diff --git a/data/ff/flir_common400/eval_pairs.jsonl b/data/ff/flir_common400/eval_pairs.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..bf9d8a226d8c6ef5f681d3a83581060eec94df0f
--- /dev/null
+++ b/data/ff/flir_common400/eval_pairs.jsonl
@@ -0,0 +1,400 @@
+{"id": "FLIR_00006", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00006_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00006.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00006_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00007", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00007_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00007.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00007_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00013", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00013_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00013.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00013_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00018", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00018_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00018.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00018_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00024", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00024_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00024.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00024_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00045", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00045_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00045.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00045_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00051", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00051_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00051.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00051_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00053", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00053_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00053.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00053_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00097", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00097_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00097.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00097_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00100", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00100_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00100.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00100_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00109", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00109_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00109.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00109_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00113", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00113_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00113.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00113_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00114", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00114_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00114.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00114_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00116", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00116_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00116.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00116_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00121", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00121_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00121.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00121_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00124", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00124_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00124.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00124_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00160", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00160_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00160.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00160_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00163", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00163_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00163.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00163_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00176", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00176_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00176.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00176_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00187", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00187_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00187.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00187_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00189", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00189_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00189.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00189_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00207", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00207_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00207.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00207_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00210", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00210_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00210.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00210_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00211", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00211_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00211.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00211_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00213", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00213_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00213.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00213_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00216", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00216_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00216.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00216_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00222", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00222_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00222.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00222_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00227", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00227_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00227.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00227_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00232", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00232_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00232.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00232_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00235", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00235_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00235.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00235_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00241", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00241_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00241.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00241_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00244", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00244_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00244.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00244_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00253", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00253_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00253.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00253_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00260", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00260_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00260.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00260_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00273", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00273_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00273.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00273_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00287", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00287_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00287.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00287_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00289", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00289_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00289.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00289_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00293", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00293_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00293.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00293_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00304", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00304_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00304.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00304_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00307", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00307_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00307.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00307_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00308", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00308_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00308.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00308_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00312", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00312_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00312.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00312_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00314", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00314_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00314.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00314_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00321", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00321_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00321.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00321_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00322", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00322_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00322.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00322_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00344", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00344_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00344.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00344_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00353", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00353_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00353.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00353_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00358", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00358_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00358.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00358_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00359", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00359_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00359.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00359_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00366", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00366_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00366.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00366_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00367", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00367_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00367.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00367_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00369", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00369_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00369.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00369_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00376", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00376_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00376.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00376_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00378", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00378_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00378.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00378_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00381", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00381_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00381.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00381_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00392", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00392_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00392.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00392_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00394", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00394_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00394.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00394_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00395", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00395_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00395.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00395_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00402", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00402_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00402.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00402_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00403", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00403_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00403.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00403_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00417", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00417_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00417.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00417_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00424", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00424_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00424.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00424_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00426", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00426_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00426.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00426_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00429", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00429_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00429.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00429_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00431", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00431_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00431.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00431_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00441", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00441_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00441.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00441_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00445", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00445_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00445.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00445_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00450", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00450_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00450.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00450_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00452", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00452_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00452.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00452_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00465", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00465_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00465.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00465_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00470", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00470_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00470.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00470_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00479", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00479_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00479.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00479_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00480", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00480_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00480.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00480_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00485", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00485_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00485.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00485_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00486", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00486_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00486.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00486_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00488", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00488_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00488.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00488_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00493", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00493_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00493.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00493_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00562", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00562_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00562.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00562_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00566", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00566_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00566.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00566_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00570", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00570_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00570.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00570_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00571", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00571_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00571.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00571_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00576", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00576_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00576.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00576_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00578", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00578_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00578.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00578_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00582", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00582_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00582.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00582_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00584", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00584_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00584.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00584_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00596", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00596_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00596.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00596_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00606", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00606_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00606.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00606_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00611", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00611_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00611.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00611_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00622", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00622_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00622.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00622_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00628", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00628_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00628.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00628_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00631", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00631_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00631.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00631_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00638", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00638_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00638.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00638_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00641", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00641_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00641.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00641_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00644", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00644_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00644.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00644_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00645", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00645_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00645.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00645_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00647", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00647_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00647.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00647_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00651", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00651_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00651.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00651_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00685", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00685_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00685.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00685_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00693", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00693_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00693.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00693_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00696", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00696_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00696.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00696_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00699", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00699_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00699.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00699_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00703", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00703_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00703.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00703_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00704", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00704_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00704.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00704_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00710", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00710_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00710.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00710_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00714", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00714_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00714.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00714_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00716", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00716_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00716.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00716_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00722", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00722_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00722.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00722_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00724", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00724_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00724.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00724_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00728", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00728_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00728.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00728_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00730", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00730_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00730.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00730_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00747", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00747_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00747.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00747_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00753", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00753_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00753.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00753_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00754", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00754_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00754.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00754_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00759", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00759_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00759.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00759_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00766", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00766_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00766.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00766_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00773", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00773_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00773.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00773_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00782", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00782_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00782.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00782_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00783", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00783_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00783.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00783_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00794", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00794_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00794.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00794_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00810", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00810_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00810.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00810_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00812", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00812_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00812.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00812_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00816", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00816_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00816.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00816_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00817", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00817_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00817.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00817_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00820", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00820_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00820.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00820_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00824", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00824_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00824.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00824_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00828", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00828_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00828.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00828_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00835", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00835_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00835.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00835_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00840", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00840_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00840.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00840_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00843", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00843_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00843.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00843_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00847", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00847_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00847.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00847_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00854", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00854_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00854.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00854_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00858", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00858_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00858.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00858_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00865", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00865_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00865.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00865_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00866", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00866_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00866.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00866_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00869", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00869_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00869.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00869_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00870", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00870_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00870.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00870_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00877", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00877_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00877.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00877_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00881", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00881_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00881.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00881_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00884", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00884_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00884.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00884_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00892", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00892_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00892.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00892_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00919", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00919_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00919.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00919_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00922", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00922_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00922.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00922_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00935", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00935_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00935.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00935_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00939", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00939_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00939.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00939_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00940", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00940_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00940.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00940_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00946", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00946_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00946.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00946_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00947", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00947_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00947.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00947_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00950", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00950_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00950.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00950_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00951", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00951_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00951.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00951_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00952", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00952_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00952.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00952_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00954", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00954_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00954.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00954_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00956", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00956_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00956.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00956_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00958", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00958_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00958.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00958_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00960", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00960_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00960.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00960_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00961", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00961_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00961.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00961_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00962", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00962_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00962.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00962_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00965", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00965_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00965.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00965_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00973", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00973_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00973.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00973_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00979", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00979_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00979.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00979_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00980", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00980_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00980.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00980_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00984", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00984_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00984.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00984_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00992", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00992_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00992.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00992_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00994", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00994_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00994.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00994_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_00996", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00996_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_00996.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_00996_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_01001", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01001_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_01001.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01001_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_01005", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01005_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_01005.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01005_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_01006", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01006_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_01006.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01006_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_01007", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01007_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_01007.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01007_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_01008", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01008_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_01008.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01008_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_01014", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01014_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_01014.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01014_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_01017", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01017_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_01017.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01017_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_01019", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01019_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_01019.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01019_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_01021", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01021_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_01021.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01021_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_01027", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01027_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_01027.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01027_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_01030", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01030_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_01030.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01030_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_01047", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01047_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_01047.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01047_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_01059", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01059_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_01059.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01059_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_01068", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01068_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_01068.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01068_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_01075", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01075_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_01075.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01075_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_01082", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01082_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_01082.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01082_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_01083", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01083_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_01083.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01083_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_01084", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01084_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_01084.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01084_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_01092", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01092_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_01092.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01092_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_01096", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01096_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_01096.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01096_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_01130", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01130_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_01130.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01130_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_01133", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01133_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_01133.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01133_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_01157", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01157_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_01157.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01157_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_01219", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01219_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_01219.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01219_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_01237", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01237_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_01237.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01237_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_01239", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01239_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_01239.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01239_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_01264", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01264_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_01264.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01264_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_01439", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01439_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_01439.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01439_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_01495", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01495_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_01495.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01495_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_01509", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01509_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_01509.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01509_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_01561", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01561_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_01561.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01561_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_01584", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01584_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_01584.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01584_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_01603", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01603_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_01603.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01603_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_01604", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01604_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_01604.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01604_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_01605", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01605_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_01605.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01605_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_01619", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01619_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_01619.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01619_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_01629", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01629_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_01629.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01629_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_01696", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01696_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_01696.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01696_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_01698", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01698_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_01698.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01698_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_01701", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01701_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_01701.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01701_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_01702", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01702_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_01702.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01702_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_01705", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01705_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_01705.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01705_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_01709", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01709_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_01709.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01709_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_01717", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01717_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_01717.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01717_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_01724", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01724_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_01724.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01724_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_01727", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01727_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_01727.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01727_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_01728", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01728_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_01728.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01728_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_01729", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01729_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_01729.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01729_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_01730", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01730_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_01730.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01730_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_01732", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01732_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_01732.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01732_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_01737", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01737_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_01737.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01737_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_01742", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01742_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_01742.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01742_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_01757", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01757_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_01757.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01757_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_01759", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01759_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_01759.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01759_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_01760", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01760_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_01760.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01760_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_01768", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01768_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_01768.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01768_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_01772", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01772_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_01772.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01772_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_01778", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01778_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_01778.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01778_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_01782", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01782_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_01782.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01782_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_01791", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01791_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_01791.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01791_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_01795", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01795_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_01795.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01795_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_01799", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01799_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_01799.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01799_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_01811", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01811_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_01811.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01811_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_01814", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01814_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_01814.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01814_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_01816", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01816_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_01816.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01816_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_01817", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01817_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_01817.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01817_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_01819", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01819_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_01819.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01819_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_01825", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01825_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_01825.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01825_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_01833", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01833_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_01833.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01833_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_01841", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01841_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_01841.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01841_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_01842", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01842_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_01842.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01842_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_01845", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01845_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_01845.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01845_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_01848", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01848_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_01848.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01848_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_01850", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01850_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_01850.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01850_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_01853", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01853_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_01853.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01853_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_01857", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01857_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_01857.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01857_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_01858", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01858_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_01858.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01858_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_01859", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01859_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_01859.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01859_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_01861", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01861_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_01861.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01861_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_01862", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01862_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_01862.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01862_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_01866", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01866_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_01866.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01866_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_01867", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01867_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_01867.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01867_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_01883", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01883_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_01883.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01883_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_01888", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01888_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_01888.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01888_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_01894", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01894_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_01894.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01894_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_01895", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01895_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_01895.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01895_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_01901", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01901_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_01901.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01901_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_01903", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01903_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_01903.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01903_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_01909", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01909_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_01909.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01909_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_01916", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01916_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_01916.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01916_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_01922", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01922_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_01922.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01922_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_01927", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01927_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_01927.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01927_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_01928", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01928_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_01928.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01928_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_01931", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01931_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_01931.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01931_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_01935", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01935_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_01935.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01935_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_01937", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01937_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_01937.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01937_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_01938", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01938_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_01938.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01938_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_01940", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01940_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_01940.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01940_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_01942", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01942_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_01942.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01942_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_01947", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01947_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_01947.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01947_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_01950", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01950_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_01950.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01950_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_01954", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01954_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_01954.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01954_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_01955", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01955_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_01955.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01955_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_01960", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01960_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_01960.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01960_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_01963", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01963_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_01963.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01963_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_01967", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01967_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_01967.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01967_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_01972", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01972_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_01972.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01972_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_01980", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01980_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_01980.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01980_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_01987", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01987_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_01987.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01987_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_01990", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01990_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_01990.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01990_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_01993", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01993_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_01993.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01993_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_01996", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01996_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_01996.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_01996_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_02026", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02026_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_02026.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02026_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_02029", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02029_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_02029.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02029_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_02031", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02031_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_02031.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02031_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_02037", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02037_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_02037.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02037_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_02041", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02041_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_02041.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02041_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_02045", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02045_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_02045.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02045_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_02047", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02047_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_02047.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02047_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_02053", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02053_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_02053.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02053_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_02055", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02055_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_02055.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02055_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_02057", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02057_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_02057.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02057_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_02068", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02068_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_02068.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02068_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_02069", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02069_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_02069.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02069_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_02100", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02100_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_02100.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02100_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_02101", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02101_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_02101.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02101_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_02102", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02102_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_02102.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02102_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_02105", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02105_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_02105.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02105_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_02111", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02111_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_02111.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02111_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_02112", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02112_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_02112.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02112_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_02115", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02115_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_02115.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02115_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_02121", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02121_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_02121.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02121_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_02124", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02124_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_02124.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02124_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_02129", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02129_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_02129.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02129_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_02140", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02140_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_02140.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02140_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_02143", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02143_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_02143.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02143_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_02145", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02145_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_02145.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02145_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_02147", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02147_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_02147.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02147_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_02150", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02150_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_02150.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02150_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_02151", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02151_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_02151.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02151_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_02159", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02159_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_02159.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02159_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_02163", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02163_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_02163.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02163_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_02166", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02166_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_02166.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02166_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_02168", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02168_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_02168.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02168_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_02174", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02174_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_02174.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02174_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_02176", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02176_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_02176.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02176_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_02178", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02178_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_02178.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02178_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_02181", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02181_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_02181.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02181_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_02193", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02193_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_02193.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02193_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_02199", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02199_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_02199.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02199_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_02204", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02204_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_02204.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02204_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_02206", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02206_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_02206.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02206_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_02215", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02215_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_02215.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02215_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_02217", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02217_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_02217.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02217_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_02221", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02221_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_02221.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02221_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_02222", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02222_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_02222.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02222_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_02242", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02242_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_02242.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02242_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_02258", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02258_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_02258.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02258_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_02259", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02259_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_02259.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02259_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_02282", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02282_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_02282.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02282_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_02287", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02287_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_02287.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02287_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_02288", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02288_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_02288.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02288_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_02289", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02289_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_02289.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02289_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_02309", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02309_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_02309.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02309_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_02311", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02311_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_02311.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02311_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_02317", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02317_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_02317.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02317_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_02322", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02322_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_02322.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02322_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_02328", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02328_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_02328.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02328_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_02330", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02330_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_02330.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02330_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_02335", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02335_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_02335.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02335_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_02336", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02336_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_02336.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02336_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_02337", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02337_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_02337.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02337_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_02357", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02357_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_02357.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02357_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_02362", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02362_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_02362.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02362_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_02363", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02363_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_02363.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02363_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_02365", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02365_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_02365.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02365_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_02367", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02367_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_02367.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02367_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_02369", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02369_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_02369.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02369_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_02378", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02378_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_02378.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02378_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_02381", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02381_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_02381.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02381_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_02385", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02385_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_02385.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02385_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_02387", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02387_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_02387.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02387_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_02394", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02394_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_02394.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02394_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_02398", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02398_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_02398.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02398_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_02401", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02401_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_02401.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02401_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_02416", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02416_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_02416.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02416_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_02417", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02417_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_02417.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02417_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_02419", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02419_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_02419.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02419_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_02425", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02425_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_02425.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02425_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_02431", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02431_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_02431.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02431_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_02434", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02434_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_02434.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02434_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_02437", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02437_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_02437.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02437_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_02444", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02444_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_02444.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02444_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_02446", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02446_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_02446.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02446_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_02450", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02450_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_02450.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02450_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_02453", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02453_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_02453.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02453_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_02455", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02455_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_02455.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02455_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_02457", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02457_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_02457.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02457_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_02458", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02458_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_02458.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02458_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_02459", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02459_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_02459.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02459_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_02465", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02465_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_02465.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02465_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_02468", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02468_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_02468.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02468_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_02475", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02475_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_02475.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02475_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_02477", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02477_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_02477.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02477_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_02480", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02480_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_02480.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02480_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_02482", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02482_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_02482.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02482_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_02484", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02484_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_02484.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02484_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_02494", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02494_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_02494.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02494_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_02498", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02498_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_02498.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02498_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_02506", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02506_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_02506.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02506_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_02520", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02520_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_02520.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02520_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_02521", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02521_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_02521.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02521_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_02523", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02523_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_02523.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02523_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_02524", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02524_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_02524.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02524_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_02539", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02539_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_02539.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02539_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_02551", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02551_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_02551.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02551_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_02561", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02561_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_02561.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02561_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_02563", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02563_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_02563.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02563_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_02572", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02572_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_02572.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02572_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_02573", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02573_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_02573.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02573_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_02575", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02575_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_02575.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02575_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_02576", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02576_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_02576.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02576_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_02578", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02578_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_02578.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02578_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_02580", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02580_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_02580.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02580_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_02586", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02586_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_02586.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02586_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_02588", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02588_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_02588.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02588_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_02601", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02601_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_02601.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02601_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_02602", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02602_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_02602.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02602_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_02610", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02610_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_02610.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02610_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_02612", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02612_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_02612.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02612_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_02618", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02618_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_02618.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02618_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_02625", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02625_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_02625.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02625_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_02630", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02630_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_02630.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02630_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_02631", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02631_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_02631.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02631_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_02633", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02633_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_02633.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02633_PreviewData.jpeg", "width": 640, "height": 512}
+{"id": "FLIR_02634", "dataset": "flir", "split": "common_test", "band": "lwir", "prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02634_RGB.jpg", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/flir/FLIR_02634.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/FLIR/FLIR_02634_PreviewData.jpeg", "width": 640, "height": 512}
diff --git a/data/ff/places_domain_rl/heldout.jsonl b/data/ff/places_domain_rl/heldout.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..cd1fe50d1d8171b026b0fe38623d11914eda4134
--- /dev/null
+++ b/data/ff/places_domain_rl/heldout.jsonl
@@ -0,0 +1,536 @@
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_wave_val-00000-of-00005_00899.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_wave_val-00000-of-00005_00899.png", "id": "places_wave_val-00000-of-00005_00899"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_ocean_val-00004-of-00005_05889.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_ocean_val-00004-of-00005_05889.png", "id": "places_ocean_val-00004-of-00005_05889"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_cliff_val-00003-of-00005_03261.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_cliff_val-00003-of-00005_03261.png", "id": "places_cliff_val-00003-of-00005_03261"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_sky_train-00003-of-00005_02248.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_sky_train-00003-of-00005_02248.png", "id": "places_sky_train-00003-of-00005_02248"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_lake_natural_train-00002-of-00005_03872.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_lake_natural_train-00002-of-00005_03872.png", "id": "places_lake_natural_train-00002-of-00005_03872"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_sky_train-00000-of-00005_02615.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_sky_train-00000-of-00005_02615.png", "id": "places_sky_train-00000-of-00005_02615"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_lake_natural_val-00001-of-00005_05932.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_lake_natural_val-00001-of-00005_05932.png", "id": "places_lake_natural_val-00001-of-00005_05932"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_butte_val-00001-of-00005_04610.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_butte_val-00001-of-00005_04610.png", "id": "places_butte_val-00001-of-00005_04610"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_butte_train-00000-of-00005_00892.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_butte_train-00000-of-00005_00892.png", "id": "places_butte_train-00000-of-00005_00892"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_mountain_snowy_val-00003-of-00005_03347.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_mountain_snowy_val-00003-of-00005_03347.png", "id": "places_mountain_snowy_val-00003-of-00005_03347"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_butte_train-00000-of-00005_06038.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_butte_train-00000-of-00005_06038.png", "id": "places_butte_train-00000-of-00005_06038"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_beach_val-00001-of-00005_03544.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_beach_val-00001-of-00005_03544.png", "id": "places_beach_val-00001-of-00005_03544"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_glacier_val-00000-of-00005_02664.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_glacier_val-00000-of-00005_02664.png", "id": "places_glacier_val-00000-of-00005_02664"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_beach_train-00000-of-00005_00330.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_beach_train-00000-of-00005_00330.png", "id": "places_beach_train-00000-of-00005_00330"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_iceberg_train-00004-of-00005_03372.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_iceberg_train-00004-of-00005_03372.png", "id": "places_iceberg_train-00004-of-00005_03372"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_glacier_train-00000-of-00005_02337.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_glacier_train-00000-of-00005_02337.png", "id": "places_glacier_train-00000-of-00005_02337"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_field_wild_val-00003-of-00005_03223.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_field_wild_val-00003-of-00005_03223.png", "id": "places_field_wild_val-00003-of-00005_03223"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_sky_train-00004-of-00005_06593.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_sky_train-00004-of-00005_06593.png", "id": "places_sky_train-00004-of-00005_06593"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_ocean_val-00003-of-00005_02896.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_ocean_val-00003-of-00005_02896.png", "id": "places_ocean_val-00003-of-00005_02896"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_beach_train-00002-of-00005_01597.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_beach_train-00002-of-00005_01597.png", "id": "places_beach_train-00002-of-00005_01597"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_cliff_train-00003-of-00005_06393.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_cliff_train-00003-of-00005_06393.png", "id": "places_cliff_train-00003-of-00005_06393"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_beach_val-00000-of-00005_03782.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_beach_val-00000-of-00005_03782.png", "id": "places_beach_val-00000-of-00005_03782"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_snowfield_val-00001-of-00005_03725.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_snowfield_val-00001-of-00005_03725.png", "id": "places_snowfield_val-00001-of-00005_03725"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_badlands_train-00001-of-00005_02258.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_badlands_train-00001-of-00005_02258.png", "id": "places_badlands_train-00001-of-00005_02258"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_volcano_train-00004-of-00005_03214.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_volcano_train-00004-of-00005_03214.png", "id": "places_volcano_train-00004-of-00005_03214"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_field_wild_train-00004-of-00005_00527.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_field_wild_train-00004-of-00005_00527.png", "id": "places_field_wild_train-00004-of-00005_00527"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_field_wild_train-00003-of-00005_04229.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_field_wild_train-00003-of-00005_04229.png", "id": "places_field_wild_train-00003-of-00005_04229"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_volcano_train-00003-of-00005_03002.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_volcano_train-00003-of-00005_03002.png", "id": "places_volcano_train-00003-of-00005_03002"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_butte_val-00003-of-00005_02662.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_butte_val-00003-of-00005_02662.png", "id": "places_butte_val-00003-of-00005_02662"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_canyon_train-00001-of-00005_06339.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_canyon_train-00001-of-00005_06339.png", "id": "places_canyon_train-00001-of-00005_06339"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_wave_val-00000-of-00005_04612.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_wave_val-00000-of-00005_04612.png", "id": "places_wave_val-00000-of-00005_04612"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_badlands_train-00002-of-00005_00158.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_badlands_train-00002-of-00005_00158.png", "id": "places_badlands_train-00002-of-00005_00158"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_badlands_train-00000-of-00005_01921.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_badlands_train-00000-of-00005_01921.png", "id": "places_badlands_train-00000-of-00005_01921"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_glacier_train-00000-of-00005_00715.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_glacier_train-00000-of-00005_00715.png", "id": "places_glacier_train-00000-of-00005_00715"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_mountain_val-00001-of-00005_02100.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_mountain_val-00001-of-00005_02100.png", "id": "places_mountain_val-00001-of-00005_02100"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_snowfield_val-00002-of-00005_01603.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_snowfield_val-00002-of-00005_01603.png", "id": "places_snowfield_val-00002-of-00005_01603"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_wave_train-00003-of-00005_04652.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_wave_train-00003-of-00005_04652.png", "id": "places_wave_train-00003-of-00005_04652"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_snowfield_val-00001-of-00005_02972.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_snowfield_val-00001-of-00005_02972.png", "id": "places_snowfield_val-00001-of-00005_02972"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_badlands_val-00004-of-00005_05693.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_badlands_val-00004-of-00005_05693.png", "id": "places_badlands_val-00004-of-00005_05693"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_valley_val-00003-of-00005_00553.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_valley_val-00003-of-00005_00553.png", "id": "places_valley_val-00003-of-00005_00553"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_desert_sand_val-00001-of-00005_04839.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_desert_sand_val-00001-of-00005_04839.png", "id": "places_desert_sand_val-00001-of-00005_04839"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_butte_val-00003-of-00005_01066.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_butte_val-00003-of-00005_01066.png", "id": "places_butte_val-00003-of-00005_01066"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_butte_val-00004-of-00005_00410.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_butte_val-00004-of-00005_00410.png", "id": "places_butte_val-00004-of-00005_00410"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_mountain_val-00004-of-00005_04828.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_mountain_val-00004-of-00005_04828.png", "id": "places_mountain_val-00004-of-00005_04828"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_lake_natural_train-00000-of-00005_02597.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_lake_natural_train-00000-of-00005_02597.png", "id": "places_lake_natural_train-00000-of-00005_02597"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_sky_train-00002-of-00005_04458.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_sky_train-00002-of-00005_04458.png", "id": "places_sky_train-00002-of-00005_04458"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_valley_val-00003-of-00005_00554.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_valley_val-00003-of-00005_00554.png", "id": "places_valley_val-00003-of-00005_00554"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_badlands_train-00001-of-00005_06147.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_badlands_train-00001-of-00005_06147.png", "id": "places_badlands_train-00001-of-00005_06147"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_badlands_train-00001-of-00005_03050.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_badlands_train-00001-of-00005_03050.png", "id": "places_badlands_train-00001-of-00005_03050"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_cliff_train-00001-of-00005_07198.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_cliff_train-00001-of-00005_07198.png", "id": "places_cliff_train-00001-of-00005_07198"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_sky_val-00002-of-00005_05716.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_sky_val-00002-of-00005_05716.png", "id": "places_sky_val-00002-of-00005_05716"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_butte_val-00002-of-00005_05753.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_butte_val-00002-of-00005_05753.png", "id": "places_butte_val-00002-of-00005_05753"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_lake_natural_val-00000-of-00005_00482.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_lake_natural_val-00000-of-00005_00482.png", "id": "places_lake_natural_val-00000-of-00005_00482"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_coast_train-00003-of-00005_04645.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_coast_train-00003-of-00005_04645.png", "id": "places_coast_train-00003-of-00005_04645"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_valley_val-00003-of-00005_04063.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_valley_val-00003-of-00005_04063.png", "id": "places_valley_val-00003-of-00005_04063"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_volcano_train-00000-of-00005_04669.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_volcano_train-00000-of-00005_04669.png", "id": "places_volcano_train-00000-of-00005_04669"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_canyon_train-00000-of-00005_00006.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_canyon_train-00000-of-00005_00006.png", "id": "places_canyon_train-00000-of-00005_00006"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_desert_sand_train-00002-of-00005_02587.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_desert_sand_train-00002-of-00005_02587.png", "id": "places_desert_sand_train-00002-of-00005_02587"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_beach_train-00000-of-00005_01051.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_beach_train-00000-of-00005_01051.png", "id": "places_beach_train-00000-of-00005_01051"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_tundra_val-00001-of-00005_06123.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_tundra_val-00001-of-00005_06123.png", "id": "places_tundra_val-00001-of-00005_06123"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_tundra_train-00004-of-00005_01810.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_tundra_train-00004-of-00005_01810.png", "id": "places_tundra_train-00004-of-00005_01810"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_tundra_val-00004-of-00005_05400.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_tundra_val-00004-of-00005_05400.png", "id": "places_tundra_val-00004-of-00005_05400"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_badlands_val-00003-of-00005_02982.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_badlands_val-00003-of-00005_02982.png", "id": "places_badlands_val-00003-of-00005_02982"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_snowfield_train-00004-of-00005_00134.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_snowfield_train-00004-of-00005_00134.png", "id": "places_snowfield_train-00004-of-00005_00134"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_field_wild_train-00000-of-00005_00503.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_field_wild_train-00000-of-00005_00503.png", "id": "places_field_wild_train-00000-of-00005_00503"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_valley_val-00000-of-00005_06595.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_valley_val-00000-of-00005_06595.png", "id": "places_valley_val-00000-of-00005_06595"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_field_wild_val-00003-of-00005_03578.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_field_wild_val-00003-of-00005_03578.png", "id": "places_field_wild_val-00003-of-00005_03578"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_beach_train-00002-of-00005_04411.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_beach_train-00002-of-00005_04411.png", "id": "places_beach_train-00002-of-00005_04411"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_islet_train-00003-of-00005_07129.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_islet_train-00003-of-00005_07129.png", "id": "places_islet_train-00003-of-00005_07129"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_tundra_val-00000-of-00005_03656.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_tundra_val-00000-of-00005_03656.png", "id": "places_tundra_val-00000-of-00005_03656"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_tundra_train-00002-of-00005_03290.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_tundra_train-00002-of-00005_03290.png", "id": "places_tundra_train-00002-of-00005_03290"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_valley_train-00003-of-00005_04641.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_valley_train-00003-of-00005_04641.png", "id": "places_valley_train-00003-of-00005_04641"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_mountain_val-00003-of-00005_02548.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_mountain_val-00003-of-00005_02548.png", "id": "places_mountain_val-00003-of-00005_02548"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_desert_sand_val-00003-of-00005_05107.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_desert_sand_val-00003-of-00005_05107.png", "id": "places_desert_sand_val-00003-of-00005_05107"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_volcano_val-00002-of-00005_04987.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_volcano_val-00002-of-00005_04987.png", "id": "places_volcano_val-00002-of-00005_04987"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_coast_val-00001-of-00005_03699.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_coast_val-00001-of-00005_03699.png", "id": "places_coast_val-00001-of-00005_03699"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_cliff_train-00002-of-00005_05261.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_cliff_train-00002-of-00005_05261.png", "id": "places_cliff_train-00002-of-00005_05261"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_glacier_val-00004-of-00005_00969.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_glacier_val-00004-of-00005_00969.png", "id": "places_glacier_val-00004-of-00005_00969"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_beach_val-00001-of-00005_07294.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_beach_val-00001-of-00005_07294.png", "id": "places_beach_val-00001-of-00005_07294"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_butte_train-00004-of-00005_05129.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_butte_train-00004-of-00005_05129.png", "id": "places_butte_train-00004-of-00005_05129"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_mountain_snowy_val-00003-of-00005_02686.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_mountain_snowy_val-00003-of-00005_02686.png", "id": "places_mountain_snowy_val-00003-of-00005_02686"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_cliff_val-00003-of-00005_07041.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_cliff_val-00003-of-00005_07041.png", "id": "places_cliff_val-00003-of-00005_07041"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_coast_train-00003-of-00005_03518.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_coast_train-00003-of-00005_03518.png", "id": "places_coast_train-00003-of-00005_03518"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_desert_sand_train-00000-of-00005_06699.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_desert_sand_train-00000-of-00005_06699.png", "id": "places_desert_sand_train-00000-of-00005_06699"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_butte_train-00002-of-00005_03374.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_butte_train-00002-of-00005_03374.png", "id": "places_butte_train-00002-of-00005_03374"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_islet_train-00002-of-00005_00435.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_islet_train-00002-of-00005_00435.png", "id": "places_islet_train-00002-of-00005_00435"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_islet_train-00000-of-00005_04022.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_islet_train-00000-of-00005_04022.png", "id": "places_islet_train-00000-of-00005_04022"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_field_wild_train-00001-of-00005_01396.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_field_wild_train-00001-of-00005_01396.png", "id": "places_field_wild_train-00001-of-00005_01396"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_coast_val-00002-of-00005_03051.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_coast_val-00002-of-00005_03051.png", "id": "places_coast_val-00002-of-00005_03051"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_canyon_val-00002-of-00005_04521.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_canyon_val-00002-of-00005_04521.png", "id": "places_canyon_val-00002-of-00005_04521"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_iceberg_train-00001-of-00005_06121.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_iceberg_train-00001-of-00005_06121.png", "id": "places_iceberg_train-00001-of-00005_06121"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_desert_sand_train-00003-of-00005_05107.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_desert_sand_train-00003-of-00005_05107.png", "id": "places_desert_sand_train-00003-of-00005_05107"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_ocean_val-00002-of-00005_02091.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_ocean_val-00002-of-00005_02091.png", "id": "places_ocean_val-00002-of-00005_02091"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_cliff_train-00002-of-00005_01239.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_cliff_train-00002-of-00005_01239.png", "id": "places_cliff_train-00002-of-00005_01239"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_canyon_train-00000-of-00005_05591.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_canyon_train-00000-of-00005_05591.png", "id": "places_canyon_train-00000-of-00005_05591"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_glacier_val-00001-of-00005_01214.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_glacier_val-00001-of-00005_01214.png", "id": "places_glacier_val-00001-of-00005_01214"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_snowfield_val-00000-of-00005_00526.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_snowfield_val-00000-of-00005_00526.png", "id": "places_snowfield_val-00000-of-00005_00526"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_mountain_snowy_val-00004-of-00005_05631.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_mountain_snowy_val-00004-of-00005_05631.png", "id": "places_mountain_snowy_val-00004-of-00005_05631"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_volcano_train-00003-of-00005_06499.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_volcano_train-00003-of-00005_06499.png", "id": "places_volcano_train-00003-of-00005_06499"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_cliff_train-00003-of-00005_03297.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_cliff_train-00003-of-00005_03297.png", "id": "places_cliff_train-00003-of-00005_03297"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_butte_train-00002-of-00005_02970.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_butte_train-00002-of-00005_02970.png", "id": "places_butte_train-00002-of-00005_02970"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_ocean_val-00002-of-00005_01843.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_ocean_val-00002-of-00005_01843.png", "id": "places_ocean_val-00002-of-00005_01843"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_tundra_val-00002-of-00005_04288.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_tundra_val-00002-of-00005_04288.png", "id": "places_tundra_val-00002-of-00005_04288"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_lake_natural_val-00000-of-00005_05718.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_lake_natural_val-00000-of-00005_05718.png", "id": "places_lake_natural_val-00000-of-00005_05718"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_mountain_snowy_val-00001-of-00005_03847.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_mountain_snowy_val-00001-of-00005_03847.png", "id": "places_mountain_snowy_val-00001-of-00005_03847"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_butte_train-00003-of-00005_02393.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_butte_train-00003-of-00005_02393.png", "id": "places_butte_train-00003-of-00005_02393"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_butte_train-00000-of-00005_04966.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_butte_train-00000-of-00005_04966.png", "id": "places_butte_train-00000-of-00005_04966"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_canyon_val-00002-of-00005_00452.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_canyon_val-00002-of-00005_00452.png", "id": "places_canyon_val-00002-of-00005_00452"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_iceberg_val-00003-of-00005_05063.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_iceberg_val-00003-of-00005_05063.png", "id": "places_iceberg_val-00003-of-00005_05063"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_canyon_val-00004-of-00005_06852.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_canyon_val-00004-of-00005_06852.png", "id": "places_canyon_val-00004-of-00005_06852"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_islet_val-00000-of-00005_01280.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_islet_val-00000-of-00005_01280.png", "id": "places_islet_val-00000-of-00005_01280"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_volcano_train-00003-of-00005_07084.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_volcano_train-00003-of-00005_07084.png", "id": "places_volcano_train-00003-of-00005_07084"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_beach_val-00004-of-00005_01030.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_beach_val-00004-of-00005_01030.png", "id": "places_beach_val-00004-of-00005_01030"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_mountain_val-00001-of-00005_04978.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_mountain_val-00001-of-00005_04978.png", "id": "places_mountain_val-00001-of-00005_04978"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_beach_val-00003-of-00005_03876.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_beach_val-00003-of-00005_03876.png", "id": "places_beach_val-00003-of-00005_03876"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_field_wild_val-00004-of-00005_01249.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_field_wild_val-00004-of-00005_01249.png", "id": "places_field_wild_val-00004-of-00005_01249"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_canyon_train-00004-of-00005_01370.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_canyon_train-00004-of-00005_01370.png", "id": "places_canyon_train-00004-of-00005_01370"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_beach_train-00002-of-00005_01469.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_beach_train-00002-of-00005_01469.png", "id": "places_beach_train-00002-of-00005_01469"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_cliff_val-00001-of-00005_01476.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_cliff_val-00001-of-00005_01476.png", "id": "places_cliff_val-00001-of-00005_01476"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_tundra_train-00004-of-00005_02505.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_tundra_train-00004-of-00005_02505.png", "id": "places_tundra_train-00004-of-00005_02505"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_mountain_snowy_val-00002-of-00005_03614.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_mountain_snowy_val-00002-of-00005_03614.png", "id": "places_mountain_snowy_val-00002-of-00005_03614"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_volcano_train-00004-of-00005_06934.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_volcano_train-00004-of-00005_06934.png", "id": "places_volcano_train-00004-of-00005_06934"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_islet_train-00002-of-00005_06915.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_islet_train-00002-of-00005_06915.png", "id": "places_islet_train-00002-of-00005_06915"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_islet_train-00002-of-00005_02726.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_islet_train-00002-of-00005_02726.png", "id": "places_islet_train-00002-of-00005_02726"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_volcano_val-00001-of-00005_05804.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_volcano_val-00001-of-00005_05804.png", "id": "places_volcano_val-00001-of-00005_05804"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_field_wild_train-00002-of-00005_00709.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_field_wild_train-00002-of-00005_00709.png", "id": "places_field_wild_train-00002-of-00005_00709"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_ocean_train-00004-of-00005_03304.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_ocean_train-00004-of-00005_03304.png", "id": "places_ocean_train-00004-of-00005_03304"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_islet_val-00000-of-00005_00134.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_islet_val-00000-of-00005_00134.png", "id": "places_islet_val-00000-of-00005_00134"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_beach_val-00001-of-00005_07115.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_beach_val-00001-of-00005_07115.png", "id": "places_beach_val-00001-of-00005_07115"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_sky_train-00000-of-00005_05316.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_sky_train-00000-of-00005_05316.png", "id": "places_sky_train-00000-of-00005_05316"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_lake_natural_train-00001-of-00005_00608.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_lake_natural_train-00001-of-00005_00608.png", "id": "places_lake_natural_train-00001-of-00005_00608"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_islet_val-00004-of-00005_03486.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_islet_val-00004-of-00005_03486.png", "id": "places_islet_val-00004-of-00005_03486"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_sky_val-00001-of-00005_04727.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_sky_val-00001-of-00005_04727.png", "id": "places_sky_val-00001-of-00005_04727"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_mountain_train-00000-of-00005_05543.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_mountain_train-00000-of-00005_05543.png", "id": "places_mountain_train-00000-of-00005_05543"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_butte_val-00002-of-00005_00906.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_butte_val-00002-of-00005_00906.png", "id": "places_butte_val-00002-of-00005_00906"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_mountain_val-00004-of-00005_03977.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_mountain_val-00004-of-00005_03977.png", "id": "places_mountain_val-00004-of-00005_03977"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_badlands_train-00001-of-00005_01547.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_badlands_train-00001-of-00005_01547.png", "id": "places_badlands_train-00001-of-00005_01547"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_snowfield_train-00000-of-00005_05751.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_snowfield_train-00000-of-00005_05751.png", "id": "places_snowfield_train-00000-of-00005_05751"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_beach_val-00004-of-00005_03143.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_beach_val-00004-of-00005_03143.png", "id": "places_beach_val-00004-of-00005_03143"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_sky_train-00001-of-00005_05117.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_sky_train-00001-of-00005_05117.png", "id": "places_sky_train-00001-of-00005_05117"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_mountain_val-00000-of-00005_06234.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_mountain_val-00000-of-00005_06234.png", "id": "places_mountain_val-00000-of-00005_06234"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_volcano_val-00004-of-00005_00929.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_volcano_val-00004-of-00005_00929.png", "id": "places_volcano_val-00004-of-00005_00929"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_mountain_snowy_train-00000-of-00005_06375.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_mountain_snowy_train-00000-of-00005_06375.png", "id": "places_mountain_snowy_train-00000-of-00005_06375"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_lake_natural_val-00003-of-00005_06582.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_lake_natural_val-00003-of-00005_06582.png", "id": "places_lake_natural_val-00003-of-00005_06582"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_canyon_val-00002-of-00005_02308.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_canyon_val-00002-of-00005_02308.png", "id": "places_canyon_val-00002-of-00005_02308"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_sky_train-00004-of-00005_05260.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_sky_train-00004-of-00005_05260.png", "id": "places_sky_train-00004-of-00005_05260"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_lake_natural_train-00000-of-00005_00163.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_lake_natural_train-00000-of-00005_00163.png", "id": "places_lake_natural_train-00000-of-00005_00163"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_lake_natural_train-00004-of-00005_05990.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_lake_natural_train-00004-of-00005_05990.png", "id": "places_lake_natural_train-00004-of-00005_05990"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_beach_val-00004-of-00005_06215.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_beach_val-00004-of-00005_06215.png", "id": "places_beach_val-00004-of-00005_06215"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_tundra_val-00003-of-00005_01705.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_tundra_val-00003-of-00005_01705.png", "id": "places_tundra_val-00003-of-00005_01705"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_snowfield_val-00001-of-00005_00098.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_snowfield_val-00001-of-00005_00098.png", "id": "places_snowfield_val-00001-of-00005_00098"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_mountain_val-00002-of-00005_06825.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_mountain_val-00002-of-00005_06825.png", "id": "places_mountain_val-00002-of-00005_06825"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_desert_sand_train-00003-of-00005_01490.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_desert_sand_train-00003-of-00005_01490.png", "id": "places_desert_sand_train-00003-of-00005_01490"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_beach_train-00003-of-00005_01221.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_beach_train-00003-of-00005_01221.png", "id": "places_beach_train-00003-of-00005_01221"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_volcano_val-00003-of-00005_00078.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_volcano_val-00003-of-00005_00078.png", "id": "places_volcano_val-00003-of-00005_00078"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_mountain_snowy_train-00003-of-00005_00798.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_mountain_snowy_train-00003-of-00005_00798.png", "id": "places_mountain_snowy_train-00003-of-00005_00798"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_beach_val-00000-of-00005_05657.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_beach_val-00000-of-00005_05657.png", "id": "places_beach_val-00000-of-00005_05657"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_ocean_val-00002-of-00005_06730.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_ocean_val-00002-of-00005_06730.png", "id": "places_ocean_val-00002-of-00005_06730"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_field_wild_train-00000-of-00005_05408.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_field_wild_train-00000-of-00005_05408.png", "id": "places_field_wild_train-00000-of-00005_05408"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_mountain_train-00002-of-00005_00130.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_mountain_train-00002-of-00005_00130.png", "id": "places_mountain_train-00002-of-00005_00130"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_valley_train-00003-of-00005_00553.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_valley_train-00003-of-00005_00553.png", "id": "places_valley_train-00003-of-00005_00553"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_mountain_train-00002-of-00005_01530.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_mountain_train-00002-of-00005_01530.png", "id": "places_mountain_train-00002-of-00005_01530"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_mountain_train-00004-of-00005_03977.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_mountain_train-00004-of-00005_03977.png", "id": "places_mountain_train-00004-of-00005_03977"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_cliff_val-00003-of-00005_05759.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_cliff_val-00003-of-00005_05759.png", "id": "places_cliff_val-00003-of-00005_05759"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_tundra_train-00004-of-00005_00312.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_tundra_train-00004-of-00005_00312.png", "id": "places_tundra_train-00004-of-00005_00312"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_ocean_train-00002-of-00005_06730.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_ocean_train-00002-of-00005_06730.png", "id": "places_ocean_train-00002-of-00005_06730"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_snowfield_val-00003-of-00005_03757.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_snowfield_val-00003-of-00005_03757.png", "id": "places_snowfield_val-00003-of-00005_03757"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_ocean_train-00002-of-00005_00216.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_ocean_train-00002-of-00005_00216.png", "id": "places_ocean_train-00002-of-00005_00216"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_mountain_snowy_val-00002-of-00005_02250.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_mountain_snowy_val-00002-of-00005_02250.png", "id": "places_mountain_snowy_val-00002-of-00005_02250"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_mountain_snowy_train-00003-of-00005_05912.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_mountain_snowy_train-00003-of-00005_05912.png", "id": "places_mountain_snowy_train-00003-of-00005_05912"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_snowfield_val-00002-of-00005_02912.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_snowfield_val-00002-of-00005_02912.png", "id": "places_snowfield_val-00002-of-00005_02912"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_cliff_val-00002-of-00005_01916.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_cliff_val-00002-of-00005_01916.png", "id": "places_cliff_val-00002-of-00005_01916"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_butte_val-00001-of-00005_00297.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_butte_val-00001-of-00005_00297.png", "id": "places_butte_val-00001-of-00005_00297"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_sky_train-00001-of-00005_06940.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_sky_train-00001-of-00005_06940.png", "id": "places_sky_train-00001-of-00005_06940"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_canyon_train-00003-of-00005_02802.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_canyon_train-00003-of-00005_02802.png", "id": "places_canyon_train-00003-of-00005_02802"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_islet_train-00002-of-00005_01299.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_islet_train-00002-of-00005_01299.png", "id": "places_islet_train-00002-of-00005_01299"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_field_wild_train-00001-of-00005_04224.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_field_wild_train-00001-of-00005_04224.png", "id": "places_field_wild_train-00001-of-00005_04224"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_volcano_val-00002-of-00005_06420.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_volcano_val-00002-of-00005_06420.png", "id": "places_volcano_val-00002-of-00005_06420"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_cliff_val-00001-of-00005_04684.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_cliff_val-00001-of-00005_04684.png", "id": "places_cliff_val-00001-of-00005_04684"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_coast_val-00003-of-00005_01445.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_coast_val-00003-of-00005_01445.png", "id": "places_coast_val-00003-of-00005_01445"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_iceberg_train-00003-of-00005_05063.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_iceberg_train-00003-of-00005_05063.png", "id": "places_iceberg_train-00003-of-00005_05063"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_badlands_train-00002-of-00005_05343.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_badlands_train-00002-of-00005_05343.png", "id": "places_badlands_train-00002-of-00005_05343"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_beach_train-00002-of-00005_00001.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_beach_train-00002-of-00005_00001.png", "id": "places_beach_train-00002-of-00005_00001"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_snowfield_val-00001-of-00005_04406.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_snowfield_val-00001-of-00005_04406.png", "id": "places_snowfield_val-00001-of-00005_04406"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_coast_train-00004-of-00005_03818.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_coast_train-00004-of-00005_03818.png", "id": "places_coast_train-00004-of-00005_03818"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_mountain_snowy_val-00000-of-00005_01431.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_mountain_snowy_val-00000-of-00005_01431.png", "id": "places_mountain_snowy_val-00000-of-00005_01431"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_cliff_train-00000-of-00005_06659.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_cliff_train-00000-of-00005_06659.png", "id": "places_cliff_train-00000-of-00005_06659"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_beach_val-00002-of-00005_00001.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_beach_val-00002-of-00005_00001.png", "id": "places_beach_val-00002-of-00005_00001"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_glacier_train-00002-of-00005_02857.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_glacier_train-00002-of-00005_02857.png", "id": "places_glacier_train-00002-of-00005_02857"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_lake_natural_val-00000-of-00005_02538.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_lake_natural_val-00000-of-00005_02538.png", "id": "places_lake_natural_val-00000-of-00005_02538"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_mountain_train-00001-of-00005_03604.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_mountain_train-00001-of-00005_03604.png", "id": "places_mountain_train-00001-of-00005_03604"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_lake_natural_val-00000-of-00005_03614.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_lake_natural_val-00000-of-00005_03614.png", "id": "places_lake_natural_val-00000-of-00005_03614"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_iceberg_val-00000-of-00005_02236.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_iceberg_val-00000-of-00005_02236.png", "id": "places_iceberg_val-00000-of-00005_02236"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_coast_train-00003-of-00005_07060.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_coast_train-00003-of-00005_07060.png", "id": "places_coast_train-00003-of-00005_07060"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_glacier_val-00000-of-00005_04332.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_glacier_val-00000-of-00005_04332.png", "id": "places_glacier_val-00000-of-00005_04332"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_mountain_val-00004-of-00005_02608.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_mountain_val-00004-of-00005_02608.png", "id": "places_mountain_val-00004-of-00005_02608"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_volcano_train-00000-of-00005_02781.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_volcano_train-00000-of-00005_02781.png", "id": "places_volcano_train-00000-of-00005_02781"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_coast_val-00001-of-00005_06173.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_coast_val-00001-of-00005_06173.png", "id": "places_coast_val-00001-of-00005_06173"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_field_wild_val-00000-of-00005_07117.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_field_wild_val-00000-of-00005_07117.png", "id": "places_field_wild_val-00000-of-00005_07117"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_volcano_val-00004-of-00005_02790.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_volcano_val-00004-of-00005_02790.png", "id": "places_volcano_val-00004-of-00005_02790"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_sky_val-00002-of-00005_01452.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_sky_val-00002-of-00005_01452.png", "id": "places_sky_val-00002-of-00005_01452"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_tundra_val-00000-of-00005_03566.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_tundra_val-00000-of-00005_03566.png", "id": "places_tundra_val-00000-of-00005_03566"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_glacier_val-00003-of-00005_00988.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_glacier_val-00003-of-00005_00988.png", "id": "places_glacier_val-00003-of-00005_00988"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_volcano_val-00002-of-00005_03213.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_volcano_val-00002-of-00005_03213.png", "id": "places_volcano_val-00002-of-00005_03213"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_badlands_val-00002-of-00005_07139.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_badlands_val-00002-of-00005_07139.png", "id": "places_badlands_val-00002-of-00005_07139"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_canyon_train-00004-of-00005_01215.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_canyon_train-00004-of-00005_01215.png", "id": "places_canyon_train-00004-of-00005_01215"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_volcano_val-00003-of-00005_02271.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_volcano_val-00003-of-00005_02271.png", "id": "places_volcano_val-00003-of-00005_02271"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_badlands_train-00002-of-00005_07031.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_badlands_train-00002-of-00005_07031.png", "id": "places_badlands_train-00002-of-00005_07031"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_glacier_val-00001-of-00005_05956.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_glacier_val-00001-of-00005_05956.png", "id": "places_glacier_val-00001-of-00005_05956"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_tundra_val-00002-of-00005_01532.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_tundra_val-00002-of-00005_01532.png", "id": "places_tundra_val-00002-of-00005_01532"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_cliff_val-00001-of-00005_05172.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_cliff_val-00001-of-00005_05172.png", "id": "places_cliff_val-00001-of-00005_05172"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_snowfield_val-00002-of-00005_01502.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_snowfield_val-00002-of-00005_01502.png", "id": "places_snowfield_val-00002-of-00005_01502"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_canyon_val-00002-of-00005_05335.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_canyon_val-00002-of-00005_05335.png", "id": "places_canyon_val-00002-of-00005_05335"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_mountain_snowy_train-00003-of-00005_00396.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_mountain_snowy_train-00003-of-00005_00396.png", "id": "places_mountain_snowy_train-00003-of-00005_00396"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_lake_natural_train-00004-of-00005_04211.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_lake_natural_train-00004-of-00005_04211.png", "id": "places_lake_natural_train-00004-of-00005_04211"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_volcano_train-00004-of-00005_03338.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_volcano_train-00004-of-00005_03338.png", "id": "places_volcano_train-00004-of-00005_03338"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_butte_train-00001-of-00005_04610.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_butte_train-00001-of-00005_04610.png", "id": "places_butte_train-00001-of-00005_04610"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_badlands_val-00002-of-00005_06028.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_badlands_val-00002-of-00005_06028.png", "id": "places_badlands_val-00002-of-00005_06028"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_glacier_val-00000-of-00005_04603.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_glacier_val-00000-of-00005_04603.png", "id": "places_glacier_val-00000-of-00005_04603"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_volcano_val-00003-of-00005_03864.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_volcano_val-00003-of-00005_03864.png", "id": "places_volcano_val-00003-of-00005_03864"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_cliff_val-00001-of-00005_04331.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_cliff_val-00001-of-00005_04331.png", "id": "places_cliff_val-00001-of-00005_04331"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_coast_val-00004-of-00005_06165.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_coast_val-00004-of-00005_06165.png", "id": "places_coast_val-00004-of-00005_06165"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_canyon_train-00004-of-00005_07055.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_canyon_train-00004-of-00005_07055.png", "id": "places_canyon_train-00004-of-00005_07055"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_mountain_train-00002-of-00005_07219.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_mountain_train-00002-of-00005_07219.png", "id": "places_mountain_train-00002-of-00005_07219"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_iceberg_val-00004-of-00005_00303.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_iceberg_val-00004-of-00005_00303.png", "id": "places_iceberg_val-00004-of-00005_00303"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_glacier_train-00002-of-00005_03728.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_glacier_train-00002-of-00005_03728.png", "id": "places_glacier_train-00002-of-00005_03728"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_glacier_train-00001-of-00005_06391.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_glacier_train-00001-of-00005_06391.png", "id": "places_glacier_train-00001-of-00005_06391"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_valley_val-00001-of-00005_06930.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_valley_val-00001-of-00005_06930.png", "id": "places_valley_val-00001-of-00005_06930"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_volcano_train-00004-of-00005_03748.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_volcano_train-00004-of-00005_03748.png", "id": "places_volcano_train-00004-of-00005_03748"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_mountain_snowy_train-00004-of-00005_00286.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_mountain_snowy_train-00004-of-00005_00286.png", "id": "places_mountain_snowy_train-00004-of-00005_00286"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_volcano_val-00001-of-00005_06660.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_volcano_val-00001-of-00005_06660.png", "id": "places_volcano_val-00001-of-00005_06660"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_cliff_train-00004-of-00005_03239.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_cliff_train-00004-of-00005_03239.png", "id": "places_cliff_train-00004-of-00005_03239"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_cliff_val-00002-of-00005_02933.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_cliff_val-00002-of-00005_02933.png", "id": "places_cliff_val-00002-of-00005_02933"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_lake_natural_train-00001-of-00005_05932.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_lake_natural_train-00001-of-00005_05932.png", "id": "places_lake_natural_train-00001-of-00005_05932"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_cliff_train-00004-of-00005_06633.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_cliff_train-00004-of-00005_06633.png", "id": "places_cliff_train-00004-of-00005_06633"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_snowfield_train-00003-of-00005_02459.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_snowfield_train-00003-of-00005_02459.png", "id": "places_snowfield_train-00003-of-00005_02459"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_iceberg_val-00001-of-00005_00565.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_iceberg_val-00001-of-00005_00565.png", "id": "places_iceberg_val-00001-of-00005_00565"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_butte_train-00003-of-00005_07102.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_butte_train-00003-of-00005_07102.png", "id": "places_butte_train-00003-of-00005_07102"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_glacier_val-00002-of-00005_02857.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_glacier_val-00002-of-00005_02857.png", "id": "places_glacier_val-00002-of-00005_02857"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_wave_val-00002-of-00005_03505.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_wave_val-00002-of-00005_03505.png", "id": "places_wave_val-00002-of-00005_03505"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_ocean_val-00000-of-00005_02307.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_ocean_val-00000-of-00005_02307.png", "id": "places_ocean_val-00000-of-00005_02307"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_glacier_train-00003-of-00005_04233.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_glacier_train-00003-of-00005_04233.png", "id": "places_glacier_train-00003-of-00005_04233"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_desert_sand_val-00001-of-00005_03075.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_desert_sand_val-00001-of-00005_03075.png", "id": "places_desert_sand_val-00001-of-00005_03075"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_lake_natural_train-00000-of-00005_05484.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_lake_natural_train-00000-of-00005_05484.png", "id": "places_lake_natural_train-00000-of-00005_05484"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_valley_val-00004-of-00005_01255.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_valley_val-00004-of-00005_01255.png", "id": "places_valley_val-00004-of-00005_01255"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_mountain_snowy_train-00003-of-00005_02263.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_mountain_snowy_train-00003-of-00005_02263.png", "id": "places_mountain_snowy_train-00003-of-00005_02263"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_coast_val-00004-of-00005_06230.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_coast_val-00004-of-00005_06230.png", "id": "places_coast_val-00004-of-00005_06230"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_valley_val-00002-of-00005_04875.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_valley_val-00002-of-00005_04875.png", "id": "places_valley_val-00002-of-00005_04875"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_sky_train-00000-of-00005_06561.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_sky_train-00000-of-00005_06561.png", "id": "places_sky_train-00000-of-00005_06561"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_field_wild_train-00004-of-00005_02808.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_field_wild_train-00004-of-00005_02808.png", "id": "places_field_wild_train-00004-of-00005_02808"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_cliff_train-00004-of-00005_05782.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_cliff_train-00004-of-00005_05782.png", "id": "places_cliff_train-00004-of-00005_05782"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_glacier_val-00004-of-00005_02315.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_glacier_val-00004-of-00005_02315.png", "id": "places_glacier_val-00004-of-00005_02315"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_butte_val-00000-of-00005_03088.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_butte_val-00000-of-00005_03088.png", "id": "places_butte_val-00000-of-00005_03088"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_ocean_val-00002-of-00005_00902.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_ocean_val-00002-of-00005_00902.png", "id": "places_ocean_val-00002-of-00005_00902"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_badlands_val-00004-of-00005_02199.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_badlands_val-00004-of-00005_02199.png", "id": "places_badlands_val-00004-of-00005_02199"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_field_wild_val-00001-of-00005_03829.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_field_wild_val-00001-of-00005_03829.png", "id": "places_field_wild_val-00001-of-00005_03829"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_snowfield_train-00004-of-00005_00349.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_snowfield_train-00004-of-00005_00349.png", "id": "places_snowfield_train-00004-of-00005_00349"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_valley_val-00002-of-00005_00298.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_valley_val-00002-of-00005_00298.png", "id": "places_valley_val-00002-of-00005_00298"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_volcano_train-00001-of-00005_07154.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_volcano_train-00001-of-00005_07154.png", "id": "places_volcano_train-00001-of-00005_07154"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_snowfield_val-00004-of-00005_02234.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_snowfield_val-00004-of-00005_02234.png", "id": "places_snowfield_val-00004-of-00005_02234"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_volcano_val-00001-of-00005_07154.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_volcano_val-00001-of-00005_07154.png", "id": "places_volcano_val-00001-of-00005_07154"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_mountain_snowy_val-00002-of-00005_05690.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_mountain_snowy_val-00002-of-00005_05690.png", "id": "places_mountain_snowy_val-00002-of-00005_05690"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_badlands_val-00000-of-00005_01922.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_badlands_val-00000-of-00005_01922.png", "id": "places_badlands_val-00000-of-00005_01922"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_sky_val-00003-of-00005_02248.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_sky_val-00003-of-00005_02248.png", "id": "places_sky_val-00003-of-00005_02248"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_canyon_val-00002-of-00005_04693.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_canyon_val-00002-of-00005_04693.png", "id": "places_canyon_val-00002-of-00005_04693"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_snowfield_train-00004-of-00005_06157.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_snowfield_train-00004-of-00005_06157.png", "id": "places_snowfield_train-00004-of-00005_06157"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_lake_natural_train-00000-of-00005_06720.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_lake_natural_train-00000-of-00005_06720.png", "id": "places_lake_natural_train-00000-of-00005_06720"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_mountain_snowy_val-00001-of-00005_00292.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_mountain_snowy_val-00001-of-00005_00292.png", "id": "places_mountain_snowy_val-00001-of-00005_00292"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_beach_train-00003-of-00005_00294.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_beach_train-00003-of-00005_00294.png", "id": "places_beach_train-00003-of-00005_00294"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_volcano_train-00001-of-00005_04073.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_volcano_train-00001-of-00005_04073.png", "id": "places_volcano_train-00001-of-00005_04073"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_mountain_snowy_val-00003-of-00005_00451.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_mountain_snowy_val-00003-of-00005_00451.png", "id": "places_mountain_snowy_val-00003-of-00005_00451"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_iceberg_train-00004-of-00005_01843.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_iceberg_train-00004-of-00005_01843.png", "id": "places_iceberg_train-00004-of-00005_01843"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_tundra_train-00001-of-00005_03966.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_tundra_train-00001-of-00005_03966.png", "id": "places_tundra_train-00001-of-00005_03966"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_ocean_val-00004-of-00005_03304.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_ocean_val-00004-of-00005_03304.png", "id": "places_ocean_val-00004-of-00005_03304"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_field_wild_train-00003-of-00005_05233.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_field_wild_train-00003-of-00005_05233.png", "id": "places_field_wild_train-00003-of-00005_05233"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_islet_val-00004-of-00005_06771.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_islet_val-00004-of-00005_06771.png", "id": "places_islet_val-00004-of-00005_06771"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_field_wild_train-00003-of-00005_03707.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_field_wild_train-00003-of-00005_03707.png", "id": "places_field_wild_train-00003-of-00005_03707"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_lake_natural_val-00003-of-00005_03714.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_lake_natural_val-00003-of-00005_03714.png", "id": "places_lake_natural_val-00003-of-00005_03714"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_lake_natural_train-00002-of-00005_00306.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_lake_natural_train-00002-of-00005_00306.png", "id": "places_lake_natural_train-00002-of-00005_00306"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_glacier_val-00001-of-00005_05474.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_glacier_val-00001-of-00005_05474.png", "id": "places_glacier_val-00001-of-00005_05474"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_cliff_train-00001-of-00005_04331.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_cliff_train-00001-of-00005_04331.png", "id": "places_cliff_train-00001-of-00005_04331"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_mountain_snowy_val-00003-of-00005_02494.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_mountain_snowy_val-00003-of-00005_02494.png", "id": "places_mountain_snowy_val-00003-of-00005_02494"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_mountain_snowy_val-00003-of-00005_02148.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_mountain_snowy_val-00003-of-00005_02148.png", "id": "places_mountain_snowy_val-00003-of-00005_02148"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_lake_natural_val-00002-of-00005_04438.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_lake_natural_val-00002-of-00005_04438.png", "id": "places_lake_natural_val-00002-of-00005_04438"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_valley_train-00004-of-00005_02361.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_valley_train-00004-of-00005_02361.png", "id": "places_valley_train-00004-of-00005_02361"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_field_wild_val-00002-of-00005_02229.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_field_wild_val-00002-of-00005_02229.png", "id": "places_field_wild_val-00002-of-00005_02229"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_islet_train-00001-of-00005_02683.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_islet_train-00001-of-00005_02683.png", "id": "places_islet_train-00001-of-00005_02683"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_snowfield_train-00002-of-00005_02024.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_snowfield_train-00002-of-00005_02024.png", "id": "places_snowfield_train-00002-of-00005_02024"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_butte_val-00003-of-00005_02154.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_butte_val-00003-of-00005_02154.png", "id": "places_butte_val-00003-of-00005_02154"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_field_wild_train-00001-of-00005_02804.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_field_wild_train-00001-of-00005_02804.png", "id": "places_field_wild_train-00001-of-00005_02804"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_field_wild_train-00004-of-00005_01249.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_field_wild_train-00004-of-00005_01249.png", "id": "places_field_wild_train-00004-of-00005_01249"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_field_wild_train-00003-of-00005_03077.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_field_wild_train-00003-of-00005_03077.png", "id": "places_field_wild_train-00003-of-00005_03077"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_cliff_train-00000-of-00005_06422.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_cliff_train-00000-of-00005_06422.png", "id": "places_cliff_train-00000-of-00005_06422"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_mountain_snowy_val-00002-of-00005_07298.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_mountain_snowy_val-00002-of-00005_07298.png", "id": "places_mountain_snowy_val-00002-of-00005_07298"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_wave_val-00000-of-00005_01278.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_wave_val-00000-of-00005_01278.png", "id": "places_wave_val-00000-of-00005_01278"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_coast_train-00001-of-00005_02355.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_coast_train-00001-of-00005_02355.png", "id": "places_coast_train-00001-of-00005_02355"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_valley_val-00002-of-00005_06355.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_valley_val-00002-of-00005_06355.png", "id": "places_valley_val-00002-of-00005_06355"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_valley_val-00004-of-00005_04505.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_valley_val-00004-of-00005_04505.png", "id": "places_valley_val-00004-of-00005_04505"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_iceberg_train-00003-of-00005_07215.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_iceberg_train-00003-of-00005_07215.png", "id": "places_iceberg_train-00003-of-00005_07215"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_sky_train-00004-of-00005_00922.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_sky_train-00004-of-00005_00922.png", "id": "places_sky_train-00004-of-00005_00922"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_wave_val-00002-of-00005_03912.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_wave_val-00002-of-00005_03912.png", "id": "places_wave_val-00002-of-00005_03912"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_lake_natural_train-00003-of-00005_06523.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_lake_natural_train-00003-of-00005_06523.png", "id": "places_lake_natural_train-00003-of-00005_06523"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_lake_natural_val-00003-of-00005_00097.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_lake_natural_val-00003-of-00005_00097.png", "id": "places_lake_natural_val-00003-of-00005_00097"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_wave_val-00000-of-00005_02990.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_wave_val-00000-of-00005_02990.png", "id": "places_wave_val-00000-of-00005_02990"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_coast_val-00003-of-00005_00312.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_coast_val-00003-of-00005_00312.png", "id": "places_coast_val-00003-of-00005_00312"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_tundra_train-00004-of-00005_00728.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_tundra_train-00004-of-00005_00728.png", "id": "places_tundra_train-00004-of-00005_00728"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_lake_natural_train-00004-of-00005_03191.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_lake_natural_train-00004-of-00005_03191.png", "id": "places_lake_natural_train-00004-of-00005_03191"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_tundra_val-00000-of-00005_01659.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_tundra_val-00000-of-00005_01659.png", "id": "places_tundra_val-00000-of-00005_01659"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_butte_train-00002-of-00005_02571.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_butte_train-00002-of-00005_02571.png", "id": "places_butte_train-00002-of-00005_02571"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_mountain_snowy_val-00000-of-00005_06899.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_mountain_snowy_val-00000-of-00005_06899.png", "id": "places_mountain_snowy_val-00000-of-00005_06899"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_valley_val-00000-of-00005_05345.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_valley_val-00000-of-00005_05345.png", "id": "places_valley_val-00000-of-00005_05345"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_cliff_train-00001-of-00005_02504.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_cliff_train-00001-of-00005_02504.png", "id": "places_cliff_train-00001-of-00005_02504"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_sky_val-00000-of-00005_02615.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_sky_val-00000-of-00005_02615.png", "id": "places_sky_val-00000-of-00005_02615"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_snowfield_val-00001-of-00005_06038.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_snowfield_val-00001-of-00005_06038.png", "id": "places_snowfield_val-00001-of-00005_06038"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_beach_train-00001-of-00005_02131.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_beach_train-00001-of-00005_02131.png", "id": "places_beach_train-00001-of-00005_02131"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_valley_train-00004-of-00005_01214.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_valley_train-00004-of-00005_01214.png", "id": "places_valley_train-00004-of-00005_01214"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_mountain_train-00003-of-00005_05485.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_mountain_train-00003-of-00005_05485.png", "id": "places_mountain_train-00003-of-00005_05485"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_canyon_train-00003-of-00005_03076.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_canyon_train-00003-of-00005_03076.png", "id": "places_canyon_train-00003-of-00005_03076"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_canyon_train-00004-of-00005_01013.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_canyon_train-00004-of-00005_01013.png", "id": "places_canyon_train-00004-of-00005_01013"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_iceberg_train-00003-of-00005_02175.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_iceberg_train-00003-of-00005_02175.png", "id": "places_iceberg_train-00003-of-00005_02175"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_cliff_train-00001-of-00005_04213.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_cliff_train-00001-of-00005_04213.png", "id": "places_cliff_train-00001-of-00005_04213"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_wave_val-00004-of-00005_03189.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_wave_val-00004-of-00005_03189.png", "id": "places_wave_val-00004-of-00005_03189"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_glacier_val-00004-of-00005_02487.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_glacier_val-00004-of-00005_02487.png", "id": "places_glacier_val-00004-of-00005_02487"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_field_wild_val-00004-of-00005_05777.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_field_wild_val-00004-of-00005_05777.png", "id": "places_field_wild_val-00004-of-00005_05777"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_iceberg_val-00003-of-00005_06379.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_iceberg_val-00003-of-00005_06379.png", "id": "places_iceberg_val-00003-of-00005_06379"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_glacier_val-00002-of-00005_01980.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_glacier_val-00002-of-00005_01980.png", "id": "places_glacier_val-00002-of-00005_01980"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_ocean_val-00004-of-00005_03823.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_ocean_val-00004-of-00005_03823.png", "id": "places_ocean_val-00004-of-00005_03823"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_desert_sand_val-00002-of-00005_02044.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_desert_sand_val-00002-of-00005_02044.png", "id": "places_desert_sand_val-00002-of-00005_02044"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_coast_val-00003-of-00005_00166.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_coast_val-00003-of-00005_00166.png", "id": "places_coast_val-00003-of-00005_00166"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_ocean_train-00004-of-00005_05744.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_ocean_train-00004-of-00005_05744.png", "id": "places_ocean_train-00004-of-00005_05744"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_valley_val-00001-of-00005_04627.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_valley_val-00001-of-00005_04627.png", "id": "places_valley_val-00001-of-00005_04627"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_field_wild_train-00003-of-00005_05591.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_field_wild_train-00003-of-00005_05591.png", "id": "places_field_wild_train-00003-of-00005_05591"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_tundra_val-00002-of-00005_03806.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_tundra_val-00002-of-00005_03806.png", "id": "places_tundra_val-00002-of-00005_03806"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_snowfield_train-00004-of-00005_04468.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_snowfield_train-00004-of-00005_04468.png", "id": "places_snowfield_train-00004-of-00005_04468"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_islet_train-00003-of-00005_05064.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_islet_train-00003-of-00005_05064.png", "id": "places_islet_train-00003-of-00005_05064"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_badlands_val-00003-of-00005_00327.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_badlands_val-00003-of-00005_00327.png", "id": "places_badlands_val-00003-of-00005_00327"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_snowfield_val-00000-of-00005_00839.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_snowfield_val-00000-of-00005_00839.png", "id": "places_snowfield_val-00000-of-00005_00839"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_lake_natural_val-00004-of-00005_02103.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_lake_natural_val-00004-of-00005_02103.png", "id": "places_lake_natural_val-00004-of-00005_02103"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_butte_train-00000-of-00005_07036.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_butte_train-00000-of-00005_07036.png", "id": "places_butte_train-00000-of-00005_07036"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_glacier_train-00002-of-00005_00773.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_glacier_train-00002-of-00005_00773.png", "id": "places_glacier_train-00002-of-00005_00773"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_tundra_val-00004-of-00005_05491.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_tundra_val-00004-of-00005_05491.png", "id": "places_tundra_val-00004-of-00005_05491"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_butte_train-00000-of-00005_00113.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_butte_train-00000-of-00005_00113.png", "id": "places_butte_train-00000-of-00005_00113"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_wave_train-00002-of-00005_00612.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_wave_train-00002-of-00005_00612.png", "id": "places_wave_train-00002-of-00005_00612"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_islet_val-00000-of-00005_04049.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_islet_val-00000-of-00005_04049.png", "id": "places_islet_val-00000-of-00005_04049"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_canyon_train-00001-of-00005_04197.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_canyon_train-00001-of-00005_04197.png", "id": "places_canyon_train-00001-of-00005_04197"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_desert_sand_val-00001-of-00005_01695.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_desert_sand_val-00001-of-00005_01695.png", "id": "places_desert_sand_val-00001-of-00005_01695"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_desert_sand_train-00004-of-00005_02688.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_desert_sand_train-00004-of-00005_02688.png", "id": "places_desert_sand_train-00004-of-00005_02688"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_snowfield_train-00001-of-00005_00890.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_snowfield_train-00001-of-00005_00890.png", "id": "places_snowfield_train-00001-of-00005_00890"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_wave_train-00004-of-00005_03820.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_wave_train-00004-of-00005_03820.png", "id": "places_wave_train-00004-of-00005_03820"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_desert_sand_val-00000-of-00005_02115.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_desert_sand_val-00000-of-00005_02115.png", "id": "places_desert_sand_val-00000-of-00005_02115"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_iceberg_train-00003-of-00005_05321.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_iceberg_train-00003-of-00005_05321.png", "id": "places_iceberg_train-00003-of-00005_05321"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_lake_natural_val-00002-of-00005_01225.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_lake_natural_val-00002-of-00005_01225.png", "id": "places_lake_natural_val-00002-of-00005_01225"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_islet_val-00003-of-00005_01169.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_islet_val-00003-of-00005_01169.png", "id": "places_islet_val-00003-of-00005_01169"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_mountain_snowy_train-00000-of-00005_04344.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_mountain_snowy_train-00000-of-00005_04344.png", "id": "places_mountain_snowy_train-00000-of-00005_04344"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_snowfield_train-00003-of-00005_05553.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_snowfield_train-00003-of-00005_05553.png", "id": "places_snowfield_train-00003-of-00005_05553"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_beach_val-00003-of-00005_06516.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_beach_val-00003-of-00005_06516.png", "id": "places_beach_val-00003-of-00005_06516"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_desert_sand_train-00000-of-00005_01063.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_desert_sand_train-00000-of-00005_01063.png", "id": "places_desert_sand_train-00000-of-00005_01063"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_snowfield_train-00001-of-00005_01755.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_snowfield_train-00001-of-00005_01755.png", "id": "places_snowfield_train-00001-of-00005_01755"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_field_wild_train-00002-of-00005_05211.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_field_wild_train-00002-of-00005_05211.png", "id": "places_field_wild_train-00002-of-00005_05211"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_wave_train-00004-of-00005_00543.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_wave_train-00004-of-00005_00543.png", "id": "places_wave_train-00004-of-00005_00543"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_ocean_train-00000-of-00005_04354.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_ocean_train-00000-of-00005_04354.png", "id": "places_ocean_train-00000-of-00005_04354"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_beach_train-00002-of-00005_07113.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_beach_train-00002-of-00005_07113.png", "id": "places_beach_train-00002-of-00005_07113"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_islet_val-00002-of-00005_06415.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_islet_val-00002-of-00005_06415.png", "id": "places_islet_val-00002-of-00005_06415"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_tundra_train-00000-of-00005_02212.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_tundra_train-00000-of-00005_02212.png", "id": "places_tundra_train-00000-of-00005_02212"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_valley_val-00001-of-00005_03344.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_valley_val-00001-of-00005_03344.png", "id": "places_valley_val-00001-of-00005_03344"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_cliff_train-00001-of-00005_01476.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_cliff_train-00001-of-00005_01476.png", "id": "places_cliff_train-00001-of-00005_01476"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_field_wild_train-00001-of-00005_04488.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_field_wild_train-00001-of-00005_04488.png", "id": "places_field_wild_train-00001-of-00005_04488"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_canyon_train-00000-of-00005_04225.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_canyon_train-00000-of-00005_04225.png", "id": "places_canyon_train-00000-of-00005_04225"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_valley_val-00000-of-00005_06308.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_valley_val-00000-of-00005_06308.png", "id": "places_valley_val-00000-of-00005_06308"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_valley_val-00003-of-00005_06453.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_valley_val-00003-of-00005_06453.png", "id": "places_valley_val-00003-of-00005_06453"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_volcano_val-00002-of-00005_06041.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_volcano_val-00002-of-00005_06041.png", "id": "places_volcano_val-00002-of-00005_06041"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_islet_val-00002-of-00005_04878.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_islet_val-00002-of-00005_04878.png", "id": "places_islet_val-00002-of-00005_04878"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_beach_val-00001-of-00005_01734.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_beach_val-00001-of-00005_01734.png", "id": "places_beach_val-00001-of-00005_01734"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_wave_val-00000-of-00005_04975.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_wave_val-00000-of-00005_04975.png", "id": "places_wave_val-00000-of-00005_04975"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_sky_train-00000-of-00005_00995.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_sky_train-00000-of-00005_00995.png", "id": "places_sky_train-00000-of-00005_00995"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_badlands_val-00004-of-00005_00972.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_badlands_val-00004-of-00005_00972.png", "id": "places_badlands_val-00004-of-00005_00972"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_wave_train-00001-of-00005_06835.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_wave_train-00001-of-00005_06835.png", "id": "places_wave_train-00001-of-00005_06835"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_volcano_train-00001-of-00005_02851.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_volcano_train-00001-of-00005_02851.png", "id": "places_volcano_train-00001-of-00005_02851"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_iceberg_val-00000-of-00005_03595.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_iceberg_val-00000-of-00005_03595.png", "id": "places_iceberg_val-00000-of-00005_03595"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_beach_val-00001-of-00005_01347.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_beach_val-00001-of-00005_01347.png", "id": "places_beach_val-00001-of-00005_01347"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_mountain_snowy_val-00001-of-00005_01262.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_mountain_snowy_val-00001-of-00005_01262.png", "id": "places_mountain_snowy_val-00001-of-00005_01262"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_mountain_val-00000-of-00005_07234.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_mountain_val-00000-of-00005_07234.png", "id": "places_mountain_val-00000-of-00005_07234"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_sky_train-00002-of-00005_06105.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_sky_train-00002-of-00005_06105.png", "id": "places_sky_train-00002-of-00005_06105"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_mountain_train-00003-of-00005_04665.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_mountain_train-00003-of-00005_04665.png", "id": "places_mountain_train-00003-of-00005_04665"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_islet_train-00000-of-00005_00134.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_islet_train-00000-of-00005_00134.png", "id": "places_islet_train-00000-of-00005_00134"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_iceberg_train-00002-of-00005_00615.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_iceberg_train-00002-of-00005_00615.png", "id": "places_iceberg_train-00002-of-00005_00615"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_badlands_val-00000-of-00005_04851.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_badlands_val-00000-of-00005_04851.png", "id": "places_badlands_val-00000-of-00005_04851"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_tundra_val-00003-of-00005_04743.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_tundra_val-00003-of-00005_04743.png", "id": "places_tundra_val-00003-of-00005_04743"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_field_wild_val-00001-of-00005_07065.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_field_wild_val-00001-of-00005_07065.png", "id": "places_field_wild_val-00001-of-00005_07065"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_mountain_snowy_train-00004-of-00005_06503.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_mountain_snowy_train-00004-of-00005_06503.png", "id": "places_mountain_snowy_train-00004-of-00005_06503"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_wave_train-00000-of-00005_05520.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_wave_train-00000-of-00005_05520.png", "id": "places_wave_train-00000-of-00005_05520"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_canyon_val-00000-of-00005_05591.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_canyon_val-00000-of-00005_05591.png", "id": "places_canyon_val-00000-of-00005_05591"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_tundra_train-00002-of-00005_04466.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_tundra_train-00002-of-00005_04466.png", "id": "places_tundra_train-00002-of-00005_04466"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_valley_train-00001-of-00005_07052.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_valley_train-00001-of-00005_07052.png", "id": "places_valley_train-00001-of-00005_07052"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_beach_val-00000-of-00005_03182.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_beach_val-00000-of-00005_03182.png", "id": "places_beach_val-00000-of-00005_03182"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_wave_train-00000-of-00005_06194.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_wave_train-00000-of-00005_06194.png", "id": "places_wave_train-00000-of-00005_06194"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_volcano_train-00003-of-00005_03864.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_volcano_train-00003-of-00005_03864.png", "id": "places_volcano_train-00003-of-00005_03864"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_ocean_val-00000-of-00005_05879.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_ocean_val-00000-of-00005_05879.png", "id": "places_ocean_val-00000-of-00005_05879"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_wave_train-00001-of-00005_07072.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_wave_train-00001-of-00005_07072.png", "id": "places_wave_train-00001-of-00005_07072"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_tundra_val-00002-of-00005_03632.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_tundra_val-00002-of-00005_03632.png", "id": "places_tundra_val-00002-of-00005_03632"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_desert_sand_val-00003-of-00005_00155.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_desert_sand_val-00003-of-00005_00155.png", "id": "places_desert_sand_val-00003-of-00005_00155"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_coast_val-00003-of-00005_06998.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_coast_val-00003-of-00005_06998.png", "id": "places_coast_val-00003-of-00005_06998"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_butte_train-00004-of-00005_07250.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_butte_train-00004-of-00005_07250.png", "id": "places_butte_train-00004-of-00005_07250"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_lake_natural_val-00000-of-00005_00163.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_lake_natural_val-00000-of-00005_00163.png", "id": "places_lake_natural_val-00000-of-00005_00163"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_mountain_val-00004-of-00005_00451.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_mountain_val-00004-of-00005_00451.png", "id": "places_mountain_val-00004-of-00005_00451"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_mountain_snowy_train-00003-of-00005_00451.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_mountain_snowy_train-00003-of-00005_00451.png", "id": "places_mountain_snowy_train-00003-of-00005_00451"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_snowfield_train-00001-of-00005_05561.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_snowfield_train-00001-of-00005_05561.png", "id": "places_snowfield_train-00001-of-00005_05561"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_cliff_val-00002-of-00005_03837.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_cliff_val-00002-of-00005_03837.png", "id": "places_cliff_val-00002-of-00005_03837"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_islet_val-00000-of-00005_01947.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_islet_val-00000-of-00005_01947.png", "id": "places_islet_val-00000-of-00005_01947"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_cliff_train-00003-of-00005_07027.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_cliff_train-00003-of-00005_07027.png", "id": "places_cliff_train-00003-of-00005_07027"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_snowfield_train-00001-of-00005_00388.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_snowfield_train-00001-of-00005_00388.png", "id": "places_snowfield_train-00001-of-00005_00388"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_cliff_val-00004-of-00005_06493.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_cliff_val-00004-of-00005_06493.png", "id": "places_cliff_val-00004-of-00005_06493"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_valley_val-00000-of-00005_00474.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_valley_val-00000-of-00005_00474.png", "id": "places_valley_val-00000-of-00005_00474"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_snowfield_train-00003-of-00005_02940.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_snowfield_train-00003-of-00005_02940.png", "id": "places_snowfield_train-00003-of-00005_02940"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_coast_train-00003-of-00005_06217.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_coast_train-00003-of-00005_06217.png", "id": "places_coast_train-00003-of-00005_06217"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_badlands_train-00001-of-00005_03849.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_badlands_train-00001-of-00005_03849.png", "id": "places_badlands_train-00001-of-00005_03849"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_cliff_train-00004-of-00005_05779.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_cliff_train-00004-of-00005_05779.png", "id": "places_cliff_train-00004-of-00005_05779"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_sky_val-00004-of-00005_06969.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_sky_val-00004-of-00005_06969.png", "id": "places_sky_val-00004-of-00005_06969"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_butte_train-00002-of-00005_01885.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_butte_train-00002-of-00005_01885.png", "id": "places_butte_train-00002-of-00005_01885"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_mountain_snowy_val-00003-of-00005_01411.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_mountain_snowy_val-00003-of-00005_01411.png", "id": "places_mountain_snowy_val-00003-of-00005_01411"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_desert_sand_val-00000-of-00005_02121.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_desert_sand_val-00000-of-00005_02121.png", "id": "places_desert_sand_val-00000-of-00005_02121"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_coast_val-00001-of-00005_01340.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_coast_val-00001-of-00005_01340.png", "id": "places_coast_val-00001-of-00005_01340"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_desert_sand_val-00000-of-00005_05741.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_desert_sand_val-00000-of-00005_05741.png", "id": "places_desert_sand_val-00000-of-00005_05741"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_glacier_val-00003-of-00005_00314.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_glacier_val-00003-of-00005_00314.png", "id": "places_glacier_val-00003-of-00005_00314"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_sky_train-00002-of-00005_02593.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_sky_train-00002-of-00005_02593.png", "id": "places_sky_train-00002-of-00005_02593"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_sky_val-00003-of-00005_07002.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_sky_val-00003-of-00005_07002.png", "id": "places_sky_val-00003-of-00005_07002"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_iceberg_train-00000-of-00005_03076.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_iceberg_train-00000-of-00005_03076.png", "id": "places_iceberg_train-00000-of-00005_03076"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_volcano_train-00004-of-00005_05925.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_volcano_train-00004-of-00005_05925.png", "id": "places_volcano_train-00004-of-00005_05925"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_canyon_train-00001-of-00005_01798.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_canyon_train-00001-of-00005_01798.png", "id": "places_canyon_train-00001-of-00005_01798"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_mountain_val-00001-of-00005_01193.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_mountain_val-00001-of-00005_01193.png", "id": "places_mountain_val-00001-of-00005_01193"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_wave_val-00000-of-00005_03377.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_wave_val-00000-of-00005_03377.png", "id": "places_wave_val-00000-of-00005_03377"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_cliff_val-00004-of-00005_06633.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_cliff_val-00004-of-00005_06633.png", "id": "places_cliff_val-00004-of-00005_06633"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_sky_train-00004-of-00005_00687.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_sky_train-00004-of-00005_00687.png", "id": "places_sky_train-00004-of-00005_00687"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_canyon_train-00003-of-00005_05420.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_canyon_train-00003-of-00005_05420.png", "id": "places_canyon_train-00003-of-00005_05420"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_lake_natural_train-00000-of-00005_04024.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_lake_natural_train-00000-of-00005_04024.png", "id": "places_lake_natural_train-00000-of-00005_04024"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_coast_val-00002-of-00005_00986.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_coast_val-00002-of-00005_00986.png", "id": "places_coast_val-00002-of-00005_00986"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_mountain_val-00001-of-00005_03604.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_mountain_val-00001-of-00005_03604.png", "id": "places_mountain_val-00001-of-00005_03604"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_iceberg_val-00004-of-00005_02256.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_iceberg_val-00004-of-00005_02256.png", "id": "places_iceberg_val-00004-of-00005_02256"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_sky_train-00003-of-00005_07002.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_sky_train-00003-of-00005_07002.png", "id": "places_sky_train-00003-of-00005_07002"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_beach_train-00003-of-00005_05300.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_beach_train-00003-of-00005_05300.png", "id": "places_beach_train-00003-of-00005_05300"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_tundra_train-00002-of-00005_05015.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_tundra_train-00002-of-00005_05015.png", "id": "places_tundra_train-00002-of-00005_05015"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_coast_val-00003-of-00005_07060.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_coast_val-00003-of-00005_07060.png", "id": "places_coast_val-00003-of-00005_07060"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_islet_val-00001-of-00005_04311.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_islet_val-00001-of-00005_04311.png", "id": "places_islet_val-00001-of-00005_04311"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_mountain_train-00001-of-00005_01193.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_mountain_train-00001-of-00005_01193.png", "id": "places_mountain_train-00001-of-00005_01193"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_cliff_train-00001-of-00005_04855.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_cliff_train-00001-of-00005_04855.png", "id": "places_cliff_train-00001-of-00005_04855"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_mountain_snowy_train-00004-of-00005_04597.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_mountain_snowy_train-00004-of-00005_04597.png", "id": "places_mountain_snowy_train-00004-of-00005_04597"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_desert_sand_train-00004-of-00005_06645.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_desert_sand_train-00004-of-00005_06645.png", "id": "places_desert_sand_train-00004-of-00005_06645"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_tundra_val-00000-of-00005_05145.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_tundra_val-00000-of-00005_05145.png", "id": "places_tundra_val-00000-of-00005_05145"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_desert_sand_train-00001-of-00005_04839.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_desert_sand_train-00001-of-00005_04839.png", "id": "places_desert_sand_train-00001-of-00005_04839"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_ocean_train-00000-of-00005_01801.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_ocean_train-00000-of-00005_01801.png", "id": "places_ocean_train-00000-of-00005_01801"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_volcano_train-00000-of-00005_01832.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_volcano_train-00000-of-00005_01832.png", "id": "places_volcano_train-00000-of-00005_01832"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_butte_val-00001-of-00005_03945.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_butte_val-00001-of-00005_03945.png", "id": "places_butte_val-00001-of-00005_03945"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_snowfield_train-00002-of-00005_05715.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_snowfield_train-00002-of-00005_05715.png", "id": "places_snowfield_train-00002-of-00005_05715"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_canyon_train-00002-of-00005_06636.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_canyon_train-00002-of-00005_06636.png", "id": "places_canyon_train-00002-of-00005_06636"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_beach_train-00003-of-00005_00890.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_beach_train-00003-of-00005_00890.png", "id": "places_beach_train-00003-of-00005_00890"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_tundra_val-00004-of-00005_02902.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_tundra_val-00004-of-00005_02902.png", "id": "places_tundra_val-00004-of-00005_02902"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_coast_val-00004-of-00005_01577.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_coast_val-00004-of-00005_01577.png", "id": "places_coast_val-00004-of-00005_01577"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_ocean_val-00004-of-00005_00156.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_ocean_val-00004-of-00005_00156.png", "id": "places_ocean_val-00004-of-00005_00156"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_coast_val-00000-of-00005_01213.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_coast_val-00000-of-00005_01213.png", "id": "places_coast_val-00000-of-00005_01213"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_beach_val-00002-of-00005_02530.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_beach_val-00002-of-00005_02530.png", "id": "places_beach_val-00002-of-00005_02530"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_butte_train-00001-of-00005_02483.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_butte_train-00001-of-00005_02483.png", "id": "places_butte_train-00001-of-00005_02483"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_volcano_val-00001-of-00005_04599.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_volcano_val-00001-of-00005_04599.png", "id": "places_volcano_val-00001-of-00005_04599"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_coast_train-00004-of-00005_02869.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_coast_train-00004-of-00005_02869.png", "id": "places_coast_train-00004-of-00005_02869"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_islet_val-00003-of-00005_07228.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_islet_val-00003-of-00005_07228.png", "id": "places_islet_val-00003-of-00005_07228"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_glacier_train-00002-of-00005_06711.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_glacier_train-00002-of-00005_06711.png", "id": "places_glacier_train-00002-of-00005_06711"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_cliff_train-00000-of-00005_00888.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_cliff_train-00000-of-00005_00888.png", "id": "places_cliff_train-00000-of-00005_00888"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_glacier_val-00003-of-00005_06846.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_glacier_val-00003-of-00005_06846.png", "id": "places_glacier_val-00003-of-00005_06846"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_badlands_train-00002-of-00005_05827.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_badlands_train-00002-of-00005_05827.png", "id": "places_badlands_train-00002-of-00005_05827"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_field_wild_train-00001-of-00005_01313.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_field_wild_train-00001-of-00005_01313.png", "id": "places_field_wild_train-00001-of-00005_01313"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_butte_val-00000-of-00005_03148.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_butte_val-00000-of-00005_03148.png", "id": "places_butte_val-00000-of-00005_03148"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_desert_sand_val-00001-of-00005_04775.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_desert_sand_val-00001-of-00005_04775.png", "id": "places_desert_sand_val-00001-of-00005_04775"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_wave_train-00000-of-00005_04975.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_wave_train-00000-of-00005_04975.png", "id": "places_wave_train-00000-of-00005_04975"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_volcano_train-00001-of-00005_02241.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_volcano_train-00001-of-00005_02241.png", "id": "places_volcano_train-00001-of-00005_02241"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_beach_val-00000-of-00005_07157.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_beach_val-00000-of-00005_07157.png", "id": "places_beach_val-00000-of-00005_07157"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_desert_sand_train-00000-of-00005_00906.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_desert_sand_train-00000-of-00005_00906.png", "id": "places_desert_sand_train-00000-of-00005_00906"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_field_wild_train-00002-of-00005_06303.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_field_wild_train-00002-of-00005_06303.png", "id": "places_field_wild_train-00002-of-00005_06303"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_butte_val-00000-of-00005_00313.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_butte_val-00000-of-00005_00313.png", "id": "places_butte_val-00000-of-00005_00313"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_mountain_val-00001-of-00005_03653.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_mountain_val-00001-of-00005_03653.png", "id": "places_mountain_val-00001-of-00005_03653"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_sky_train-00002-of-00005_03944.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_sky_train-00002-of-00005_03944.png", "id": "places_sky_train-00002-of-00005_03944"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_lake_natural_train-00002-of-00005_06743.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_lake_natural_train-00002-of-00005_06743.png", "id": "places_lake_natural_train-00002-of-00005_06743"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_iceberg_train-00002-of-00005_05782.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_iceberg_train-00002-of-00005_05782.png", "id": "places_iceberg_train-00002-of-00005_05782"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_desert_sand_val-00000-of-00005_06699.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_desert_sand_val-00000-of-00005_06699.png", "id": "places_desert_sand_val-00000-of-00005_06699"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_beach_train-00003-of-00005_02158.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_beach_train-00003-of-00005_02158.png", "id": "places_beach_train-00003-of-00005_02158"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_volcano_val-00003-of-00005_01759.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_volcano_val-00003-of-00005_01759.png", "id": "places_volcano_val-00003-of-00005_01759"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_ocean_train-00003-of-00005_06656.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_ocean_train-00003-of-00005_06656.png", "id": "places_ocean_train-00003-of-00005_06656"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_badlands_val-00003-of-00005_05813.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_badlands_val-00003-of-00005_05813.png", "id": "places_badlands_val-00003-of-00005_05813"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_tundra_val-00001-of-00005_03745.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_tundra_val-00001-of-00005_03745.png", "id": "places_tundra_val-00001-of-00005_03745"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_field_wild_val-00002-of-00005_05834.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_field_wild_val-00002-of-00005_05834.png", "id": "places_field_wild_val-00002-of-00005_05834"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_ocean_train-00004-of-00005_04542.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_ocean_train-00004-of-00005_04542.png", "id": "places_ocean_train-00004-of-00005_04542"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_lake_natural_val-00001-of-00005_04795.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_lake_natural_val-00001-of-00005_04795.png", "id": "places_lake_natural_val-00001-of-00005_04795"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_tundra_train-00001-of-00005_04812.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_tundra_train-00001-of-00005_04812.png", "id": "places_tundra_train-00001-of-00005_04812"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_beach_train-00000-of-00005_00039.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_beach_train-00000-of-00005_00039.png", "id": "places_beach_train-00000-of-00005_00039"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_sky_train-00003-of-00005_01429.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_sky_train-00003-of-00005_01429.png", "id": "places_sky_train-00003-of-00005_01429"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_lake_natural_val-00001-of-00005_00311.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_lake_natural_val-00001-of-00005_00311.png", "id": "places_lake_natural_val-00001-of-00005_00311"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_butte_train-00002-of-00005_06026.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_butte_train-00002-of-00005_06026.png", "id": "places_butte_train-00002-of-00005_06026"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_snowfield_val-00002-of-00005_02168.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_snowfield_val-00002-of-00005_02168.png", "id": "places_snowfield_val-00002-of-00005_02168"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_coast_val-00000-of-00005_00335.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_coast_val-00000-of-00005_00335.png", "id": "places_coast_val-00000-of-00005_00335"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_wave_train-00004-of-00005_03888.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_wave_train-00004-of-00005_03888.png", "id": "places_wave_train-00004-of-00005_03888"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_tundra_train-00003-of-00005_04747.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_tundra_train-00003-of-00005_04747.png", "id": "places_tundra_train-00003-of-00005_04747"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_badlands_train-00000-of-00005_02678.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_badlands_train-00000-of-00005_02678.png", "id": "places_badlands_train-00000-of-00005_02678"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_mountain_train-00004-of-00005_00481.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_mountain_train-00004-of-00005_00481.png", "id": "places_mountain_train-00004-of-00005_00481"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_wave_val-00003-of-00005_00305.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_wave_val-00003-of-00005_00305.png", "id": "places_wave_val-00003-of-00005_00305"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_desert_sand_train-00001-of-00005_05909.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_desert_sand_train-00001-of-00005_05909.png", "id": "places_desert_sand_train-00001-of-00005_05909"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_snowfield_val-00003-of-00005_04987.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_snowfield_val-00003-of-00005_04987.png", "id": "places_snowfield_val-00003-of-00005_04987"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_iceberg_val-00001-of-00005_01684.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_iceberg_val-00001-of-00005_01684.png", "id": "places_iceberg_val-00001-of-00005_01684"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_beach_train-00002-of-00005_03931.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_beach_train-00002-of-00005_03931.png", "id": "places_beach_train-00002-of-00005_03931"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_iceberg_val-00004-of-00005_02349.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_iceberg_val-00004-of-00005_02349.png", "id": "places_iceberg_val-00004-of-00005_02349"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_lake_natural_val-00000-of-00005_06720.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_lake_natural_val-00000-of-00005_06720.png", "id": "places_lake_natural_val-00000-of-00005_06720"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_sky_val-00002-of-00005_00170.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_sky_val-00002-of-00005_00170.png", "id": "places_sky_val-00002-of-00005_00170"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_butte_train-00003-of-00005_04291.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_butte_train-00003-of-00005_04291.png", "id": "places_butte_train-00003-of-00005_04291"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_canyon_train-00003-of-00005_06404.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_canyon_train-00003-of-00005_06404.png", "id": "places_canyon_train-00003-of-00005_06404"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_canyon_val-00003-of-00005_04361.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_canyon_val-00003-of-00005_04361.png", "id": "places_canyon_val-00003-of-00005_04361"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_beach_train-00003-of-00005_06739.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_beach_train-00003-of-00005_06739.png", "id": "places_beach_train-00003-of-00005_06739"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_field_wild_train-00003-of-00005_02073.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_field_wild_train-00003-of-00005_02073.png", "id": "places_field_wild_train-00003-of-00005_02073"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_ocean_train-00004-of-00005_00156.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_ocean_train-00004-of-00005_00156.png", "id": "places_ocean_train-00004-of-00005_00156"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_beach_train-00000-of-00005_04189.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_beach_train-00000-of-00005_04189.png", "id": "places_beach_train-00000-of-00005_04189"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_valley_train-00003-of-00005_06190.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_valley_train-00003-of-00005_06190.png", "id": "places_valley_train-00003-of-00005_06190"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_lake_natural_train-00001-of-00005_04795.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_lake_natural_train-00001-of-00005_04795.png", "id": "places_lake_natural_train-00001-of-00005_04795"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_field_wild_val-00001-of-00005_03592.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_field_wild_val-00001-of-00005_03592.png", "id": "places_field_wild_val-00001-of-00005_03592"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_sky_val-00002-of-00005_00942.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_sky_val-00002-of-00005_00942.png", "id": "places_sky_val-00002-of-00005_00942"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_field_wild_train-00004-of-00005_03725.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_field_wild_train-00004-of-00005_03725.png", "id": "places_field_wild_train-00004-of-00005_03725"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_canyon_val-00001-of-00005_02496.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_canyon_val-00001-of-00005_02496.png", "id": "places_canyon_val-00001-of-00005_02496"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_mountain_snowy_train-00001-of-00005_06390.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_mountain_snowy_train-00001-of-00005_06390.png", "id": "places_mountain_snowy_train-00001-of-00005_06390"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_iceberg_val-00001-of-00005_05694.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_iceberg_val-00001-of-00005_05694.png", "id": "places_iceberg_val-00001-of-00005_05694"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_volcano_val-00001-of-00005_06236.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_volcano_val-00001-of-00005_06236.png", "id": "places_volcano_val-00001-of-00005_06236"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_snowfield_train-00002-of-00005_04850.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_snowfield_train-00002-of-00005_04850.png", "id": "places_snowfield_train-00002-of-00005_04850"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_sky_train-00004-of-00005_05080.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_sky_train-00004-of-00005_05080.png", "id": "places_sky_train-00004-of-00005_05080"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_islet_train-00004-of-00005_03318.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_islet_train-00004-of-00005_03318.png", "id": "places_islet_train-00004-of-00005_03318"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_islet_train-00004-of-00005_06550.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_islet_train-00004-of-00005_06550.png", "id": "places_islet_train-00004-of-00005_06550"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_wave_train-00001-of-00005_01237.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_wave_train-00001-of-00005_01237.png", "id": "places_wave_train-00001-of-00005_01237"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_iceberg_val-00000-of-00005_05402.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_iceberg_val-00000-of-00005_05402.png", "id": "places_iceberg_val-00000-of-00005_05402"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_snowfield_train-00004-of-00005_05274.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_snowfield_train-00004-of-00005_05274.png", "id": "places_snowfield_train-00004-of-00005_05274"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_glacier_val-00004-of-00005_06055.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_glacier_val-00004-of-00005_06055.png", "id": "places_glacier_val-00004-of-00005_06055"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_tundra_val-00000-of-00005_02212.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_tundra_val-00000-of-00005_02212.png", "id": "places_tundra_val-00000-of-00005_02212"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_beach_val-00002-of-00005_01597.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_beach_val-00002-of-00005_01597.png", "id": "places_beach_val-00002-of-00005_01597"}
+{"prompt": "Convert this visible-light photo into a long-wave thermal infrared image of the same scene, as captured by a FLIR thermal camera in white-hot grayscale.", "image": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/rgb/places_glacier_train-00001-of-00005_03149.png", "gt_ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/backgrounds/ir_sft_a128_full_ckpt12/places_glacier_train-00001-of-00005_03149.png", "id": "places_glacier_train-00001-of-00005_03149"}
diff --git a/data/ff/vedai_common/eval_pairs.jsonl b/data/ff/vedai_common/eval_pairs.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..7c5560c06ec69da62c3095d2999e6dbd89839c7b
--- /dev/null
+++ b/data/ff/vedai_common/eval_pairs.jsonl
@@ -0,0 +1,268 @@
+{"id": "00000001", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000001_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000001.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000001_ir.png", "width": 512, "height": 512}
+{"id": "00000002", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000002_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000002.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000002_ir.png", "width": 512, "height": 512}
+{"id": "00000009", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000009_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000009.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000009_ir.png", "width": 512, "height": 512}
+{"id": "00000010", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000010_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000010.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000010_ir.png", "width": 512, "height": 512}
+{"id": "00000015", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000015_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000015.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000015_ir.png", "width": 512, "height": 512}
+{"id": "00000016", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000016_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000016.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000016_ir.png", "width": 512, "height": 512}
+{"id": "00000026", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000026_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000026.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000026_ir.png", "width": 512, "height": 512}
+{"id": "00000045", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000045_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000045.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000045_ir.png", "width": 512, "height": 512}
+{"id": "00000049", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000049_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000049.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000049_ir.png", "width": 512, "height": 512}
+{"id": "00000057", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000057_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000057.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000057_ir.png", "width": 512, "height": 512}
+{"id": "00000060", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000060_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000060.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000060_ir.png", "width": 512, "height": 512}
+{"id": "00000064", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000064_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000064.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000064_ir.png", "width": 512, "height": 512}
+{"id": "00000065", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000065_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000065.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000065_ir.png", "width": 512, "height": 512}
+{"id": "00000066", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000066_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000066.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000066_ir.png", "width": 512, "height": 512}
+{"id": "00000069", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000069_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000069.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000069_ir.png", "width": 512, "height": 512}
+{"id": "00000071", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000071_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000071.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000071_ir.png", "width": 512, "height": 512}
+{"id": "00000072", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000072_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000072.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000072_ir.png", "width": 512, "height": 512}
+{"id": "00000081", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000081_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000081.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000081_ir.png", "width": 512, "height": 512}
+{"id": "00000082", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000082_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000082.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000082_ir.png", "width": 512, "height": 512}
+{"id": "00000089", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000089_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000089.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000089_ir.png", "width": 512, "height": 512}
+{"id": "00000090", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000090_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000090.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000090_ir.png", "width": 512, "height": 512}
+{"id": "00000095", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000095_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000095.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000095_ir.png", "width": 512, "height": 512}
+{"id": "00000100", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000100_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000100.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000100_ir.png", "width": 512, "height": 512}
+{"id": "00000107", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000107_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000107.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000107_ir.png", "width": 512, "height": 512}
+{"id": "00000113", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000113_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000113.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000113_ir.png", "width": 512, "height": 512}
+{"id": "00000116", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000116_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000116.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000116_ir.png", "width": 512, "height": 512}
+{"id": "00000117", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000117_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000117.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000117_ir.png", "width": 512, "height": 512}
+{"id": "00000121", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000121_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000121.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000121_ir.png", "width": 512, "height": 512}
+{"id": "00000125", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000125_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000125.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000125_ir.png", "width": 512, "height": 512}
+{"id": "00000127", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000127_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000127.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000127_ir.png", "width": 512, "height": 512}
+{"id": "00000129", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000129_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000129.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000129_ir.png", "width": 512, "height": 512}
+{"id": "00000131", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000131_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000131.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000131_ir.png", "width": 512, "height": 512}
+{"id": "00000138", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000138_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000138.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000138_ir.png", "width": 512, "height": 512}
+{"id": "00000141", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000141_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000141.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000141_ir.png", "width": 512, "height": 512}
+{"id": "00000142", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000142_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000142.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000142_ir.png", "width": 512, "height": 512}
+{"id": "00000145", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000145_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000145.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000145_ir.png", "width": 512, "height": 512}
+{"id": "00000148", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000148_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000148.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000148_ir.png", "width": 512, "height": 512}
+{"id": "00000149", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000149_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000149.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000149_ir.png", "width": 512, "height": 512}
+{"id": "00000152", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000152_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000152.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000152_ir.png", "width": 512, "height": 512}
+{"id": "00000153", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000153_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000153.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000153_ir.png", "width": 512, "height": 512}
+{"id": "00000161", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000161_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000161.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000161_ir.png", "width": 512, "height": 512}
+{"id": "00000165", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000165_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000165.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000165_ir.png", "width": 512, "height": 512}
+{"id": "00000168", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000168_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000168.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000168_ir.png", "width": 512, "height": 512}
+{"id": "00000177", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000177_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000177.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000177_ir.png", "width": 512, "height": 512}
+{"id": "00000182", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000182_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000182.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000182_ir.png", "width": 512, "height": 512}
+{"id": "00000185", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000185_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000185.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000185_ir.png", "width": 512, "height": 512}
+{"id": "00000193", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000193_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000193.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000193_ir.png", "width": 512, "height": 512}
+{"id": "00000197", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000197_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000197.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000197_ir.png", "width": 512, "height": 512}
+{"id": "00000200", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000200_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000200.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000200_ir.png", "width": 512, "height": 512}
+{"id": "00000201", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000201_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000201.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000201_ir.png", "width": 512, "height": 512}
+{"id": "00000205", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000205_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000205.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000205_ir.png", "width": 512, "height": 512}
+{"id": "00000208", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000208_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000208.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000208_ir.png", "width": 512, "height": 512}
+{"id": "00000224", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000224_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000224.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000224_ir.png", "width": 512, "height": 512}
+{"id": "00000225", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000225_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000225.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000225_ir.png", "width": 512, "height": 512}
+{"id": "00000230", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000230_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000230.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000230_ir.png", "width": 512, "height": 512}
+{"id": "00000236", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000236_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000236.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000236_ir.png", "width": 512, "height": 512}
+{"id": "00000241", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000241_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000241.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000241_ir.png", "width": 512, "height": 512}
+{"id": "00000251", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000251_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000251.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000251_ir.png", "width": 512, "height": 512}
+{"id": "00000254", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000254_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000254.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000254_ir.png", "width": 512, "height": 512}
+{"id": "00000257", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000257_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000257.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000257_ir.png", "width": 512, "height": 512}
+{"id": "00000259", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000259_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000259.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000259_ir.png", "width": 512, "height": 512}
+{"id": "00000262", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000262_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000262.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000262_ir.png", "width": 512, "height": 512}
+{"id": "00000264", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000264_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000264.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000264_ir.png", "width": 512, "height": 512}
+{"id": "00000269", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000269_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000269.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000269_ir.png", "width": 512, "height": 512}
+{"id": "00000274", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000274_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000274.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000274_ir.png", "width": 512, "height": 512}
+{"id": "00000277", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000277_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000277.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000277_ir.png", "width": 512, "height": 512}
+{"id": "00000278", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000278_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000278.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000278_ir.png", "width": 512, "height": 512}
+{"id": "00000298", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000298_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000298.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000298_ir.png", "width": 512, "height": 512}
+{"id": "00000299", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000299_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000299.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000299_ir.png", "width": 512, "height": 512}
+{"id": "00000310", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000310_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000310.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000310_ir.png", "width": 512, "height": 512}
+{"id": "00000313", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000313_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000313.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000313_ir.png", "width": 512, "height": 512}
+{"id": "00000314", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000314_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000314.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000314_ir.png", "width": 512, "height": 512}
+{"id": "00000316", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000316_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000316.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000316_ir.png", "width": 512, "height": 512}
+{"id": "00000320", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000320_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000320.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000320_ir.png", "width": 512, "height": 512}
+{"id": "00000328", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000328_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000328.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000328_ir.png", "width": 512, "height": 512}
+{"id": "00000330", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000330_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000330.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000330_ir.png", "width": 512, "height": 512}
+{"id": "00000333", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000333_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000333.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000333_ir.png", "width": 512, "height": 512}
+{"id": "00000334", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000334_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000334.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000334_ir.png", "width": 512, "height": 512}
+{"id": "00000340", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000340_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000340.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000340_ir.png", "width": 512, "height": 512}
+{"id": "00000342", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000342_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000342.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000342_ir.png", "width": 512, "height": 512}
+{"id": "00000346", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000346_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000346.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000346_ir.png", "width": 512, "height": 512}
+{"id": "00000358", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000358_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000358.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000358_ir.png", "width": 512, "height": 512}
+{"id": "00000366", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000366_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000366.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000366_ir.png", "width": 512, "height": 512}
+{"id": "00000385", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000385_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000385.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000385_ir.png", "width": 512, "height": 512}
+{"id": "00000388", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000388_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000388.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000388_ir.png", "width": 512, "height": 512}
+{"id": "00000399", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000399_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000399.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000399_ir.png", "width": 512, "height": 512}
+{"id": "00000403", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000403_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000403.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000403_ir.png", "width": 512, "height": 512}
+{"id": "00000416", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000416_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000416.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000416_ir.png", "width": 512, "height": 512}
+{"id": "00000418", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000418_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000418.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000418_ir.png", "width": 512, "height": 512}
+{"id": "00000421", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000421_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000421.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000421_ir.png", "width": 512, "height": 512}
+{"id": "00000429", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000429_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000429.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000429_ir.png", "width": 512, "height": 512}
+{"id": "00000442", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000442_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000442.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000442_ir.png", "width": 512, "height": 512}
+{"id": "00000451", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000451_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000451.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000451_ir.png", "width": 512, "height": 512}
+{"id": "00000453", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000453_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000453.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000453_ir.png", "width": 512, "height": 512}
+{"id": "00000461", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000461_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000461.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000461_ir.png", "width": 512, "height": 512}
+{"id": "00000462", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000462_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000462.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000462_ir.png", "width": 512, "height": 512}
+{"id": "00000464", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000464_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000464.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000464_ir.png", "width": 512, "height": 512}
+{"id": "00000471", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000471_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000471.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000471_ir.png", "width": 512, "height": 512}
+{"id": "00000474", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000474_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000474.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000474_ir.png", "width": 512, "height": 512}
+{"id": "00000479", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000479_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000479.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000479_ir.png", "width": 512, "height": 512}
+{"id": "00000483", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000483_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000483.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000483_ir.png", "width": 512, "height": 512}
+{"id": "00000485", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000485_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000485.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000485_ir.png", "width": 512, "height": 512}
+{"id": "00000490", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000490_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000490.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000490_ir.png", "width": 512, "height": 512}
+{"id": "00000501", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000501_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000501.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000501_ir.png", "width": 512, "height": 512}
+{"id": "00000502", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000502_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000502.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000502_ir.png", "width": 512, "height": 512}
+{"id": "00000504", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000504_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000504.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000504_ir.png", "width": 512, "height": 512}
+{"id": "00000506", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000506_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000506.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000506_ir.png", "width": 512, "height": 512}
+{"id": "00000507", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000507_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000507.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000507_ir.png", "width": 512, "height": 512}
+{"id": "00000511", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000511_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000511.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000511_ir.png", "width": 512, "height": 512}
+{"id": "00000512", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000512_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000512.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000512_ir.png", "width": 512, "height": 512}
+{"id": "00000515", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000515_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000515.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000515_ir.png", "width": 512, "height": 512}
+{"id": "00000519", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000519_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000519.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000519_ir.png", "width": 512, "height": 512}
+{"id": "00000521", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000521_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000521.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000521_ir.png", "width": 512, "height": 512}
+{"id": "00000524", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000524_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000524.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000524_ir.png", "width": 512, "height": 512}
+{"id": "00000533", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000533_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000533.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000533_ir.png", "width": 512, "height": 512}
+{"id": "00000535", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000535_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000535.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000535_ir.png", "width": 512, "height": 512}
+{"id": "00000550", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000550_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000550.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000550_ir.png", "width": 512, "height": 512}
+{"id": "00000554", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000554_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000554.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000554_ir.png", "width": 512, "height": 512}
+{"id": "00000557", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000557_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000557.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000557_ir.png", "width": 512, "height": 512}
+{"id": "00000558", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000558_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000558.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000558_ir.png", "width": 512, "height": 512}
+{"id": "00000562", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000562_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000562.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000562_ir.png", "width": 512, "height": 512}
+{"id": "00000564", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000564_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000564.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000564_ir.png", "width": 512, "height": 512}
+{"id": "00000566", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000566_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000566.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000566_ir.png", "width": 512, "height": 512}
+{"id": "00000568", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000568_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000568.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000568_ir.png", "width": 512, "height": 512}
+{"id": "00000569", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000569_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000569.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000569_ir.png", "width": 512, "height": 512}
+{"id": "00000572", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000572_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000572.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000572_ir.png", "width": 512, "height": 512}
+{"id": "00000586", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000586_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000586.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000586_ir.png", "width": 512, "height": 512}
+{"id": "00000592", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000592_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000592.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000592_ir.png", "width": 512, "height": 512}
+{"id": "00000610", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000610_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000610.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000610_ir.png", "width": 512, "height": 512}
+{"id": "00000611", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000611_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000611.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000611_ir.png", "width": 512, "height": 512}
+{"id": "00000615", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000615_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000615.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000615_ir.png", "width": 512, "height": 512}
+{"id": "00000616", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000616_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000616.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000616_ir.png", "width": 512, "height": 512}
+{"id": "00000618", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000618_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000618.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000618_ir.png", "width": 512, "height": 512}
+{"id": "00000619", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000619_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000619.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000619_ir.png", "width": 512, "height": 512}
+{"id": "00000626", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000626_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000626.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000626_ir.png", "width": 512, "height": 512}
+{"id": "00000627", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000627_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000627.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000627_ir.png", "width": 512, "height": 512}
+{"id": "00000629", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000629_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000629.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000629_ir.png", "width": 512, "height": 512}
+{"id": "00000644", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000644_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000644.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000644_ir.png", "width": 512, "height": 512}
+{"id": "00000647", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000647_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000647.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000647_ir.png", "width": 512, "height": 512}
+{"id": "00000652", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000652_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000652.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000652_ir.png", "width": 512, "height": 512}
+{"id": "00000654", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000654_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000654.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000654_ir.png", "width": 512, "height": 512}
+{"id": "00000656", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000656_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000656.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000656_ir.png", "width": 512, "height": 512}
+{"id": "00000662", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000662_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000662.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000662_ir.png", "width": 512, "height": 512}
+{"id": "00000664", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000664_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000664.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000664_ir.png", "width": 512, "height": 512}
+{"id": "00000678", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000678_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000678.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000678_ir.png", "width": 512, "height": 512}
+{"id": "00000681", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000681_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000681.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000681_ir.png", "width": 512, "height": 512}
+{"id": "00000682", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000682_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000682.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000682_ir.png", "width": 512, "height": 512}
+{"id": "00000685", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000685_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000685.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000685_ir.png", "width": 512, "height": 512}
+{"id": "00000691", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000691_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000691.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000691_ir.png", "width": 512, "height": 512}
+{"id": "00000694", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000694_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000694.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000694_ir.png", "width": 512, "height": 512}
+{"id": "00000695", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000695_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000695.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000695_ir.png", "width": 512, "height": 512}
+{"id": "00000697", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000697_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000697.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000697_ir.png", "width": 512, "height": 512}
+{"id": "00000703", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000703_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000703.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000703_ir.png", "width": 512, "height": 512}
+{"id": "00000707", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000707_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000707.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000707_ir.png", "width": 512, "height": 512}
+{"id": "00000709", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000709_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000709.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000709_ir.png", "width": 512, "height": 512}
+{"id": "00000710", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000710_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000710.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000710_ir.png", "width": 512, "height": 512}
+{"id": "00000712", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000712_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000712.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000712_ir.png", "width": 512, "height": 512}
+{"id": "00000714", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000714_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000714.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000714_ir.png", "width": 512, "height": 512}
+{"id": "00000723", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000723_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000723.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000723_ir.png", "width": 512, "height": 512}
+{"id": "00000739", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000739_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000739.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000739_ir.png", "width": 512, "height": 512}
+{"id": "00000744", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000744_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000744.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000744_ir.png", "width": 512, "height": 512}
+{"id": "00000746", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000746_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000746.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000746_ir.png", "width": 512, "height": 512}
+{"id": "00000752", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000752_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000752.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000752_ir.png", "width": 512, "height": 512}
+{"id": "00000754", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000754_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000754.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000754_ir.png", "width": 512, "height": 512}
+{"id": "00000766", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000766_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000766.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000766_ir.png", "width": 512, "height": 512}
+{"id": "00000767", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000767_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000767.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000767_ir.png", "width": 512, "height": 512}
+{"id": "00000772", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000772_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000772.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000772_ir.png", "width": 512, "height": 512}
+{"id": "00000777", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000777_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000777.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000777_ir.png", "width": 512, "height": 512}
+{"id": "00000779", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000779_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000779.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000779_ir.png", "width": 512, "height": 512}
+{"id": "00000783", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000783_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000783.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000783_ir.png", "width": 512, "height": 512}
+{"id": "00000784", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000784_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000784.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000784_ir.png", "width": 512, "height": 512}
+{"id": "00000788", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000788_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000788.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000788_ir.png", "width": 512, "height": 512}
+{"id": "00000790", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000790_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000790.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000790_ir.png", "width": 512, "height": 512}
+{"id": "00000794", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000794_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000794.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000794_ir.png", "width": 512, "height": 512}
+{"id": "00000796", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000796_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000796.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000796_ir.png", "width": 512, "height": 512}
+{"id": "00000807", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000807_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000807.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000807_ir.png", "width": 512, "height": 512}
+{"id": "00000822", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000822_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000822.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000822_ir.png", "width": 512, "height": 512}
+{"id": "00000829", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000829_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000829.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000829_ir.png", "width": 512, "height": 512}
+{"id": "00000836", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000836_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000836.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000836_ir.png", "width": 512, "height": 512}
+{"id": "00000840", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000840_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000840.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000840_ir.png", "width": 512, "height": 512}
+{"id": "00000845", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000845_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000845.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000845_ir.png", "width": 512, "height": 512}
+{"id": "00000846", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000846_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000846.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000846_ir.png", "width": 512, "height": 512}
+{"id": "00000852", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000852_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000852.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000852_ir.png", "width": 512, "height": 512}
+{"id": "00000855", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000855_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000855.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000855_ir.png", "width": 512, "height": 512}
+{"id": "00000860", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000860_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000860.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000860_ir.png", "width": 512, "height": 512}
+{"id": "00000872", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000872_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000872.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000872_ir.png", "width": 512, "height": 512}
+{"id": "00000875", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000875_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000875.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000875_ir.png", "width": 512, "height": 512}
+{"id": "00000879", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000879_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000879.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000879_ir.png", "width": 512, "height": 512}
+{"id": "00000884", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000884_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000884.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000884_ir.png", "width": 512, "height": 512}
+{"id": "00000885", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000885_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000885.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000885_ir.png", "width": 512, "height": 512}
+{"id": "00000886", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000886_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000886.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000886_ir.png", "width": 512, "height": 512}
+{"id": "00000888", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000888_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000888.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000888_ir.png", "width": 512, "height": 512}
+{"id": "00000890", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000890_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000890.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000890_ir.png", "width": 512, "height": 512}
+{"id": "00000892", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000892_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000892.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000892_ir.png", "width": 512, "height": 512}
+{"id": "00000894", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000894_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000894.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000894_ir.png", "width": 512, "height": 512}
+{"id": "00000897", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000897_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000897.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000897_ir.png", "width": 512, "height": 512}
+{"id": "00000898", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000898_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000898.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000898_ir.png", "width": 512, "height": 512}
+{"id": "00000906", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000906_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000906.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000906_ir.png", "width": 512, "height": 512}
+{"id": "00000918", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000918_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000918.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000918_ir.png", "width": 512, "height": 512}
+{"id": "00000921", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000921_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000921.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000921_ir.png", "width": 512, "height": 512}
+{"id": "00000923", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000923_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000923.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000923_ir.png", "width": 512, "height": 512}
+{"id": "00000928", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000928_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000928.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000928_ir.png", "width": 512, "height": 512}
+{"id": "00000939", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000939_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000939.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000939_ir.png", "width": 512, "height": 512}
+{"id": "00000940", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000940_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000940.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000940_ir.png", "width": 512, "height": 512}
+{"id": "00000945", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000945_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000945.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000945_ir.png", "width": 512, "height": 512}
+{"id": "00000946", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000946_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000946.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000946_ir.png", "width": 512, "height": 512}
+{"id": "00000949", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000949_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000949.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000949_ir.png", "width": 512, "height": 512}
+{"id": "00000950", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000950_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000950.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000950_ir.png", "width": 512, "height": 512}
+{"id": "00000961", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000961_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000961.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000961_ir.png", "width": 512, "height": 512}
+{"id": "00000962", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000962_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000962.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000962_ir.png", "width": 512, "height": 512}
+{"id": "00000967", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000967_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000967.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000967_ir.png", "width": 512, "height": 512}
+{"id": "00000968", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000968_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000968.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000968_ir.png", "width": 512, "height": 512}
+{"id": "00000969", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000969_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000969.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000969_ir.png", "width": 512, "height": 512}
+{"id": "00000974", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000974_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000974.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000974_ir.png", "width": 512, "height": 512}
+{"id": "00000977", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000977_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000977.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000977_ir.png", "width": 512, "height": 512}
+{"id": "00000980", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000980_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000980.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000980_ir.png", "width": 512, "height": 512}
+{"id": "00000989", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000989_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000989.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000989_ir.png", "width": 512, "height": 512}
+{"id": "00000994", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000994_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00000994.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00000994_ir.png", "width": 512, "height": 512}
+{"id": "00001001", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00001001_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00001001.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00001001_ir.png", "width": 512, "height": 512}
+{"id": "00001012", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00001012_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00001012.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00001012_ir.png", "width": 512, "height": 512}
+{"id": "00001014", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00001014_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00001014.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00001014_ir.png", "width": 512, "height": 512}
+{"id": "00001015", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00001015_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00001015.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00001015_ir.png", "width": 512, "height": 512}
+{"id": "00001016", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00001016_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00001016.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00001016_ir.png", "width": 512, "height": 512}
+{"id": "00001021", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00001021_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00001021.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00001021_ir.png", "width": 512, "height": 512}
+{"id": "00001023", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00001023_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00001023.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00001023_ir.png", "width": 512, "height": 512}
+{"id": "00001031", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00001031_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00001031.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00001031_ir.png", "width": 512, "height": 512}
+{"id": "00001038", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00001038_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00001038.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00001038_ir.png", "width": 512, "height": 512}
+{"id": "00001043", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00001043_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00001043.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00001043_ir.png", "width": 512, "height": 512}
+{"id": "00001046", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00001046_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00001046.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00001046_ir.png", "width": 512, "height": 512}
+{"id": "00001055", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00001055_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00001055.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00001055_ir.png", "width": 512, "height": 512}
+{"id": "00001060", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00001060_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00001060.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00001060_ir.png", "width": 512, "height": 512}
+{"id": "00001069", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00001069_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00001069.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00001069_ir.png", "width": 512, "height": 512}
+{"id": "00001071", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00001071_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00001071.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00001071_ir.png", "width": 512, "height": 512}
+{"id": "00001072", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00001072_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00001072.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00001072_ir.png", "width": 512, "height": 512}
+{"id": "00001073", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00001073_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00001073.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00001073_ir.png", "width": 512, "height": 512}
+{"id": "00001074", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00001074_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00001074.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00001074_ir.png", "width": 512, "height": 512}
+{"id": "00001077", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00001077_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00001077.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00001077_ir.png", "width": 512, "height": 512}
+{"id": "00001079", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00001079_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00001079.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00001079_ir.png", "width": 512, "height": 512}
+{"id": "00001086", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00001086_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00001086.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00001086_ir.png", "width": 512, "height": 512}
+{"id": "00001101", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00001101_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00001101.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00001101_ir.png", "width": 512, "height": 512}
+{"id": "00001103", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00001103_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00001103.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00001103_ir.png", "width": 512, "height": 512}
+{"id": "00001104", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00001104_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00001104.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00001104_ir.png", "width": 512, "height": 512}
+{"id": "00001114", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00001114_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00001114.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00001114_ir.png", "width": 512, "height": 512}
+{"id": "00001116", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00001116_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00001116.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00001116_ir.png", "width": 512, "height": 512}
+{"id": "00001119", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00001119_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00001119.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00001119_ir.png", "width": 512, "height": 512}
+{"id": "00001120", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00001120_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00001120.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00001120_ir.png", "width": 512, "height": 512}
+{"id": "00001124", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00001124_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00001124.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00001124_ir.png", "width": 512, "height": 512}
+{"id": "00001125", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00001125_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00001125.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00001125_ir.png", "width": 512, "height": 512}
+{"id": "00001153", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00001153_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00001153.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00001153_ir.png", "width": 512, "height": 512}
+{"id": "00001168", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00001168_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00001168.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00001168_ir.png", "width": 512, "height": 512}
+{"id": "00001172", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00001172_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00001172.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00001172_ir.png", "width": 512, "height": 512}
+{"id": "00001183", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00001183_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00001183.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00001183_ir.png", "width": 512, "height": 512}
+{"id": "00001193", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00001193_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00001193.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00001193_ir.png", "width": 512, "height": 512}
+{"id": "00001199", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00001199_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00001199.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00001199_ir.png", "width": 512, "height": 512}
+{"id": "00001202", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00001202_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00001202.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00001202_ir.png", "width": 512, "height": 512}
+{"id": "00001203", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00001203_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00001203.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00001203_ir.png", "width": 512, "height": 512}
+{"id": "00001223", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00001223_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00001223.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00001223_ir.png", "width": 512, "height": 512}
+{"id": "00001230", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00001230_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00001230.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00001230_ir.png", "width": 512, "height": 512}
+{"id": "00001231", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00001231_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00001231.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00001231_ir.png", "width": 512, "height": 512}
+{"id": "00001232", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00001232_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00001232.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00001232_ir.png", "width": 512, "height": 512}
+{"id": "00001240", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00001240_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00001240.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00001240_ir.png", "width": 512, "height": 512}
+{"id": "00001245", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00001245_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00001245.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00001245_ir.png", "width": 512, "height": 512}
+{"id": "00001256", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00001256_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00001256.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00001256_ir.png", "width": 512, "height": 512}
+{"id": "00001261", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00001261_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00001261.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00001261_ir.png", "width": 512, "height": 512}
+{"id": "00001262", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00001262_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00001262.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00001262_ir.png", "width": 512, "height": 512}
+{"id": "00001265", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00001265_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00001265.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00001265_ir.png", "width": 512, "height": 512}
+{"id": "00001268", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00001268_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00001268.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00001268_ir.png", "width": 512, "height": 512}
+{"id": "00001271", "dataset": "vedai", "split": "common_test", "band": "nir", "prompt": "Convert this visible-light aerial photo into a near-infrared image of the same scene, as captured by an aerial near-infrared camera in grayscale.", "rgb": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00001271_co.png", "ir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/targets/vedai/00001271.png", "ir_raw": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/raw/20260922pbw/VEDAI/00001271_ir.png", "width": 512, "height": 512}
diff --git a/eval/bicyclegan_flir_full/flir_common/metrics.csv b/eval/bicyclegan_flir_full/flir_common/metrics.csv
new file mode 100644
index 0000000000000000000000000000000000000000..fbc43269f16561ee769a5d6dbc979fb153f81f9e
--- /dev/null
+++ b/eval/bicyclegan_flir_full/flir_common/metrics.csv
@@ -0,0 +1,1447 @@
+id,psnr,ssim,ms_ssim,l1,lpips,dists
+FLIR_00006,15.650035858154297,0.3413006663322449,0.3202632963657379,0.13185402750968933,0.5166745781898499,0.3245377540588379
+FLIR_00007,13.18662166595459,0.3321085572242737,0.3736986815929413,0.17434434592723846,0.4993152618408203,0.25806236267089844
+FLIR_00013,12.963068008422852,0.40504223108291626,0.4697667360305786,0.18424642086029053,0.4263697564601898,0.24432170391082764
+FLIR_00018,14.605945587158203,0.37754395604133606,0.41192537546157837,0.15356746315956116,0.47697213292121887,0.23142027854919434
+FLIR_00024,20.89797592163086,0.5303366780281067,0.5992535948753357,0.06662394851446152,0.4639642834663391,0.2016451358795166
+FLIR_00045,17.802417755126953,0.35549190640449524,0.384339302778244,0.09682072699069977,0.47289371490478516,0.2523123025894165
+FLIR_00051,16.213314056396484,0.3623178005218506,0.3933083713054657,0.11155202239751816,0.3897480070590973,0.2153947353363037
+FLIR_00053,19.42661476135254,0.49100345373153687,0.5854740142822266,0.07608690112829208,0.5092213153839111,0.2248218059539795
+FLIR_00097,16.184589385986328,0.3288583755493164,0.3319869339466095,0.12535816431045532,0.5874500870704651,0.27783846855163574
+FLIR_00100,20.238685607910156,0.654552161693573,0.7033711671829224,0.07507073879241943,0.4708201587200165,0.18996715545654297
+FLIR_00109,16.308570861816406,0.3682023882865906,0.3684864640235901,0.1140909418463707,0.5470803380012512,0.2750849723815918
+FLIR_00113,15.635398864746094,0.6332885026931763,0.6706776022911072,0.1467321366071701,0.5422453880310059,0.23263144493103027
+FLIR_00114,17.302209854125977,0.41624608635902405,0.43901708722114563,0.09611298888921738,0.5516015291213989,0.25895512104034424
+FLIR_00116,17.967426300048828,0.42700546979904175,0.4477916955947876,0.09070700407028198,0.4669922888278961,0.2554192543029785
+FLIR_00121,17.553447723388672,0.39369088411331177,0.4003342092037201,0.09470292925834656,0.5238872766494751,0.25666797161102295
+FLIR_00124,21.15541648864746,0.5682547092437744,0.6286150217056274,0.06539304554462433,0.4668344557285309,0.2121344804763794
+FLIR_00160,8.222308158874512,0.2931031286716461,0.0,0.33675238490104675,0.6551956534385681,0.3205038905143738
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diff --git a/eval/bicyclegan_flir_full/flir_common/metrics.json b/eval/bicyclegan_flir_full/flir_common/metrics.json
new file mode 100644
index 0000000000000000000000000000000000000000..b6782d268273c0df39e6cd485c35ce7c7ebca1c2
--- /dev/null
+++ b/eval/bicyclegan_flir_full/flir_common/metrics.json
@@ -0,0 +1,21 @@
+{
+  "protocol": "luma-fp32",
+  "pairs": "data/ff/flir_common/eval_pairs.jsonl",
+  "pred_dir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/eval/bicyclegan_flir_full/flir_common",
+  "n": 1446,
+  "psnr": 16.478546180171097,
+  "ssim": 0.4040446837083598,
+  "ms_ssim": 0.4304691339594365,
+  "l1": 0.12004971295404525,
+  "lpips": 0.4817612639745571,
+  "dists": 0.2509585608255484,
+  "psnr_std": 2.5093639888441017,
+  "ssim_std": 0.10148136905206598,
+  "ms_ssim_std": 0.11303438943706123,
+  "l1_std": 0.03603384880528141,
+  "lpips_std": 0.08504902989152259,
+  "dists_std": 0.0463501605114877,
+  "fid": 123.09633369010982,
+  "kid": 0.11487939834594726,
+  "kid_std": 0.0028648124053299636
+}
\ No newline at end of file
diff --git a/eval/bicyclegan_flir_full/flir_common_clean300/metrics.json b/eval/bicyclegan_flir_full/flir_common_clean300/metrics.json
new file mode 100644
index 0000000000000000000000000000000000000000..8619e0890636087bc7d1258914f7a671075a9b24
--- /dev/null
+++ b/eval/bicyclegan_flir_full/flir_common_clean300/metrics.json
@@ -0,0 +1,21 @@
+{
+  "protocol": "luma-fp32",
+  "pairs": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/flir_common_clean300/eval_pairs.jsonl",
+  "pred_dir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/eval/bicyclegan_flir_full/flir_common",
+  "n": 300,
+  "psnr": 16.25941445350647,
+  "ssim": 0.3824283443391323,
+  "ms_ssim": 0.41281774794061976,
+  "l1": 0.11927162807434798,
+  "lpips": 0.45876566295822463,
+  "dists": 0.2409961348772049,
+  "psnr_std": 2.4085322326209018,
+  "ssim_std": 0.09334852165261293,
+  "ms_ssim_std": 0.10885111085836173,
+  "l1_std": 0.031050394366158467,
+  "lpips_std": 0.06873904861464925,
+  "dists_std": 0.039917201873900555,
+  "fid": 171.35573035174144,
+  "kid": 0.11789105653762817,
+  "kid_std": 0.0060922931211273455
+}
\ No newline at end of file
diff --git a/eval/bicyclegan_flir_sel/flir_common/metrics.csv b/eval/bicyclegan_flir_sel/flir_common/metrics.csv
new file mode 100644
index 0000000000000000000000000000000000000000..4a93a3064291266f40d01c84840104573d980631
--- /dev/null
+++ b/eval/bicyclegan_flir_sel/flir_common/metrics.csv
@@ -0,0 +1,1447 @@
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diff --git a/eval/bicyclegan_flir_sel/flir_common/metrics.json b/eval/bicyclegan_flir_sel/flir_common/metrics.json
new file mode 100644
index 0000000000000000000000000000000000000000..82ac0dfe20edeab83fbdd3e6821d1457ae1c2812
--- /dev/null
+++ b/eval/bicyclegan_flir_sel/flir_common/metrics.json
@@ -0,0 +1,21 @@
+{
+  "protocol": "luma-fp32",
+  "pairs": "data/ff/flir_common/eval_pairs.jsonl",
+  "pred_dir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/eval/bicyclegan_flir_sel/flir_common",
+  "n": 1446,
+  "psnr": 17.115134386930556,
+  "ssim": 0.43563280428758466,
+  "ms_ssim": 0.45860179336445295,
+  "l1": 0.11184158003058177,
+  "lpips": 0.4381160193005703,
+  "dists": 0.23992556230490963,
+  "psnr_std": 2.6153693103453155,
+  "ssim_std": 0.11508069960266612,
+  "ms_ssim_std": 0.13158143320270915,
+  "l1_std": 0.03852913274204266,
+  "lpips_std": 0.0797033645239495,
+  "dists_std": 0.05695194796965788,
+  "fid": 113.62071593173363,
+  "kid": 0.09799922704696655,
+  "kid_std": 0.0022971410768545205
+}
\ No newline at end of file
diff --git a/eval/bicyclegan_flir_sel/flir_common400/metrics.json b/eval/bicyclegan_flir_sel/flir_common400/metrics.json
new file mode 100644
index 0000000000000000000000000000000000000000..837c8a4f1bfd2d9d0395cec7a9cd18a4e6e4aea9
--- /dev/null
+++ b/eval/bicyclegan_flir_sel/flir_common400/metrics.json
@@ -0,0 +1,21 @@
+{
+  "protocol": "luma-fp32",
+  "pairs": "data/ff/flir_common400/eval_pairs.jsonl",
+  "pred_dir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/eval/bicyclegan_flir_sel/flir_common",
+  "n": 400,
+  "psnr": 18.60005564928055,
+  "ssim": 0.5295517998933792,
+  "ms_ssim": 0.5669420563802123,
+  "l1": 0.09243210216984153,
+  "lpips": 0.4053483182191849,
+  "dists": 0.21188633799552917,
+  "psnr_std": 2.376190192080797,
+  "ssim_std": 0.13000159546000237,
+  "ms_ssim_std": 0.12051311771945161,
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+  "lpips_std": 0.07539507905539355,
+  "dists_std": 0.029547557531128973,
+  "fid": 125.30571169170057,
+  "kid": 0.08827685594558715,
+  "kid_std": 0.004755465612789891
+}
\ No newline at end of file
diff --git a/eval/bicyclegan_vedai_full/vedai_common/metrics.csv b/eval/bicyclegan_vedai_full/vedai_common/metrics.csv
new file mode 100644
index 0000000000000000000000000000000000000000..05f3ce2ea42cf3b2c78fc3fe9a2ba6d3e545d7cb
--- /dev/null
+++ b/eval/bicyclegan_vedai_full/vedai_common/metrics.csv
@@ -0,0 +1,269 @@
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diff --git a/eval/bicyclegan_vedai_full/vedai_common/metrics.json b/eval/bicyclegan_vedai_full/vedai_common/metrics.json
new file mode 100644
index 0000000000000000000000000000000000000000..701e202f5e83bab38fcef5570ebc426cddc660b1
--- /dev/null
+++ b/eval/bicyclegan_vedai_full/vedai_common/metrics.json
@@ -0,0 +1,21 @@
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diff --git a/eval/codec_audit/codec_audit_all.json b/eval/codec_audit/codec_audit_all.json
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index 0000000000000000000000000000000000000000..a4b949fe2a08933ee74d28bd6a9380961384980e
--- /dev/null
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\ No newline at end of file
diff --git a/eval/infragan2_flir_full/flir_common/metrics.csv b/eval/infragan2_flir_full/flir_common/metrics.csv
new file mode 100644
index 0000000000000000000000000000000000000000..50e8f11c8fd26c847ace4f22115fb7cc430f2919
--- /dev/null
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diff --git a/eval/infragan2_flir_full/flir_common/metrics.json b/eval/infragan2_flir_full/flir_common/metrics.json
new file mode 100644
index 0000000000000000000000000000000000000000..def2ee6aec8123973cf3b5909aa2bf2160974e4c
--- /dev/null
+++ b/eval/infragan2_flir_full/flir_common/metrics.json
@@ -0,0 +1,21 @@
+{
+  "protocol": "luma-fp32",
+  "pairs": "data/ff/flir_common/eval_pairs.jsonl",
+  "pred_dir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/eval/infragan2_flir_full/flir_common",
+  "n": 1446,
+  "psnr": 16.309593002166327,
+  "ssim": 0.35230906062436135,
+  "ms_ssim": 0.40300724567796514,
+  "l1": 0.12238420477525987,
+  "lpips": 0.7548448726596674,
+  "dists": 0.44877589128159223,
+  "psnr_std": 2.4518073851023696,
+  "ssim_std": 0.0950346244103459,
+  "ms_ssim_std": 0.11153802959007154,
+  "l1_std": 0.03715867281173088,
+  "lpips_std": 0.0804576098847599,
+  "dists_std": 0.04446341749736727,
+  "fid": 320.259609885862,
+  "kid": 0.3815693974494934,
+  "kid_std": 0.0035522779247367906
+}
\ No newline at end of file
diff --git a/eval/infragan2_flir_full/flir_common_clean300/metrics.json b/eval/infragan2_flir_full/flir_common_clean300/metrics.json
new file mode 100644
index 0000000000000000000000000000000000000000..c0fd6dd93d067ffc946d38c655327e9b3880bbb2
--- /dev/null
+++ b/eval/infragan2_flir_full/flir_common_clean300/metrics.json
@@ -0,0 +1,21 @@
+{
+  "protocol": "luma-fp32",
+  "pairs": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/flir_common_clean300/eval_pairs.jsonl",
+  "pred_dir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/eval/infragan2_flir_full/flir_common",
+  "n": 300,
+  "psnr": 15.97076600710551,
+  "ssim": 0.3262746042013168,
+  "ms_ssim": 0.37779506132006646,
+  "l1": 0.12332369588315487,
+  "lpips": 0.7424140860637028,
+  "dists": 0.45085400581359864,
+  "psnr_std": 2.268342104117839,
+  "ssim_std": 0.09770245401001539,
+  "ms_ssim_std": 0.11542853013269753,
+  "l1_std": 0.03098054272354019,
+  "lpips_std": 0.08508269459146829,
+  "dists_std": 0.041643308472980964,
+  "fid": 372.4599201405101,
+  "kid": 0.38631019353866575,
+  "kid_std": 0.007326574533038089
+}
\ No newline at end of file
diff --git a/eval/infragan2_flir_sel/flir_common/metrics.csv b/eval/infragan2_flir_sel/flir_common/metrics.csv
new file mode 100644
index 0000000000000000000000000000000000000000..7c88c7fed78040a28ae020e73ece7ec4b4700f5b
--- /dev/null
+++ b/eval/infragan2_flir_sel/flir_common/metrics.csv
@@ -0,0 +1,1447 @@
+id,psnr,ssim,ms_ssim,l1,lpips,dists
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diff --git a/eval/infragan2_flir_sel/flir_common/metrics.json b/eval/infragan2_flir_sel/flir_common/metrics.json
new file mode 100644
index 0000000000000000000000000000000000000000..027b56d695971046c1dee548d813c88cfc18acdf
--- /dev/null
+++ b/eval/infragan2_flir_sel/flir_common/metrics.json
@@ -0,0 +1,21 @@
+{
+  "protocol": "luma-fp32",
+  "pairs": "data/ff/flir_common/eval_pairs.jsonl",
+  "pred_dir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/eval/infragan2_flir_sel/flir_common",
+  "n": 1446,
+  "psnr": 16.6657758161405,
+  "ssim": 0.2921603776277513,
+  "ms_ssim": 0.37982580706188623,
+  "l1": 0.11816279179729614,
+  "lpips": 0.6369603091173317,
+  "dists": 0.3968994264203648,
+  "psnr_std": 2.4490546539431755,
+  "ssim_std": 0.07400312647792906,
+  "ms_ssim_std": 0.11855774253628781,
+  "l1_std": 0.038779735562070955,
+  "lpips_std": 0.05480715590291749,
+  "dists_std": 0.03627174277682642,
+  "fid": 310.8260202690889,
+  "kid": 0.34578030824661254,
+  "kid_std": 0.0030269617061711718
+}
\ No newline at end of file
diff --git a/eval/infragan2_vedai_sel/vedai_common/metrics.csv b/eval/infragan2_vedai_sel/vedai_common/metrics.csv
new file mode 100644
index 0000000000000000000000000000000000000000..5ace02255efb6647d0ddd0cf2945f7c788fcd866
--- /dev/null
+++ b/eval/infragan2_vedai_sel/vedai_common/metrics.csv
@@ -0,0 +1,269 @@
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diff --git a/eval/infragan2_vedai_sel/vedai_common/metrics.json b/eval/infragan2_vedai_sel/vedai_common/metrics.json
new file mode 100644
index 0000000000000000000000000000000000000000..8817f3592cd281c75c111d494a2134da3873cc9f
--- /dev/null
+++ b/eval/infragan2_vedai_sel/vedai_common/metrics.json
@@ -0,0 +1,21 @@
+{
+  "protocol": "luma-fp32",
+  "pairs": "data/ff/vedai_common/eval_pairs.jsonl",
+  "pred_dir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/eval/infragan2_vedai_sel/vedai_common",
+  "n": 268,
+  "psnr": 17.234730494556143,
+  "ssim": 0.6443737843143407,
+  "ms_ssim": 0.6657238465858929,
+  "l1": 0.10898197361671214,
+  "lpips": 0.435809878827031,
+  "dists": 0.2903862855772474,
+  "psnr_std": 2.1695643521694823,
+  "ssim_std": 0.06422771663164335,
+  "ms_ssim_std": 0.09656230943690024,
+  "l1_std": 0.04379150085772673,
+  "lpips_std": 0.07095307933897078,
+  "dists_std": 0.037175469542673575,
+  "fid": 190.7861619469942,
+  "kid": 0.11150995492935181,
+  "kid_std": 0.005161269266182685
+}
\ No newline at end of file
diff --git a/eval/klein4b_dmd2_1_flir_from_ep72/checkpoint-150_cfg1.0_s1/flir_common/metrics.csv b/eval/klein4b_dmd2_1_flir_from_ep72/checkpoint-150_cfg1.0_s1/flir_common/metrics.csv
new file mode 100644
index 0000000000000000000000000000000000000000..0e7e5c984b472385663ef13e0582e6e369e3ea97
--- /dev/null
+++ b/eval/klein4b_dmd2_1_flir_from_ep72/checkpoint-150_cfg1.0_s1/flir_common/metrics.csv
@@ -0,0 +1,1447 @@
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diff --git a/eval/klein4b_dmd2_1_flir_from_ep72/checkpoint-150_cfg1.0_s1/flir_common/metrics.json b/eval/klein4b_dmd2_1_flir_from_ep72/checkpoint-150_cfg1.0_s1/flir_common/metrics.json
new file mode 100644
index 0000000000000000000000000000000000000000..953d3ec77cbf5a06acfdf6e983f3c4a9821aadad
--- /dev/null
+++ b/eval/klein4b_dmd2_1_flir_from_ep72/checkpoint-150_cfg1.0_s1/flir_common/metrics.json
@@ -0,0 +1,21 @@
+{
+  "protocol": "luma-fp32",
+  "pairs": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/flir_common/eval_pairs.jsonl",
+  "pred_dir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/eval/klein4b_dmd2_1_flir_from_ep72/checkpoint-150_cfg1.0_s1/flir_common",
+  "n": 1446,
+  "psnr": 20.541527328649497,
+  "ssim": 0.5968789611408489,
+  "ms_ssim": 0.6728087612125059,
+  "l1": 0.07413757963938172,
+  "lpips": 0.260620090610671,
+  "dists": 0.15389723904729713,
+  "psnr_std": 2.522069916609048,
+  "ssim_std": 0.0899242380724616,
+  "ms_ssim_std": 0.0816171662246969,
+  "l1_std": 0.025855619468249314,
+  "lpips_std": 0.07299337398514649,
+  "dists_std": 0.03699871934327252,
+  "fid": 35.893117514792834,
+  "kid": 0.014795269966125488,
+  "kid_std": 0.0007724999009280579
+}
\ No newline at end of file
diff --git a/eval/klein4b_dmd2_1_flir_from_ep72/checkpoint-150_cfg1.0_s1/flir_common/sampling.json b/eval/klein4b_dmd2_1_flir_from_ep72/checkpoint-150_cfg1.0_s1/flir_common/sampling.json
new file mode 100644
index 0000000000000000000000000000000000000000..adcf3471860064b69b9d94b2b1088c44713a6779
--- /dev/null
+++ b/eval/klein4b_dmd2_1_flir_from_ep72/checkpoint-150_cfg1.0_s1/flir_common/sampling.json
@@ -0,0 +1,12 @@
+{
+  "pairs": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/flir_common/eval_pairs.jsonl",
+  "out": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/eval/klein4b_dmd2_1_flir_from_ep72/checkpoint-150_cfg1.0_s1/flir_common",
+  "model": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/models/klein4b_sft_flir_ep72_merged",
+  "lora": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/runs/klein4b_dmd2_1_flir_from_ep72/saves/klein4b_dmd2_1_flir_from_ep72/checkpoints/checkpoint-150",
+  "steps": 1,
+  "cfg": 1.0,
+  "seed": 0,
+  "prompt": null,
+  "max": null,
+  "gpus": "0,1,2,3,4,5,6,7"
+}
\ No newline at end of file
diff --git a/eval/klein4b_dmd2_1_flir_from_nft/checkpoint-150_cfg1.0_s1/flir_common/metrics.csv b/eval/klein4b_dmd2_1_flir_from_nft/checkpoint-150_cfg1.0_s1/flir_common/metrics.csv
new file mode 100644
index 0000000000000000000000000000000000000000..cb8cee707ca72b3c08abf6802aac9e1d270104aa
--- /dev/null
+++ b/eval/klein4b_dmd2_1_flir_from_nft/checkpoint-150_cfg1.0_s1/flir_common/metrics.csv
@@ -0,0 +1,1447 @@
+id,psnr,ssim,ms_ssim,l1,lpips,dists
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diff --git a/eval/klein4b_dmd2_1_flir_from_nft/checkpoint-150_cfg1.0_s1/flir_common/metrics.json b/eval/klein4b_dmd2_1_flir_from_nft/checkpoint-150_cfg1.0_s1/flir_common/metrics.json
new file mode 100644
index 0000000000000000000000000000000000000000..2374e63dacee321b4543ed77e76aac42db1e25e1
--- /dev/null
+++ b/eval/klein4b_dmd2_1_flir_from_nft/checkpoint-150_cfg1.0_s1/flir_common/metrics.json
@@ -0,0 +1,21 @@
+{
+  "protocol": "luma-fp32",
+  "pairs": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/flir_common/eval_pairs.jsonl",
+  "pred_dir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/eval/klein4b_dmd2_1_flir_from_nft/checkpoint-150_cfg1.0_s1/flir_common",
+  "n": 1446,
+  "psnr": 20.873865061610896,
+  "ssim": 0.6260044648109795,
+  "ms_ssim": 0.6965269361459038,
+  "l1": 0.07049206460866031,
+  "lpips": 0.2638623253551839,
+  "dists": 0.15238966606299742,
+  "psnr_std": 2.2741945479023697,
+  "ssim_std": 0.09355767921092357,
+  "ms_ssim_std": 0.08362017398765932,
+  "l1_std": 0.0215522156173283,
+  "lpips_std": 0.05439040711921332,
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+}
\ No newline at end of file
diff --git a/eval/klein4b_dmd2_1_flir_from_nft/checkpoint-150_cfg1.0_s1/flir_common/sampling.json b/eval/klein4b_dmd2_1_flir_from_nft/checkpoint-150_cfg1.0_s1/flir_common/sampling.json
new file mode 100644
index 0000000000000000000000000000000000000000..9635107335d7fe333cec78467cede1e950830e90
--- /dev/null
+++ b/eval/klein4b_dmd2_1_flir_from_nft/checkpoint-150_cfg1.0_s1/flir_common/sampling.json
@@ -0,0 +1,12 @@
+{
+  "pairs": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/flir_common/eval_pairs.jsonl",
+  "out": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/eval/klein4b_dmd2_1_flir_from_nft/checkpoint-150_cfg1.0_s1/flir_common",
+  "model": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/models/klein4b_nft_flir_ep72_ckpt80_merged",
+  "lora": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/runs/klein4b_dmd2_1_flir_from_nft/saves/klein4b_dmd2_1_flir_from_nft/checkpoints/checkpoint-150",
+  "steps": 1,
+  "cfg": 1.0,
+  "seed": 0,
+  "prompt": null,
+  "max": null,
+  "gpus": "0,1,2,3,4,5,6,7"
+}
\ No newline at end of file
diff --git a/eval/klein4b_dmd2_1_vedai_from_ep144/checkpoint-150_cfg1.0_s1/vedai_common/metrics.csv b/eval/klein4b_dmd2_1_vedai_from_ep144/checkpoint-150_cfg1.0_s1/vedai_common/metrics.csv
new file mode 100644
index 0000000000000000000000000000000000000000..b426efe3067a45d5967ecb38aff3e061bf589dce
--- /dev/null
+++ b/eval/klein4b_dmd2_1_vedai_from_ep144/checkpoint-150_cfg1.0_s1/vedai_common/metrics.csv
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diff --git a/eval/klein4b_dmd2_1_vedai_from_ep144/checkpoint-150_cfg1.0_s1/vedai_common/metrics.json b/eval/klein4b_dmd2_1_vedai_from_ep144/checkpoint-150_cfg1.0_s1/vedai_common/metrics.json
new file mode 100644
index 0000000000000000000000000000000000000000..babd2e0be5deda5c488745685a8a3c6733bf0f92
--- /dev/null
+++ b/eval/klein4b_dmd2_1_vedai_from_ep144/checkpoint-150_cfg1.0_s1/vedai_common/metrics.json
@@ -0,0 +1,21 @@
+{
+  "protocol": "luma-fp32",
+  "pairs": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/vedai_common/eval_pairs.jsonl",
+  "pred_dir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/eval/klein4b_dmd2_1_vedai_from_ep144/checkpoint-150_cfg1.0_s1/vedai_common",
+  "n": 268,
+  "psnr": 27.79561928137025,
+  "ssim": 0.8947120747014657,
+  "ms_ssim": 0.9177044874696589,
+  "l1": 0.03212787541769334,
+  "lpips": 0.08951926249454716,
+  "dists": 0.07977508319847619,
+  "psnr_std": 2.217364186603418,
+  "ssim_std": 0.0286871955446381,
+  "ms_ssim_std": 0.025300234484990945,
+  "l1_std": 0.00954218350392497,
+  "lpips_std": 0.02930768692494898,
+  "dists_std": 0.018487232935432054,
+  "fid": 44.10691571988855,
+  "kid": -0.0014586710929870605,
+  "kid_std": 0.000692942635080259
+}
\ No newline at end of file
diff --git a/eval/klein4b_dmd2_1_vedai_from_ep144/checkpoint-150_cfg1.0_s1/vedai_common/sampling.json b/eval/klein4b_dmd2_1_vedai_from_ep144/checkpoint-150_cfg1.0_s1/vedai_common/sampling.json
new file mode 100644
index 0000000000000000000000000000000000000000..2378cd5cf769d86c328705994d8966a54530c3f9
--- /dev/null
+++ b/eval/klein4b_dmd2_1_vedai_from_ep144/checkpoint-150_cfg1.0_s1/vedai_common/sampling.json
@@ -0,0 +1,12 @@
+{
+  "pairs": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/vedai_common/eval_pairs.jsonl",
+  "out": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/eval/klein4b_dmd2_1_vedai_from_ep144/checkpoint-150_cfg1.0_s1/vedai_common",
+  "model": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/models/klein4b_sft_vedai_ep144_merged",
+  "lora": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/runs/klein4b_dmd2_1_vedai_from_ep144/saves/klein4b_dmd2_1_vedai_from_ep144/checkpoints/checkpoint-150",
+  "steps": 1,
+  "cfg": 1.0,
+  "seed": 0,
+  "prompt": null,
+  "max": null,
+  "gpus": "0,1,2,3,4,5,6,7"
+}
\ No newline at end of file
diff --git a/eval/klein4b_dmd2_4_flir_from_ep72_lr2e-6/checkpoint-150_cfg1.0_s4/flir_common/metrics.csv b/eval/klein4b_dmd2_4_flir_from_ep72_lr2e-6/checkpoint-150_cfg1.0_s4/flir_common/metrics.csv
new file mode 100644
index 0000000000000000000000000000000000000000..6b4114b7a8f800134a6f33140e2b416509e427bf
--- /dev/null
+++ b/eval/klein4b_dmd2_4_flir_from_ep72_lr2e-6/checkpoint-150_cfg1.0_s4/flir_common/metrics.csv
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diff --git a/eval/klein4b_dmd2_4_flir_from_ep72_lr2e-6/checkpoint-150_cfg1.0_s4/flir_common/metrics.json b/eval/klein4b_dmd2_4_flir_from_ep72_lr2e-6/checkpoint-150_cfg1.0_s4/flir_common/metrics.json
new file mode 100644
index 0000000000000000000000000000000000000000..088d6020d772b4f9988654dcee17ea4fdfeab27e
--- /dev/null
+++ b/eval/klein4b_dmd2_4_flir_from_ep72_lr2e-6/checkpoint-150_cfg1.0_s4/flir_common/metrics.json
@@ -0,0 +1,21 @@
+{
+  "protocol": "luma-fp32",
+  "pairs": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/flir_common/eval_pairs.jsonl",
+  "pred_dir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/eval/klein4b_dmd2_4_flir_from_ep72_lr2e-6/checkpoint-150_cfg1.0_s4/flir_common",
+  "n": 1446,
+  "psnr": 21.917499055506283,
+  "ssim": 0.6808032782987935,
+  "ms_ssim": 0.7483960473149973,
+  "l1": 0.06418147899292893,
+  "lpips": 0.2889172849380624,
+  "dists": 0.17251409847871563,
+  "psnr_std": 3.1429035953135647,
+  "ssim_std": 0.10985119211407973,
+  "ms_ssim_std": 0.09556964919974105,
+  "l1_std": 0.026770012317474334,
+  "lpips_std": 0.09735833170995431,
+  "dists_std": 0.040503534799664315,
+  "fid": 50.22808728686749,
+  "kid": 0.032000534534454346,
+  "kid_std": 0.0011328073952644499
+}
\ No newline at end of file
diff --git a/eval/klein4b_dmd2_4_flir_from_ep72_lr2e-6/checkpoint-150_cfg1.0_s4/flir_common/sampling.json b/eval/klein4b_dmd2_4_flir_from_ep72_lr2e-6/checkpoint-150_cfg1.0_s4/flir_common/sampling.json
new file mode 100644
index 0000000000000000000000000000000000000000..8b00716bb9f1e089650b76e459c653b12e6dd5b9
--- /dev/null
+++ b/eval/klein4b_dmd2_4_flir_from_ep72_lr2e-6/checkpoint-150_cfg1.0_s4/flir_common/sampling.json
@@ -0,0 +1,12 @@
+{
+  "pairs": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/flir_common/eval_pairs.jsonl",
+  "out": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/eval/klein4b_dmd2_4_flir_from_ep72_lr2e-6/checkpoint-150_cfg1.0_s4/flir_common",
+  "model": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/models/klein4b_sft_flir_ep72_merged",
+  "lora": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/runs/klein4b_dmd2_4_flir_from_ep72_lr2e-6/saves/klein4b_dmd2_4_flir_from_ep72_lr2e-6/checkpoints/checkpoint-150",
+  "steps": 4,
+  "cfg": 1.0,
+  "seed": 0,
+  "prompt": null,
+  "max": null,
+  "gpus": "0,1,2,3,4,5,6,7"
+}
\ No newline at end of file
diff --git a/eval/klein4b_nft_vedai_full_ssim_dists_psnr/checkpoint-100_cfg1.0_s28/vedai_common/metrics.csv b/eval/klein4b_nft_vedai_full_ssim_dists_psnr/checkpoint-100_cfg1.0_s28/vedai_common/metrics.csv
new file mode 100644
index 0000000000000000000000000000000000000000..0d8f10fcbfb53d02ab72bff577d72f87541a91af
--- /dev/null
+++ b/eval/klein4b_nft_vedai_full_ssim_dists_psnr/checkpoint-100_cfg1.0_s28/vedai_common/metrics.csv
@@ -0,0 +1,269 @@
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diff --git a/eval/klein4b_nft_vedai_full_ssim_dists_psnr/checkpoint-100_cfg1.0_s28/vedai_common/metrics.json b/eval/klein4b_nft_vedai_full_ssim_dists_psnr/checkpoint-100_cfg1.0_s28/vedai_common/metrics.json
new file mode 100644
index 0000000000000000000000000000000000000000..9ad65926a0ed96a15613717f0276fc8904d0f296
--- /dev/null
+++ b/eval/klein4b_nft_vedai_full_ssim_dists_psnr/checkpoint-100_cfg1.0_s28/vedai_common/metrics.json
@@ -0,0 +1,21 @@
+{
+  "protocol": "luma-fp32",
+  "pairs": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/vedai_common/eval_pairs.jsonl",
+  "pred_dir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/eval/klein4b_nft_vedai_full_ssim_dists_psnr/checkpoint-100_cfg1.0_s28/vedai_common",
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+  "fid": 38.828617851498336,
+  "kid": -0.0002596473693847656,
+  "kid_std": 0.0008086007125432155
+}
\ No newline at end of file
diff --git a/eval/klein4b_nft_vedai_full_ssim_dists_psnr/checkpoint-100_cfg1.0_s28/vedai_common/sampling.json b/eval/klein4b_nft_vedai_full_ssim_dists_psnr/checkpoint-100_cfg1.0_s28/vedai_common/sampling.json
new file mode 100644
index 0000000000000000000000000000000000000000..b88be8f9245cbf7eac35f9457d8e72a7a0e8775a
--- /dev/null
+++ b/eval/klein4b_nft_vedai_full_ssim_dists_psnr/checkpoint-100_cfg1.0_s28/vedai_common/sampling.json
@@ -0,0 +1,12 @@
+{
+  "pairs": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/vedai_common/eval_pairs.jsonl",
+  "out": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/eval/klein4b_nft_vedai_full_ssim_dists_psnr/checkpoint-100_cfg1.0_s28/vedai_common",
+  "model": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/flow_factory_gpu_gate_models/FLUX.2-klein-base-4B",
+  "lora": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/runs/klein4b_nft_vedai_full_ssim_dists_psnr/saves/klein4b_nft_vedai_full_ssim_dists_psnr/checkpoints/checkpoint-100",
+  "steps": 28,
+  "cfg": 1.0,
+  "seed": 0,
+  "prompt": null,
+  "max": null,
+  "gpus": "4,5,6,7"
+}
\ No newline at end of file
diff --git a/eval/klein4b_nft_vedai_full_ssim_dists_psnr/checkpoint-100_cfg1.0_s8/vedai_common/metrics.csv b/eval/klein4b_nft_vedai_full_ssim_dists_psnr/checkpoint-100_cfg1.0_s8/vedai_common/metrics.csv
new file mode 100644
index 0000000000000000000000000000000000000000..7f1f0297eca61695a0432c21c8a52dc55568df43
--- /dev/null
+++ b/eval/klein4b_nft_vedai_full_ssim_dists_psnr/checkpoint-100_cfg1.0_s8/vedai_common/metrics.csv
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diff --git a/eval/klein4b_nft_vedai_full_ssim_dists_psnr/checkpoint-100_cfg1.0_s8/vedai_common/metrics.json b/eval/klein4b_nft_vedai_full_ssim_dists_psnr/checkpoint-100_cfg1.0_s8/vedai_common/metrics.json
new file mode 100644
index 0000000000000000000000000000000000000000..231c3db5355bbcccb5a8c85109c4970a669a5119
--- /dev/null
+++ b/eval/klein4b_nft_vedai_full_ssim_dists_psnr/checkpoint-100_cfg1.0_s8/vedai_common/metrics.json
@@ -0,0 +1,21 @@
+{
+  "protocol": "luma-fp32",
+  "pairs": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/vedai_common/eval_pairs.jsonl",
+  "pred_dir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/eval/klein4b_nft_vedai_full_ssim_dists_psnr/checkpoint-100_cfg1.0_s8/vedai_common",
+  "n": 268,
+  "psnr": 29.530692755286374,
+  "ssim": 0.9313296833589896,
+  "ms_ssim": 0.9468910591815834,
+  "l1": 0.02660090521103077,
+  "lpips": 0.08106073305539008,
+  "dists": 0.06853811287168246,
+  "psnr_std": 2.401039899218032,
+  "ssim_std": 0.023908341010736914,
+  "ms_ssim_std": 0.021089793251347643,
+  "l1_std": 0.008947199119044276,
+  "lpips_std": 0.026873596456372665,
+  "dists_std": 0.01926159844878932,
+  "fid": 37.40144078364801,
+  "kid": -0.0009777569770812989,
+  "kid_std": 0.0006385111531820264
+}
\ No newline at end of file
diff --git a/eval/klein4b_nft_vedai_full_ssim_dists_psnr/checkpoint-100_cfg1.0_s8/vedai_common/sampling.json b/eval/klein4b_nft_vedai_full_ssim_dists_psnr/checkpoint-100_cfg1.0_s8/vedai_common/sampling.json
new file mode 100644
index 0000000000000000000000000000000000000000..b8913957771d73b229d5a4b5c71cc505cbe3e55e
--- /dev/null
+++ b/eval/klein4b_nft_vedai_full_ssim_dists_psnr/checkpoint-100_cfg1.0_s8/vedai_common/sampling.json
@@ -0,0 +1,12 @@
+{
+  "pairs": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/vedai_common/eval_pairs.jsonl",
+  "out": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/eval/klein4b_nft_vedai_full_ssim_dists_psnr/checkpoint-100_cfg1.0_s8/vedai_common",
+  "model": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/flow_factory_gpu_gate_models/FLUX.2-klein-base-4B",
+  "lora": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/runs/klein4b_nft_vedai_full_ssim_dists_psnr/saves/klein4b_nft_vedai_full_ssim_dists_psnr/checkpoints/checkpoint-100",
+  "steps": 8,
+  "cfg": 1.0,
+  "seed": 0,
+  "prompt": null,
+  "max": null,
+  "gpus": "4,5,6,7"
+}
\ No newline at end of file
diff --git a/eval/klein4b_sft_flir_full_r64_lr1e-4/checkpoint-12_cfg1.0_s28/flir_common/metrics.csv b/eval/klein4b_sft_flir_full_r64_lr1e-4/checkpoint-12_cfg1.0_s28/flir_common/metrics.csv
new file mode 100644
index 0000000000000000000000000000000000000000..86757f67ff531089035afcbce82c69bdb3dec046
--- /dev/null
+++ b/eval/klein4b_sft_flir_full_r64_lr1e-4/checkpoint-12_cfg1.0_s28/flir_common/metrics.csv
@@ -0,0 +1,1447 @@
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diff --git a/eval/klein4b_sft_flir_full_r64_lr1e-4/checkpoint-12_cfg1.0_s28/flir_common/metrics.json b/eval/klein4b_sft_flir_full_r64_lr1e-4/checkpoint-12_cfg1.0_s28/flir_common/metrics.json
new file mode 100644
index 0000000000000000000000000000000000000000..a03127d578c81c69104b5a2b68421b5615686459
--- /dev/null
+++ b/eval/klein4b_sft_flir_full_r64_lr1e-4/checkpoint-12_cfg1.0_s28/flir_common/metrics.json
@@ -0,0 +1,21 @@
+{
+  "protocol": "luma-fp32",
+  "pairs": "data/ff/flir_common/eval_pairs.jsonl",
+  "pred_dir": "eval/klein4b_sft_flir_full_r64_lr1e-4/checkpoint-12_cfg1.0_s28/flir_common",
+  "n": 1446,
+  "psnr": 18.83004151867963,
+  "ssim": 0.5648777333202863,
+  "ms_ssim": 0.6298731485301858,
+  "l1": 0.09245645760057461,
+  "lpips": 0.31686289436898785,
+  "dists": 0.16346279644042788,
+  "psnr_std": 2.7021508666151672,
+  "ssim_std": 0.10306341464343688,
+  "ms_ssim_std": 0.10101660433459984,
+  "l1_std": 0.03382394332934543,
+  "lpips_std": 0.08520428016539063,
+  "dists_std": 0.03824018902677182,
+  "fid": 27.200255148458098,
+  "kid": 0.005333728790283203,
+  "kid_std": 0.0005960652063088228
+}
\ No newline at end of file
diff --git a/eval/klein4b_sft_flir_full_r64_lr1e-4/checkpoint-12_cfg1.0_s28/flir_common/sampling.json b/eval/klein4b_sft_flir_full_r64_lr1e-4/checkpoint-12_cfg1.0_s28/flir_common/sampling.json
new file mode 100644
index 0000000000000000000000000000000000000000..7fcfe796a19111d44fbf3514b132b6fe8b90c8e2
--- /dev/null
+++ b/eval/klein4b_sft_flir_full_r64_lr1e-4/checkpoint-12_cfg1.0_s28/flir_common/sampling.json
@@ -0,0 +1,12 @@
+{
+  "pairs": "data/ff/flir_common/eval_pairs.jsonl",
+  "out": "eval/klein4b_sft_flir_full_r64_lr1e-4/checkpoint-12_cfg1.0_s28/flir_common",
+  "model": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/flow_factory_gpu_gate_models/FLUX.2-klein-base-4B",
+  "lora": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/runs/klein4b_sft_flir_full_r64_lr1e-4/saves/klein4b_sft_flir_full_r64_lr1e-4/checkpoints/checkpoint-12",
+  "steps": 28,
+  "cfg": 1.0,
+  "seed": 0,
+  "prompt": null,
+  "max": null,
+  "gpus": "0,1,2,3,4,5,6,7"
+}
\ No newline at end of file
diff --git a/eval/klein4b_sft_flir_full_r64_lr1e-4/checkpoint-12_cfg1.0_s28/flir_common400/metrics.csv b/eval/klein4b_sft_flir_full_r64_lr1e-4/checkpoint-12_cfg1.0_s28/flir_common400/metrics.csv
new file mode 100644
index 0000000000000000000000000000000000000000..7b1471fd3793dbdc53c6ca45722f977f42f03684
--- /dev/null
+++ b/eval/klein4b_sft_flir_full_r64_lr1e-4/checkpoint-12_cfg1.0_s28/flir_common400/metrics.csv
@@ -0,0 +1,401 @@
+id,psnr,ssim,ms_ssim,l1,lpips,dists
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diff --git a/eval/klein4b_sft_flir_full_r64_lr1e-4/checkpoint-12_cfg1.0_s28/flir_common400/metrics.json b/eval/klein4b_sft_flir_full_r64_lr1e-4/checkpoint-12_cfg1.0_s28/flir_common400/metrics.json
new file mode 100644
index 0000000000000000000000000000000000000000..33d4b95b871d2ab32755049a290f8fed3d97e764
--- /dev/null
+++ b/eval/klein4b_sft_flir_full_r64_lr1e-4/checkpoint-12_cfg1.0_s28/flir_common400/metrics.json
@@ -0,0 +1,21 @@
+{
+  "protocol": "luma-fp32",
+  "pairs": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/flir_common400/eval_pairs.jsonl",
+  "pred_dir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/eval/klein4b_sft_flir_full_r64_lr1e-4/checkpoint-12_cfg1.0_s28/flir_common400",
+  "n": 400,
+  "psnr": 19.56461889743805,
+  "ssim": 0.6343745220452547,
+  "ms_ssim": 0.6922188183665275,
+  "l1": 0.08709918997250497,
+  "lpips": 0.33125731131061914,
+  "dists": 0.16950481429696082,
+  "psnr_std": 2.699407536478935,
+  "ssim_std": 0.10156696381602286,
+  "ms_ssim_std": 0.0971922849951205,
+  "l1_std": 0.03517621694712379,
+  "lpips_std": 0.10242808488679547,
+  "dists_std": 0.04106413334113817,
+  "fid": 56.843217481655756,
+  "kid": 0.0034052085876464843,
+  "kid_std": 0.0010495239376831142
+}
\ No newline at end of file
diff --git a/eval/klein4b_sft_flir_full_r64_lr1e-4/checkpoint-12_cfg1.0_s28/flir_common400/sampling.json b/eval/klein4b_sft_flir_full_r64_lr1e-4/checkpoint-12_cfg1.0_s28/flir_common400/sampling.json
new file mode 100644
index 0000000000000000000000000000000000000000..02a2a0c588e339fec534efc298a96c8731b2652a
--- /dev/null
+++ b/eval/klein4b_sft_flir_full_r64_lr1e-4/checkpoint-12_cfg1.0_s28/flir_common400/sampling.json
@@ -0,0 +1,12 @@
+{
+  "pairs": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/flir_common400/eval_pairs.jsonl",
+  "out": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/eval/klein4b_sft_flir_full_r64_lr1e-4/checkpoint-12_cfg1.0_s28/flir_common400",
+  "model": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/flow_factory_gpu_gate_models/FLUX.2-klein-base-4B",
+  "lora": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/runs/klein4b_sft_flir_full_r64_lr1e-4/saves/klein4b_sft_flir_full_r64_lr1e-4/checkpoints/checkpoint-12",
+  "steps": 28,
+  "cfg": 1.0,
+  "seed": 0,
+  "prompt": null,
+  "max": null,
+  "gpus": "0,1,2,3,4,5,6,7"
+}
\ No newline at end of file
diff --git a/eval/klein4b_sft_flir_full_r64_lr1e-4/checkpoint-12_cfg1.0_s4/flir_common400/metrics.csv b/eval/klein4b_sft_flir_full_r64_lr1e-4/checkpoint-12_cfg1.0_s4/flir_common400/metrics.csv
new file mode 100644
index 0000000000000000000000000000000000000000..20372fa6cd808962a0df559d06a38bbed0f46a44
--- /dev/null
+++ b/eval/klein4b_sft_flir_full_r64_lr1e-4/checkpoint-12_cfg1.0_s4/flir_common400/metrics.csv
@@ -0,0 +1,401 @@
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diff --git a/eval/klein4b_sft_flir_full_r64_lr1e-4/checkpoint-12_cfg1.0_s4/flir_common400/metrics.json b/eval/klein4b_sft_flir_full_r64_lr1e-4/checkpoint-12_cfg1.0_s4/flir_common400/metrics.json
new file mode 100644
index 0000000000000000000000000000000000000000..948fa859e97a02646c272f3d204429698643642f
--- /dev/null
+++ b/eval/klein4b_sft_flir_full_r64_lr1e-4/checkpoint-12_cfg1.0_s4/flir_common400/metrics.json
@@ -0,0 +1,21 @@
+{
+  "protocol": "luma-fp32",
+  "pairs": "data/ff/flir_common400/eval_pairs.jsonl",
+  "pred_dir": "eval/klein4b_sft_flir_full_r64_lr1e-4/checkpoint-12_cfg1.0_s4/flir_common400",
+  "n": 400,
+  "psnr": 20.37204303264618,
+  "ssim": 0.696478276476264,
+  "ms_ssim": 0.7457328917086125,
+  "l1": 0.08017682777717709,
+  "lpips": 0.3607337509095669,
+  "dists": 0.21588604107499124,
+  "psnr_std": 2.6998261413197384,
+  "ssim_std": 0.11091203501069838,
+  "ms_ssim_std": 0.10097562253773937,
+  "l1_std": 0.03378220663550151,
+  "lpips_std": 0.08394552467631697,
+  "dists_std": 0.03719082431041865,
+  "fid": 83.10776472353518,
+  "kid": 0.03431826114654541,
+  "kid_std": 0.0024699663463175637
+}
\ No newline at end of file
diff --git a/eval/klein4b_sft_flir_full_r64_lr1e-4/checkpoint-12_cfg1.0_s4/flir_common400/sampling.json b/eval/klein4b_sft_flir_full_r64_lr1e-4/checkpoint-12_cfg1.0_s4/flir_common400/sampling.json
new file mode 100644
index 0000000000000000000000000000000000000000..096ba4b6013d78404f994eb5f00566e9f220ae5e
--- /dev/null
+++ b/eval/klein4b_sft_flir_full_r64_lr1e-4/checkpoint-12_cfg1.0_s4/flir_common400/sampling.json
@@ -0,0 +1,12 @@
+{
+  "pairs": "data/ff/flir_common400/eval_pairs.jsonl",
+  "out": "eval/klein4b_sft_flir_full_r64_lr1e-4/checkpoint-12_cfg1.0_s4/flir_common400",
+  "model": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/flow_factory_gpu_gate_models/FLUX.2-klein-base-4B",
+  "lora": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/runs/klein4b_sft_flir_full_r64_lr1e-4/saves/klein4b_sft_flir_full_r64_lr1e-4/checkpoints/checkpoint-12",
+  "steps": 4,
+  "cfg": 1.0,
+  "seed": 0,
+  "prompt": null,
+  "max": null,
+  "gpus": "0,1,2,3,4,5,6,7"
+}
\ No newline at end of file
diff --git a/eval/klein4b_sft_flir_full_r64_lr1e-4/checkpoint-12_cfg1.0_s50/flir_common400/metrics.csv b/eval/klein4b_sft_flir_full_r64_lr1e-4/checkpoint-12_cfg1.0_s50/flir_common400/metrics.csv
new file mode 100644
index 0000000000000000000000000000000000000000..4281770d7bb7c2e86fb9158b0133775e3f4e3b5d
--- /dev/null
+++ b/eval/klein4b_sft_flir_full_r64_lr1e-4/checkpoint-12_cfg1.0_s50/flir_common400/metrics.csv
@@ -0,0 +1,401 @@
+id,psnr,ssim,ms_ssim,l1,lpips,dists
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diff --git a/eval/klein4b_sft_flir_full_r64_lr1e-4/checkpoint-12_cfg1.0_s50/flir_common400/metrics.json b/eval/klein4b_sft_flir_full_r64_lr1e-4/checkpoint-12_cfg1.0_s50/flir_common400/metrics.json
new file mode 100644
index 0000000000000000000000000000000000000000..3a2d49afb6524a4cc2bf753ece66ffa9a2acc9da
--- /dev/null
+++ b/eval/klein4b_sft_flir_full_r64_lr1e-4/checkpoint-12_cfg1.0_s50/flir_common400/metrics.json
@@ -0,0 +1,21 @@
+{
+  "protocol": "luma-fp32",
+  "pairs": "data/ff/flir_common400/eval_pairs.jsonl",
+  "pred_dir": "eval/klein4b_sft_flir_full_r64_lr1e-4/checkpoint-12_cfg1.0_s50/flir_common400",
+  "n": 400,
+  "psnr": 19.438358414173127,
+  "ssim": 0.6207410282641649,
+  "ms_ssim": 0.6809893976897001,
+  "l1": 0.08823112715966999,
+  "lpips": 0.3336781147494912,
+  "dists": 0.16841997981071472,
+  "psnr_std": 2.687410705664536,
+  "ssim_std": 0.10084952403611684,
+  "ms_ssim_std": 0.09681101078222891,
+  "l1_std": 0.03526869424665258,
+  "lpips_std": 0.10674275394602588,
+  "dists_std": 0.043220606178226574,
+  "fid": 56.16924199434925,
+  "kid": 0.0025207686424255373,
+  "kid_std": 0.0009368657702692192
+}
\ No newline at end of file
diff --git a/eval/klein4b_sft_flir_full_r64_lr1e-4/checkpoint-12_cfg1.0_s50/flir_common400/sampling.json b/eval/klein4b_sft_flir_full_r64_lr1e-4/checkpoint-12_cfg1.0_s50/flir_common400/sampling.json
new file mode 100644
index 0000000000000000000000000000000000000000..b7d97d54868c25224fe7f7958bbfda6465668645
--- /dev/null
+++ b/eval/klein4b_sft_flir_full_r64_lr1e-4/checkpoint-12_cfg1.0_s50/flir_common400/sampling.json
@@ -0,0 +1,12 @@
+{
+  "pairs": "data/ff/flir_common400/eval_pairs.jsonl",
+  "out": "eval/klein4b_sft_flir_full_r64_lr1e-4/checkpoint-12_cfg1.0_s50/flir_common400",
+  "model": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/flow_factory_gpu_gate_models/FLUX.2-klein-base-4B",
+  "lora": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/runs/klein4b_sft_flir_full_r64_lr1e-4/saves/klein4b_sft_flir_full_r64_lr1e-4/checkpoints/checkpoint-12",
+  "steps": 50,
+  "cfg": 1.0,
+  "seed": 0,
+  "prompt": null,
+  "max": null,
+  "gpus": "0,1,2,3,4,5,6,7"
+}
\ No newline at end of file
diff --git a/eval/klein4b_sft_flir_full_r64_lr1e-4/checkpoint-12_cfg1.0_s8/flir_common400/metrics.csv b/eval/klein4b_sft_flir_full_r64_lr1e-4/checkpoint-12_cfg1.0_s8/flir_common400/metrics.csv
new file mode 100644
index 0000000000000000000000000000000000000000..854ac19899097b8ca60bcd5e52f3457510e91d51
--- /dev/null
+++ b/eval/klein4b_sft_flir_full_r64_lr1e-4/checkpoint-12_cfg1.0_s8/flir_common400/metrics.csv
@@ -0,0 +1,401 @@
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diff --git a/eval/klein4b_sft_flir_full_r64_lr1e-4/checkpoint-12_cfg1.0_s8/flir_common400/metrics.json b/eval/klein4b_sft_flir_full_r64_lr1e-4/checkpoint-12_cfg1.0_s8/flir_common400/metrics.json
new file mode 100644
index 0000000000000000000000000000000000000000..a21e12f912a7ebf45ab0a2951c808d61569d41f9
--- /dev/null
+++ b/eval/klein4b_sft_flir_full_r64_lr1e-4/checkpoint-12_cfg1.0_s8/flir_common400/metrics.json
@@ -0,0 +1,21 @@
+{
+  "protocol": "luma-fp32",
+  "pairs": "data/ff/flir_common400/eval_pairs.jsonl",
+  "pred_dir": "eval/klein4b_sft_flir_full_r64_lr1e-4/checkpoint-12_cfg1.0_s8/flir_common400",
+  "n": 400,
+  "psnr": 20.01341842651367,
+  "ssim": 0.67852978117764,
+  "ms_ssim": 0.7287541534751654,
+  "l1": 0.08331378443166614,
+  "lpips": 0.3401905605569482,
+  "dists": 0.18914849311113358,
+  "psnr_std": 2.741623867359499,
+  "ssim_std": 0.1062105143678054,
+  "ms_ssim_std": 0.09871047668197978,
+  "l1_std": 0.03489027206714893,
+  "lpips_std": 0.08816543009282721,
+  "dists_std": 0.03500460103971545,
+  "fid": 69.0161350951391,
+  "kid": 0.017960212230682372,
+  "kid_std": 0.0018485998943435556
+}
\ No newline at end of file
diff --git a/eval/klein4b_sft_flir_full_r64_lr1e-4/checkpoint-12_cfg1.0_s8/flir_common400/sampling.json b/eval/klein4b_sft_flir_full_r64_lr1e-4/checkpoint-12_cfg1.0_s8/flir_common400/sampling.json
new file mode 100644
index 0000000000000000000000000000000000000000..c6ef75c32db837309e75bd072bc10eaf5b7f3133
--- /dev/null
+++ b/eval/klein4b_sft_flir_full_r64_lr1e-4/checkpoint-12_cfg1.0_s8/flir_common400/sampling.json
@@ -0,0 +1,12 @@
+{
+  "pairs": "data/ff/flir_common400/eval_pairs.jsonl",
+  "out": "eval/klein4b_sft_flir_full_r64_lr1e-4/checkpoint-12_cfg1.0_s8/flir_common400",
+  "model": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/flow_factory_gpu_gate_models/FLUX.2-klein-base-4B",
+  "lora": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/runs/klein4b_sft_flir_full_r64_lr1e-4/saves/klein4b_sft_flir_full_r64_lr1e-4/checkpoints/checkpoint-12",
+  "steps": 8,
+  "cfg": 1.0,
+  "seed": 0,
+  "prompt": null,
+  "max": null,
+  "gpus": "0,1,2,3,4,5,6,7"
+}
\ No newline at end of file
diff --git a/eval/klein4b_sft_flir_full_r64a128_lr1e-4/checkpoint-12_cfg1.0_s28/flir_common/metrics.csv b/eval/klein4b_sft_flir_full_r64a128_lr1e-4/checkpoint-12_cfg1.0_s28/flir_common/metrics.csv
new file mode 100644
index 0000000000000000000000000000000000000000..57176150fc46c9d144a84523315494c873494a61
--- /dev/null
+++ b/eval/klein4b_sft_flir_full_r64a128_lr1e-4/checkpoint-12_cfg1.0_s28/flir_common/metrics.csv
@@ -0,0 +1,1447 @@
+id,psnr,ssim,ms_ssim,l1,lpips,dists
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diff --git a/eval/klein4b_sft_flir_full_r64a128_lr1e-4/checkpoint-12_cfg1.0_s28/flir_common/metrics.json b/eval/klein4b_sft_flir_full_r64a128_lr1e-4/checkpoint-12_cfg1.0_s28/flir_common/metrics.json
new file mode 100644
index 0000000000000000000000000000000000000000..60746925628d393a25eb6c47fb81870e6034236e
--- /dev/null
+++ b/eval/klein4b_sft_flir_full_r64a128_lr1e-4/checkpoint-12_cfg1.0_s28/flir_common/metrics.json
@@ -0,0 +1,21 @@
+{
+  "protocol": "luma-fp32",
+  "pairs": "data/ff/flir_common/eval_pairs.jsonl",
+  "pred_dir": "eval/klein4b_sft_flir_full_r64a128_lr1e-4/checkpoint-12_cfg1.0_s28/flir_common",
+  "n": 1446,
+  "psnr": 19.14711127076736,
+  "ssim": 0.5804430976159675,
+  "ms_ssim": 0.6453086732787545,
+  "l1": 0.0894407843140419,
+  "lpips": 0.31618423716140653,
+  "dists": 0.16435165923825604,
+  "psnr_std": 2.777821888639173,
+  "ssim_std": 0.10791240102268847,
+  "ms_ssim_std": 0.10345483538617559,
+  "l1_std": 0.03323574932625597,
+  "lpips_std": 0.09612275461848889,
+  "dists_std": 0.04243037737466971,
+  "fid": 27.557986928995604,
+  "kid": 0.00671323299407959,
+  "kid_std": 0.0007130911834975335
+}
\ No newline at end of file
diff --git a/eval/klein4b_sft_flir_full_r64a128_lr1e-4/checkpoint-12_cfg1.0_s28/flir_common/sampling.json b/eval/klein4b_sft_flir_full_r64a128_lr1e-4/checkpoint-12_cfg1.0_s28/flir_common/sampling.json
new file mode 100644
index 0000000000000000000000000000000000000000..ceeb14d3a928df3330e3f8e8465a79272f210984
--- /dev/null
+++ b/eval/klein4b_sft_flir_full_r64a128_lr1e-4/checkpoint-12_cfg1.0_s28/flir_common/sampling.json
@@ -0,0 +1,12 @@
+{
+  "pairs": "data/ff/flir_common/eval_pairs.jsonl",
+  "out": "eval/klein4b_sft_flir_full_r64a128_lr1e-4/checkpoint-12_cfg1.0_s28/flir_common",
+  "model": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/flow_factory_gpu_gate_models/FLUX.2-klein-base-4B",
+  "lora": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/runs/klein4b_sft_flir_full_r64a128_lr1e-4/saves/klein4b_sft_flir_full_r64a128_lr1e-4/checkpoints/checkpoint-12",
+  "steps": 28,
+  "cfg": 1.0,
+  "seed": 0,
+  "prompt": null,
+  "max": null,
+  "gpus": "0,1,2,3"
+}
\ No newline at end of file
diff --git a/eval/klein4b_sft_flir_full_r64a128_lr1e-4/checkpoint-12_cfg1.0_s28/flir_common400/metrics.csv b/eval/klein4b_sft_flir_full_r64a128_lr1e-4/checkpoint-12_cfg1.0_s28/flir_common400/metrics.csv
new file mode 100644
index 0000000000000000000000000000000000000000..07f5cf6c9e4bf29abc70317e37776a7cbfa92b1d
--- /dev/null
+++ b/eval/klein4b_sft_flir_full_r64a128_lr1e-4/checkpoint-12_cfg1.0_s28/flir_common400/metrics.csv
@@ -0,0 +1,401 @@
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diff --git a/eval/klein4b_sft_flir_full_r64a128_lr1e-4/checkpoint-12_cfg1.0_s28/flir_common400/metrics.json b/eval/klein4b_sft_flir_full_r64a128_lr1e-4/checkpoint-12_cfg1.0_s28/flir_common400/metrics.json
new file mode 100644
index 0000000000000000000000000000000000000000..76e2dbad1949d91cf50590a452f4652322c80dce
--- /dev/null
+++ b/eval/klein4b_sft_flir_full_r64a128_lr1e-4/checkpoint-12_cfg1.0_s28/flir_common400/metrics.json
@@ -0,0 +1,21 @@
+{
+  "protocol": "luma-fp32",
+  "pairs": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/flir_common400/eval_pairs.jsonl",
+  "pred_dir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/eval/klein4b_sft_flir_full_r64a128_lr1e-4/checkpoint-12_cfg1.0_s28/flir_common400",
+  "n": 400,
+  "psnr": 19.867423317432404,
+  "ssim": 0.6530153978615999,
+  "ms_ssim": 0.7087126452475786,
+  "l1": 0.08511285811197013,
+  "lpips": 0.3226831918396056,
+  "dists": 0.1685422293841839,
+  "psnr_std": 2.918631903670729,
+  "ssim_std": 0.11141635879511344,
+  "ms_ssim_std": 0.10250113547984778,
+  "l1_std": 0.03637171593045748,
+  "lpips_std": 0.11430113865759015,
+  "dists_std": 0.04548030811296026,
+  "fid": 55.42376713777236,
+  "kid": 0.003442952632904053,
+  "kid_std": 0.0010474478426940985
+}
\ No newline at end of file
diff --git a/eval/klein4b_sft_flir_full_r64a128_lr1e-4/checkpoint-12_cfg1.0_s28/flir_common400/sampling.json b/eval/klein4b_sft_flir_full_r64a128_lr1e-4/checkpoint-12_cfg1.0_s28/flir_common400/sampling.json
new file mode 100644
index 0000000000000000000000000000000000000000..623073184f493dcd33dd1732a70c00222f8ff215
--- /dev/null
+++ b/eval/klein4b_sft_flir_full_r64a128_lr1e-4/checkpoint-12_cfg1.0_s28/flir_common400/sampling.json
@@ -0,0 +1,12 @@
+{
+  "pairs": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/flir_common400/eval_pairs.jsonl",
+  "out": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/eval/klein4b_sft_flir_full_r64a128_lr1e-4/checkpoint-12_cfg1.0_s28/flir_common400",
+  "model": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/flow_factory_gpu_gate_models/FLUX.2-klein-base-4B",
+  "lora": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/runs/klein4b_sft_flir_full_r64a128_lr1e-4/saves/klein4b_sft_flir_full_r64a128_lr1e-4/checkpoints/checkpoint-12",
+  "steps": 28,
+  "cfg": 1.0,
+  "seed": 0,
+  "prompt": null,
+  "max": null,
+  "gpus": "4,5,6,7"
+}
\ No newline at end of file
diff --git a/eval/klein4b_sft_flir_full_r64a128_lr1e-4/checkpoint-12_cfg1.0_s8/flir_common/metrics.csv b/eval/klein4b_sft_flir_full_r64a128_lr1e-4/checkpoint-12_cfg1.0_s8/flir_common/metrics.csv
new file mode 100644
index 0000000000000000000000000000000000000000..15ae7f7b86ca4557aafc94fed19d8453108623e9
--- /dev/null
+++ b/eval/klein4b_sft_flir_full_r64a128_lr1e-4/checkpoint-12_cfg1.0_s8/flir_common/metrics.csv
@@ -0,0 +1,1447 @@
+id,psnr,ssim,ms_ssim,l1,lpips,dists
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diff --git a/eval/klein4b_sft_flir_full_r64a128_lr1e-4/checkpoint-12_cfg1.0_s8/flir_common/metrics.json b/eval/klein4b_sft_flir_full_r64a128_lr1e-4/checkpoint-12_cfg1.0_s8/flir_common/metrics.json
new file mode 100644
index 0000000000000000000000000000000000000000..f530a424f675c403351790af70e621ce5d8c0208
--- /dev/null
+++ b/eval/klein4b_sft_flir_full_r64a128_lr1e-4/checkpoint-12_cfg1.0_s8/flir_common/metrics.json
@@ -0,0 +1,21 @@
+{
+  "protocol": "luma-fp32",
+  "pairs": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/flir_common/eval_pairs.jsonl",
+  "pred_dir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/eval/klein4b_sft_flir_full_r64a128_lr1e-4/checkpoint-12_cfg1.0_s8/flir_common",
+  "n": 1446,
+  "psnr": 19.53454000689347,
+  "ssim": 0.6119766969692327,
+  "ms_ssim": 0.6724087306572688,
+  "l1": 0.08591937793761409,
+  "lpips": 0.34799176487520356,
+  "dists": 0.19415535133739065,
+  "psnr_std": 2.8084456357873906,
+  "ssim_std": 0.10881973701522436,
+  "ms_ssim_std": 0.10213047505172294,
+  "l1_std": 0.03233665701844804,
+  "lpips_std": 0.09372106295418714,
+  "dists_std": 0.040031675619178905,
+  "fid": 46.809872174111035,
+  "kid": 0.0285545015335083,
+  "kid_std": 0.0012360220800008426
+}
\ No newline at end of file
diff --git a/eval/klein4b_sft_flir_full_r64a128_lr1e-4/checkpoint-12_cfg1.0_s8/flir_common/sampling.json b/eval/klein4b_sft_flir_full_r64a128_lr1e-4/checkpoint-12_cfg1.0_s8/flir_common/sampling.json
new file mode 100644
index 0000000000000000000000000000000000000000..1eb07210d4f7ef40dd5b561da268f5fe564582b7
--- /dev/null
+++ b/eval/klein4b_sft_flir_full_r64a128_lr1e-4/checkpoint-12_cfg1.0_s8/flir_common/sampling.json
@@ -0,0 +1,12 @@
+{
+  "pairs": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/flir_common/eval_pairs.jsonl",
+  "out": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/eval/klein4b_sft_flir_full_r64a128_lr1e-4/checkpoint-12_cfg1.0_s8/flir_common",
+  "model": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/flow_factory_gpu_gate_models/FLUX.2-klein-base-4B",
+  "lora": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/runs/klein4b_sft_flir_full_r64a128_lr1e-4/saves/klein4b_sft_flir_full_r64a128_lr1e-4/checkpoints/checkpoint-12",
+  "steps": 8,
+  "cfg": 1.0,
+  "seed": 0,
+  "prompt": null,
+  "max": null,
+  "gpus": "0,1,2,3"
+}
\ No newline at end of file
diff --git a/eval/klein4b_sft_flir_full_r64a128_lr1e-4_gas1_ep144/checkpoint-144_cfg1.0_s28/flir_common/metrics.csv b/eval/klein4b_sft_flir_full_r64a128_lr1e-4_gas1_ep144/checkpoint-144_cfg1.0_s28/flir_common/metrics.csv
new file mode 100644
index 0000000000000000000000000000000000000000..785c46a383a207e997084781667d0625cabf9799
--- /dev/null
+++ b/eval/klein4b_sft_flir_full_r64a128_lr1e-4_gas1_ep144/checkpoint-144_cfg1.0_s28/flir_common/metrics.csv
@@ -0,0 +1,1447 @@
+id,psnr,ssim,ms_ssim,l1,lpips,dists
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diff --git a/eval/klein4b_sft_flir_full_r64a128_lr1e-4_gas1_ep144/checkpoint-144_cfg1.0_s28/flir_common/metrics.json b/eval/klein4b_sft_flir_full_r64a128_lr1e-4_gas1_ep144/checkpoint-144_cfg1.0_s28/flir_common/metrics.json
new file mode 100644
index 0000000000000000000000000000000000000000..76ec4832c62a5de8f55ea35c36f94689ca632dd3
--- /dev/null
+++ b/eval/klein4b_sft_flir_full_r64a128_lr1e-4_gas1_ep144/checkpoint-144_cfg1.0_s28/flir_common/metrics.json
@@ -0,0 +1,21 @@
+{
+  "protocol": "luma-fp32",
+  "pairs": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/flir_common/eval_pairs.jsonl",
+  "pred_dir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/eval/klein4b_sft_flir_full_r64a128_lr1e-4_gas1_ep144/checkpoint-144_cfg1.0_s28/flir_common",
+  "n": 1446,
+  "psnr": 21.11073655541342,
+  "ssim": 0.6474372329058984,
+  "ms_ssim": 0.7202503232705313,
+  "l1": 0.0730291093283566,
+  "lpips": 0.26819799662043403,
+  "dists": 0.14399329425553234,
+  "psnr_std": 3.6953978137170562,
+  "ssim_std": 0.12128270064606125,
+  "ms_ssim_std": 0.10778558145288929,
+  "l1_std": 0.03346921387561471,
+  "lpips_std": 0.13195370660707256,
+  "dists_std": 0.05707958614387107,
+  "fid": 24.981788439301056,
+  "kid": 0.007099027633666992,
+  "kid_std": 0.000741023216611403
+}
\ No newline at end of file
diff --git a/eval/klein4b_sft_flir_full_r64a128_lr1e-4_gas1_ep144/checkpoint-144_cfg1.0_s28/flir_common/sampling.json b/eval/klein4b_sft_flir_full_r64a128_lr1e-4_gas1_ep144/checkpoint-144_cfg1.0_s28/flir_common/sampling.json
new file mode 100644
index 0000000000000000000000000000000000000000..699bce247f41c2bfa49b3bc77afc126e5cc8104f
--- /dev/null
+++ b/eval/klein4b_sft_flir_full_r64a128_lr1e-4_gas1_ep144/checkpoint-144_cfg1.0_s28/flir_common/sampling.json
@@ -0,0 +1,12 @@
+{
+  "pairs": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/flir_common/eval_pairs.jsonl",
+  "out": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/eval/klein4b_sft_flir_full_r64a128_lr1e-4_gas1_ep144/checkpoint-144_cfg1.0_s28/flir_common",
+  "model": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/flow_factory_gpu_gate_models/FLUX.2-klein-base-4B",
+  "lora": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/runs/klein4b_sft_flir_full_r64a128_lr1e-4_gas1_ep144/saves/klein4b_sft_flir_full_r64a128_lr1e-4_gas1_ep144/checkpoints/checkpoint-144",
+  "steps": 28,
+  "cfg": 1.0,
+  "seed": 0,
+  "prompt": null,
+  "max": null,
+  "gpus": "0,1,2,3,4,5,6,7"
+}
\ No newline at end of file
diff --git a/eval/klein4b_sft_flir_full_r64a128_lr1e-4_gas4_ep48_n3584/checkpoint-48_cfg1.0_s28/flir_common400/metrics.csv b/eval/klein4b_sft_flir_full_r64a128_lr1e-4_gas4_ep48_n3584/checkpoint-48_cfg1.0_s28/flir_common400/metrics.csv
new file mode 100644
index 0000000000000000000000000000000000000000..79dbfa3e8faf40754c51079f733676552b26a2c2
--- /dev/null
+++ b/eval/klein4b_sft_flir_full_r64a128_lr1e-4_gas4_ep48_n3584/checkpoint-48_cfg1.0_s28/flir_common400/metrics.csv
@@ -0,0 +1,401 @@
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diff --git a/eval/klein4b_sft_flir_full_r64a128_lr1e-4_gas4_ep48_n3584/checkpoint-48_cfg1.0_s28/flir_common400/metrics.json b/eval/klein4b_sft_flir_full_r64a128_lr1e-4_gas4_ep48_n3584/checkpoint-48_cfg1.0_s28/flir_common400/metrics.json
new file mode 100644
index 0000000000000000000000000000000000000000..979a12d09664eed53a1ec74bf9b6a722806894a2
--- /dev/null
+++ b/eval/klein4b_sft_flir_full_r64a128_lr1e-4_gas4_ep48_n3584/checkpoint-48_cfg1.0_s28/flir_common400/metrics.json
@@ -0,0 +1,21 @@
+{
+  "protocol": "luma-fp32",
+  "pairs": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/flir_common400/eval_pairs.jsonl",
+  "pred_dir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/eval/klein4b_sft_flir_full_r64a128_lr1e-4_gas4_ep48_n3584/checkpoint-48_cfg1.0_s28/flir_common400",
+  "n": 400,
+  "psnr": 19.805257065296175,
+  "ssim": 0.6209942194074393,
+  "ms_ssim": 0.6869451960176229,
+  "l1": 0.08448277755640447,
+  "lpips": 0.33287418508902195,
+  "dists": 0.16459268629550933,
+  "psnr_std": 2.6994992202635335,
+  "ssim_std": 0.08964879407812867,
+  "ms_ssim_std": 0.08683126327553198,
+  "l1_std": 0.034911301362459325,
+  "lpips_std": 0.11858813524972585,
+  "dists_std": 0.04603319742695365,
+  "fid": 58.057767296149905,
+  "kid": 0.004867982864379883,
+  "kid_std": 0.0013040351725919792
+}
\ No newline at end of file
diff --git a/eval/klein4b_sft_flir_full_r64a128_lr1e-4_gas4_ep48_n3584/checkpoint-48_cfg1.0_s28/flir_common400/sampling.json b/eval/klein4b_sft_flir_full_r64a128_lr1e-4_gas4_ep48_n3584/checkpoint-48_cfg1.0_s28/flir_common400/sampling.json
new file mode 100644
index 0000000000000000000000000000000000000000..f441e06ed7eb3f3198b7d684206de8f3e7282bb5
--- /dev/null
+++ b/eval/klein4b_sft_flir_full_r64a128_lr1e-4_gas4_ep48_n3584/checkpoint-48_cfg1.0_s28/flir_common400/sampling.json
@@ -0,0 +1,12 @@
+{
+  "pairs": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/flir_common400/eval_pairs.jsonl",
+  "out": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/eval/klein4b_sft_flir_full_r64a128_lr1e-4_gas4_ep48_n3584/checkpoint-48_cfg1.0_s28/flir_common400",
+  "model": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/flow_factory_gpu_gate_models/FLUX.2-klein-base-4B",
+  "lora": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/runs/klein4b_sft_flir_full_r64a128_lr1e-4_gas4_ep48_n3584/saves/klein4b_sft_flir_full_r64a128_lr1e-4_gas4_ep48_n3584/checkpoints/checkpoint-48",
+  "steps": 28,
+  "cfg": 1.0,
+  "seed": 0,
+  "prompt": null,
+  "max": null,
+  "gpus": "0,1,2,3,4,5,6,7"
+}
\ No newline at end of file
diff --git a/eval/pix2pix_flir_sel/flir_common/metrics.csv b/eval/pix2pix_flir_sel/flir_common/metrics.csv
new file mode 100644
index 0000000000000000000000000000000000000000..7d3c28b5eb1129a254d49308c77ace95388eb9e5
--- /dev/null
+++ b/eval/pix2pix_flir_sel/flir_common/metrics.csv
@@ -0,0 +1,1447 @@
+id,psnr,ssim,ms_ssim,l1,lpips,dists
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diff --git a/eval/pix2pix_flir_sel/flir_common/metrics.json b/eval/pix2pix_flir_sel/flir_common/metrics.json
new file mode 100644
index 0000000000000000000000000000000000000000..8479ffaea04a5a5c3fb37398f1304593c6d6464e
--- /dev/null
+++ b/eval/pix2pix_flir_sel/flir_common/metrics.json
@@ -0,0 +1,21 @@
+{
+  "protocol": "luma-fp32",
+  "pairs": "data/ff/flir_common/eval_pairs.jsonl",
+  "pred_dir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/eval/pix2pix_flir_sel/flir_common",
+  "n": 1446,
+  "psnr": 18.250612563612055,
+  "ssim": 0.4610914086837986,
+  "ms_ssim": 0.5077993531362315,
+  "l1": 0.09806783091828207,
+  "lpips": 0.40376153676918763,
+  "dists": 0.23828284798980914,
+  "psnr_std": 2.6761459811752055,
+  "ssim_std": 0.10944705410818123,
+  "ms_ssim_std": 0.12567126285300279,
+  "l1_std": 0.0357829184853866,
+  "lpips_std": 0.08849313713083388,
+  "dists_std": 0.0509325827369595,
+  "fid": 146.57365636056608,
+  "kid": 0.1346540355682373,
+  "kid_std": 0.002364201491689835
+}
\ No newline at end of file
diff --git a/eval/qwen21_sft_vedai_full_r64a128_lr1e-4_bs1_gas1_ep144/checkpoint-144_cfg1.0_s28/vedai_common/metrics.csv b/eval/qwen21_sft_vedai_full_r64a128_lr1e-4_bs1_gas1_ep144/checkpoint-144_cfg1.0_s28/vedai_common/metrics.csv
new file mode 100644
index 0000000000000000000000000000000000000000..8008b40cbe4560a96b7eb31fa025c76c03691bf3
--- /dev/null
+++ b/eval/qwen21_sft_vedai_full_r64a128_lr1e-4_bs1_gas1_ep144/checkpoint-144_cfg1.0_s28/vedai_common/metrics.csv
@@ -0,0 +1,269 @@
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diff --git a/eval/qwen21_sft_vedai_full_r64a128_lr1e-4_bs1_gas1_ep144/checkpoint-144_cfg1.0_s28/vedai_common/metrics.json b/eval/qwen21_sft_vedai_full_r64a128_lr1e-4_bs1_gas1_ep144/checkpoint-144_cfg1.0_s28/vedai_common/metrics.json
new file mode 100644
index 0000000000000000000000000000000000000000..8912cba0f80a6bcc6e463a92e7b3bf31cfdea4ed
--- /dev/null
+++ b/eval/qwen21_sft_vedai_full_r64a128_lr1e-4_bs1_gas1_ep144/checkpoint-144_cfg1.0_s28/vedai_common/metrics.json
@@ -0,0 +1,21 @@
+{
+  "protocol": "luma-fp32",
+  "pairs": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/vedai_common/eval_pairs.jsonl",
+  "pred_dir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/eval/qwen21_sft_vedai_full_r64a128_lr1e-4_bs1_gas1_ep144/checkpoint-144_cfg1.0_s28/vedai_common",
+  "n": 268,
+  "psnr": 29.64544493404787,
+  "ssim": 0.9174476401574576,
+  "ms_ssim": 0.9395418376175325,
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+  "l1_std": 0.007954729434749602,
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+  "dists_std": 0.015082447319182315,
+  "fid": 33.257269560389545,
+  "kid": -0.0021655941009521484,
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+}
\ No newline at end of file
diff --git a/eval/qwen21_sft_vedai_full_r64a128_lr1e-4_bs1_gas1_ep144/checkpoint-144_cfg1.0_s28/vedai_common/sampling.json b/eval/qwen21_sft_vedai_full_r64a128_lr1e-4_bs1_gas1_ep144/checkpoint-144_cfg1.0_s28/vedai_common/sampling.json
new file mode 100644
index 0000000000000000000000000000000000000000..b7a165d4af8f981e44f0967a3a0786059ca4e1c1
--- /dev/null
+++ b/eval/qwen21_sft_vedai_full_r64a128_lr1e-4_bs1_gas1_ep144/checkpoint-144_cfg1.0_s28/vedai_common/sampling.json
@@ -0,0 +1,12 @@
+{
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+  "out": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/eval/qwen21_sft_vedai_full_r64a128_lr1e-4_bs1_gas1_ep144/checkpoint-144_cfg1.0_s28/vedai_common",
+  "model": "/root/models/Qwen-Image-2.1",
+  "lora": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/runs/qwen21_sft_vedai_full_r64a128_lr1e-4_bs1_gas1_ep144/saves/qwen21_sft_vedai_full_r64a128_lr1e-4_bs1_gas1_ep144/checkpoints/checkpoint-144",
+  "steps": 28,
+  "cfg": 1.0,
+  "seed": 0,
+  "prompt": null,
+  "max": null,
+  "gpus": "0,1,2,3,4,5,6,7"
+}
\ No newline at end of file
diff --git a/eval/qwen21_sft_vedai_full_r64a128_lr1e-4_bs1_gas1_ep144/checkpoint-144_cfg1.0_s8/vedai_common/metrics.csv b/eval/qwen21_sft_vedai_full_r64a128_lr1e-4_bs1_gas1_ep144/checkpoint-144_cfg1.0_s8/vedai_common/metrics.csv
new file mode 100644
index 0000000000000000000000000000000000000000..ef27861430d1cf0af984920fa31326502f1ec2f8
--- /dev/null
+++ b/eval/qwen21_sft_vedai_full_r64a128_lr1e-4_bs1_gas1_ep144/checkpoint-144_cfg1.0_s8/vedai_common/metrics.csv
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diff --git a/eval/qwen21_sft_vedai_full_r64a128_lr1e-4_bs1_gas1_ep144/checkpoint-144_cfg1.0_s8/vedai_common/metrics.json b/eval/qwen21_sft_vedai_full_r64a128_lr1e-4_bs1_gas1_ep144/checkpoint-144_cfg1.0_s8/vedai_common/metrics.json
new file mode 100644
index 0000000000000000000000000000000000000000..fe7580fd1b084f76d71dab06ff254483024a6bef
--- /dev/null
+++ b/eval/qwen21_sft_vedai_full_r64a128_lr1e-4_bs1_gas1_ep144/checkpoint-144_cfg1.0_s8/vedai_common/metrics.json
@@ -0,0 +1,21 @@
+{
+  "protocol": "luma-fp32",
+  "pairs": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/vedai_common/eval_pairs.jsonl",
+  "pred_dir": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/eval/qwen21_sft_vedai_full_r64a128_lr1e-4_bs1_gas1_ep144/checkpoint-144_cfg1.0_s8/vedai_common",
+  "n": 268,
+  "psnr": 29.554811563064803,
+  "ssim": 0.9204009760226777,
+  "ms_ssim": 0.9419556186715169,
+  "l1": 0.025918481791558774,
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+  "dists_std": 0.016760431698232433,
+  "fid": 34.37044801422127,
+  "kid": -0.0015207982063293456,
+  "kid_std": 0.0006556883659995241
+}
\ No newline at end of file
diff --git a/eval/qwen21_sft_vedai_full_r64a128_lr1e-4_bs1_gas1_ep144/checkpoint-144_cfg1.0_s8/vedai_common/sampling.json b/eval/qwen21_sft_vedai_full_r64a128_lr1e-4_bs1_gas1_ep144/checkpoint-144_cfg1.0_s8/vedai_common/sampling.json
new file mode 100644
index 0000000000000000000000000000000000000000..2828a2c0de6903b9afbfba1aab133dbdbda44a78
--- /dev/null
+++ b/eval/qwen21_sft_vedai_full_r64a128_lr1e-4_bs1_gas1_ep144/checkpoint-144_cfg1.0_s8/vedai_common/sampling.json
@@ -0,0 +1,12 @@
+{
+  "pairs": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/vedai_common/eval_pairs.jsonl",
+  "out": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/eval/qwen21_sft_vedai_full_r64a128_lr1e-4_bs1_gas1_ep144/checkpoint-144_cfg1.0_s8/vedai_common",
+  "model": "/root/models/Qwen-Image-2.1",
+  "lora": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/runs/qwen21_sft_vedai_full_r64a128_lr1e-4_bs1_gas1_ep144/saves/qwen21_sft_vedai_full_r64a128_lr1e-4_bs1_gas1_ep144/checkpoints/checkpoint-144",
+  "steps": 8,
+  "cfg": 1.0,
+  "seed": 0,
+  "prompt": null,
+  "max": null,
+  "gpus": "0,1,2,3,4,5,6,7"
+}
\ No newline at end of file
diff --git a/eval/qwen21_sft_vedai_full_r64a128_lr1e-4_bs1_gas1_ep48/checkpoint-48_cfg1.0_s28/vedai_common/metrics.csv b/eval/qwen21_sft_vedai_full_r64a128_lr1e-4_bs1_gas1_ep48/checkpoint-48_cfg1.0_s28/vedai_common/metrics.csv
new file mode 100644
index 0000000000000000000000000000000000000000..9341dec4c526df1e80ae2a711042ea3fa5e087c4
--- /dev/null
+++ b/eval/qwen21_sft_vedai_full_r64a128_lr1e-4_bs1_gas1_ep48/checkpoint-48_cfg1.0_s28/vedai_common/metrics.csv
@@ -0,0 +1,269 @@
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diff --git a/eval/qwen21_sft_vedai_full_r64a128_lr1e-4_bs1_gas1_ep48/checkpoint-48_cfg1.0_s28/vedai_common/metrics.json b/eval/qwen21_sft_vedai_full_r64a128_lr1e-4_bs1_gas1_ep48/checkpoint-48_cfg1.0_s28/vedai_common/metrics.json
new file mode 100644
index 0000000000000000000000000000000000000000..3812a1fa11735b643f824b4631d6bd983cf4f82b
--- /dev/null
+++ b/eval/qwen21_sft_vedai_full_r64a128_lr1e-4_bs1_gas1_ep48/checkpoint-48_cfg1.0_s28/vedai_common/metrics.json
@@ -0,0 +1,21 @@
+{
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\ No newline at end of file
diff --git a/eval/qwen21_sft_vedai_full_r64a128_lr1e-4_bs1_gas1_ep48/checkpoint-48_cfg1.0_s28/vedai_common/sampling.json b/eval/qwen21_sft_vedai_full_r64a128_lr1e-4_bs1_gas1_ep48/checkpoint-48_cfg1.0_s28/vedai_common/sampling.json
new file mode 100644
index 0000000000000000000000000000000000000000..30ffd574e1e70810d1452b40d89a1b619200c577
--- /dev/null
+++ b/eval/qwen21_sft_vedai_full_r64a128_lr1e-4_bs1_gas1_ep48/checkpoint-48_cfg1.0_s28/vedai_common/sampling.json
@@ -0,0 +1,12 @@
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+  "model": "/root/models/Qwen-Image-2.1",
+  "lora": "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/runs/qwen21_sft_vedai_full_r64a128_lr1e-4_bs1_gas1_ep48/saves/qwen21_sft_vedai_full_r64a128_lr1e-4_bs1_gas1_ep48/checkpoints/checkpoint-48",
+  "steps": 28,
+  "cfg": 1.0,
+  "seed": 0,
+  "prompt": null,
+  "max": null,
+  "gpus": "0,1,2,3,4,5,6,7"
+}
\ No newline at end of file
diff --git a/eval/thermalgen_xl2_ft_vedai/vedai_common/metrics.csv b/eval/thermalgen_xl2_ft_vedai/vedai_common/metrics.csv
new file mode 100644
index 0000000000000000000000000000000000000000..a37b63bc6251d714d8cd7dfd236691cca1a7492d
--- /dev/null
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new file mode 100644
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index 0000000000000000000000000000000000000000..c50dba806167da7614c604093760f0e4c2e01655
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+    "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/runs/SIRST320r40_ours72_dm_n262/UIUNet_seed5/SIRST320r40_ours72_dm_n262/UIUNet_350.pth.tar": {
+      "epoch": 350,
+      "n": 96,
+      "iou": 0.713520749665328,
+      "niou": 0.7437990562584732,
+      "pd": 0.9607843137254902,
+      "fa": 1.3441765077004675e-05
+    }
+  }
+}
diff --git a/irstd/runs/SIRST320r40_ours72_dm_n262/UIUNet_seed5/val_scores.json b/irstd/runs/SIRST320r40_ours72_dm_n262/UIUNet_seed5/val_scores.json
new file mode 100644
index 0000000000000000000000000000000000000000..167310e0fc2aaaf1df2d5c1653ce8871644b05ab
--- /dev/null
+++ b/irstd/runs/SIRST320r40_ours72_dm_n262/UIUNet_seed5/val_scores.json
@@ -0,0 +1,75 @@
+{
+  "model": "UIUNet",
+  "dataset": "SIRST320r40_ours72_dm_n262",
+  "threshold": 0.5,
+  "norm": {
+    "mean": 116.2063217163086,
+    "std": 29.8194637298584
+  },
+  "scores": {
+    "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/runs/SIRST320r40_ours72_dm_n262/UIUNet_seed5/SIRST320r40_ours72_dm_n262/UIUNet_100.pth.tar": {
+      "epoch": 100,
+      "n": 64,
+      "iou": 0.6187076044269904,
+      "niou": 0.6878798513152369,
+      "pd": 0.9066666666666666,
+      "fa": 3.050430223083822e-06
+    },
+    "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/runs/SIRST320r40_ours72_dm_n262/UIUNet_seed5/SIRST320r40_ours72_dm_n262/UIUNet_150.pth.tar": {
+      "epoch": 150,
+      "n": 64,
+      "iou": 0.6461086637298091,
+      "niou": 0.6986415721868524,
+      "pd": 0.9466666666666667,
+      "fa": 2.5811332307057455e-05
+    },
+    "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/runs/SIRST320r40_ours72_dm_n262/UIUNet_seed5/SIRST320r40_ours72_dm_n262/UIUNet_200.pth.tar": {
+      "epoch": 200,
+      "n": 64,
+      "iou": 0.6014466546112116,
+      "niou": 0.6929927898816608,
+      "pd": 0.8933333333333333,
+      "fa": 4.223672476655338e-06
+    },
+    "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/runs/SIRST320r40_ours72_dm_n262/UIUNet_seed5/SIRST320r40_ours72_dm_n262/UIUNet_250.pth.tar": {
+      "epoch": 250,
+      "n": 64,
+      "iou": 0.6313894888408927,
+      "niou": 0.7194593002920056,
+      "pd": 0.92,
+      "fa": 2.111836238327669e-06
+    },
+    "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/runs/SIRST320r40_ours72_dm_n262/UIUNet_seed5/SIRST320r40_ours72_dm_n262/UIUNet_300.pth.tar": {
+      "epoch": 300,
+      "n": 64,
+      "iou": 0.6293197526373226,
+      "niou": 0.7246885474451461,
+      "pd": 0.92,
+      "fa": 9.385939279127342e-07
+    },
+    "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/runs/SIRST320r40_ours72_dm_n262/UIUNet_seed5/SIRST320r40_ours72_dm_n262/UIUNet_350.pth.tar": {
+      "epoch": 350,
+      "n": 64,
+      "iou": 0.6397270114942529,
+      "niou": 0.7272998711968512,
+      "pd": 0.9333333333333333,
+      "fa": 2.5811332307057455e-06
+    },
+    "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/runs/SIRST320r40_ours72_dm_n262/UIUNet_seed5/SIRST320r40_ours72_dm_n262/UIUNet_400.pth.tar": {
+      "epoch": 400,
+      "n": 64,
+      "iou": 0.6384725196288366,
+      "niou": 0.7233073991320487,
+      "pd": 0.9333333333333333,
+      "fa": 5.162266461411491e-06
+    },
+    "/apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/runs/SIRST320r40_ours72_dm_n262/UIUNet_seed5/SIRST320r40_ours72_dm_n262/UIUNet_50.pth.tar": {
+      "epoch": 50,
+      "n": 64,
+      "iou": 0.5779122541603631,
+      "niou": 0.6599947132297985,
+      "pd": 0.9066666666666666,
+      "fa": 1.2201720892335288e-05
+    }
+  }
+}
diff --git a/runs/klein4b_dmd2_1_flir_from_ep72/saves/klein4b_dmd2_1_flir_from_ep72/checkpoints/checkpoint-150/README.md b/runs/klein4b_dmd2_1_flir_from_ep72/saves/klein4b_dmd2_1_flir_from_ep72/checkpoints/checkpoint-150/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..32205326f359655a9478df7e720d429061c94c10
--- /dev/null
+++ b/runs/klein4b_dmd2_1_flir_from_ep72/saves/klein4b_dmd2_1_flir_from_ep72/checkpoints/checkpoint-150/README.md
@@ -0,0 +1,204 @@
+---
+base_model: /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/models/klein4b_sft_flir_ep72_merged/transformer
+library_name: peft
+tags:
+- lora
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]
+### Framework versions
+
+- PEFT 0.20.0
\ No newline at end of file
diff --git a/runs/klein4b_dmd2_1_flir_from_ep72/saves/klein4b_dmd2_1_flir_from_ep72/checkpoints/checkpoint-150/adapter_config.json b/runs/klein4b_dmd2_1_flir_from_ep72/saves/klein4b_dmd2_1_flir_from_ep72/checkpoints/checkpoint-150/adapter_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..df8135ad3014e181fa220280d125151b88338b88
--- /dev/null
+++ b/runs/klein4b_dmd2_1_flir_from_ep72/saves/klein4b_dmd2_1_flir_from_ep72/checkpoints/checkpoint-150/adapter_config.json
@@ -0,0 +1,59 @@
+{
+  "alora_invocation_tokens": null,
+  "alpha_pattern": {},
+  "arrow_config": null,
+  "auto_mapping": {
+    "base_model_class": "Flux2Transformer2DModel",
+    "parent_library": "diffusers.models.transformers.transformer_flux2"
+  },
+  "base_model_name_or_path": null,
+  "bias": "none",
+  "corda_config": null,
+  "ensure_weight_tying": false,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": "gaussian",
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 128,
+  "lora_bias": false,
+  "lora_dropout": 0.0,
+  "lora_ga_config": null,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "monteclora_config": null,
+  "peft_type": "LORA",
+  "peft_version": "0.20.0",
+  "qalora_group_size": 16,
+  "r": 64,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "attn.to_qkv_mlp_proj",
+    "attn.add_k_proj",
+    "attn.to_v",
+    "attn.add_q_proj",
+    "attn.to_q",
+    "ff_context.linear_out",
+    "attn.to_add_out",
+    "attn.to_out.0",
+    "ff.linear_out",
+    "ff_context.linear_in",
+    "attn.add_v_proj",
+    "ff.linear_in",
+    "attn.to_k"
+  ],
+  "target_parameters": null,
+  "task_type": null,
+  "trainable_token_indices": null,
+  "use_bdlora": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false,
+  "velora_config": null
+}
\ No newline at end of file
diff --git a/runs/klein4b_dmd2_1_flir_from_ep72/saves/klein4b_dmd2_1_flir_from_ep72/checkpoints/checkpoint-150/manifest.json b/runs/klein4b_dmd2_1_flir_from_ep72/saves/klein4b_dmd2_1_flir_from_ep72/checkpoints/checkpoint-150/manifest.json
new file mode 100644
index 0000000000000000000000000000000000000000..36e2c39fb45c2aba7867fcd33a35c043a2309413
--- /dev/null
+++ b/runs/klein4b_dmd2_1_flir_from_ep72/saves/klein4b_dmd2_1_flir_from_ep72/checkpoints/checkpoint-150/manifest.json
@@ -0,0 +1,21 @@
+{
+  "base_role": "generator",
+  "entries": [
+    {
+      "component": "transformer",
+      "path": ".",
+      "role": "generator"
+    },
+    {
+      "component": "transformer",
+      "path": "roles/fake",
+      "role": "fake"
+    }
+  ],
+  "finetune_type": "lora",
+  "format_version": 1,
+  "primary": {
+    "component": "transformer",
+    "path": "."
+  }
+}
diff --git a/runs/klein4b_dmd2_1_flir_from_ep72/saves/klein4b_dmd2_1_flir_from_ep72/checkpoints/checkpoint-150/roles/fake/README.md b/runs/klein4b_dmd2_1_flir_from_ep72/saves/klein4b_dmd2_1_flir_from_ep72/checkpoints/checkpoint-150/roles/fake/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..32205326f359655a9478df7e720d429061c94c10
--- /dev/null
+++ b/runs/klein4b_dmd2_1_flir_from_ep72/saves/klein4b_dmd2_1_flir_from_ep72/checkpoints/checkpoint-150/roles/fake/README.md
@@ -0,0 +1,204 @@
+---
+base_model: /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/models/klein4b_sft_flir_ep72_merged/transformer
+library_name: peft
+tags:
+- lora
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]
+### Framework versions
+
+- PEFT 0.20.0
\ No newline at end of file
diff --git a/runs/klein4b_dmd2_1_flir_from_ep72/saves/klein4b_dmd2_1_flir_from_ep72/checkpoints/checkpoint-150/roles/fake/adapter_config.json b/runs/klein4b_dmd2_1_flir_from_ep72/saves/klein4b_dmd2_1_flir_from_ep72/checkpoints/checkpoint-150/roles/fake/adapter_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..df8135ad3014e181fa220280d125151b88338b88
--- /dev/null
+++ b/runs/klein4b_dmd2_1_flir_from_ep72/saves/klein4b_dmd2_1_flir_from_ep72/checkpoints/checkpoint-150/roles/fake/adapter_config.json
@@ -0,0 +1,59 @@
+{
+  "alora_invocation_tokens": null,
+  "alpha_pattern": {},
+  "arrow_config": null,
+  "auto_mapping": {
+    "base_model_class": "Flux2Transformer2DModel",
+    "parent_library": "diffusers.models.transformers.transformer_flux2"
+  },
+  "base_model_name_or_path": null,
+  "bias": "none",
+  "corda_config": null,
+  "ensure_weight_tying": false,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": "gaussian",
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 128,
+  "lora_bias": false,
+  "lora_dropout": 0.0,
+  "lora_ga_config": null,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "monteclora_config": null,
+  "peft_type": "LORA",
+  "peft_version": "0.20.0",
+  "qalora_group_size": 16,
+  "r": 64,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "attn.to_qkv_mlp_proj",
+    "attn.add_k_proj",
+    "attn.to_v",
+    "attn.add_q_proj",
+    "attn.to_q",
+    "ff_context.linear_out",
+    "attn.to_add_out",
+    "attn.to_out.0",
+    "ff.linear_out",
+    "ff_context.linear_in",
+    "attn.add_v_proj",
+    "ff.linear_in",
+    "attn.to_k"
+  ],
+  "target_parameters": null,
+  "task_type": null,
+  "trainable_token_indices": null,
+  "use_bdlora": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false,
+  "velora_config": null
+}
\ No newline at end of file
diff --git a/runs/klein4b_dmd2_1_flir_from_nft/saves/klein4b_dmd2_1_flir_from_nft/checkpoints/checkpoint-150/README.md b/runs/klein4b_dmd2_1_flir_from_nft/saves/klein4b_dmd2_1_flir_from_nft/checkpoints/checkpoint-150/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..42c13c74dbc118e3106bbe4b8f67d406d6cb58f4
--- /dev/null
+++ b/runs/klein4b_dmd2_1_flir_from_nft/saves/klein4b_dmd2_1_flir_from_nft/checkpoints/checkpoint-150/README.md
@@ -0,0 +1,204 @@
+---
+base_model: /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/models/klein4b_nft_flir_ep72_ckpt80_merged/transformer
+library_name: peft
+tags:
+- lora
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]
+### Framework versions
+
+- PEFT 0.20.0
\ No newline at end of file
diff --git a/runs/klein4b_dmd2_1_flir_from_nft/saves/klein4b_dmd2_1_flir_from_nft/checkpoints/checkpoint-150/adapter_config.json b/runs/klein4b_dmd2_1_flir_from_nft/saves/klein4b_dmd2_1_flir_from_nft/checkpoints/checkpoint-150/adapter_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..4c8945761db5d97127ba5d2cf5ba0bdf9499c4ca
--- /dev/null
+++ b/runs/klein4b_dmd2_1_flir_from_nft/saves/klein4b_dmd2_1_flir_from_nft/checkpoints/checkpoint-150/adapter_config.json
@@ -0,0 +1,59 @@
+{
+  "alora_invocation_tokens": null,
+  "alpha_pattern": {},
+  "arrow_config": null,
+  "auto_mapping": {
+    "base_model_class": "Flux2Transformer2DModel",
+    "parent_library": "diffusers.models.transformers.transformer_flux2"
+  },
+  "base_model_name_or_path": null,
+  "bias": "none",
+  "corda_config": null,
+  "ensure_weight_tying": false,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": "gaussian",
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 128,
+  "lora_bias": false,
+  "lora_dropout": 0.0,
+  "lora_ga_config": null,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "monteclora_config": null,
+  "peft_type": "LORA",
+  "peft_version": "0.20.0",
+  "qalora_group_size": 16,
+  "r": 64,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "attn.to_qkv_mlp_proj",
+    "attn.add_k_proj",
+    "attn.to_out.0",
+    "attn.to_v",
+    "ff_context.linear_in",
+    "attn.to_k",
+    "attn.to_add_out",
+    "ff_context.linear_out",
+    "ff.linear_out",
+    "ff.linear_in",
+    "attn.add_q_proj",
+    "attn.to_q",
+    "attn.add_v_proj"
+  ],
+  "target_parameters": null,
+  "task_type": null,
+  "trainable_token_indices": null,
+  "use_bdlora": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false,
+  "velora_config": null
+}
\ No newline at end of file
diff --git a/runs/klein4b_dmd2_1_flir_from_nft/saves/klein4b_dmd2_1_flir_from_nft/checkpoints/checkpoint-150/manifest.json b/runs/klein4b_dmd2_1_flir_from_nft/saves/klein4b_dmd2_1_flir_from_nft/checkpoints/checkpoint-150/manifest.json
new file mode 100644
index 0000000000000000000000000000000000000000..36e2c39fb45c2aba7867fcd33a35c043a2309413
--- /dev/null
+++ b/runs/klein4b_dmd2_1_flir_from_nft/saves/klein4b_dmd2_1_flir_from_nft/checkpoints/checkpoint-150/manifest.json
@@ -0,0 +1,21 @@
+{
+  "base_role": "generator",
+  "entries": [
+    {
+      "component": "transformer",
+      "path": ".",
+      "role": "generator"
+    },
+    {
+      "component": "transformer",
+      "path": "roles/fake",
+      "role": "fake"
+    }
+  ],
+  "finetune_type": "lora",
+  "format_version": 1,
+  "primary": {
+    "component": "transformer",
+    "path": "."
+  }
+}
diff --git a/runs/klein4b_dmd2_1_flir_from_nft/saves/klein4b_dmd2_1_flir_from_nft/checkpoints/checkpoint-150/roles/fake/README.md b/runs/klein4b_dmd2_1_flir_from_nft/saves/klein4b_dmd2_1_flir_from_nft/checkpoints/checkpoint-150/roles/fake/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..42c13c74dbc118e3106bbe4b8f67d406d6cb58f4
--- /dev/null
+++ b/runs/klein4b_dmd2_1_flir_from_nft/saves/klein4b_dmd2_1_flir_from_nft/checkpoints/checkpoint-150/roles/fake/README.md
@@ -0,0 +1,204 @@
+---
+base_model: /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/models/klein4b_nft_flir_ep72_ckpt80_merged/transformer
+library_name: peft
+tags:
+- lora
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]
+### Framework versions
+
+- PEFT 0.20.0
\ No newline at end of file
diff --git a/runs/klein4b_dmd2_1_flir_from_nft/saves/klein4b_dmd2_1_flir_from_nft/checkpoints/checkpoint-150/roles/fake/adapter_config.json b/runs/klein4b_dmd2_1_flir_from_nft/saves/klein4b_dmd2_1_flir_from_nft/checkpoints/checkpoint-150/roles/fake/adapter_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..e38ffe29643fa27229fe7b672da4c68c0623edf1
--- /dev/null
+++ b/runs/klein4b_dmd2_1_flir_from_nft/saves/klein4b_dmd2_1_flir_from_nft/checkpoints/checkpoint-150/roles/fake/adapter_config.json
@@ -0,0 +1,59 @@
+{
+  "alora_invocation_tokens": null,
+  "alpha_pattern": {},
+  "arrow_config": null,
+  "auto_mapping": {
+    "base_model_class": "Flux2Transformer2DModel",
+    "parent_library": "diffusers.models.transformers.transformer_flux2"
+  },
+  "base_model_name_or_path": null,
+  "bias": "none",
+  "corda_config": null,
+  "ensure_weight_tying": false,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": "gaussian",
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 128,
+  "lora_bias": false,
+  "lora_dropout": 0.0,
+  "lora_ga_config": null,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "monteclora_config": null,
+  "peft_type": "LORA",
+  "peft_version": "0.20.0",
+  "qalora_group_size": 16,
+  "r": 64,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "attn.to_qkv_mlp_proj",
+    "attn.add_k_proj",
+    "attn.to_out.0",
+    "attn.to_v",
+    "ff_context.linear_in",
+    "attn.to_q",
+    "ff_context.linear_out",
+    "ff.linear_out",
+    "ff.linear_in",
+    "attn.add_q_proj",
+    "attn.to_add_out",
+    "attn.add_v_proj",
+    "attn.to_k"
+  ],
+  "target_parameters": null,
+  "task_type": null,
+  "trainable_token_indices": null,
+  "use_bdlora": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false,
+  "velora_config": null
+}
\ No newline at end of file
diff --git a/runs/klein4b_dmd2_1_vedai_from_ep144/saves/klein4b_dmd2_1_vedai_from_ep144/checkpoints/checkpoint-150/README.md b/runs/klein4b_dmd2_1_vedai_from_ep144/saves/klein4b_dmd2_1_vedai_from_ep144/checkpoints/checkpoint-150/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..4356e22087c0101c61d8bbc4f5679d805f294e3a
--- /dev/null
+++ b/runs/klein4b_dmd2_1_vedai_from_ep144/saves/klein4b_dmd2_1_vedai_from_ep144/checkpoints/checkpoint-150/README.md
@@ -0,0 +1,204 @@
+---
+base_model: /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/models/klein4b_sft_vedai_ep144_merged/transformer
+library_name: peft
+tags:
+- lora
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]
+### Framework versions
+
+- PEFT 0.20.0
\ No newline at end of file
diff --git a/runs/klein4b_dmd2_1_vedai_from_ep144/saves/klein4b_dmd2_1_vedai_from_ep144/checkpoints/checkpoint-150/adapter_config.json b/runs/klein4b_dmd2_1_vedai_from_ep144/saves/klein4b_dmd2_1_vedai_from_ep144/checkpoints/checkpoint-150/adapter_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..84728c72adef005e86ce210e050ceb7fb91e99b5
--- /dev/null
+++ b/runs/klein4b_dmd2_1_vedai_from_ep144/saves/klein4b_dmd2_1_vedai_from_ep144/checkpoints/checkpoint-150/adapter_config.json
@@ -0,0 +1,59 @@
+{
+  "alora_invocation_tokens": null,
+  "alpha_pattern": {},
+  "arrow_config": null,
+  "auto_mapping": {
+    "base_model_class": "Flux2Transformer2DModel",
+    "parent_library": "diffusers.models.transformers.transformer_flux2"
+  },
+  "base_model_name_or_path": null,
+  "bias": "none",
+  "corda_config": null,
+  "ensure_weight_tying": false,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": "gaussian",
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 128,
+  "lora_bias": false,
+  "lora_dropout": 0.0,
+  "lora_ga_config": null,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "monteclora_config": null,
+  "peft_type": "LORA",
+  "peft_version": "0.20.0",
+  "qalora_group_size": 16,
+  "r": 64,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "attn.to_out.0",
+    "attn.to_k",
+    "ff_context.linear_out",
+    "attn.to_v",
+    "attn.to_add_out",
+    "attn.add_q_proj",
+    "ff.linear_in",
+    "attn.to_qkv_mlp_proj",
+    "ff_context.linear_in",
+    "attn.to_q",
+    "attn.add_k_proj",
+    "ff.linear_out",
+    "attn.add_v_proj"
+  ],
+  "target_parameters": null,
+  "task_type": null,
+  "trainable_token_indices": null,
+  "use_bdlora": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false,
+  "velora_config": null
+}
\ No newline at end of file
diff --git a/runs/klein4b_dmd2_1_vedai_from_ep144/saves/klein4b_dmd2_1_vedai_from_ep144/checkpoints/checkpoint-150/manifest.json b/runs/klein4b_dmd2_1_vedai_from_ep144/saves/klein4b_dmd2_1_vedai_from_ep144/checkpoints/checkpoint-150/manifest.json
new file mode 100644
index 0000000000000000000000000000000000000000..36e2c39fb45c2aba7867fcd33a35c043a2309413
--- /dev/null
+++ b/runs/klein4b_dmd2_1_vedai_from_ep144/saves/klein4b_dmd2_1_vedai_from_ep144/checkpoints/checkpoint-150/manifest.json
@@ -0,0 +1,21 @@
+{
+  "base_role": "generator",
+  "entries": [
+    {
+      "component": "transformer",
+      "path": ".",
+      "role": "generator"
+    },
+    {
+      "component": "transformer",
+      "path": "roles/fake",
+      "role": "fake"
+    }
+  ],
+  "finetune_type": "lora",
+  "format_version": 1,
+  "primary": {
+    "component": "transformer",
+    "path": "."
+  }
+}
diff --git a/runs/klein4b_dmd2_1_vedai_from_ep144/saves/klein4b_dmd2_1_vedai_from_ep144/checkpoints/checkpoint-150/roles/fake/README.md b/runs/klein4b_dmd2_1_vedai_from_ep144/saves/klein4b_dmd2_1_vedai_from_ep144/checkpoints/checkpoint-150/roles/fake/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..4356e22087c0101c61d8bbc4f5679d805f294e3a
--- /dev/null
+++ b/runs/klein4b_dmd2_1_vedai_from_ep144/saves/klein4b_dmd2_1_vedai_from_ep144/checkpoints/checkpoint-150/roles/fake/README.md
@@ -0,0 +1,204 @@
+---
+base_model: /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/models/klein4b_sft_vedai_ep144_merged/transformer
+library_name: peft
+tags:
+- lora
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]
+### Framework versions
+
+- PEFT 0.20.0
\ No newline at end of file
diff --git a/runs/klein4b_dmd2_1_vedai_from_ep144/saves/klein4b_dmd2_1_vedai_from_ep144/checkpoints/checkpoint-150/roles/fake/adapter_config.json b/runs/klein4b_dmd2_1_vedai_from_ep144/saves/klein4b_dmd2_1_vedai_from_ep144/checkpoints/checkpoint-150/roles/fake/adapter_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..84728c72adef005e86ce210e050ceb7fb91e99b5
--- /dev/null
+++ b/runs/klein4b_dmd2_1_vedai_from_ep144/saves/klein4b_dmd2_1_vedai_from_ep144/checkpoints/checkpoint-150/roles/fake/adapter_config.json
@@ -0,0 +1,59 @@
+{
+  "alora_invocation_tokens": null,
+  "alpha_pattern": {},
+  "arrow_config": null,
+  "auto_mapping": {
+    "base_model_class": "Flux2Transformer2DModel",
+    "parent_library": "diffusers.models.transformers.transformer_flux2"
+  },
+  "base_model_name_or_path": null,
+  "bias": "none",
+  "corda_config": null,
+  "ensure_weight_tying": false,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": "gaussian",
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 128,
+  "lora_bias": false,
+  "lora_dropout": 0.0,
+  "lora_ga_config": null,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "monteclora_config": null,
+  "peft_type": "LORA",
+  "peft_version": "0.20.0",
+  "qalora_group_size": 16,
+  "r": 64,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "attn.to_out.0",
+    "attn.to_k",
+    "ff_context.linear_out",
+    "attn.to_v",
+    "attn.to_add_out",
+    "attn.add_q_proj",
+    "ff.linear_in",
+    "attn.to_qkv_mlp_proj",
+    "ff_context.linear_in",
+    "attn.to_q",
+    "attn.add_k_proj",
+    "ff.linear_out",
+    "attn.add_v_proj"
+  ],
+  "target_parameters": null,
+  "task_type": null,
+  "trainable_token_indices": null,
+  "use_bdlora": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false,
+  "velora_config": null
+}
\ No newline at end of file
diff --git a/runs/klein4b_dmd2_1_vedai_from_nft/saves/klein4b_dmd2_1_vedai_from_nft/checkpoints/checkpoint-150/README.md b/runs/klein4b_dmd2_1_vedai_from_nft/saves/klein4b_dmd2_1_vedai_from_nft/checkpoints/checkpoint-150/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..fa4202985e8d8128b8b78f962e2811b1b8a452a5
--- /dev/null
+++ b/runs/klein4b_dmd2_1_vedai_from_nft/saves/klein4b_dmd2_1_vedai_from_nft/checkpoints/checkpoint-150/README.md
@@ -0,0 +1,204 @@
+---
+base_model: /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/models/klein4b_nft_vedai_ep144_ckpt80_merged/transformer
+library_name: peft
+tags:
+- lora
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]
+### Framework versions
+
+- PEFT 0.20.0
\ No newline at end of file
diff --git a/runs/klein4b_dmd2_1_vedai_from_nft/saves/klein4b_dmd2_1_vedai_from_nft/checkpoints/checkpoint-150/adapter_config.json b/runs/klein4b_dmd2_1_vedai_from_nft/saves/klein4b_dmd2_1_vedai_from_nft/checkpoints/checkpoint-150/adapter_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..b855ada4cbc548dd4e4e495aaa59ffd543c82c16
--- /dev/null
+++ b/runs/klein4b_dmd2_1_vedai_from_nft/saves/klein4b_dmd2_1_vedai_from_nft/checkpoints/checkpoint-150/adapter_config.json
@@ -0,0 +1,59 @@
+{
+  "alora_invocation_tokens": null,
+  "alpha_pattern": {},
+  "arrow_config": null,
+  "auto_mapping": {
+    "base_model_class": "Flux2Transformer2DModel",
+    "parent_library": "diffusers.models.transformers.transformer_flux2"
+  },
+  "base_model_name_or_path": null,
+  "bias": "none",
+  "corda_config": null,
+  "ensure_weight_tying": false,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": "gaussian",
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 128,
+  "lora_bias": false,
+  "lora_dropout": 0.0,
+  "lora_ga_config": null,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "monteclora_config": null,
+  "peft_type": "LORA",
+  "peft_version": "0.20.0",
+  "qalora_group_size": 16,
+  "r": 64,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "attn.to_v",
+    "attn.to_out.0",
+    "attn.to_q",
+    "attn.to_add_out",
+    "attn.to_k",
+    "attn.add_v_proj",
+    "ff_context.linear_out",
+    "attn.add_k_proj",
+    "ff_context.linear_in",
+    "ff.linear_out",
+    "ff.linear_in",
+    "attn.to_qkv_mlp_proj",
+    "attn.add_q_proj"
+  ],
+  "target_parameters": null,
+  "task_type": null,
+  "trainable_token_indices": null,
+  "use_bdlora": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false,
+  "velora_config": null
+}
\ No newline at end of file
diff --git a/runs/klein4b_dmd2_1_vedai_from_nft/saves/klein4b_dmd2_1_vedai_from_nft/checkpoints/checkpoint-150/manifest.json b/runs/klein4b_dmd2_1_vedai_from_nft/saves/klein4b_dmd2_1_vedai_from_nft/checkpoints/checkpoint-150/manifest.json
new file mode 100644
index 0000000000000000000000000000000000000000..36e2c39fb45c2aba7867fcd33a35c043a2309413
--- /dev/null
+++ b/runs/klein4b_dmd2_1_vedai_from_nft/saves/klein4b_dmd2_1_vedai_from_nft/checkpoints/checkpoint-150/manifest.json
@@ -0,0 +1,21 @@
+{
+  "base_role": "generator",
+  "entries": [
+    {
+      "component": "transformer",
+      "path": ".",
+      "role": "generator"
+    },
+    {
+      "component": "transformer",
+      "path": "roles/fake",
+      "role": "fake"
+    }
+  ],
+  "finetune_type": "lora",
+  "format_version": 1,
+  "primary": {
+    "component": "transformer",
+    "path": "."
+  }
+}
diff --git a/runs/klein4b_dmd2_1_vedai_from_nft/saves/klein4b_dmd2_1_vedai_from_nft/checkpoints/checkpoint-150/roles/fake/README.md b/runs/klein4b_dmd2_1_vedai_from_nft/saves/klein4b_dmd2_1_vedai_from_nft/checkpoints/checkpoint-150/roles/fake/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..fa4202985e8d8128b8b78f962e2811b1b8a452a5
--- /dev/null
+++ b/runs/klein4b_dmd2_1_vedai_from_nft/saves/klein4b_dmd2_1_vedai_from_nft/checkpoints/checkpoint-150/roles/fake/README.md
@@ -0,0 +1,204 @@
+---
+base_model: /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/models/klein4b_nft_vedai_ep144_ckpt80_merged/transformer
+library_name: peft
+tags:
+- lora
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]
+### Framework versions
+
+- PEFT 0.20.0
\ No newline at end of file
diff --git a/runs/klein4b_dmd2_1_vedai_from_nft/saves/klein4b_dmd2_1_vedai_from_nft/checkpoints/checkpoint-150/roles/fake/adapter_config.json b/runs/klein4b_dmd2_1_vedai_from_nft/saves/klein4b_dmd2_1_vedai_from_nft/checkpoints/checkpoint-150/roles/fake/adapter_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..b7afbeab2645cb74ea4cd21a2f2be578644a5dbb
--- /dev/null
+++ b/runs/klein4b_dmd2_1_vedai_from_nft/saves/klein4b_dmd2_1_vedai_from_nft/checkpoints/checkpoint-150/roles/fake/adapter_config.json
@@ -0,0 +1,59 @@
+{
+  "alora_invocation_tokens": null,
+  "alpha_pattern": {},
+  "arrow_config": null,
+  "auto_mapping": {
+    "base_model_class": "Flux2Transformer2DModel",
+    "parent_library": "diffusers.models.transformers.transformer_flux2"
+  },
+  "base_model_name_or_path": null,
+  "bias": "none",
+  "corda_config": null,
+  "ensure_weight_tying": false,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": "gaussian",
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 128,
+  "lora_bias": false,
+  "lora_dropout": 0.0,
+  "lora_ga_config": null,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "monteclora_config": null,
+  "peft_type": "LORA",
+  "peft_version": "0.20.0",
+  "qalora_group_size": 16,
+  "r": 64,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "attn.to_out.0",
+    "attn.to_add_out",
+    "attn.to_q",
+    "attn.to_k",
+    "attn.add_v_proj",
+    "ff_context.linear_out",
+    "attn.add_k_proj",
+    "ff_context.linear_in",
+    "attn.to_v",
+    "ff.linear_out",
+    "ff.linear_in",
+    "attn.to_qkv_mlp_proj",
+    "attn.add_q_proj"
+  ],
+  "target_parameters": null,
+  "task_type": null,
+  "trainable_token_indices": null,
+  "use_bdlora": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false,
+  "velora_config": null
+}
\ No newline at end of file
diff --git a/runs/klein4b_dmd2_2_flir_from_ep72/saves/klein4b_dmd2_2_flir_from_ep72/checkpoints/checkpoint-150/README.md b/runs/klein4b_dmd2_2_flir_from_ep72/saves/klein4b_dmd2_2_flir_from_ep72/checkpoints/checkpoint-150/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..32205326f359655a9478df7e720d429061c94c10
--- /dev/null
+++ b/runs/klein4b_dmd2_2_flir_from_ep72/saves/klein4b_dmd2_2_flir_from_ep72/checkpoints/checkpoint-150/README.md
@@ -0,0 +1,204 @@
+---
+base_model: /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/models/klein4b_sft_flir_ep72_merged/transformer
+library_name: peft
+tags:
+- lora
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]
+### Framework versions
+
+- PEFT 0.20.0
\ No newline at end of file
diff --git a/runs/klein4b_dmd2_2_flir_from_ep72/saves/klein4b_dmd2_2_flir_from_ep72/checkpoints/checkpoint-150/adapter_config.json b/runs/klein4b_dmd2_2_flir_from_ep72/saves/klein4b_dmd2_2_flir_from_ep72/checkpoints/checkpoint-150/adapter_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..36b5a7c95cf9943a06d2ab62ec4b556a7d88e7a3
--- /dev/null
+++ b/runs/klein4b_dmd2_2_flir_from_ep72/saves/klein4b_dmd2_2_flir_from_ep72/checkpoints/checkpoint-150/adapter_config.json
@@ -0,0 +1,59 @@
+{
+  "alora_invocation_tokens": null,
+  "alpha_pattern": {},
+  "arrow_config": null,
+  "auto_mapping": {
+    "base_model_class": "Flux2Transformer2DModel",
+    "parent_library": "diffusers.models.transformers.transformer_flux2"
+  },
+  "base_model_name_or_path": null,
+  "bias": "none",
+  "corda_config": null,
+  "ensure_weight_tying": false,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": "gaussian",
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 128,
+  "lora_bias": false,
+  "lora_dropout": 0.0,
+  "lora_ga_config": null,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "monteclora_config": null,
+  "peft_type": "LORA",
+  "peft_version": "0.20.0",
+  "qalora_group_size": 16,
+  "r": 64,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "ff_context.linear_out",
+    "ff.linear_in",
+    "attn.add_q_proj",
+    "attn.to_add_out",
+    "ff_context.linear_in",
+    "attn.add_k_proj",
+    "attn.to_k",
+    "attn.to_qkv_mlp_proj",
+    "attn.add_v_proj",
+    "attn.to_q",
+    "ff.linear_out",
+    "attn.to_v",
+    "attn.to_out.0"
+  ],
+  "target_parameters": null,
+  "task_type": null,
+  "trainable_token_indices": null,
+  "use_bdlora": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false,
+  "velora_config": null
+}
\ No newline at end of file
diff --git a/runs/klein4b_dmd2_2_flir_from_ep72/saves/klein4b_dmd2_2_flir_from_ep72/checkpoints/checkpoint-150/manifest.json b/runs/klein4b_dmd2_2_flir_from_ep72/saves/klein4b_dmd2_2_flir_from_ep72/checkpoints/checkpoint-150/manifest.json
new file mode 100644
index 0000000000000000000000000000000000000000..36e2c39fb45c2aba7867fcd33a35c043a2309413
--- /dev/null
+++ b/runs/klein4b_dmd2_2_flir_from_ep72/saves/klein4b_dmd2_2_flir_from_ep72/checkpoints/checkpoint-150/manifest.json
@@ -0,0 +1,21 @@
+{
+  "base_role": "generator",
+  "entries": [
+    {
+      "component": "transformer",
+      "path": ".",
+      "role": "generator"
+    },
+    {
+      "component": "transformer",
+      "path": "roles/fake",
+      "role": "fake"
+    }
+  ],
+  "finetune_type": "lora",
+  "format_version": 1,
+  "primary": {
+    "component": "transformer",
+    "path": "."
+  }
+}
diff --git a/runs/klein4b_dmd2_2_flir_from_ep72/saves/klein4b_dmd2_2_flir_from_ep72/checkpoints/checkpoint-150/roles/fake/README.md b/runs/klein4b_dmd2_2_flir_from_ep72/saves/klein4b_dmd2_2_flir_from_ep72/checkpoints/checkpoint-150/roles/fake/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..32205326f359655a9478df7e720d429061c94c10
--- /dev/null
+++ b/runs/klein4b_dmd2_2_flir_from_ep72/saves/klein4b_dmd2_2_flir_from_ep72/checkpoints/checkpoint-150/roles/fake/README.md
@@ -0,0 +1,204 @@
+---
+base_model: /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/models/klein4b_sft_flir_ep72_merged/transformer
+library_name: peft
+tags:
+- lora
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]
+### Framework versions
+
+- PEFT 0.20.0
\ No newline at end of file
diff --git a/runs/klein4b_dmd2_2_flir_from_ep72/saves/klein4b_dmd2_2_flir_from_ep72/checkpoints/checkpoint-150/roles/fake/adapter_config.json b/runs/klein4b_dmd2_2_flir_from_ep72/saves/klein4b_dmd2_2_flir_from_ep72/checkpoints/checkpoint-150/roles/fake/adapter_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..1ad8562c6fddd527c093ba9372dfdeac1e6da12e
--- /dev/null
+++ b/runs/klein4b_dmd2_2_flir_from_ep72/saves/klein4b_dmd2_2_flir_from_ep72/checkpoints/checkpoint-150/roles/fake/adapter_config.json
@@ -0,0 +1,59 @@
+{
+  "alora_invocation_tokens": null,
+  "alpha_pattern": {},
+  "arrow_config": null,
+  "auto_mapping": {
+    "base_model_class": "Flux2Transformer2DModel",
+    "parent_library": "diffusers.models.transformers.transformer_flux2"
+  },
+  "base_model_name_or_path": null,
+  "bias": "none",
+  "corda_config": null,
+  "ensure_weight_tying": false,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": "gaussian",
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 128,
+  "lora_bias": false,
+  "lora_dropout": 0.0,
+  "lora_ga_config": null,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "monteclora_config": null,
+  "peft_type": "LORA",
+  "peft_version": "0.20.0",
+  "qalora_group_size": 16,
+  "r": 64,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "ff_context.linear_out",
+    "attn.add_q_proj",
+    "ff.linear_in",
+    "attn.to_add_out",
+    "ff_context.linear_in",
+    "attn.add_k_proj",
+    "attn.to_k",
+    "attn.to_qkv_mlp_proj",
+    "attn.add_v_proj",
+    "attn.to_q",
+    "ff.linear_out",
+    "attn.to_v",
+    "attn.to_out.0"
+  ],
+  "target_parameters": null,
+  "task_type": null,
+  "trainable_token_indices": null,
+  "use_bdlora": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false,
+  "velora_config": null
+}
\ No newline at end of file
diff --git a/runs/klein4b_dmd2_4_flir_from_ep72/saves/klein4b_dmd2_4_flir_from_ep72/checkpoints/checkpoint-150/README.md b/runs/klein4b_dmd2_4_flir_from_ep72/saves/klein4b_dmd2_4_flir_from_ep72/checkpoints/checkpoint-150/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..32205326f359655a9478df7e720d429061c94c10
--- /dev/null
+++ b/runs/klein4b_dmd2_4_flir_from_ep72/saves/klein4b_dmd2_4_flir_from_ep72/checkpoints/checkpoint-150/README.md
@@ -0,0 +1,204 @@
+---
+base_model: /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/models/klein4b_sft_flir_ep72_merged/transformer
+library_name: peft
+tags:
+- lora
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]
+### Framework versions
+
+- PEFT 0.20.0
\ No newline at end of file
diff --git a/runs/klein4b_dmd2_4_flir_from_ep72/saves/klein4b_dmd2_4_flir_from_ep72/checkpoints/checkpoint-150/adapter_config.json b/runs/klein4b_dmd2_4_flir_from_ep72/saves/klein4b_dmd2_4_flir_from_ep72/checkpoints/checkpoint-150/adapter_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..a75d9cc1a868bd428e727fe9c86e9748b9eda885
--- /dev/null
+++ b/runs/klein4b_dmd2_4_flir_from_ep72/saves/klein4b_dmd2_4_flir_from_ep72/checkpoints/checkpoint-150/adapter_config.json
@@ -0,0 +1,59 @@
+{
+  "alora_invocation_tokens": null,
+  "alpha_pattern": {},
+  "arrow_config": null,
+  "auto_mapping": {
+    "base_model_class": "Flux2Transformer2DModel",
+    "parent_library": "diffusers.models.transformers.transformer_flux2"
+  },
+  "base_model_name_or_path": null,
+  "bias": "none",
+  "corda_config": null,
+  "ensure_weight_tying": false,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": "gaussian",
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 128,
+  "lora_bias": false,
+  "lora_dropout": 0.0,
+  "lora_ga_config": null,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "monteclora_config": null,
+  "peft_type": "LORA",
+  "peft_version": "0.20.0",
+  "qalora_group_size": 16,
+  "r": 64,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "attn.to_qkv_mlp_proj",
+    "attn.add_q_proj",
+    "attn.to_q",
+    "attn.add_k_proj",
+    "attn.to_out.0",
+    "attn.add_v_proj",
+    "ff.linear_out",
+    "ff_context.linear_out",
+    "ff.linear_in",
+    "ff_context.linear_in",
+    "attn.to_add_out",
+    "attn.to_v",
+    "attn.to_k"
+  ],
+  "target_parameters": null,
+  "task_type": null,
+  "trainable_token_indices": null,
+  "use_bdlora": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false,
+  "velora_config": null
+}
\ No newline at end of file
diff --git a/runs/klein4b_dmd2_4_flir_from_ep72/saves/klein4b_dmd2_4_flir_from_ep72/checkpoints/checkpoint-150/manifest.json b/runs/klein4b_dmd2_4_flir_from_ep72/saves/klein4b_dmd2_4_flir_from_ep72/checkpoints/checkpoint-150/manifest.json
new file mode 100644
index 0000000000000000000000000000000000000000..36e2c39fb45c2aba7867fcd33a35c043a2309413
--- /dev/null
+++ b/runs/klein4b_dmd2_4_flir_from_ep72/saves/klein4b_dmd2_4_flir_from_ep72/checkpoints/checkpoint-150/manifest.json
@@ -0,0 +1,21 @@
+{
+  "base_role": "generator",
+  "entries": [
+    {
+      "component": "transformer",
+      "path": ".",
+      "role": "generator"
+    },
+    {
+      "component": "transformer",
+      "path": "roles/fake",
+      "role": "fake"
+    }
+  ],
+  "finetune_type": "lora",
+  "format_version": 1,
+  "primary": {
+    "component": "transformer",
+    "path": "."
+  }
+}
diff --git a/runs/klein4b_dmd2_4_flir_from_ep72/saves/klein4b_dmd2_4_flir_from_ep72/checkpoints/checkpoint-150/roles/fake/README.md b/runs/klein4b_dmd2_4_flir_from_ep72/saves/klein4b_dmd2_4_flir_from_ep72/checkpoints/checkpoint-150/roles/fake/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..32205326f359655a9478df7e720d429061c94c10
--- /dev/null
+++ b/runs/klein4b_dmd2_4_flir_from_ep72/saves/klein4b_dmd2_4_flir_from_ep72/checkpoints/checkpoint-150/roles/fake/README.md
@@ -0,0 +1,204 @@
+---
+base_model: /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/models/klein4b_sft_flir_ep72_merged/transformer
+library_name: peft
+tags:
+- lora
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]
+### Framework versions
+
+- PEFT 0.20.0
\ No newline at end of file
diff --git a/runs/klein4b_dmd2_4_flir_from_ep72/saves/klein4b_dmd2_4_flir_from_ep72/checkpoints/checkpoint-150/roles/fake/adapter_config.json b/runs/klein4b_dmd2_4_flir_from_ep72/saves/klein4b_dmd2_4_flir_from_ep72/checkpoints/checkpoint-150/roles/fake/adapter_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..a75d9cc1a868bd428e727fe9c86e9748b9eda885
--- /dev/null
+++ b/runs/klein4b_dmd2_4_flir_from_ep72/saves/klein4b_dmd2_4_flir_from_ep72/checkpoints/checkpoint-150/roles/fake/adapter_config.json
@@ -0,0 +1,59 @@
+{
+  "alora_invocation_tokens": null,
+  "alpha_pattern": {},
+  "arrow_config": null,
+  "auto_mapping": {
+    "base_model_class": "Flux2Transformer2DModel",
+    "parent_library": "diffusers.models.transformers.transformer_flux2"
+  },
+  "base_model_name_or_path": null,
+  "bias": "none",
+  "corda_config": null,
+  "ensure_weight_tying": false,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": "gaussian",
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 128,
+  "lora_bias": false,
+  "lora_dropout": 0.0,
+  "lora_ga_config": null,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "monteclora_config": null,
+  "peft_type": "LORA",
+  "peft_version": "0.20.0",
+  "qalora_group_size": 16,
+  "r": 64,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "attn.to_qkv_mlp_proj",
+    "attn.add_q_proj",
+    "attn.to_q",
+    "attn.add_k_proj",
+    "attn.to_out.0",
+    "attn.add_v_proj",
+    "ff.linear_out",
+    "ff_context.linear_out",
+    "ff.linear_in",
+    "ff_context.linear_in",
+    "attn.to_add_out",
+    "attn.to_v",
+    "attn.to_k"
+  ],
+  "target_parameters": null,
+  "task_type": null,
+  "trainable_token_indices": null,
+  "use_bdlora": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false,
+  "velora_config": null
+}
\ No newline at end of file
diff --git a/runs/klein4b_dmd2_4_flir_from_ep72_lr2e-6/saves/klein4b_dmd2_4_flir_from_ep72_lr2e-6/checkpoints/checkpoint-150/README.md b/runs/klein4b_dmd2_4_flir_from_ep72_lr2e-6/saves/klein4b_dmd2_4_flir_from_ep72_lr2e-6/checkpoints/checkpoint-150/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..32205326f359655a9478df7e720d429061c94c10
--- /dev/null
+++ b/runs/klein4b_dmd2_4_flir_from_ep72_lr2e-6/saves/klein4b_dmd2_4_flir_from_ep72_lr2e-6/checkpoints/checkpoint-150/README.md
@@ -0,0 +1,204 @@
+---
+base_model: /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/models/klein4b_sft_flir_ep72_merged/transformer
+library_name: peft
+tags:
+- lora
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]
+### Framework versions
+
+- PEFT 0.20.0
\ No newline at end of file
diff --git a/runs/klein4b_dmd2_4_flir_from_ep72_lr2e-6/saves/klein4b_dmd2_4_flir_from_ep72_lr2e-6/checkpoints/checkpoint-150/adapter_config.json b/runs/klein4b_dmd2_4_flir_from_ep72_lr2e-6/saves/klein4b_dmd2_4_flir_from_ep72_lr2e-6/checkpoints/checkpoint-150/adapter_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..83ea45374912979a727f109c0d8f101b98d3fe53
--- /dev/null
+++ b/runs/klein4b_dmd2_4_flir_from_ep72_lr2e-6/saves/klein4b_dmd2_4_flir_from_ep72_lr2e-6/checkpoints/checkpoint-150/adapter_config.json
@@ -0,0 +1,59 @@
+{
+  "alora_invocation_tokens": null,
+  "alpha_pattern": {},
+  "arrow_config": null,
+  "auto_mapping": {
+    "base_model_class": "Flux2Transformer2DModel",
+    "parent_library": "diffusers.models.transformers.transformer_flux2"
+  },
+  "base_model_name_or_path": null,
+  "bias": "none",
+  "corda_config": null,
+  "ensure_weight_tying": false,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": "gaussian",
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 128,
+  "lora_bias": false,
+  "lora_dropout": 0.0,
+  "lora_ga_config": null,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "monteclora_config": null,
+  "peft_type": "LORA",
+  "peft_version": "0.20.0",
+  "qalora_group_size": 16,
+  "r": 64,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "attn.add_v_proj",
+    "attn.add_q_proj",
+    "ff_context.linear_in",
+    "attn.to_out.0",
+    "ff_context.linear_out",
+    "attn.to_add_out",
+    "attn.to_v",
+    "attn.to_qkv_mlp_proj",
+    "attn.add_k_proj",
+    "ff.linear_in",
+    "ff.linear_out",
+    "attn.to_k",
+    "attn.to_q"
+  ],
+  "target_parameters": null,
+  "task_type": null,
+  "trainable_token_indices": null,
+  "use_bdlora": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false,
+  "velora_config": null
+}
\ No newline at end of file
diff --git a/runs/klein4b_dmd2_4_flir_from_ep72_lr2e-6/saves/klein4b_dmd2_4_flir_from_ep72_lr2e-6/checkpoints/checkpoint-150/manifest.json b/runs/klein4b_dmd2_4_flir_from_ep72_lr2e-6/saves/klein4b_dmd2_4_flir_from_ep72_lr2e-6/checkpoints/checkpoint-150/manifest.json
new file mode 100644
index 0000000000000000000000000000000000000000..36e2c39fb45c2aba7867fcd33a35c043a2309413
--- /dev/null
+++ b/runs/klein4b_dmd2_4_flir_from_ep72_lr2e-6/saves/klein4b_dmd2_4_flir_from_ep72_lr2e-6/checkpoints/checkpoint-150/manifest.json
@@ -0,0 +1,21 @@
+{
+  "base_role": "generator",
+  "entries": [
+    {
+      "component": "transformer",
+      "path": ".",
+      "role": "generator"
+    },
+    {
+      "component": "transformer",
+      "path": "roles/fake",
+      "role": "fake"
+    }
+  ],
+  "finetune_type": "lora",
+  "format_version": 1,
+  "primary": {
+    "component": "transformer",
+    "path": "."
+  }
+}
diff --git a/runs/klein4b_dmd2_4_flir_from_ep72_lr2e-6/saves/klein4b_dmd2_4_flir_from_ep72_lr2e-6/checkpoints/checkpoint-150/roles/fake/README.md b/runs/klein4b_dmd2_4_flir_from_ep72_lr2e-6/saves/klein4b_dmd2_4_flir_from_ep72_lr2e-6/checkpoints/checkpoint-150/roles/fake/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..32205326f359655a9478df7e720d429061c94c10
--- /dev/null
+++ b/runs/klein4b_dmd2_4_flir_from_ep72_lr2e-6/saves/klein4b_dmd2_4_flir_from_ep72_lr2e-6/checkpoints/checkpoint-150/roles/fake/README.md
@@ -0,0 +1,204 @@
+---
+base_model: /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/models/klein4b_sft_flir_ep72_merged/transformer
+library_name: peft
+tags:
+- lora
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]
+### Framework versions
+
+- PEFT 0.20.0
\ No newline at end of file
diff --git a/runs/klein4b_dmd2_4_flir_from_ep72_lr2e-6/saves/klein4b_dmd2_4_flir_from_ep72_lr2e-6/checkpoints/checkpoint-150/roles/fake/adapter_config.json b/runs/klein4b_dmd2_4_flir_from_ep72_lr2e-6/saves/klein4b_dmd2_4_flir_from_ep72_lr2e-6/checkpoints/checkpoint-150/roles/fake/adapter_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..83ea45374912979a727f109c0d8f101b98d3fe53
--- /dev/null
+++ b/runs/klein4b_dmd2_4_flir_from_ep72_lr2e-6/saves/klein4b_dmd2_4_flir_from_ep72_lr2e-6/checkpoints/checkpoint-150/roles/fake/adapter_config.json
@@ -0,0 +1,59 @@
+{
+  "alora_invocation_tokens": null,
+  "alpha_pattern": {},
+  "arrow_config": null,
+  "auto_mapping": {
+    "base_model_class": "Flux2Transformer2DModel",
+    "parent_library": "diffusers.models.transformers.transformer_flux2"
+  },
+  "base_model_name_or_path": null,
+  "bias": "none",
+  "corda_config": null,
+  "ensure_weight_tying": false,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": "gaussian",
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 128,
+  "lora_bias": false,
+  "lora_dropout": 0.0,
+  "lora_ga_config": null,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "monteclora_config": null,
+  "peft_type": "LORA",
+  "peft_version": "0.20.0",
+  "qalora_group_size": 16,
+  "r": 64,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "attn.add_v_proj",
+    "attn.add_q_proj",
+    "ff_context.linear_in",
+    "attn.to_out.0",
+    "ff_context.linear_out",
+    "attn.to_add_out",
+    "attn.to_v",
+    "attn.to_qkv_mlp_proj",
+    "attn.add_k_proj",
+    "ff.linear_in",
+    "ff.linear_out",
+    "attn.to_k",
+    "attn.to_q"
+  ],
+  "target_parameters": null,
+  "task_type": null,
+  "trainable_token_indices": null,
+  "use_bdlora": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false,
+  "velora_config": null
+}
\ No newline at end of file
diff --git a/runs/klein4b_grpo_flir_full_ssim_dists/train.log b/runs/klein4b_grpo_flir_full_ssim_dists/train.log
new file mode 100644
index 0000000000000000000000000000000000000000..75c2e7dae64bccd293c20f4fb6aef6eec926b5c6
--- /dev/null
+++ b/runs/klein4b_grpo_flir_full_ssim_dists/train.log
@@ -0,0 +1,2914 @@
+[2026-10-01 16:05:32,121] [INFO] [flow_factory.cli]: ============================================================
+[2026-10-01 16:05:32,122] [INFO] [flow_factory.cli]: Flow-Factory Launch Configuration
+[2026-10-01 16:05:32,122] [INFO] [flow_factory.cli]:   Num machines:       1
+[2026-10-01 16:05:32,122] [INFO] [flow_factory.cli]:   Machine rank:       0
+[2026-10-01 16:05:32,122] [INFO] [flow_factory.cli]:   Num processes:      8
+[2026-10-01 16:05:32,122] [INFO] [flow_factory.cli]:   Master node:        localhost:29513
+[2026-10-01 16:05:32,123] [INFO] [flow_factory.cli]:   Mixed precision:    bf16
+[2026-10-01 16:05:32,123] [INFO] [flow_factory.cli]:   Accelerate config:  config/deepspeed/deepspeed_zero2.yaml
+[2026-10-01 16:05:32,123] [INFO] [flow_factory.cli]: ============================================================
+W1001 16:05:40.450000 2822096 site-packages/torch/distributed/run.py:852] 
+W1001 16:05:40.450000 2822096 site-packages/torch/distributed/run.py:852] *****************************************
+W1001 16:05:40.450000 2822096 site-packages/torch/distributed/run.py:852] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
+W1001 16:05:40.450000 2822096 site-packages/torch/distributed/run.py:852] *****************************************
+[2026-10-01 16:05:47] [Rank 0] [WARNING] [flow_factory.hparams.abc]: GRPOTrainingArguments.from_dict captured 1 unknown key(s) into extra_kwargs: ['condition_image_size']. Verify these are intentional (e.g., adapter-specific kwargs); typos against declared fields will be silently accepted otherwise.
+[2026-10-01 16:05:47] [Rank 0] [INFO] [flow_factory.hparams.training_args._base]: World Size: 8
+[2026-10-01 16:05:47] [Rank 0] [WARNING] [flow_factory.hparams.abc]: EvaluationArguments.from_dict captured 1 unknown key(s) into extra_kwargs: ['condition_image_size']. Verify these are intentional (e.g., adapter-specific kwargs); typos against declared fields will be silently accepted otherwise.
+[2026-10-01 16:05:47,534] [INFO] [flow_factory.train]: ====================================================================================================
+[2026-10-01 16:05:47,534] [INFO] [flow_factory.train]: Flow-Factory Training Initialized
+[2026-10-01 16:05:47,534] [INFO] [flow_factory.train]: World Size: 8
+[2026-10-01 16:05:47,534] [INFO] [flow_factory.train]: ====================================================================================================
+[2026-10-01 16:05:47,534] [INFO] [flow_factory.train]: Config: /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/runs/klein4b_grpo_flir_full_ssim_dists/train.yaml
+[2026-10-01 16:05:47,541] [INFO] [flow_factory.train]: 
+launcher: accelerate
+config_file: config/deepspeed/deepspeed_zero2.yaml
+num_processes: 8
+main_process_port: 29513
+mixed_precision: bf16
+data:
+  dataset_dir: data
+  cache_dir: ~/.cache/flow_factory/datasets
+  image_dir: null
+  video_dir: null
+  audio_dir: null
+  preprocessing_batch_size: 8
+  dataloader_num_workers: 8
+  enable_preprocess: true
+  force_reprocess: false
+  max_dataset_size: null
+  preprocess_parallelism: local
+  sampler_type: group_contiguous
+  sampler_subgroup_size: null
+  datasets:
+  - extra_kwargs: {}
+    name: flir_rl_full
+    dataset_dir: /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/flir_rl_full
+    image_dir: null
+    video_dir: null
+    audio_dir: null
+    train:
+      extra_kwargs: {}
+      enabled: true
+      split: train
+      weight: 1
+      max_dataset_size: null
+      unique_sample_num_per_epoch: 48
+      num_batches_per_epoch: null
+    eval:
+      extra_kwargs: {}
+      enabled: true
+      split: test
+      max_dataset_size: null
+      resolution: null
+      num_inference_steps: null
+      guidance_scale: null
+    source_id: 0
+model:
+  model_name_or_path: /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/flow_factory_gpu_gate_models/FLUX.2-klein-base-4B
+  finetune_type: lora
+  trainable_parameters_dtype: bfloat16
+  component_load_dtypes: null
+  frozen_parameters_dtype: null
+  target_components:
+  - transformer
+  target_modules: default
+  model_type: flux2-klein
+  lora_rank: 64
+  lora_alpha: 128
+  resume_path: /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/runs/klein4b_sft_flir_full_r64a128_lr1e-4/saves/klein4b_sft_flir_full_r64a128_lr1e-4/checkpoints/checkpoint-12
+  resume_type: lora
+scheduler:
+  dynamics_type: Flow-SDE
+  noise_level: 0.7
+  num_sde_steps: 1
+  sde_steps:
+  - 1
+  - 2
+  - 3
+  seed: 42
+training:
+  trainer_type: grpo
+  resolution:
+  - 512
+  - 640
+  height: 512
+  width: 640
+  num_frames: null
+  frame_rate: null
+  max_epochs: 200
+  per_device_batch_size: 1
+  gradient_step_per_epoch: 2
+  max_grad_norm: 1.0
+  num_batches_per_epoch: 96
+  gradient_accumulation_steps: 48
+  num_inner_epochs: 1
+  group_size: 16
+  unique_sample_num_per_epoch: 48
+  num_inference_steps: 8
+  guidance_scale: 1.0
+  ref_param_device: cuda
+  seed: 42
+  learning_rate: 1.0e-05
+  adam_weight_decay: 0.0001
+  adam_betas:
+  - 0.9
+  - 0.999
+  adam_epsilon: 1.0e-08
+  enable_gradient_checkpointing: false
+  offload_samples_to_cpu: false
+  ema_decay: 0.9
+  ema_update_interval: 4
+  ema_device: cuda
+  ema_decay_schedule: power
+  latent_storage_dtype: fp16
+  shuffle_samples: true
+  reward_optimization_overlap: false
+  reward_optimization_overlap_mode: ready
+  reward_optimization_overlap_poll_interval: 0.05
+  global_std: false
+  advantage_aggregation: gdpo
+  clip_range:
+  - -0.0001
+  - 0.0001
+  adv_clip_range:
+  - -5.0
+  - 5.0
+  kl_type: v-based
+  kl_beta: 0.0
+  condition_image_size: 1024
+eval:
+  resolution:
+  - 512
+  - 640
+  height: 512
+  width: 640
+  num_frames: null
+  frame_rate: null
+  per_device_batch_size: 1
+  seed: 42
+  guidance_scale: 1.0
+  num_inference_steps: 28
+  eval_freq: 10
+  condition_image_size: 1024
+log:
+  run_name: klein4b_grpo_flir_full_ssim_dists
+  project: IR-Engine
+  logging_backend: wandb
+  save_dir: /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/runs/klein4b_grpo_flir_full_ssim_dists/saves
+  save_freq: 20
+  save_model_only: true
+  verbose: true
+acceleration:
+  shared: []
+  rollout: []
+reward:
+  reward_0:
+    name: ssim
+    reward_model: ir_rewards.fidelity.SSIMReward
+    weight:
+      flir_rl_full: 1.0
+    dtype: float32
+    device: cuda
+    batch_size: 16
+    async_reward: false
+    num_workers: 1
+    applicable_datasets:
+    - flir_rl_full
+  reward_1:
+    name: dists
+    reward_model: ir_rewards.fidelity.DISTSReward
+    weight:
+      flir_rl_full: 1.0
+    dtype: float32
+    device: cuda
+    batch_size: 16
+    async_reward: false
+    num_workers: 1
+    applicable_datasets:
+    - flir_rl_full
+eval_reward:
+  reward_0:
+    name: ssim
+    reward_model: ir_rewards.fidelity.SSIMReward
+    weight:
+      flir_rl_full: 1.0
+    dtype: float32
+    device: cuda
+    batch_size: 16
+    async_reward: false
+    num_workers: 1
+    applicable_datasets:
+    - flir_rl_full
+  reward_1:
+    name: psnr
+    reward_model: ir_rewards.fidelity.PSNRReward
+    weight:
+      flir_rl_full: 1.0
+    dtype: float32
+    device: cuda
+    batch_size: 16
+    async_reward: false
+    num_workers: 1
+    applicable_datasets:
+    - flir_rl_full
+  reward_2:
+    name: dists
+    reward_model: ir_rewards.fidelity.DISTSReward
+    weight:
+      flir_rl_full: 1.0
+    dtype: float32
+    device: cuda
+    batch_size: 16
+    async_reward: false
+    num_workers: 1
+    applicable_datasets:
+    - flir_rl_full
+optimizer:
+  optimizer_configs:
+  - extra_kwargs: {}
+    name: default
+    optimizer: adamw
+    learning_rate: 0.0003
+    weight_decay: 0.0001
+    max_grad_norm: 1.0
+    update_frequency: 1
+    betas:
+    - 0.9
+    - 0.999
+    eps: 1.0e-08
+
+[2026-10-01 16:05:47,541] [INFO] [flow_factory.train]: ====================================================================================================
+[2026-10-01 16:05:57] [Rank 3] [INFO] [flow_factory.models.loader]: Loading model architecture: flux2-klein...
+[2026-10-01 16:05:57] [Rank 4] [INFO] [flow_factory.models.loader]: Loading model architecture: flux2-klein...
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+[2026-10-01 16:05:57] [Rank 6] [INFO] [flow_factory.models.loader]: Loading model architecture: flux2-klein...
+[2026-10-01 16:05:57,622] [WARNING] [accelerate.utils.other]: [RANK 0] Detected kernel version 5.4.241, which is below the recommended minimum of 5.5.0; this can cause the process to hang. It is recommended to upgrade the kernel to the minimum version or higher.
+[2026-10-01 16:05:57] [Rank 0] [INFO] [flow_factory.models.loader]: Loading model architecture: flux2-klein...
+[2026-10-01 16:05:57] [Rank 2] [INFO] [flow_factory.models.loader]: Loading model architecture: flux2-klein...
+
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+
Loading pipeline components...:  20%|██        | 1/5 [00:21<01:26, 21.74s/it][2026-10-01 16:06:19] [Rank 1] [INFO] [flow_factory.scheduler.loader]: Loaded SDE scheduler: FlowMatchEulerDiscreteSDEScheduler
+
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+[2026-10-01 16:06:20] [Rank 7] [INFO] [flow_factory.scheduler.loader]: Loaded SDE scheduler: FlowMatchEulerDiscreteSDEScheduler
+[2026-10-01 16:06:20] [Rank 5] [INFO] [flow_factory.scheduler.loader]: Loaded SDE scheduler: FlowMatchEulerDiscreteSDEScheduler
+
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+
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+[2026-10-01 16:06:20] [Rank 0] [INFO] [flow_factory.scheduler.loader]: Loaded SDE scheduler: FlowMatchEulerDiscreteSDEScheduler
+[2026-10-01 16:06:20] [Rank 3] [INFO] [flow_factory.scheduler.loader]: Loaded SDE scheduler: FlowMatchEulerDiscreteSDEScheduler
+
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+[2026-10-01 16:06:20] [Rank 4] [INFO] [flow_factory.scheduler.loader]: Loaded SDE scheduler: FlowMatchEulerDiscreteSDEScheduler
+[2026-10-01 16:06:20] [Rank 2] [INFO] [flow_factory.scheduler.loader]: Loaded SDE scheduler: FlowMatchEulerDiscreteSDEScheduler
+[2026-10-01 16:06:21] [Rank 0] [INFO] [flow_factory.models.abc]: LoRA adapter loaded for transformer role=base from /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/runs/klein4b_sft_flir_full_r64a128_lr1e-4/saves/klein4b_sft_flir_full_r64a128_lr1e-4/checkpoints/checkpoint-12
+[2026-10-01 16:06:22] [Rank 0] [INFO] [flow_factory.models.abc]: Checkpoint loaded successfully from /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/runs/klein4b_sft_flir_full_r64a128_lr1e-4/saves/klein4b_sft_flir_full_r64a128_lr1e-4/checkpoints/checkpoint-12 (type=lora)
+[2026-10-01 16:06:22] [Rank 2] [INFO] [flow_factory.models.abc]: Froze ALL transformer parameters
+[2026-10-01 16:06:22] [Rank 2] [INFO] [flow_factory.models.abc]: Component transformer already has 'default' adapter. Skipping initialization but enabling gradients.
+[2026-10-01 16:06:22] [Rank 3] [INFO] [flow_factory.models.abc]: Froze ALL transformer parameters
+[2026-10-01 16:06:22] [Rank 5] [INFO] [flow_factory.models.abc]: Froze ALL transformer parameters
+[2026-10-01 16:06:22] [Rank 4] [INFO] [flow_factory.models.abc]: Froze ALL transformer parameters
+[2026-10-01 16:06:22] [Rank 6] [INFO] [flow_factory.models.abc]: Froze ALL transformer parameters
+[2026-10-01 16:06:22] [Rank 1] [INFO] [flow_factory.models.abc]: Froze ALL transformer parameters
+[2026-10-01 16:06:22] [Rank 0] [INFO] [flow_factory.models.abc]: Froze ALL transformer parameters
+[2026-10-01 16:06:22] [Rank 1] [INFO] [flow_factory.models.abc]: Component transformer already has 'default' adapter. Skipping initialization but enabling gradients.
+[2026-10-01 16:06:22] [Rank 3] [INFO] [flow_factory.models.abc]: Component transformer already has 'default' adapter. Skipping initialization but enabling gradients.
+[2026-10-01 16:06:22] [Rank 5] [INFO] [flow_factory.models.abc]: Component transformer already has 'default' adapter. Skipping initialization but enabling gradients.
+[2026-10-01 16:06:22] [Rank 7] [INFO] [flow_factory.models.abc]: Froze ALL transformer parameters
+[2026-10-01 16:06:22] [Rank 4] [INFO] [flow_factory.models.abc]: Component transformer already has 'default' adapter. Skipping initialization but enabling gradients.
+[2026-10-01 16:06:22] [Rank 6] [INFO] [flow_factory.models.abc]: Component transformer already has 'default' adapter. Skipping initialization but enabling gradients.
+[2026-10-01 16:06:22] [Rank 0] [INFO] [flow_factory.models.abc]: Component transformer already has 'default' adapter. Skipping initialization but enabling gradients.
+[2026-10-01 16:06:22] [Rank 7] [INFO] [flow_factory.models.abc]: Component transformer already has 'default' adapter. Skipping initialization but enabling gradients.
+[2026-10-01 16:06:22] [Rank 2] [INFO] [flow_factory.models.abc]: Set 160 trainable parameters to torch.bfloat16; frozen component policy -> {'text_encoder': None, 'transformer': None, 'vae': None}
+[2026-10-01 16:06:22] [Rank 2] [INFO] [flow_factory.models.loader]: Successfully loaded Flux2KleinAdapter
+[2026-10-01 16:06:22] [Rank 3] [INFO] [flow_factory.models.abc]: Set 160 trainable parameters to torch.bfloat16; frozen component policy -> {'text_encoder': None, 'transformer': None, 'vae': None}
+[2026-10-01 16:06:22] [Rank 6] [INFO] [flow_factory.models.abc]: Set 160 trainable parameters to torch.bfloat16; frozen component policy -> {'text_encoder': None, 'transformer': None, 'vae': None}
+[2026-10-01 16:06:22] [Rank 3] [INFO] [flow_factory.models.loader]: Successfully loaded Flux2KleinAdapter
+[2026-10-01 16:06:22] [Rank 6] [INFO] [flow_factory.models.loader]: Successfully loaded Flux2KleinAdapter
+[2026-10-01 16:06:22] [Rank 1] [INFO] [flow_factory.models.abc]: Set 160 trainable parameters to torch.bfloat16; frozen component policy -> {'text_encoder': None, 'transformer': None, 'vae': None}
+[2026-10-01 16:06:22] [Rank 4] [INFO] [flow_factory.models.abc]: Set 160 trainable parameters to torch.bfloat16; frozen component policy -> {'text_encoder': None, 'transformer': None, 'vae': None}
+[2026-10-01 16:06:22] [Rank 5] [INFO] [flow_factory.models.abc]: Set 160 trainable parameters to torch.bfloat16; frozen component policy -> {'text_encoder': None, 'transformer': None, 'vae': None}
+[2026-10-01 16:06:22] [Rank 0] [INFO] [flow_factory.models.abc]: Set 160 trainable parameters to torch.bfloat16; frozen component policy -> {'text_encoder': None, 'transformer': None, 'vae': None}
+[2026-10-01 16:06:22] [Rank 1] [INFO] [flow_factory.models.loader]: Successfully loaded Flux2KleinAdapter
+[2026-10-01 16:06:22] [Rank 7] [INFO] [flow_factory.models.abc]: Set 160 trainable parameters to torch.bfloat16; frozen component policy -> {'text_encoder': None, 'transformer': None, 'vae': None}
+[2026-10-01 16:06:22] [Rank 4] [INFO] [flow_factory.models.loader]: Successfully loaded Flux2KleinAdapter
+[2026-10-01 16:06:22] [Rank 5] [INFO] [flow_factory.models.loader]: Successfully loaded Flux2KleinAdapter
+[2026-10-01 16:06:22] [Rank 7] [INFO] [flow_factory.models.loader]: Successfully loaded Flux2KleinAdapter
+[2026-10-01 16:06:22] [Rank 0] [INFO] [flow_factory.models.abc]: Precision inventory stage=initialize component=text_encoder {'trainable': {}, 'frozen': {'torch.bfloat16': 4022468096}}
+[2026-10-01 16:06:22] [Rank 0] [INFO] [flow_factory.models.abc]: Precision inventory stage=initialize component=transformer {'trainable': {'torch.bfloat16': 76677120}, 'frozen': {'torch.float32': 3875544576}}
+[2026-10-01 16:06:22] [Rank 0] [INFO] [flow_factory.models.abc]: Precision inventory stage=initialize component=vae {'trainable': {}, 'frozen': {'torch.float32': 84046115}}
+[2026-10-01 16:06:22] [Rank 0] [INFO] [flow_factory.models.loader]: Successfully loaded Flux2KleinAdapter
+[2026-10-01 16:06:45] [Rank 1] [INFO] [flow_factory.data_utils.loader]: Preprocessing train dataset shard 0001/0007 -> /root/.cache/flow_factory/datasets/baae786eff625d7a251da3508a68a964.tmp/_parts/rank_0001_of_0008/cache-baae786eff625d7a251da3508a68a964_shard0001of0007.arrow
+[2026-10-01 16:06:45] [Rank 3] [INFO] [flow_factory.data_utils.loader]: Preprocessing train dataset shard 0003/0007 -> /root/.cache/flow_factory/datasets/baae786eff625d7a251da3508a68a964.tmp/_parts/rank_0003_of_0008/cache-baae786eff625d7a251da3508a68a964_shard0003of0007.arrow
+[2026-10-01 16:06:45] [Rank 7] [INFO] [flow_factory.data_utils.loader]: Preprocessing train dataset shard 0007/0007 -> /root/.cache/flow_factory/datasets/baae786eff625d7a251da3508a68a964.tmp/_parts/rank_0007_of_0008/cache-baae786eff625d7a251da3508a68a964_shard0007of0007.arrow
+[2026-10-01 16:06:45] [Rank 5] [INFO] [flow_factory.data_utils.loader]: Preprocessing train dataset shard 0005/0007 -> /root/.cache/flow_factory/datasets/baae786eff625d7a251da3508a68a964.tmp/_parts/rank_0005_of_0008/cache-baae786eff625d7a251da3508a68a964_shard0005of0007.arrow
+[2026-10-01 16:06:45] [Rank 2] [INFO] [flow_factory.data_utils.loader]: Preprocessing train dataset shard 0002/0007 -> /root/.cache/flow_factory/datasets/baae786eff625d7a251da3508a68a964.tmp/_parts/rank_0002_of_0008/cache-baae786eff625d7a251da3508a68a964_shard0002of0007.arrow
+[2026-10-01 16:06:45] [Rank 4] [INFO] [flow_factory.data_utils.loader]: Preprocessing train dataset shard 0004/0007 -> /root/.cache/flow_factory/datasets/baae786eff625d7a251da3508a68a964.tmp/_parts/rank_0004_of_0008/cache-baae786eff625d7a251da3508a68a964_shard0004of0007.arrow
+[2026-10-01 16:06:45] [Rank 6] [INFO] [flow_factory.data_utils.loader]: Preprocessing train dataset shard 0006/0007 -> /root/.cache/flow_factory/datasets/baae786eff625d7a251da3508a68a964.tmp/_parts/rank_0006_of_0008/cache-baae786eff625d7a251da3508a68a964_shard0006of0007.arrow
+[2026-10-01 16:06:45] [Rank 0] [INFO] [flow_factory.data_utils.loader]: Preprocessing train dataset shard 0000/0007 -> /root/.cache/flow_factory/datasets/baae786eff625d7a251da3508a68a964.tmp/_parts/rank_0000_of_0008/cache-baae786eff625d7a251da3508a68a964_shard0000of0007.arrow
+/opt/conda/envs/ff-qi21/lib/python3.10/contextlib.py:135: UserWarning: CUDA reports that you have 8 available devices, and you have used fork_rng without explicitly specifying which devices are being used. For safety, we initialize *every* CUDA device by default, which can be quite slow if you have a lot of CUDAs. If you know that you are only making use of a few CUDA devices, set the environment variable CUDA_VISIBLE_DEVICES or the 'devices' keyword argument of fork_rng with the set of devices you are actually using. For example, if you are using CPU only, set device.upper()_VISIBLE_DEVICES= or devices=[]; if you are using device 0 only, set CUDA_VISIBLE_DEVICES=0 or devices=[0].  To initialize all devices and suppress this warning, set the 'devices' keyword argument to `range(torch.cuda.device_count())`.
+  return next(self.gen)
+/opt/conda/envs/ff-qi21/lib/python3.10/contextlib.py:135: UserWarning: CUDA reports that you have 8 available devices, and you have used fork_rng without explicitly specifying which devices are being used. For safety, we initialize *every* CUDA device by default, which can be quite slow if you have a lot of CUDAs. If you know that you are only making use of a few CUDA devices, set the environment variable CUDA_VISIBLE_DEVICES or the 'devices' keyword argument of fork_rng with the set of devices you are actually using. For example, if you are using CPU only, set device.upper()_VISIBLE_DEVICES= or devices=[]; if you are using device 0 only, set CUDA_VISIBLE_DEVICES=0 or devices=[0].  To initialize all devices and suppress this warning, set the 'devices' keyword argument to `range(torch.cuda.device_count())`.
+  return next(self.gen)
+/opt/conda/envs/ff-qi21/lib/python3.10/contextlib.py:135: UserWarning: CUDA reports that you have 8 available devices, and you have used fork_rng without explicitly specifying which devices are being used. For safety, we initialize *every* CUDA device by default, which can be quite slow if you have a lot of CUDAs. If you know that you are only making use of a few CUDA devices, set the environment variable CUDA_VISIBLE_DEVICES or the 'devices' keyword argument of fork_rng with the set of devices you are actually using. For example, if you are using CPU only, set device.upper()_VISIBLE_DEVICES= or devices=[]; if you are using device 0 only, set CUDA_VISIBLE_DEVICES=0 or devices=[0].  To initialize all devices and suppress this warning, set the 'devices' keyword argument to `range(torch.cuda.device_count())`.
+  return next(self.gen)
+/opt/conda/envs/ff-qi21/lib/python3.10/contextlib.py:135: UserWarning: CUDA reports that you have 8 available devices, and you have used fork_rng without explicitly specifying which devices are being used. For safety, we initialize *every* CUDA device by default, which can be quite slow if you have a lot of CUDAs. If you know that you are only making use of a few CUDA devices, set the environment variable CUDA_VISIBLE_DEVICES or the 'devices' keyword argument of fork_rng with the set of devices you are actually using. For example, if you are using CPU only, set device.upper()_VISIBLE_DEVICES= or devices=[]; if you are using device 0 only, set CUDA_VISIBLE_DEVICES=0 or devices=[0].  To initialize all devices and suppress this warning, set the 'devices' keyword argument to `range(torch.cuda.device_count())`.
+  return next(self.gen)
+/opt/conda/envs/ff-qi21/lib/python3.10/contextlib.py:135: UserWarning: CUDA reports that you have 8 available devices, and you have used fork_rng without explicitly specifying which devices are being used. For safety, we initialize *every* CUDA device by default, which can be quite slow if you have a lot of CUDAs. If you know that you are only making use of a few CUDA devices, set the environment variable CUDA_VISIBLE_DEVICES or the 'devices' keyword argument of fork_rng with the set of devices you are actually using. For example, if you are using CPU only, set device.upper()_VISIBLE_DEVICES= or devices=[]; if you are using device 0 only, set CUDA_VISIBLE_DEVICES=0 or devices=[0].  To initialize all devices and suppress this warning, set the 'devices' keyword argument to `range(torch.cuda.device_count())`.
+  return next(self.gen)
+/opt/conda/envs/ff-qi21/lib/python3.10/contextlib.py:135: UserWarning: CUDA reports that you have 8 available devices, and you have used fork_rng without explicitly specifying which devices are being used. For safety, we initialize *every* CUDA device by default, which can be quite slow if you have a lot of CUDAs. If you know that you are only making use of a few CUDA devices, set the environment variable CUDA_VISIBLE_DEVICES or the 'devices' keyword argument of fork_rng with the set of devices you are actually using. For example, if you are using CPU only, set device.upper()_VISIBLE_DEVICES= or devices=[]; if you are using device 0 only, set CUDA_VISIBLE_DEVICES=0 or devices=[0].  To initialize all devices and suppress this warning, set the 'devices' keyword argument to `range(torch.cuda.device_count())`.
+  return next(self.gen)
+/opt/conda/envs/ff-qi21/lib/python3.10/contextlib.py:135: UserWarning: CUDA reports that you have 8 available devices, and you have used fork_rng without explicitly specifying which devices are being used. For safety, we initialize *every* CUDA device by default, which can be quite slow if you have a lot of CUDAs. If you know that you are only making use of a few CUDA devices, set the environment variable CUDA_VISIBLE_DEVICES or the 'devices' keyword argument of fork_rng with the set of devices you are actually using. For example, if you are using CPU only, set device.upper()_VISIBLE_DEVICES= or devices=[]; if you are using device 0 only, set CUDA_VISIBLE_DEVICES=0 or devices=[0].  To initialize all devices and suppress this warning, set the 'devices' keyword argument to `range(torch.cuda.device_count())`.
+  return next(self.gen)
+/opt/conda/envs/ff-qi21/lib/python3.10/contextlib.py:135: UserWarning: CUDA reports that you have 8 available devices, and you have used fork_rng without explicitly specifying which devices are being used. For safety, we initialize *every* CUDA device by default, which can be quite slow if you have a lot of CUDAs. If you know that you are only making use of a few CUDA devices, set the environment variable CUDA_VISIBLE_DEVICES or the 'devices' keyword argument of fork_rng with the set of devices you are actually using. For example, if you are using CPU only, set device.upper()_VISIBLE_DEVICES= or devices=[]; if you are using device 0 only, set CUDA_VISIBLE_DEVICES=0 or devices=[0].  To initialize all devices and suppress this warning, set the 'devices' keyword argument to `range(torch.cuda.device_count())`.
+  return next(self.gen)
+
[Preprocessing train dataset] Shard 0000/0007:   0%|          | 0/449 [00:00<?, ? examples/s]
[Preprocessing train dataset] Shard 0000/0007:   2%|▏         | 8/449 [00:02<02:25,  3.02 examples/s]
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[Preprocessing train dataset] Shard 0000/0007:  11%|█         | 48/449 [00:18<02:52,  2.33 examples/s]
[Preprocessing train dataset] Shard 0000/0007:  12%|█▏        | 56/449 [00:21<02:43,  2.40 examples/s]
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[Preprocessing train dataset] Shard 0000/0007:  16%|█▌        | 72/449 [00:27<02:24,  2.60 examples/s]
[Preprocessing train dataset] Shard 0000/0007:  18%|█▊        | 80/449 [00:29<02:15,  2.72 examples/s]
[Preprocessing train dataset] Shard 0000/0007:  20%|█▉        | 88/449 [00:32<02:07,  2.83 examples/s]
[Preprocessing train dataset] Shard 0000/0007:  21%|██▏       | 96/449 [00:35<02:04,  2.83 examples/s]
[Preprocessing train dataset] Shard 0000/0007:  23%|██▎       | 104/449 [00:38<02:00,  2.87 examples/s]
[Preprocessing train dataset] Shard 0000/0007:  25%|██▍       | 112/449 [00:40<01:55,  2.91 examples/s]
[Preprocessing train dataset] Shard 0000/0007:  27%|██▋       | 120/449 [00:43<01:48,  3.04 examples/s]
[Preprocessing train dataset] Shard 0000/0007:  29%|██▊       | 128/449 [00:45<01:44,  3.08 examples/s]
[Preprocessing train dataset] Shard 0000/0007:  30%|███       | 136/449 [00:48<01:42,  3.06 examples/s]
[Preprocessing train dataset] Shard 0000/0007:  32%|███▏      | 144/449 [00:50<01:40,  3.04 examples/s]
[Preprocessing train dataset] Shard 0000/0007:  34%|███▍      | 152/449 [00:53<01:38,  3.02 examples/s]
[Preprocessing train dataset] Shard 0000/0007:  36%|███▌      | 160/449 [00:56<01:34,  3.06 examples/s]
[Preprocessing train dataset] Shard 0000/0007:  37%|███▋      | 168/449 [00:58<01:31,  3.06 examples/s]
[Preprocessing train dataset] Shard 0000/0007:  39%|███▉      | 176/449 [01:01<01:29,  3.04 examples/s]
[Preprocessing train dataset] Shard 0000/0007:  41%|████      | 184/449 [01:04<01:27,  3.02 examples/s]
[Preprocessing train dataset] Shard 0000/0007:  43%|████▎     | 192/449 [01:06<01:25,  3.01 examples/s]
[Preprocessing train dataset] Shard 0000/0007:  45%|████▍     | 200/449 [01:09<01:22,  3.02 examples/s]
[Preprocessing train dataset] Shard 0000/0007:  46%|████▋     | 208/449 [01:11<01:18,  3.06 examples/s]
[Preprocessing train dataset] Shard 0000/0007:  48%|████▊     | 216/449 [01:14<01:16,  3.05 examples/s]
[Preprocessing train dataset] Shard 0000/0007:  50%|████▉     | 224/449 [01:18<01:26,  2.59 examples/s]
[Preprocessing train dataset] Shard 0000/0007:  52%|█████▏    | 232/449 [01:21<01:20,  2.69 examples/s]
[Preprocessing train dataset] Shard 0000/0007:  53%|█████▎    | 240/449 [01:24<01:15,  2.76 examples/s]
[Preprocessing train dataset] Shard 0000/0007:  55%|█████▌    | 248/449 [01:26<01:10,  2.85 examples/s]
[Preprocessing train dataset] Shard 0000/0007:  57%|█████▋    | 256/449 [01:29<01:06,  2.89 examples/s]
[Preprocessing train dataset] Shard 0000/0007:  59%|█████▉    | 264/449 [01:32<01:03,  2.92 examples/s]
[Preprocessing train dataset] Shard 0000/0007:  61%|██████    | 272/449 [01:34<00:59,  2.98 examples/s]
[Preprocessing train dataset] Shard 0000/0007:  62%|██████▏   | 280/449 [01:37<00:56,  3.01 examples/s]
[Preprocessing train dataset] Shard 0000/0007:  64%|██████▍   | 288/449 [01:40<00:53,  2.99 examples/s]
[Preprocessing train dataset] Shard 0000/0007:  66%|██████▌   | 296/449 [01:42<00:51,  2.99 examples/s]
[Preprocessing train dataset] Shard 0000/0007:  68%|██████▊   | 304/449 [01:45<00:48,  2.98 examples/s]
[Preprocessing train dataset] Shard 0000/0007:  69%|██████▉   | 312/449 [01:48<00:45,  2.99 examples/s]
[Preprocessing train dataset] Shard 0000/0007:  71%|███████▏  | 320/449 [01:50<00:43,  2.99 examples/s]
[Preprocessing train dataset] Shard 0000/0007:  73%|███████▎  | 328/449 [01:53<00:41,  2.95 examples/s]
[Preprocessing train dataset] Shard 0000/0007:  75%|███████▍  | 336/449 [01:56<00:38,  2.97 examples/s]
[Preprocessing train dataset] Shard 0000/0007:  77%|███████▋  | 344/449 [01:58<00:34,  3.02 examples/s]
[Preprocessing train dataset] Shard 0000/0007:  78%|███████▊  | 352/449 [02:01<00:31,  3.05 examples/s]
[Preprocessing train dataset] Shard 0000/0007:  80%|████████  | 360/449 [02:03<00:29,  3.06 examples/s]
[Preprocessing train dataset] Shard 0000/0007:  82%|████████▏ | 368/449 [02:06<00:26,  3.10 examples/s]
[Preprocessing train dataset] Shard 0000/0007:  84%|████████▎ | 376/449 [02:09<00:23,  3.08 examples/s]
[Preprocessing train dataset] Shard 0000/0007:  86%|████████▌ | 384/449 [02:11<00:21,  3.04 examples/s]
[Preprocessing train dataset] Shard 0000/0007:  87%|████████▋ | 392/449 [02:14<00:18,  3.04 examples/s]
[Preprocessing train dataset] Shard 0000/0007:  89%|████████▉ | 400/449 [02:18<00:19,  2.52 examples/s]
[Preprocessing train dataset] Shard 0000/0007:  91%|█████████ | 408/449 [02:21<00:15,  2.61 examples/s]
[Preprocessing train dataset] Shard 0000/0007:  93%|█████████▎| 416/449 [02:24<00:12,  2.73 examples/s]
[Preprocessing train dataset] Shard 0000/0007:  94%|█████████▍| 424/449 [02:26<00:08,  2.84 examples/s]
[Preprocessing train dataset] Shard 0000/0007:  96%|█████████▌| 432/449 [02:29<00:05,  2.90 examples/s]
[Preprocessing train dataset] Shard 0000/0007:  98%|█████████▊| 440/449 [02:32<00:03,  2.90 examples/s]
[Preprocessing train dataset] Shard 0000/0007: 100%|█████████▉| 448/449 [02:34<00:00,  2.89 examples/s]
[Preprocessing train dataset] Shard 0000/0007: 100%|██████████| 449/449 [02:35<00:00,  2.82 examples/s]
[Preprocessing train dataset] Shard 0000/0007: 100%|██████████| 449/449 [02:35<00:00,  2.89 examples/s]
+[2026-10-01 16:09:31] [Rank 0] [INFO] [flow_factory.data_utils.loader]: [local] Consolidated 8 part(s) for train split -> /root/.cache/flow_factory/datasets/baae786eff625d7a251da3508a68a964
+[2026-10-01 16:09:31] [Rank 5] [INFO] [flow_factory.data_utils.loader]: Multi-source: built DataLoader for 'flir_rl_full' (dataset_size=3599, unique_sample_num_per_epoch=48, num_batches_per_epoch=96)
+[2026-10-01 16:09:31] [Rank 7] [INFO] [flow_factory.data_utils.loader]: Multi-source: built DataLoader for 'flir_rl_full' (dataset_size=3599, unique_sample_num_per_epoch=48, num_batches_per_epoch=96)
+[2026-10-01 16:09:31] [Rank 1] [INFO] [flow_factory.data_utils.loader]: Multi-source: built DataLoader for 'flir_rl_full' (dataset_size=3599, unique_sample_num_per_epoch=48, num_batches_per_epoch=96)
+[2026-10-01 16:09:31] [Rank 4] [INFO] [flow_factory.data_utils.loader]: Multi-source: built DataLoader for 'flir_rl_full' (dataset_size=3599, unique_sample_num_per_epoch=48, num_batches_per_epoch=96)
+[2026-10-01 16:09:31] [Rank 0] [INFO] [flow_factory.data_utils.loader]: Multi-source: built DataLoader for 'flir_rl_full' (dataset_size=3599, unique_sample_num_per_epoch=48, num_batches_per_epoch=96)
+[2026-10-01 16:09:31] [Rank 2] [INFO] [flow_factory.data_utils.loader]: Multi-source: built DataLoader for 'flir_rl_full' (dataset_size=3599, unique_sample_num_per_epoch=48, num_batches_per_epoch=96)
+[2026-10-01 16:09:31] [Rank 3] [INFO] [flow_factory.data_utils.loader]: Multi-source: built DataLoader for 'flir_rl_full' (dataset_size=3599, unique_sample_num_per_epoch=48, num_batches_per_epoch=96)
+[2026-10-01 16:09:31] [Rank 6] [INFO] [flow_factory.data_utils.loader]: Multi-source: built DataLoader for 'flir_rl_full' (dataset_size=3599, unique_sample_num_per_epoch=48, num_batches_per_epoch=96)
+[2026-10-01 16:09:31] [Rank 3] [INFO] [flow_factory.data_utils.loader]: Preprocessing test dataset shard 0003/0007 -> /root/.cache/flow_factory/datasets/b5da67312a677dd412c582089e5bc14b.tmp/_parts/rank_0003_of_0008/cache-b5da67312a677dd412c582089e5bc14b_shard0003of0007.arrow
+[2026-10-01 16:09:31] [Rank 1] [INFO] [flow_factory.data_utils.loader]: Preprocessing test dataset shard 0001/0007 -> /root/.cache/flow_factory/datasets/b5da67312a677dd412c582089e5bc14b.tmp/_parts/rank_0001_of_0008/cache-b5da67312a677dd412c582089e5bc14b_shard0001of0007.arrow
+[2026-10-01 16:09:31] [Rank 2] [INFO] [flow_factory.data_utils.loader]: Preprocessing test dataset shard 0002/0007 -> /root/.cache/flow_factory/datasets/b5da67312a677dd412c582089e5bc14b.tmp/_parts/rank_0002_of_0008/cache-b5da67312a677dd412c582089e5bc14b_shard0002of0007.arrow
+[2026-10-01 16:09:31] [Rank 7] [INFO] [flow_factory.data_utils.loader]: Preprocessing test dataset shard 0007/0007 -> /root/.cache/flow_factory/datasets/b5da67312a677dd412c582089e5bc14b.tmp/_parts/rank_0007_of_0008/cache-b5da67312a677dd412c582089e5bc14b_shard0007of0007.arrow
+[2026-10-01 16:09:31] [Rank 5] [INFO] [flow_factory.data_utils.loader]: Preprocessing test dataset shard 0005/0007 -> /root/.cache/flow_factory/datasets/b5da67312a677dd412c582089e5bc14b.tmp/_parts/rank_0005_of_0008/cache-b5da67312a677dd412c582089e5bc14b_shard0005of0007.arrow
+[2026-10-01 16:09:31] [Rank 0] [INFO] [flow_factory.data_utils.loader]: Preprocessing test dataset shard 0000/0007 -> /root/.cache/flow_factory/datasets/b5da67312a677dd412c582089e5bc14b.tmp/_parts/rank_0000_of_0008/cache-b5da67312a677dd412c582089e5bc14b_shard0000of0007.arrow
+[2026-10-01 16:09:31] [Rank 6] [INFO] [flow_factory.data_utils.loader]: Preprocessing test dataset shard 0006/0007 -> /root/.cache/flow_factory/datasets/b5da67312a677dd412c582089e5bc14b.tmp/_parts/rank_0006_of_0008/cache-b5da67312a677dd412c582089e5bc14b_shard0006of0007.arrow
+[2026-10-01 16:09:31] [Rank 4] [INFO] [flow_factory.data_utils.loader]: Preprocessing test dataset shard 0004/0007 -> /root/.cache/flow_factory/datasets/b5da67312a677dd412c582089e5bc14b.tmp/_parts/rank_0004_of_0008/cache-b5da67312a677dd412c582089e5bc14b_shard0004of0007.arrow
+
[Preprocessing test dataset] Shard 0000/0007:   0%|          | 0/8 [00:00<?, ? examples/s]
[Preprocessing test dataset] Shard 0000/0007: 100%|██████████| 8/8 [00:02<00:00,  2.99 examples/s]
[Preprocessing test dataset] Shard 0000/0007: 100%|██████████| 8/8 [00:02<00:00,  2.98 examples/s]
+[2026-10-01 16:09:36] [Rank 0] [INFO] [flow_factory.data_utils.loader]: [local] Consolidated 8 part(s) for test split -> /root/.cache/flow_factory/datasets/b5da67312a677dd412c582089e5bc14b
+/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.
+  warnings.warn(
+/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.
+  warnings.warn(
+/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.
+  warnings.warn(
+/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.
+  warnings.warn(
+/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.
+  warnings.warn(
+/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.
+  warnings.warn(
+/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.
+  warnings.warn(
+/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.
+  warnings.warn(
+/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=VGG16_Weights.IMAGENET1K_V1`. You can also use `weights=VGG16_Weights.DEFAULT` to get the most up-to-date weights.
+  warnings.warn(msg)
+/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=VGG16_Weights.IMAGENET1K_V1`. You can also use `weights=VGG16_Weights.DEFAULT` to get the most up-to-date weights.
+  warnings.warn(msg)
+/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=VGG16_Weights.IMAGENET1K_V1`. You can also use `weights=VGG16_Weights.DEFAULT` to get the most up-to-date weights.
+  warnings.warn(msg)
+/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=VGG16_Weights.IMAGENET1K_V1`. You can also use `weights=VGG16_Weights.DEFAULT` to get the most up-to-date weights.
+  warnings.warn(msg)
+/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=VGG16_Weights.IMAGENET1K_V1`. You can also use `weights=VGG16_Weights.DEFAULT` to get the most up-to-date weights.
+  warnings.warn(msg)
+/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=VGG16_Weights.IMAGENET1K_V1`. You can also use `weights=VGG16_Weights.DEFAULT` to get the most up-to-date weights.
+  warnings.warn(msg)
+/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=VGG16_Weights.IMAGENET1K_V1`. You can also use `weights=VGG16_Weights.DEFAULT` to get the most up-to-date weights.
+  warnings.warn(msg)
+/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=VGG16_Weights.IMAGENET1K_V1`. You can also use `weights=VGG16_Weights.DEFAULT` to get the most up-to-date weights.
+  warnings.warn(msg)
+wandb: [wandb.login()] Loaded credentials for https://api.wandb.ai from /root/.netrc.
+wandb: Currently logged in as: 315229706 (315229706-xi-an-jiaotong-university-) to https://api.wandb.ai. Use `wandb login --relogin` to force relogin
+wandb: setting up run 3ze7q3px
+wandb: Tracking run with wandb version 0.28.2
+wandb: Run data is saved locally in /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/runs/klein4b_grpo_flir_full_ssim_dists/wandb/run-20261001_160954-3ze7q3px
+wandb: Run `wandb offline` to turn off syncing.
+wandb: Syncing run klein4b_grpo_flir_full_ssim_dists
+wandb: ⭐️ View project at https://wandb.ai/315229706-xi-an-jiaotong-university-/IR-Engine
+wandb: 🚀 View run at https://wandb.ai/315229706-xi-an-jiaotong-university-/IR-Engine/runs/3ze7q3px
+[2026-10-01 16:09:55] [Rank 0] [INFO] [flow_factory.models.abc]: ======================================================================
+[2026-10-01 16:09:55] [Rank 0] [INFO] [flow_factory.models.abc]: Transformer Trainable Parameters:
+[2026-10-01 16:09:55] [Rank 0] [INFO] [flow_factory.models.abc]:   Total:        3,952,221,696 (  7.36 GB)
+[2026-10-01 16:09:55] [Rank 0] [INFO] [flow_factory.models.abc]:   Trainable:       76,677,120 (  0.14 GB)
+[2026-10-01 16:09:55] [Rank 0] [INFO] [flow_factory.models.abc]:   Percentage:           1.94%
+[2026-10-01 16:09:55] [Rank 0] [INFO] [flow_factory.models.abc]: ======================================================================
+[2026-10-01 16:09:55] [Rank 0] [INFO] [flow_factory.models.abc]: Saving LoRA weights for transformer role=base to /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/runs/klein4b_grpo_flir_full_ssim_dists/saves/klein4b_grpo_flir_full_ssim_dists/checkpoints/checkpoint-0
+[2026-10-01 16:09:55] [Rank 0] [INFO] [flow_factory.models.abc]: Checkpoint saved successfully to /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/runs/klein4b_grpo_flir_full_ssim_dists/saves/klein4b_grpo_flir_full_ssim_dists/checkpoints/checkpoint-0
+
Eval/flir_rl_full:   0%|          | 0/8 [00:00<?, ?it/s][2026-10-01 16:09:56] [Rank 1] [WARNING] [flow_factory.models.flux.flux2_klein]: FLUX.2 does not support batch inference with varying condition images per sample. Falling back to single-sample inference. This warning will only appear once.
+[2026-10-01 16:09:56] [Rank 5] [WARNING] [flow_factory.models.flux.flux2_klein]: FLUX.2 does not support batch inference with varying condition images per sample. Falling back to single-sample inference. This warning will only appear once.
+[2026-10-01 16:09:56] [Rank 4] [WARNING] [flow_factory.models.flux.flux2_klein]: FLUX.2 does not support batch inference with varying condition images per sample. Falling back to single-sample inference. This warning will only appear once.
+[2026-10-01 16:09:56] [Rank 3] [WARNING] [flow_factory.models.flux.flux2_klein]: FLUX.2 does not support batch inference with varying condition images per sample. Falling back to single-sample inference. This warning will only appear once.
+[2026-10-01 16:09:56] [Rank 2] [WARNING] [flow_factory.models.flux.flux2_klein]: FLUX.2 does not support batch inference with varying condition images per sample. Falling back to single-sample inference. This warning will only appear once.
+[2026-10-01 16:09:56] [Rank 6] [WARNING] [flow_factory.models.flux.flux2_klein]: FLUX.2 does not support batch inference with varying condition images per sample. Falling back to single-sample inference. This warning will only appear once.
+[2026-10-01 16:09:56] [Rank 0] [WARNING] [flow_factory.models.flux.flux2_klein]: FLUX.2 does not support batch inference with varying condition images per sample. Falling back to single-sample inference. This warning will only appear once.
+[2026-10-01 16:09:56] [Rank 7] [WARNING] [flow_factory.models.flux.flux2_klein]: FLUX.2 does not support batch inference with varying condition images per sample. Falling back to single-sample inference. This warning will only appear once.
+
Eval/flir_rl_full:  12%|█▎        | 1/8 [00:07<00:52,  7.49s/it]
Eval/flir_rl_full:  25%|██▌       | 2/8 [00:13<00:40,  6.77s/it]
Eval/flir_rl_full:  38%|███▊      | 3/8 [00:20<00:32,  6.56s/it]
Eval/flir_rl_full:  50%|█████     | 4/8 [00:26<00:25,  6.46s/it]
Eval/flir_rl_full:  62%|██████▎   | 5/8 [00:32<00:19,  6.40s/it]
Eval/flir_rl_full:  75%|███████▌  | 6/8 [00:38<00:12,  6.37s/it]
Eval/flir_rl_full:  88%|████████▊ | 7/8 [00:45<00:06,  6.35s/it]
Eval/flir_rl_full: 100%|██████████| 8/8 [00:51<00:00,  6.44s/it]
Eval/flir_rl_full: 100%|██████████| 8/8 [00:51<00:00,  6.49s/it]
+
Pointwise Rewards: ssim:   0%|          | 0/1 [00:00<?, ?it/s]
Pointwise Rewards: ssim: 100%|██████████| 1/1 [00:00<00:00,  6.21it/s]
Pointwise Rewards: ssim: 100%|██████████| 1/1 [00:00<00:00,  6.18it/s]
+
Pointwise Rewards: psnr:   0%|          | 0/1 [00:00<?, ?it/s]
Pointwise Rewards: psnr: 100%|██████████| 1/1 [00:00<00:00, 10.94it/s]
+
Pointwise Rewards: dists:   0%|          | 0/1 [00:00<?, ?it/s]
Pointwise Rewards: dists: 100%|██████████| 1/1 [00:00<00:00,  6.77it/s]
Pointwise Rewards: dists: 100%|██████████| 1/1 [00:00<00:00,  6.75it/s]
+[2026-10-01 16:10:49] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0000 | Epoch 000] eval/flir_rl_full/reward_dists_mean=0.8262 eval/flir_rl_full/reward_dists_std=0.0368 eval/flir_rl_full/reward_psnr_mean=19.3257 eval/flir_rl_full/reward_psnr_std=3.0044 eval/flir_rl_full/reward_ssim_mean=0.5843 eval/flir_rl_full/reward_ssim_std=0.0870
+
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+[2026-10-01 16:13:52] [Rank 5] [WARNING] [flow_factory.models.flux.flux2_klein]: Flux.2: Ragged I2I detected (varying condition image sizes). Processing samples individually (warning shown once).
+[2026-10-01 16:13:52] [Rank 2] [WARNING] [flow_factory.models.flux.flux2_klein]: Flux.2: Ragged I2I detected (varying condition image sizes). Processing samples individually (warning shown once).
+[2026-10-01 16:13:52] [Rank 6] [WARNING] [flow_factory.models.flux.flux2_klein]: Flux.2: Ragged I2I detected (varying condition image sizes). Processing samples individually (warning shown once).
+[2026-10-01 16:13:52] [Rank 3] [WARNING] [flow_factory.models.flux.flux2_klein]: Flux.2: Ragged I2I detected (varying condition image sizes). Processing samples individually (warning shown once).
+[2026-10-01 16:13:52] [Rank 4] [WARNING] [flow_factory.models.flux.flux2_klein]: Flux.2: Ragged I2I detected (varying condition image sizes). Processing samples individually (warning shown once).
+[2026-10-01 16:13:52] [Rank 7] [WARNING] [flow_factory.models.flux.flux2_klein]: Flux.2: Ragged I2I detected (varying condition image sizes). Processing samples individually (warning shown once).
+[2026-10-01 16:13:52] [Rank 1] [WARNING] [flow_factory.models.flux.flux2_klein]: Flux.2: Ragged I2I detected (varying condition image sizes). Processing samples individually (warning shown once).
+[2026-10-01 16:13:53] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0000 | Epoch 000] train/reward_dists_mean=0.8222 train/reward_dists_std=0.0354 train/reward_ssim_mean=0.6332 train/reward_ssim_std=0.1017 train/reward_dists_group_std_mean=0.0222 train/reward_dists_group_std_max=0.0478 train/reward_dists_group_std_min=0.0085 train/reward_dists_group_mean_std=0.0259 train/reward_ssim_group_std_mean=0.0249 train/reward_ssim_group_std_max=0.0626 train/reward_ssim_group_std_min=0.0079 train/reward_ssim_group_mean_std=0.0976 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.8663 train/adv_max=4.7276 train/adv_abs_mean=1.2672
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+
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Epoch 0 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 16:14:18] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0000 | Epoch 000] train/clipped_loss_min=-3.7856 train/clipped_loss_max=5 train/clipped_loss_mean=0.0276 train/clipped_loss_std=1.5963 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-3.7856 train/unclipped_loss_max=5 train/unclipped_loss_mean=0.0276 train/unclipped_loss_std=1.5963 train/policy_loss=0.0276 train/loss=0.0276 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0001
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Epoch 0 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 16:14:43] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0001 | Epoch 000] train/clipped_loss_min=-4.7274 train/clipped_loss_max=4.9372 train/clipped_loss_mean=-0.0298 train/clipped_loss_std=1.6135 train/ratio_min=0.9999 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0.0002 train/unclipped_loss_min=-4.7274 train/unclipped_loss_max=4.9372 train/unclipped_loss_mean=-0.0298 train/unclipped_loss_std=1.6135 train/policy_loss=-0.0298 train/loss=-0.0298 train/clip_frac_high=0 train/clip_frac_low=0.0078 train/clip_frac_total=0.0078 train/grad_norm=0.0001
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+[2026-10-01 16:14:43] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0002 | Epoch 000] timing/rollout_seconds=180.0828 timing/feedback_seconds=5.8269 timing/optimization_seconds=50.5602 timing/cycle_seconds=233.9142
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+
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+[2026-10-01 16:17:46] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0002 | Epoch 001] train/reward_dists_mean=0.8221 train/reward_dists_std=0.0319 train/reward_ssim_mean=0.5410 train/reward_ssim_std=0.0993 train/reward_dists_group_std_mean=0.0124 train/reward_dists_group_std_max=0.0264 train/reward_dists_group_std_min=0.0044 train/reward_dists_group_mean_std=0.0289 train/reward_ssim_group_std_mean=0.0321 train/reward_ssim_group_std_max=0.0548 train/reward_ssim_group_std_min=0.0146 train/reward_ssim_group_mean_std=0.0935 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.0906 train/adv_max=5.4891 train/adv_abs_mean=1.2695
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Epoch 1 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 16:18:11] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0002 | Epoch 001] train/clipped_loss_min=-5 train/clipped_loss_max=4.2619 train/clipped_loss_mean=0.0166 train/clipped_loss_std=1.5677 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-5 train/unclipped_loss_max=4.2619 train/unclipped_loss_mean=0.0166 train/unclipped_loss_std=1.5677 train/policy_loss=0.0166 train/loss=0.0166 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
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Epoch 1 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 16:18:36] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0003 | Epoch 001] train/clipped_loss_min=-4.9995 train/clipped_loss_max=4.9995 train/clipped_loss_mean=-0.0145 train/clipped_loss_std=1.6366 train/ratio_min=0.9997 train/ratio_max=1.0000 train/ratio_mean=0.9999 train/ratio_std=0 train/unclipped_loss_min=-4.9994 train/unclipped_loss_max=4.9992 train/unclipped_loss_mean=-0.0145 train/unclipped_loss_std=1.6366 train/policy_loss=-0.0145 train/loss=-0.0145 train/clip_frac_high=0 train/clip_frac_low=0.5286 train/clip_frac_total=0.5286 train/grad_norm=0.0006
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+[2026-10-01 16:18:36] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0004 | Epoch 001] timing/rollout_seconds=179.4805 timing/feedback_seconds=5.4118 timing/optimization_seconds=50.5645 timing/cycle_seconds=233.2501
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+
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+[2026-10-01 16:21:39] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0004 | Epoch 002] train/reward_dists_mean=0.8159 train/reward_dists_std=0.0414 train/reward_ssim_mean=0.5398 train/reward_ssim_std=0.0946 train/reward_dists_group_std_mean=0.0086 train/reward_dists_group_std_max=0.0173 train/reward_dists_group_std_min=0.0057 train/reward_dists_group_mean_std=0.0404 train/reward_ssim_group_std_mean=0.0175 train/reward_ssim_group_std_max=0.0352 train/reward_ssim_group_std_min=0.0088 train/reward_ssim_group_mean_std=0.0928 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.4060 train/adv_max=4.0082 train/adv_abs_mean=1.2173
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Epoch 2 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 16:22:04] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0004 | Epoch 002] train/clipped_loss_min=-3.5220 train/clipped_loss_max=4.4060 train/clipped_loss_mean=-0.0011 train/clipped_loss_std=1.4594 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-3.5220 train/unclipped_loss_max=4.4060 train/unclipped_loss_mean=-0.0011 train/unclipped_loss_std=1.4594 train/policy_loss=-0.0011 train/loss=-0.0011 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0002
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Epoch 2 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 16:22:29] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0005 | Epoch 002] train/clipped_loss_min=-4.0079 train/clipped_loss_max=4.3866 train/clipped_loss_mean=0.0011 train/clipped_loss_std=1.5809 train/ratio_min=0.9998 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-4.0079 train/unclipped_loss_max=4.3866 train/unclipped_loss_mean=0.0011 train/unclipped_loss_std=1.5809 train/policy_loss=0.0011 train/loss=0.0011 train/clip_frac_high=0 train/clip_frac_low=0.0495 train/clip_frac_total=0.0495 train/grad_norm=0.0002
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+[2026-10-01 16:22:29] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0006 | Epoch 002] timing/rollout_seconds=179.5099 timing/feedback_seconds=5.0769 timing/optimization_seconds=50.4656 timing/cycle_seconds=233.1677
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+
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+[2026-10-01 16:25:32] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0006 | Epoch 003] train/reward_dists_mean=0.8139 train/reward_dists_std=0.0308 train/reward_ssim_mean=0.5509 train/reward_ssim_std=0.0857 train/reward_dists_group_std_mean=0.0089 train/reward_dists_group_std_max=0.0161 train/reward_dists_group_std_min=0.0037 train/reward_dists_group_mean_std=0.0294 train/reward_ssim_group_std_mean=0.0136 train/reward_ssim_group_std_max=0.0246 train/reward_ssim_group_std_min=0.0073 train/reward_ssim_group_mean_std=0.0845 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.6642 train/adv_max=3.9344 train/adv_abs_mean=1.1415
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Epoch 3 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 16:25:57] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0006 | Epoch 003] train/clipped_loss_min=-3.5664 train/clipped_loss_max=3.8895 train/clipped_loss_mean=-0.0093 train/clipped_loss_std=1.4640 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-3.5664 train/unclipped_loss_max=3.8895 train/unclipped_loss_mean=-0.0093 train/unclipped_loss_std=1.4640 train/policy_loss=-0.0093 train/loss=-0.0093 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
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Epoch 3 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 16:26:22] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0007 | Epoch 003] train/clipped_loss_min=-3.9343 train/clipped_loss_max=4.9998 train/clipped_loss_mean=0.0076 train/clipped_loss_std=1.4212 train/ratio_min=0.9999 train/ratio_max=1.0000 train/ratio_mean=0.9999 train/ratio_std=0.0002 train/unclipped_loss_min=-3.9343 train/unclipped_loss_max=4.9998 train/unclipped_loss_mean=0.0076 train/unclipped_loss_std=1.4212 train/policy_loss=0.0076 train/loss=0.0076 train/clip_frac_high=0 train/clip_frac_low=0.0547 train/clip_frac_total=0.0547 train/grad_norm=0.0003
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+[2026-10-01 16:26:22] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0008 | Epoch 003] timing/rollout_seconds=179.6280 timing/feedback_seconds=5.2913 timing/optimization_seconds=50.4222 timing/cycle_seconds=233.2074
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+
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+[2026-10-01 16:29:26] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0008 | Epoch 004] train/reward_dists_mean=0.8103 train/reward_dists_std=0.0275 train/reward_ssim_mean=0.5444 train/reward_ssim_std=0.0782 train/reward_dists_group_std_mean=0.0087 train/reward_dists_group_std_max=0.0176 train/reward_dists_group_std_min=0.0039 train/reward_dists_group_mean_std=0.0259 train/reward_ssim_group_std_mean=0.0110 train/reward_ssim_group_std_max=0.0202 train/reward_ssim_group_std_min=0.0048 train/reward_ssim_group_mean_std=0.0774 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.7717 train/adv_max=4.0828 train/adv_abs_mean=1.2187
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Epoch 4 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 16:29:51] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0008 | Epoch 004] train/clipped_loss_min=-3.3486 train/clipped_loss_max=4.7717 train/clipped_loss_mean=0.1236 train/clipped_loss_std=1.5306 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-3.3486 train/unclipped_loss_max=4.7717 train/unclipped_loss_mean=0.1236 train/unclipped_loss_std=1.5306 train/policy_loss=0.1236 train/loss=0.1236 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0001
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Epoch 4 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 16:30:16] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0009 | Epoch 004] train/clipped_loss_min=-4.0828 train/clipped_loss_max=4.7588 train/clipped_loss_mean=-0.1236 train/clipped_loss_std=1.5091 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0.0001 train/unclipped_loss_min=-4.0828 train/unclipped_loss_max=4.7588 train/unclipped_loss_mean=-0.1236 train/unclipped_loss_std=1.5091 train/policy_loss=-0.1236 train/loss=-0.1236 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0001
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+[2026-10-01 16:30:16] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0010 | Epoch 004] timing/rollout_seconds=179.6463 timing/feedback_seconds=5.3915 timing/optimization_seconds=50.5202 timing/cycle_seconds=233.3484
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+[2026-10-01 16:33:19] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0010 | Epoch 005] train/reward_dists_mean=0.8011 train/reward_dists_std=0.0280 train/reward_ssim_mean=0.5901 train/reward_ssim_std=0.0957 train/reward_dists_group_std_mean=0.0080 train/reward_dists_group_std_max=0.0156 train/reward_dists_group_std_min=0.0039 train/reward_dists_group_mean_std=0.0267 train/reward_ssim_group_std_mean=0.0083 train/reward_ssim_group_std_max=0.0261 train/reward_ssim_group_std_min=0.0032 train/reward_ssim_group_mean_std=0.0953 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.4059 train/adv_max=3.7405 train/adv_abs_mean=1.2297
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Epoch 5 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 16:33:44] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0010 | Epoch 005] train/clipped_loss_min=-3.6884 train/clipped_loss_max=4.8063 train/clipped_loss_mean=0.0916 train/clipped_loss_std=1.5381 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-3.6884 train/unclipped_loss_max=4.8063 train/unclipped_loss_mean=0.0916 train/unclipped_loss_std=1.5381 train/policy_loss=0.0916 train/loss=0.0916 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0002
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Epoch 5 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 16:34:09] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0011 | Epoch 005] train/clipped_loss_min=-3.7404 train/clipped_loss_max=4.9995 train/clipped_loss_mean=-0.0926 train/clipped_loss_std=1.5393 train/ratio_min=0.9999 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-3.7404 train/unclipped_loss_max=4.9995 train/unclipped_loss_mean=-0.0926 train/unclipped_loss_std=1.5393 train/policy_loss=-0.0926 train/loss=-0.0926 train/clip_frac_high=0 train/clip_frac_low=0.0026 train/clip_frac_total=0.0026 train/grad_norm=0.0002
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+[2026-10-01 16:34:09] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0012 | Epoch 005] timing/rollout_seconds=179.6175 timing/feedback_seconds=5.3550 timing/optimization_seconds=50.6242 timing/cycle_seconds=233.3032
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+[2026-10-01 16:37:13] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0012 | Epoch 006] train/reward_dists_mean=0.7979 train/reward_dists_std=0.0374 train/reward_ssim_mean=0.5618 train/reward_ssim_std=0.1031 train/reward_dists_group_std_mean=0.0067 train/reward_dists_group_std_max=0.0131 train/reward_dists_group_std_min=0.0034 train/reward_dists_group_mean_std=0.0367 train/reward_ssim_group_std_mean=0.0071 train/reward_ssim_group_std_max=0.0117 train/reward_ssim_group_std_min=0.0034 train/reward_ssim_group_mean_std=0.1028 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.3354 train/adv_max=4.6015 train/adv_abs_mean=1.2351
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Epoch 6 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 16:37:37] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0012 | Epoch 006] train/clipped_loss_min=-3.5868 train/clipped_loss_max=4.1845 train/clipped_loss_mean=-0.0101 train/clipped_loss_std=1.5128 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-3.5868 train/unclipped_loss_max=4.1845 train/unclipped_loss_mean=-0.0101 train/unclipped_loss_std=1.5128 train/policy_loss=-0.0101 train/loss=-0.0101 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0004
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Epoch 6 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 16:38:03] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0013 | Epoch 006] train/clipped_loss_min=-4.6011 train/clipped_loss_max=4.3351 train/clipped_loss_mean=0.0100 train/clipped_loss_std=1.5513 train/ratio_min=0.9998 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-4.6011 train/unclipped_loss_max=4.3351 train/unclipped_loss_mean=0.0100 train/unclipped_loss_std=1.5513 train/policy_loss=0.0100 train/loss=0.0100 train/clip_frac_high=0 train/clip_frac_low=0.0130 train/clip_frac_total=0.0130 train/grad_norm=0.0004
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+[2026-10-01 16:38:03] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0014 | Epoch 006] timing/rollout_seconds=179.6248 timing/feedback_seconds=5.6677 timing/optimization_seconds=50.8968 timing/cycle_seconds=233.7590
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+[2026-10-01 16:41:08] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0014 | Epoch 007] train/reward_dists_mean=0.7838 train/reward_dists_std=0.0285 train/reward_ssim_mean=0.5755 train/reward_ssim_std=0.0896 train/reward_dists_group_std_mean=0.0069 train/reward_dists_group_std_max=0.0120 train/reward_dists_group_std_min=0.0041 train/reward_dists_group_mean_std=0.0276 train/reward_ssim_group_std_mean=0.0068 train/reward_ssim_group_std_max=0.0157 train/reward_ssim_group_std_min=0.0033 train/reward_ssim_group_mean_std=0.0893 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.4514 train/adv_max=4.6710 train/adv_abs_mean=1.2678
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Epoch 7 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 16:41:33] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0014 | Epoch 007] train/clipped_loss_min=-4.6710 train/clipped_loss_max=5 train/clipped_loss_mean=-0.0677 train/clipped_loss_std=1.6078 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-4.6710 train/unclipped_loss_max=5 train/unclipped_loss_mean=-0.0677 train/unclipped_loss_std=1.6078 train/policy_loss=-0.0677 train/loss=-0.0677 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0004
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Epoch 7 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 16:41:58] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0015 | Epoch 007] train/clipped_loss_min=-4.0357 train/clipped_loss_max=4.1909 train/clipped_loss_mean=0.0666 train/clipped_loss_std=1.5535 train/ratio_min=0.9998 train/ratio_max=1.0000 train/ratio_mean=0.9999 train/ratio_std=0.0002 train/unclipped_loss_min=-4.0357 train/unclipped_loss_max=4.1909 train/unclipped_loss_mean=0.0666 train/unclipped_loss_std=1.5534 train/policy_loss=0.0666 train/loss=0.0666 train/clip_frac_high=0 train/clip_frac_low=0.3464 train/clip_frac_total=0.3464 train/grad_norm=0.0005
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+[2026-10-01 16:41:58] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0016 | Epoch 007] timing/rollout_seconds=179.5506 timing/feedback_seconds=6.9585 timing/optimization_seconds=50.4339 timing/cycle_seconds=234.9011
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+
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+[2026-10-01 16:45:01] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0016 | Epoch 008] train/reward_dists_mean=0.7667 train/reward_dists_std=0.0363 train/reward_ssim_mean=0.5774 train/reward_ssim_std=0.1055 train/reward_dists_group_std_mean=0.0068 train/reward_dists_group_std_max=0.0098 train/reward_dists_group_std_min=0.0047 train/reward_dists_group_mean_std=0.0357 train/reward_ssim_group_std_mean=0.0071 train/reward_ssim_group_std_max=0.0175 train/reward_ssim_group_std_min=0.0036 train/reward_ssim_group_mean_std=0.1052 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.4829 train/adv_max=4.3100 train/adv_abs_mean=1.2543
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+
                                                               
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+
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+
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+
                                                               
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+
Epoch 8 Timestep:   0%|          | 0/1 [00:00<?, ?it/s]W1001 16:45:16.984000 2822096 site-packages/torch/distributed/elastic/agent/server/api.py:739] Received Signals.SIGTERM death signal, shutting down workers
+W1001 16:45:16.998000 2822096 site-packages/torch/distributed/elastic/multiprocessing/api.py:1010] Sending process 2822281 closing signal SIGTERM
+W1001 16:45:16.998000 2822096 site-packages/torch/distributed/elastic/multiprocessing/api.py:1010] Sending process 2822282 closing signal SIGTERM
+W1001 16:45:16.999000 2822096 site-packages/torch/distributed/elastic/multiprocessing/api.py:1010] Sending process 2822283 closing signal SIGTERM
+W1001 16:45:16.999000 2822096 site-packages/torch/distributed/elastic/multiprocessing/api.py:1010] Sending process 2822284 closing signal SIGTERM
+W1001 16:45:16.999000 2822096 site-packages/torch/distributed/elastic/multiprocessing/api.py:1010] Sending process 2822285 closing signal SIGTERM
+W1001 16:45:16.999000 2822096 site-packages/torch/distributed/elastic/multiprocessing/api.py:1010] Sending process 2822286 closing signal SIGTERM
+W1001 16:45:17.000000 2822096 site-packages/torch/distributed/elastic/multiprocessing/api.py:1010] Sending process 2822287 closing signal SIGTERM
+W1001 16:45:17.000000 2822096 site-packages/torch/distributed/elastic/multiprocessing/api.py:1010] Sending process 2822288 closing signal SIGTERM
+Traceback (most recent call last):
+  File "/opt/conda/envs/ff-qi21/bin/accelerate", line 6, in <module>
+    sys.exit(main())
+  File "/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/accelerate/commands/accelerate_cli.py", line 50, in main
+    args.func(args)
+  File "/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/accelerate/commands/launch.py", line 1392, in launch_command
+    deepspeed_launcher(args)
+  File "/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/accelerate/commands/launch.py", line 1076, in deepspeed_launcher
+    distrib_run.run(args)
+  File "/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torch/distributed/run.py", line 982, in run
+    elastic_launch(
+  File "/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torch/distributed/launcher/api.py", line 170, in __call__
+    return launch_agent(self._config, self._entrypoint, list(args))
+  File "/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torch/distributed/launcher/api.py", line 308, in launch_agent
+    result = agent.run()
+  File "/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torch/distributed/elastic/metrics/api.py", line 134, in wrapper
+    result = f(*args, **kwargs)
+  File "/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torch/distributed/elastic/agent/server/api.py", line 731, in run
+    result = self._invoke_run(role)
+  File "/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torch/distributed/elastic/agent/server/api.py", line 908, in _invoke_run
+    time.sleep(monitor_interval)
+  File "/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torch/distributed/elastic/multiprocessing/api.py", line 86, in _terminate_process_handler
+    raise SignalException(f"Process {os.getpid()} got signal: {sigval}", sigval=sigval)
+torch.distributed.elastic.multiprocessing.api.SignalException: Process 2822096 got signal: 15
diff --git a/runs/klein4b_grpo_flir_full_ssim_dists_lr5e-5/saves/klein4b_grpo_flir_full_ssim_dists_lr5e-5/checkpoints/checkpoint-180/README.md b/runs/klein4b_grpo_flir_full_ssim_dists_lr5e-5/saves/klein4b_grpo_flir_full_ssim_dists_lr5e-5/checkpoints/checkpoint-180/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..2b78a7a995873d5db3090fca56885fe89cdf97e1
--- /dev/null
+++ b/runs/klein4b_grpo_flir_full_ssim_dists_lr5e-5/saves/klein4b_grpo_flir_full_ssim_dists_lr5e-5/checkpoints/checkpoint-180/README.md
@@ -0,0 +1,204 @@
+---
+base_model: /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/flow_factory_gpu_gate_models/FLUX.2-klein-base-4B/transformer
+library_name: peft
+tags:
+- lora
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]
+### Framework versions
+
+- PEFT 0.20.0
\ No newline at end of file
diff --git a/runs/klein4b_grpo_flir_full_ssim_dists_lr5e-5/saves/klein4b_grpo_flir_full_ssim_dists_lr5e-5/checkpoints/checkpoint-180/adapter_config.json b/runs/klein4b_grpo_flir_full_ssim_dists_lr5e-5/saves/klein4b_grpo_flir_full_ssim_dists_lr5e-5/checkpoints/checkpoint-180/adapter_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..ac2ab86ab8cf3a235d89b1a56c2fd2e78517a625
--- /dev/null
+++ b/runs/klein4b_grpo_flir_full_ssim_dists_lr5e-5/saves/klein4b_grpo_flir_full_ssim_dists_lr5e-5/checkpoints/checkpoint-180/adapter_config.json
@@ -0,0 +1,59 @@
+{
+  "alora_invocation_tokens": null,
+  "alpha_pattern": {},
+  "arrow_config": null,
+  "auto_mapping": {
+    "base_model_class": "Flux2Transformer2DModel",
+    "parent_library": "diffusers.models.transformers.transformer_flux2"
+  },
+  "base_model_name_or_path": null,
+  "bias": "none",
+  "corda_config": null,
+  "ensure_weight_tying": false,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": "gaussian",
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 128,
+  "lora_bias": false,
+  "lora_dropout": 0.0,
+  "lora_ga_config": null,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "monteclora_config": null,
+  "peft_type": "LORA",
+  "peft_version": "0.20.0",
+  "qalora_group_size": 16,
+  "r": 64,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "attn.to_q",
+    "attn.to_out.0",
+    "ff.linear_in",
+    "attn.to_k",
+    "attn.add_k_proj",
+    "attn.to_add_out",
+    "ff.linear_out",
+    "attn.to_qkv_mlp_proj",
+    "ff_context.linear_in",
+    "attn.add_v_proj",
+    "ff_context.linear_out",
+    "attn.to_v",
+    "attn.add_q_proj"
+  ],
+  "target_parameters": null,
+  "task_type": null,
+  "trainable_token_indices": null,
+  "use_bdlora": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false,
+  "velora_config": null
+}
\ No newline at end of file
diff --git a/runs/klein4b_grpo_flir_full_ssim_dists_lr5e-5/saves/klein4b_grpo_flir_full_ssim_dists_lr5e-5/checkpoints/checkpoint-180/manifest.json b/runs/klein4b_grpo_flir_full_ssim_dists_lr5e-5/saves/klein4b_grpo_flir_full_ssim_dists_lr5e-5/checkpoints/checkpoint-180/manifest.json
new file mode 100644
index 0000000000000000000000000000000000000000..4443fd995e0f6f3bd9b7e3dacea93df8d345ecfb
--- /dev/null
+++ b/runs/klein4b_grpo_flir_full_ssim_dists_lr5e-5/saves/klein4b_grpo_flir_full_ssim_dists_lr5e-5/checkpoints/checkpoint-180/manifest.json
@@ -0,0 +1,16 @@
+{
+  "base_role": "base",
+  "entries": [
+    {
+      "component": "transformer",
+      "path": ".",
+      "role": "base"
+    }
+  ],
+  "finetune_type": "lora",
+  "format_version": 1,
+  "primary": {
+    "component": "transformer",
+    "path": "."
+  }
+}
diff --git a/runs/klein4b_grpo_flir_full_ssim_dists_lr5e-5/train.log b/runs/klein4b_grpo_flir_full_ssim_dists_lr5e-5/train.log
new file mode 100644
index 0000000000000000000000000000000000000000..c6dcf5403288937fbc76c7dd826b2c455420b0ca
--- /dev/null
+++ b/runs/klein4b_grpo_flir_full_ssim_dists_lr5e-5/train.log
@@ -0,0 +1,59701 @@
+[2026-10-01 16:45:25,409] [INFO] [flow_factory.cli]: ============================================================
+[2026-10-01 16:45:25,409] [INFO] [flow_factory.cli]: Flow-Factory Launch Configuration
+[2026-10-01 16:45:25,409] [INFO] [flow_factory.cli]:   Num machines:       1
+[2026-10-01 16:45:25,409] [INFO] [flow_factory.cli]:   Machine rank:       0
+[2026-10-01 16:45:25,410] [INFO] [flow_factory.cli]:   Num processes:      8
+[2026-10-01 16:45:25,410] [INFO] [flow_factory.cli]:   Master node:        localhost:29513
+[2026-10-01 16:45:25,410] [INFO] [flow_factory.cli]:   Mixed precision:    bf16
+[2026-10-01 16:45:25,410] [INFO] [flow_factory.cli]:   Accelerate config:  config/deepspeed/deepspeed_zero2.yaml
+[2026-10-01 16:45:25,410] [INFO] [flow_factory.cli]: ============================================================
+W1001 16:45:33.225000 2847381 site-packages/torch/distributed/run.py:852] 
+W1001 16:45:33.225000 2847381 site-packages/torch/distributed/run.py:852] *****************************************
+W1001 16:45:33.225000 2847381 site-packages/torch/distributed/run.py:852] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
+W1001 16:45:33.225000 2847381 site-packages/torch/distributed/run.py:852] *****************************************
+[2026-10-01 16:45:40] [Rank 0] [WARNING] [flow_factory.hparams.abc]: GRPOTrainingArguments.from_dict captured 1 unknown key(s) into extra_kwargs: ['condition_image_size']. Verify these are intentional (e.g., adapter-specific kwargs); typos against declared fields will be silently accepted otherwise.
+[2026-10-01 16:45:40] [Rank 0] [INFO] [flow_factory.hparams.training_args._base]: World Size: 8
+[2026-10-01 16:45:40] [Rank 0] [WARNING] [flow_factory.hparams.abc]: EvaluationArguments.from_dict captured 1 unknown key(s) into extra_kwargs: ['condition_image_size']. Verify these are intentional (e.g., adapter-specific kwargs); typos against declared fields will be silently accepted otherwise.
+[2026-10-01 16:45:40,157] [INFO] [flow_factory.train]: ====================================================================================================
+[2026-10-01 16:45:40,157] [INFO] [flow_factory.train]: Flow-Factory Training Initialized
+[2026-10-01 16:45:40,157] [INFO] [flow_factory.train]: World Size: 8
+[2026-10-01 16:45:40,157] [INFO] [flow_factory.train]: ====================================================================================================
+[2026-10-01 16:45:40,157] [INFO] [flow_factory.train]: Config: /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/runs/klein4b_grpo_flir_full_ssim_dists_lr5e-5/train.yaml
+[2026-10-01 16:45:40,164] [INFO] [flow_factory.train]: 
+launcher: accelerate
+config_file: config/deepspeed/deepspeed_zero2.yaml
+num_processes: 8
+main_process_port: 29513
+mixed_precision: bf16
+data:
+  dataset_dir: data
+  cache_dir: ~/.cache/flow_factory/datasets
+  image_dir: null
+  video_dir: null
+  audio_dir: null
+  preprocessing_batch_size: 8
+  dataloader_num_workers: 8
+  enable_preprocess: true
+  force_reprocess: false
+  max_dataset_size: null
+  preprocess_parallelism: local
+  sampler_type: group_contiguous
+  sampler_subgroup_size: null
+  datasets:
+  - extra_kwargs: {}
+    name: flir_rl_full
+    dataset_dir: /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/flir_rl_full
+    image_dir: null
+    video_dir: null
+    audio_dir: null
+    train:
+      extra_kwargs: {}
+      enabled: true
+      split: train
+      weight: 1
+      max_dataset_size: null
+      unique_sample_num_per_epoch: 48
+      num_batches_per_epoch: null
+    eval:
+      extra_kwargs: {}
+      enabled: true
+      split: test
+      max_dataset_size: null
+      resolution: null
+      num_inference_steps: null
+      guidance_scale: null
+    source_id: 0
+model:
+  model_name_or_path: /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/flow_factory_gpu_gate_models/FLUX.2-klein-base-4B
+  finetune_type: lora
+  trainable_parameters_dtype: bfloat16
+  component_load_dtypes: null
+  frozen_parameters_dtype: null
+  target_components:
+  - transformer
+  target_modules: default
+  model_type: flux2-klein
+  lora_rank: 64
+  lora_alpha: 128
+  resume_path: /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/runs/klein4b_sft_flir_full_r64a128_lr1e-4/saves/klein4b_sft_flir_full_r64a128_lr1e-4/checkpoints/checkpoint-12
+  resume_type: lora
+scheduler:
+  dynamics_type: Flow-SDE
+  noise_level: 0.7
+  num_sde_steps: 1
+  sde_steps:
+  - 1
+  - 2
+  - 3
+  seed: 42
+training:
+  trainer_type: grpo
+  resolution:
+  - 512
+  - 640
+  height: 512
+  width: 640
+  num_frames: null
+  frame_rate: null
+  max_epochs: 200
+  per_device_batch_size: 1
+  gradient_step_per_epoch: 2
+  max_grad_norm: 1.0
+  num_batches_per_epoch: 96
+  gradient_accumulation_steps: 48
+  num_inner_epochs: 1
+  group_size: 16
+  unique_sample_num_per_epoch: 48
+  num_inference_steps: 8
+  guidance_scale: 1.0
+  ref_param_device: cuda
+  seed: 42
+  learning_rate: 1.0e-05
+  adam_weight_decay: 0.0001
+  adam_betas:
+  - 0.9
+  - 0.999
+  adam_epsilon: 1.0e-08
+  enable_gradient_checkpointing: false
+  offload_samples_to_cpu: false
+  ema_decay: 0.9
+  ema_update_interval: 4
+  ema_device: cuda
+  ema_decay_schedule: power
+  latent_storage_dtype: fp16
+  shuffle_samples: true
+  reward_optimization_overlap: false
+  reward_optimization_overlap_mode: ready
+  reward_optimization_overlap_poll_interval: 0.05
+  global_std: false
+  advantage_aggregation: gdpo
+  clip_range:
+  - -0.0001
+  - 0.0001
+  adv_clip_range:
+  - -5.0
+  - 5.0
+  kl_type: v-based
+  kl_beta: 0.0
+  condition_image_size: 1024
+eval:
+  resolution:
+  - 512
+  - 640
+  height: 512
+  width: 640
+  num_frames: null
+  frame_rate: null
+  per_device_batch_size: 1
+  seed: 42
+  guidance_scale: 1.0
+  num_inference_steps: 28
+  eval_freq: 10
+  condition_image_size: 1024
+log:
+  run_name: klein4b_grpo_flir_full_ssim_dists_lr5e-5
+  project: IR-Engine
+  logging_backend: wandb
+  save_dir: /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/runs/klein4b_grpo_flir_full_ssim_dists_lr5e-5/saves
+  save_freq: 20
+  save_model_only: true
+  verbose: true
+acceleration:
+  shared: []
+  rollout: []
+reward:
+  reward_0:
+    name: ssim
+    reward_model: ir_rewards.fidelity.SSIMReward
+    weight:
+      flir_rl_full: 1.0
+    dtype: float32
+    device: cuda
+    batch_size: 16
+    async_reward: false
+    num_workers: 1
+    applicable_datasets:
+    - flir_rl_full
+  reward_1:
+    name: dists
+    reward_model: ir_rewards.fidelity.DISTSReward
+    weight:
+      flir_rl_full: 1.0
+    dtype: float32
+    device: cuda
+    batch_size: 16
+    async_reward: false
+    num_workers: 1
+    applicable_datasets:
+    - flir_rl_full
+eval_reward:
+  reward_0:
+    name: ssim
+    reward_model: ir_rewards.fidelity.SSIMReward
+    weight:
+      flir_rl_full: 1.0
+    dtype: float32
+    device: cuda
+    batch_size: 16
+    async_reward: false
+    num_workers: 1
+    applicable_datasets:
+    - flir_rl_full
+  reward_1:
+    name: psnr
+    reward_model: ir_rewards.fidelity.PSNRReward
+    weight:
+      flir_rl_full: 1.0
+    dtype: float32
+    device: cuda
+    batch_size: 16
+    async_reward: false
+    num_workers: 1
+    applicable_datasets:
+    - flir_rl_full
+  reward_2:
+    name: dists
+    reward_model: ir_rewards.fidelity.DISTSReward
+    weight:
+      flir_rl_full: 1.0
+    dtype: float32
+    device: cuda
+    batch_size: 16
+    async_reward: false
+    num_workers: 1
+    applicable_datasets:
+    - flir_rl_full
+optimizer:
+  optimizer_configs:
+  - extra_kwargs: {}
+    name: default
+    optimizer: adamw
+    learning_rate: 5.0e-05
+    weight_decay: 0.0001
+    max_grad_norm: 1.0
+    update_frequency: 1
+    betas:
+    - 0.9
+    - 0.999
+    eps: 1.0e-08
+
+[2026-10-01 16:45:40,165] [INFO] [flow_factory.train]: ====================================================================================================
+[2026-10-01 16:45:49] [Rank 5] [INFO] [flow_factory.models.loader]: Loading model architecture: flux2-klein...
+[2026-10-01 16:45:49] [Rank 7] [INFO] [flow_factory.models.loader]: Loading model architecture: flux2-klein...
+[2026-10-01 16:45:49] [Rank 6] [INFO] [flow_factory.models.loader]: Loading model architecture: flux2-klein...
+[2026-10-01 16:45:49] [Rank 1] [INFO] [flow_factory.models.loader]: Loading model architecture: flux2-klein...
+[2026-10-01 16:45:49] [Rank 3] [INFO] [flow_factory.models.loader]: Loading model architecture: flux2-klein...
+[2026-10-01 16:45:49] [Rank 4] [INFO] [flow_factory.models.loader]: Loading model architecture: flux2-klein...
+[2026-10-01 16:45:49] [Rank 2] [INFO] [flow_factory.models.loader]: Loading model architecture: flux2-klein...
+[2026-10-01 16:45:49,658] [WARNING] [accelerate.utils.other]: [RANK 0] Detected kernel version 5.4.241, which is below the recommended minimum of 5.5.0; this can cause the process to hang. It is recommended to upgrade the kernel to the minimum version or higher.
+[2026-10-01 16:45:49] [Rank 0] [INFO] [flow_factory.models.loader]: Loading model architecture: flux2-klein...
+
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+
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+
Loading pipeline components...:  40%|████      | 2/5 [00:25<00:44, 14.75s/it][2026-10-01 16:46:15] [Rank 2] [INFO] [flow_factory.scheduler.loader]: Loaded SDE scheduler: FlowMatchEulerDiscreteSDEScheduler
+
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+[2026-10-01 16:46:15] [Rank 7] [INFO] [flow_factory.scheduler.loader]: Loaded SDE scheduler: FlowMatchEulerDiscreteSDEScheduler
+
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+
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+
Loading pipeline components...: 100%|██████████| 5/5 [00:26<00:00,  4.05s/it][2026-10-01 16:46:16] [Rank 3] [INFO] [flow_factory.scheduler.loader]: Loaded SDE scheduler: FlowMatchEulerDiscreteSDEScheduler
+
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+[2026-10-01 16:46:16] [Rank 0] [INFO] [flow_factory.scheduler.loader]: Loaded SDE scheduler: FlowMatchEulerDiscreteSDEScheduler
+[2026-10-01 16:46:16] [Rank 5] [INFO] [flow_factory.scheduler.loader]: Loaded SDE scheduler: FlowMatchEulerDiscreteSDEScheduler
+
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+[2026-10-01 16:46:16] [Rank 4] [INFO] [flow_factory.scheduler.loader]: Loaded SDE scheduler: FlowMatchEulerDiscreteSDEScheduler
+
Loading weights: 100%|██████████| 398/398 [00:00<00:00, 8974.43it/s]
+[2026-10-01 16:46:16] [Rank 1] [INFO] [flow_factory.scheduler.loader]: Loaded SDE scheduler: FlowMatchEulerDiscreteSDEScheduler
+[2026-10-01 16:46:16] [Rank 0] [INFO] [flow_factory.models.abc]: LoRA adapter loaded for transformer role=base from /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/runs/klein4b_sft_flir_full_r64a128_lr1e-4/saves/klein4b_sft_flir_full_r64a128_lr1e-4/checkpoints/checkpoint-12
+[2026-10-01 16:46:17] [Rank 0] [INFO] [flow_factory.models.abc]: Checkpoint loaded successfully from /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/runs/klein4b_sft_flir_full_r64a128_lr1e-4/saves/klein4b_sft_flir_full_r64a128_lr1e-4/checkpoints/checkpoint-12 (type=lora)
+[2026-10-01 16:46:17] [Rank 1] [INFO] [flow_factory.models.abc]: Froze ALL transformer parameters
+[2026-10-01 16:46:17] [Rank 6] [INFO] [flow_factory.models.abc]: Froze ALL transformer parameters
+[2026-10-01 16:46:17] [Rank 4] [INFO] [flow_factory.models.abc]: Froze ALL transformer parameters
+[2026-10-01 16:46:17] [Rank 3] [INFO] [flow_factory.models.abc]: Froze ALL transformer parameters
+[2026-10-01 16:46:17] [Rank 7] [INFO] [flow_factory.models.abc]: Froze ALL transformer parameters
+[2026-10-01 16:46:17] [Rank 2] [INFO] [flow_factory.models.abc]: Froze ALL transformer parameters
+[2026-10-01 16:46:17] [Rank 1] [INFO] [flow_factory.models.abc]: Component transformer already has 'default' adapter. Skipping initialization but enabling gradients.
+[2026-10-01 16:46:17] [Rank 6] [INFO] [flow_factory.models.abc]: Component transformer already has 'default' adapter. Skipping initialization but enabling gradients.
+[2026-10-01 16:46:17] [Rank 0] [INFO] [flow_factory.models.abc]: Froze ALL transformer parameters
+[2026-10-01 16:46:17] [Rank 5] [INFO] [flow_factory.models.abc]: Froze ALL transformer parameters
+[2026-10-01 16:46:17] [Rank 4] [INFO] [flow_factory.models.abc]: Component transformer already has 'default' adapter. Skipping initialization but enabling gradients.
+[2026-10-01 16:46:17] [Rank 3] [INFO] [flow_factory.models.abc]: Component transformer already has 'default' adapter. Skipping initialization but enabling gradients.
+[2026-10-01 16:46:17] [Rank 7] [INFO] [flow_factory.models.abc]: Component transformer already has 'default' adapter. Skipping initialization but enabling gradients.
+[2026-10-01 16:46:17] [Rank 2] [INFO] [flow_factory.models.abc]: Component transformer already has 'default' adapter. Skipping initialization but enabling gradients.
+[2026-10-01 16:46:17] [Rank 0] [INFO] [flow_factory.models.abc]: Component transformer already has 'default' adapter. Skipping initialization but enabling gradients.
+[2026-10-01 16:46:17] [Rank 5] [INFO] [flow_factory.models.abc]: Component transformer already has 'default' adapter. Skipping initialization but enabling gradients.
+[2026-10-01 16:46:17] [Rank 1] [INFO] [flow_factory.models.abc]: Set 160 trainable parameters to torch.bfloat16; frozen component policy -> {'text_encoder': None, 'transformer': None, 'vae': None}
+[2026-10-01 16:46:17] [Rank 6] [INFO] [flow_factory.models.abc]: Set 160 trainable parameters to torch.bfloat16; frozen component policy -> {'text_encoder': None, 'transformer': None, 'vae': None}
+[2026-10-01 16:46:17] [Rank 1] [INFO] [flow_factory.models.loader]: Successfully loaded Flux2KleinAdapter
+[2026-10-01 16:46:17] [Rank 6] [INFO] [flow_factory.models.loader]: Successfully loaded Flux2KleinAdapter
+[2026-10-01 16:46:17] [Rank 3] [INFO] [flow_factory.models.abc]: Set 160 trainable parameters to torch.bfloat16; frozen component policy -> {'text_encoder': None, 'transformer': None, 'vae': None}
+[2026-10-01 16:46:17] [Rank 3] [INFO] [flow_factory.models.loader]: Successfully loaded Flux2KleinAdapter
+[2026-10-01 16:46:17] [Rank 4] [INFO] [flow_factory.models.abc]: Set 160 trainable parameters to torch.bfloat16; frozen component policy -> {'text_encoder': None, 'transformer': None, 'vae': None}
+[2026-10-01 16:46:17] [Rank 7] [INFO] [flow_factory.models.abc]: Set 160 trainable parameters to torch.bfloat16; frozen component policy -> {'text_encoder': None, 'transformer': None, 'vae': None}
+[2026-10-01 16:46:17] [Rank 4] [INFO] [flow_factory.models.loader]: Successfully loaded Flux2KleinAdapter
+[2026-10-01 16:46:17] [Rank 2] [INFO] [flow_factory.models.abc]: Set 160 trainable parameters to torch.bfloat16; frozen component policy -> {'text_encoder': None, 'transformer': None, 'vae': None}
+[2026-10-01 16:46:17] [Rank 7] [INFO] [flow_factory.models.loader]: Successfully loaded Flux2KleinAdapter
+[2026-10-01 16:46:17] [Rank 0] [INFO] [flow_factory.models.abc]: Set 160 trainable parameters to torch.bfloat16; frozen component policy -> {'text_encoder': None, 'transformer': None, 'vae': None}
+[2026-10-01 16:46:17] [Rank 5] [INFO] [flow_factory.models.abc]: Set 160 trainable parameters to torch.bfloat16; frozen component policy -> {'text_encoder': None, 'transformer': None, 'vae': None}
+[2026-10-01 16:46:17] [Rank 2] [INFO] [flow_factory.models.loader]: Successfully loaded Flux2KleinAdapter
+[2026-10-01 16:46:17] [Rank 5] [INFO] [flow_factory.models.loader]: Successfully loaded Flux2KleinAdapter
+[2026-10-01 16:46:17] [Rank 0] [INFO] [flow_factory.models.abc]: Precision inventory stage=initialize component=text_encoder {'trainable': {}, 'frozen': {'torch.bfloat16': 4022468096}}
+[2026-10-01 16:46:17] [Rank 0] [INFO] [flow_factory.models.abc]: Precision inventory stage=initialize component=transformer {'trainable': {'torch.bfloat16': 76677120}, 'frozen': {'torch.float32': 3875544576}}
+[2026-10-01 16:46:17] [Rank 0] [INFO] [flow_factory.models.abc]: Precision inventory stage=initialize component=vae {'trainable': {}, 'frozen': {'torch.float32': 84046115}}
+[2026-10-01 16:46:17] [Rank 0] [INFO] [flow_factory.models.loader]: Successfully loaded Flux2KleinAdapter
+[2026-10-01 16:46:42] [Rank 3] [INFO] [flow_factory.data_utils.loader]: Multi-source: built DataLoader for 'flir_rl_full' (dataset_size=3599, unique_sample_num_per_epoch=48, num_batches_per_epoch=96)
+[2026-10-01 16:46:43] [Rank 2] [INFO] [flow_factory.data_utils.loader]: Multi-source: built DataLoader for 'flir_rl_full' (dataset_size=3599, unique_sample_num_per_epoch=48, num_batches_per_epoch=96)
+[2026-10-01 16:46:43] [Rank 6] [INFO] [flow_factory.data_utils.loader]: Multi-source: built DataLoader for 'flir_rl_full' (dataset_size=3599, unique_sample_num_per_epoch=48, num_batches_per_epoch=96)
+[2026-10-01 16:46:43] [Rank 0] [INFO] [flow_factory.data_utils.loader]: Loading train dataset from merged cache: /root/.cache/flow_factory/datasets/baae786eff625d7a251da3508a68a964
+[2026-10-01 16:46:43] [Rank 7] [INFO] [flow_factory.data_utils.loader]: Multi-source: built DataLoader for 'flir_rl_full' (dataset_size=3599, unique_sample_num_per_epoch=48, num_batches_per_epoch=96)
+[2026-10-01 16:46:43] [Rank 1] [INFO] [flow_factory.data_utils.loader]: Multi-source: built DataLoader for 'flir_rl_full' (dataset_size=3599, unique_sample_num_per_epoch=48, num_batches_per_epoch=96)
+[2026-10-01 16:46:43] [Rank 5] [INFO] [flow_factory.data_utils.loader]: Multi-source: built DataLoader for 'flir_rl_full' (dataset_size=3599, unique_sample_num_per_epoch=48, num_batches_per_epoch=96)
+[2026-10-01 16:46:43] [Rank 0] [INFO] [flow_factory.data_utils.loader]: Multi-source: built DataLoader for 'flir_rl_full' (dataset_size=3599, unique_sample_num_per_epoch=48, num_batches_per_epoch=96)
+[2026-10-01 16:46:43] [Rank 4] [INFO] [flow_factory.data_utils.loader]: Multi-source: built DataLoader for 'flir_rl_full' (dataset_size=3599, unique_sample_num_per_epoch=48, num_batches_per_epoch=96)
+[2026-10-01 16:46:43] [Rank 0] [INFO] [flow_factory.data_utils.loader]: Loading test dataset from merged cache: /root/.cache/flow_factory/datasets/b5da67312a677dd412c582089e5bc14b
+/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.
+  warnings.warn(
+/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.
+  warnings.warn(
+/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.
+  warnings.warn(
+/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.
+  warnings.warn(
+/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.
+  warnings.warn(
+/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.
+  warnings.warn(
+/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.
+  warnings.warn(
+/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.
+  warnings.warn(
+/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=VGG16_Weights.IMAGENET1K_V1`. You can also use `weights=VGG16_Weights.DEFAULT` to get the most up-to-date weights.
+  warnings.warn(msg)
+/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=VGG16_Weights.IMAGENET1K_V1`. You can also use `weights=VGG16_Weights.DEFAULT` to get the most up-to-date weights.
+  warnings.warn(msg)
+/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=VGG16_Weights.IMAGENET1K_V1`. You can also use `weights=VGG16_Weights.DEFAULT` to get the most up-to-date weights.
+  warnings.warn(msg)
+/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=VGG16_Weights.IMAGENET1K_V1`. You can also use `weights=VGG16_Weights.DEFAULT` to get the most up-to-date weights.
+  warnings.warn(msg)
+/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=VGG16_Weights.IMAGENET1K_V1`. You can also use `weights=VGG16_Weights.DEFAULT` to get the most up-to-date weights.
+  warnings.warn(msg)
+/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=VGG16_Weights.IMAGENET1K_V1`. You can also use `weights=VGG16_Weights.DEFAULT` to get the most up-to-date weights.
+  warnings.warn(msg)
+/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=VGG16_Weights.IMAGENET1K_V1`. You can also use `weights=VGG16_Weights.DEFAULT` to get the most up-to-date weights.
+  warnings.warn(msg)
+/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=VGG16_Weights.IMAGENET1K_V1`. You can also use `weights=VGG16_Weights.DEFAULT` to get the most up-to-date weights.
+  warnings.warn(msg)
+wandb: [wandb.login()] Loaded credentials for https://api.wandb.ai from /root/.netrc.
+wandb: Currently logged in as: 315229706 (315229706-xi-an-jiaotong-university-) to https://api.wandb.ai. Use `wandb login --relogin` to force relogin
+wandb: setting up run juvo095a
+wandb: Tracking run with wandb version 0.28.2
+wandb: Run data is saved locally in /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/runs/klein4b_grpo_flir_full_ssim_dists_lr5e-5/wandb/run-20261001_164700-juvo095a
+wandb: Run `wandb offline` to turn off syncing.
+wandb: Syncing run klein4b_grpo_flir_full_ssim_dists_lr5e-5
+wandb: ⭐️ View project at https://wandb.ai/315229706-xi-an-jiaotong-university-/IR-Engine
+wandb: 🚀 View run at https://wandb.ai/315229706-xi-an-jiaotong-university-/IR-Engine/runs/juvo095a
+[2026-10-01 16:47:04] [Rank 0] [INFO] [flow_factory.models.abc]: ======================================================================
+[2026-10-01 16:47:04] [Rank 0] [INFO] [flow_factory.models.abc]: Transformer Trainable Parameters:
+[2026-10-01 16:47:04] [Rank 0] [INFO] [flow_factory.models.abc]:   Total:        3,952,221,696 (  7.36 GB)
+[2026-10-01 16:47:04] [Rank 0] [INFO] [flow_factory.models.abc]:   Trainable:       76,677,120 (  0.14 GB)
+[2026-10-01 16:47:04] [Rank 0] [INFO] [flow_factory.models.abc]:   Percentage:           1.94%
+[2026-10-01 16:47:04] [Rank 0] [INFO] [flow_factory.models.abc]: ======================================================================
+[2026-10-01 16:47:04] [Rank 0] [INFO] [flow_factory.models.abc]: Saving LoRA weights for transformer role=base to /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/runs/klein4b_grpo_flir_full_ssim_dists_lr5e-5/saves/klein4b_grpo_flir_full_ssim_dists_lr5e-5/checkpoints/checkpoint-0
+[2026-10-01 16:47:04] [Rank 0] [INFO] [flow_factory.models.abc]: Checkpoint saved successfully to /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/runs/klein4b_grpo_flir_full_ssim_dists_lr5e-5/saves/klein4b_grpo_flir_full_ssim_dists_lr5e-5/checkpoints/checkpoint-0
+
Eval/flir_rl_full:   0%|          | 0/8 [00:00<?, ?it/s][2026-10-01 16:47:05] [Rank 1] [WARNING] [flow_factory.models.flux.flux2_klein]: FLUX.2 does not support batch inference with varying condition images per sample. Falling back to single-sample inference. This warning will only appear once.
+[2026-10-01 16:47:05] [Rank 2] [WARNING] [flow_factory.models.flux.flux2_klein]: FLUX.2 does not support batch inference with varying condition images per sample. Falling back to single-sample inference. This warning will only appear once.
+[2026-10-01 16:47:05] [Rank 7] [WARNING] [flow_factory.models.flux.flux2_klein]: FLUX.2 does not support batch inference with varying condition images per sample. Falling back to single-sample inference. This warning will only appear once.
+[2026-10-01 16:47:05] [Rank 5] [WARNING] [flow_factory.models.flux.flux2_klein]: FLUX.2 does not support batch inference with varying condition images per sample. Falling back to single-sample inference. This warning will only appear once.
+[2026-10-01 16:47:05] [Rank 6] [WARNING] [flow_factory.models.flux.flux2_klein]: FLUX.2 does not support batch inference with varying condition images per sample. Falling back to single-sample inference. This warning will only appear once.
+[2026-10-01 16:47:06] [Rank 3] [WARNING] [flow_factory.models.flux.flux2_klein]: FLUX.2 does not support batch inference with varying condition images per sample. Falling back to single-sample inference. This warning will only appear once.
+[2026-10-01 16:47:06] [Rank 4] [WARNING] [flow_factory.models.flux.flux2_klein]: FLUX.2 does not support batch inference with varying condition images per sample. Falling back to single-sample inference. This warning will only appear once.
+[2026-10-01 16:47:06] [Rank 0] [WARNING] [flow_factory.models.flux.flux2_klein]: FLUX.2 does not support batch inference with varying condition images per sample. Falling back to single-sample inference. This warning will only appear once.
+
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Eval/flir_rl_full: 100%|██████████| 8/8 [00:52<00:00,  6.54s/it]
+
Pointwise Rewards: ssim:   0%|          | 0/1 [00:00<?, ?it/s]
Pointwise Rewards: ssim: 100%|██████████| 1/1 [00:00<00:00,  6.04it/s]
Pointwise Rewards: ssim: 100%|██████████| 1/1 [00:00<00:00,  6.02it/s]
+
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Pointwise Rewards: psnr: 100%|██████████| 1/1 [00:00<00:00, 10.83it/s]
+
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Pointwise Rewards: dists: 100%|██████████| 1/1 [00:00<00:00,  6.82it/s]
+[2026-10-01 16:47:58] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0000 | Epoch 000] eval/flir_rl_full/reward_dists_mean=0.8262 eval/flir_rl_full/reward_dists_std=0.0368 eval/flir_rl_full/reward_psnr_mean=19.3257 eval/flir_rl_full/reward_psnr_std=3.0044 eval/flir_rl_full/reward_ssim_mean=0.5843 eval/flir_rl_full/reward_ssim_std=0.0870
+
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+
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Pointwise Rewards: ssim:  83%|████████▎ | 5/6 [00:00<00:00,  5.33it/s]
Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:01<00:00,  5.53it/s]
Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:01<00:00,  5.47it/s]
+
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Pointwise Rewards: dists:  83%|████████▎ | 5/6 [00:01<00:00,  3.21it/s]
Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.27it/s]
Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.25it/s]
+[2026-10-01 16:51:00] [Rank 3] [WARNING] [flow_factory.models.flux.flux2_klein]: Flux.2: Ragged I2I detected (varying condition image sizes). Processing samples individually (warning shown once).
+[2026-10-01 16:51:00] [Rank 4] [WARNING] [flow_factory.models.flux.flux2_klein]: Flux.2: Ragged I2I detected (varying condition image sizes). Processing samples individually (warning shown once).
+[2026-10-01 16:51:00] [Rank 6] [WARNING] [flow_factory.models.flux.flux2_klein]: Flux.2: Ragged I2I detected (varying condition image sizes). Processing samples individually (warning shown once).
+[2026-10-01 16:51:00] [Rank 2] [WARNING] [flow_factory.models.flux.flux2_klein]: Flux.2: Ragged I2I detected (varying condition image sizes). Processing samples individually (warning shown once).
+[2026-10-01 16:51:00] [Rank 7] [WARNING] [flow_factory.models.flux.flux2_klein]: Flux.2: Ragged I2I detected (varying condition image sizes). Processing samples individually (warning shown once).
+[2026-10-01 16:51:00] [Rank 1] [WARNING] [flow_factory.models.flux.flux2_klein]: Flux.2: Ragged I2I detected (varying condition image sizes). Processing samples individually (warning shown once).
+[2026-10-01 16:51:00] [Rank 5] [WARNING] [flow_factory.models.flux.flux2_klein]: Flux.2: Ragged I2I detected (varying condition image sizes). Processing samples individually (warning shown once).
+[2026-10-01 16:51:01] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0000 | Epoch 000] train/reward_dists_mean=0.8222 train/reward_dists_std=0.0354 train/reward_ssim_mean=0.6332 train/reward_ssim_std=0.1017 train/reward_dists_group_std_mean=0.0222 train/reward_dists_group_std_max=0.0478 train/reward_dists_group_std_min=0.0085 train/reward_dists_group_mean_std=0.0259 train/reward_ssim_group_std_mean=0.0249 train/reward_ssim_group_std_max=0.0626 train/reward_ssim_group_std_min=0.0079 train/reward_ssim_group_mean_std=0.0976 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.8663 train/adv_max=4.7276 train/adv_abs_mean=1.2672
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Epoch 0 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 16:51:26] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0000 | Epoch 000] train/clipped_loss_min=-3.7856 train/clipped_loss_max=5 train/clipped_loss_mean=0.0276 train/clipped_loss_std=1.5963 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-3.7856 train/unclipped_loss_max=5 train/unclipped_loss_mean=0.0276 train/unclipped_loss_std=1.5963 train/policy_loss=0.0276 train/loss=0.0276 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0001
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Epoch 0 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 16:51:51] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0001 | Epoch 000] train/clipped_loss_min=-4.7276 train/clipped_loss_max=4.9373 train/clipped_loss_mean=-0.0298 train/clipped_loss_std=1.6136 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0.0003 train/unclipped_loss_min=-4.7276 train/unclipped_loss_max=4.9373 train/unclipped_loss_mean=-0.0298 train/unclipped_loss_std=1.6136 train/policy_loss=-0.0298 train/loss=-0.0298 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0001
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+[2026-10-01 16:51:51] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0002 | Epoch 000] timing/rollout_seconds=179.4287 timing/feedback_seconds=4.8964 timing/optimization_seconds=50.6045 timing/cycle_seconds=233.0336
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Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:01<00:00,  5.43it/s]
+
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Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.27it/s]
+[2026-10-01 16:54:54] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0002 | Epoch 001] train/reward_dists_mean=0.8307 train/reward_dists_std=0.0366 train/reward_ssim_mean=0.6377 train/reward_ssim_std=0.1141 train/reward_dists_group_std_mean=0.0220 train/reward_dists_group_std_max=0.0416 train/reward_dists_group_std_min=0.0072 train/reward_dists_group_mean_std=0.0277 train/reward_ssim_group_std_mean=0.0237 train/reward_ssim_group_std_max=0.0587 train/reward_ssim_group_std_min=0.0099 train/reward_ssim_group_mean_std=0.1110 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-6.0304 train/adv_max=6.2218 train/adv_abs_mean=1.1686
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Epoch 1 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 16:55:19] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0002 | Epoch 001] train/clipped_loss_min=-5 train/clipped_loss_max=5 train/clipped_loss_mean=-0.0202 train/clipped_loss_std=1.4689 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-5 train/unclipped_loss_max=5 train/unclipped_loss_mean=-0.0202 train/unclipped_loss_std=1.4689 train/policy_loss=-0.0202 train/loss=-0.0202 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0002
+
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Epoch 1 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 16:55:44] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0003 | Epoch 001] train/clipped_loss_min=-5.0000 train/clipped_loss_max=5.0000 train/clipped_loss_mean=0.0229 train/clipped_loss_std=1.5176 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0.0001 train/unclipped_loss_min=-5.0000 train/unclipped_loss_max=5.0000 train/unclipped_loss_mean=0.0229 train/unclipped_loss_std=1.5176 train/policy_loss=0.0229 train/loss=0.0229 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0002
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+[2026-10-01 16:55:44] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0004 | Epoch 001] timing/rollout_seconds=179.3328 timing/feedback_seconds=4.4744 timing/optimization_seconds=50.5271 timing/cycle_seconds=232.8107
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+[2026-10-01 16:58:47] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0004 | Epoch 002] train/reward_dists_mean=0.8269 train/reward_dists_std=0.0404 train/reward_ssim_mean=0.6164 train/reward_ssim_std=0.0980 train/reward_dists_group_std_mean=0.0179 train/reward_dists_group_std_max=0.0375 train/reward_dists_group_std_min=0.0059 train/reward_dists_group_mean_std=0.0354 train/reward_ssim_group_std_mean=0.0257 train/reward_ssim_group_std_max=0.0646 train/reward_ssim_group_std_min=0.0097 train/reward_ssim_group_mean_std=0.0938 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.3134 train/adv_max=6.0511 train/adv_abs_mean=1.1217
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Epoch 2 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 16:59:12] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0004 | Epoch 002] train/clipped_loss_min=-5 train/clipped_loss_max=5 train/clipped_loss_mean=-0.0034 train/clipped_loss_std=1.4573 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-5 train/unclipped_loss_max=5 train/unclipped_loss_mean=-0.0034 train/unclipped_loss_std=1.4573 train/policy_loss=-0.0034 train/loss=-0.0034 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0002
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Epoch 2 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 16:59:37] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0005 | Epoch 002] train/clipped_loss_min=-4.1221 train/clipped_loss_max=4.7626 train/clipped_loss_mean=0.0053 train/clipped_loss_std=1.4275 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-4.1221 train/unclipped_loss_max=4.7626 train/unclipped_loss_mean=0.0053 train/unclipped_loss_std=1.4275 train/policy_loss=0.0053 train/loss=0.0053 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0002
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+[2026-10-01 16:59:37] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0006 | Epoch 002] timing/rollout_seconds=179.4336 timing/feedback_seconds=4.5409 timing/optimization_seconds=50.5666 timing/cycle_seconds=232.9509
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+
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+[2026-10-01 17:02:40] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0006 | Epoch 003] train/reward_dists_mean=0.8303 train/reward_dists_std=0.0362 train/reward_ssim_mean=0.6218 train/reward_ssim_std=0.1140 train/reward_dists_group_std_mean=0.0170 train/reward_dists_group_std_max=0.0359 train/reward_dists_group_std_min=0.0063 train/reward_dists_group_mean_std=0.0312 train/reward_ssim_group_std_mean=0.0244 train/reward_ssim_group_std_max=0.0948 train/reward_ssim_group_std_min=0.0077 train/reward_ssim_group_mean_std=0.1101 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.1718 train/adv_max=4.4404 train/adv_abs_mean=1.1677
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Epoch 3 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 17:03:05] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0006 | Epoch 003] train/clipped_loss_min=-4.1883 train/clipped_loss_max=3.7233 train/clipped_loss_mean=-0.0887 train/clipped_loss_std=1.4534 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-4.1883 train/unclipped_loss_max=3.7233 train/unclipped_loss_mean=-0.0887 train/unclipped_loss_std=1.4534 train/policy_loss=-0.0887 train/loss=-0.0887 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0004
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Epoch 3 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 17:03:30] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0007 | Epoch 003] train/clipped_loss_min=-4.4404 train/clipped_loss_max=5.0000 train/clipped_loss_mean=0.0883 train/clipped_loss_std=1.5088 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0.0001 train/unclipped_loss_min=-4.4404 train/unclipped_loss_max=5.0000 train/unclipped_loss_mean=0.0883 train/unclipped_loss_std=1.5088 train/policy_loss=0.0883 train/loss=0.0883 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0004
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+[2026-10-01 17:03:30] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0008 | Epoch 003] timing/rollout_seconds=179.5522 timing/feedback_seconds=4.4485 timing/optimization_seconds=50.5725 timing/cycle_seconds=233.0043
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+
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+
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+[2026-10-01 17:06:32] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0008 | Epoch 004] train/reward_dists_mean=0.8338 train/reward_dists_std=0.0320 train/reward_ssim_mean=0.6103 train/reward_ssim_std=0.1021 train/reward_dists_group_std_mean=0.0155 train/reward_dists_group_std_max=0.0333 train/reward_dists_group_std_min=0.0058 train/reward_dists_group_mean_std=0.0271 train/reward_ssim_group_std_mean=0.0203 train/reward_ssim_group_std_max=0.0427 train/reward_ssim_group_std_min=0.0071 train/reward_ssim_group_mean_std=0.0996 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.2203 train/adv_max=3.7079 train/adv_abs_mean=1.0977
+
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Epoch 4 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 17:06:58] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0008 | Epoch 004] train/clipped_loss_min=-3.7079 train/clipped_loss_max=4.2203 train/clipped_loss_mean=-0.0318 train/clipped_loss_std=1.4083 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-3.7079 train/unclipped_loss_max=4.2203 train/unclipped_loss_mean=-0.0318 train/unclipped_loss_std=1.4083 train/policy_loss=-0.0318 train/loss=-0.0318 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0001
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Epoch 4 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 17:07:23] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0009 | Epoch 004] train/clipped_loss_min=-3.5490 train/clipped_loss_max=3.8381 train/clipped_loss_mean=0.0318 train/clipped_loss_std=1.3601 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0.0001 train/unclipped_loss_min=-3.5490 train/unclipped_loss_max=3.8381 train/unclipped_loss_mean=0.0318 train/unclipped_loss_std=1.3601 train/policy_loss=0.0318 train/loss=0.0318 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0001
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+[2026-10-01 17:07:23] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0010 | Epoch 004] timing/rollout_seconds=179.3616 timing/feedback_seconds=5.3640 timing/optimization_seconds=50.7319 timing/cycle_seconds=233.0232
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+
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+[2026-10-01 17:10:26] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0010 | Epoch 005] train/reward_dists_mean=0.8246 train/reward_dists_std=0.0365 train/reward_ssim_mean=0.6374 train/reward_ssim_std=0.1014 train/reward_dists_group_std_mean=0.0186 train/reward_dists_group_std_max=0.0394 train/reward_dists_group_std_min=0.0073 train/reward_dists_group_mean_std=0.0306 train/reward_ssim_group_std_mean=0.0206 train/reward_ssim_group_std_max=0.0625 train/reward_ssim_group_std_min=0.0069 train/reward_ssim_group_mean_std=0.0985 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.6160 train/adv_max=3.7781 train/adv_abs_mean=1.1646
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Epoch 5 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 17:10:51] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0010 | Epoch 005] train/clipped_loss_min=-3.5834 train/clipped_loss_max=4.6160 train/clipped_loss_mean=0.0492 train/clipped_loss_std=1.5077 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-3.5834 train/unclipped_loss_max=4.6160 train/unclipped_loss_mean=0.0492 train/unclipped_loss_std=1.5077 train/policy_loss=0.0492 train/loss=0.0492 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0002
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Epoch 5 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 17:11:16] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0011 | Epoch 005] train/clipped_loss_min=-3.7781 train/clipped_loss_max=4.4170 train/clipped_loss_mean=-0.0492 train/clipped_loss_std=1.4573 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-3.7781 train/unclipped_loss_max=4.4170 train/unclipped_loss_mean=-0.0492 train/unclipped_loss_std=1.4573 train/policy_loss=-0.0492 train/loss=-0.0492 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0002
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+[2026-10-01 17:11:16] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0012 | Epoch 005] timing/rollout_seconds=179.4086 timing/feedback_seconds=5.4561 timing/optimization_seconds=50.5501 timing/cycle_seconds=232.9953
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+
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+
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+[2026-10-01 17:14:19] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0012 | Epoch 006] train/reward_dists_mean=0.8230 train/reward_dists_std=0.0392 train/reward_ssim_mean=0.6303 train/reward_ssim_std=0.1306 train/reward_dists_group_std_mean=0.0186 train/reward_dists_group_std_max=0.0373 train/reward_dists_group_std_min=0.0084 train/reward_dists_group_mean_std=0.0339 train/reward_ssim_group_std_mean=0.0159 train/reward_ssim_group_std_max=0.0381 train/reward_ssim_group_std_min=0.0052 train/reward_ssim_group_mean_std=0.1294 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.2615 train/adv_max=3.7454 train/adv_abs_mean=1.1623
+
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Epoch 6 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 17:14:44] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0012 | Epoch 006] train/clipped_loss_min=-3.6569 train/clipped_loss_max=5 train/clipped_loss_mean=0.0193 train/clipped_loss_std=1.4341 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-3.6569 train/unclipped_loss_max=5 train/unclipped_loss_mean=0.0193 train/unclipped_loss_std=1.4341 train/policy_loss=0.0193 train/loss=0.0193 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0004
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Epoch 6 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 17:15:09] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0013 | Epoch 006] train/clipped_loss_min=-3.7454 train/clipped_loss_max=4.8898 train/clipped_loss_mean=-0.0200 train/clipped_loss_std=1.5013 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-3.7454 train/unclipped_loss_max=4.8898 train/unclipped_loss_mean=-0.0200 train/unclipped_loss_std=1.5013 train/policy_loss=-0.0200 train/loss=-0.0200 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0004
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+[2026-10-01 17:15:09] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0014 | Epoch 006] timing/rollout_seconds=179.4363 timing/feedback_seconds=4.9056 timing/optimization_seconds=50.7355 timing/cycle_seconds=233.1187
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+[2026-10-01 17:18:12] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0014 | Epoch 007] train/reward_dists_mean=0.8210 train/reward_dists_std=0.0356 train/reward_ssim_mean=0.6437 train/reward_ssim_std=0.1022 train/reward_dists_group_std_mean=0.0192 train/reward_dists_group_std_max=0.0308 train/reward_dists_group_std_min=0.0101 train/reward_dists_group_mean_std=0.0296 train/reward_ssim_group_std_mean=0.0161 train/reward_ssim_group_std_max=0.0527 train/reward_ssim_group_std_min=0.0049 train/reward_ssim_group_mean_std=0.1005 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.5608 train/adv_max=3.8420 train/adv_abs_mean=1.2417
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Epoch 7 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 17:19:02] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0015 | Epoch 007] train/clipped_loss_min=-3.2164 train/clipped_loss_max=5.0000 train/clipped_loss_mean=0.0183 train/clipped_loss_std=1.5941 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-3.2164 train/unclipped_loss_max=5.0000 train/unclipped_loss_mean=0.0183 train/unclipped_loss_std=1.5941 train/policy_loss=0.0183 train/loss=0.0183 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0004
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+[2026-10-01 17:19:02] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0016 | Epoch 007] timing/rollout_seconds=179.5125 timing/feedback_seconds=4.7075 timing/optimization_seconds=50.5432 timing/cycle_seconds=233.0342
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+
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+
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+[2026-10-01 17:22:07] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0016 | Epoch 008] train/reward_dists_mean=0.8111 train/reward_dists_std=0.0397 train/reward_ssim_mean=0.6414 train/reward_ssim_std=0.0958 train/reward_dists_group_std_mean=0.0174 train/reward_dists_group_std_max=0.0321 train/reward_dists_group_std_min=0.0087 train/reward_dists_group_mean_std=0.0353 train/reward_ssim_group_std_mean=0.0176 train/reward_ssim_group_std_max=0.0385 train/reward_ssim_group_std_min=0.0071 train/reward_ssim_group_mean_std=0.0938 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.9468 train/adv_max=5.1498 train/adv_abs_mean=1.2951
+
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Epoch 8 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 17:22:32] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0016 | Epoch 008] train/clipped_loss_min=-3.5000 train/clipped_loss_max=5 train/clipped_loss_mean=-0.0410 train/clipped_loss_std=1.4814 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-3.5000 train/unclipped_loss_max=5 train/unclipped_loss_mean=-0.0410 train/unclipped_loss_std=1.4814 train/policy_loss=-0.0410 train/loss=-0.0410 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
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Epoch 8 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 17:22:57] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0017 | Epoch 008] train/clipped_loss_min=-5.0000 train/clipped_loss_max=5.0000 train/clipped_loss_mean=0.0361 train/clipped_loss_std=1.7204 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-5.0000 train/unclipped_loss_max=5.0000 train/unclipped_loss_mean=0.0361 train/unclipped_loss_std=1.7204 train/policy_loss=0.0361 train/loss=0.0361 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
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+[2026-10-01 17:22:57] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0018 | Epoch 008] timing/rollout_seconds=179.4247 timing/feedback_seconds=6.7388 timing/optimization_seconds=50.4572 timing/cycle_seconds=235.1106
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+
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+
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+[2026-10-01 17:26:00] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0018 | Epoch 009] train/reward_dists_mean=0.8232 train/reward_dists_std=0.0341 train/reward_ssim_mean=0.6569 train/reward_ssim_std=0.0988 train/reward_dists_group_std_mean=0.0163 train/reward_dists_group_std_max=0.0260 train/reward_dists_group_std_min=0.0086 train/reward_dists_group_mean_std=0.0296 train/reward_ssim_group_std_mean=0.0162 train/reward_ssim_group_std_max=0.0430 train/reward_ssim_group_std_min=0.0046 train/reward_ssim_group_mean_std=0.0971 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.8442 train/adv_max=4.2162 train/adv_abs_mean=1.3439
+
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Epoch 9 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 17:26:25] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0018 | Epoch 009] train/clipped_loss_min=-4.2162 train/clipped_loss_max=3.9736 train/clipped_loss_mean=-0.0310 train/clipped_loss_std=1.5964 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-4.2162 train/unclipped_loss_max=3.9736 train/unclipped_loss_mean=-0.0310 train/unclipped_loss_std=1.5964 train/policy_loss=-0.0310 train/loss=-0.0310 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
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Epoch 9 Training:  99%|█████████▉| 95/96 [00:49<00:00,  1.93it/s]
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Epoch 9 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 17:26:50] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0019 | Epoch 009] train/clipped_loss_min=-3.8712 train/clipped_loss_max=4.8442 train/clipped_loss_mean=0.0310 train/clipped_loss_std=1.7134 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-3.8712 train/unclipped_loss_max=4.8442 train/unclipped_loss_mean=0.0310 train/unclipped_loss_std=1.7134 train/policy_loss=0.0310 train/loss=0.0310 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
+
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Epoch 9 Timestep: 100%|██████████| 1/1 [00:00<00:00,  1.90it/s]
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Epoch 9 Training: 100%|██████████| 96/96 [00:49<00:00,  1.86it/s]
Epoch 9 Training: 100%|██████████| 96/96 [00:49<00:00,  1.92it/s]
+[2026-10-01 17:26:50] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0020 | Epoch 009] timing/rollout_seconds=179.5384 timing/feedback_seconds=5.1635 timing/optimization_seconds=50.6046 timing/cycle_seconds=233.3796
+
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Eval/flir_rl_full:  25%|██▌       | 2/8 [00:14<00:41,  6.99s/it]
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Eval/flir_rl_full:  62%|██████▎   | 5/8 [00:33<00:19,  6.45s/it]
Eval/flir_rl_full:  75%|███████▌  | 6/8 [00:39<00:12,  6.41s/it]
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Eval/flir_rl_full: 100%|██████████| 8/8 [00:52<00:00,  6.49s/it]
Eval/flir_rl_full: 100%|██████████| 8/8 [00:52<00:00,  6.57s/it]
+
Pointwise Rewards: ssim:   0%|          | 0/1 [00:00<?, ?it/s]
Pointwise Rewards: ssim: 100%|██████████| 1/1 [00:00<00:00,  5.58it/s]
Pointwise Rewards: ssim: 100%|██████████| 1/1 [00:00<00:00,  5.56it/s]
+
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Pointwise Rewards: psnr: 100%|██████████| 1/1 [00:00<00:00, 11.63it/s]
+
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+[2026-10-01 17:27:44] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0020 | Epoch 010] eval/flir_rl_full/reward_dists_mean=0.8084 eval/flir_rl_full/reward_dists_std=0.0380 eval/flir_rl_full/reward_psnr_mean=18.4959 eval/flir_rl_full/reward_psnr_std=2.6886 eval/flir_rl_full/reward_ssim_mean=0.6085 eval/flir_rl_full/reward_ssim_std=0.0904
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+
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+
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+[2026-10-01 17:30:47] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0020 | Epoch 010] train/reward_dists_mean=0.8244 train/reward_dists_std=0.0311 train/reward_ssim_mean=0.6091 train/reward_ssim_std=0.1020 train/reward_dists_group_std_mean=0.0162 train/reward_dists_group_std_max=0.0305 train/reward_dists_group_std_min=0.0051 train/reward_dists_group_mean_std=0.0260 train/reward_ssim_group_std_mean=0.0168 train/reward_ssim_group_std_max=0.0340 train/reward_ssim_group_std_min=0.0066 train/reward_ssim_group_mean_std=0.1004 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.5361 train/adv_max=4.1942 train/adv_abs_mean=1.1482
+
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Epoch 10 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 17:31:12] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0020 | Epoch 010] train/clipped_loss_min=-4.1942 train/clipped_loss_max=4.9631 train/clipped_loss_mean=-0.0596 train/clipped_loss_std=1.4172 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-4.1942 train/unclipped_loss_max=4.9631 train/unclipped_loss_mean=-0.0596 train/unclipped_loss_std=1.4172 train/policy_loss=-0.0596 train/loss=-0.0596 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0001
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Epoch 10 Training:  99%|█████████▉| 95/96 [00:49<00:00,  1.93it/s]
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Epoch 10 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 17:31:37] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0021 | Epoch 010] train/clipped_loss_min=-3.6137 train/clipped_loss_max=5.0000 train/clipped_loss_mean=0.0582 train/clipped_loss_std=1.4688 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0.0002 train/unclipped_loss_min=-3.6137 train/unclipped_loss_max=5.0000 train/unclipped_loss_mean=0.0582 train/unclipped_loss_std=1.4688 train/policy_loss=0.0582 train/loss=0.0582 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0001
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Epoch 10 Timestep: 100%|██████████| 1/1 [00:00<00:00,  1.90it/s]
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Epoch 10 Training: 100%|██████████| 96/96 [00:49<00:00,  1.86it/s]
Epoch 10 Training: 100%|██████████| 96/96 [00:49<00:00,  1.93it/s]
+[2026-10-01 17:31:37] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0022 | Epoch 010] timing/rollout_seconds=179.8746 timing/feedback_seconds=5.4235 timing/optimization_seconds=50.3928 timing/cycle_seconds=233.2179
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+
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+[2026-10-01 17:34:40] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0022 | Epoch 011] train/reward_dists_mean=0.8184 train/reward_dists_std=0.0405 train/reward_ssim_mean=0.6126 train/reward_ssim_std=0.1082 train/reward_dists_group_std_mean=0.0137 train/reward_dists_group_std_max=0.0246 train/reward_dists_group_std_min=0.0064 train/reward_dists_group_mean_std=0.0378 train/reward_ssim_group_std_mean=0.0160 train/reward_ssim_group_std_max=0.0449 train/reward_ssim_group_std_min=0.0063 train/reward_ssim_group_mean_std=0.1067 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.0323 train/adv_max=3.8920 train/adv_abs_mean=1.1419
+
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Epoch 11 Training:  48%|████▊     | 46/96 [00:23<00:25,  1.93it/s]
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Epoch 11 Training:  49%|████▉     | 47/96 [00:24<00:25,  1.93it/s]
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Epoch 11 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 17:35:05] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0022 | Epoch 011] train/clipped_loss_min=-3.8920 train/clipped_loss_max=4.3893 train/clipped_loss_mean=0.0090 train/clipped_loss_std=1.5090 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-3.8920 train/unclipped_loss_max=4.3893 train/unclipped_loss_mean=0.0090 train/unclipped_loss_std=1.5090 train/policy_loss=0.0090 train/loss=0.0090 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0005
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Epoch 11 Training:  66%|██████▌   | 63/96 [00:32<00:17,  1.93it/s]
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Epoch 11 Training:  75%|███████▌  | 72/96 [00:37<00:12,  1.93it/s]
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Epoch 11 Training:  81%|████████▏ | 78/96 [00:40<00:09,  1.93it/s]
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Epoch 11 Training:  82%|████████▏ | 79/96 [00:40<00:08,  1.93it/s]
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Epoch 11 Training:  83%|████████▎ | 80/96 [00:41<00:08,  1.93it/s]
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Epoch 11 Training:  85%|████████▌ | 82/96 [00:42<00:07,  1.93it/s]
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Epoch 11 Training:  86%|████████▋ | 83/96 [00:42<00:06,  1.93it/s]
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Epoch 11 Training:  88%|████████▊ | 84/96 [00:43<00:06,  1.93it/s]
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Epoch 11 Training:  89%|████████▊ | 85/96 [00:44<00:05,  1.93it/s]
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Epoch 11 Training:  90%|████████▉ | 86/96 [00:44<00:05,  1.93it/s]
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Epoch 11 Training:  96%|█████████▌| 92/96 [00:47<00:02,  1.93it/s]
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Epoch 11 Training:  97%|█████████▋| 93/96 [00:48<00:01,  1.93it/s]
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Epoch 11 Training:  98%|█████████▊| 94/96 [00:48<00:01,  1.93it/s]
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Epoch 11 Training:  99%|█████████▉| 95/96 [00:49<00:00,  1.93it/s]
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Epoch 11 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 17:35:30] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0023 | Epoch 011] train/clipped_loss_min=-3.5146 train/clipped_loss_max=4.9999 train/clipped_loss_mean=-0.0091 train/clipped_loss_std=1.3997 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-3.5146 train/unclipped_loss_max=4.9999 train/unclipped_loss_mean=-0.0091 train/unclipped_loss_std=1.3997 train/policy_loss=-0.0091 train/loss=-0.0091 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0005
+
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Epoch 11 Timestep: 100%|██████████| 1/1 [00:00<00:00,  1.90it/s]
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Epoch 11 Training: 100%|██████████| 96/96 [00:49<00:00,  1.86it/s]
Epoch 11 Training: 100%|██████████| 96/96 [00:49<00:00,  1.93it/s]
+[2026-10-01 17:35:30] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0024 | Epoch 011] timing/rollout_seconds=179.6673 timing/feedback_seconds=4.4896 timing/optimization_seconds=50.4133 timing/cycle_seconds=232.9639
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+
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Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:01<00:00,  5.56it/s]
Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:01<00:00,  5.58it/s]
+
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Pointwise Rewards: dists:  50%|█████     | 3/6 [00:00<00:00,  3.27it/s]
Pointwise Rewards: dists:  67%|██████▋   | 4/6 [00:01<00:00,  3.31it/s]
Pointwise Rewards: dists:  83%|████████▎ | 5/6 [00:01<00:00,  3.28it/s]
Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.24it/s]
Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.27it/s]
+[2026-10-01 17:38:33] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0024 | Epoch 012] train/reward_dists_mean=0.8275 train/reward_dists_std=0.0283 train/reward_ssim_mean=0.6299 train/reward_ssim_std=0.1120 train/reward_dists_group_std_mean=0.0132 train/reward_dists_group_std_max=0.0365 train/reward_dists_group_std_min=0.0047 train/reward_dists_group_mean_std=0.0242 train/reward_ssim_group_std_mean=0.0175 train/reward_ssim_group_std_max=0.0455 train/reward_ssim_group_std_min=0.0049 train/reward_ssim_group_mean_std=0.1101 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.6204 train/adv_max=4.5910 train/adv_abs_mean=1.1726
+
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Epoch 12 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 17:38:58] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0024 | Epoch 012] train/clipped_loss_min=-4.5910 train/clipped_loss_max=4.6204 train/clipped_loss_mean=0.0766 train/clipped_loss_std=1.4828 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-4.5910 train/unclipped_loss_max=4.6204 train/unclipped_loss_mean=0.0766 train/unclipped_loss_std=1.4828 train/policy_loss=0.0766 train/loss=0.0766 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
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Epoch 12 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 17:39:23] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0025 | Epoch 012] train/clipped_loss_min=-3.9715 train/clipped_loss_max=3.6827 train/clipped_loss_mean=-0.0766 train/clipped_loss_std=1.4529 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-3.9715 train/unclipped_loss_max=3.6827 train/unclipped_loss_mean=-0.0766 train/unclipped_loss_std=1.4529 train/policy_loss=-0.0766 train/loss=-0.0766 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
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+[2026-10-01 17:39:23] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0026 | Epoch 012] timing/rollout_seconds=179.6072 timing/feedback_seconds=5.5581 timing/optimization_seconds=50.4247 timing/cycle_seconds=233.1338
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+
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+[2026-10-01 17:42:26] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0026 | Epoch 013] train/reward_dists_mean=0.8286 train/reward_dists_std=0.0305 train/reward_ssim_mean=0.6111 train/reward_ssim_std=0.1132 train/reward_dists_group_std_mean=0.0103 train/reward_dists_group_std_max=0.0243 train/reward_dists_group_std_min=0.0044 train/reward_dists_group_mean_std=0.0283 train/reward_ssim_group_std_mean=0.0161 train/reward_ssim_group_std_max=0.0307 train/reward_ssim_group_std_min=0.0058 train/reward_ssim_group_mean_std=0.1118 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.8248 train/adv_max=5.3433 train/adv_abs_mean=1.2029
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Epoch 13 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 17:42:51] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0026 | Epoch 013] train/clipped_loss_min=-5 train/clipped_loss_max=4.8248 train/clipped_loss_mean=0.0330 train/clipped_loss_std=1.5591 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-5 train/unclipped_loss_max=4.8248 train/unclipped_loss_mean=0.0330 train/unclipped_loss_std=1.5591 train/policy_loss=0.0330 train/loss=0.0330 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
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Epoch 13 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 17:43:16] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0027 | Epoch 013] train/clipped_loss_min=-4.5354 train/clipped_loss_max=4.4995 train/clipped_loss_mean=-0.0321 train/clipped_loss_std=1.4835 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0.0002 train/unclipped_loss_min=-4.5354 train/unclipped_loss_max=4.4995 train/unclipped_loss_mean=-0.0321 train/unclipped_loss_std=1.4835 train/policy_loss=-0.0321 train/loss=-0.0321 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
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+[2026-10-01 17:43:16] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0028 | Epoch 013] timing/rollout_seconds=179.3211 timing/feedback_seconds=5.2575 timing/optimization_seconds=50.3871 timing/cycle_seconds=232.7265
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+
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+[2026-10-01 17:46:19] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0028 | Epoch 014] train/reward_dists_mean=0.8294 train/reward_dists_std=0.0310 train/reward_ssim_mean=0.6177 train/reward_ssim_std=0.1086 train/reward_dists_group_std_mean=0.0107 train/reward_dists_group_std_max=0.0265 train/reward_dists_group_std_min=0.0048 train/reward_dists_group_mean_std=0.0288 train/reward_ssim_group_std_mean=0.0154 train/reward_ssim_group_std_max=0.0369 train/reward_ssim_group_std_min=0.0056 train/reward_ssim_group_mean_std=0.1073 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.5597 train/adv_max=5.5337 train/adv_abs_mean=1.1752
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Epoch 14 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 17:46:43] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0028 | Epoch 014] train/clipped_loss_min=-4.5798 train/clipped_loss_max=4.5284 train/clipped_loss_mean=0.0209 train/clipped_loss_std=1.5317 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-4.5798 train/unclipped_loss_max=4.5284 train/unclipped_loss_mean=0.0209 train/unclipped_loss_std=1.5317 train/policy_loss=0.0209 train/loss=0.0209 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0002
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Epoch 14 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 17:47:08] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0029 | Epoch 014] train/clipped_loss_min=-5.0000 train/clipped_loss_max=4.5597 train/clipped_loss_mean=-0.0194 train/clipped_loss_std=1.4590 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0.0002 train/unclipped_loss_min=-5.0000 train/unclipped_loss_max=4.5597 train/unclipped_loss_mean=-0.0194 train/unclipped_loss_std=1.4590 train/policy_loss=-0.0194 train/loss=-0.0194 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0001
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+[2026-10-01 17:47:08] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0030 | Epoch 014] timing/rollout_seconds=179.3567 timing/feedback_seconds=5.3162 timing/optimization_seconds=50.4276 timing/cycle_seconds=232.7031
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+[2026-10-01 17:50:11] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0030 | Epoch 015] train/reward_dists_mean=0.8301 train/reward_dists_std=0.0340 train/reward_ssim_mean=0.6478 train/reward_ssim_std=0.0881 train/reward_dists_group_std_mean=0.0116 train/reward_dists_group_std_max=0.0226 train/reward_dists_group_std_min=0.0041 train/reward_dists_group_mean_std=0.0316 train/reward_ssim_group_std_mean=0.0141 train/reward_ssim_group_std_max=0.0352 train/reward_ssim_group_std_min=0.0045 train/reward_ssim_group_mean_std=0.0866 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.6636 train/adv_max=5.2626 train/adv_abs_mean=1.1482
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Epoch 15 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 17:50:36] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0030 | Epoch 015] train/clipped_loss_min=-4.9101 train/clipped_loss_max=4.2578 train/clipped_loss_mean=0.0395 train/clipped_loss_std=1.4597 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-4.9101 train/unclipped_loss_max=4.2578 train/unclipped_loss_mean=0.0395 train/unclipped_loss_std=1.4597 train/policy_loss=0.0395 train/loss=0.0395 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
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Epoch 15 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 17:51:01] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0031 | Epoch 015] train/clipped_loss_min=-5.0000 train/clipped_loss_max=4.6636 train/clipped_loss_mean=-0.0389 train/clipped_loss_std=1.5046 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0.0001 train/unclipped_loss_min=-5.0000 train/unclipped_loss_max=4.6636 train/unclipped_loss_mean=-0.0389 train/unclipped_loss_std=1.5046 train/policy_loss=-0.0389 train/loss=-0.0389 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
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+[2026-10-01 17:51:01] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0032 | Epoch 015] timing/rollout_seconds=179.5436 timing/feedback_seconds=5.1808 timing/optimization_seconds=50.4494 timing/cycle_seconds=232.9691
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+
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+[2026-10-01 17:54:06] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0032 | Epoch 016] train/reward_dists_mean=0.8402 train/reward_dists_std=0.0296 train/reward_ssim_mean=0.6359 train/reward_ssim_std=0.0869 train/reward_dists_group_std_mean=0.0099 train/reward_dists_group_std_max=0.0180 train/reward_dists_group_std_min=0.0042 train/reward_dists_group_mean_std=0.0277 train/reward_ssim_group_std_mean=0.0127 train/reward_ssim_group_std_max=0.0211 train/reward_ssim_group_std_min=0.0060 train/reward_ssim_group_mean_std=0.0859 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.1158 train/adv_max=4.3616 train/adv_abs_mean=1.2139
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Epoch 16 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 17:54:31] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0032 | Epoch 016] train/clipped_loss_min=-4.3616 train/clipped_loss_max=5 train/clipped_loss_mean=-0.0261 train/clipped_loss_std=1.4585 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-4.3616 train/unclipped_loss_max=5 train/unclipped_loss_mean=-0.0261 train/unclipped_loss_std=1.4585 train/policy_loss=-0.0261 train/loss=-0.0261 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0002
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Epoch 16 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 17:54:56] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0033 | Epoch 016] train/clipped_loss_min=-3.6322 train/clipped_loss_max=4.1505 train/clipped_loss_mean=0.0258 train/clipped_loss_std=1.5760 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-3.6322 train/unclipped_loss_max=4.1505 train/unclipped_loss_mean=0.0258 train/unclipped_loss_std=1.5760 train/policy_loss=0.0258 train/loss=0.0258 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0002
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+[2026-10-01 17:54:56] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0034 | Epoch 016] timing/rollout_seconds=179.3900 timing/feedback_seconds=6.8509 timing/optimization_seconds=50.4025 timing/cycle_seconds=234.3030
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+
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+[2026-10-01 17:57:58] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0034 | Epoch 017] train/reward_dists_mean=0.8256 train/reward_dists_std=0.0324 train/reward_ssim_mean=0.6357 train/reward_ssim_std=0.1074 train/reward_dists_group_std_mean=0.0109 train/reward_dists_group_std_max=0.0199 train/reward_dists_group_std_min=0.0048 train/reward_dists_group_mean_std=0.0303 train/reward_ssim_group_std_mean=0.0122 train/reward_ssim_group_std_max=0.0255 train/reward_ssim_group_std_min=0.0045 train/reward_ssim_group_mean_std=0.1065 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.1193 train/adv_max=5.7909 train/adv_abs_mean=1.1813
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Epoch 17 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 17:58:23] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0034 | Epoch 017] train/clipped_loss_min=-5 train/clipped_loss_max=4.1193 train/clipped_loss_mean=-0.0747 train/clipped_loss_std=1.4907 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-5 train/unclipped_loss_max=4.1193 train/unclipped_loss_mean=-0.0747 train/unclipped_loss_std=1.4907 train/policy_loss=-0.0747 train/loss=-0.0747 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0004
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Epoch 17 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 17:58:48] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0035 | Epoch 017] train/clipped_loss_min=-3.8237 train/clipped_loss_max=4.0169 train/clipped_loss_mean=0.0780 train/clipped_loss_std=1.4866 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0.0001 train/unclipped_loss_min=-3.8237 train/unclipped_loss_max=4.0169 train/unclipped_loss_mean=0.0780 train/unclipped_loss_std=1.4866 train/policy_loss=0.0780 train/loss=0.0780 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0004
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+[2026-10-01 17:58:48] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0036 | Epoch 017] timing/rollout_seconds=179.3307 timing/feedback_seconds=5.0793 timing/optimization_seconds=50.3958 timing/cycle_seconds=232.6748
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+
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+[2026-10-01 18:01:51] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0036 | Epoch 018] train/reward_dists_mean=0.8289 train/reward_dists_std=0.0331 train/reward_ssim_mean=0.6262 train/reward_ssim_std=0.1010 train/reward_dists_group_std_mean=0.0108 train/reward_dists_group_std_max=0.0237 train/reward_dists_group_std_min=0.0037 train/reward_dists_group_mean_std=0.0310 train/reward_ssim_group_std_mean=0.0112 train/reward_ssim_group_std_max=0.0236 train/reward_ssim_group_std_min=0.0039 train/reward_ssim_group_mean_std=0.1003 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.6790 train/adv_max=4.3412 train/adv_abs_mean=1.1630
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Epoch 18 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 18:02:17] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0036 | Epoch 018] train/clipped_loss_min=-4.3412 train/clipped_loss_max=4.4074 train/clipped_loss_mean=0.0938 train/clipped_loss_std=1.5139 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-4.3412 train/unclipped_loss_max=4.4074 train/unclipped_loss_mean=0.0938 train/unclipped_loss_std=1.5139 train/policy_loss=0.0938 train/loss=0.0938 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0005
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Epoch 18 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 18:02:41] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0037 | Epoch 018] train/clipped_loss_min=-3.4326 train/clipped_loss_max=4.6789 train/clipped_loss_mean=-0.0938 train/clipped_loss_std=1.4272 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0.0001 train/unclipped_loss_min=-3.4326 train/unclipped_loss_max=4.6789 train/unclipped_loss_mean=-0.0938 train/unclipped_loss_std=1.4272 train/policy_loss=-0.0938 train/loss=-0.0938 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0005
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+[2026-10-01 18:02:41] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0038 | Epoch 018] timing/rollout_seconds=179.3765 timing/feedback_seconds=5.1278 timing/optimization_seconds=50.7954 timing/cycle_seconds=233.2138
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+
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+[2026-10-01 18:05:44] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0038 | Epoch 019] train/reward_dists_mean=0.8321 train/reward_dists_std=0.0291 train/reward_ssim_mean=0.6403 train/reward_ssim_std=0.1000 train/reward_dists_group_std_mean=0.0103 train/reward_dists_group_std_max=0.0208 train/reward_dists_group_std_min=0.0030 train/reward_dists_group_mean_std=0.0269 train/reward_ssim_group_std_mean=0.0095 train/reward_ssim_group_std_max=0.0224 train/reward_ssim_group_std_min=0.0043 train/reward_ssim_group_mean_std=0.0995 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.9224 train/adv_max=4.8201 train/adv_abs_mean=1.1503
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Epoch 19 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 18:06:09] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0038 | Epoch 019] train/clipped_loss_min=-4.8201 train/clipped_loss_max=4.9224 train/clipped_loss_mean=-0.0036 train/clipped_loss_std=1.4388 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-4.8201 train/unclipped_loss_max=4.9224 train/unclipped_loss_mean=-0.0036 train/unclipped_loss_std=1.4388 train/policy_loss=-0.0036 train/loss=-0.0036 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
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Epoch 19 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 18:06:34] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0039 | Epoch 019] train/clipped_loss_min=-4.0995 train/clipped_loss_max=3.8158 train/clipped_loss_mean=0.0036 train/clipped_loss_std=1.4493 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-4.0995 train/unclipped_loss_max=3.8158 train/unclipped_loss_mean=0.0036 train/unclipped_loss_std=1.4493 train/policy_loss=0.0036 train/loss=0.0036 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
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+[2026-10-01 18:06:34] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0040 | Epoch 019] timing/rollout_seconds=179.3946 timing/feedback_seconds=5.3554 timing/optimization_seconds=50.5579 timing/cycle_seconds=232.7933
+[2026-10-01 18:06:34] [Rank 0] [INFO] [flow_factory.models.abc]: Saving LoRA weights for transformer role=base to /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/runs/klein4b_grpo_flir_full_ssim_dists_lr5e-5/saves/klein4b_grpo_flir_full_ssim_dists_lr5e-5/checkpoints/checkpoint-20
+[2026-10-01 18:06:35] [Rank 0] [INFO] [flow_factory.models.abc]: Checkpoint saved successfully to /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/runs/klein4b_grpo_flir_full_ssim_dists_lr5e-5/saves/klein4b_grpo_flir_full_ssim_dists_lr5e-5/checkpoints/checkpoint-20
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+[2026-10-01 18:07:28] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0040 | Epoch 020] eval/flir_rl_full/reward_dists_mean=0.8340 eval/flir_rl_full/reward_dists_std=0.0351 eval/flir_rl_full/reward_psnr_mean=19.5561 eval/flir_rl_full/reward_psnr_std=2.8645 eval/flir_rl_full/reward_ssim_mean=0.6131 eval/flir_rl_full/reward_ssim_std=0.0897
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+[2026-10-01 18:10:31] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0040 | Epoch 020] train/reward_dists_mean=0.8310 train/reward_dists_std=0.0324 train/reward_ssim_mean=0.6312 train/reward_ssim_std=0.1026 train/reward_dists_group_std_mean=0.0100 train/reward_dists_group_std_max=0.0209 train/reward_dists_group_std_min=0.0053 train/reward_dists_group_mean_std=0.0306 train/reward_ssim_group_std_mean=0.0102 train/reward_ssim_group_std_max=0.0253 train/reward_ssim_group_std_min=0.0044 train/reward_ssim_group_mean_std=0.1020 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.4021 train/adv_max=4.8502 train/adv_abs_mean=1.1419
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Epoch 20 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 18:10:56] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0040 | Epoch 020] train/clipped_loss_min=-4.8502 train/clipped_loss_max=4.4021 train/clipped_loss_mean=0.0605 train/clipped_loss_std=1.4520 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-4.8502 train/unclipped_loss_max=4.4021 train/unclipped_loss_mean=0.0605 train/unclipped_loss_std=1.4520 train/policy_loss=0.0605 train/loss=0.0605 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0002
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Epoch 20 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 18:11:21] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0041 | Epoch 020] train/clipped_loss_min=-4.3047 train/clipped_loss_max=4.1179 train/clipped_loss_mean=-0.0605 train/clipped_loss_std=1.4322 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-4.3047 train/unclipped_loss_max=4.1179 train/unclipped_loss_mean=-0.0605 train/unclipped_loss_std=1.4322 train/policy_loss=-0.0605 train/loss=-0.0605 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0002
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+[2026-10-01 18:11:21] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0042 | Epoch 020] timing/rollout_seconds=179.4642 timing/feedback_seconds=5.0463 timing/optimization_seconds=50.4775 timing/cycle_seconds=232.7953
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+[2026-10-01 18:14:24] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0042 | Epoch 021] train/reward_dists_mean=0.8273 train/reward_dists_std=0.0292 train/reward_ssim_mean=0.6132 train/reward_ssim_std=0.1180 train/reward_dists_group_std_mean=0.0096 train/reward_dists_group_std_max=0.0221 train/reward_dists_group_std_min=0.0043 train/reward_dists_group_mean_std=0.0273 train/reward_ssim_group_std_mean=0.0104 train/reward_ssim_group_std_max=0.0247 train/reward_ssim_group_std_min=0.0039 train/reward_ssim_group_mean_std=0.1175 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.5545 train/adv_max=4.6344 train/adv_abs_mean=1.1786
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Epoch 21 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 18:14:49] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0042 | Epoch 021] train/clipped_loss_min=-4.6344 train/clipped_loss_max=5 train/clipped_loss_mean=-0.0093 train/clipped_loss_std=1.5521 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-4.6344 train/unclipped_loss_max=5 train/unclipped_loss_mean=-0.0093 train/unclipped_loss_std=1.5521 train/policy_loss=-0.0093 train/loss=-0.0093 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
+
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Epoch 21 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 18:15:14] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0043 | Epoch 021] train/clipped_loss_min=-4.1714 train/clipped_loss_max=4.5859 train/clipped_loss_mean=0.0079 train/clipped_loss_std=1.4708 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-4.1714 train/unclipped_loss_max=4.5859 train/unclipped_loss_mean=0.0079 train/unclipped_loss_std=1.4708 train/policy_loss=0.0079 train/loss=0.0079 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
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Epoch 21 Training: 100%|██████████| 96/96 [00:50<00:00,  1.91it/s]
+[2026-10-01 18:15:14] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0044 | Epoch 021] timing/rollout_seconds=179.4971 timing/feedback_seconds=5.4868 timing/optimization_seconds=50.9267 timing/cycle_seconds=233.4549
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Pointwise Rewards: ssim:  83%|████████▎ | 5/6 [00:00<00:00,  5.84it/s]
Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:01<00:00,  5.76it/s]
Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:01<00:00,  5.69it/s]
+
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Pointwise Rewards: dists:  50%|█████     | 3/6 [00:00<00:00,  3.27it/s]
Pointwise Rewards: dists:  67%|██████▋   | 4/6 [00:01<00:00,  3.26it/s]
Pointwise Rewards: dists:  83%|████████▎ | 5/6 [00:01<00:00,  3.35it/s]
Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.29it/s]
Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.30it/s]
+[2026-10-01 18:18:17] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0044 | Epoch 022] train/reward_dists_mean=0.8268 train/reward_dists_std=0.0315 train/reward_ssim_mean=0.6159 train/reward_ssim_std=0.1032 train/reward_dists_group_std_mean=0.0097 train/reward_dists_group_std_max=0.0182 train/reward_dists_group_std_min=0.0041 train/reward_dists_group_mean_std=0.0298 train/reward_ssim_group_std_mean=0.0078 train/reward_ssim_group_std_max=0.0160 train/reward_ssim_group_std_min=0.0033 train/reward_ssim_group_mean_std=0.1029 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.4633 train/adv_max=4.4966 train/adv_abs_mean=1.1157
+
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Epoch 22 Training:   9%|▉         | 9/96 [00:04<00:45,  1.93it/s]
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Epoch 22 Training:  11%|█▏        | 11/96 [00:05<00:44,  1.93it/s]
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Epoch 22 Training:  12%|█▎        | 12/96 [00:06<00:43,  1.93it/s]
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Epoch 22 Training:  14%|█▎        | 13/96 [00:06<00:43,  1.93it/s]
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Epoch 22 Training:  15%|█▍        | 14/96 [00:07<00:42,  1.93it/s]
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Epoch 22 Training:  16%|█▌        | 15/96 [00:07<00:42,  1.93it/s]
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Epoch 22 Training:  17%|█▋        | 16/96 [00:08<00:41,  1.93it/s]
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Epoch 22 Training:  18%|█▊        | 17/96 [00:08<00:40,  1.93it/s]
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Epoch 22 Training:  19%|█▉        | 18/96 [00:09<00:40,  1.93it/s]
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Epoch 22 Training:  22%|██▏       | 21/96 [00:10<00:38,  1.93it/s]
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Epoch 22 Training:  23%|██▎       | 22/96 [00:11<00:38,  1.93it/s]
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Epoch 22 Training:  24%|██▍       | 23/96 [00:11<00:37,  1.93it/s]
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Epoch 22 Training:  25%|██▌       | 24/96 [00:12<00:37,  1.93it/s]
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Epoch 22 Training:  26%|██▌       | 25/96 [00:12<00:36,  1.93it/s]
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Epoch 22 Training:  27%|██▋       | 26/96 [00:13<00:36,  1.93it/s]
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Epoch 22 Training:  28%|██▊       | 27/96 [00:13<00:35,  1.93it/s]
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Epoch 22 Training:  29%|██▉       | 28/96 [00:14<00:35,  1.93it/s]
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Epoch 22 Training:  30%|███       | 29/96 [00:14<00:34,  1.93it/s]
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Epoch 22 Training:  31%|███▏      | 30/96 [00:15<00:34,  1.93it/s]
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Epoch 22 Training:  32%|███▏      | 31/96 [00:16<00:33,  1.93it/s]
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Epoch 22 Training:  33%|███▎      | 32/96 [00:16<00:33,  1.93it/s]
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Epoch 22 Training:  34%|███▍      | 33/96 [00:17<00:32,  1.93it/s]
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Epoch 22 Training:  35%|███▌      | 34/96 [00:17<00:32,  1.93it/s]
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Epoch 22 Training:  36%|███▋      | 35/96 [00:18<00:31,  1.93it/s]
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Epoch 22 Training:  38%|███▊      | 36/96 [00:18<00:31,  1.93it/s]
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Epoch 22 Training:  39%|███▊      | 37/96 [00:19<00:30,  1.93it/s]
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Epoch 22 Training:  40%|███▉      | 38/96 [00:19<00:30,  1.93it/s]
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Epoch 22 Training:  41%|████      | 39/96 [00:20<00:29,  1.93it/s]
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Epoch 22 Training:  42%|████▏     | 40/96 [00:20<00:29,  1.93it/s]
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Epoch 22 Training:  43%|████▎     | 41/96 [00:21<00:28,  1.93it/s]
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Epoch 22 Training:  44%|████▍     | 42/96 [00:21<00:27,  1.93it/s]
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Epoch 22 Training:  45%|████▍     | 43/96 [00:22<00:27,  1.93it/s]
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Epoch 22 Training:  46%|████▌     | 44/96 [00:22<00:26,  1.93it/s]
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Epoch 22 Training:  47%|████▋     | 45/96 [00:23<00:26,  1.93it/s]
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Epoch 22 Training:  48%|████▊     | 46/96 [00:23<00:25,  1.93it/s]
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Epoch 22 Training:  49%|████▉     | 47/96 [00:24<00:25,  1.93it/s]
+
Epoch 22 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 18:18:42] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0044 | Epoch 022] train/clipped_loss_min=-4.4966 train/clipped_loss_max=4.2894 train/clipped_loss_mean=0.1486 train/clipped_loss_std=1.4658 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-4.4966 train/unclipped_loss_max=4.2894 train/unclipped_loss_mean=0.1486 train/unclipped_loss_std=1.4658 train/policy_loss=0.1486 train/loss=0.1486 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0005
+
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Epoch 22 Timestep: 100%|██████████| 1/1 [00:00<00:00,  1.90it/s]
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Epoch 22 Training:  51%|█████     | 49/96 [00:25<00:24,  1.94it/s]
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Epoch 22 Training:  53%|█████▎    | 51/96 [00:26<00:23,  1.93it/s]
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Epoch 22 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 18:19:07] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0045 | Epoch 022] train/clipped_loss_min=-3.3808 train/clipped_loss_max=4.4632 train/clipped_loss_mean=-0.1486 train/clipped_loss_std=1.3532 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-3.3808 train/unclipped_loss_max=4.4632 train/unclipped_loss_mean=-0.1486 train/unclipped_loss_std=1.3532 train/policy_loss=-0.1486 train/loss=-0.1486 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0006
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+[2026-10-01 18:19:07] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0046 | Epoch 022] timing/rollout_seconds=179.4101 timing/feedback_seconds=5.3590 timing/optimization_seconds=50.4521 timing/cycle_seconds=232.8570
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+
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+
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Pointwise Rewards: dists:  50%|█████     | 3/6 [00:00<00:00,  3.20it/s]
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Pointwise Rewards: dists:  83%|████████▎ | 5/6 [00:01<00:00,  3.21it/s]
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Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.21it/s]
+[2026-10-01 18:22:10] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0046 | Epoch 023] train/reward_dists_mean=0.8301 train/reward_dists_std=0.0284 train/reward_ssim_mean=0.6333 train/reward_ssim_std=0.1002 train/reward_dists_group_std_mean=0.0089 train/reward_dists_group_std_max=0.0175 train/reward_dists_group_std_min=0.0040 train/reward_dists_group_mean_std=0.0268 train/reward_ssim_group_std_mean=0.0105 train/reward_ssim_group_std_max=0.0215 train/reward_ssim_group_std_min=0.0042 train/reward_ssim_group_mean_std=0.0996 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.6839 train/adv_max=4.4850 train/adv_abs_mean=1.1804
+
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Epoch 23 Training:  48%|████▊     | 46/96 [00:23<00:25,  1.93it/s]
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Epoch 23 Training:  49%|████▉     | 47/96 [00:24<00:25,  1.93it/s]
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Epoch 23 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 18:22:35] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0046 | Epoch 023] train/clipped_loss_min=-4.4850 train/clipped_loss_max=4.6839 train/clipped_loss_mean=-0.0631 train/clipped_loss_std=1.4936 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-4.4850 train/unclipped_loss_max=4.6839 train/unclipped_loss_mean=-0.0631 train/unclipped_loss_std=1.4936 train/policy_loss=-0.0631 train/loss=-0.0631 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0002
+
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Epoch 23 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 18:23:00] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0047 | Epoch 023] train/clipped_loss_min=-4.0872 train/clipped_loss_max=4.5794 train/clipped_loss_mean=0.0631 train/clipped_loss_std=1.5032 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-4.0872 train/unclipped_loss_max=4.5794 train/unclipped_loss_mean=0.0631 train/unclipped_loss_std=1.5032 train/policy_loss=0.0631 train/loss=0.0631 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0002
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+[2026-10-01 18:23:00] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0048 | Epoch 023] timing/rollout_seconds=179.4187 timing/feedback_seconds=4.8041 timing/optimization_seconds=50.4568 timing/cycle_seconds=232.8413
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+
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+
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Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.31it/s]
+[2026-10-01 18:26:04] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0048 | Epoch 024] train/reward_dists_mean=0.8336 train/reward_dists_std=0.0331 train/reward_ssim_mean=0.6400 train/reward_ssim_std=0.0850 train/reward_dists_group_std_mean=0.0085 train/reward_dists_group_std_max=0.0166 train/reward_dists_group_std_min=0.0039 train/reward_dists_group_mean_std=0.0318 train/reward_ssim_group_std_mean=0.0090 train/reward_ssim_group_std_max=0.0220 train/reward_ssim_group_std_min=0.0030 train/reward_ssim_group_mean_std=0.0844 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.7487 train/adv_max=4.0777 train/adv_abs_mean=1.2121
+
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Epoch 24 Training:  49%|████▉     | 47/96 [00:24<00:25,  1.93it/s]
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Epoch 24 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 18:26:29] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0048 | Epoch 024] train/clipped_loss_min=-4.0777 train/clipped_loss_max=5 train/clipped_loss_mean=-0.0370 train/clipped_loss_std=1.5284 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-4.0777 train/unclipped_loss_max=5 train/unclipped_loss_mean=-0.0370 train/unclipped_loss_std=1.5284 train/policy_loss=-0.0370 train/loss=-0.0370 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
+
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Epoch 24 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 18:26:54] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0049 | Epoch 024] train/clipped_loss_min=-4.0701 train/clipped_loss_max=4.6595 train/clipped_loss_mean=0.0350 train/clipped_loss_std=1.5204 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0.0002 train/unclipped_loss_min=-4.0701 train/unclipped_loss_max=4.6595 train/unclipped_loss_mean=0.0350 train/unclipped_loss_std=1.5204 train/policy_loss=0.0350 train/loss=0.0350 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
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+[2026-10-01 18:26:54] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0050 | Epoch 024] timing/rollout_seconds=179.4926 timing/feedback_seconds=5.3225 timing/optimization_seconds=51.1125 timing/cycle_seconds=233.7531
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+
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+
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Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.30it/s]
+[2026-10-01 18:29:57] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0050 | Epoch 025] train/reward_dists_mean=0.8312 train/reward_dists_std=0.0247 train/reward_ssim_mean=0.6291 train/reward_ssim_std=0.1008 train/reward_dists_group_std_mean=0.0087 train/reward_dists_group_std_max=0.0208 train/reward_dists_group_std_min=0.0040 train/reward_dists_group_mean_std=0.0229 train/reward_ssim_group_std_mean=0.0097 train/reward_ssim_group_std_max=0.0208 train/reward_ssim_group_std_min=0.0038 train/reward_ssim_group_mean_std=0.1003 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-6.0121 train/adv_max=5.0907 train/adv_abs_mean=1.2497
+
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Epoch 25 Training:  39%|███▊      | 37/96 [00:19<00:30,  1.92it/s]
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Epoch 25 Training:  49%|████▉     | 47/96 [00:24<00:25,  1.92it/s]
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Epoch 25 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 18:30:22] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0050 | Epoch 025] train/clipped_loss_min=-4.3708 train/clipped_loss_max=5 train/clipped_loss_mean=-0.0195 train/clipped_loss_std=1.5224 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-4.3708 train/unclipped_loss_max=5 train/unclipped_loss_mean=-0.0195 train/unclipped_loss_std=1.5224 train/policy_loss=-0.0195 train/loss=-0.0195 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0005
+
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Epoch 25 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 18:30:47] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0051 | Epoch 025] train/clipped_loss_min=-5.0000 train/clipped_loss_max=4.4161 train/clipped_loss_mean=0.0171 train/clipped_loss_std=1.6402 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-5.0000 train/unclipped_loss_max=4.4161 train/unclipped_loss_mean=0.0171 train/unclipped_loss_std=1.6402 train/policy_loss=0.0171 train/loss=0.0171 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0006
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+[2026-10-01 18:30:47] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0052 | Epoch 025] timing/rollout_seconds=179.6101 timing/feedback_seconds=5.5540 timing/optimization_seconds=50.5332 timing/cycle_seconds=233.1206
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+
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+
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Pointwise Rewards: dists:  50%|█████     | 3/6 [00:00<00:00,  3.24it/s]
Pointwise Rewards: dists:  67%|██████▋   | 4/6 [00:01<00:00,  3.34it/s]
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Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.32it/s]
+[2026-10-01 18:33:50] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0052 | Epoch 026] train/reward_dists_mean=0.8351 train/reward_dists_std=0.0369 train/reward_ssim_mean=0.6541 train/reward_ssim_std=0.1008 train/reward_dists_group_std_mean=0.0087 train/reward_dists_group_std_max=0.0183 train/reward_dists_group_std_min=0.0031 train/reward_dists_group_mean_std=0.0358 train/reward_ssim_group_std_mean=0.0085 train/reward_ssim_group_std_max=0.0220 train/reward_ssim_group_std_min=0.0033 train/reward_ssim_group_mean_std=0.1004 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.4747 train/adv_max=5.6218 train/adv_abs_mean=1.0984
+
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Epoch 26 Training:   7%|▋         | 7/96 [00:03<00:46,  1.92it/s]
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Epoch 26 Training:   8%|▊         | 8/96 [00:04<00:45,  1.94it/s]
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Epoch 26 Training:   9%|▉         | 9/96 [00:04<00:44,  1.94it/s]
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Epoch 26 Training:  10%|█         | 10/96 [00:05<00:44,  1.93it/s]
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Epoch 26 Training:  11%|█▏        | 11/96 [00:05<00:44,  1.93it/s]
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Epoch 26 Training:  12%|█▎        | 12/96 [00:06<00:43,  1.93it/s]
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Epoch 26 Training:  14%|█▎        | 13/96 [00:06<00:42,  1.93it/s]
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Epoch 26 Training:  15%|█▍        | 14/96 [00:07<00:42,  1.93it/s]
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Epoch 26 Training:  16%|█▌        | 15/96 [00:07<00:42,  1.93it/s]
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Epoch 26 Training:  17%|█▋        | 16/96 [00:08<00:41,  1.93it/s]
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Epoch 26 Training:  18%|█▊        | 17/96 [00:08<00:40,  1.93it/s]
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Epoch 26 Training:  19%|█▉        | 18/96 [00:09<00:40,  1.93it/s]
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Epoch 26 Training:  29%|██▉       | 28/96 [00:14<00:35,  1.93it/s]
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Epoch 26 Training:  31%|███▏      | 30/96 [00:15<00:34,  1.93it/s]
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Epoch 26 Training:  39%|███▊      | 37/96 [00:19<00:30,  1.93it/s]
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Epoch 26 Training:  40%|███▉      | 38/96 [00:19<00:30,  1.92it/s]
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Epoch 26 Training:  41%|████      | 39/96 [00:20<00:29,  1.93it/s]
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Epoch 26 Training:  44%|████▍     | 42/96 [00:21<00:28,  1.93it/s]
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Epoch 26 Training:  45%|████▍     | 43/96 [00:22<00:27,  1.93it/s]
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Epoch 26 Training:  46%|████▌     | 44/96 [00:22<00:26,  1.93it/s]
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Epoch 26 Training:  47%|████▋     | 45/96 [00:23<00:26,  1.93it/s]
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Epoch 26 Training:  48%|████▊     | 46/96 [00:23<00:25,  1.93it/s]
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Epoch 26 Training:  49%|████▉     | 47/96 [00:24<00:25,  1.93it/s]
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Epoch 26 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 18:34:15] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0052 | Epoch 026] train/clipped_loss_min=-4.2430 train/clipped_loss_max=4.4747 train/clipped_loss_mean=-0.0666 train/clipped_loss_std=1.4506 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-4.2430 train/unclipped_loss_max=4.4747 train/unclipped_loss_mean=-0.0666 train/unclipped_loss_std=1.4506 train/policy_loss=-0.0666 train/loss=-0.0666 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
+
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Epoch 26 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 18:34:40] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0053 | Epoch 026] train/clipped_loss_min=-5.0000 train/clipped_loss_max=3.9825 train/clipped_loss_mean=0.0682 train/clipped_loss_std=1.3312 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0.0001 train/unclipped_loss_min=-5.0000 train/unclipped_loss_max=3.9825 train/unclipped_loss_mean=0.0682 train/unclipped_loss_std=1.3312 train/policy_loss=0.0682 train/loss=0.0682 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
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+[2026-10-01 18:34:40] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0054 | Epoch 026] timing/rollout_seconds=179.5068 timing/feedback_seconds=5.0808 timing/optimization_seconds=50.4753 timing/cycle_seconds=232.8894
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+
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+
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Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.20it/s]
+[2026-10-01 18:37:43] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0054 | Epoch 027] train/reward_dists_mean=0.8197 train/reward_dists_std=0.0347 train/reward_ssim_mean=0.6364 train/reward_ssim_std=0.1145 train/reward_dists_group_std_mean=0.0107 train/reward_dists_group_std_max=0.0205 train/reward_dists_group_std_min=0.0050 train/reward_dists_group_mean_std=0.0327 train/reward_ssim_group_std_mean=0.0097 train/reward_ssim_group_std_max=0.0306 train/reward_ssim_group_std_min=0.0029 train/reward_ssim_group_mean_std=0.1139 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.5687 train/adv_max=4.9875 train/adv_abs_mean=1.1828
+
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Epoch 27 Training:  39%|███▊      | 37/96 [00:19<00:30,  1.93it/s]
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Epoch 27 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 18:38:08] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0054 | Epoch 027] train/clipped_loss_min=-4.9875 train/clipped_loss_max=4.3860 train/clipped_loss_mean=-0.0337 train/clipped_loss_std=1.5520 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-4.9875 train/unclipped_loss_max=4.3860 train/unclipped_loss_mean=-0.0337 train/unclipped_loss_std=1.5520 train/policy_loss=-0.0337 train/loss=-0.0337 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
+
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Epoch 27 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 18:38:32] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0055 | Epoch 027] train/clipped_loss_min=-4.6784 train/clipped_loss_max=4.5687 train/clipped_loss_mean=0.0337 train/clipped_loss_std=1.4752 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-4.6784 train/unclipped_loss_max=4.5687 train/unclipped_loss_mean=0.0337 train/unclipped_loss_std=1.4752 train/policy_loss=0.0337 train/loss=0.0337 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
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+[2026-10-01 18:38:32] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0056 | Epoch 027] timing/rollout_seconds=179.5234 timing/feedback_seconds=4.9783 timing/optimization_seconds=50.4345 timing/cycle_seconds=232.9199
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+
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+
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Pointwise Rewards: dists:  50%|█████     | 3/6 [00:00<00:00,  3.27it/s]
Pointwise Rewards: dists:  67%|██████▋   | 4/6 [00:01<00:00,  3.28it/s]
Pointwise Rewards: dists:  83%|████████▎ | 5/6 [00:01<00:00,  3.25it/s]
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Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.25it/s]
+[2026-10-01 18:41:36] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0056 | Epoch 028] train/reward_dists_mean=0.8323 train/reward_dists_std=0.0281 train/reward_ssim_mean=0.6337 train/reward_ssim_std=0.0762 train/reward_dists_group_std_mean=0.0097 train/reward_dists_group_std_max=0.0204 train/reward_dists_group_std_min=0.0046 train/reward_dists_group_mean_std=0.0261 train/reward_ssim_group_std_mean=0.0096 train/reward_ssim_group_std_max=0.0287 train/reward_ssim_group_std_min=0.0029 train/reward_ssim_group_mean_std=0.0754 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.2328 train/adv_max=4.2029 train/adv_abs_mean=1.1826
+
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Epoch 28 Training:   7%|▋         | 7/96 [00:03<00:45,  1.94it/s]
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Epoch 28 Training:   8%|▊         | 8/96 [00:04<00:45,  1.94it/s]
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Epoch 28 Training:   9%|▉         | 9/96 [00:04<00:44,  1.93it/s]
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Epoch 28 Training:  10%|█         | 10/96 [00:05<00:44,  1.93it/s]
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Epoch 28 Training:  11%|█▏        | 11/96 [00:05<00:44,  1.93it/s]
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Epoch 28 Training:  12%|█▎        | 12/96 [00:06<00:43,  1.93it/s]
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Epoch 28 Training:  14%|█▎        | 13/96 [00:06<00:42,  1.93it/s]
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Epoch 28 Training:  15%|█▍        | 14/96 [00:07<00:42,  1.93it/s]
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Epoch 28 Training:  16%|█▌        | 15/96 [00:07<00:41,  1.93it/s]
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Epoch 28 Training:  17%|█▋        | 16/96 [00:08<00:41,  1.93it/s]
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Epoch 28 Training:  18%|█▊        | 17/96 [00:08<00:40,  1.93it/s]
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Epoch 28 Training:  19%|█▉        | 18/96 [00:09<00:40,  1.93it/s]
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Epoch 28 Training:  26%|██▌       | 25/96 [00:12<00:36,  1.93it/s]
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Epoch 28 Training:  28%|██▊       | 27/96 [00:13<00:35,  1.93it/s]
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Epoch 28 Training:  29%|██▉       | 28/96 [00:14<00:35,  1.93it/s]
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Epoch 28 Training:  30%|███       | 29/96 [00:14<00:34,  1.93it/s]
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Epoch 28 Training:  32%|███▏      | 31/96 [00:16<00:33,  1.93it/s]
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Epoch 28 Training:  33%|███▎      | 32/96 [00:16<00:33,  1.93it/s]
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Epoch 28 Training:  34%|███▍      | 33/96 [00:17<00:32,  1.93it/s]
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Epoch 28 Training:  39%|███▊      | 37/96 [00:19<00:30,  1.93it/s]
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Epoch 28 Training:  40%|███▉      | 38/96 [00:19<00:30,  1.93it/s]
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Epoch 28 Training:  41%|████      | 39/96 [00:20<00:29,  1.93it/s]
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Epoch 28 Training:  44%|████▍     | 42/96 [00:21<00:28,  1.93it/s]
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Epoch 28 Training:  45%|████▍     | 43/96 [00:22<00:27,  1.93it/s]
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Epoch 28 Training:  46%|████▌     | 44/96 [00:22<00:26,  1.93it/s]
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Epoch 28 Training:  47%|████▋     | 45/96 [00:23<00:26,  1.93it/s]
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Epoch 28 Training:  48%|████▊     | 46/96 [00:23<00:25,  1.93it/s]
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Epoch 28 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 18:42:26] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0057 | Epoch 028] train/clipped_loss_min=-4.2029 train/clipped_loss_max=5.0000 train/clipped_loss_mean=-0.0597 train/clipped_loss_std=1.4682 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-4.2029 train/unclipped_loss_max=5.0000 train/unclipped_loss_mean=-0.0597 train/unclipped_loss_std=1.4682 train/policy_loss=-0.0597 train/loss=-0.0597 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0002
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+[2026-10-01 18:42:26] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0058 | Epoch 028] timing/rollout_seconds=179.6253 timing/feedback_seconds=5.3165 timing/optimization_seconds=50.4511 timing/cycle_seconds=233.0319
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+
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+
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+[2026-10-01 18:45:28] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0058 | Epoch 029] train/reward_dists_mean=0.8347 train/reward_dists_std=0.0335 train/reward_ssim_mean=0.6422 train/reward_ssim_std=0.1107 train/reward_dists_group_std_mean=0.0101 train/reward_dists_group_std_max=0.0193 train/reward_dists_group_std_min=0.0044 train/reward_dists_group_mean_std=0.0318 train/reward_ssim_group_std_mean=0.0094 train/reward_ssim_group_std_max=0.0313 train/reward_ssim_group_std_min=0.0031 train/reward_ssim_group_mean_std=0.1101 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.5340 train/adv_max=4.9137 train/adv_abs_mean=1.1913
+
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Epoch 29 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 18:45:53] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0058 | Epoch 029] train/clipped_loss_min=-4.9137 train/clipped_loss_max=4.5104 train/clipped_loss_mean=-0.0196 train/clipped_loss_std=1.4574 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-4.9137 train/unclipped_loss_max=4.5104 train/unclipped_loss_mean=-0.0196 train/unclipped_loss_std=1.4574 train/policy_loss=-0.0196 train/loss=-0.0196 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0004
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Epoch 29 Training:  99%|█████████▉| 95/96 [00:49<00:00,  1.92it/s]
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Epoch 29 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 18:46:18] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0059 | Epoch 029] train/clipped_loss_min=-4.1012 train/clipped_loss_max=4.5340 train/clipped_loss_mean=0.0196 train/clipped_loss_std=1.5921 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-4.1012 train/unclipped_loss_max=4.5340 train/unclipped_loss_mean=0.0196 train/unclipped_loss_std=1.5921 train/policy_loss=0.0196 train/loss=0.0196 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0005
+
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Epoch 29 Training: 100%|██████████| 96/96 [00:49<00:00,  1.85it/s]
Epoch 29 Training: 100%|██████████| 96/96 [00:49<00:00,  1.92it/s]
+[2026-10-01 18:46:18] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0060 | Epoch 029] timing/rollout_seconds=179.2885 timing/feedback_seconds=4.8943 timing/optimization_seconds=50.5412 timing/cycle_seconds=232.8772
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Eval/flir_rl_full:  25%|██▌       | 2/8 [00:14<00:41,  6.97s/it]
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Eval/flir_rl_full: 100%|██████████| 8/8 [00:52<00:00,  6.51s/it]
Eval/flir_rl_full: 100%|██████████| 8/8 [00:52<00:00,  6.57s/it]
+
Pointwise Rewards: ssim:   0%|          | 0/1 [00:00<?, ?it/s]
Pointwise Rewards: ssim: 100%|██████████| 1/1 [00:00<00:00,  6.74it/s]
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+
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Pointwise Rewards: dists: 100%|██████████| 1/1 [00:00<00:00,  7.03it/s]
+[2026-10-01 18:47:12] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0060 | Epoch 030] eval/flir_rl_full/reward_dists_mean=0.8388 eval/flir_rl_full/reward_dists_std=0.0363 eval/flir_rl_full/reward_psnr_mean=19.7328 eval/flir_rl_full/reward_psnr_std=2.8622 eval/flir_rl_full/reward_ssim_mean=0.6142 eval/flir_rl_full/reward_ssim_std=0.0883
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+
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+
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Pointwise Rewards: dists:  67%|██████▋   | 4/6 [00:01<00:00,  3.31it/s]
Pointwise Rewards: dists:  83%|████████▎ | 5/6 [00:01<00:00,  3.25it/s]
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Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.25it/s]
+[2026-10-01 18:50:15] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0060 | Epoch 030] train/reward_dists_mean=0.8325 train/reward_dists_std=0.0331 train/reward_ssim_mean=0.6124 train/reward_ssim_std=0.0970 train/reward_dists_group_std_mean=0.0092 train/reward_dists_group_std_max=0.0177 train/reward_dists_group_std_min=0.0027 train/reward_dists_group_mean_std=0.0316 train/reward_ssim_group_std_mean=0.0098 train/reward_ssim_group_std_max=0.0220 train/reward_ssim_group_std_min=0.0037 train/reward_ssim_group_mean_std=0.0964 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.4106 train/adv_max=4.2619 train/adv_abs_mean=1.2129
+
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Epoch 30 Training:  48%|████▊     | 46/96 [00:24<00:25,  1.93it/s]
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Epoch 30 Training:  49%|████▉     | 47/96 [00:24<00:25,  1.93it/s]
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Epoch 30 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 18:50:40] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0060 | Epoch 030] train/clipped_loss_min=-3.7271 train/clipped_loss_max=4.4106 train/clipped_loss_mean=0.0608 train/clipped_loss_std=1.4959 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-3.7271 train/unclipped_loss_max=4.4106 train/unclipped_loss_mean=0.0608 train/unclipped_loss_std=1.4959 train/policy_loss=0.0608 train/loss=0.0608 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0002
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Epoch 30 Training:  55%|█████▌    | 53/96 [00:27<00:22,  1.93it/s]
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Epoch 30 Training:  56%|█████▋    | 54/96 [00:28<00:21,  1.93it/s]
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Epoch 30 Training:  57%|█████▋    | 55/96 [00:28<00:21,  1.93it/s]
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Epoch 30 Training:  58%|█████▊    | 56/96 [00:29<00:20,  1.93it/s]
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Epoch 30 Training:  59%|█████▉    | 57/96 [00:29<00:20,  1.93it/s]
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Epoch 30 Training:  60%|██████    | 58/96 [00:30<00:19,  1.93it/s]
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Epoch 30 Training:  61%|██████▏   | 59/96 [00:30<00:19,  1.93it/s]
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Epoch 30 Training:  62%|██████▎   | 60/96 [00:31<00:18,  1.93it/s]
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Epoch 30 Training:  64%|██████▎   | 61/96 [00:31<00:18,  1.93it/s]
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Epoch 30 Training:  65%|██████▍   | 62/96 [00:32<00:17,  1.93it/s]
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Epoch 30 Training:  66%|██████▌   | 63/96 [00:33<00:17,  1.93it/s]
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Epoch 30 Training:  67%|██████▋   | 64/96 [00:33<00:16,  1.93it/s]
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Epoch 30 Training:  68%|██████▊   | 65/96 [00:34<00:16,  1.93it/s]
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Epoch 30 Training:  69%|██████▉   | 66/96 [00:34<00:15,  1.93it/s]
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Epoch 30 Training:  70%|██████▉   | 67/96 [00:35<00:15,  1.93it/s]
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Epoch 30 Training:  71%|███████   | 68/96 [00:35<00:14,  1.93it/s]
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Epoch 30 Training:  72%|███████▏  | 69/96 [00:36<00:14,  1.93it/s]
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Epoch 30 Training:  73%|███████▎  | 70/96 [00:36<00:13,  1.93it/s]
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Epoch 30 Training:  74%|███████▍  | 71/96 [00:37<00:12,  1.93it/s]
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Epoch 30 Training:  75%|███████▌  | 72/96 [00:37<00:12,  1.93it/s]
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Epoch 30 Training:  76%|███████▌  | 73/96 [00:38<00:11,  1.93it/s]
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Epoch 30 Training:  77%|███████▋  | 74/96 [00:38<00:11,  1.93it/s]
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Epoch 30 Training:  78%|███████▊  | 75/96 [00:39<00:10,  1.93it/s]
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Epoch 30 Training:  79%|███████▉  | 76/96 [00:39<00:10,  1.93it/s]
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Epoch 30 Training:  80%|████████  | 77/96 [00:40<00:09,  1.93it/s]
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Epoch 30 Training:  81%|████████▏ | 78/96 [00:40<00:09,  1.93it/s]
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Epoch 30 Training:  82%|████████▏ | 79/96 [00:41<00:08,  1.93it/s]
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Epoch 30 Training:  83%|████████▎ | 80/96 [00:41<00:08,  1.93it/s]
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Epoch 30 Training:  84%|████████▍ | 81/96 [00:42<00:07,  1.93it/s]
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Epoch 30 Training:  85%|████████▌ | 82/96 [00:42<00:07,  1.93it/s]
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Epoch 30 Training:  86%|████████▋ | 83/96 [00:43<00:06,  1.93it/s]
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Epoch 30 Training:  88%|████████▊ | 84/96 [00:43<00:06,  1.93it/s]
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Epoch 30 Training:  89%|████████▊ | 85/96 [00:44<00:05,  1.93it/s]
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Epoch 30 Training:  90%|████████▉ | 86/96 [00:44<00:05,  1.93it/s]
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Epoch 30 Training:  91%|█████████ | 87/96 [00:45<00:04,  1.93it/s]
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Epoch 30 Training:  92%|█████████▏| 88/96 [00:45<00:04,  1.93it/s]
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Epoch 30 Training:  93%|█████████▎| 89/96 [00:46<00:03,  1.93it/s]
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Epoch 30 Training:  96%|█████████▌| 92/96 [00:48<00:02,  1.93it/s]
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Epoch 30 Training:  97%|█████████▋| 93/96 [00:48<00:01,  1.92it/s]
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Epoch 30 Training:  98%|█████████▊| 94/96 [00:49<00:01,  1.93it/s]
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Epoch 30 Training:  99%|█████████▉| 95/96 [00:49<00:00,  1.91it/s]
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Epoch 30 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 18:51:05] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0061 | Epoch 030] train/clipped_loss_min=-4.2619 train/clipped_loss_max=4.3938 train/clipped_loss_mean=-0.0608 train/clipped_loss_std=1.4639 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-4.2619 train/unclipped_loss_max=4.3938 train/unclipped_loss_mean=-0.0608 train/unclipped_loss_std=1.4639 train/policy_loss=-0.0608 train/loss=-0.0608 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0002
+
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Epoch 30 Timestep: 100%|██████████| 1/1 [00:00<00:00,  1.90it/s]
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Epoch 30 Training: 100%|██████████| 96/96 [00:50<00:00,  1.85it/s]
Epoch 30 Training: 100%|██████████| 96/96 [00:50<00:00,  1.91it/s]
+[2026-10-01 18:51:05] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0062 | Epoch 030] timing/rollout_seconds=179.4183 timing/feedback_seconds=4.7954 timing/optimization_seconds=50.8586 timing/cycle_seconds=233.1708
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+
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+
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Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.32it/s]
+[2026-10-01 18:54:08] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0062 | Epoch 031] train/reward_dists_mean=0.8284 train/reward_dists_std=0.0336 train/reward_ssim_mean=0.6248 train/reward_ssim_std=0.1171 train/reward_dists_group_std_mean=0.0101 train/reward_dists_group_std_max=0.0215 train/reward_dists_group_std_min=0.0046 train/reward_dists_group_mean_std=0.0318 train/reward_ssim_group_std_mean=0.0097 train/reward_ssim_group_std_max=0.0226 train/reward_ssim_group_std_min=0.0039 train/reward_ssim_group_mean_std=0.1165 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.1988 train/adv_max=6.1804 train/adv_abs_mean=1.2135
+
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Epoch 31 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 18:54:33] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0062 | Epoch 031] train/clipped_loss_min=-3.9582 train/clipped_loss_max=4.4953 train/clipped_loss_mean=0.0361 train/clipped_loss_std=1.4090 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-3.9582 train/unclipped_loss_max=4.4953 train/unclipped_loss_mean=0.0361 train/unclipped_loss_std=1.4090 train/policy_loss=0.0361 train/loss=0.0361 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
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Epoch 31 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 18:54:57] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0063 | Epoch 031] train/clipped_loss_min=-5.0000 train/clipped_loss_max=5.0000 train/clipped_loss_mean=-0.0336 train/clipped_loss_std=1.6496 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0.0002 train/unclipped_loss_min=-5.0000 train/unclipped_loss_max=5.0000 train/unclipped_loss_mean=-0.0336 train/unclipped_loss_std=1.6496 train/policy_loss=-0.0336 train/loss=-0.0336 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
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Epoch 31 Training: 100%|██████████| 96/96 [00:49<00:00,  1.93it/s]
+[2026-10-01 18:54:57] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0064 | Epoch 031] timing/rollout_seconds=179.3594 timing/feedback_seconds=4.6702 timing/optimization_seconds=50.4729 timing/cycle_seconds=232.6923
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+[2026-10-01 18:58:01] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0064 | Epoch 032] train/reward_dists_mean=0.8234 train/reward_dists_std=0.0350 train/reward_ssim_mean=0.6386 train/reward_ssim_std=0.1002 train/reward_dists_group_std_mean=0.0099 train/reward_dists_group_std_max=0.0165 train/reward_dists_group_std_min=0.0038 train/reward_dists_group_mean_std=0.0334 train/reward_ssim_group_std_mean=0.0084 train/reward_ssim_group_std_max=0.0200 train/reward_ssim_group_std_min=0.0025 train/reward_ssim_group_mean_std=0.0998 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.0742 train/adv_max=4.0291 train/adv_abs_mean=1.1749
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Epoch 32 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 18:58:25] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0064 | Epoch 032] train/clipped_loss_min=-3.4075 train/clipped_loss_max=5 train/clipped_loss_mean=-0.0041 train/clipped_loss_std=1.4947 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-3.4075 train/unclipped_loss_max=5 train/unclipped_loss_mean=-0.0041 train/unclipped_loss_std=1.4947 train/policy_loss=-0.0041 train/loss=-0.0041 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0005
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Epoch 32 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 18:58:50] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0065 | Epoch 032] train/clipped_loss_min=-4.0291 train/clipped_loss_max=4.3141 train/clipped_loss_mean=0.0038 train/clipped_loss_std=1.4686 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-4.0291 train/unclipped_loss_max=4.3141 train/unclipped_loss_mean=0.0038 train/unclipped_loss_std=1.4686 train/policy_loss=0.0038 train/loss=0.0038 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0004
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+[2026-10-01 18:58:50] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0066 | Epoch 032] timing/rollout_seconds=179.4439 timing/feedback_seconds=5.1429 timing/optimization_seconds=50.4856 timing/cycle_seconds=232.8664
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+
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+[2026-10-01 19:01:53] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0066 | Epoch 033] train/reward_dists_mean=0.8277 train/reward_dists_std=0.0325 train/reward_ssim_mean=0.6377 train/reward_ssim_std=0.1087 train/reward_dists_group_std_mean=0.0096 train/reward_dists_group_std_max=0.0172 train/reward_dists_group_std_min=0.0048 train/reward_dists_group_mean_std=0.0309 train/reward_ssim_group_std_mean=0.0095 train/reward_ssim_group_std_max=0.0262 train/reward_ssim_group_std_min=0.0031 train/reward_ssim_group_mean_std=0.1082 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.4570 train/adv_max=4.0922 train/adv_abs_mean=1.1799
+
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Epoch 33 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 19:02:19] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0066 | Epoch 033] train/clipped_loss_min=-4.0922 train/clipped_loss_max=3.8665 train/clipped_loss_mean=-0.0205 train/clipped_loss_std=1.4857 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-4.0922 train/unclipped_loss_max=3.8665 train/unclipped_loss_mean=-0.0205 train/unclipped_loss_std=1.4857 train/policy_loss=-0.0205 train/loss=-0.0205 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0005
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+[2026-10-01 19:02:44] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0068 | Epoch 033] timing/rollout_seconds=179.3682 timing/feedback_seconds=5.2107 timing/optimization_seconds=50.9870 timing/cycle_seconds=233.3594
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+
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+[2026-10-01 19:05:47] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0068 | Epoch 034] train/reward_dists_mean=0.8324 train/reward_dists_std=0.0357 train/reward_ssim_mean=0.6405 train/reward_ssim_std=0.1241 train/reward_dists_group_std_mean=0.0102 train/reward_dists_group_std_max=0.0223 train/reward_dists_group_std_min=0.0041 train/reward_dists_group_mean_std=0.0339 train/reward_ssim_group_std_mean=0.0098 train/reward_ssim_group_std_max=0.0227 train/reward_ssim_group_std_min=0.0034 train/reward_ssim_group_mean_std=0.1237 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.0157 train/adv_max=3.7329 train/adv_abs_mean=1.1738
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Epoch 34 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 19:06:12] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0068 | Epoch 034] train/clipped_loss_min=-3.3653 train/clipped_loss_max=5 train/clipped_loss_mean=0.0242 train/clipped_loss_std=1.4983 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-3.3653 train/unclipped_loss_max=5 train/unclipped_loss_mean=0.0242 train/unclipped_loss_std=1.4983 train/policy_loss=0.0242 train/loss=0.0242 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0004
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Epoch 34 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 19:06:37] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0069 | Epoch 034] train/clipped_loss_min=-3.7329 train/clipped_loss_max=4.9146 train/clipped_loss_mean=-0.0242 train/clipped_loss_std=1.4870 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0.0001 train/unclipped_loss_min=-3.7329 train/unclipped_loss_max=4.9146 train/unclipped_loss_mean=-0.0242 train/unclipped_loss_std=1.4870 train/policy_loss=-0.0242 train/loss=-0.0242 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0005
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+[2026-10-01 19:06:37] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0070 | Epoch 034] timing/rollout_seconds=179.4115 timing/feedback_seconds=5.3017 timing/optimization_seconds=50.5323 timing/cycle_seconds=232.8494
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+
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+[2026-10-01 19:09:39] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0070 | Epoch 035] train/reward_dists_mean=0.8332 train/reward_dists_std=0.0358 train/reward_ssim_mean=0.6396 train/reward_ssim_std=0.1102 train/reward_dists_group_std_mean=0.0098 train/reward_dists_group_std_max=0.0268 train/reward_dists_group_std_min=0.0033 train/reward_dists_group_mean_std=0.0342 train/reward_ssim_group_std_mean=0.0099 train/reward_ssim_group_std_max=0.0212 train/reward_ssim_group_std_min=0.0055 train/reward_ssim_group_mean_std=0.1097 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.1222 train/adv_max=4.8589 train/adv_abs_mean=1.1947
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Epoch 35 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 19:10:04] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0070 | Epoch 035] train/clipped_loss_min=-4.8589 train/clipped_loss_max=4.8246 train/clipped_loss_mean=0.0414 train/clipped_loss_std=1.4862 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-4.8589 train/unclipped_loss_max=4.8246 train/unclipped_loss_mean=0.0414 train/unclipped_loss_std=1.4862 train/policy_loss=0.0414 train/loss=0.0414 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0004
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Epoch 35 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 19:10:29] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0071 | Epoch 035] train/clipped_loss_min=-3.8297 train/clipped_loss_max=5.0000 train/clipped_loss_mean=-0.0417 train/clipped_loss_std=1.5472 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0.0001 train/unclipped_loss_min=-3.8297 train/unclipped_loss_max=5.0000 train/unclipped_loss_mean=-0.0417 train/unclipped_loss_std=1.5472 train/policy_loss=-0.0417 train/loss=-0.0417 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0004
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+[2026-10-01 19:10:29] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0072 | Epoch 035] timing/rollout_seconds=179.3342 timing/feedback_seconds=5.0404 timing/optimization_seconds=50.3874 timing/cycle_seconds=232.5726
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+
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+[2026-10-01 19:13:32] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0072 | Epoch 036] train/reward_dists_mean=0.8375 train/reward_dists_std=0.0236 train/reward_ssim_mean=0.6280 train/reward_ssim_std=0.1027 train/reward_dists_group_std_mean=0.0091 train/reward_dists_group_std_max=0.0177 train/reward_dists_group_std_min=0.0041 train/reward_dists_group_mean_std=0.0216 train/reward_ssim_group_std_mean=0.0100 train/reward_ssim_group_std_max=0.0217 train/reward_ssim_group_std_min=0.0051 train/reward_ssim_group_mean_std=0.1021 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.2215 train/adv_max=4.8930 train/adv_abs_mean=1.2312
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Epoch 36 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 19:13:57] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0072 | Epoch 036] train/clipped_loss_min=-3.5645 train/clipped_loss_max=5 train/clipped_loss_mean=0.1134 train/clipped_loss_std=1.5300 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-3.5645 train/unclipped_loss_max=5 train/unclipped_loss_mean=0.1134 train/unclipped_loss_std=1.5300 train/policy_loss=0.1134 train/loss=0.1134 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
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Epoch 36 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 19:14:22] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0073 | Epoch 036] train/clipped_loss_min=-4.8930 train/clipped_loss_max=3.9459 train/clipped_loss_mean=-0.1140 train/clipped_loss_std=1.5409 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-4.8930 train/unclipped_loss_max=3.9459 train/unclipped_loss_mean=-0.1140 train/unclipped_loss_std=1.5409 train/policy_loss=-0.1140 train/loss=-0.1140 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
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+[2026-10-01 19:14:22] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0074 | Epoch 036] timing/rollout_seconds=179.3444 timing/feedback_seconds=5.1177 timing/optimization_seconds=50.5504 timing/cycle_seconds=232.9349
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+[2026-10-01 19:17:25] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0074 | Epoch 037] train/reward_dists_mean=0.8365 train/reward_dists_std=0.0313 train/reward_ssim_mean=0.6360 train/reward_ssim_std=0.1073 train/reward_dists_group_std_mean=0.0095 train/reward_dists_group_std_max=0.0207 train/reward_dists_group_std_min=0.0033 train/reward_dists_group_mean_std=0.0297 train/reward_ssim_group_std_mean=0.0102 train/reward_ssim_group_std_max=0.0243 train/reward_ssim_group_std_min=0.0038 train/reward_ssim_group_mean_std=0.1067 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.5910 train/adv_max=5.0216 train/adv_abs_mean=1.1367
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Epoch 37 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 19:17:50] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0074 | Epoch 037] train/clipped_loss_min=-4.9573 train/clipped_loss_max=3.8188 train/clipped_loss_mean=-0.0700 train/clipped_loss_std=1.4587 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-4.9573 train/unclipped_loss_max=3.8188 train/unclipped_loss_mean=-0.0700 train/unclipped_loss_std=1.4587 train/policy_loss=-0.0700 train/loss=-0.0700 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0001
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Epoch 37 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 19:18:15] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0075 | Epoch 037] train/clipped_loss_min=-5.0000 train/clipped_loss_max=4.5910 train/clipped_loss_mean=0.0700 train/clipped_loss_std=1.4315 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-5.0000 train/unclipped_loss_max=4.5910 train/unclipped_loss_mean=0.0700 train/unclipped_loss_std=1.4315 train/policy_loss=0.0700 train/loss=0.0700 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0001
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+[2026-10-01 19:18:15] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0076 | Epoch 037] timing/rollout_seconds=179.4717 timing/feedback_seconds=5.3013 timing/optimization_seconds=50.4587 timing/cycle_seconds=232.7628
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+[2026-10-01 19:21:18] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0076 | Epoch 038] train/reward_dists_mean=0.8363 train/reward_dists_std=0.0299 train/reward_ssim_mean=0.6487 train/reward_ssim_std=0.0903 train/reward_dists_group_std_mean=0.0091 train/reward_dists_group_std_max=0.0249 train/reward_dists_group_std_min=0.0046 train/reward_dists_group_mean_std=0.0282 train/reward_ssim_group_std_mean=0.0097 train/reward_ssim_group_std_max=0.0226 train/reward_ssim_group_std_min=0.0039 train/reward_ssim_group_mean_std=0.0897 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.4334 train/adv_max=4.1704 train/adv_abs_mean=1.1846
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Epoch 38 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 19:21:43] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0076 | Epoch 038] train/clipped_loss_min=-4.1704 train/clipped_loss_max=4.3430 train/clipped_loss_mean=-0.0829 train/clipped_loss_std=1.4820 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-4.1704 train/unclipped_loss_max=4.3430 train/unclipped_loss_mean=-0.0829 train/unclipped_loss_std=1.4820 train/policy_loss=-0.0829 train/loss=-0.0829 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
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Epoch 38 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 19:22:07] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0077 | Epoch 038] train/clipped_loss_min=-3.9196 train/clipped_loss_max=4.4334 train/clipped_loss_mean=0.0829 train/clipped_loss_std=1.5031 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-3.9196 train/unclipped_loss_max=4.4334 train/unclipped_loss_mean=0.0829 train/unclipped_loss_std=1.5031 train/policy_loss=0.0829 train/loss=0.0829 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
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+[2026-10-01 19:22:07] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0078 | Epoch 038] timing/rollout_seconds=179.2956 timing/feedback_seconds=4.9462 timing/optimization_seconds=50.4177 timing/cycle_seconds=232.5932
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+
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+[2026-10-01 19:25:10] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0078 | Epoch 039] train/reward_dists_mean=0.8452 train/reward_dists_std=0.0294 train/reward_ssim_mean=0.6240 train/reward_ssim_std=0.0916 train/reward_dists_group_std_mean=0.0084 train/reward_dists_group_std_max=0.0205 train/reward_dists_group_std_min=0.0038 train/reward_dists_group_mean_std=0.0280 train/reward_ssim_group_std_mean=0.0108 train/reward_ssim_group_std_max=0.0212 train/reward_ssim_group_std_min=0.0046 train/reward_ssim_group_mean_std=0.0909 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.1030 train/adv_max=3.9608 train/adv_abs_mean=1.1184
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Epoch 39 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 19:25:35] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0078 | Epoch 039] train/clipped_loss_min=-3.9608 train/clipped_loss_max=5 train/clipped_loss_mean=-0.0623 train/clipped_loss_std=1.3861 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-3.9608 train/unclipped_loss_max=5 train/unclipped_loss_mean=-0.0623 train/unclipped_loss_std=1.3861 train/policy_loss=-0.0623 train/loss=-0.0623 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0001
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+[2026-10-01 19:26:00] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0080 | Epoch 039] timing/rollout_seconds=179.4337 timing/feedback_seconds=4.8540 timing/optimization_seconds=50.6312 timing/cycle_seconds=232.9428
+[2026-10-01 19:26:00] [Rank 0] [INFO] [flow_factory.models.abc]: Saving LoRA weights for transformer role=base to /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/runs/klein4b_grpo_flir_full_ssim_dists_lr5e-5/saves/klein4b_grpo_flir_full_ssim_dists_lr5e-5/checkpoints/checkpoint-40
+[2026-10-01 19:26:01] [Rank 0] [INFO] [flow_factory.models.abc]: Checkpoint saved successfully to /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/runs/klein4b_grpo_flir_full_ssim_dists_lr5e-5/saves/klein4b_grpo_flir_full_ssim_dists_lr5e-5/checkpoints/checkpoint-40
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+[2026-10-01 19:26:54] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0080 | Epoch 040] eval/flir_rl_full/reward_dists_mean=0.8395 eval/flir_rl_full/reward_dists_std=0.0373 eval/flir_rl_full/reward_psnr_mean=19.9598 eval/flir_rl_full/reward_psnr_std=2.9598 eval/flir_rl_full/reward_ssim_mean=0.6170 eval/flir_rl_full/reward_ssim_std=0.0890
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+[2026-10-01 19:29:57] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0080 | Epoch 040] train/reward_dists_mean=0.8458 train/reward_dists_std=0.0287 train/reward_ssim_mean=0.6429 train/reward_ssim_std=0.0936 train/reward_dists_group_std_mean=0.0087 train/reward_dists_group_std_max=0.0165 train/reward_dists_group_std_min=0.0042 train/reward_dists_group_mean_std=0.0272 train/reward_ssim_group_std_mean=0.0110 train/reward_ssim_group_std_max=0.0221 train/reward_ssim_group_std_min=0.0057 train/reward_ssim_group_mean_std=0.0929 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.8483 train/adv_max=5.2860 train/adv_abs_mean=1.1472
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Epoch 40 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 19:30:22] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0080 | Epoch 040] train/clipped_loss_min=-5 train/clipped_loss_max=4.8483 train/clipped_loss_mean=0.0035 train/clipped_loss_std=1.5519 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-5 train/unclipped_loss_max=4.8483 train/unclipped_loss_mean=0.0035 train/unclipped_loss_std=1.5519 train/policy_loss=0.0035 train/loss=0.0035 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0001
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Epoch 40 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 19:30:47] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0081 | Epoch 040] train/clipped_loss_min=-4.6501 train/clipped_loss_max=3.5154 train/clipped_loss_mean=-0.0027 train/clipped_loss_std=1.3600 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0.0001 train/unclipped_loss_min=-4.6501 train/unclipped_loss_max=3.5154 train/unclipped_loss_mean=-0.0027 train/unclipped_loss_std=1.3600 train/policy_loss=-0.0027 train/loss=-0.0027 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0001
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+[2026-10-01 19:30:47] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0082 | Epoch 040] timing/rollout_seconds=179.5838 timing/feedback_seconds=5.2369 timing/optimization_seconds=50.4466 timing/cycle_seconds=232.9382
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+[2026-10-01 19:33:50] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0082 | Epoch 041] train/reward_dists_mean=0.8375 train/reward_dists_std=0.0327 train/reward_ssim_mean=0.6425 train/reward_ssim_std=0.1057 train/reward_dists_group_std_mean=0.0093 train/reward_dists_group_std_max=0.0208 train/reward_dists_group_std_min=0.0046 train/reward_dists_group_mean_std=0.0311 train/reward_ssim_group_std_mean=0.0118 train/reward_ssim_group_std_max=0.0206 train/reward_ssim_group_std_min=0.0048 train/reward_ssim_group_mean_std=0.1050 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.0887 train/adv_max=4.7808 train/adv_abs_mean=1.1945
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Epoch 41 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 19:34:15] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0082 | Epoch 041] train/clipped_loss_min=-4.7808 train/clipped_loss_max=5 train/clipped_loss_mean=-0.0930 train/clipped_loss_std=1.5075 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-4.7808 train/unclipped_loss_max=5 train/unclipped_loss_mean=-0.0930 train/unclipped_loss_std=1.5075 train/policy_loss=-0.0930 train/loss=-0.0930 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0004
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Epoch 41 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 19:34:40] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0083 | Epoch 041] train/clipped_loss_min=-4.7512 train/clipped_loss_max=4.3093 train/clipped_loss_mean=0.0928 train/clipped_loss_std=1.4884 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0.0001 train/unclipped_loss_min=-4.7512 train/unclipped_loss_max=4.3093 train/unclipped_loss_mean=0.0928 train/unclipped_loss_std=1.4884 train/policy_loss=0.0928 train/loss=0.0928 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0004
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+[2026-10-01 19:34:40] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0084 | Epoch 041] timing/rollout_seconds=179.4354 timing/feedback_seconds=5.2173 timing/optimization_seconds=50.4185 timing/cycle_seconds=232.8012
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+[2026-10-01 19:37:43] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0084 | Epoch 042] train/reward_dists_mean=0.8367 train/reward_dists_std=0.0272 train/reward_ssim_mean=0.6115 train/reward_ssim_std=0.0887 train/reward_dists_group_std_mean=0.0084 train/reward_dists_group_std_max=0.0157 train/reward_dists_group_std_min=0.0038 train/reward_dists_group_mean_std=0.0257 train/reward_ssim_group_std_mean=0.0114 train/reward_ssim_group_std_max=0.0223 train/reward_ssim_group_std_min=0.0047 train/reward_ssim_group_mean_std=0.0879 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.1702 train/adv_max=5.3806 train/adv_abs_mean=1.1705
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Epoch 42 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 19:38:08] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0084 | Epoch 042] train/clipped_loss_min=-5 train/clipped_loss_max=4.1702 train/clipped_loss_mean=0.1130 train/clipped_loss_std=1.5170 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-5 train/unclipped_loss_max=4.1702 train/unclipped_loss_mean=0.1130 train/unclipped_loss_std=1.5170 train/policy_loss=0.1130 train/loss=0.1130 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
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Epoch 42 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 19:38:33] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0085 | Epoch 042] train/clipped_loss_min=-5.0000 train/clipped_loss_max=3.3013 train/clipped_loss_mean=-0.1115 train/clipped_loss_std=1.4233 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-5.0000 train/unclipped_loss_max=3.3013 train/unclipped_loss_mean=-0.1115 train/unclipped_loss_std=1.4233 train/policy_loss=-0.1115 train/loss=-0.1115 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
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+[2026-10-01 19:38:33] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0086 | Epoch 042] timing/rollout_seconds=179.3983 timing/feedback_seconds=5.3505 timing/optimization_seconds=50.6988 timing/cycle_seconds=233.0706
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+[2026-10-01 19:41:36] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0086 | Epoch 043] train/reward_dists_mean=0.8468 train/reward_dists_std=0.0299 train/reward_ssim_mean=0.6198 train/reward_ssim_std=0.0953 train/reward_dists_group_std_mean=0.0081 train/reward_dists_group_std_max=0.0149 train/reward_dists_group_std_min=0.0034 train/reward_dists_group_mean_std=0.0286 train/reward_ssim_group_std_mean=0.0101 train/reward_ssim_group_std_max=0.0242 train/reward_ssim_group_std_min=0.0032 train/reward_ssim_group_mean_std=0.0946 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.7597 train/adv_max=5.2610 train/adv_abs_mean=1.1920
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Epoch 43 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 19:42:01] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0086 | Epoch 043] train/clipped_loss_min=-3.8874 train/clipped_loss_max=4.4148 train/clipped_loss_mean=-0.0115 train/clipped_loss_std=1.4973 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-3.8874 train/unclipped_loss_max=4.4148 train/unclipped_loss_mean=-0.0115 train/unclipped_loss_std=1.4973 train/policy_loss=-0.0115 train/loss=-0.0115 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0001
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Epoch 43 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 19:42:26] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0087 | Epoch 043] train/clipped_loss_min=-5.0000 train/clipped_loss_max=4.7597 train/clipped_loss_mean=0.0121 train/clipped_loss_std=1.4618 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0.0001 train/unclipped_loss_min=-5.0000 train/unclipped_loss_max=4.7597 train/unclipped_loss_mean=0.0121 train/unclipped_loss_std=1.4618 train/policy_loss=0.0121 train/loss=0.0121 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0001
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+[2026-10-01 19:42:26] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0088 | Epoch 043] timing/rollout_seconds=179.3155 timing/feedback_seconds=5.1743 timing/optimization_seconds=50.4763 timing/cycle_seconds=232.8699
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+
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+[2026-10-01 19:45:29] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0088 | Epoch 044] train/reward_dists_mean=0.8456 train/reward_dists_std=0.0310 train/reward_ssim_mean=0.6544 train/reward_ssim_std=0.0774 train/reward_dists_group_std_mean=0.0078 train/reward_dists_group_std_max=0.0148 train/reward_dists_group_std_min=0.0030 train/reward_dists_group_mean_std=0.0299 train/reward_ssim_group_std_mean=0.0106 train/reward_ssim_group_std_max=0.0207 train/reward_ssim_group_std_min=0.0044 train/reward_ssim_group_mean_std=0.0765 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.4591 train/adv_max=5.4620 train/adv_abs_mean=1.1713
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Epoch 44 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 19:45:54] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0088 | Epoch 044] train/clipped_loss_min=-4.0851 train/clipped_loss_max=4.4591 train/clipped_loss_mean=0.0250 train/clipped_loss_std=1.4243 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-4.0851 train/unclipped_loss_max=4.4591 train/unclipped_loss_mean=0.0250 train/unclipped_loss_std=1.4243 train/policy_loss=0.0250 train/loss=0.0250 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0002
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Epoch 44 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 19:46:19] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0089 | Epoch 044] train/clipped_loss_min=-5.0000 train/clipped_loss_max=4.1465 train/clipped_loss_mean=-0.0238 train/clipped_loss_std=1.5226 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0.0001 train/unclipped_loss_min=-5.0000 train/unclipped_loss_max=4.1465 train/unclipped_loss_mean=-0.0238 train/unclipped_loss_std=1.5226 train/policy_loss=-0.0238 train/loss=-0.0238 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0002
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+[2026-10-01 19:46:19] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0090 | Epoch 044] timing/rollout_seconds=179.4797 timing/feedback_seconds=5.4507 timing/optimization_seconds=50.7158 timing/cycle_seconds=233.1133
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+[2026-10-01 19:49:22] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0090 | Epoch 045] train/reward_dists_mean=0.8435 train/reward_dists_std=0.0306 train/reward_ssim_mean=0.6365 train/reward_ssim_std=0.1031 train/reward_dists_group_std_mean=0.0083 train/reward_dists_group_std_max=0.0153 train/reward_dists_group_std_min=0.0039 train/reward_dists_group_mean_std=0.0293 train/reward_ssim_group_std_mean=0.0105 train/reward_ssim_group_std_max=0.0338 train/reward_ssim_group_std_min=0.0036 train/reward_ssim_group_mean_std=0.1024 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.1133 train/adv_max=4.8218 train/adv_abs_mean=1.1858
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Epoch 45 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 19:49:47] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0090 | Epoch 045] train/clipped_loss_min=-4.8218 train/clipped_loss_max=5 train/clipped_loss_mean=-0.0654 train/clipped_loss_std=1.4972 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-4.8218 train/unclipped_loss_max=5 train/unclipped_loss_mean=-0.0654 train/unclipped_loss_std=1.4972 train/policy_loss=-0.0654 train/loss=-0.0654 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0004
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Epoch 45 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 19:50:12] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0091 | Epoch 045] train/clipped_loss_min=-4.2273 train/clipped_loss_max=5.0000 train/clipped_loss_mean=0.0648 train/clipped_loss_std=1.5221 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-4.2273 train/unclipped_loss_max=5.0000 train/unclipped_loss_mean=0.0648 train/unclipped_loss_std=1.5221 train/policy_loss=0.0648 train/loss=0.0648 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0004
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+[2026-10-01 19:50:12] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0092 | Epoch 045] timing/rollout_seconds=179.4461 timing/feedback_seconds=5.4538 timing/optimization_seconds=50.6709 timing/cycle_seconds=233.0455
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+[2026-10-01 19:53:15] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0092 | Epoch 046] train/reward_dists_mean=0.8461 train/reward_dists_std=0.0270 train/reward_ssim_mean=0.6397 train/reward_ssim_std=0.0969 train/reward_dists_group_std_mean=0.0078 train/reward_dists_group_std_max=0.0160 train/reward_dists_group_std_min=0.0038 train/reward_dists_group_mean_std=0.0257 train/reward_ssim_group_std_mean=0.0093 train/reward_ssim_group_std_max=0.0198 train/reward_ssim_group_std_min=0.0036 train/reward_ssim_group_mean_std=0.0964 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-6.0327 train/adv_max=5.4000 train/adv_abs_mean=1.1533
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Epoch 46 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 19:53:40] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0092 | Epoch 046] train/clipped_loss_min=-5 train/clipped_loss_max=5 train/clipped_loss_mean=-0.0193 train/clipped_loss_std=1.4636 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-5 train/unclipped_loss_max=5 train/unclipped_loss_mean=-0.0193 train/unclipped_loss_std=1.4636 train/policy_loss=-0.0193 train/loss=-0.0193 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0004
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Epoch 46 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 19:54:05] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0093 | Epoch 046] train/clipped_loss_min=-5.0000 train/clipped_loss_max=4.4484 train/clipped_loss_mean=0.0183 train/clipped_loss_std=1.4466 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-5.0000 train/unclipped_loss_max=4.4484 train/unclipped_loss_mean=0.0183 train/unclipped_loss_std=1.4466 train/policy_loss=0.0183 train/loss=0.0183 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0004
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+[2026-10-01 19:54:05] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0094 | Epoch 046] timing/rollout_seconds=179.4805 timing/feedback_seconds=5.5562 timing/optimization_seconds=50.4098 timing/cycle_seconds=232.8880
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+[2026-10-01 19:57:09] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0094 | Epoch 047] train/reward_dists_mean=0.8477 train/reward_dists_std=0.0293 train/reward_ssim_mean=0.6625 train/reward_ssim_std=0.0925 train/reward_dists_group_std_mean=0.0081 train/reward_dists_group_std_max=0.0145 train/reward_dists_group_std_min=0.0043 train/reward_dists_group_mean_std=0.0280 train/reward_ssim_group_std_mean=0.0097 train/reward_ssim_group_std_max=0.0207 train/reward_ssim_group_std_min=0.0043 train/reward_ssim_group_mean_std=0.0919 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.4689 train/adv_max=3.8992 train/adv_abs_mean=1.1793
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Epoch 47 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 19:57:34] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0094 | Epoch 047] train/clipped_loss_min=-3.8886 train/clipped_loss_max=3.9289 train/clipped_loss_mean=0.0260 train/clipped_loss_std=1.4570 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-3.8886 train/unclipped_loss_max=3.9289 train/unclipped_loss_mean=0.0260 train/unclipped_loss_std=1.4570 train/policy_loss=0.0260 train/loss=0.0260 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0001
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Epoch 47 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 19:57:59] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0095 | Epoch 047] train/clipped_loss_min=-3.8992 train/clipped_loss_max=5.0000 train/clipped_loss_mean=-0.0272 train/clipped_loss_std=1.5095 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-3.8992 train/unclipped_loss_max=5.0000 train/unclipped_loss_mean=-0.0272 train/unclipped_loss_std=1.5095 train/policy_loss=-0.0272 train/loss=-0.0272 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0001
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+[2026-10-01 19:57:59] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0096 | Epoch 047] timing/rollout_seconds=179.7717 timing/feedback_seconds=5.1625 timing/optimization_seconds=50.7483 timing/cycle_seconds=233.5989
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Epoch 48 Sampling:  85%|████████▌ | 82/96 [02:31<00:25,  1.85s/it]
Epoch 48 Sampling:  86%|████████▋ | 83/96 [02:33<00:23,  1.85s/it]
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Epoch 48 Sampling:  91%|█████████ | 87/96 [02:40<00:16,  1.85s/it]
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Epoch 48 Sampling:  95%|█████████▍| 91/96 [02:48<00:09,  1.84s/it]
Epoch 48 Sampling:  96%|█████████▌| 92/96 [02:49<00:07,  1.84s/it]
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Epoch 48 Sampling: 100%|██████████| 96/96 [02:57<00:00,  1.85s/it]
+
Pointwise Rewards: ssim:   0%|          | 0/6 [00:00<?, ?it/s]
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Pointwise Rewards: ssim:  67%|██████▋   | 4/6 [00:00<00:00,  5.79it/s]
Pointwise Rewards: ssim:  83%|████████▎ | 5/6 [00:00<00:00,  5.90it/s]
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Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:01<00:00,  5.80it/s]
+
Pointwise Rewards: dists:   0%|          | 0/6 [00:00<?, ?it/s]
Pointwise Rewards: dists:  17%|█▋        | 1/6 [00:00<00:01,  3.37it/s]
Pointwise Rewards: dists:  33%|███▎      | 2/6 [00:00<00:01,  3.44it/s]
Pointwise Rewards: dists:  50%|█████     | 3/6 [00:00<00:00,  3.35it/s]
Pointwise Rewards: dists:  67%|██████▋   | 4/6 [00:01<00:00,  3.35it/s]
Pointwise Rewards: dists:  83%|████████▎ | 5/6 [00:01<00:00,  3.36it/s]
Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.33it/s]
Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.35it/s]
+[2026-10-01 20:01:03] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0096 | Epoch 048] train/reward_dists_mean=0.8349 train/reward_dists_std=0.0251 train/reward_ssim_mean=0.6376 train/reward_ssim_std=0.0994 train/reward_dists_group_std_mean=0.0090 train/reward_dists_group_std_max=0.0220 train/reward_dists_group_std_min=0.0030 train/reward_dists_group_mean_std=0.0231 train/reward_ssim_group_std_mean=0.0102 train/reward_ssim_group_std_max=0.0250 train/reward_ssim_group_std_min=0.0039 train/reward_ssim_group_mean_std=0.0987 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.2858 train/adv_max=4.7633 train/adv_abs_mean=1.2003
+
Epoch 48 Training:   0%|          | 0/96 [00:00<?, ?it/s]
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Epoch 48 Training:   2%|▏         | 2/96 [00:00<00:46,  2.02it/s]
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Epoch 48 Training:   4%|▍         | 4/96 [00:02<00:47,  1.95it/s]
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Epoch 48 Training:   5%|▌         | 5/96 [00:02<00:46,  1.94it/s]
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Epoch 48 Training:   6%|▋         | 6/96 [00:03<00:46,  1.94it/s]
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Epoch 48 Training:   7%|▋         | 7/96 [00:03<00:46,  1.93it/s]
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Epoch 48 Training:   8%|▊         | 8/96 [00:04<00:45,  1.93it/s]
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+
                                                                
Epoch 48 Training:   9%|▉         | 9/96 [00:04<00:45,  1.93it/s]
+
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Epoch 48 Training:  10%|█         | 10/96 [00:05<00:44,  1.93it/s]
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Epoch 48 Training:  11%|█▏        | 11/96 [00:05<00:44,  1.92it/s]
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Epoch 48 Training:  12%|█▎        | 12/96 [00:06<00:43,  1.92it/s]
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Epoch 48 Training:  14%|█▎        | 13/96 [00:06<00:43,  1.92it/s]
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+
                                                                
Epoch 48 Training:  15%|█▍        | 14/96 [00:07<00:42,  1.92it/s]
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+
                                                                
Epoch 48 Training:  16%|█▌        | 15/96 [00:07<00:42,  1.92it/s]
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Epoch 48 Training:  17%|█▋        | 16/96 [00:08<00:41,  1.92it/s]
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Epoch 48 Training:  18%|█▊        | 17/96 [00:08<00:41,  1.92it/s]
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Epoch 48 Training:  19%|█▉        | 18/96 [00:09<00:40,  1.92it/s]
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Epoch 48 Training:  20%|█▉        | 19/96 [00:09<00:40,  1.92it/s]
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Epoch 48 Training:  21%|██        | 20/96 [00:10<00:39,  1.92it/s]
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Epoch 48 Training:  22%|██▏       | 21/96 [00:10<00:38,  1.92it/s]
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Epoch 48 Training:  23%|██▎       | 22/96 [00:11<00:38,  1.92it/s]
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Epoch 48 Training:  24%|██▍       | 23/96 [00:11<00:37,  1.92it/s]
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Epoch 48 Training:  25%|██▌       | 24/96 [00:12<00:37,  1.92it/s]
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Epoch 48 Training:  26%|██▌       | 25/96 [00:12<00:36,  1.92it/s]
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Epoch 48 Training:  27%|██▋       | 26/96 [00:13<00:36,  1.92it/s]
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Epoch 48 Training:  28%|██▊       | 27/96 [00:13<00:35,  1.92it/s]
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Epoch 48 Training:  29%|██▉       | 28/96 [00:14<00:35,  1.92it/s]
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Epoch 48 Training:  30%|███       | 29/96 [00:15<00:34,  1.92it/s]
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Epoch 48 Training:  31%|███▏      | 30/96 [00:15<00:34,  1.92it/s]
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Epoch 48 Training:  32%|███▏      | 31/96 [00:16<00:33,  1.92it/s]
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Epoch 48 Training:  33%|███▎      | 32/96 [00:16<00:33,  1.92it/s]
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Epoch 48 Training:  34%|███▍      | 33/96 [00:17<00:32,  1.92it/s]
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Epoch 48 Training:  35%|███▌      | 34/96 [00:17<00:32,  1.92it/s]
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Epoch 48 Training:  36%|███▋      | 35/96 [00:18<00:31,  1.92it/s]
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Epoch 48 Training:  38%|███▊      | 36/96 [00:18<00:31,  1.92it/s]
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Epoch 48 Training:  39%|███▊      | 37/96 [00:19<00:30,  1.92it/s]
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Epoch 48 Training:  40%|███▉      | 38/96 [00:19<00:30,  1.92it/s]
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Epoch 48 Training:  41%|████      | 39/96 [00:20<00:29,  1.92it/s]
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Epoch 48 Training:  42%|████▏     | 40/96 [00:20<00:29,  1.92it/s]
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Epoch 48 Training:  43%|████▎     | 41/96 [00:21<00:28,  1.93it/s]
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Epoch 48 Training:  44%|████▍     | 42/96 [00:21<00:28,  1.93it/s]
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Epoch 48 Training:  45%|████▍     | 43/96 [00:22<00:27,  1.92it/s]
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Epoch 48 Training:  46%|████▌     | 44/96 [00:22<00:27,  1.92it/s]
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Epoch 48 Training:  47%|████▋     | 45/96 [00:23<00:26,  1.92it/s]
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Epoch 48 Training:  48%|████▊     | 46/96 [00:23<00:25,  1.92it/s]
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Epoch 48 Training:  49%|████▉     | 47/96 [00:24<00:25,  1.92it/s]
+
Epoch 48 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 20:01:27] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0096 | Epoch 048] train/clipped_loss_min=-4.7633 train/clipped_loss_max=4.1136 train/clipped_loss_mean=-0.0917 train/clipped_loss_std=1.4997 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-4.7633 train/unclipped_loss_max=4.1136 train/unclipped_loss_mean=-0.0917 train/unclipped_loss_std=1.4997 train/policy_loss=-0.0917 train/loss=-0.0917 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
+
+
Epoch 48 Timestep: 100%|██████████| 1/1 [00:00<00:00,  1.90it/s]
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Epoch 48 Training:  50%|█████     | 48/96 [00:24<00:25,  1.86it/s]
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Epoch 48 Training:  51%|█████     | 49/96 [00:25<00:24,  1.94it/s]
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Epoch 48 Training:  52%|█████▏    | 50/96 [00:25<00:23,  1.93it/s]
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Epoch 48 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 20:01:52] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0097 | Epoch 048] train/clipped_loss_min=-3.5839 train/clipped_loss_max=4.2858 train/clipped_loss_mean=0.0917 train/clipped_loss_std=1.5242 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0.0001 train/unclipped_loss_min=-3.5839 train/unclipped_loss_max=4.2858 train/unclipped_loss_mean=0.0917 train/unclipped_loss_std=1.5242 train/policy_loss=0.0917 train/loss=0.0917 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
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+[2026-10-01 20:01:52] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0098 | Epoch 048] timing/rollout_seconds=180.4323 timing/feedback_seconds=5.3745 timing/optimization_seconds=50.5552 timing/cycle_seconds=233.8824
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+
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+
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+[2026-10-01 20:04:55] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0098 | Epoch 049] train/reward_dists_mean=0.8336 train/reward_dists_std=0.0297 train/reward_ssim_mean=0.6358 train/reward_ssim_std=0.1100 train/reward_dists_group_std_mean=0.0091 train/reward_dists_group_std_max=0.0182 train/reward_dists_group_std_min=0.0037 train/reward_dists_group_mean_std=0.0281 train/reward_ssim_group_std_mean=0.0088 train/reward_ssim_group_std_max=0.0173 train/reward_ssim_group_std_min=0.0030 train/reward_ssim_group_mean_std=0.1096 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.8913 train/adv_max=4.5587 train/adv_abs_mean=1.2261
+
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Epoch 49 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 20:05:20] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0098 | Epoch 049] train/clipped_loss_min=-4.5587 train/clipped_loss_max=4.8913 train/clipped_loss_mean=-0.0802 train/clipped_loss_std=1.5756 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-4.5587 train/unclipped_loss_max=4.8913 train/unclipped_loss_mean=-0.0802 train/unclipped_loss_std=1.5756 train/policy_loss=-0.0802 train/loss=-0.0802 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0005
+
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Epoch 49 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 20:05:45] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0099 | Epoch 049] train/clipped_loss_min=-3.6777 train/clipped_loss_max=4.2234 train/clipped_loss_mean=0.0802 train/clipped_loss_std=1.4950 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-3.6777 train/unclipped_loss_max=4.2234 train/unclipped_loss_mean=0.0802 train/unclipped_loss_std=1.4950 train/policy_loss=0.0802 train/loss=0.0802 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0004
+
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Epoch 49 Training: 100%|██████████| 96/96 [00:49<00:00,  1.92it/s]
+[2026-10-01 20:05:45] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0100 | Epoch 049] timing/rollout_seconds=179.4061 timing/feedback_seconds=5.4620 timing/optimization_seconds=50.5109 timing/cycle_seconds=232.8775
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+
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+[2026-10-01 20:06:39] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0100 | Epoch 050] eval/flir_rl_full/reward_dists_mean=0.8425 eval/flir_rl_full/reward_dists_std=0.0376 eval/flir_rl_full/reward_psnr_mean=20.0874 eval/flir_rl_full/reward_psnr_std=2.9781 eval/flir_rl_full/reward_ssim_mean=0.6177 eval/flir_rl_full/reward_ssim_std=0.0882
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+
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+[2026-10-01 20:09:42] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0100 | Epoch 050] train/reward_dists_mean=0.8386 train/reward_dists_std=0.0292 train/reward_ssim_mean=0.6580 train/reward_ssim_std=0.1096 train/reward_dists_group_std_mean=0.0090 train/reward_dists_group_std_max=0.0167 train/reward_dists_group_std_min=0.0043 train/reward_dists_group_mean_std=0.0276 train/reward_ssim_group_std_mean=0.0096 train/reward_ssim_group_std_max=0.0185 train/reward_ssim_group_std_min=0.0033 train/reward_ssim_group_mean_std=0.1091 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.0934 train/adv_max=4.6587 train/adv_abs_mean=1.2818
+
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Epoch 50 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 20:10:32] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0101 | Epoch 050] train/clipped_loss_min=-4.5798 train/clipped_loss_max=5.0000 train/clipped_loss_mean=0.0413 train/clipped_loss_std=1.6577 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-4.5798 train/unclipped_loss_max=5.0000 train/unclipped_loss_mean=0.0413 train/unclipped_loss_std=1.6577 train/policy_loss=0.0413 train/loss=0.0413 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
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+[2026-10-01 20:10:32] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0102 | Epoch 050] timing/rollout_seconds=179.6154 timing/feedback_seconds=5.7412 timing/optimization_seconds=50.4464 timing/cycle_seconds=233.1006
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+
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+
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+[2026-10-01 20:13:35] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0102 | Epoch 051] train/reward_dists_mean=0.8249 train/reward_dists_std=0.0370 train/reward_ssim_mean=0.6185 train/reward_ssim_std=0.1099 train/reward_dists_group_std_mean=0.0110 train/reward_dists_group_std_max=0.0211 train/reward_dists_group_std_min=0.0029 train/reward_dists_group_mean_std=0.0351 train/reward_ssim_group_std_mean=0.0097 train/reward_ssim_group_std_max=0.0212 train/reward_ssim_group_std_min=0.0036 train/reward_ssim_group_mean_std=0.1094 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.0982 train/adv_max=4.4228 train/adv_abs_mean=1.2073
+
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Epoch 51 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 20:14:00] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0102 | Epoch 051] train/clipped_loss_min=-4.2304 train/clipped_loss_max=5 train/clipped_loss_mean=-0.0220 train/clipped_loss_std=1.4607 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-4.2304 train/unclipped_loss_max=5 train/unclipped_loss_mean=-0.0220 train/unclipped_loss_std=1.4607 train/policy_loss=-0.0220 train/loss=-0.0220 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0001
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Epoch 51 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 20:14:25] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0103 | Epoch 051] train/clipped_loss_min=-4.4228 train/clipped_loss_max=4.5730 train/clipped_loss_mean=0.0218 train/clipped_loss_std=1.5746 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0.0002 train/unclipped_loss_min=-4.4228 train/unclipped_loss_max=4.5730 train/unclipped_loss_mean=0.0218 train/unclipped_loss_std=1.5746 train/policy_loss=0.0218 train/loss=0.0218 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0001
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+[2026-10-01 20:14:25] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0104 | Epoch 051] timing/rollout_seconds=179.6524 timing/feedback_seconds=5.8373 timing/optimization_seconds=50.6029 timing/cycle_seconds=233.2276
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+
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+[2026-10-01 20:17:28] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0104 | Epoch 052] train/reward_dists_mean=0.8251 train/reward_dists_std=0.0354 train/reward_ssim_mean=0.6347 train/reward_ssim_std=0.1088 train/reward_dists_group_std_mean=0.0104 train/reward_dists_group_std_max=0.0190 train/reward_dists_group_std_min=0.0041 train/reward_dists_group_mean_std=0.0337 train/reward_ssim_group_std_mean=0.0087 train/reward_ssim_group_std_max=0.0184 train/reward_ssim_group_std_min=0.0035 train/reward_ssim_group_mean_std=0.1084 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.3235 train/adv_max=4.0548 train/adv_abs_mean=1.2094
+
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Epoch 52 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 20:18:18] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0105 | Epoch 052] train/clipped_loss_min=-3.8337 train/clipped_loss_max=4.0629 train/clipped_loss_mean=-0.0571 train/clipped_loss_std=1.5068 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-3.8337 train/unclipped_loss_max=4.0629 train/unclipped_loss_mean=-0.0571 train/unclipped_loss_std=1.5068 train/policy_loss=-0.0571 train/loss=-0.0571 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0001
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+[2026-10-01 20:18:18] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0106 | Epoch 052] timing/rollout_seconds=179.7037 timing/feedback_seconds=5.7977 timing/optimization_seconds=50.4660 timing/cycle_seconds=233.1311
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+[2026-10-01 20:21:21] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0106 | Epoch 053] train/reward_dists_mean=0.8304 train/reward_dists_std=0.0388 train/reward_ssim_mean=0.6466 train/reward_ssim_std=0.1196 train/reward_dists_group_std_mean=0.0105 train/reward_dists_group_std_max=0.0271 train/reward_dists_group_std_min=0.0045 train/reward_dists_group_mean_std=0.0372 train/reward_ssim_group_std_mean=0.0084 train/reward_ssim_group_std_max=0.0205 train/reward_ssim_group_std_min=0.0027 train/reward_ssim_group_mean_std=0.1193 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.2972 train/adv_max=5.5291 train/adv_abs_mean=1.1970
+
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Epoch 53 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 20:22:11] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0107 | Epoch 053] train/clipped_loss_min=-4.4847 train/clipped_loss_max=4.9999 train/clipped_loss_mean=-0.0487 train/clipped_loss_std=1.5088 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0.0002 train/unclipped_loss_min=-4.4847 train/unclipped_loss_max=4.9999 train/unclipped_loss_mean=-0.0487 train/unclipped_loss_std=1.5088 train/policy_loss=-0.0487 train/loss=-0.0487 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0004
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+[2026-10-01 20:22:11] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0108 | Epoch 053] timing/rollout_seconds=179.5418 timing/feedback_seconds=5.4017 timing/optimization_seconds=50.4308 timing/cycle_seconds=232.9857
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+
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+
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Pointwise Rewards: dists:  67%|██████▋   | 4/6 [00:01<00:00,  3.19it/s]
Pointwise Rewards: dists:  83%|████████▎ | 5/6 [00:01<00:00,  3.18it/s]
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+[2026-10-01 20:25:15] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0108 | Epoch 054] train/reward_dists_mean=0.8303 train/reward_dists_std=0.0324 train/reward_ssim_mean=0.6244 train/reward_ssim_std=0.1090 train/reward_dists_group_std_mean=0.0101 train/reward_dists_group_std_max=0.0172 train/reward_dists_group_std_min=0.0047 train/reward_dists_group_mean_std=0.0306 train/reward_ssim_group_std_mean=0.0089 train/reward_ssim_group_std_max=0.0196 train/reward_ssim_group_std_min=0.0039 train/reward_ssim_group_mean_std=0.1085 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.1805 train/adv_max=3.9332 train/adv_abs_mean=1.1603
+
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Epoch 54 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 20:25:40] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0108 | Epoch 054] train/clipped_loss_min=-3.9332 train/clipped_loss_max=4.0287 train/clipped_loss_mean=-0.0314 train/clipped_loss_std=1.4577 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-3.9332 train/unclipped_loss_max=4.0287 train/unclipped_loss_mean=-0.0314 train/unclipped_loss_std=1.4577 train/policy_loss=-0.0314 train/loss=-0.0314 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0001
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Epoch 54 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 20:26:04] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0109 | Epoch 054] train/clipped_loss_min=-3.5363 train/clipped_loss_max=4.1805 train/clipped_loss_mean=0.0314 train/clipped_loss_std=1.4690 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-3.5363 train/unclipped_loss_max=4.1805 train/unclipped_loss_mean=0.0314 train/unclipped_loss_std=1.4690 train/policy_loss=0.0314 train/loss=0.0314 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0001
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+[2026-10-01 20:26:04] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0110 | Epoch 054] timing/rollout_seconds=179.7424 timing/feedback_seconds=5.8006 timing/optimization_seconds=50.3914 timing/cycle_seconds=233.1452
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+
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+[2026-10-01 20:29:08] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0110 | Epoch 055] train/reward_dists_mean=0.8368 train/reward_dists_std=0.0284 train/reward_ssim_mean=0.6508 train/reward_ssim_std=0.1064 train/reward_dists_group_std_mean=0.0095 train/reward_dists_group_std_max=0.0189 train/reward_dists_group_std_min=0.0041 train/reward_dists_group_mean_std=0.0266 train/reward_ssim_group_std_mean=0.0089 train/reward_ssim_group_std_max=0.0202 train/reward_ssim_group_std_min=0.0025 train/reward_ssim_group_mean_std=0.1059 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.2934 train/adv_max=3.9542 train/adv_abs_mean=1.2271
+
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Epoch 55 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 20:29:33] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0110 | Epoch 055] train/clipped_loss_min=-3.5569 train/clipped_loss_max=5 train/clipped_loss_mean=0.0554 train/clipped_loss_std=1.6007 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-3.5569 train/unclipped_loss_max=5 train/unclipped_loss_mean=0.0554 train/unclipped_loss_std=1.6007 train/policy_loss=0.0554 train/loss=0.0554 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0005
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+[2026-10-01 20:29:57] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0112 | Epoch 055] timing/rollout_seconds=179.4990 timing/feedback_seconds=5.6836 timing/optimization_seconds=50.5002 timing/cycle_seconds=232.9911
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+
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Pointwise Rewards: dists:  67%|██████▋   | 4/6 [00:01<00:00,  3.28it/s]
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+[2026-10-01 20:33:00] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0112 | Epoch 056] train/reward_dists_mean=0.8395 train/reward_dists_std=0.0277 train/reward_ssim_mean=0.6539 train/reward_ssim_std=0.0907 train/reward_dists_group_std_mean=0.0087 train/reward_dists_group_std_max=0.0239 train/reward_dists_group_std_min=0.0038 train/reward_dists_group_mean_std=0.0260 train/reward_ssim_group_std_mean=0.0084 train/reward_ssim_group_std_max=0.0181 train/reward_ssim_group_std_min=0.0036 train/reward_ssim_group_mean_std=0.0902 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.7607 train/adv_max=4.1974 train/adv_abs_mean=1.1718
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Epoch 56 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 20:33:26] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0112 | Epoch 056] train/clipped_loss_min=-4.1974 train/clipped_loss_max=5 train/clipped_loss_mean=-0.0094 train/clipped_loss_std=1.4925 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-4.1974 train/unclipped_loss_max=5 train/unclipped_loss_mean=-0.0094 train/unclipped_loss_std=1.4925 train/policy_loss=-0.0094 train/loss=-0.0094 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0005
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Epoch 56 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 20:33:50] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0113 | Epoch 056] train/clipped_loss_min=-4.1658 train/clipped_loss_max=4.9966 train/clipped_loss_mean=0.0074 train/clipped_loss_std=1.5072 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-4.1658 train/unclipped_loss_max=4.9966 train/unclipped_loss_mean=0.0074 train/unclipped_loss_std=1.5072 train/policy_loss=0.0074 train/loss=0.0074 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0004
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+[2026-10-01 20:33:50] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0114 | Epoch 056] timing/rollout_seconds=179.5169 timing/feedback_seconds=5.3871 timing/optimization_seconds=50.5831 timing/cycle_seconds=233.0043
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+
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+[2026-10-01 20:36:54] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0114 | Epoch 057] train/reward_dists_mean=0.8418 train/reward_dists_std=0.0318 train/reward_ssim_mean=0.6356 train/reward_ssim_std=0.0825 train/reward_dists_group_std_mean=0.0084 train/reward_dists_group_std_max=0.0198 train/reward_dists_group_std_min=0.0032 train/reward_dists_group_mean_std=0.0306 train/reward_ssim_group_std_mean=0.0106 train/reward_ssim_group_std_max=0.0314 train/reward_ssim_group_std_min=0.0024 train/reward_ssim_group_mean_std=0.0816 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.2215 train/adv_max=4.2346 train/adv_abs_mean=1.2076
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Epoch 57 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 20:37:19] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0114 | Epoch 057] train/clipped_loss_min=-3.6123 train/clipped_loss_max=4.4710 train/clipped_loss_mean=0.0942 train/clipped_loss_std=1.5189 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-3.6123 train/unclipped_loss_max=4.4710 train/unclipped_loss_mean=0.0942 train/unclipped_loss_std=1.5189 train/policy_loss=0.0942 train/loss=0.0942 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0001
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Epoch 57 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 20:37:43] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0115 | Epoch 057] train/clipped_loss_min=-4.2346 train/clipped_loss_max=5.0000 train/clipped_loss_mean=-0.0948 train/clipped_loss_std=1.5114 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-4.2346 train/unclipped_loss_max=5.0000 train/unclipped_loss_mean=-0.0948 train/unclipped_loss_std=1.5114 train/policy_loss=-0.0948 train/loss=-0.0948 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0001
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+[2026-10-01 20:37:43] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0116 | Epoch 057] timing/rollout_seconds=179.5697 timing/feedback_seconds=4.9818 timing/optimization_seconds=50.4746 timing/cycle_seconds=233.0571
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+
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+[2026-10-01 20:40:46] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0116 | Epoch 058] train/reward_dists_mean=0.8397 train/reward_dists_std=0.0248 train/reward_ssim_mean=0.6132 train/reward_ssim_std=0.0900 train/reward_dists_group_std_mean=0.0081 train/reward_dists_group_std_max=0.0165 train/reward_dists_group_std_min=0.0028 train/reward_dists_group_mean_std=0.0232 train/reward_ssim_group_std_mean=0.0091 train/reward_ssim_group_std_max=0.0282 train/reward_ssim_group_std_min=0.0034 train/reward_ssim_group_mean_std=0.0894 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-7.0158 train/adv_max=4.7944 train/adv_abs_mean=1.2033
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Epoch 58 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 20:41:11] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0116 | Epoch 058] train/clipped_loss_min=-4.4219 train/clipped_loss_max=4.8461 train/clipped_loss_mean=-0.0124 train/clipped_loss_std=1.4759 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-4.4219 train/unclipped_loss_max=4.8461 train/unclipped_loss_mean=-0.0124 train/unclipped_loss_std=1.4759 train/policy_loss=-0.0124 train/loss=-0.0124 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
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Epoch 58 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 20:41:36] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0117 | Epoch 058] train/clipped_loss_min=-4.7944 train/clipped_loss_max=5.0000 train/clipped_loss_mean=0.0064 train/clipped_loss_std=1.5613 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-4.7944 train/unclipped_loss_max=5.0000 train/unclipped_loss_mean=0.0064 train/unclipped_loss_std=1.5613 train/policy_loss=0.0064 train/loss=0.0064 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
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+[2026-10-01 20:41:36] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0118 | Epoch 058] timing/rollout_seconds=179.4366 timing/feedback_seconds=5.3211 timing/optimization_seconds=50.4704 timing/cycle_seconds=232.8073
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+
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+[2026-10-01 20:44:39] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0118 | Epoch 059] train/reward_dists_mean=0.8392 train/reward_dists_std=0.0253 train/reward_ssim_mean=0.6263 train/reward_ssim_std=0.1088 train/reward_dists_group_std_mean=0.0086 train/reward_dists_group_std_max=0.0175 train/reward_dists_group_std_min=0.0037 train/reward_dists_group_mean_std=0.0236 train/reward_ssim_group_std_mean=0.0105 train/reward_ssim_group_std_max=0.0248 train/reward_ssim_group_std_min=0.0038 train/reward_ssim_group_mean_std=0.1082 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.1089 train/adv_max=5.0555 train/adv_abs_mean=1.2241
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Epoch 59 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 20:45:04] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0118 | Epoch 059] train/clipped_loss_min=-5 train/clipped_loss_max=4.1911 train/clipped_loss_mean=0.0526 train/clipped_loss_std=1.5126 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-5 train/unclipped_loss_max=4.1911 train/unclipped_loss_mean=0.0526 train/unclipped_loss_std=1.5126 train/policy_loss=0.0526 train/loss=0.0526 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0001
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Epoch 59 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 20:45:29] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0119 | Epoch 059] train/clipped_loss_min=-4.5384 train/clipped_loss_max=5.0000 train/clipped_loss_mean=-0.0527 train/clipped_loss_std=1.5154 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0.0002 train/unclipped_loss_min=-4.5384 train/unclipped_loss_max=5.0000 train/unclipped_loss_mean=-0.0527 train/unclipped_loss_std=1.5154 train/policy_loss=-0.0527 train/loss=-0.0527 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0001
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+[2026-10-01 20:45:29] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0120 | Epoch 059] timing/rollout_seconds=179.5365 timing/feedback_seconds=5.0862 timing/optimization_seconds=50.4620 timing/cycle_seconds=232.8956
+[2026-10-01 20:45:29] [Rank 0] [INFO] [flow_factory.models.abc]: Saving LoRA weights for transformer role=base to /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/runs/klein4b_grpo_flir_full_ssim_dists_lr5e-5/saves/klein4b_grpo_flir_full_ssim_dists_lr5e-5/checkpoints/checkpoint-60
+[2026-10-01 20:45:29] [Rank 0] [INFO] [flow_factory.models.abc]: Checkpoint saved successfully to /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/runs/klein4b_grpo_flir_full_ssim_dists_lr5e-5/saves/klein4b_grpo_flir_full_ssim_dists_lr5e-5/checkpoints/checkpoint-60
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+[2026-10-01 20:46:23] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0120 | Epoch 060] eval/flir_rl_full/reward_dists_mean=0.8407 eval/flir_rl_full/reward_dists_std=0.0369 eval/flir_rl_full/reward_psnr_mean=20.2443 eval/flir_rl_full/reward_psnr_std=3.0430 eval/flir_rl_full/reward_ssim_mean=0.6188 eval/flir_rl_full/reward_ssim_std=0.0869
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+[2026-10-01 20:49:26] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0120 | Epoch 060] train/reward_dists_mean=0.8424 train/reward_dists_std=0.0243 train/reward_ssim_mean=0.6556 train/reward_ssim_std=0.1013 train/reward_dists_group_std_mean=0.0079 train/reward_dists_group_std_max=0.0160 train/reward_dists_group_std_min=0.0034 train/reward_dists_group_mean_std=0.0228 train/reward_ssim_group_std_mean=0.0094 train/reward_ssim_group_std_max=0.0276 train/reward_ssim_group_std_min=0.0032 train/reward_ssim_group_mean_std=0.1007 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.9070 train/adv_max=4.6913 train/adv_abs_mean=1.1933
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Epoch 60 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 20:49:51] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0120 | Epoch 060] train/clipped_loss_min=-3.7424 train/clipped_loss_max=4.9070 train/clipped_loss_mean=0.0177 train/clipped_loss_std=1.5219 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-3.7424 train/unclipped_loss_max=4.9070 train/unclipped_loss_mean=0.0177 train/unclipped_loss_std=1.5219 train/policy_loss=0.0177 train/loss=0.0177 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0005
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Epoch 60 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 20:50:16] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0121 | Epoch 060] train/clipped_loss_min=-4.6913 train/clipped_loss_max=4.8987 train/clipped_loss_mean=-0.0177 train/clipped_loss_std=1.4714 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-4.6913 train/unclipped_loss_max=4.8987 train/unclipped_loss_mean=-0.0177 train/unclipped_loss_std=1.4714 train/policy_loss=-0.0177 train/loss=-0.0177 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0005
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+[2026-10-01 20:50:16] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0122 | Epoch 060] timing/rollout_seconds=179.4748 timing/feedback_seconds=5.2411 timing/optimization_seconds=50.4387 timing/cycle_seconds=232.9682
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+
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+[2026-10-01 20:53:19] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0122 | Epoch 061] train/reward_dists_mean=0.8357 train/reward_dists_std=0.0284 train/reward_ssim_mean=0.6441 train/reward_ssim_std=0.1154 train/reward_dists_group_std_mean=0.0093 train/reward_dists_group_std_max=0.0236 train/reward_dists_group_std_min=0.0038 train/reward_dists_group_mean_std=0.0265 train/reward_ssim_group_std_mean=0.0094 train/reward_ssim_group_std_max=0.0200 train/reward_ssim_group_std_min=0.0029 train/reward_ssim_group_mean_std=0.1149 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.0986 train/adv_max=3.5863 train/adv_abs_mean=1.1530
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Epoch 61 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 20:53:44] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0122 | Epoch 061] train/clipped_loss_min=-3.5102 train/clipped_loss_max=4.4175 train/clipped_loss_mean=-0.0180 train/clipped_loss_std=1.5285 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-3.5102 train/unclipped_loss_max=4.4175 train/unclipped_loss_mean=-0.0180 train/unclipped_loss_std=1.5285 train/policy_loss=-0.0180 train/loss=-0.0180 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
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Epoch 61 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 20:54:09] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0123 | Epoch 061] train/clipped_loss_min=-3.5862 train/clipped_loss_max=5.0000 train/clipped_loss_mean=0.0177 train/clipped_loss_std=1.3590 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-3.5862 train/unclipped_loss_max=5.0000 train/unclipped_loss_mean=0.0177 train/unclipped_loss_std=1.3590 train/policy_loss=0.0177 train/loss=0.0177 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
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+[2026-10-01 20:54:09] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0124 | Epoch 061] timing/rollout_seconds=179.3419 timing/feedback_seconds=5.3197 timing/optimization_seconds=50.4665 timing/cycle_seconds=232.8590
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+[2026-10-01 20:57:12] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0124 | Epoch 062] train/reward_dists_mean=0.8332 train/reward_dists_std=0.0302 train/reward_ssim_mean=0.6393 train/reward_ssim_std=0.1005 train/reward_dists_group_std_mean=0.0098 train/reward_dists_group_std_max=0.0157 train/reward_dists_group_std_min=0.0044 train/reward_dists_group_mean_std=0.0284 train/reward_ssim_group_std_mean=0.0101 train/reward_ssim_group_std_max=0.0190 train/reward_ssim_group_std_min=0.0027 train/reward_ssim_group_mean_std=0.0999 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.1268 train/adv_max=4.8669 train/adv_abs_mean=1.2406
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Epoch 62 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 20:57:37] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0124 | Epoch 062] train/clipped_loss_min=-4.8669 train/clipped_loss_max=4.1268 train/clipped_loss_mean=0.0044 train/clipped_loss_std=1.4956 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-4.8669 train/unclipped_loss_max=4.1268 train/unclipped_loss_mean=0.0044 train/unclipped_loss_std=1.4956 train/policy_loss=0.0044 train/loss=0.0044 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0002
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Epoch 62 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 20:58:02] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0125 | Epoch 062] train/clipped_loss_min=-4.7275 train/clipped_loss_max=3.8260 train/clipped_loss_mean=-0.0044 train/clipped_loss_std=1.5931 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-4.7275 train/unclipped_loss_max=3.8260 train/unclipped_loss_mean=-0.0044 train/unclipped_loss_std=1.5931 train/policy_loss=-0.0044 train/loss=-0.0044 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0002
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+[2026-10-01 20:58:02] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0126 | Epoch 062] timing/rollout_seconds=179.5075 timing/feedback_seconds=5.4752 timing/optimization_seconds=50.4363 timing/cycle_seconds=232.8467
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+[2026-10-01 21:01:06] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0126 | Epoch 063] train/reward_dists_mean=0.8311 train/reward_dists_std=0.0345 train/reward_ssim_mean=0.6308 train/reward_ssim_std=0.1044 train/reward_dists_group_std_mean=0.0087 train/reward_dists_group_std_max=0.0187 train/reward_dists_group_std_min=0.0031 train/reward_dists_group_mean_std=0.0332 train/reward_ssim_group_std_mean=0.0076 train/reward_ssim_group_std_max=0.0160 train/reward_ssim_group_std_min=0.0032 train/reward_ssim_group_mean_std=0.1041 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.1409 train/adv_max=4.1571 train/adv_abs_mean=1.1703
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Epoch 63 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 21:01:31] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0126 | Epoch 063] train/clipped_loss_min=-4.1543 train/clipped_loss_max=4.1759 train/clipped_loss_mean=0.0262 train/clipped_loss_std=1.4361 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-4.1543 train/unclipped_loss_max=4.1759 train/unclipped_loss_mean=0.0262 train/unclipped_loss_std=1.4361 train/policy_loss=0.0262 train/loss=0.0262 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
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Epoch 63 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 21:01:56] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0127 | Epoch 063] train/clipped_loss_min=-4.1571 train/clipped_loss_max=5.0000 train/clipped_loss_mean=-0.0266 train/clipped_loss_std=1.4977 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0.0001 train/unclipped_loss_min=-4.1571 train/unclipped_loss_max=5.0000 train/unclipped_loss_mean=-0.0266 train/unclipped_loss_std=1.4977 train/policy_loss=-0.0266 train/loss=-0.0266 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
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+[2026-10-01 21:01:56] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0128 | Epoch 063] timing/rollout_seconds=179.4114 timing/feedback_seconds=6.7448 timing/optimization_seconds=50.5794 timing/cycle_seconds=234.2901
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+[2026-10-01 21:04:59] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0128 | Epoch 064] train/reward_dists_mean=0.8323 train/reward_dists_std=0.0322 train/reward_ssim_mean=0.6595 train/reward_ssim_std=0.1089 train/reward_dists_group_std_mean=0.0097 train/reward_dists_group_std_max=0.0160 train/reward_dists_group_std_min=0.0050 train/reward_dists_group_mean_std=0.0306 train/reward_ssim_group_std_mean=0.0100 train/reward_ssim_group_std_max=0.0190 train/reward_ssim_group_std_min=0.0038 train/reward_ssim_group_mean_std=0.1083 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.9151 train/adv_max=4.1814 train/adv_abs_mean=1.2567
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Epoch 64 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 21:05:24] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0128 | Epoch 064] train/clipped_loss_min=-4.1814 train/clipped_loss_max=5 train/clipped_loss_mean=0.0210 train/clipped_loss_std=1.6309 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-4.1814 train/unclipped_loss_max=5 train/unclipped_loss_mean=0.0210 train/unclipped_loss_std=1.6309 train/policy_loss=0.0210 train/loss=0.0210 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
+
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Epoch 64 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 21:05:49] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0129 | Epoch 064] train/clipped_loss_min=-3.4947 train/clipped_loss_max=4.7361 train/clipped_loss_mean=-0.0243 train/clipped_loss_std=1.5185 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0.0001 train/unclipped_loss_min=-3.4947 train/unclipped_loss_max=4.7361 train/unclipped_loss_mean=-0.0243 train/unclipped_loss_std=1.5185 train/policy_loss=-0.0243 train/loss=-0.0243 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
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+[2026-10-01 21:05:49] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0130 | Epoch 064] timing/rollout_seconds=179.4694 timing/feedback_seconds=5.1876 timing/optimization_seconds=50.4475 timing/cycle_seconds=232.9171
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+[2026-10-01 21:08:52] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0130 | Epoch 065] train/reward_dists_mean=0.8187 train/reward_dists_std=0.0345 train/reward_ssim_mean=0.6310 train/reward_ssim_std=0.0830 train/reward_dists_group_std_mean=0.0094 train/reward_dists_group_std_max=0.0166 train/reward_dists_group_std_min=0.0046 train/reward_dists_group_mean_std=0.0331 train/reward_ssim_group_std_mean=0.0079 train/reward_ssim_group_std_max=0.0181 train/reward_ssim_group_std_min=0.0031 train/reward_ssim_group_mean_std=0.0826 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.2672 train/adv_max=4.0606 train/adv_abs_mean=1.2529
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Epoch 65 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 21:09:17] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0130 | Epoch 065] train/clipped_loss_min=-4.0606 train/clipped_loss_max=4.5759 train/clipped_loss_mean=-0.0022 train/clipped_loss_std=1.5944 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-4.0606 train/unclipped_loss_max=4.5759 train/unclipped_loss_mean=-0.0022 train/unclipped_loss_std=1.5944 train/policy_loss=-0.0022 train/loss=-0.0022 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0005
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Epoch 65 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 21:09:42] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0131 | Epoch 065] train/clipped_loss_min=-3.6595 train/clipped_loss_max=5.0000 train/clipped_loss_mean=0.0015 train/clipped_loss_std=1.5236 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-3.6595 train/unclipped_loss_max=5.0000 train/unclipped_loss_mean=0.0015 train/unclipped_loss_std=1.5236 train/policy_loss=0.0015 train/loss=0.0015 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0005
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+[2026-10-01 21:09:42] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0132 | Epoch 065] timing/rollout_seconds=179.5877 timing/feedback_seconds=5.5702 timing/optimization_seconds=50.7616 timing/cycle_seconds=233.2255
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+
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Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.21it/s]
+[2026-10-01 21:12:45] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0132 | Epoch 066] train/reward_dists_mean=0.8324 train/reward_dists_std=0.0337 train/reward_ssim_mean=0.6283 train/reward_ssim_std=0.1005 train/reward_dists_group_std_mean=0.0093 train/reward_dists_group_std_max=0.0231 train/reward_dists_group_std_min=0.0027 train/reward_dists_group_mean_std=0.0321 train/reward_ssim_group_std_mean=0.0083 train/reward_ssim_group_std_max=0.0196 train/reward_ssim_group_std_min=0.0037 train/reward_ssim_group_mean_std=0.1001 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.9769 train/adv_max=4.1053 train/adv_abs_mean=1.2107
+
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Epoch 66 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 21:13:10] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0132 | Epoch 066] train/clipped_loss_min=-4.1053 train/clipped_loss_max=3.7986 train/clipped_loss_mean=-0.0727 train/clipped_loss_std=1.4996 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-4.1053 train/unclipped_loss_max=3.7986 train/unclipped_loss_mean=-0.0727 train/unclipped_loss_std=1.4996 train/policy_loss=-0.0727 train/loss=-0.0727 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0002
+
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Epoch 66 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 21:13:35] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0133 | Epoch 066] train/clipped_loss_min=-4.0607 train/clipped_loss_max=5.0000 train/clipped_loss_mean=0.0695 train/clipped_loss_std=1.4984 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-4.0607 train/unclipped_loss_max=5.0000 train/unclipped_loss_mean=0.0695 train/unclipped_loss_std=1.4984 train/policy_loss=0.0695 train/loss=0.0695 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0002
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+[2026-10-01 21:13:35] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0134 | Epoch 066] timing/rollout_seconds=179.6164 timing/feedback_seconds=5.4962 timing/optimization_seconds=50.4738 timing/cycle_seconds=233.0559
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+
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+[2026-10-01 21:16:38] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0134 | Epoch 067] train/reward_dists_mean=0.8383 train/reward_dists_std=0.0330 train/reward_ssim_mean=0.6544 train/reward_ssim_std=0.1144 train/reward_dists_group_std_mean=0.0091 train/reward_dists_group_std_max=0.0190 train/reward_dists_group_std_min=0.0035 train/reward_dists_group_mean_std=0.0315 train/reward_ssim_group_std_mean=0.0086 train/reward_ssim_group_std_max=0.0208 train/reward_ssim_group_std_min=0.0033 train/reward_ssim_group_mean_std=0.1141 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.5817 train/adv_max=5.2323 train/adv_abs_mean=1.1573
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Epoch 67 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 21:17:03] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0134 | Epoch 067] train/clipped_loss_min=-4.0239 train/clipped_loss_max=5 train/clipped_loss_mean=0.0851 train/clipped_loss_std=1.4358 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-4.0239 train/unclipped_loss_max=5 train/unclipped_loss_mean=0.0851 train/unclipped_loss_std=1.4358 train/policy_loss=0.0851 train/loss=0.0851 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0002
+
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Epoch 67 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 21:17:28] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0135 | Epoch 067] train/clipped_loss_min=-5.0000 train/clipped_loss_max=4.0538 train/clipped_loss_mean=-0.0860 train/clipped_loss_std=1.4958 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0.0001 train/unclipped_loss_min=-5.0000 train/unclipped_loss_max=4.0538 train/unclipped_loss_mean=-0.0860 train/unclipped_loss_std=1.4958 train/policy_loss=-0.0860 train/loss=-0.0860 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0002
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+[2026-10-01 21:17:28] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0136 | Epoch 067] timing/rollout_seconds=179.6309 timing/feedback_seconds=5.1016 timing/optimization_seconds=50.3955 timing/cycle_seconds=232.9781
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+
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+
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+[2026-10-01 21:20:31] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0136 | Epoch 068] train/reward_dists_mean=0.8323 train/reward_dists_std=0.0273 train/reward_ssim_mean=0.6430 train/reward_ssim_std=0.0958 train/reward_dists_group_std_mean=0.0088 train/reward_dists_group_std_max=0.0243 train/reward_dists_group_std_min=0.0041 train/reward_dists_group_mean_std=0.0255 train/reward_ssim_group_std_mean=0.0077 train/reward_ssim_group_std_max=0.0158 train/reward_ssim_group_std_min=0.0026 train/reward_ssim_group_mean_std=0.0954 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.3286 train/adv_max=4.0658 train/adv_abs_mean=1.1971
+
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Epoch 68 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 21:20:56] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0136 | Epoch 068] train/clipped_loss_min=-3.5188 train/clipped_loss_max=3.5731 train/clipped_loss_mean=-0.0233 train/clipped_loss_std=1.3709 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-3.5188 train/unclipped_loss_max=3.5731 train/unclipped_loss_mean=-0.0233 train/unclipped_loss_std=1.3709 train/policy_loss=-0.0233 train/loss=-0.0233 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0004
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Epoch 68 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 21:21:21] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0137 | Epoch 068] train/clipped_loss_min=-4.0657 train/clipped_loss_max=5.0000 train/clipped_loss_mean=0.0225 train/clipped_loss_std=1.5900 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0.0001 train/unclipped_loss_min=-4.0657 train/unclipped_loss_max=5.0000 train/unclipped_loss_mean=0.0225 train/unclipped_loss_std=1.5900 train/policy_loss=0.0225 train/loss=0.0225 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0005
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+[2026-10-01 21:21:21] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0138 | Epoch 068] timing/rollout_seconds=179.3346 timing/feedback_seconds=5.2481 timing/optimization_seconds=50.7202 timing/cycle_seconds=232.9435
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Epoch 69 Sampling: 100%|██████████| 96/96 [02:57<00:00,  1.85s/it]
+
Pointwise Rewards: ssim:   0%|          | 0/6 [00:00<?, ?it/s]
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Pointwise Rewards: ssim:  83%|████████▎ | 5/6 [00:00<00:00,  5.71it/s]
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Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:01<00:00,  5.64it/s]
+
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Pointwise Rewards: dists:  17%|█▋        | 1/6 [00:00<00:01,  3.30it/s]
Pointwise Rewards: dists:  33%|███▎      | 2/6 [00:00<00:01,  3.22it/s]
Pointwise Rewards: dists:  50%|█████     | 3/6 [00:00<00:00,  3.22it/s]
Pointwise Rewards: dists:  67%|██████▋   | 4/6 [00:01<00:00,  3.30it/s]
Pointwise Rewards: dists:  83%|████████▎ | 5/6 [00:01<00:00,  3.28it/s]
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Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.27it/s]
+[2026-10-01 21:24:24] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0138 | Epoch 069] train/reward_dists_mean=0.8284 train/reward_dists_std=0.0341 train/reward_ssim_mean=0.6132 train/reward_ssim_std=0.1050 train/reward_dists_group_std_mean=0.0095 train/reward_dists_group_std_max=0.0182 train/reward_dists_group_std_min=0.0044 train/reward_dists_group_mean_std=0.0326 train/reward_ssim_group_std_mean=0.0087 train/reward_ssim_group_std_max=0.0258 train/reward_ssim_group_std_min=0.0033 train/reward_ssim_group_mean_std=0.1046 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.8181 train/adv_max=3.8520 train/adv_abs_mean=1.1983
+
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Epoch 69 Training:   4%|▍         | 4/96 [00:02<00:49,  1.86it/s]
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Epoch 69 Training:   5%|▌         | 5/96 [00:02<00:48,  1.88it/s]
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Epoch 69 Training:   6%|▋         | 6/96 [00:03<00:47,  1.89it/s]
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Epoch 69 Training:   7%|▋         | 7/96 [00:03<00:46,  1.91it/s]
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Epoch 69 Training:   8%|▊         | 8/96 [00:04<00:46,  1.91it/s]
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Epoch 69 Training:   9%|▉         | 9/96 [00:04<00:45,  1.91it/s]
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Epoch 69 Training:  10%|█         | 10/96 [00:05<00:44,  1.92it/s]
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Epoch 69 Training:  11%|█▏        | 11/96 [00:05<00:44,  1.92it/s]
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Epoch 69 Training:  12%|█▎        | 12/96 [00:06<00:43,  1.92it/s]
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Epoch 69 Training:  14%|█▎        | 13/96 [00:06<00:43,  1.92it/s]
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Epoch 69 Training:  15%|█▍        | 14/96 [00:07<00:42,  1.92it/s]
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Epoch 69 Training:  16%|█▌        | 15/96 [00:07<00:42,  1.92it/s]
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Epoch 69 Training:  17%|█▋        | 16/96 [00:08<00:41,  1.92it/s]
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Epoch 69 Training:  18%|█▊        | 17/96 [00:08<00:41,  1.92it/s]
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Epoch 69 Training:  19%|█▉        | 18/96 [00:09<00:40,  1.92it/s]
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Epoch 69 Training:  20%|█▉        | 19/96 [00:09<00:40,  1.92it/s]
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Epoch 69 Training:  21%|██        | 20/96 [00:10<00:39,  1.92it/s]
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Epoch 69 Training:  22%|██▏       | 21/96 [00:10<00:39,  1.92it/s]
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Epoch 69 Training:  23%|██▎       | 22/96 [00:11<00:38,  1.92it/s]
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Epoch 69 Training:  24%|██▍       | 23/96 [00:11<00:37,  1.92it/s]
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Epoch 69 Training:  25%|██▌       | 24/96 [00:12<00:37,  1.92it/s]
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Epoch 69 Training:  26%|██▌       | 25/96 [00:13<00:36,  1.92it/s]
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Epoch 69 Training:  27%|██▋       | 26/96 [00:13<00:36,  1.92it/s]
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Epoch 69 Training:  28%|██▊       | 27/96 [00:14<00:35,  1.92it/s]
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Epoch 69 Training:  29%|██▉       | 28/96 [00:14<00:35,  1.92it/s]
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Epoch 69 Training:  30%|███       | 29/96 [00:15<00:34,  1.92it/s]
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Epoch 69 Training:  31%|███▏      | 30/96 [00:15<00:34,  1.92it/s]
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Epoch 69 Training:  32%|███▏      | 31/96 [00:16<00:33,  1.92it/s]
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Epoch 69 Training:  33%|███▎      | 32/96 [00:16<00:33,  1.92it/s]
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Epoch 69 Training:  34%|███▍      | 33/96 [00:17<00:32,  1.92it/s]
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Epoch 69 Training:  35%|███▌      | 34/96 [00:17<00:32,  1.92it/s]
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Epoch 69 Training:  36%|███▋      | 35/96 [00:18<00:31,  1.92it/s]
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Epoch 69 Training:  38%|███▊      | 36/96 [00:18<00:31,  1.92it/s]
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Epoch 69 Training:  39%|███▊      | 37/96 [00:19<00:30,  1.92it/s]
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Epoch 69 Training:  40%|███▉      | 38/96 [00:19<00:30,  1.92it/s]
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Epoch 69 Training:  41%|████      | 39/96 [00:20<00:29,  1.92it/s]
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Epoch 69 Training:  42%|████▏     | 40/96 [00:20<00:29,  1.92it/s]
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Epoch 69 Training:  43%|████▎     | 41/96 [00:21<00:28,  1.92it/s]
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Epoch 69 Training:  44%|████▍     | 42/96 [00:21<00:28,  1.92it/s]
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Epoch 69 Training:  45%|████▍     | 43/96 [00:22<00:27,  1.92it/s]
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Epoch 69 Training:  46%|████▌     | 44/96 [00:22<00:27,  1.92it/s]
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Epoch 69 Training:  47%|████▋     | 45/96 [00:23<00:26,  1.92it/s]
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Epoch 69 Training:  48%|████▊     | 46/96 [00:23<00:25,  1.92it/s]
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Epoch 69 Training:  49%|████▉     | 47/96 [00:24<00:25,  1.92it/s]
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Epoch 69 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 21:24:49] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0138 | Epoch 069] train/clipped_loss_min=-3.8520 train/clipped_loss_max=4.8181 train/clipped_loss_mean=0.0747 train/clipped_loss_std=1.5091 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-3.8520 train/unclipped_loss_max=4.8181 train/unclipped_loss_mean=0.0747 train/unclipped_loss_std=1.5091 train/policy_loss=0.0747 train/loss=0.0747 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0005
+
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Epoch 69 Timestep: 100%|██████████| 1/1 [00:00<00:00,  1.90it/s]
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Epoch 69 Training:  51%|█████     | 49/96 [00:25<00:24,  1.94it/s]
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Epoch 69 Training:  53%|█████▎    | 51/96 [00:26<00:23,  1.93it/s]
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Epoch 69 Training:  54%|█████▍    | 52/96 [00:27<00:22,  1.93it/s]
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Epoch 69 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 21:25:14] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0139 | Epoch 069] train/clipped_loss_min=-3.7212 train/clipped_loss_max=3.4184 train/clipped_loss_mean=-0.0747 train/clipped_loss_std=1.4706 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0.0001 train/unclipped_loss_min=-3.7212 train/unclipped_loss_max=3.4184 train/unclipped_loss_mean=-0.0747 train/unclipped_loss_std=1.4706 train/policy_loss=-0.0747 train/loss=-0.0747 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0005
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+[2026-10-01 21:25:14] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0140 | Epoch 069] timing/rollout_seconds=179.4039 timing/feedback_seconds=5.4410 timing/optimization_seconds=50.6440 timing/cycle_seconds=232.9610
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+
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+[2026-10-01 21:26:07] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0140 | Epoch 070] eval/flir_rl_full/reward_dists_mean=0.8394 eval/flir_rl_full/reward_dists_std=0.0368 eval/flir_rl_full/reward_psnr_mean=20.2588 eval/flir_rl_full/reward_psnr_std=3.0517 eval/flir_rl_full/reward_ssim_mean=0.6200 eval/flir_rl_full/reward_ssim_std=0.0872
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+
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+
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Pointwise Rewards: dists:  50%|█████     | 3/6 [00:00<00:00,  3.27it/s]
Pointwise Rewards: dists:  67%|██████▋   | 4/6 [00:01<00:00,  3.26it/s]
Pointwise Rewards: dists:  83%|████████▎ | 5/6 [00:01<00:00,  3.22it/s]
Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.20it/s]
Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.23it/s]
+[2026-10-01 21:29:10] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0140 | Epoch 070] train/reward_dists_mean=0.8392 train/reward_dists_std=0.0267 train/reward_ssim_mean=0.6391 train/reward_ssim_std=0.0797 train/reward_dists_group_std_mean=0.0079 train/reward_dists_group_std_max=0.0157 train/reward_dists_group_std_min=0.0044 train/reward_dists_group_mean_std=0.0254 train/reward_ssim_group_std_mean=0.0091 train/reward_ssim_group_std_max=0.0195 train/reward_ssim_group_std_min=0.0034 train/reward_ssim_group_mean_std=0.0791 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.1505 train/adv_max=4.2727 train/adv_abs_mean=1.1726
+
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Epoch 70 Training:  11%|█▏        | 11/96 [00:05<00:44,  1.93it/s]
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Epoch 70 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 21:29:35] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0140 | Epoch 070] train/clipped_loss_min=-3.7817 train/clipped_loss_max=4.6347 train/clipped_loss_mean=0.0152 train/clipped_loss_std=1.5129 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-3.7817 train/unclipped_loss_max=4.6347 train/unclipped_loss_mean=0.0152 train/unclipped_loss_std=1.5129 train/policy_loss=0.0152 train/loss=0.0152 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0005
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Epoch 70 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 21:30:00] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0141 | Epoch 070] train/clipped_loss_min=-4.2727 train/clipped_loss_max=5.0000 train/clipped_loss_mean=-0.0156 train/clipped_loss_std=1.4780 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0.0001 train/unclipped_loss_min=-4.2727 train/unclipped_loss_max=5.0000 train/unclipped_loss_mean=-0.0156 train/unclipped_loss_std=1.4780 train/policy_loss=-0.0156 train/loss=-0.0156 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0005
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+[2026-10-01 21:30:00] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0142 | Epoch 070] timing/rollout_seconds=179.4936 timing/feedback_seconds=5.0363 timing/optimization_seconds=50.4199 timing/cycle_seconds=232.8141
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+
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Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:01<00:00,  5.71it/s]
+
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Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.30it/s]
+[2026-10-01 21:33:03] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0142 | Epoch 071] train/reward_dists_mean=0.8423 train/reward_dists_std=0.0349 train/reward_ssim_mean=0.6339 train/reward_ssim_std=0.0955 train/reward_dists_group_std_mean=0.0070 train/reward_dists_group_std_max=0.0137 train/reward_dists_group_std_min=0.0029 train/reward_dists_group_mean_std=0.0340 train/reward_ssim_group_std_mean=0.0098 train/reward_ssim_group_std_max=0.0241 train/reward_ssim_group_std_min=0.0034 train/reward_ssim_group_mean_std=0.0948 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.6437 train/adv_max=4.1319 train/adv_abs_mean=1.1502
+
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Epoch 71 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 21:33:28] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0142 | Epoch 071] train/clipped_loss_min=-3.8110 train/clipped_loss_max=5 train/clipped_loss_mean=-0.0305 train/clipped_loss_std=1.4410 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-3.8110 train/unclipped_loss_max=5 train/unclipped_loss_mean=-0.0305 train/unclipped_loss_std=1.4410 train/policy_loss=-0.0305 train/loss=-0.0305 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0002
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Epoch 71 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 21:33:53] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0143 | Epoch 071] train/clipped_loss_min=-4.1319 train/clipped_loss_max=4.7454 train/clipped_loss_mean=0.0289 train/clipped_loss_std=1.4913 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-4.1319 train/unclipped_loss_max=4.7454 train/unclipped_loss_mean=0.0289 train/unclipped_loss_std=1.4913 train/policy_loss=0.0289 train/loss=0.0289 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0002
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+[2026-10-01 21:33:53] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0144 | Epoch 071] timing/rollout_seconds=179.6598 timing/feedback_seconds=4.9692 timing/optimization_seconds=50.4557 timing/cycle_seconds=233.0697
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+
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+
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+[2026-10-01 21:36:56] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0144 | Epoch 072] train/reward_dists_mean=0.8528 train/reward_dists_std=0.0236 train/reward_ssim_mean=0.6523 train/reward_ssim_std=0.0952 train/reward_dists_group_std_mean=0.0069 train/reward_dists_group_std_max=0.0124 train/reward_dists_group_std_min=0.0031 train/reward_dists_group_mean_std=0.0224 train/reward_ssim_group_std_mean=0.0106 train/reward_ssim_group_std_max=0.0221 train/reward_ssim_group_std_min=0.0045 train/reward_ssim_group_mean_std=0.0945 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.1866 train/adv_max=5.1746 train/adv_abs_mean=1.2545
+
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Epoch 72 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 21:37:21] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0144 | Epoch 072] train/clipped_loss_min=-5 train/clipped_loss_max=3.4729 train/clipped_loss_mean=-0.0395 train/clipped_loss_std=1.5701 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-5 train/unclipped_loss_max=3.4729 train/unclipped_loss_mean=-0.0395 train/unclipped_loss_std=1.5701 train/policy_loss=-0.0395 train/loss=-0.0395 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0002
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Epoch 72 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 21:37:46] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0145 | Epoch 072] train/clipped_loss_min=-4.2476 train/clipped_loss_max=4.1866 train/clipped_loss_mean=0.0400 train/clipped_loss_std=1.5402 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0.0002 train/unclipped_loss_min=-4.2476 train/unclipped_loss_max=4.1866 train/unclipped_loss_mean=0.0400 train/unclipped_loss_std=1.5402 train/policy_loss=0.0400 train/loss=0.0400 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0002
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+[2026-10-01 21:37:46] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0146 | Epoch 072] timing/rollout_seconds=179.3314 timing/feedback_seconds=4.9277 timing/optimization_seconds=50.4231 timing/cycle_seconds=232.6627
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+
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+
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+[2026-10-01 21:40:49] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0146 | Epoch 073] train/reward_dists_mean=0.8454 train/reward_dists_std=0.0234 train/reward_ssim_mean=0.6249 train/reward_ssim_std=0.0933 train/reward_dists_group_std_mean=0.0070 train/reward_dists_group_std_max=0.0153 train/reward_dists_group_std_min=0.0038 train/reward_dists_group_mean_std=0.0222 train/reward_ssim_group_std_mean=0.0100 train/reward_ssim_group_std_max=0.0273 train/reward_ssim_group_std_min=0.0042 train/reward_ssim_group_mean_std=0.0926 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.1950 train/adv_max=4.1103 train/adv_abs_mean=1.1931
+
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Epoch 73 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 21:41:14] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0146 | Epoch 073] train/clipped_loss_min=-3.8034 train/clipped_loss_max=4.0365 train/clipped_loss_mean=-0.0566 train/clipped_loss_std=1.4646 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-3.8034 train/unclipped_loss_max=4.0365 train/unclipped_loss_mean=-0.0566 train/unclipped_loss_std=1.4646 train/policy_loss=-0.0566 train/loss=-0.0566 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0005
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Epoch 73 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 21:41:38] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0147 | Epoch 073] train/clipped_loss_min=-4.1103 train/clipped_loss_max=4.1951 train/clipped_loss_mean=0.0566 train/clipped_loss_std=1.4850 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0.0001 train/unclipped_loss_min=-4.1103 train/unclipped_loss_max=4.1951 train/unclipped_loss_mean=0.0566 train/unclipped_loss_std=1.4850 train/policy_loss=0.0566 train/loss=0.0566 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0005
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+[2026-10-01 21:41:38] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0148 | Epoch 073] timing/rollout_seconds=179.4678 timing/feedback_seconds=5.5110 timing/optimization_seconds=50.4246 timing/cycle_seconds=232.8317
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+
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+[2026-10-01 21:44:41] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0148 | Epoch 074] train/reward_dists_mean=0.8426 train/reward_dists_std=0.0345 train/reward_ssim_mean=0.6318 train/reward_ssim_std=0.1249 train/reward_dists_group_std_mean=0.0078 train/reward_dists_group_std_max=0.0231 train/reward_dists_group_std_min=0.0037 train/reward_dists_group_mean_std=0.0335 train/reward_ssim_group_std_mean=0.0091 train/reward_ssim_group_std_max=0.0182 train/reward_ssim_group_std_min=0.0036 train/reward_ssim_group_mean_std=0.1245 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.3354 train/adv_max=4.5855 train/adv_abs_mean=1.1915
+
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Epoch 74 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 21:45:06] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0148 | Epoch 074] train/clipped_loss_min=-4.4091 train/clipped_loss_max=5 train/clipped_loss_mean=0.0674 train/clipped_loss_std=1.5464 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-4.4091 train/unclipped_loss_max=5 train/unclipped_loss_mean=0.0674 train/unclipped_loss_std=1.5464 train/policy_loss=0.0674 train/loss=0.0674 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0005
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Epoch 74 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 21:45:31] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0149 | Epoch 074] train/clipped_loss_min=-4.5855 train/clipped_loss_max=3.5876 train/clipped_loss_mean=-0.0683 train/clipped_loss_std=1.4786 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0.0001 train/unclipped_loss_min=-4.5855 train/unclipped_loss_max=3.5876 train/unclipped_loss_mean=-0.0683 train/unclipped_loss_std=1.4786 train/policy_loss=-0.0683 train/loss=-0.0683 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0005
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+[2026-10-01 21:45:31] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0150 | Epoch 074] timing/rollout_seconds=179.4005 timing/feedback_seconds=5.0450 timing/optimization_seconds=50.5441 timing/cycle_seconds=232.8260
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+
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+[2026-10-01 21:48:34] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0150 | Epoch 075] train/reward_dists_mean=0.8386 train/reward_dists_std=0.0280 train/reward_ssim_mean=0.6533 train/reward_ssim_std=0.1087 train/reward_dists_group_std_mean=0.0091 train/reward_dists_group_std_max=0.0169 train/reward_dists_group_std_min=0.0034 train/reward_dists_group_mean_std=0.0263 train/reward_ssim_group_std_mean=0.0107 train/reward_ssim_group_std_max=0.0216 train/reward_ssim_group_std_min=0.0045 train/reward_ssim_group_mean_std=0.1081 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.5277 train/adv_max=4.9746 train/adv_abs_mean=1.2572
+
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Epoch 75 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 21:48:59] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0150 | Epoch 075] train/clipped_loss_min=-4.9746 train/clipped_loss_max=5 train/clipped_loss_mean=0.1910 train/clipped_loss_std=1.5435 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-4.9746 train/unclipped_loss_max=5 train/unclipped_loss_mean=0.1910 train/unclipped_loss_std=1.5435 train/policy_loss=0.1910 train/loss=0.1910 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0002
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Epoch 75 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 21:49:24] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0151 | Epoch 075] train/clipped_loss_min=-4.0729 train/clipped_loss_max=3.8855 train/clipped_loss_mean=-0.1924 train/clipped_loss_std=1.5772 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-4.0729 train/unclipped_loss_max=3.8855 train/unclipped_loss_mean=-0.1924 train/unclipped_loss_std=1.5772 train/policy_loss=-0.1924 train/loss=-0.1924 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0002
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+[2026-10-01 21:49:24] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0152 | Epoch 075] timing/rollout_seconds=179.2992 timing/feedback_seconds=4.8942 timing/optimization_seconds=50.6635 timing/cycle_seconds=232.8158
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+
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+[2026-10-01 21:52:27] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0152 | Epoch 076] train/reward_dists_mean=0.8358 train/reward_dists_std=0.0306 train/reward_ssim_mean=0.6507 train/reward_ssim_std=0.0859 train/reward_dists_group_std_mean=0.0083 train/reward_dists_group_std_max=0.0205 train/reward_dists_group_std_min=0.0038 train/reward_dists_group_mean_std=0.0293 train/reward_ssim_group_std_mean=0.0086 train/reward_ssim_group_std_max=0.0237 train/reward_ssim_group_std_min=0.0038 train/reward_ssim_group_mean_std=0.0854 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.9726 train/adv_max=5.4522 train/adv_abs_mean=1.2038
+
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Epoch 76 Training:  49%|████▉     | 47/96 [00:24<00:25,  1.93it/s]
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Epoch 76 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 21:52:52] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0152 | Epoch 076] train/clipped_loss_min=-5 train/clipped_loss_max=4.9283 train/clipped_loss_mean=0.0338 train/clipped_loss_std=1.4774 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-5 train/unclipped_loss_max=4.9283 train/unclipped_loss_mean=0.0338 train/unclipped_loss_std=1.4774 train/policy_loss=0.0338 train/loss=0.0338 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0005
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Epoch 76 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 21:53:17] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0153 | Epoch 076] train/clipped_loss_min=-3.5984 train/clipped_loss_max=4.9726 train/clipped_loss_mean=-0.0319 train/clipped_loss_std=1.5814 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0.0002 train/unclipped_loss_min=-3.5984 train/unclipped_loss_max=4.9726 train/unclipped_loss_mean=-0.0319 train/unclipped_loss_std=1.5814 train/policy_loss=-0.0319 train/loss=-0.0319 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0005
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+[2026-10-01 21:53:17] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0154 | Epoch 076] timing/rollout_seconds=179.5455 timing/feedback_seconds=5.5995 timing/optimization_seconds=50.3374 timing/cycle_seconds=232.7606
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+
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+[2026-10-01 21:56:20] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0154 | Epoch 077] train/reward_dists_mean=0.8290 train/reward_dists_std=0.0354 train/reward_ssim_mean=0.6565 train/reward_ssim_std=0.1132 train/reward_dists_group_std_mean=0.0096 train/reward_dists_group_std_max=0.0176 train/reward_dists_group_std_min=0.0034 train/reward_dists_group_mean_std=0.0340 train/reward_ssim_group_std_mean=0.0083 train/reward_ssim_group_std_max=0.0208 train/reward_ssim_group_std_min=0.0031 train/reward_ssim_group_mean_std=0.1128 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.6398 train/adv_max=4.2447 train/adv_abs_mean=1.2884
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Epoch 77 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 21:56:45] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0154 | Epoch 077] train/clipped_loss_min=-4.1696 train/clipped_loss_max=4.4178 train/clipped_loss_mean=0.0010 train/clipped_loss_std=1.5640 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-4.1696 train/unclipped_loss_max=4.4178 train/unclipped_loss_mean=0.0010 train/unclipped_loss_std=1.5640 train/policy_loss=0.0010 train/loss=0.0010 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
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Epoch 77 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 21:57:10] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0155 | Epoch 077] train/clipped_loss_min=-4.2447 train/clipped_loss_max=5.0000 train/clipped_loss_mean=-0.0027 train/clipped_loss_std=1.6360 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0.0001 train/unclipped_loss_min=-4.2447 train/unclipped_loss_max=5.0000 train/unclipped_loss_mean=-0.0027 train/unclipped_loss_std=1.6360 train/policy_loss=-0.0027 train/loss=-0.0027 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
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+[2026-10-01 21:57:10] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0156 | Epoch 077] timing/rollout_seconds=179.5902 timing/feedback_seconds=5.1917 timing/optimization_seconds=50.8206 timing/cycle_seconds=233.2983
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+
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Pointwise Rewards: dists:  67%|██████▋   | 4/6 [00:01<00:00,  3.37it/s]
Pointwise Rewards: dists:  83%|████████▎ | 5/6 [00:01<00:00,  3.40it/s]
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+[2026-10-01 22:00:13] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0156 | Epoch 078] train/reward_dists_mean=0.8351 train/reward_dists_std=0.0319 train/reward_ssim_mean=0.6777 train/reward_ssim_std=0.1223 train/reward_dists_group_std_mean=0.0091 train/reward_dists_group_std_max=0.0179 train/reward_dists_group_std_min=0.0046 train/reward_dists_group_mean_std=0.0305 train/reward_ssim_group_std_mean=0.0090 train/reward_ssim_group_std_max=0.0205 train/reward_ssim_group_std_min=0.0039 train/reward_ssim_group_mean_std=0.1219 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.5445 train/adv_max=4.0920 train/adv_abs_mean=1.2547
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Epoch 78 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 22:00:38] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0156 | Epoch 078] train/clipped_loss_min=-3.2798 train/clipped_loss_max=5 train/clipped_loss_mean=-0.0423 train/clipped_loss_std=1.5211 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-3.2798 train/unclipped_loss_max=5 train/unclipped_loss_mean=-0.0423 train/unclipped_loss_std=1.5211 train/policy_loss=-0.0423 train/loss=-0.0423 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0005
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Epoch 78 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 22:01:03] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0157 | Epoch 078] train/clipped_loss_min=-4.0920 train/clipped_loss_max=5.0000 train/clipped_loss_mean=0.0402 train/clipped_loss_std=1.6135 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-4.0920 train/unclipped_loss_max=5.0000 train/unclipped_loss_mean=0.0402 train/unclipped_loss_std=1.6135 train/policy_loss=0.0402 train/loss=0.0402 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0005
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+[2026-10-01 22:01:03] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0158 | Epoch 078] timing/rollout_seconds=179.6069 timing/feedback_seconds=5.5976 timing/optimization_seconds=50.3841 timing/cycle_seconds=232.8813
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+
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+[2026-10-01 22:04:06] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0158 | Epoch 079] train/reward_dists_mean=0.8338 train/reward_dists_std=0.0378 train/reward_ssim_mean=0.6752 train/reward_ssim_std=0.1121 train/reward_dists_group_std_mean=0.0090 train/reward_dists_group_std_max=0.0184 train/reward_dists_group_std_min=0.0042 train/reward_dists_group_mean_std=0.0365 train/reward_ssim_group_std_mean=0.0081 train/reward_ssim_group_std_max=0.0164 train/reward_ssim_group_std_min=0.0031 train/reward_ssim_group_mean_std=0.1118 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.4756 train/adv_max=4.1725 train/adv_abs_mean=1.2295
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Epoch 79 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 22:04:31] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0158 | Epoch 079] train/clipped_loss_min=-3.3211 train/clipped_loss_max=5 train/clipped_loss_mean=0.1136 train/clipped_loss_std=1.5565 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-3.3211 train/unclipped_loss_max=5 train/unclipped_loss_mean=0.1136 train/unclipped_loss_std=1.5565 train/policy_loss=0.1136 train/loss=0.1136 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0002
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+[2026-10-01 22:04:56] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0160 | Epoch 079] timing/rollout_seconds=179.4397 timing/feedback_seconds=5.4970 timing/optimization_seconds=50.4682 timing/cycle_seconds=233.0792
+[2026-10-01 22:04:56] [Rank 0] [INFO] [flow_factory.models.abc]: Saving LoRA weights for transformer role=base to /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/runs/klein4b_grpo_flir_full_ssim_dists_lr5e-5/saves/klein4b_grpo_flir_full_ssim_dists_lr5e-5/checkpoints/checkpoint-80
+[2026-10-01 22:04:56] [Rank 0] [INFO] [flow_factory.models.abc]: Checkpoint saved successfully to /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/runs/klein4b_grpo_flir_full_ssim_dists_lr5e-5/saves/klein4b_grpo_flir_full_ssim_dists_lr5e-5/checkpoints/checkpoint-80
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+[2026-10-01 22:05:50] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0160 | Epoch 080] eval/flir_rl_full/reward_dists_mean=0.8380 eval/flir_rl_full/reward_dists_std=0.0368 eval/flir_rl_full/reward_psnr_mean=20.2679 eval/flir_rl_full/reward_psnr_std=3.0290 eval/flir_rl_full/reward_ssim_mean=0.6225 eval/flir_rl_full/reward_ssim_std=0.0874
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+[2026-10-01 22:08:53] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0160 | Epoch 080] train/reward_dists_mean=0.8303 train/reward_dists_std=0.0290 train/reward_ssim_mean=0.6455 train/reward_ssim_std=0.1057 train/reward_dists_group_std_mean=0.0083 train/reward_dists_group_std_max=0.0177 train/reward_dists_group_std_min=0.0039 train/reward_dists_group_mean_std=0.0276 train/reward_ssim_group_std_mean=0.0076 train/reward_ssim_group_std_max=0.0197 train/reward_ssim_group_std_min=0.0030 train/reward_ssim_group_mean_std=0.1054 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.9876 train/adv_max=5.2538 train/adv_abs_mean=1.2594
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Epoch 80 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 22:09:18] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0160 | Epoch 080] train/clipped_loss_min=-5 train/clipped_loss_max=5 train/clipped_loss_mean=-0.0347 train/clipped_loss_std=1.5468 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-5 train/unclipped_loss_max=5 train/unclipped_loss_mean=-0.0347 train/unclipped_loss_std=1.5468 train/policy_loss=-0.0347 train/loss=-0.0347 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0004
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Epoch 80 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 22:09:43] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0161 | Epoch 080] train/clipped_loss_min=-4.1944 train/clipped_loss_max=4.7124 train/clipped_loss_mean=0.0327 train/clipped_loss_std=1.6105 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0.0001 train/unclipped_loss_min=-4.1944 train/unclipped_loss_max=4.7124 train/unclipped_loss_mean=0.0327 train/unclipped_loss_std=1.6105 train/policy_loss=0.0327 train/loss=0.0327 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0004
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+[2026-10-01 22:09:43] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0162 | Epoch 080] timing/rollout_seconds=179.5686 timing/feedback_seconds=4.9140 timing/optimization_seconds=50.6632 timing/cycle_seconds=233.1374
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+
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+[2026-10-01 22:12:46] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0162 | Epoch 081] train/reward_dists_mean=0.8334 train/reward_dists_std=0.0345 train/reward_ssim_mean=0.6480 train/reward_ssim_std=0.1152 train/reward_dists_group_std_mean=0.0083 train/reward_dists_group_std_max=0.0166 train/reward_dists_group_std_min=0.0048 train/reward_dists_group_mean_std=0.0334 train/reward_ssim_group_std_mean=0.0080 train/reward_ssim_group_std_max=0.0168 train/reward_ssim_group_std_min=0.0031 train/reward_ssim_group_mean_std=0.1149 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.2257 train/adv_max=3.6652 train/adv_abs_mean=1.1806
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Epoch 81 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 22:13:11] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0162 | Epoch 081] train/clipped_loss_min=-3.6194 train/clipped_loss_max=5 train/clipped_loss_mean=0.0984 train/clipped_loss_std=1.4735 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-3.6194 train/unclipped_loss_max=5 train/unclipped_loss_mean=0.0984 train/unclipped_loss_std=1.4735 train/policy_loss=0.0984 train/loss=0.0984 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
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Epoch 81 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 22:13:36] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0163 | Epoch 081] train/clipped_loss_min=-3.6652 train/clipped_loss_max=3.9830 train/clipped_loss_mean=-0.0990 train/clipped_loss_std=1.4697 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-3.6652 train/unclipped_loss_max=3.9830 train/unclipped_loss_mean=-0.0990 train/unclipped_loss_std=1.4697 train/policy_loss=-0.0990 train/loss=-0.0990 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
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+[2026-10-01 22:13:36] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0164 | Epoch 081] timing/rollout_seconds=179.4801 timing/feedback_seconds=5.4403 timing/optimization_seconds=50.4434 timing/cycle_seconds=232.8650
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+
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+[2026-10-01 22:16:39] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0164 | Epoch 082] train/reward_dists_mean=0.8251 train/reward_dists_std=0.0315 train/reward_ssim_mean=0.6391 train/reward_ssim_std=0.0962 train/reward_dists_group_std_mean=0.0082 train/reward_dists_group_std_max=0.0152 train/reward_dists_group_std_min=0.0037 train/reward_dists_group_mean_std=0.0303 train/reward_ssim_group_std_mean=0.0080 train/reward_ssim_group_std_max=0.0159 train/reward_ssim_group_std_min=0.0035 train/reward_ssim_group_mean_std=0.0958 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.2350 train/adv_max=4.6782 train/adv_abs_mean=1.2177
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Epoch 82 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 22:17:04] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0164 | Epoch 082] train/clipped_loss_min=-3.9523 train/clipped_loss_max=5 train/clipped_loss_mean=0.0505 train/clipped_loss_std=1.5508 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-3.9523 train/unclipped_loss_max=5 train/unclipped_loss_mean=0.0505 train/unclipped_loss_std=1.5508 train/policy_loss=0.0505 train/loss=0.0505 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0004
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Epoch 82 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 22:17:29] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0165 | Epoch 082] train/clipped_loss_min=-4.6782 train/clipped_loss_max=4.3018 train/clipped_loss_mean=-0.0511 train/clipped_loss_std=1.5228 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-4.6782 train/unclipped_loss_max=4.3018 train/unclipped_loss_mean=-0.0511 train/unclipped_loss_std=1.5228 train/policy_loss=-0.0511 train/loss=-0.0511 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0004
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+[2026-10-01 22:17:29] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0166 | Epoch 082] timing/rollout_seconds=179.4647 timing/feedback_seconds=5.1311 timing/optimization_seconds=50.4135 timing/cycle_seconds=232.7776
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+[2026-10-01 22:20:32] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0166 | Epoch 083] train/reward_dists_mean=0.8343 train/reward_dists_std=0.0284 train/reward_ssim_mean=0.6381 train/reward_ssim_std=0.0764 train/reward_dists_group_std_mean=0.0079 train/reward_dists_group_std_max=0.0156 train/reward_dists_group_std_min=0.0037 train/reward_dists_group_mean_std=0.0272 train/reward_ssim_group_std_mean=0.0077 train/reward_ssim_group_std_max=0.0162 train/reward_ssim_group_std_min=0.0032 train/reward_ssim_group_mean_std=0.0759 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.9564 train/adv_max=4.3000 train/adv_abs_mean=1.1745
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Epoch 83 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 22:20:57] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0166 | Epoch 083] train/clipped_loss_min=-3.9008 train/clipped_loss_max=4.9564 train/clipped_loss_mean=0.0494 train/clipped_loss_std=1.4388 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-3.9008 train/unclipped_loss_max=4.9564 train/unclipped_loss_mean=0.0494 train/unclipped_loss_std=1.4388 train/policy_loss=0.0494 train/loss=0.0494 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0002
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Epoch 83 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 22:21:21] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0167 | Epoch 083] train/clipped_loss_min=-4.3000 train/clipped_loss_max=4.0563 train/clipped_loss_mean=-0.0494 train/clipped_loss_std=1.4979 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0.0002 train/unclipped_loss_min=-4.3000 train/unclipped_loss_max=4.0563 train/unclipped_loss_mean=-0.0494 train/unclipped_loss_std=1.4979 train/policy_loss=-0.0494 train/loss=-0.0494 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0002
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+[2026-10-01 22:21:21] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0168 | Epoch 083] timing/rollout_seconds=179.4896 timing/feedback_seconds=5.4078 timing/optimization_seconds=50.4430 timing/cycle_seconds=232.8213
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+[2026-10-01 22:24:25] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0168 | Epoch 084] train/reward_dists_mean=0.8465 train/reward_dists_std=0.0267 train/reward_ssim_mean=0.6275 train/reward_ssim_std=0.1020 train/reward_dists_group_std_mean=0.0072 train/reward_dists_group_std_max=0.0143 train/reward_dists_group_std_min=0.0032 train/reward_dists_group_mean_std=0.0256 train/reward_ssim_group_std_mean=0.0095 train/reward_ssim_group_std_max=0.0266 train/reward_ssim_group_std_min=0.0041 train/reward_ssim_group_mean_std=0.1015 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.5899 train/adv_max=4.2327 train/adv_abs_mean=1.2261
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Epoch 84 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 22:24:49] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0168 | Epoch 084] train/clipped_loss_min=-4.0708 train/clipped_loss_max=4.5899 train/clipped_loss_mean=-0.0134 train/clipped_loss_std=1.5343 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-4.0708 train/unclipped_loss_max=4.5899 train/unclipped_loss_mean=-0.0134 train/unclipped_loss_std=1.5343 train/policy_loss=-0.0134 train/loss=-0.0134 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0002
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Epoch 84 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 22:25:14] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0169 | Epoch 084] train/clipped_loss_min=-4.2327 train/clipped_loss_max=4.2247 train/clipped_loss_mean=0.0134 train/clipped_loss_std=1.5247 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0.0003 train/unclipped_loss_min=-4.2327 train/unclipped_loss_max=4.2247 train/unclipped_loss_mean=0.0134 train/unclipped_loss_std=1.5247 train/policy_loss=0.0134 train/loss=0.0134 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0002
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+[2026-10-01 22:25:14] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0170 | Epoch 084] timing/rollout_seconds=179.4328 timing/feedback_seconds=5.3602 timing/optimization_seconds=50.4354 timing/cycle_seconds=232.8226
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+[2026-10-01 22:28:17] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0170 | Epoch 085] train/reward_dists_mean=0.8491 train/reward_dists_std=0.0210 train/reward_ssim_mean=0.6227 train/reward_ssim_std=0.0950 train/reward_dists_group_std_mean=0.0066 train/reward_dists_group_std_max=0.0107 train/reward_dists_group_std_min=0.0034 train/reward_dists_group_mean_std=0.0199 train/reward_ssim_group_std_mean=0.0099 train/reward_ssim_group_std_max=0.0225 train/reward_ssim_group_std_min=0.0037 train/reward_ssim_group_mean_std=0.0944 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.0490 train/adv_max=4.3660 train/adv_abs_mean=1.2090
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Epoch 85 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 22:28:42] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0170 | Epoch 085] train/clipped_loss_min=-4.3617 train/clipped_loss_max=5 train/clipped_loss_mean=-0.0767 train/clipped_loss_std=1.4685 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-4.3617 train/unclipped_loss_max=5 train/unclipped_loss_mean=-0.0767 train/unclipped_loss_std=1.4685 train/policy_loss=-0.0767 train/loss=-0.0767 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0002
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Epoch 85 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 22:29:07] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0171 | Epoch 085] train/clipped_loss_min=-4.3659 train/clipped_loss_max=4.0067 train/clipped_loss_mean=0.0766 train/clipped_loss_std=1.5595 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0.0001 train/unclipped_loss_min=-4.3659 train/unclipped_loss_max=4.0067 train/unclipped_loss_mean=0.0766 train/unclipped_loss_std=1.5595 train/policy_loss=0.0766 train/loss=0.0766 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0002
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+[2026-10-01 22:29:07] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0172 | Epoch 085] timing/rollout_seconds=179.4350 timing/feedback_seconds=5.4600 timing/optimization_seconds=50.4391 timing/cycle_seconds=232.7857
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+[2026-10-01 22:32:10] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0172 | Epoch 086] train/reward_dists_mean=0.8384 train/reward_dists_std=0.0317 train/reward_ssim_mean=0.6272 train/reward_ssim_std=0.1054 train/reward_dists_group_std_mean=0.0079 train/reward_dists_group_std_max=0.0157 train/reward_dists_group_std_min=0.0041 train/reward_dists_group_mean_std=0.0305 train/reward_ssim_group_std_mean=0.0092 train/reward_ssim_group_std_max=0.0213 train/reward_ssim_group_std_min=0.0043 train/reward_ssim_group_mean_std=0.1050 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.3182 train/adv_max=5.0491 train/adv_abs_mean=1.1731
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Epoch 86 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 22:32:35] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0172 | Epoch 086] train/clipped_loss_min=-5 train/clipped_loss_max=4.0552 train/clipped_loss_mean=-0.0861 train/clipped_loss_std=1.4271 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-5 train/unclipped_loss_max=4.0552 train/unclipped_loss_mean=-0.0861 train/unclipped_loss_std=1.4271 train/policy_loss=-0.0861 train/loss=-0.0861 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
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Epoch 86 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 22:33:00] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0173 | Epoch 086] train/clipped_loss_min=-4.7114 train/clipped_loss_max=5.0000 train/clipped_loss_mean=0.0854 train/clipped_loss_std=1.5504 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0.0002 train/unclipped_loss_min=-4.7114 train/unclipped_loss_max=5.0000 train/unclipped_loss_mean=0.0854 train/unclipped_loss_std=1.5504 train/policy_loss=0.0854 train/loss=0.0854 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
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+[2026-10-01 22:33:00] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0174 | Epoch 086] timing/rollout_seconds=179.5242 timing/feedback_seconds=5.4966 timing/optimization_seconds=50.3734 timing/cycle_seconds=232.8109
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Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.22it/s]
+[2026-10-01 22:36:03] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0174 | Epoch 087] train/reward_dists_mean=0.8358 train/reward_dists_std=0.0320 train/reward_ssim_mean=0.6275 train/reward_ssim_std=0.1055 train/reward_dists_group_std_mean=0.0075 train/reward_dists_group_std_max=0.0117 train/reward_dists_group_std_min=0.0027 train/reward_dists_group_mean_std=0.0310 train/reward_ssim_group_std_mean=0.0081 train/reward_ssim_group_std_max=0.0147 train/reward_ssim_group_std_min=0.0038 train/reward_ssim_group_mean_std=0.1052 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.3124 train/adv_max=4.3113 train/adv_abs_mean=1.1753
+
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Epoch 87 Training:  49%|████▉     | 47/96 [00:24<00:25,  1.92it/s]
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Epoch 87 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 22:36:28] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0174 | Epoch 087] train/clipped_loss_min=-4.3113 train/clipped_loss_max=4.6474 train/clipped_loss_mean=0.0364 train/clipped_loss_std=1.4512 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-4.3113 train/unclipped_loss_max=4.6474 train/unclipped_loss_mean=0.0364 train/unclipped_loss_std=1.4512 train/policy_loss=0.0364 train/loss=0.0364 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0005
+
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Epoch 87 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 22:36:53] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0175 | Epoch 087] train/clipped_loss_min=-4.1828 train/clipped_loss_max=5.0000 train/clipped_loss_mean=-0.0372 train/clipped_loss_std=1.5435 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-4.1828 train/unclipped_loss_max=5.0000 train/unclipped_loss_mean=-0.0372 train/unclipped_loss_std=1.5435 train/policy_loss=-0.0372 train/loss=-0.0372 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0006
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Epoch 87 Training: 100%|██████████| 96/96 [00:50<00:00,  1.92it/s]
+[2026-10-01 22:36:53] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0176 | Epoch 087] timing/rollout_seconds=179.5603 timing/feedback_seconds=5.4033 timing/optimization_seconds=50.7297 timing/cycle_seconds=233.2298
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Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:01<00:00,  5.88it/s]
+
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Pointwise Rewards: dists:  83%|████████▎ | 5/6 [00:01<00:00,  3.26it/s]
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Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.31it/s]
+[2026-10-01 22:39:56] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0176 | Epoch 088] train/reward_dists_mean=0.8391 train/reward_dists_std=0.0318 train/reward_ssim_mean=0.6584 train/reward_ssim_std=0.1018 train/reward_dists_group_std_mean=0.0077 train/reward_dists_group_std_max=0.0160 train/reward_dists_group_std_min=0.0031 train/reward_dists_group_mean_std=0.0307 train/reward_ssim_group_std_mean=0.0083 train/reward_ssim_group_std_max=0.0178 train/reward_ssim_group_std_min=0.0035 train/reward_ssim_group_mean_std=0.1014 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.5700 train/adv_max=4.9610 train/adv_abs_mean=1.1845
+
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Epoch 88 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 22:40:21] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0176 | Epoch 088] train/clipped_loss_min=-4.9610 train/clipped_loss_max=4.5700 train/clipped_loss_mean=-0.0736 train/clipped_loss_std=1.5003 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-4.9610 train/unclipped_loss_max=4.5700 train/unclipped_loss_mean=-0.0736 train/unclipped_loss_std=1.5003 train/policy_loss=-0.0736 train/loss=-0.0736 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0006
+
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Epoch 88 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 22:40:46] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0177 | Epoch 088] train/clipped_loss_min=-4.7193 train/clipped_loss_max=4.5440 train/clipped_loss_mean=0.0736 train/clipped_loss_std=1.5144 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0.0001 train/unclipped_loss_min=-4.7193 train/unclipped_loss_max=4.5440 train/unclipped_loss_mean=0.0736 train/unclipped_loss_std=1.5144 train/policy_loss=0.0736 train/loss=0.0736 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0006
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+[2026-10-01 22:40:46] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0178 | Epoch 088] timing/rollout_seconds=179.3629 timing/feedback_seconds=5.1177 timing/optimization_seconds=50.4690 timing/cycle_seconds=232.7684
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Epoch 89 Sampling: 100%|██████████| 96/96 [02:56<00:00,  1.84s/it]
+
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Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:01<00:00,  5.87it/s]
+
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Pointwise Rewards: dists:  33%|███▎      | 2/6 [00:00<00:01,  3.35it/s]
Pointwise Rewards: dists:  50%|█████     | 3/6 [00:00<00:00,  3.43it/s]
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Pointwise Rewards: dists:  83%|████████▎ | 5/6 [00:01<00:00,  3.35it/s]
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Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.35it/s]
+[2026-10-01 22:43:49] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0178 | Epoch 089] train/reward_dists_mean=0.8252 train/reward_dists_std=0.0367 train/reward_ssim_mean=0.5934 train/reward_ssim_std=0.1055 train/reward_dists_group_std_mean=0.0087 train/reward_dists_group_std_max=0.0205 train/reward_dists_group_std_min=0.0029 train/reward_dists_group_mean_std=0.0354 train/reward_ssim_group_std_mean=0.0079 train/reward_ssim_group_std_max=0.0147 train/reward_ssim_group_std_min=0.0026 train/reward_ssim_group_mean_std=0.1052 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.2393 train/adv_max=3.8547 train/adv_abs_mean=1.1448
+
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Epoch 89 Training:  26%|██▌       | 25/96 [00:13<00:36,  1.93it/s]
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Epoch 89 Training:  49%|████▉     | 47/96 [00:24<00:25,  1.92it/s]
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Epoch 89 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 22:44:14] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0178 | Epoch 089] train/clipped_loss_min=-3.6944 train/clipped_loss_max=4.2393 train/clipped_loss_mean=-0.1166 train/clipped_loss_std=1.4218 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-3.6944 train/unclipped_loss_max=4.2393 train/unclipped_loss_mean=-0.1166 train/unclipped_loss_std=1.4218 train/policy_loss=-0.1166 train/loss=-0.1166 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0005
+
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Epoch 89 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 22:44:39] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0179 | Epoch 089] train/clipped_loss_min=-3.8547 train/clipped_loss_max=4.2051 train/clipped_loss_mean=0.1166 train/clipped_loss_std=1.4836 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-3.8547 train/unclipped_loss_max=4.2051 train/unclipped_loss_mean=0.1166 train/unclipped_loss_std=1.4836 train/policy_loss=0.1166 train/loss=0.1166 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0005
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Epoch 89 Training: 100%|██████████| 96/96 [00:50<00:00,  1.91it/s]
+[2026-10-01 22:44:39] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0180 | Epoch 089] timing/rollout_seconds=179.4445 timing/feedback_seconds=5.4301 timing/optimization_seconds=50.8207 timing/cycle_seconds=233.2277
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+[2026-10-01 22:45:32] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0180 | Epoch 090] eval/flir_rl_full/reward_dists_mean=0.8389 eval/flir_rl_full/reward_dists_std=0.0372 eval/flir_rl_full/reward_psnr_mean=20.2759 eval/flir_rl_full/reward_psnr_std=3.0423 eval/flir_rl_full/reward_ssim_mean=0.6232 eval/flir_rl_full/reward_ssim_std=0.0869
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+
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+
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Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.20it/s]
+[2026-10-01 22:48:35] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0180 | Epoch 090] train/reward_dists_mean=0.8361 train/reward_dists_std=0.0355 train/reward_ssim_mean=0.6669 train/reward_ssim_std=0.0877 train/reward_dists_group_std_mean=0.0086 train/reward_dists_group_std_max=0.0256 train/reward_dists_group_std_min=0.0026 train/reward_dists_group_mean_std=0.0342 train/reward_ssim_group_std_mean=0.0079 train/reward_ssim_group_std_max=0.0182 train/reward_ssim_group_std_min=0.0035 train/reward_ssim_group_mean_std=0.0873 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.6191 train/adv_max=5.4509 train/adv_abs_mean=1.1829
+
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Epoch 90 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 22:49:00] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0180 | Epoch 090] train/clipped_loss_min=-5 train/clipped_loss_max=4.6191 train/clipped_loss_mean=0.0253 train/clipped_loss_std=1.5429 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-5 train/unclipped_loss_max=4.6191 train/unclipped_loss_mean=0.0253 train/unclipped_loss_std=1.5429 train/policy_loss=0.0253 train/loss=0.0253 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
+
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Epoch 90 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 22:49:25] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0181 | Epoch 090] train/clipped_loss_min=-3.6239 train/clipped_loss_max=4.5594 train/clipped_loss_mean=-0.0241 train/clipped_loss_std=1.4379 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0.0001 train/unclipped_loss_min=-3.6239 train/unclipped_loss_max=4.5594 train/unclipped_loss_mean=-0.0241 train/unclipped_loss_std=1.4379 train/policy_loss=-0.0241 train/loss=-0.0241 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
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+[2026-10-01 22:49:25] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0182 | Epoch 090] timing/rollout_seconds=179.5464 timing/feedback_seconds=5.6847 timing/optimization_seconds=50.3957 timing/cycle_seconds=233.0599
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+
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+[2026-10-01 22:52:28] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0182 | Epoch 091] train/reward_dists_mean=0.8311 train/reward_dists_std=0.0366 train/reward_ssim_mean=0.6221 train/reward_ssim_std=0.1172 train/reward_dists_group_std_mean=0.0093 train/reward_dists_group_std_max=0.0183 train/reward_dists_group_std_min=0.0029 train/reward_dists_group_mean_std=0.0352 train/reward_ssim_group_std_mean=0.0077 train/reward_ssim_group_std_max=0.0160 train/reward_ssim_group_std_min=0.0031 train/reward_ssim_group_mean_std=0.1169 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-3.8722 train/adv_max=4.6659 train/adv_abs_mean=1.1619
+
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Epoch 91 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 22:53:18] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0183 | Epoch 091] train/clipped_loss_min=-3.1744 train/clipped_loss_max=3.8085 train/clipped_loss_mean=0.0213 train/clipped_loss_std=1.4112 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-3.1744 train/unclipped_loss_max=3.8085 train/unclipped_loss_mean=0.0213 train/unclipped_loss_std=1.4112 train/policy_loss=0.0213 train/loss=0.0213 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0005
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+[2026-10-01 22:53:18] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0184 | Epoch 091] timing/rollout_seconds=179.4796 timing/feedback_seconds=5.1302 timing/optimization_seconds=50.4687 timing/cycle_seconds=232.8708
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+
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+[2026-10-01 22:56:21] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0184 | Epoch 092] train/reward_dists_mean=0.8412 train/reward_dists_std=0.0301 train/reward_ssim_mean=0.6564 train/reward_ssim_std=0.1041 train/reward_dists_group_std_mean=0.0082 train/reward_dists_group_std_max=0.0196 train/reward_dists_group_std_min=0.0049 train/reward_dists_group_mean_std=0.0288 train/reward_ssim_group_std_mean=0.0089 train/reward_ssim_group_std_max=0.0219 train/reward_ssim_group_std_min=0.0032 train/reward_ssim_group_mean_std=0.1036 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.2029 train/adv_max=4.7080 train/adv_abs_mean=1.2489
+
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Epoch 92 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 22:56:46] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0184 | Epoch 092] train/clipped_loss_min=-3.8598 train/clipped_loss_max=5 train/clipped_loss_mean=-0.0283 train/clipped_loss_std=1.5585 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-3.8598 train/unclipped_loss_max=5 train/unclipped_loss_mean=-0.0283 train/unclipped_loss_std=1.5585 train/policy_loss=-0.0283 train/loss=-0.0283 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
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Epoch 92 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 22:57:11] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0185 | Epoch 092] train/clipped_loss_min=-4.7080 train/clipped_loss_max=4.9340 train/clipped_loss_mean=0.0278 train/clipped_loss_std=1.5548 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0.0002 train/unclipped_loss_min=-4.7080 train/unclipped_loss_max=4.9340 train/unclipped_loss_mean=0.0278 train/unclipped_loss_std=1.5548 train/policy_loss=0.0278 train/loss=0.0278 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
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+[2026-10-01 22:57:11] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0186 | Epoch 092] timing/rollout_seconds=179.4734 timing/feedback_seconds=5.5496 timing/optimization_seconds=50.7435 timing/cycle_seconds=233.2770
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+
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+[2026-10-01 23:00:14] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0186 | Epoch 093] train/reward_dists_mean=0.8353 train/reward_dists_std=0.0310 train/reward_ssim_mean=0.6091 train/reward_ssim_std=0.1169 train/reward_dists_group_std_mean=0.0079 train/reward_dists_group_std_max=0.0136 train/reward_dists_group_std_min=0.0033 train/reward_dists_group_mean_std=0.0299 train/reward_ssim_group_std_mean=0.0091 train/reward_ssim_group_std_max=0.0179 train/reward_ssim_group_std_min=0.0033 train/reward_ssim_group_mean_std=0.1165 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.0854 train/adv_max=4.3860 train/adv_abs_mean=1.2183
+
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Epoch 93 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 23:00:39] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0186 | Epoch 093] train/clipped_loss_min=-4.0261 train/clipped_loss_max=5 train/clipped_loss_mean=-0.0317 train/clipped_loss_std=1.5269 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-4.0261 train/unclipped_loss_max=5 train/unclipped_loss_mean=-0.0317 train/unclipped_loss_std=1.5269 train/policy_loss=-0.0317 train/loss=-0.0317 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0004
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Epoch 93 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 23:01:04] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0187 | Epoch 093] train/clipped_loss_min=-4.3860 train/clipped_loss_max=3.9868 train/clipped_loss_mean=0.0315 train/clipped_loss_std=1.5107 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-4.3860 train/unclipped_loss_max=3.9868 train/unclipped_loss_mean=0.0315 train/unclipped_loss_std=1.5107 train/policy_loss=0.0315 train/loss=0.0315 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
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+[2026-10-01 23:01:04] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0188 | Epoch 093] timing/rollout_seconds=179.4815 timing/feedback_seconds=5.4589 timing/optimization_seconds=50.3682 timing/cycle_seconds=232.8509
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+
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+
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Pointwise Rewards: dists:  83%|████████▎ | 5/6 [00:01<00:00,  3.20it/s]
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+[2026-10-01 23:04:07] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0188 | Epoch 094] train/reward_dists_mean=0.8451 train/reward_dists_std=0.0262 train/reward_ssim_mean=0.6450 train/reward_ssim_std=0.1104 train/reward_dists_group_std_mean=0.0076 train/reward_dists_group_std_max=0.0220 train/reward_dists_group_std_min=0.0035 train/reward_dists_group_mean_std=0.0248 train/reward_ssim_group_std_mean=0.0098 train/reward_ssim_group_std_max=0.0266 train/reward_ssim_group_std_min=0.0041 train/reward_ssim_group_mean_std=0.1099 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.5495 train/adv_max=4.5157 train/adv_abs_mean=1.2170
+
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Epoch 94 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 23:04:32] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0188 | Epoch 094] train/clipped_loss_min=-4.5157 train/clipped_loss_max=5 train/clipped_loss_mean=0.1524 train/clipped_loss_std=1.5406 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-4.5157 train/unclipped_loss_max=5 train/unclipped_loss_mean=0.1524 train/unclipped_loss_std=1.5406 train/policy_loss=0.1524 train/loss=0.1524 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0005
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Epoch 94 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 23:04:57] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0189 | Epoch 094] train/clipped_loss_min=-4.4962 train/clipped_loss_max=3.3070 train/clipped_loss_mean=-0.1540 train/clipped_loss_std=1.4949 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0.0002 train/unclipped_loss_min=-4.4962 train/unclipped_loss_max=3.3070 train/unclipped_loss_mean=-0.1540 train/unclipped_loss_std=1.4949 train/policy_loss=-0.1540 train/loss=-0.1540 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0005
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+[2026-10-01 23:04:57] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0190 | Epoch 094] timing/rollout_seconds=179.5779 timing/feedback_seconds=5.5234 timing/optimization_seconds=50.5762 timing/cycle_seconds=233.2054
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+
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+
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Pointwise Rewards: dists:  83%|████████▎ | 5/6 [00:01<00:00,  3.23it/s]
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Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.27it/s]
+[2026-10-01 23:08:00] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0190 | Epoch 095] train/reward_dists_mean=0.8379 train/reward_dists_std=0.0279 train/reward_ssim_mean=0.6262 train/reward_ssim_std=0.1034 train/reward_dists_group_std_mean=0.0074 train/reward_dists_group_std_max=0.0152 train/reward_dists_group_std_min=0.0033 train/reward_dists_group_mean_std=0.0268 train/reward_ssim_group_std_mean=0.0081 train/reward_ssim_group_std_max=0.0247 train/reward_ssim_group_std_min=0.0035 train/reward_ssim_group_mean_std=0.1030 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.8821 train/adv_max=4.4623 train/adv_abs_mean=1.1734
+
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Epoch 95 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 23:08:25] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0190 | Epoch 095] train/clipped_loss_min=-4.2495 train/clipped_loss_max=4.0857 train/clipped_loss_mean=-0.0574 train/clipped_loss_std=1.3830 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-4.2495 train/unclipped_loss_max=4.0857 train/unclipped_loss_mean=-0.0574 train/unclipped_loss_std=1.3830 train/policy_loss=-0.0574 train/loss=-0.0574 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0005
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Epoch 95 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 23:08:50] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0191 | Epoch 095] train/clipped_loss_min=-4.4623 train/clipped_loss_max=4.8820 train/clipped_loss_mean=0.0574 train/clipped_loss_std=1.5661 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0.0001 train/unclipped_loss_min=-4.4623 train/unclipped_loss_max=4.8820 train/unclipped_loss_mean=0.0574 train/unclipped_loss_std=1.5661 train/policy_loss=0.0574 train/loss=0.0574 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0005
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+[2026-10-01 23:08:50] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0192 | Epoch 095] timing/rollout_seconds=179.4939 timing/feedback_seconds=4.9805 timing/optimization_seconds=50.4816 timing/cycle_seconds=232.8959
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Epoch 96 Sampling: 100%|██████████| 96/96 [02:57<00:00,  1.84s/it]
+
Pointwise Rewards: ssim:   0%|          | 0/6 [00:00<?, ?it/s]
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Pointwise Rewards: ssim:  67%|██████▋   | 4/6 [00:00<00:00,  5.93it/s]
Pointwise Rewards: ssim:  83%|████████▎ | 5/6 [00:00<00:00,  5.91it/s]
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Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:01<00:00,  5.93it/s]
+
Pointwise Rewards: dists:   0%|          | 0/6 [00:00<?, ?it/s]
Pointwise Rewards: dists:  17%|█▋        | 1/6 [00:00<00:01,  3.17it/s]
Pointwise Rewards: dists:  33%|███▎      | 2/6 [00:00<00:01,  3.32it/s]
Pointwise Rewards: dists:  50%|█████     | 3/6 [00:00<00:00,  3.33it/s]
Pointwise Rewards: dists:  67%|██████▋   | 4/6 [00:01<00:00,  3.35it/s]
Pointwise Rewards: dists:  83%|████████▎ | 5/6 [00:01<00:00,  3.32it/s]
Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.38it/s]
Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.34it/s]
+[2026-10-01 23:11:53] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0192 | Epoch 096] train/reward_dists_mean=0.8385 train/reward_dists_std=0.0298 train/reward_ssim_mean=0.6387 train/reward_ssim_std=0.1130 train/reward_dists_group_std_mean=0.0077 train/reward_dists_group_std_max=0.0174 train/reward_dists_group_std_min=0.0032 train/reward_dists_group_mean_std=0.0287 train/reward_ssim_group_std_mean=0.0083 train/reward_ssim_group_std_max=0.0191 train/reward_ssim_group_std_min=0.0039 train/reward_ssim_group_mean_std=0.1126 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.7499 train/adv_max=3.7607 train/adv_abs_mean=1.2281
+
Epoch 96 Training:   0%|          | 0/96 [00:00<?, ?it/s]
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Epoch 96 Training:   7%|▋         | 7/96 [00:03<00:46,  1.93it/s]
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Epoch 96 Training:   8%|▊         | 8/96 [00:04<00:45,  1.93it/s]
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Epoch 96 Training:   9%|▉         | 9/96 [00:04<00:45,  1.93it/s]
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Epoch 96 Training:  10%|█         | 10/96 [00:05<00:44,  1.93it/s]
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Epoch 96 Training:  11%|█▏        | 11/96 [00:05<00:44,  1.93it/s]
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Epoch 96 Training:  14%|█▎        | 13/96 [00:06<00:43,  1.92it/s]
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Epoch 96 Training:  15%|█▍        | 14/96 [00:07<00:42,  1.92it/s]
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Epoch 96 Training:  17%|█▋        | 16/96 [00:08<00:41,  1.93it/s]
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Epoch 96 Training:  18%|█▊        | 17/96 [00:08<00:41,  1.92it/s]
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Epoch 96 Training:  19%|█▉        | 18/96 [00:09<00:40,  1.93it/s]
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Epoch 96 Training:  27%|██▋       | 26/96 [00:13<00:36,  1.93it/s]
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Epoch 96 Training:  28%|██▊       | 27/96 [00:13<00:35,  1.93it/s]
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Epoch 96 Training:  29%|██▉       | 28/96 [00:14<00:35,  1.93it/s]
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Epoch 96 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 23:12:18] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0192 | Epoch 096] train/clipped_loss_min=-3.6930 train/clipped_loss_max=4.7499 train/clipped_loss_mean=0.0388 train/clipped_loss_std=1.4714 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-3.6930 train/unclipped_loss_max=4.7499 train/unclipped_loss_mean=0.0388 train/unclipped_loss_std=1.4714 train/policy_loss=0.0388 train/loss=0.0388 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0005
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Epoch 96 Training:  77%|███████▋  | 74/96 [00:38<00:11,  1.93it/s]
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Epoch 96 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 23:12:43] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0193 | Epoch 096] train/clipped_loss_min=-3.7607 train/clipped_loss_max=4.7134 train/clipped_loss_mean=-0.0388 train/clipped_loss_std=1.5714 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0.0001 train/unclipped_loss_min=-3.7607 train/unclipped_loss_max=4.7134 train/unclipped_loss_mean=-0.0388 train/unclipped_loss_std=1.5714 train/policy_loss=-0.0388 train/loss=-0.0388 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0005
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Epoch 96 Timestep: 100%|██████████| 1/1 [00:00<00:00,  1.90it/s]
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Epoch 96 Training: 100%|██████████| 96/96 [00:49<00:00,  1.86it/s]
Epoch 96 Training: 100%|██████████| 96/96 [00:49<00:00,  1.93it/s]
+[2026-10-01 23:12:43] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0194 | Epoch 096] timing/rollout_seconds=179.5373 timing/feedback_seconds=5.2084 timing/optimization_seconds=50.4745 timing/cycle_seconds=233.0423
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+
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Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.27it/s]
+[2026-10-01 23:15:46] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0194 | Epoch 097] train/reward_dists_mean=0.8362 train/reward_dists_std=0.0317 train/reward_ssim_mean=0.6058 train/reward_ssim_std=0.0861 train/reward_dists_group_std_mean=0.0084 train/reward_dists_group_std_max=0.0169 train/reward_dists_group_std_min=0.0038 train/reward_dists_group_mean_std=0.0304 train/reward_ssim_group_std_mean=0.0081 train/reward_ssim_group_std_max=0.0183 train/reward_ssim_group_std_min=0.0032 train/reward_ssim_group_mean_std=0.0856 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.8492 train/adv_max=4.5406 train/adv_abs_mean=1.2091
+
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Epoch 97 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 23:16:11] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0194 | Epoch 097] train/clipped_loss_min=-3.9450 train/clipped_loss_max=4.3037 train/clipped_loss_mean=-0.0449 train/clipped_loss_std=1.5256 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-3.9450 train/unclipped_loss_max=4.3037 train/unclipped_loss_mean=-0.0449 train/unclipped_loss_std=1.5256 train/policy_loss=-0.0449 train/loss=-0.0449 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0002
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Epoch 97 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 23:16:36] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0195 | Epoch 097] train/clipped_loss_min=-4.5406 train/clipped_loss_max=4.8491 train/clipped_loss_mean=0.0449 train/clipped_loss_std=1.4766 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0.0001 train/unclipped_loss_min=-4.5406 train/unclipped_loss_max=4.8491 train/unclipped_loss_mean=0.0449 train/unclipped_loss_std=1.4766 train/policy_loss=0.0449 train/loss=0.0449 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0002
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Epoch 97 Training: 100%|██████████| 96/96 [00:49<00:00,  1.93it/s]
+[2026-10-01 23:16:36] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0196 | Epoch 097] timing/rollout_seconds=179.5655 timing/feedback_seconds=5.5571 timing/optimization_seconds=50.4363 timing/cycle_seconds=232.9916
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+[2026-10-01 23:19:39] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0196 | Epoch 098] train/reward_dists_mean=0.8339 train/reward_dists_std=0.0364 train/reward_ssim_mean=0.6477 train/reward_ssim_std=0.1070 train/reward_dists_group_std_mean=0.0086 train/reward_dists_group_std_max=0.0181 train/reward_dists_group_std_min=0.0041 train/reward_dists_group_mean_std=0.0352 train/reward_ssim_group_std_mean=0.0078 train/reward_ssim_group_std_max=0.0168 train/reward_ssim_group_std_min=0.0035 train/reward_ssim_group_mean_std=0.1066 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.9283 train/adv_max=4.1681 train/adv_abs_mean=1.2397
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Epoch 98 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 23:20:04] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0196 | Epoch 098] train/clipped_loss_min=-4.1681 train/clipped_loss_max=4.9283 train/clipped_loss_mean=0.0515 train/clipped_loss_std=1.5530 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-4.1681 train/unclipped_loss_max=4.9283 train/unclipped_loss_mean=0.0515 train/unclipped_loss_std=1.5530 train/policy_loss=0.0515 train/loss=0.0515 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
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Epoch 98 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 23:20:29] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0197 | Epoch 098] train/clipped_loss_min=-4.1350 train/clipped_loss_max=3.7967 train/clipped_loss_mean=-0.0515 train/clipped_loss_std=1.5085 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-4.1350 train/unclipped_loss_max=3.7967 train/unclipped_loss_mean=-0.0515 train/unclipped_loss_std=1.5085 train/policy_loss=-0.0515 train/loss=-0.0515 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
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Epoch 98 Timestep: 100%|██████████| 1/1 [00:00<00:00,  1.89it/s]
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Epoch 98 Training: 100%|██████████| 96/96 [00:49<00:00,  1.92it/s]
+[2026-10-01 23:20:29] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0198 | Epoch 098] timing/rollout_seconds=179.4618 timing/feedback_seconds=5.2688 timing/optimization_seconds=50.5086 timing/cycle_seconds=232.9160
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Pointwise Rewards: ssim:   0%|          | 0/6 [00:00<?, ?it/s]
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Pointwise Rewards: ssim:  50%|█████     | 3/6 [00:00<00:00,  5.66it/s]
Pointwise Rewards: ssim:  67%|██████▋   | 4/6 [00:00<00:00,  5.67it/s]
Pointwise Rewards: ssim:  83%|████████▎ | 5/6 [00:00<00:00,  5.65it/s]
Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:01<00:00,  5.95it/s]
Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:01<00:00,  5.67it/s]
+
Pointwise Rewards: dists:   0%|          | 0/6 [00:00<?, ?it/s]
Pointwise Rewards: dists:  17%|█▋        | 1/6 [00:00<00:01,  3.10it/s]
Pointwise Rewards: dists:  33%|███▎      | 2/6 [00:00<00:01,  3.15it/s]
Pointwise Rewards: dists:  50%|█████     | 3/6 [00:00<00:00,  3.23it/s]
Pointwise Rewards: dists:  67%|██████▋   | 4/6 [00:01<00:00,  3.22it/s]
Pointwise Rewards: dists:  83%|████████▎ | 5/6 [00:01<00:00,  3.21it/s]
Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.32it/s]
Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.25it/s]
+[2026-10-01 23:23:32] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0198 | Epoch 099] train/reward_dists_mean=0.8250 train/reward_dists_std=0.0387 train/reward_ssim_mean=0.6362 train/reward_ssim_std=0.1326 train/reward_dists_group_std_mean=0.0095 train/reward_dists_group_std_max=0.0234 train/reward_dists_group_std_min=0.0042 train/reward_dists_group_mean_std=0.0373 train/reward_ssim_group_std_mean=0.0074 train/reward_ssim_group_std_max=0.0178 train/reward_ssim_group_std_min=0.0030 train/reward_ssim_group_mean_std=0.1324 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.8513 train/adv_max=3.8322 train/adv_abs_mean=1.2589
+
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Epoch 99 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 23:23:57] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0198 | Epoch 099] train/clipped_loss_min=-3.8322 train/clipped_loss_max=4.3897 train/clipped_loss_mean=-0.1273 train/clipped_loss_std=1.5335 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-3.8322 train/unclipped_loss_max=4.3897 train/unclipped_loss_mean=-0.1273 train/unclipped_loss_std=1.5335 train/policy_loss=-0.1273 train/loss=-0.1273 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0005
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+[2026-10-01 23:24:22] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0200 | Epoch 099] timing/rollout_seconds=179.4037 timing/feedback_seconds=5.3751 timing/optimization_seconds=50.4361 timing/cycle_seconds=232.6913
+[2026-10-01 23:24:22] [Rank 0] [INFO] [flow_factory.models.abc]: Saving LoRA weights for transformer role=base to /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/runs/klein4b_grpo_flir_full_ssim_dists_lr5e-5/saves/klein4b_grpo_flir_full_ssim_dists_lr5e-5/checkpoints/checkpoint-100
+[2026-10-01 23:24:22] [Rank 0] [INFO] [flow_factory.models.abc]: Checkpoint saved successfully to /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/runs/klein4b_grpo_flir_full_ssim_dists_lr5e-5/saves/klein4b_grpo_flir_full_ssim_dists_lr5e-5/checkpoints/checkpoint-100
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+[2026-10-01 23:25:15] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0200 | Epoch 100] eval/flir_rl_full/reward_dists_mean=0.8381 eval/flir_rl_full/reward_dists_std=0.0374 eval/flir_rl_full/reward_psnr_mean=20.1141 eval/flir_rl_full/reward_psnr_std=2.9843 eval/flir_rl_full/reward_ssim_mean=0.6210 eval/flir_rl_full/reward_ssim_std=0.0867
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+[2026-10-01 23:28:18] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0200 | Epoch 100] train/reward_dists_mean=0.8290 train/reward_dists_std=0.0352 train/reward_ssim_mean=0.6342 train/reward_ssim_std=0.1129 train/reward_dists_group_std_mean=0.0096 train/reward_dists_group_std_max=0.0291 train/reward_dists_group_std_min=0.0034 train/reward_dists_group_mean_std=0.0336 train/reward_ssim_group_std_mean=0.0079 train/reward_ssim_group_std_max=0.0147 train/reward_ssim_group_std_min=0.0037 train/reward_ssim_group_mean_std=0.1126 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.6874 train/adv_max=4.8859 train/adv_abs_mean=1.2018
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Epoch 100 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 23:28:43] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0200 | Epoch 100] train/clipped_loss_min=-4.5097 train/clipped_loss_max=4.3139 train/clipped_loss_mean=0.0229 train/clipped_loss_std=1.5396 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-4.5097 train/unclipped_loss_max=4.3139 train/unclipped_loss_mean=0.0229 train/unclipped_loss_std=1.5396 train/policy_loss=0.0229 train/loss=0.0229 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0005
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Epoch 100 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 23:29:08] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0201 | Epoch 100] train/clipped_loss_min=-4.8859 train/clipped_loss_max=4.6874 train/clipped_loss_mean=-0.0229 train/clipped_loss_std=1.4981 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-4.8859 train/unclipped_loss_max=4.6874 train/unclipped_loss_mean=-0.0229 train/unclipped_loss_std=1.4981 train/policy_loss=-0.0229 train/loss=-0.0229 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0005
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+[2026-10-01 23:29:08] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0202 | Epoch 100] timing/rollout_seconds=179.4433 timing/feedback_seconds=4.9777 timing/optimization_seconds=50.4712 timing/cycle_seconds=232.8439
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+[2026-10-01 23:32:11] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0202 | Epoch 101] train/reward_dists_mean=0.8330 train/reward_dists_std=0.0276 train/reward_ssim_mean=0.6090 train/reward_ssim_std=0.0964 train/reward_dists_group_std_mean=0.0088 train/reward_dists_group_std_max=0.0182 train/reward_dists_group_std_min=0.0037 train/reward_dists_group_mean_std=0.0260 train/reward_ssim_group_std_mean=0.0078 train/reward_ssim_group_std_max=0.0187 train/reward_ssim_group_std_min=0.0030 train/reward_ssim_group_mean_std=0.0960 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.4142 train/adv_max=5.3467 train/adv_abs_mean=1.1191
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Epoch 101 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 23:32:36] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0202 | Epoch 101] train/clipped_loss_min=-4.4895 train/clipped_loss_max=5 train/clipped_loss_mean=0.0469 train/clipped_loss_std=1.4735 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-4.4895 train/unclipped_loss_max=5 train/unclipped_loss_mean=0.0469 train/unclipped_loss_std=1.4735 train/policy_loss=0.0469 train/loss=0.0469 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0005
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Epoch 101 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 23:33:01] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0203 | Epoch 101] train/clipped_loss_min=-5.0000 train/clipped_loss_max=4.3723 train/clipped_loss_mean=-0.0471 train/clipped_loss_std=1.3845 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0.0002 train/unclipped_loss_min=-5.0000 train/unclipped_loss_max=4.3723 train/unclipped_loss_mean=-0.0471 train/unclipped_loss_std=1.3845 train/policy_loss=-0.0471 train/loss=-0.0471 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0004
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+[2026-10-01 23:33:01] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0204 | Epoch 101] timing/rollout_seconds=179.4063 timing/feedback_seconds=4.6898 timing/optimization_seconds=50.5459 timing/cycle_seconds=232.9104
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+[2026-10-01 23:36:04] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0204 | Epoch 102] train/reward_dists_mean=0.8346 train/reward_dists_std=0.0307 train/reward_ssim_mean=0.5874 train/reward_ssim_std=0.1047 train/reward_dists_group_std_mean=0.0087 train/reward_dists_group_std_max=0.0165 train/reward_dists_group_std_min=0.0047 train/reward_dists_group_mean_std=0.0293 train/reward_ssim_group_std_mean=0.0072 train/reward_ssim_group_std_max=0.0186 train/reward_ssim_group_std_min=0.0025 train/reward_ssim_group_mean_std=0.1044 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.0753 train/adv_max=4.0044 train/adv_abs_mean=1.1634
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Epoch 102 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 23:36:29] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0204 | Epoch 102] train/clipped_loss_min=-4.0044 train/clipped_loss_max=5 train/clipped_loss_mean=0.0663 train/clipped_loss_std=1.4698 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-4.0044 train/unclipped_loss_max=5 train/unclipped_loss_mean=0.0663 train/unclipped_loss_std=1.4698 train/policy_loss=0.0663 train/loss=0.0663 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0002
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Epoch 102 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 23:36:54] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0205 | Epoch 102] train/clipped_loss_min=-3.6760 train/clipped_loss_max=4.3055 train/clipped_loss_mean=-0.0665 train/clipped_loss_std=1.4072 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-3.6760 train/unclipped_loss_max=4.3055 train/unclipped_loss_mean=-0.0665 train/unclipped_loss_std=1.4072 train/policy_loss=-0.0665 train/loss=-0.0665 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0002
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+[2026-10-01 23:36:54] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0206 | Epoch 102] timing/rollout_seconds=179.2875 timing/feedback_seconds=4.8837 timing/optimization_seconds=50.4262 timing/cycle_seconds=232.6434
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+[2026-10-01 23:39:57] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0206 | Epoch 103] train/reward_dists_mean=0.8406 train/reward_dists_std=0.0300 train/reward_ssim_mean=0.6499 train/reward_ssim_std=0.1125 train/reward_dists_group_std_mean=0.0085 train/reward_dists_group_std_max=0.0180 train/reward_dists_group_std_min=0.0038 train/reward_dists_group_mean_std=0.0286 train/reward_ssim_group_std_mean=0.0080 train/reward_ssim_group_std_max=0.0162 train/reward_ssim_group_std_min=0.0029 train/reward_ssim_group_mean_std=0.1121 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.2696 train/adv_max=4.6344 train/adv_abs_mean=1.2153
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Epoch 103 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 23:40:22] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0206 | Epoch 103] train/clipped_loss_min=-4.1109 train/clipped_loss_max=3.7742 train/clipped_loss_mean=0.0035 train/clipped_loss_std=1.4888 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-4.1109 train/unclipped_loss_max=3.7742 train/unclipped_loss_mean=0.0035 train/unclipped_loss_std=1.4888 train/policy_loss=0.0035 train/loss=0.0035 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0005
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Epoch 103 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 23:40:47] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0207 | Epoch 103] train/clipped_loss_min=-4.6344 train/clipped_loss_max=4.2695 train/clipped_loss_mean=-0.0035 train/clipped_loss_std=1.5742 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-4.6344 train/unclipped_loss_max=4.2695 train/unclipped_loss_mean=-0.0035 train/unclipped_loss_std=1.5742 train/policy_loss=-0.0035 train/loss=-0.0035 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0005
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+[2026-10-01 23:40:47] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0208 | Epoch 103] timing/rollout_seconds=179.5962 timing/feedback_seconds=5.4198 timing/optimization_seconds=50.7218 timing/cycle_seconds=233.0831
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+
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+[2026-10-01 23:43:50] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0208 | Epoch 104] train/reward_dists_mean=0.8433 train/reward_dists_std=0.0284 train/reward_ssim_mean=0.6195 train/reward_ssim_std=0.1081 train/reward_dists_group_std_mean=0.0087 train/reward_dists_group_std_max=0.0185 train/reward_dists_group_std_min=0.0035 train/reward_dists_group_mean_std=0.0269 train/reward_ssim_group_std_mean=0.0084 train/reward_ssim_group_std_max=0.0165 train/reward_ssim_group_std_min=0.0027 train/reward_ssim_group_mean_std=0.1077 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.4567 train/adv_max=4.6708 train/adv_abs_mean=1.1649
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Epoch 104 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 23:44:15] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0208 | Epoch 104] train/clipped_loss_min=-4.6708 train/clipped_loss_max=4.2501 train/clipped_loss_mean=0.0026 train/clipped_loss_std=1.4848 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-4.6708 train/unclipped_loss_max=4.2501 train/unclipped_loss_mean=0.0026 train/unclipped_loss_std=1.4848 train/policy_loss=0.0026 train/loss=0.0026 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
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Epoch 104 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 23:44:40] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0209 | Epoch 104] train/clipped_loss_min=-4.3634 train/clipped_loss_max=4.4567 train/clipped_loss_mean=-0.0026 train/clipped_loss_std=1.4349 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-4.3634 train/unclipped_loss_max=4.4567 train/unclipped_loss_mean=-0.0026 train/unclipped_loss_std=1.4349 train/policy_loss=-0.0026 train/loss=-0.0026 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
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+[2026-10-01 23:44:40] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0210 | Epoch 104] timing/rollout_seconds=179.6766 timing/feedback_seconds=5.5799 timing/optimization_seconds=50.6238 timing/cycle_seconds=233.1791
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+[2026-10-01 23:47:43] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0210 | Epoch 105] train/reward_dists_mean=0.8412 train/reward_dists_std=0.0285 train/reward_ssim_mean=0.6090 train/reward_ssim_std=0.0948 train/reward_dists_group_std_mean=0.0080 train/reward_dists_group_std_max=0.0140 train/reward_dists_group_std_min=0.0021 train/reward_dists_group_mean_std=0.0272 train/reward_ssim_group_std_mean=0.0078 train/reward_ssim_group_std_max=0.0264 train/reward_ssim_group_std_min=0.0028 train/reward_ssim_group_mean_std=0.0944 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.5005 train/adv_max=5.3959 train/adv_abs_mean=1.1619
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Epoch 105 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 23:48:08] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0210 | Epoch 105] train/clipped_loss_min=-3.6254 train/clipped_loss_max=4.1992 train/clipped_loss_mean=0.0625 train/clipped_loss_std=1.4803 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-3.6254 train/unclipped_loss_max=4.1992 train/unclipped_loss_mean=0.0625 train/unclipped_loss_std=1.4803 train/policy_loss=0.0625 train/loss=0.0625 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
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Epoch 105 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 23:48:33] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0211 | Epoch 105] train/clipped_loss_min=-5.0000 train/clipped_loss_max=4.5005 train/clipped_loss_mean=-0.0614 train/clipped_loss_std=1.4767 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0.0001 train/unclipped_loss_min=-5.0000 train/unclipped_loss_max=4.5005 train/unclipped_loss_mean=-0.0614 train/unclipped_loss_std=1.4767 train/policy_loss=-0.0614 train/loss=-0.0614 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
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+[2026-10-01 23:48:33] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0212 | Epoch 105] timing/rollout_seconds=179.5414 timing/feedback_seconds=4.8839 timing/optimization_seconds=50.3947 timing/cycle_seconds=232.7129
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+[2026-10-01 23:51:36] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0212 | Epoch 106] train/reward_dists_mean=0.8435 train/reward_dists_std=0.0306 train/reward_ssim_mean=0.6472 train/reward_ssim_std=0.1033 train/reward_dists_group_std_mean=0.0082 train/reward_dists_group_std_max=0.0156 train/reward_dists_group_std_min=0.0041 train/reward_dists_group_mean_std=0.0293 train/reward_ssim_group_std_mean=0.0071 train/reward_ssim_group_std_max=0.0148 train/reward_ssim_group_std_min=0.0032 train/reward_ssim_group_mean_std=0.1030 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.5238 train/adv_max=4.4898 train/adv_abs_mean=1.1798
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Epoch 106 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 23:52:01] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0212 | Epoch 106] train/clipped_loss_min=-3.7221 train/clipped_loss_max=4.5238 train/clipped_loss_mean=-0.0685 train/clipped_loss_std=1.4529 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-3.7221 train/unclipped_loss_max=4.5238 train/unclipped_loss_mean=-0.0685 train/unclipped_loss_std=1.4529 train/policy_loss=-0.0685 train/loss=-0.0685 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
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Epoch 106 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 23:52:25] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0213 | Epoch 106] train/clipped_loss_min=-4.4898 train/clipped_loss_max=4.2391 train/clipped_loss_mean=0.0685 train/clipped_loss_std=1.4985 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0.0001 train/unclipped_loss_min=-4.4898 train/unclipped_loss_max=4.2391 train/unclipped_loss_mean=0.0685 train/unclipped_loss_std=1.4985 train/policy_loss=0.0685 train/loss=0.0685 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
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+[2026-10-01 23:52:25] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0214 | Epoch 106] timing/rollout_seconds=179.5552 timing/feedback_seconds=5.5036 timing/optimization_seconds=50.3812 timing/cycle_seconds=232.8139
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+[2026-10-01 23:55:28] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0214 | Epoch 107] train/reward_dists_mean=0.8437 train/reward_dists_std=0.0286 train/reward_ssim_mean=0.6536 train/reward_ssim_std=0.0901 train/reward_dists_group_std_mean=0.0079 train/reward_dists_group_std_max=0.0140 train/reward_dists_group_std_min=0.0025 train/reward_dists_group_mean_std=0.0274 train/reward_ssim_group_std_mean=0.0075 train/reward_ssim_group_std_max=0.0211 train/reward_ssim_group_std_min=0.0029 train/reward_ssim_group_mean_std=0.0897 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.2369 train/adv_max=4.3609 train/adv_abs_mean=1.2045
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Epoch 107 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 23:55:53] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0214 | Epoch 107] train/clipped_loss_min=-3.6551 train/clipped_loss_max=4.3531 train/clipped_loss_mean=0.0191 train/clipped_loss_std=1.4464 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-3.6551 train/unclipped_loss_max=4.3531 train/unclipped_loss_mean=0.0191 train/unclipped_loss_std=1.4464 train/policy_loss=0.0191 train/loss=0.0191 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
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Epoch 107 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 23:56:18] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0215 | Epoch 107] train/clipped_loss_min=-4.3609 train/clipped_loss_max=5.0000 train/clipped_loss_mean=-0.0197 train/clipped_loss_std=1.5790 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0.0002 train/unclipped_loss_min=-4.3609 train/unclipped_loss_max=5.0000 train/unclipped_loss_mean=-0.0197 train/unclipped_loss_std=1.5790 train/policy_loss=-0.0197 train/loss=-0.0197 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
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+[2026-10-01 23:56:18] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0216 | Epoch 107] timing/rollout_seconds=179.4332 timing/feedback_seconds=4.9302 timing/optimization_seconds=50.4210 timing/cycle_seconds=232.7152
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+[2026-10-01 23:59:21] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0216 | Epoch 108] train/reward_dists_mean=0.8390 train/reward_dists_std=0.0294 train/reward_ssim_mean=0.6315 train/reward_ssim_std=0.0771 train/reward_dists_group_std_mean=0.0074 train/reward_dists_group_std_max=0.0156 train/reward_dists_group_std_min=0.0036 train/reward_dists_group_mean_std=0.0283 train/reward_ssim_group_std_mean=0.0072 train/reward_ssim_group_std_max=0.0167 train/reward_ssim_group_std_min=0.0027 train/reward_ssim_group_mean_std=0.0767 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.1057 train/adv_max=4.4158 train/adv_abs_mean=1.1706
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Epoch 108 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 23:59:46] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0216 | Epoch 108] train/clipped_loss_min=-4.0358 train/clipped_loss_max=4.9567 train/clipped_loss_mean=0.0091 train/clipped_loss_std=1.5074 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-4.0358 train/unclipped_loss_max=4.9567 train/unclipped_loss_mean=0.0091 train/unclipped_loss_std=1.5074 train/policy_loss=0.0091 train/loss=0.0091 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
+
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Epoch 108 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 00:00:11] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0217 | Epoch 108] train/clipped_loss_min=-4.4157 train/clipped_loss_max=5.0000 train/clipped_loss_mean=-0.0094 train/clipped_loss_std=1.4225 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-4.4157 train/unclipped_loss_max=5.0000 train/unclipped_loss_mean=-0.0094 train/unclipped_loss_std=1.4225 train/policy_loss=-0.0094 train/loss=-0.0094 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
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+[2026-10-02 00:00:11] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0218 | Epoch 108] timing/rollout_seconds=179.5292 timing/feedback_seconds=5.3058 timing/optimization_seconds=50.4101 timing/cycle_seconds=232.8538
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+
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Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.33it/s]
+[2026-10-02 00:03:14] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0218 | Epoch 109] train/reward_dists_mean=0.8409 train/reward_dists_std=0.0276 train/reward_ssim_mean=0.6316 train/reward_ssim_std=0.0918 train/reward_dists_group_std_mean=0.0070 train/reward_dists_group_std_max=0.0137 train/reward_dists_group_std_min=0.0038 train/reward_dists_group_mean_std=0.0266 train/reward_ssim_group_std_mean=0.0075 train/reward_ssim_group_std_max=0.0147 train/reward_ssim_group_std_min=0.0034 train/reward_ssim_group_mean_std=0.0914 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.4540 train/adv_max=4.3999 train/adv_abs_mean=1.1409
+
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Epoch 109 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 00:03:39] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0218 | Epoch 109] train/clipped_loss_min=-3.7359 train/clipped_loss_max=5 train/clipped_loss_mean=0.0106 train/clipped_loss_std=1.4002 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-3.7359 train/unclipped_loss_max=5 train/unclipped_loss_mean=0.0106 train/unclipped_loss_std=1.4002 train/policy_loss=0.0106 train/loss=0.0106 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
+
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Epoch 109 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 00:04:04] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0219 | Epoch 109] train/clipped_loss_min=-4.3999 train/clipped_loss_max=4.2211 train/clipped_loss_mean=-0.0118 train/clipped_loss_std=1.4727 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0.0002 train/unclipped_loss_min=-4.3999 train/unclipped_loss_max=4.2211 train/unclipped_loss_mean=-0.0118 train/unclipped_loss_std=1.4727 train/policy_loss=-0.0118 train/loss=-0.0118 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
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+[2026-10-02 00:04:04] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0220 | Epoch 109] timing/rollout_seconds=179.4478 timing/feedback_seconds=5.3540 timing/optimization_seconds=50.4117 timing/cycle_seconds=232.6881
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+[2026-10-02 00:04:57] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0220 | Epoch 110] eval/flir_rl_full/reward_dists_mean=0.8374 eval/flir_rl_full/reward_dists_std=0.0372 eval/flir_rl_full/reward_psnr_mean=20.0827 eval/flir_rl_full/reward_psnr_std=2.9459 eval/flir_rl_full/reward_ssim_mean=0.6194 eval/flir_rl_full/reward_ssim_std=0.0863
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+
Pointwise Rewards: ssim:   0%|          | 0/6 [00:00<?, ?it/s]
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Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:01<00:00,  5.67it/s]
+
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Pointwise Rewards: dists:  33%|███▎      | 2/6 [00:00<00:01,  3.15it/s]
Pointwise Rewards: dists:  50%|█████     | 3/6 [00:00<00:00,  3.23it/s]
Pointwise Rewards: dists:  67%|██████▋   | 4/6 [00:01<00:00,  3.25it/s]
Pointwise Rewards: dists:  83%|████████▎ | 5/6 [00:01<00:00,  3.27it/s]
Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.28it/s]
Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.25it/s]
+[2026-10-02 00:08:00] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0220 | Epoch 110] train/reward_dists_mean=0.8430 train/reward_dists_std=0.0314 train/reward_ssim_mean=0.6423 train/reward_ssim_std=0.0953 train/reward_dists_group_std_mean=0.0070 train/reward_dists_group_std_max=0.0130 train/reward_dists_group_std_min=0.0041 train/reward_dists_group_mean_std=0.0305 train/reward_ssim_group_std_mean=0.0080 train/reward_ssim_group_std_max=0.0170 train/reward_ssim_group_std_min=0.0030 train/reward_ssim_group_mean_std=0.0949 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.4108 train/adv_max=5.0169 train/adv_abs_mean=1.1951
+
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Epoch 110 Training:   7%|▋         | 7/96 [00:03<00:45,  1.94it/s]
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Epoch 110 Training:   8%|▊         | 8/96 [00:04<00:45,  1.94it/s]
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Epoch 110 Training:   9%|▉         | 9/96 [00:04<00:44,  1.93it/s]
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Epoch 110 Training:  10%|█         | 10/96 [00:05<00:44,  1.93it/s]
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Epoch 110 Training:  11%|█▏        | 11/96 [00:05<00:44,  1.93it/s]
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Epoch 110 Training:  12%|█▎        | 12/96 [00:06<00:43,  1.93it/s]
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Epoch 110 Training:  14%|█▎        | 13/96 [00:06<00:42,  1.93it/s]
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Epoch 110 Training:  15%|█▍        | 14/96 [00:07<00:42,  1.93it/s]
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Epoch 110 Training:  16%|█▌        | 15/96 [00:07<00:41,  1.93it/s]
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Epoch 110 Training:  17%|█▋        | 16/96 [00:08<00:41,  1.93it/s]
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Epoch 110 Training:  18%|█▊        | 17/96 [00:08<00:40,  1.93it/s]
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Epoch 110 Training:  19%|█▉        | 18/96 [00:09<00:40,  1.93it/s]
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Epoch 110 Training:  20%|█▉        | 19/96 [00:09<00:39,  1.93it/s]
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Epoch 110 Training:  23%|██▎       | 22/96 [00:11<00:38,  1.93it/s]
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Epoch 110 Training:  24%|██▍       | 23/96 [00:11<00:37,  1.93it/s]
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Epoch 110 Training:  25%|██▌       | 24/96 [00:12<00:37,  1.93it/s]
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Epoch 110 Training:  26%|██▌       | 25/96 [00:12<00:36,  1.93it/s]
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Epoch 110 Training:  27%|██▋       | 26/96 [00:13<00:36,  1.93it/s]
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Epoch 110 Training:  28%|██▊       | 27/96 [00:13<00:35,  1.93it/s]
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Epoch 110 Training:  29%|██▉       | 28/96 [00:14<00:35,  1.93it/s]
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Epoch 110 Training:  30%|███       | 29/96 [00:14<00:34,  1.93it/s]
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Epoch 110 Training:  31%|███▏      | 30/96 [00:15<00:34,  1.93it/s]
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Epoch 110 Training:  32%|███▏      | 31/96 [00:16<00:33,  1.93it/s]
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Epoch 110 Training:  33%|███▎      | 32/96 [00:16<00:33,  1.93it/s]
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Epoch 110 Training:  34%|███▍      | 33/96 [00:17<00:32,  1.93it/s]
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Epoch 110 Training:  35%|███▌      | 34/96 [00:17<00:32,  1.93it/s]
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Epoch 110 Training:  38%|███▊      | 36/96 [00:18<00:31,  1.93it/s]
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Epoch 110 Training:  39%|███▊      | 37/96 [00:19<00:30,  1.93it/s]
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Epoch 110 Training:  40%|███▉      | 38/96 [00:19<00:30,  1.93it/s]
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Epoch 110 Training:  41%|████      | 39/96 [00:20<00:29,  1.93it/s]
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Epoch 110 Training:  43%|████▎     | 41/96 [00:21<00:28,  1.93it/s]
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Epoch 110 Training:  44%|████▍     | 42/96 [00:21<00:27,  1.93it/s]
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Epoch 110 Training:  45%|████▍     | 43/96 [00:22<00:27,  1.93it/s]
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Epoch 110 Training:  46%|████▌     | 44/96 [00:22<00:26,  1.93it/s]
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Epoch 110 Training:  47%|████▋     | 45/96 [00:23<00:26,  1.93it/s]
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Epoch 110 Training:  48%|████▊     | 46/96 [00:23<00:25,  1.93it/s]
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Epoch 110 Training:  49%|████▉     | 47/96 [00:24<00:25,  1.93it/s]
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Epoch 110 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 00:08:25] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0220 | Epoch 110] train/clipped_loss_min=-5 train/clipped_loss_max=4.4108 train/clipped_loss_mean=-0.0743 train/clipped_loss_std=1.4178 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-5 train/unclipped_loss_max=4.4108 train/unclipped_loss_mean=-0.0743 train/unclipped_loss_std=1.4178 train/policy_loss=-0.0743 train/loss=-0.0743 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
+
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Epoch 110 Training:  51%|█████     | 49/96 [00:25<00:24,  1.94it/s]
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Epoch 110 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 00:08:50] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0221 | Epoch 110] train/clipped_loss_min=-3.6060 train/clipped_loss_max=3.8982 train/clipped_loss_mean=0.0744 train/clipped_loss_std=1.5343 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-3.6060 train/unclipped_loss_max=3.8982 train/unclipped_loss_mean=0.0744 train/unclipped_loss_std=1.5343 train/policy_loss=0.0744 train/loss=0.0744 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
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+[2026-10-02 00:08:50] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0222 | Epoch 110] timing/rollout_seconds=179.4753 timing/feedback_seconds=4.9900 timing/optimization_seconds=50.3775 timing/cycle_seconds=232.7692
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+
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+
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Pointwise Rewards: dists:  50%|█████     | 3/6 [00:00<00:00,  3.16it/s]
Pointwise Rewards: dists:  67%|██████▋   | 4/6 [00:01<00:00,  3.20it/s]
Pointwise Rewards: dists:  83%|████████▎ | 5/6 [00:01<00:00,  3.20it/s]
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Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.19it/s]
+[2026-10-02 00:11:53] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0222 | Epoch 111] train/reward_dists_mean=0.8428 train/reward_dists_std=0.0294 train/reward_ssim_mean=0.6341 train/reward_ssim_std=0.1126 train/reward_dists_group_std_mean=0.0073 train/reward_dists_group_std_max=0.0171 train/reward_dists_group_std_min=0.0035 train/reward_dists_group_mean_std=0.0284 train/reward_ssim_group_std_mean=0.0073 train/reward_ssim_group_std_max=0.0148 train/reward_ssim_group_std_min=0.0036 train/reward_ssim_group_mean_std=0.1124 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.4888 train/adv_max=4.4016 train/adv_abs_mean=1.1823
+
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Epoch 111 Training:   7%|▋         | 7/96 [00:03<00:46,  1.93it/s]
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Epoch 111 Training:   8%|▊         | 8/96 [00:04<00:45,  1.93it/s]
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Epoch 111 Training:   9%|▉         | 9/96 [00:04<00:45,  1.92it/s]
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Epoch 111 Training:  10%|█         | 10/96 [00:05<00:44,  1.92it/s]
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Epoch 111 Training:  11%|█▏        | 11/96 [00:05<00:44,  1.92it/s]
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Epoch 111 Training:  12%|█▎        | 12/96 [00:06<00:43,  1.92it/s]
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Epoch 111 Training:  14%|█▎        | 13/96 [00:06<00:43,  1.92it/s]
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Epoch 111 Training:  15%|█▍        | 14/96 [00:07<00:42,  1.92it/s]
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Epoch 111 Training:  16%|█▌        | 15/96 [00:07<00:42,  1.92it/s]
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Epoch 111 Training:  17%|█▋        | 16/96 [00:08<00:41,  1.92it/s]
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Epoch 111 Training:  18%|█▊        | 17/96 [00:08<00:41,  1.92it/s]
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Epoch 111 Training:  19%|█▉        | 18/96 [00:09<00:40,  1.92it/s]
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Epoch 111 Training:  20%|█▉        | 19/96 [00:09<00:40,  1.92it/s]
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Epoch 111 Training:  24%|██▍       | 23/96 [00:11<00:37,  1.92it/s]
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Epoch 111 Training:  25%|██▌       | 24/96 [00:12<00:37,  1.92it/s]
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Epoch 111 Training:  26%|██▌       | 25/96 [00:12<00:36,  1.92it/s]
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Epoch 111 Training:  27%|██▋       | 26/96 [00:13<00:36,  1.92it/s]
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Epoch 111 Training:  28%|██▊       | 27/96 [00:13<00:35,  1.92it/s]
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Epoch 111 Training:  29%|██▉       | 28/96 [00:14<00:35,  1.92it/s]
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Epoch 111 Training:  30%|███       | 29/96 [00:15<00:34,  1.92it/s]
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Epoch 111 Training:  31%|███▏      | 30/96 [00:15<00:34,  1.92it/s]
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Epoch 111 Training:  32%|███▏      | 31/96 [00:16<00:33,  1.92it/s]
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Epoch 111 Training:  33%|███▎      | 32/96 [00:16<00:33,  1.92it/s]
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Epoch 111 Training:  34%|███▍      | 33/96 [00:17<00:32,  1.92it/s]
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Epoch 111 Training:  35%|███▌      | 34/96 [00:17<00:32,  1.92it/s]
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Epoch 111 Training:  36%|███▋      | 35/96 [00:18<00:31,  1.92it/s]
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Epoch 111 Training:  38%|███▊      | 36/96 [00:18<00:31,  1.92it/s]
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Epoch 111 Training:  39%|███▊      | 37/96 [00:19<00:30,  1.92it/s]
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Epoch 111 Training:  40%|███▉      | 38/96 [00:19<00:30,  1.92it/s]
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Epoch 111 Training:  41%|████      | 39/96 [00:20<00:29,  1.92it/s]
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Epoch 111 Training:  43%|████▎     | 41/96 [00:21<00:28,  1.92it/s]
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Epoch 111 Training:  44%|████▍     | 42/96 [00:21<00:28,  1.92it/s]
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Epoch 111 Training:  45%|████▍     | 43/96 [00:22<00:27,  1.92it/s]
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Epoch 111 Training:  46%|████▌     | 44/96 [00:22<00:27,  1.92it/s]
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Epoch 111 Training:  47%|████▋     | 45/96 [00:23<00:26,  1.92it/s]
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Epoch 111 Training:  48%|████▊     | 46/96 [00:23<00:26,  1.92it/s]
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Epoch 111 Training:  49%|████▉     | 47/96 [00:24<00:25,  1.92it/s]
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Epoch 111 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 00:12:18] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0222 | Epoch 111] train/clipped_loss_min=-3.9271 train/clipped_loss_max=4.4888 train/clipped_loss_mean=-0.0057 train/clipped_loss_std=1.4489 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-3.9271 train/unclipped_loss_max=4.4888 train/unclipped_loss_mean=-0.0057 train/unclipped_loss_std=1.4489 train/policy_loss=-0.0057 train/loss=-0.0057 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0002
+
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Epoch 111 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 00:12:43] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0223 | Epoch 111] train/clipped_loss_min=-4.4016 train/clipped_loss_max=3.9234 train/clipped_loss_mean=0.0057 train/clipped_loss_std=1.4826 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0.0002 train/unclipped_loss_min=-4.4016 train/unclipped_loss_max=3.9234 train/unclipped_loss_mean=0.0057 train/unclipped_loss_std=1.4826 train/policy_loss=0.0057 train/loss=0.0057 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0002
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+[2026-10-02 00:12:43] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0224 | Epoch 111] timing/rollout_seconds=180.0472 timing/feedback_seconds=5.8471 timing/optimization_seconds=50.5911 timing/cycle_seconds=233.6126
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+
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+
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Pointwise Rewards: dists:  67%|██████▋   | 4/6 [00:01<00:00,  3.31it/s]
Pointwise Rewards: dists:  83%|████████▎ | 5/6 [00:01<00:00,  3.30it/s]
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Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.29it/s]
+[2026-10-02 00:15:46] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0224 | Epoch 112] train/reward_dists_mean=0.8419 train/reward_dists_std=0.0262 train/reward_ssim_mean=0.6372 train/reward_ssim_std=0.1078 train/reward_dists_group_std_mean=0.0070 train/reward_dists_group_std_max=0.0158 train/reward_dists_group_std_min=0.0035 train/reward_dists_group_mean_std=0.0251 train/reward_ssim_group_std_mean=0.0082 train/reward_ssim_group_std_max=0.0157 train/reward_ssim_group_std_min=0.0038 train/reward_ssim_group_mean_std=0.1075 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.8545 train/adv_max=4.9654 train/adv_abs_mean=1.1804
+
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Epoch 112 Training:   7%|▋         | 7/96 [00:03<00:46,  1.93it/s]
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Epoch 112 Training:   8%|▊         | 8/96 [00:04<00:45,  1.93it/s]
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Epoch 112 Training:   9%|▉         | 9/96 [00:04<00:45,  1.93it/s]
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Epoch 112 Training:  10%|█         | 10/96 [00:05<00:44,  1.93it/s]
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Epoch 112 Training:  11%|█▏        | 11/96 [00:05<00:44,  1.93it/s]
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Epoch 112 Training:  12%|█▎        | 12/96 [00:06<00:43,  1.92it/s]
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Epoch 112 Training:  14%|█▎        | 13/96 [00:06<00:43,  1.93it/s]
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Epoch 112 Training:  15%|█▍        | 14/96 [00:07<00:42,  1.92it/s]
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Epoch 112 Training:  16%|█▌        | 15/96 [00:07<00:42,  1.93it/s]
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Epoch 112 Training:  18%|█▊        | 17/96 [00:08<00:41,  1.93it/s]
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Epoch 112 Training:  19%|█▉        | 18/96 [00:09<00:40,  1.92it/s]
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Epoch 112 Training:  26%|██▌       | 25/96 [00:12<00:36,  1.93it/s]
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Epoch 112 Training:  27%|██▋       | 26/96 [00:13<00:36,  1.92it/s]
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Epoch 112 Training:  29%|██▉       | 28/96 [00:14<00:35,  1.92it/s]
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Epoch 112 Training:  30%|███       | 29/96 [00:15<00:34,  1.92it/s]
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Epoch 112 Training:  31%|███▏      | 30/96 [00:15<00:34,  1.92it/s]
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Epoch 112 Training:  32%|███▏      | 31/96 [00:16<00:33,  1.92it/s]
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Epoch 112 Training:  35%|███▌      | 34/96 [00:17<00:32,  1.92it/s]
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Epoch 112 Training:  39%|███▊      | 37/96 [00:19<00:30,  1.92it/s]
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Epoch 112 Training:  40%|███▉      | 38/96 [00:19<00:30,  1.92it/s]
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Epoch 112 Training:  41%|████      | 39/96 [00:20<00:29,  1.92it/s]
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Epoch 112 Training:  44%|████▍     | 42/96 [00:21<00:28,  1.93it/s]
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Epoch 112 Training:  45%|████▍     | 43/96 [00:22<00:27,  1.92it/s]
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Epoch 112 Training:  47%|████▋     | 45/96 [00:23<00:26,  1.93it/s]
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Epoch 112 Training:  48%|████▊     | 46/96 [00:23<00:25,  1.93it/s]
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Epoch 112 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 00:16:11] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0224 | Epoch 112] train/clipped_loss_min=-4.9654 train/clipped_loss_max=4.8545 train/clipped_loss_mean=-0.0438 train/clipped_loss_std=1.4921 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-4.9654 train/unclipped_loss_max=4.8545 train/unclipped_loss_mean=-0.0438 train/unclipped_loss_std=1.4921 train/policy_loss=-0.0438 train/loss=-0.0438 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
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Epoch 112 Timestep: 100%|██████████| 1/1 [00:00<00:00,  1.90it/s]
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Epoch 112 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 00:16:36] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0225 | Epoch 112] train/clipped_loss_min=-3.8624 train/clipped_loss_max=3.9502 train/clipped_loss_mean=0.0438 train/clipped_loss_std=1.4689 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-3.8624 train/unclipped_loss_max=3.9502 train/unclipped_loss_mean=0.0438 train/unclipped_loss_std=1.4689 train/policy_loss=0.0438 train/loss=0.0438 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
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Epoch 112 Training: 100%|██████████| 96/96 [00:49<00:00,  1.92it/s]
+[2026-10-02 00:16:36] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0226 | Epoch 112] timing/rollout_seconds=179.4558 timing/feedback_seconds=4.9773 timing/optimization_seconds=50.4821 timing/cycle_seconds=232.7953
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+
Pointwise Rewards: ssim:   0%|          | 0/6 [00:00<?, ?it/s]
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Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:01<00:00,  5.94it/s]
+
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Pointwise Rewards: dists:  50%|█████     | 3/6 [00:00<00:00,  3.27it/s]
Pointwise Rewards: dists:  67%|██████▋   | 4/6 [00:01<00:00,  3.27it/s]
Pointwise Rewards: dists:  83%|████████▎ | 5/6 [00:01<00:00,  3.32it/s]
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Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.35it/s]
+[2026-10-02 00:19:39] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0226 | Epoch 113] train/reward_dists_mean=0.8384 train/reward_dists_std=0.0279 train/reward_ssim_mean=0.6318 train/reward_ssim_std=0.0990 train/reward_dists_group_std_mean=0.0076 train/reward_dists_group_std_max=0.0175 train/reward_dists_group_std_min=0.0033 train/reward_dists_group_mean_std=0.0267 train/reward_ssim_group_std_mean=0.0081 train/reward_ssim_group_std_max=0.0145 train/reward_ssim_group_std_min=0.0030 train/reward_ssim_group_mean_std=0.0986 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.1073 train/adv_max=4.0642 train/adv_abs_mean=1.0968
+
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Epoch 113 Training:   9%|▉         | 9/96 [00:04<00:47,  1.85it/s]
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Epoch 113 Training:  10%|█         | 10/96 [00:05<00:45,  1.87it/s]
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Epoch 113 Training:  11%|█▏        | 11/96 [00:05<00:45,  1.89it/s]
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Epoch 113 Training:  19%|█▉        | 18/96 [00:09<00:40,  1.92it/s]
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Epoch 113 Training:  29%|██▉       | 28/96 [00:14<00:35,  1.93it/s]
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Epoch 113 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 00:20:04] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0226 | Epoch 113] train/clipped_loss_min=-4.0642 train/clipped_loss_max=4.1073 train/clipped_loss_mean=-0.0663 train/clipped_loss_std=1.3740 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-4.0642 train/unclipped_loss_max=4.1073 train/unclipped_loss_mean=-0.0663 train/unclipped_loss_std=1.3740 train/policy_loss=-0.0663 train/loss=-0.0663 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0001
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Epoch 113 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 00:20:29] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0227 | Epoch 113] train/clipped_loss_min=-3.9373 train/clipped_loss_max=3.6871 train/clipped_loss_mean=0.0663 train/clipped_loss_std=1.3605 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0.0001 train/unclipped_loss_min=-3.9373 train/unclipped_loss_max=3.6871 train/unclipped_loss_mean=0.0663 train/unclipped_loss_std=1.3605 train/policy_loss=0.0663 train/loss=0.0663 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0002
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+[2026-10-02 00:20:29] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0228 | Epoch 113] timing/rollout_seconds=179.4016 timing/feedback_seconds=5.1719 timing/optimization_seconds=50.7129 timing/cycle_seconds=233.1406
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+
Pointwise Rewards: ssim:   0%|          | 0/6 [00:00<?, ?it/s]
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Pointwise Rewards: ssim:  67%|██████▋   | 4/6 [00:00<00:00,  6.24it/s]
Pointwise Rewards: ssim:  83%|████████▎ | 5/6 [00:00<00:00,  6.14it/s]
Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:00<00:00,  6.20it/s]
Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:00<00:00,  6.16it/s]
+
Pointwise Rewards: dists:   0%|          | 0/6 [00:00<?, ?it/s]
Pointwise Rewards: dists:  17%|█▋        | 1/6 [00:00<00:01,  3.45it/s]
Pointwise Rewards: dists:  33%|███▎      | 2/6 [00:00<00:01,  3.47it/s]
Pointwise Rewards: dists:  50%|█████     | 3/6 [00:00<00:00,  3.47it/s]
Pointwise Rewards: dists:  67%|██████▋   | 4/6 [00:01<00:00,  3.40it/s]
Pointwise Rewards: dists:  83%|████████▎ | 5/6 [00:01<00:00,  3.36it/s]
Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.37it/s]
Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.39it/s]
+[2026-10-02 00:23:32] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0228 | Epoch 114] train/reward_dists_mean=0.8404 train/reward_dists_std=0.0298 train/reward_ssim_mean=0.6190 train/reward_ssim_std=0.0839 train/reward_dists_group_std_mean=0.0068 train/reward_dists_group_std_max=0.0134 train/reward_dists_group_std_min=0.0037 train/reward_dists_group_mean_std=0.0290 train/reward_ssim_group_std_mean=0.0079 train/reward_ssim_group_std_max=0.0190 train/reward_ssim_group_std_min=0.0025 train/reward_ssim_group_mean_std=0.0834 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.6410 train/adv_max=4.9888 train/adv_abs_mean=1.1646
+
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Epoch 114 Training:   1%|          | 1/96 [00:00<00:43,  2.17it/s]
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Epoch 114 Training:   2%|▏         | 2/96 [00:00<00:46,  2.02it/s]
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Epoch 114 Training:   4%|▍         | 4/96 [00:02<00:47,  1.95it/s]
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Epoch 114 Training:   5%|▌         | 5/96 [00:02<00:46,  1.95it/s]
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Epoch 114 Training:   6%|▋         | 6/96 [00:03<00:46,  1.94it/s]
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Epoch 114 Training:   7%|▋         | 7/96 [00:03<00:45,  1.94it/s]
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Epoch 114 Training:   8%|▊         | 8/96 [00:04<00:45,  1.93it/s]
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Epoch 114 Training:   9%|▉         | 9/96 [00:04<00:45,  1.93it/s]
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Epoch 114 Training:  10%|█         | 10/96 [00:05<00:44,  1.93it/s]
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Epoch 114 Training:  11%|█▏        | 11/96 [00:05<00:44,  1.93it/s]
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Epoch 114 Training:  12%|█▎        | 12/96 [00:06<00:43,  1.93it/s]
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Epoch 114 Training:  14%|█▎        | 13/96 [00:06<00:43,  1.93it/s]
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Epoch 114 Training:  15%|█▍        | 14/96 [00:07<00:42,  1.93it/s]
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Epoch 114 Training:  16%|█▌        | 15/96 [00:07<00:42,  1.93it/s]
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Epoch 114 Training:  17%|█▋        | 16/96 [00:08<00:41,  1.93it/s]
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Epoch 114 Training:  18%|█▊        | 17/96 [00:08<00:40,  1.93it/s]
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Epoch 114 Training:  19%|█▉        | 18/96 [00:09<00:40,  1.93it/s]
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Epoch 114 Training:  20%|█▉        | 19/96 [00:09<00:39,  1.93it/s]
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Epoch 114 Training:  21%|██        | 20/96 [00:10<00:39,  1.93it/s]
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Epoch 114 Training:  22%|██▏       | 21/96 [00:10<00:38,  1.93it/s]
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Epoch 114 Training:  23%|██▎       | 22/96 [00:11<00:38,  1.93it/s]
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Epoch 114 Training:  24%|██▍       | 23/96 [00:11<00:37,  1.93it/s]
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Epoch 114 Training:  25%|██▌       | 24/96 [00:12<00:37,  1.93it/s]
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Epoch 114 Training:  26%|██▌       | 25/96 [00:12<00:36,  1.93it/s]
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Epoch 114 Training:  27%|██▋       | 26/96 [00:13<00:36,  1.93it/s]
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Epoch 114 Training:  28%|██▊       | 27/96 [00:13<00:35,  1.93it/s]
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Epoch 114 Training:  29%|██▉       | 28/96 [00:14<00:35,  1.93it/s]
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Epoch 114 Training:  30%|███       | 29/96 [00:14<00:34,  1.93it/s]
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Epoch 114 Training:  31%|███▏      | 30/96 [00:15<00:34,  1.92it/s]
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Epoch 114 Training:  32%|███▏      | 31/96 [00:16<00:33,  1.92it/s]
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Epoch 114 Training:  33%|███▎      | 32/96 [00:16<00:33,  1.93it/s]
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Epoch 114 Training:  34%|███▍      | 33/96 [00:17<00:32,  1.93it/s]
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Epoch 114 Training:  35%|███▌      | 34/96 [00:17<00:32,  1.93it/s]
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Epoch 114 Training:  36%|███▋      | 35/96 [00:18<00:31,  1.93it/s]
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Epoch 114 Training:  38%|███▊      | 36/96 [00:18<00:31,  1.93it/s]
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Epoch 114 Training:  39%|███▊      | 37/96 [00:19<00:30,  1.93it/s]
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Epoch 114 Training:  40%|███▉      | 38/96 [00:19<00:30,  1.93it/s]
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Epoch 114 Training:  41%|████      | 39/96 [00:20<00:29,  1.93it/s]
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Epoch 114 Training:  42%|████▏     | 40/96 [00:20<00:29,  1.93it/s]
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Epoch 114 Training:  43%|████▎     | 41/96 [00:21<00:28,  1.93it/s]
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Epoch 114 Training:  49%|████▉     | 47/96 [00:24<00:25,  1.93it/s]
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Epoch 114 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 00:23:57] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0228 | Epoch 114] train/clipped_loss_min=-3.7058 train/clipped_loss_max=4.4258 train/clipped_loss_mean=-0.0359 train/clipped_loss_std=1.4269 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-3.7058 train/unclipped_loss_max=4.4258 train/unclipped_loss_mean=-0.0359 train/unclipped_loss_std=1.4269 train/policy_loss=-0.0359 train/loss=-0.0359 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0002
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Epoch 114 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 00:24:22] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0229 | Epoch 114] train/clipped_loss_min=-4.9888 train/clipped_loss_max=4.6410 train/clipped_loss_mean=0.0359 train/clipped_loss_std=1.4984 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-4.9888 train/unclipped_loss_max=4.6410 train/unclipped_loss_mean=0.0359 train/unclipped_loss_std=1.4984 train/policy_loss=0.0359 train/loss=0.0359 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0002
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Epoch 114 Training: 100%|██████████| 96/96 [00:49<00:00,  1.93it/s]
+[2026-10-02 00:24:22] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0230 | Epoch 114] timing/rollout_seconds=179.4320 timing/feedback_seconds=5.2744 timing/optimization_seconds=50.4610 timing/cycle_seconds=232.7654
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+
Pointwise Rewards: ssim:   0%|          | 0/6 [00:00<?, ?it/s]
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Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:01<00:00,  5.85it/s]
+
Pointwise Rewards: dists:   0%|          | 0/6 [00:00<?, ?it/s]
Pointwise Rewards: dists:  17%|█▋        | 1/6 [00:00<00:01,  3.54it/s]
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Pointwise Rewards: dists:  67%|██████▋   | 4/6 [00:01<00:00,  3.28it/s]
Pointwise Rewards: dists:  83%|████████▎ | 5/6 [00:01<00:00,  3.29it/s]
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Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.31it/s]
+[2026-10-02 00:27:25] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0230 | Epoch 115] train/reward_dists_mean=0.8375 train/reward_dists_std=0.0275 train/reward_ssim_mean=0.6372 train/reward_ssim_std=0.1053 train/reward_dists_group_std_mean=0.0069 train/reward_dists_group_std_max=0.0104 train/reward_dists_group_std_min=0.0031 train/reward_dists_group_mean_std=0.0265 train/reward_ssim_group_std_mean=0.0083 train/reward_ssim_group_std_max=0.0192 train/reward_ssim_group_std_min=0.0031 train/reward_ssim_group_mean_std=0.1049 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.7593 train/adv_max=4.3844 train/adv_abs_mean=1.2245
+
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Epoch 115 Training:   7%|▋         | 7/96 [00:03<00:46,  1.93it/s]
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Epoch 115 Training:   8%|▊         | 8/96 [00:04<00:45,  1.93it/s]
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Epoch 115 Training:   9%|▉         | 9/96 [00:04<00:45,  1.93it/s]
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Epoch 115 Training:  11%|█▏        | 11/96 [00:05<00:44,  1.93it/s]
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Epoch 115 Training:  14%|█▎        | 13/96 [00:06<00:43,  1.92it/s]
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Epoch 115 Training:  15%|█▍        | 14/96 [00:07<00:42,  1.92it/s]
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Epoch 115 Training:  17%|█▋        | 16/96 [00:08<00:41,  1.92it/s]
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Epoch 115 Training:  18%|█▊        | 17/96 [00:08<00:41,  1.92it/s]
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Epoch 115 Training:  19%|█▉        | 18/96 [00:09<00:40,  1.92it/s]
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Epoch 115 Training:  26%|██▌       | 25/96 [00:12<00:36,  1.92it/s]
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Epoch 115 Training:  27%|██▋       | 26/96 [00:13<00:36,  1.92it/s]
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Epoch 115 Training:  28%|██▊       | 27/96 [00:13<00:35,  1.92it/s]
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Epoch 115 Training:  29%|██▉       | 28/96 [00:14<00:35,  1.92it/s]
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Epoch 115 Training:  31%|███▏      | 30/96 [00:15<00:34,  1.92it/s]
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Epoch 115 Training:  32%|███▏      | 31/96 [00:16<00:33,  1.93it/s]
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Epoch 115 Training:  39%|███▊      | 37/96 [00:19<00:30,  1.92it/s]
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Epoch 115 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 00:27:50] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0230 | Epoch 115] train/clipped_loss_min=-4.3123 train/clipped_loss_max=4.7593 train/clipped_loss_mean=-0.0132 train/clipped_loss_std=1.5487 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-4.3123 train/unclipped_loss_max=4.7593 train/unclipped_loss_mean=-0.0132 train/unclipped_loss_std=1.5487 train/policy_loss=-0.0132 train/loss=-0.0132 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
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Epoch 115 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 00:28:15] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0231 | Epoch 115] train/clipped_loss_min=-4.3843 train/clipped_loss_max=4.3363 train/clipped_loss_mean=0.0132 train/clipped_loss_std=1.4985 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-4.3843 train/unclipped_loss_max=4.3363 train/unclipped_loss_mean=0.0132 train/unclipped_loss_std=1.4985 train/policy_loss=0.0132 train/loss=0.0132 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
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Epoch 115 Training: 100%|██████████| 96/96 [00:50<00:00,  1.92it/s]
+[2026-10-02 00:28:15] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0232 | Epoch 115] timing/rollout_seconds=179.3817 timing/feedback_seconds=5.4929 timing/optimization_seconds=50.7506 timing/cycle_seconds=233.0065
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+
Pointwise Rewards: ssim:   0%|          | 0/6 [00:00<?, ?it/s]
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Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:01<00:00,  5.54it/s]
+
Pointwise Rewards: dists:   0%|          | 0/6 [00:00<?, ?it/s]
Pointwise Rewards: dists:  17%|█▋        | 1/6 [00:00<00:01,  3.23it/s]
Pointwise Rewards: dists:  33%|███▎      | 2/6 [00:00<00:01,  3.20it/s]
Pointwise Rewards: dists:  50%|█████     | 3/6 [00:00<00:00,  3.16it/s]
Pointwise Rewards: dists:  67%|██████▋   | 4/6 [00:01<00:00,  3.28it/s]
Pointwise Rewards: dists:  83%|████████▎ | 5/6 [00:01<00:00,  3.23it/s]
Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.24it/s]
Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.23it/s]
+[2026-10-02 00:31:18] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0232 | Epoch 116] train/reward_dists_mean=0.8281 train/reward_dists_std=0.0312 train/reward_ssim_mean=0.6140 train/reward_ssim_std=0.0970 train/reward_dists_group_std_mean=0.0072 train/reward_dists_group_std_max=0.0171 train/reward_dists_group_std_min=0.0043 train/reward_dists_group_mean_std=0.0303 train/reward_ssim_group_std_mean=0.0071 train/reward_ssim_group_std_max=0.0170 train/reward_ssim_group_std_min=0.0023 train/reward_ssim_group_mean_std=0.0967 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.0658 train/adv_max=5.3143 train/adv_abs_mean=1.2105
+
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Epoch 116 Training:   1%|          | 1/96 [00:00<00:43,  2.16it/s]
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Epoch 116 Training:   2%|▏         | 2/96 [00:00<00:46,  2.01it/s]
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Epoch 116 Training:   3%|▎         | 3/96 [00:01<00:58,  1.59it/s]
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Epoch 116 Training:   4%|▍         | 4/96 [00:02<00:53,  1.71it/s]
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Epoch 116 Training:   5%|▌         | 5/96 [00:02<00:51,  1.78it/s]
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Epoch 116 Training:   7%|▋         | 7/96 [00:03<00:47,  1.86it/s]
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Epoch 116 Training:   8%|▊         | 8/96 [00:04<00:46,  1.88it/s]
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Epoch 116 Training:   9%|▉         | 9/96 [00:04<00:45,  1.89it/s]
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Epoch 116 Training:  10%|█         | 10/96 [00:05<00:45,  1.90it/s]
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Epoch 116 Training:  11%|█▏        | 11/96 [00:05<00:44,  1.91it/s]
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Epoch 116 Training:  12%|█▎        | 12/96 [00:06<00:43,  1.92it/s]
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Epoch 116 Training:  14%|█▎        | 13/96 [00:06<00:43,  1.92it/s]
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Epoch 116 Training:  15%|█▍        | 14/96 [00:07<00:42,  1.92it/s]
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Epoch 116 Training:  16%|█▌        | 15/96 [00:07<00:42,  1.92it/s]
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Epoch 116 Training:  17%|█▋        | 16/96 [00:08<00:41,  1.92it/s]
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Epoch 116 Training:  18%|█▊        | 17/96 [00:09<00:41,  1.93it/s]
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Epoch 116 Training:  19%|█▉        | 18/96 [00:09<00:40,  1.92it/s]
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Epoch 116 Training:  20%|█▉        | 19/96 [00:10<00:39,  1.93it/s]
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Epoch 116 Training:  21%|██        | 20/96 [00:10<00:39,  1.93it/s]
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Epoch 116 Training:  22%|██▏       | 21/96 [00:11<00:38,  1.93it/s]
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Epoch 116 Training:  23%|██▎       | 22/96 [00:11<00:38,  1.93it/s]
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Epoch 116 Training:  24%|██▍       | 23/96 [00:12<00:37,  1.93it/s]
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Epoch 116 Training:  25%|██▌       | 24/96 [00:12<00:37,  1.93it/s]
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Epoch 116 Training:  26%|██▌       | 25/96 [00:13<00:36,  1.93it/s]
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Epoch 116 Training:  27%|██▋       | 26/96 [00:13<00:36,  1.93it/s]
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Epoch 116 Training:  28%|██▊       | 27/96 [00:14<00:35,  1.93it/s]
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Epoch 116 Training:  29%|██▉       | 28/96 [00:14<00:35,  1.93it/s]
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Epoch 116 Training:  30%|███       | 29/96 [00:15<00:34,  1.93it/s]
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Epoch 116 Training:  31%|███▏      | 30/96 [00:15<00:34,  1.93it/s]
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Epoch 116 Training:  32%|███▏      | 31/96 [00:16<00:33,  1.93it/s]
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Epoch 116 Training:  33%|███▎      | 32/96 [00:16<00:33,  1.93it/s]
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Epoch 116 Training:  34%|███▍      | 33/96 [00:17<00:32,  1.93it/s]
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Epoch 116 Training:  35%|███▌      | 34/96 [00:17<00:32,  1.93it/s]
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Epoch 116 Training:  36%|███▋      | 35/96 [00:18<00:31,  1.93it/s]
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Epoch 116 Training:  38%|███▊      | 36/96 [00:18<00:31,  1.93it/s]
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Epoch 116 Training:  49%|████▉     | 47/96 [00:24<00:25,  1.92it/s]
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Epoch 116 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 00:31:43] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0232 | Epoch 116] train/clipped_loss_min=-5 train/clipped_loss_max=3.8072 train/clipped_loss_mean=-0.0228 train/clipped_loss_std=1.4918 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-5 train/unclipped_loss_max=3.8072 train/unclipped_loss_mean=-0.0228 train/unclipped_loss_std=1.4918 train/policy_loss=-0.0228 train/loss=-0.0228 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
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Epoch 116 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 00:32:08] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0233 | Epoch 116] train/clipped_loss_min=-4.2917 train/clipped_loss_max=4.0658 train/clipped_loss_mean=0.0237 train/clipped_loss_std=1.5071 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-4.2917 train/unclipped_loss_max=4.0658 train/unclipped_loss_mean=0.0237 train/unclipped_loss_std=1.5071 train/policy_loss=0.0237 train/loss=0.0237 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
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Epoch 116 Training: 100%|██████████| 96/96 [00:50<00:00,  1.92it/s]
+[2026-10-02 00:32:08] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0234 | Epoch 116] timing/rollout_seconds=179.3658 timing/feedback_seconds=4.8799 timing/optimization_seconds=50.7150 timing/cycle_seconds=233.0168
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+
Pointwise Rewards: ssim:   0%|          | 0/6 [00:00<?, ?it/s]
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Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:01<00:00,  5.63it/s]
+
Pointwise Rewards: dists:   0%|          | 0/6 [00:00<?, ?it/s]
Pointwise Rewards: dists:  17%|█▋        | 1/6 [00:00<00:01,  3.17it/s]
Pointwise Rewards: dists:  33%|███▎      | 2/6 [00:00<00:01,  3.20it/s]
Pointwise Rewards: dists:  50%|█████     | 3/6 [00:00<00:00,  3.22it/s]
Pointwise Rewards: dists:  67%|██████▋   | 4/6 [00:01<00:00,  3.21it/s]
Pointwise Rewards: dists:  83%|████████▎ | 5/6 [00:01<00:00,  3.28it/s]
Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.31it/s]
Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.26it/s]
+[2026-10-02 00:35:11] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0234 | Epoch 117] train/reward_dists_mean=0.8418 train/reward_dists_std=0.0286 train/reward_ssim_mean=0.6632 train/reward_ssim_std=0.1010 train/reward_dists_group_std_mean=0.0068 train/reward_dists_group_std_max=0.0116 train/reward_dists_group_std_min=0.0036 train/reward_dists_group_mean_std=0.0278 train/reward_ssim_group_std_mean=0.0068 train/reward_ssim_group_std_max=0.0150 train/reward_ssim_group_std_min=0.0030 train/reward_ssim_group_mean_std=0.1008 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.1322 train/adv_max=4.2928 train/adv_abs_mean=1.1231
+
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Epoch 117 Training:   9%|▉         | 9/96 [00:04<00:45,  1.93it/s]
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Epoch 117 Training:  11%|█▏        | 11/96 [00:05<00:44,  1.93it/s]
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Epoch 117 Training:  12%|█▎        | 12/96 [00:06<00:43,  1.93it/s]
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Epoch 117 Training:  14%|█▎        | 13/96 [00:06<00:43,  1.93it/s]
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Epoch 117 Training:  15%|█▍        | 14/96 [00:07<00:42,  1.93it/s]
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Epoch 117 Training:  18%|█▊        | 17/96 [00:08<00:41,  1.93it/s]
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Epoch 117 Training:  25%|██▌       | 24/96 [00:12<00:37,  1.93it/s]
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Epoch 117 Training:  26%|██▌       | 25/96 [00:12<00:36,  1.93it/s]
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Epoch 117 Training:  27%|██▋       | 26/96 [00:13<00:36,  1.93it/s]
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Epoch 117 Training:  29%|██▉       | 28/96 [00:14<00:35,  1.93it/s]
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Epoch 117 Training:  43%|████▎     | 41/96 [00:21<00:28,  1.93it/s]
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Epoch 117 Training:  48%|████▊     | 46/96 [00:23<00:25,  1.93it/s]
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Epoch 117 Training:  49%|████▉     | 47/96 [00:24<00:25,  1.93it/s]
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Epoch 117 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 00:35:36] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0234 | Epoch 117] train/clipped_loss_min=-4.2928 train/clipped_loss_max=4.1322 train/clipped_loss_mean=0.0103 train/clipped_loss_std=1.4264 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-4.2928 train/unclipped_loss_max=4.1322 train/unclipped_loss_mean=0.0103 train/unclipped_loss_std=1.4264 train/policy_loss=0.0103 train/loss=0.0103 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
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Epoch 117 Timestep: 100%|██████████| 1/1 [00:00<00:00,  1.90it/s]
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Epoch 117 Training:  54%|█████▍    | 52/96 [00:26<00:22,  1.93it/s]
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Epoch 117 Training:  55%|█████▌    | 53/96 [00:27<00:22,  1.93it/s]
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Epoch 117 Training:  56%|█████▋    | 54/96 [00:27<00:21,  1.93it/s]
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Epoch 117 Training:  57%|█████▋    | 55/96 [00:28<00:21,  1.93it/s]
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Epoch 117 Training:  58%|█████▊    | 56/96 [00:29<00:20,  1.93it/s]
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Epoch 117 Training:  59%|█████▉    | 57/96 [00:29<00:20,  1.93it/s]
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Epoch 117 Training:  60%|██████    | 58/96 [00:30<00:19,  1.93it/s]
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Epoch 117 Training:  61%|██████▏   | 59/96 [00:30<00:19,  1.93it/s]
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Epoch 117 Training:  62%|██████▎   | 60/96 [00:31<00:18,  1.93it/s]
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Epoch 117 Training:  64%|██████▎   | 61/96 [00:31<00:18,  1.93it/s]
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Epoch 117 Training:  65%|██████▍   | 62/96 [00:32<00:17,  1.93it/s]
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Epoch 117 Training:  66%|██████▌   | 63/96 [00:32<00:17,  1.93it/s]
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Epoch 117 Training:  67%|██████▋   | 64/96 [00:33<00:16,  1.93it/s]
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Epoch 117 Training:  68%|██████▊   | 65/96 [00:33<00:16,  1.93it/s]
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Epoch 117 Training:  69%|██████▉   | 66/96 [00:34<00:15,  1.93it/s]
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Epoch 117 Training:  70%|██████▉   | 67/96 [00:34<00:15,  1.93it/s]
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Epoch 117 Training:  71%|███████   | 68/96 [00:35<00:14,  1.93it/s]
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Epoch 117 Training:  72%|███████▏  | 69/96 [00:35<00:14,  1.93it/s]
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Epoch 117 Training:  73%|███████▎  | 70/96 [00:36<00:13,  1.93it/s]
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Epoch 117 Training:  74%|███████▍  | 71/96 [00:36<00:12,  1.93it/s]
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Epoch 117 Training:  75%|███████▌  | 72/96 [00:37<00:12,  1.93it/s]
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Epoch 117 Training:  76%|███████▌  | 73/96 [00:37<00:11,  1.93it/s]
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Epoch 117 Training:  77%|███████▋  | 74/96 [00:38<00:11,  1.93it/s]
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Epoch 117 Training:  78%|███████▊  | 75/96 [00:38<00:10,  1.93it/s]
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Epoch 117 Training:  79%|███████▉  | 76/96 [00:39<00:10,  1.93it/s]
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Epoch 117 Training:  80%|████████  | 77/96 [00:39<00:09,  1.93it/s]
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Epoch 117 Training:  81%|████████▏ | 78/96 [00:40<00:09,  1.93it/s]
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Epoch 117 Training:  82%|████████▏ | 79/96 [00:40<00:08,  1.93it/s]
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Epoch 117 Training:  83%|████████▎ | 80/96 [00:41<00:08,  1.93it/s]
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Epoch 117 Training:  84%|████████▍ | 81/96 [00:41<00:07,  1.93it/s]
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Epoch 117 Training:  85%|████████▌ | 82/96 [00:42<00:07,  1.93it/s]
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Epoch 117 Training:  86%|████████▋ | 83/96 [00:43<00:06,  1.93it/s]
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Epoch 117 Training:  88%|████████▊ | 84/96 [00:43<00:06,  1.93it/s]
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Epoch 117 Training:  89%|████████▊ | 85/96 [00:44<00:05,  1.93it/s]
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Epoch 117 Training:  90%|████████▉ | 86/96 [00:44<00:05,  1.93it/s]
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Epoch 117 Training:  91%|█████████ | 87/96 [00:45<00:04,  1.93it/s]
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Epoch 117 Training:  96%|█████████▌| 92/96 [00:47<00:02,  1.93it/s]
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Epoch 117 Training:  97%|█████████▋| 93/96 [00:48<00:01,  1.93it/s]
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Epoch 117 Training:  98%|█████████▊| 94/96 [00:48<00:01,  1.93it/s]
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Epoch 117 Training:  99%|█████████▉| 95/96 [00:49<00:00,  1.93it/s]
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Epoch 117 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 00:36:01] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0235 | Epoch 117] train/clipped_loss_min=-3.9106 train/clipped_loss_max=4.0833 train/clipped_loss_mean=-0.0103 train/clipped_loss_std=1.4253 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0.0002 train/unclipped_loss_min=-3.9106 train/unclipped_loss_max=4.0833 train/unclipped_loss_mean=-0.0103 train/unclipped_loss_std=1.4253 train/policy_loss=-0.0103 train/loss=-0.0103 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
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Epoch 117 Timestep: 100%|██████████| 1/1 [00:00<00:00,  1.90it/s]
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Epoch 117 Training: 100%|██████████| 96/96 [00:49<00:00,  1.86it/s]
Epoch 117 Training: 100%|██████████| 96/96 [00:49<00:00,  1.93it/s]
+[2026-10-02 00:36:01] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0236 | Epoch 117] timing/rollout_seconds=179.6865 timing/feedback_seconds=5.7136 timing/optimization_seconds=50.4380 timing/cycle_seconds=233.0627
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Epoch 118 Sampling: 100%|██████████| 96/96 [02:57<00:00,  1.85s/it]
+
Pointwise Rewards: ssim:   0%|          | 0/6 [00:00<?, ?it/s]
Pointwise Rewards: ssim:  17%|█▋        | 1/6 [00:00<00:01,  4.98it/s]
Pointwise Rewards: ssim:  33%|███▎      | 2/6 [00:00<00:00,  5.52it/s]
Pointwise Rewards: ssim:  50%|█████     | 3/6 [00:00<00:00,  5.75it/s]
Pointwise Rewards: ssim:  67%|██████▋   | 4/6 [00:02<00:02,  1.01s/it]
Pointwise Rewards: ssim:  83%|████████▎ | 5/6 [00:03<00:00,  1.41it/s]
Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:03<00:00,  1.89it/s]
Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:03<00:00,  1.89it/s]
+
Pointwise Rewards: dists:   0%|          | 0/6 [00:00<?, ?it/s]
Pointwise Rewards: dists:  17%|█▋        | 1/6 [00:00<00:01,  3.15it/s]
Pointwise Rewards: dists:  33%|███▎      | 2/6 [00:00<00:01,  3.23it/s]
Pointwise Rewards: dists:  50%|█████     | 3/6 [00:00<00:00,  3.26it/s]
Pointwise Rewards: dists:  67%|██████▋   | 4/6 [00:01<00:00,  3.21it/s]
Pointwise Rewards: dists:  83%|████████▎ | 5/6 [00:01<00:00,  3.24it/s]
Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.22it/s]
Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.22it/s]
+[2026-10-02 00:39:06] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0236 | Epoch 118] train/reward_dists_mean=0.8400 train/reward_dists_std=0.0335 train/reward_ssim_mean=0.6062 train/reward_ssim_std=0.1009 train/reward_dists_group_std_mean=0.0073 train/reward_dists_group_std_max=0.0153 train/reward_dists_group_std_min=0.0035 train/reward_dists_group_mean_std=0.0326 train/reward_ssim_group_std_mean=0.0070 train/reward_ssim_group_std_max=0.0163 train/reward_ssim_group_std_min=0.0029 train/reward_ssim_group_mean_std=0.1006 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.8396 train/adv_max=4.7004 train/adv_abs_mean=1.0976
+
Epoch 118 Training:   0%|          | 0/96 [00:00<?, ?it/s]
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Epoch 118 Timestep: 100%|██████████| 1/1 [00:00<00:00,  2.19it/s]
+
                                                                 
Epoch 118 Training:   1%|          | 1/96 [00:00<00:43,  2.17it/s]
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Epoch 118 Training:   2%|▏         | 2/96 [00:00<00:46,  2.02it/s]
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Epoch 118 Training:   4%|▍         | 4/96 [00:02<00:47,  1.96it/s]
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Epoch 118 Training:   5%|▌         | 5/96 [00:02<00:46,  1.94it/s]
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Epoch 118 Training:   6%|▋         | 6/96 [00:03<00:46,  1.94it/s]
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Epoch 118 Training:   7%|▋         | 7/96 [00:03<00:45,  1.93it/s]
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Epoch 118 Training:   8%|▊         | 8/96 [00:04<00:45,  1.93it/s]
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Epoch 118 Training:   9%|▉         | 9/96 [00:04<00:45,  1.92it/s]
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Epoch 118 Training:  10%|█         | 10/96 [00:05<00:44,  1.93it/s]
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Epoch 118 Training:  11%|█▏        | 11/96 [00:05<00:44,  1.93it/s]
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Epoch 118 Training:  12%|█▎        | 12/96 [00:06<00:43,  1.93it/s]
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Epoch 118 Training:  14%|█▎        | 13/96 [00:06<00:43,  1.93it/s]
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Epoch 118 Timestep: 100%|██████████| 1/1 [00:00<00:00,  2.16it/s]
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Epoch 118 Training:  15%|█▍        | 14/96 [00:07<00:42,  1.93it/s]
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Epoch 118 Training:  16%|█▌        | 15/96 [00:07<00:42,  1.93it/s]
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Epoch 118 Training:  17%|█▋        | 16/96 [00:08<00:41,  1.93it/s]
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Epoch 118 Timestep: 100%|██████████| 1/1 [00:00<00:00,  2.16it/s]
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Epoch 118 Training:  18%|█▊        | 17/96 [00:08<00:40,  1.93it/s]
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Epoch 118 Timestep: 100%|██████████| 1/1 [00:00<00:00,  2.16it/s]
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Epoch 118 Training:  19%|█▉        | 18/96 [00:09<00:40,  1.93it/s]
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Epoch 118 Training:  20%|█▉        | 19/96 [00:09<00:39,  1.93it/s]
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Epoch 118 Training:  21%|██        | 20/96 [00:10<00:39,  1.93it/s]
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Epoch 118 Training:  22%|██▏       | 21/96 [00:10<00:38,  1.93it/s]
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Epoch 118 Training:  23%|██▎       | 22/96 [00:11<00:38,  1.93it/s]
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Epoch 118 Training:  24%|██▍       | 23/96 [00:11<00:37,  1.93it/s]
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Epoch 118 Training:  25%|██▌       | 24/96 [00:12<00:37,  1.93it/s]
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Epoch 118 Timestep: 100%|██████████| 1/1 [00:00<00:00,  2.16it/s]
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Epoch 118 Training:  26%|██▌       | 25/96 [00:12<00:36,  1.93it/s]
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Epoch 118 Training:  27%|██▋       | 26/96 [00:13<00:36,  1.93it/s]
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Epoch 118 Training:  28%|██▊       | 27/96 [00:13<00:35,  1.93it/s]
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Epoch 118 Training:  29%|██▉       | 28/96 [00:14<00:35,  1.93it/s]
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Epoch 118 Training:  30%|███       | 29/96 [00:14<00:34,  1.93it/s]
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Epoch 118 Timestep: 100%|██████████| 1/1 [00:00<00:00,  2.16it/s]
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Epoch 118 Training:  31%|███▏      | 30/96 [00:15<00:34,  1.93it/s]
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Epoch 118 Training:  32%|███▏      | 31/96 [00:16<00:33,  1.93it/s]
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Epoch 118 Training:  33%|███▎      | 32/96 [00:16<00:33,  1.93it/s]
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Epoch 118 Training:  39%|███▊      | 37/96 [00:19<00:30,  1.93it/s]
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Epoch 118 Training:  47%|████▋     | 45/96 [00:23<00:26,  1.93it/s]
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Epoch 118 Training:  48%|████▊     | 46/96 [00:23<00:25,  1.93it/s]
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Epoch 118 Training:  49%|████▉     | 47/96 [00:24<00:25,  1.93it/s]
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Epoch 118 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 00:39:31] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0236 | Epoch 118] train/clipped_loss_min=-4.7004 train/clipped_loss_max=4.8396 train/clipped_loss_mean=0.0390 train/clipped_loss_std=1.4007 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-4.7004 train/unclipped_loss_max=4.8396 train/unclipped_loss_mean=0.0390 train/unclipped_loss_std=1.4007 train/policy_loss=0.0390 train/loss=0.0390 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0002
+
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Epoch 118 Timestep: 100%|██████████| 1/1 [00:00<00:00,  1.90it/s]
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Epoch 118 Training:  53%|█████▎    | 51/96 [00:26<00:23,  1.93it/s]
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Epoch 118 Training:  54%|█████▍    | 52/96 [00:26<00:22,  1.93it/s]
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Epoch 118 Training:  55%|█████▌    | 53/96 [00:27<00:22,  1.93it/s]
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Epoch 118 Training:  56%|█████▋    | 54/96 [00:27<00:21,  1.93it/s]
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Epoch 118 Training:  57%|█████▋    | 55/96 [00:28<00:21,  1.93it/s]
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Epoch 118 Training:  58%|█████▊    | 56/96 [00:29<00:20,  1.93it/s]
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Epoch 118 Training:  59%|█████▉    | 57/96 [00:29<00:20,  1.93it/s]
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Epoch 118 Training:  60%|██████    | 58/96 [00:30<00:19,  1.93it/s]
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Epoch 118 Training:  61%|██████▏   | 59/96 [00:30<00:19,  1.93it/s]
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Epoch 118 Training:  62%|██████▎   | 60/96 [00:31<00:18,  1.93it/s]
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Epoch 118 Training:  64%|██████▎   | 61/96 [00:31<00:18,  1.93it/s]
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Epoch 118 Training:  65%|██████▍   | 62/96 [00:32<00:17,  1.93it/s]
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Epoch 118 Training:  66%|██████▌   | 63/96 [00:32<00:17,  1.93it/s]
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Epoch 118 Training:  67%|██████▋   | 64/96 [00:33<00:16,  1.93it/s]
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Epoch 118 Training:  68%|██████▊   | 65/96 [00:33<00:16,  1.93it/s]
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Epoch 118 Training:  69%|██████▉   | 66/96 [00:34<00:15,  1.93it/s]
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Epoch 118 Training:  70%|██████▉   | 67/96 [00:34<00:15,  1.93it/s]
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Epoch 118 Training:  71%|███████   | 68/96 [00:35<00:14,  1.93it/s]
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Epoch 118 Training:  72%|███████▏  | 69/96 [00:35<00:14,  1.93it/s]
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Epoch 118 Training:  73%|███████▎  | 70/96 [00:36<00:13,  1.93it/s]
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Epoch 118 Training:  74%|███████▍  | 71/96 [00:36<00:12,  1.93it/s]
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Epoch 118 Training:  75%|███████▌  | 72/96 [00:37<00:12,  1.93it/s]
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Epoch 118 Training:  76%|███████▌  | 73/96 [00:37<00:11,  1.93it/s]
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Epoch 118 Training:  77%|███████▋  | 74/96 [00:38<00:11,  1.93it/s]
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Epoch 118 Training:  78%|███████▊  | 75/96 [00:38<00:10,  1.93it/s]
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Epoch 118 Training:  79%|███████▉  | 76/96 [00:39<00:10,  1.93it/s]
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Epoch 118 Training:  80%|████████  | 77/96 [00:39<00:09,  1.93it/s]
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Epoch 118 Training:  81%|████████▏ | 78/96 [00:40<00:09,  1.93it/s]
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Epoch 118 Training:  82%|████████▏ | 79/96 [00:40<00:08,  1.93it/s]
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Epoch 118 Training:  83%|████████▎ | 80/96 [00:41<00:08,  1.93it/s]
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Epoch 118 Training:  84%|████████▍ | 81/96 [00:41<00:07,  1.93it/s]
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Epoch 118 Training:  85%|████████▌ | 82/96 [00:42<00:07,  1.93it/s]
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Epoch 118 Training:  86%|████████▋ | 83/96 [00:43<00:06,  1.93it/s]
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Epoch 118 Training:  88%|████████▊ | 84/96 [00:43<00:06,  1.93it/s]
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Epoch 118 Training:  89%|████████▊ | 85/96 [00:44<00:05,  1.93it/s]
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Epoch 118 Training:  90%|████████▉ | 86/96 [00:44<00:05,  1.93it/s]
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Epoch 118 Training:  91%|█████████ | 87/96 [00:45<00:04,  1.93it/s]
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Epoch 118 Training:  92%|█████████▏| 88/96 [00:45<00:04,  1.93it/s]
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Epoch 118 Training:  93%|█████████▎| 89/96 [00:46<00:03,  1.93it/s]
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Epoch 118 Training:  96%|█████████▌| 92/96 [00:47<00:02,  1.93it/s]
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Epoch 118 Training:  97%|█████████▋| 93/96 [00:48<00:01,  1.93it/s]
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Epoch 118 Training:  98%|█████████▊| 94/96 [00:48<00:01,  1.92it/s]
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Epoch 118 Training:  99%|█████████▉| 95/96 [00:49<00:00,  1.92it/s]
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Epoch 118 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 00:39:56] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0237 | Epoch 118] train/clipped_loss_min=-4.2433 train/clipped_loss_max=4.7081 train/clipped_loss_mean=-0.0390 train/clipped_loss_std=1.3801 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0.0001 train/unclipped_loss_min=-4.2433 train/unclipped_loss_max=4.7081 train/unclipped_loss_mean=-0.0390 train/unclipped_loss_std=1.3801 train/policy_loss=-0.0390 train/loss=-0.0390 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0002
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Epoch 118 Timestep: 100%|██████████| 1/1 [00:00<00:00,  1.90it/s]
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Epoch 118 Training: 100%|██████████| 96/96 [00:49<00:00,  1.86it/s]
Epoch 118 Training: 100%|██████████| 96/96 [00:49<00:00,  1.93it/s]
+[2026-10-02 00:39:56] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0238 | Epoch 118] timing/rollout_seconds=179.3964 timing/feedback_seconds=7.2029 timing/optimization_seconds=50.4372 timing/cycle_seconds=234.8736
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+
Pointwise Rewards: ssim:   0%|          | 0/6 [00:00<?, ?it/s]
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Pointwise Rewards: ssim:  67%|██████▋   | 4/6 [00:00<00:00,  5.71it/s]
Pointwise Rewards: ssim:  83%|████████▎ | 5/6 [00:00<00:00,  5.93it/s]
Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:01<00:00,  5.93it/s]
Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:01<00:00,  5.73it/s]
+
Pointwise Rewards: dists:   0%|          | 0/6 [00:00<?, ?it/s]
Pointwise Rewards: dists:  17%|█▋        | 1/6 [00:00<00:01,  3.14it/s]
Pointwise Rewards: dists:  33%|███▎      | 2/6 [00:00<00:01,  3.25it/s]
Pointwise Rewards: dists:  50%|█████     | 3/6 [00:00<00:00,  3.22it/s]
Pointwise Rewards: dists:  67%|██████▋   | 4/6 [00:01<00:00,  3.24it/s]
Pointwise Rewards: dists:  83%|████████▎ | 5/6 [00:01<00:00,  3.28it/s]
Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.27it/s]
Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.25it/s]
+[2026-10-02 00:42:59] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0238 | Epoch 119] train/reward_dists_mean=0.8400 train/reward_dists_std=0.0325 train/reward_ssim_mean=0.6408 train/reward_ssim_std=0.0795 train/reward_dists_group_std_mean=0.0068 train/reward_dists_group_std_max=0.0136 train/reward_dists_group_std_min=0.0027 train/reward_dists_group_mean_std=0.0317 train/reward_ssim_group_std_mean=0.0069 train/reward_ssim_group_std_max=0.0142 train/reward_ssim_group_std_min=0.0024 train/reward_ssim_group_mean_std=0.0791 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.7507 train/adv_max=3.9070 train/adv_abs_mean=1.1351
+
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Epoch 119 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 00:43:24] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0238 | Epoch 119] train/clipped_loss_min=-3.2280 train/clipped_loss_max=4.7507 train/clipped_loss_mean=-0.0547 train/clipped_loss_std=1.4697 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-3.2280 train/unclipped_loss_max=4.7507 train/unclipped_loss_mean=-0.0547 train/unclipped_loss_std=1.4697 train/policy_loss=-0.0547 train/loss=-0.0547 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0002
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+[2026-10-02 00:43:49] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0240 | Epoch 119] timing/rollout_seconds=179.5567 timing/feedback_seconds=5.4664 timing/optimization_seconds=50.4542 timing/cycle_seconds=232.8991
+[2026-10-02 00:43:49] [Rank 0] [INFO] [flow_factory.models.abc]: Saving LoRA weights for transformer role=base to /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/runs/klein4b_grpo_flir_full_ssim_dists_lr5e-5/saves/klein4b_grpo_flir_full_ssim_dists_lr5e-5/checkpoints/checkpoint-120
+[2026-10-02 00:43:49] [Rank 0] [INFO] [flow_factory.models.abc]: Checkpoint saved successfully to /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/runs/klein4b_grpo_flir_full_ssim_dists_lr5e-5/saves/klein4b_grpo_flir_full_ssim_dists_lr5e-5/checkpoints/checkpoint-120
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+[2026-10-02 00:44:43] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0240 | Epoch 120] eval/flir_rl_full/reward_dists_mean=0.8391 eval/flir_rl_full/reward_dists_std=0.0371 eval/flir_rl_full/reward_psnr_mean=20.2357 eval/flir_rl_full/reward_psnr_std=3.0023 eval/flir_rl_full/reward_ssim_mean=0.6207 eval/flir_rl_full/reward_ssim_std=0.0850
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+[2026-10-02 00:47:45] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0240 | Epoch 120] train/reward_dists_mean=0.8399 train/reward_dists_std=0.0291 train/reward_ssim_mean=0.6495 train/reward_ssim_std=0.0912 train/reward_dists_group_std_mean=0.0060 train/reward_dists_group_std_max=0.0096 train/reward_dists_group_std_min=0.0031 train/reward_dists_group_mean_std=0.0285 train/reward_ssim_group_std_mean=0.0065 train/reward_ssim_group_std_max=0.0164 train/reward_ssim_group_std_min=0.0024 train/reward_ssim_group_mean_std=0.0909 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.7653 train/adv_max=5.3284 train/adv_abs_mean=1.1763
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Epoch 120 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 00:48:10] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0240 | Epoch 120] train/clipped_loss_min=-3.8048 train/clipped_loss_max=4.4878 train/clipped_loss_mean=0.0369 train/clipped_loss_std=1.4356 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-3.8048 train/unclipped_loss_max=4.4878 train/unclipped_loss_mean=0.0369 train/unclipped_loss_std=1.4356 train/policy_loss=0.0369 train/loss=0.0369 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0005
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Epoch 120 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 00:48:35] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0241 | Epoch 120] train/clipped_loss_min=-5.0000 train/clipped_loss_max=4.7652 train/clipped_loss_mean=-0.0360 train/clipped_loss_std=1.4918 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-5.0000 train/unclipped_loss_max=4.7652 train/unclipped_loss_mean=-0.0360 train/unclipped_loss_std=1.4918 train/policy_loss=-0.0360 train/loss=-0.0360 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0005
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+[2026-10-02 00:48:35] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0242 | Epoch 120] timing/rollout_seconds=179.4311 timing/feedback_seconds=4.9008 timing/optimization_seconds=50.4047 timing/cycle_seconds=232.7189
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+
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+[2026-10-02 00:51:38] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0242 | Epoch 121] train/reward_dists_mean=0.8393 train/reward_dists_std=0.0311 train/reward_ssim_mean=0.6241 train/reward_ssim_std=0.1090 train/reward_dists_group_std_mean=0.0068 train/reward_dists_group_std_max=0.0138 train/reward_dists_group_std_min=0.0031 train/reward_dists_group_mean_std=0.0303 train/reward_ssim_group_std_mean=0.0065 train/reward_ssim_group_std_max=0.0157 train/reward_ssim_group_std_min=0.0022 train/reward_ssim_group_mean_std=0.1087 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.4914 train/adv_max=5.3900 train/adv_abs_mean=1.1790
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Epoch 121 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 00:52:03] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0242 | Epoch 121] train/clipped_loss_min=-5 train/clipped_loss_max=4.4914 train/clipped_loss_mean=-0.0172 train/clipped_loss_std=1.5381 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-5 train/unclipped_loss_max=4.4914 train/unclipped_loss_mean=-0.0172 train/unclipped_loss_std=1.5381 train/policy_loss=-0.0172 train/loss=-0.0172 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
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Epoch 121 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 00:52:28] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0243 | Epoch 121] train/clipped_loss_min=-3.4790 train/clipped_loss_max=4.2738 train/clipped_loss_mean=0.0182 train/clipped_loss_std=1.4320 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-3.4790 train/unclipped_loss_max=4.2738 train/unclipped_loss_mean=0.0182 train/unclipped_loss_std=1.4320 train/policy_loss=0.0182 train/loss=0.0182 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
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+[2026-10-02 00:52:28] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0244 | Epoch 121] timing/rollout_seconds=179.5683 timing/feedback_seconds=5.5414 timing/optimization_seconds=50.4698 timing/cycle_seconds=232.8631
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+
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+[2026-10-02 00:55:31] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0244 | Epoch 122] train/reward_dists_mean=0.8400 train/reward_dists_std=0.0365 train/reward_ssim_mean=0.6394 train/reward_ssim_std=0.0935 train/reward_dists_group_std_mean=0.0068 train/reward_dists_group_std_max=0.0130 train/reward_dists_group_std_min=0.0031 train/reward_dists_group_mean_std=0.0358 train/reward_ssim_group_std_mean=0.0076 train/reward_ssim_group_std_max=0.0154 train/reward_ssim_group_std_min=0.0021 train/reward_ssim_group_mean_std=0.0931 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.7259 train/adv_max=4.9491 train/adv_abs_mean=1.1869
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Epoch 122 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 00:55:56] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0244 | Epoch 122] train/clipped_loss_min=-3.9409 train/clipped_loss_max=4.7259 train/clipped_loss_mean=0.0616 train/clipped_loss_std=1.4499 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-3.9409 train/unclipped_loss_max=4.7259 train/unclipped_loss_mean=0.0616 train/unclipped_loss_std=1.4499 train/policy_loss=0.0616 train/loss=0.0616 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0002
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Epoch 122 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 00:56:21] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0245 | Epoch 122] train/clipped_loss_min=-4.9491 train/clipped_loss_max=4.2318 train/clipped_loss_mean=-0.0616 train/clipped_loss_std=1.5285 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0.0002 train/unclipped_loss_min=-4.9491 train/unclipped_loss_max=4.2318 train/unclipped_loss_mean=-0.0616 train/unclipped_loss_std=1.5285 train/policy_loss=-0.0616 train/loss=-0.0616 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0002
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+[2026-10-02 00:56:21] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0246 | Epoch 122] timing/rollout_seconds=179.3319 timing/feedback_seconds=5.3933 timing/optimization_seconds=50.5717 timing/cycle_seconds=232.8366
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+[2026-10-02 00:59:24] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0246 | Epoch 123] train/reward_dists_mean=0.8426 train/reward_dists_std=0.0383 train/reward_ssim_mean=0.6189 train/reward_ssim_std=0.1292 train/reward_dists_group_std_mean=0.0069 train/reward_dists_group_std_max=0.0141 train/reward_dists_group_std_min=0.0028 train/reward_dists_group_mean_std=0.0375 train/reward_ssim_group_std_mean=0.0077 train/reward_ssim_group_std_max=0.0147 train/reward_ssim_group_std_min=0.0034 train/reward_ssim_group_mean_std=0.1289 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.5219 train/adv_max=5.0786 train/adv_abs_mean=1.1890
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Epoch 123 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 00:59:49] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0246 | Epoch 123] train/clipped_loss_min=-5 train/clipped_loss_max=5 train/clipped_loss_mean=0.0636 train/clipped_loss_std=1.5031 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-5 train/unclipped_loss_max=5 train/unclipped_loss_mean=0.0636 train/unclipped_loss_std=1.5031 train/policy_loss=0.0636 train/loss=0.0636 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0002
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Epoch 123 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 01:00:14] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0247 | Epoch 123] train/clipped_loss_min=-4.5304 train/clipped_loss_max=4.4345 train/clipped_loss_mean=-0.0648 train/clipped_loss_std=1.4967 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-4.5304 train/unclipped_loss_max=4.4345 train/unclipped_loss_mean=-0.0648 train/unclipped_loss_std=1.4967 train/policy_loss=-0.0648 train/loss=-0.0648 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0002
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+[2026-10-02 01:00:14] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0248 | Epoch 123] timing/rollout_seconds=179.3817 timing/feedback_seconds=5.6432 timing/optimization_seconds=50.4792 timing/cycle_seconds=232.8344
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+
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+[2026-10-02 01:03:17] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0248 | Epoch 124] train/reward_dists_mean=0.8474 train/reward_dists_std=0.0262 train/reward_ssim_mean=0.6378 train/reward_ssim_std=0.1026 train/reward_dists_group_std_mean=0.0065 train/reward_dists_group_std_max=0.0146 train/reward_dists_group_std_min=0.0038 train/reward_dists_group_mean_std=0.0253 train/reward_ssim_group_std_mean=0.0077 train/reward_ssim_group_std_max=0.0166 train/reward_ssim_group_std_min=0.0027 train/reward_ssim_group_mean_std=0.1022 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.0593 train/adv_max=4.8907 train/adv_abs_mean=1.1918
+
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Epoch 124 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 01:03:42] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0248 | Epoch 124] train/clipped_loss_min=-4.8907 train/clipped_loss_max=5 train/clipped_loss_mean=0.0593 train/clipped_loss_std=1.5238 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-4.8907 train/unclipped_loss_max=5 train/unclipped_loss_mean=0.0593 train/unclipped_loss_std=1.5238 train/policy_loss=0.0593 train/loss=0.0593 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0002
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Epoch 124 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 01:04:07] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0249 | Epoch 124] train/clipped_loss_min=-3.6079 train/clipped_loss_max=4.7228 train/clipped_loss_mean=-0.0595 train/clipped_loss_std=1.4668 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-3.6079 train/unclipped_loss_max=4.7228 train/unclipped_loss_mean=-0.0595 train/unclipped_loss_std=1.4668 train/policy_loss=-0.0595 train/loss=-0.0595 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0002
+
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+[2026-10-02 01:04:07] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0250 | Epoch 124] timing/rollout_seconds=179.4470 timing/feedback_seconds=5.6133 timing/optimization_seconds=50.3827 timing/cycle_seconds=232.8638
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+
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+
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+[2026-10-02 01:07:10] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0250 | Epoch 125] train/reward_dists_mean=0.8473 train/reward_dists_std=0.0314 train/reward_ssim_mean=0.6622 train/reward_ssim_std=0.1126 train/reward_dists_group_std_mean=0.0069 train/reward_dists_group_std_max=0.0149 train/reward_dists_group_std_min=0.0025 train/reward_dists_group_mean_std=0.0306 train/reward_ssim_group_std_mean=0.0069 train/reward_ssim_group_std_max=0.0245 train/reward_ssim_group_std_min=0.0029 train/reward_ssim_group_mean_std=0.1123 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.7377 train/adv_max=3.9553 train/adv_abs_mean=1.1815
+
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Epoch 125 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 01:07:35] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0250 | Epoch 125] train/clipped_loss_min=-3.9553 train/clipped_loss_max=4.5771 train/clipped_loss_mean=-0.0792 train/clipped_loss_std=1.4529 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-3.9553 train/unclipped_loss_max=4.5771 train/unclipped_loss_mean=-0.0792 train/unclipped_loss_std=1.4529 train/policy_loss=-0.0792 train/loss=-0.0792 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
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Epoch 125 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 01:08:00] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0251 | Epoch 125] train/clipped_loss_min=-3.9036 train/clipped_loss_max=4.7377 train/clipped_loss_mean=0.0792 train/clipped_loss_std=1.5209 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-3.9036 train/unclipped_loss_max=4.7377 train/unclipped_loss_mean=0.0792 train/unclipped_loss_std=1.5209 train/policy_loss=0.0792 train/loss=0.0792 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
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+[2026-10-02 01:08:00] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0252 | Epoch 125] timing/rollout_seconds=179.5039 timing/feedback_seconds=5.3554 timing/optimization_seconds=50.8410 timing/cycle_seconds=233.2786
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+
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+[2026-10-02 01:11:03] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0252 | Epoch 126] train/reward_dists_mean=0.8466 train/reward_dists_std=0.0296 train/reward_ssim_mean=0.6351 train/reward_ssim_std=0.0958 train/reward_dists_group_std_mean=0.0065 train/reward_dists_group_std_max=0.0120 train/reward_dists_group_std_min=0.0036 train/reward_dists_group_mean_std=0.0288 train/reward_ssim_group_std_mean=0.0066 train/reward_ssim_group_std_max=0.0157 train/reward_ssim_group_std_min=0.0019 train/reward_ssim_group_mean_std=0.0955 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.9269 train/adv_max=4.0378 train/adv_abs_mean=1.1720
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Epoch 126 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 01:11:28] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0252 | Epoch 126] train/clipped_loss_min=-3.8524 train/clipped_loss_max=4.9269 train/clipped_loss_mean=-0.0935 train/clipped_loss_std=1.4653 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-3.8524 train/unclipped_loss_max=4.9269 train/unclipped_loss_mean=-0.0935 train/unclipped_loss_std=1.4653 train/policy_loss=-0.0935 train/loss=-0.0935 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0005
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Epoch 126 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 01:11:53] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0253 | Epoch 126] train/clipped_loss_min=-4.0378 train/clipped_loss_max=4.2860 train/clipped_loss_mean=0.0935 train/clipped_loss_std=1.4638 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0.0002 train/unclipped_loss_min=-4.0378 train/unclipped_loss_max=4.2860 train/unclipped_loss_mean=0.0935 train/unclipped_loss_std=1.4638 train/policy_loss=0.0935 train/loss=0.0935 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0005
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+[2026-10-02 01:11:53] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0254 | Epoch 126] timing/rollout_seconds=179.6801 timing/feedback_seconds=5.4191 timing/optimization_seconds=50.4165 timing/cycle_seconds=232.9610
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+
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+[2026-10-02 01:14:56] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0254 | Epoch 127] train/reward_dists_mean=0.8384 train/reward_dists_std=0.0280 train/reward_ssim_mean=0.6477 train/reward_ssim_std=0.1085 train/reward_dists_group_std_mean=0.0076 train/reward_dists_group_std_max=0.0157 train/reward_dists_group_std_min=0.0039 train/reward_dists_group_mean_std=0.0268 train/reward_ssim_group_std_mean=0.0069 train/reward_ssim_group_std_max=0.0142 train/reward_ssim_group_std_min=0.0025 train/reward_ssim_group_mean_std=0.1083 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.6481 train/adv_max=4.7690 train/adv_abs_mean=1.2226
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Epoch 127 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 01:15:21] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0254 | Epoch 127] train/clipped_loss_min=-4.4065 train/clipped_loss_max=4.1743 train/clipped_loss_mean=0.0065 train/clipped_loss_std=1.4986 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-4.4065 train/unclipped_loss_max=4.1743 train/unclipped_loss_mean=0.0065 train/unclipped_loss_std=1.4986 train/policy_loss=0.0065 train/loss=0.0065 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0005
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Epoch 127 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 01:15:46] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0255 | Epoch 127] train/clipped_loss_min=-4.7690 train/clipped_loss_max=4.6480 train/clipped_loss_mean=-0.0066 train/clipped_loss_std=1.5857 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0.0001 train/unclipped_loss_min=-4.7690 train/unclipped_loss_max=4.6480 train/unclipped_loss_mean=-0.0066 train/unclipped_loss_std=1.5857 train/policy_loss=-0.0066 train/loss=-0.0066 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0006
+
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+[2026-10-02 01:15:46] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0256 | Epoch 127] timing/rollout_seconds=179.4374 timing/feedback_seconds=5.5503 timing/optimization_seconds=50.6244 timing/cycle_seconds=233.0633
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Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.27it/s]
Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.23it/s]
+[2026-10-02 01:18:49] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0256 | Epoch 128] train/reward_dists_mean=0.8335 train/reward_dists_std=0.0323 train/reward_ssim_mean=0.6489 train/reward_ssim_std=0.1086 train/reward_dists_group_std_mean=0.0076 train/reward_dists_group_std_max=0.0116 train/reward_dists_group_std_min=0.0031 train/reward_dists_group_mean_std=0.0313 train/reward_ssim_group_std_mean=0.0062 train/reward_ssim_group_std_max=0.0128 train/reward_ssim_group_std_min=0.0029 train/reward_ssim_group_mean_std=0.1084 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.5966 train/adv_max=4.4070 train/adv_abs_mean=1.2111
+
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Epoch 128 Training:   5%|▌         | 5/96 [00:02<00:46,  1.95it/s]
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Epoch 128 Training:   7%|▋         | 7/96 [00:03<00:45,  1.94it/s]
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Epoch 128 Training:   8%|▊         | 8/96 [00:04<00:45,  1.93it/s]
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Epoch 128 Training:   9%|▉         | 9/96 [00:04<00:44,  1.93it/s]
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Epoch 128 Training:  10%|█         | 10/96 [00:05<00:51,  1.67it/s]
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Epoch 128 Training:  11%|█▏        | 11/96 [00:05<00:48,  1.74it/s]
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Epoch 128 Training:  12%|█▎        | 12/96 [00:06<00:46,  1.80it/s]
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Epoch 128 Training:  14%|█▎        | 13/96 [00:06<00:45,  1.83it/s]
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Epoch 128 Training:  15%|█▍        | 14/96 [00:07<00:44,  1.86it/s]
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Epoch 128 Training:  16%|█▌        | 15/96 [00:07<00:43,  1.88it/s]
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Epoch 128 Training:  17%|█▋        | 16/96 [00:08<00:42,  1.90it/s]
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Epoch 128 Training:  18%|█▊        | 17/96 [00:09<00:41,  1.91it/s]
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Epoch 128 Training:  19%|█▉        | 18/96 [00:09<00:40,  1.91it/s]
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Epoch 128 Training:  20%|█▉        | 19/96 [00:10<00:40,  1.92it/s]
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Epoch 128 Training:  21%|██        | 20/96 [00:10<00:39,  1.92it/s]
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Epoch 128 Training:  22%|██▏       | 21/96 [00:11<00:38,  1.92it/s]
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Epoch 128 Training:  23%|██▎       | 22/96 [00:11<00:38,  1.92it/s]
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Epoch 128 Training:  24%|██▍       | 23/96 [00:12<00:37,  1.92it/s]
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Epoch 128 Training:  25%|██▌       | 24/96 [00:12<00:37,  1.93it/s]
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Epoch 128 Training:  26%|██▌       | 25/96 [00:13<00:36,  1.93it/s]
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Epoch 128 Training:  27%|██▋       | 26/96 [00:13<00:36,  1.93it/s]
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Epoch 128 Training:  28%|██▊       | 27/96 [00:14<00:35,  1.93it/s]
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Epoch 128 Training:  29%|██▉       | 28/96 [00:14<00:35,  1.93it/s]
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Epoch 128 Training:  30%|███       | 29/96 [00:15<00:34,  1.93it/s]
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Epoch 128 Training:  31%|███▏      | 30/96 [00:15<00:34,  1.93it/s]
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Epoch 128 Training:  32%|███▏      | 31/96 [00:16<00:33,  1.93it/s]
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Epoch 128 Training:  33%|███▎      | 32/96 [00:16<00:33,  1.93it/s]
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Epoch 128 Training:  34%|███▍      | 33/96 [00:17<00:32,  1.93it/s]
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Epoch 128 Training:  35%|███▌      | 34/96 [00:17<00:32,  1.93it/s]
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Epoch 128 Training:  36%|███▋      | 35/96 [00:18<00:31,  1.93it/s]
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Epoch 128 Training:  38%|███▊      | 36/96 [00:18<00:31,  1.93it/s]
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Epoch 128 Training:  39%|███▊      | 37/96 [00:19<00:30,  1.93it/s]
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Epoch 128 Training:  40%|███▉      | 38/96 [00:19<00:30,  1.93it/s]
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Epoch 128 Training:  41%|████      | 39/96 [00:20<00:29,  1.93it/s]
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Epoch 128 Training:  42%|████▏     | 40/96 [00:20<00:29,  1.93it/s]
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Epoch 128 Training:  43%|████▎     | 41/96 [00:21<00:28,  1.93it/s]
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Epoch 128 Training:  44%|████▍     | 42/96 [00:21<00:27,  1.93it/s]
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Epoch 128 Training:  45%|████▍     | 43/96 [00:22<00:27,  1.93it/s]
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Epoch 128 Training:  46%|████▌     | 44/96 [00:23<00:26,  1.93it/s]
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Epoch 128 Training:  47%|████▋     | 45/96 [00:23<00:26,  1.93it/s]
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Epoch 128 Training:  48%|████▊     | 46/96 [00:24<00:25,  1.93it/s]
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Epoch 128 Training:  49%|████▉     | 47/96 [00:24<00:25,  1.93it/s]
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Epoch 128 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 01:19:14] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0256 | Epoch 128] train/clipped_loss_min=-4.0315 train/clipped_loss_max=4.5966 train/clipped_loss_mean=-0.0828 train/clipped_loss_std=1.5021 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-4.0315 train/unclipped_loss_max=4.5966 train/unclipped_loss_mean=-0.0828 train/unclipped_loss_std=1.5021 train/policy_loss=-0.0828 train/loss=-0.0828 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0005
+
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Epoch 128 Timestep: 100%|██████████| 1/1 [00:00<00:00,  1.90it/s]
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Epoch 128 Training:  50%|█████     | 48/96 [00:25<00:25,  1.86it/s]
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Epoch 128 Training:  51%|█████     | 49/96 [00:25<00:24,  1.94it/s]
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Epoch 128 Training:  53%|█████▎    | 51/96 [00:26<00:23,  1.93it/s]
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Epoch 128 Training:  54%|█████▍    | 52/96 [00:27<00:22,  1.93it/s]
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Epoch 128 Training:  56%|█████▋    | 54/96 [00:28<00:21,  1.93it/s]
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Epoch 128 Training:  57%|█████▋    | 55/96 [00:28<00:21,  1.93it/s]
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Epoch 128 Training:  58%|█████▊    | 56/96 [00:29<00:20,  1.93it/s]
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Epoch 128 Training:  59%|█████▉    | 57/96 [00:29<00:20,  1.93it/s]
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Epoch 128 Training:  61%|██████▏   | 59/96 [00:30<00:19,  1.93it/s]
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Epoch 128 Training:  62%|██████▎   | 60/96 [00:31<00:18,  1.93it/s]
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Epoch 128 Training:  64%|██████▎   | 61/96 [00:31<00:18,  1.93it/s]
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Epoch 128 Training:  65%|██████▍   | 62/96 [00:32<00:17,  1.93it/s]
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Epoch 128 Training:  66%|██████▌   | 63/96 [00:32<00:17,  1.93it/s]
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Epoch 128 Training:  67%|██████▋   | 64/96 [00:33<00:16,  1.93it/s]
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Epoch 128 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 01:19:40] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0257 | Epoch 128] train/clipped_loss_min=-4.4069 train/clipped_loss_max=4.3910 train/clipped_loss_mean=0.0828 train/clipped_loss_std=1.5467 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0.0002 train/unclipped_loss_min=-4.4069 train/unclipped_loss_max=4.3910 train/unclipped_loss_mean=0.0828 train/unclipped_loss_std=1.5467 train/policy_loss=0.0828 train/loss=0.0828 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0006
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+[2026-10-02 01:19:40] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0258 | Epoch 128] timing/rollout_seconds=179.5995 timing/feedback_seconds=5.8089 timing/optimization_seconds=50.9634 timing/cycle_seconds=233.6432
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Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.32it/s]
+[2026-10-02 01:22:43] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0258 | Epoch 129] train/reward_dists_mean=0.8319 train/reward_dists_std=0.0295 train/reward_ssim_mean=0.6401 train/reward_ssim_std=0.0865 train/reward_dists_group_std_mean=0.0078 train/reward_dists_group_std_max=0.0149 train/reward_dists_group_std_min=0.0035 train/reward_dists_group_mean_std=0.0283 train/reward_ssim_group_std_mean=0.0072 train/reward_ssim_group_std_max=0.0159 train/reward_ssim_group_std_min=0.0033 train/reward_ssim_group_mean_std=0.0861 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.1049 train/adv_max=4.6918 train/adv_abs_mean=1.1657
+
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Epoch 129 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 01:23:08] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0258 | Epoch 129] train/clipped_loss_min=-4.2967 train/clipped_loss_max=5 train/clipped_loss_mean=0.0568 train/clipped_loss_std=1.5167 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-4.2967 train/unclipped_loss_max=5 train/unclipped_loss_mean=0.0568 train/unclipped_loss_std=1.5167 train/policy_loss=0.0568 train/loss=0.0568 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0002
+
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Epoch 129 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 01:23:32] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0259 | Epoch 129] train/clipped_loss_min=-4.6918 train/clipped_loss_max=4.4944 train/clipped_loss_mean=-0.0571 train/clipped_loss_std=1.4078 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0.0001 train/unclipped_loss_min=-4.6918 train/unclipped_loss_max=4.4944 train/unclipped_loss_mean=-0.0571 train/unclipped_loss_std=1.4078 train/policy_loss=-0.0571 train/loss=-0.0571 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0002
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+[2026-10-02 01:23:32] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0260 | Epoch 129] timing/rollout_seconds=179.4146 timing/feedback_seconds=4.9319 timing/optimization_seconds=50.4237 timing/cycle_seconds=232.8383
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+[2026-10-02 01:24:26] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0260 | Epoch 130] eval/flir_rl_full/reward_dists_mean=0.8387 eval/flir_rl_full/reward_dists_std=0.0371 eval/flir_rl_full/reward_psnr_mean=20.2804 eval/flir_rl_full/reward_psnr_std=3.0043 eval/flir_rl_full/reward_ssim_mean=0.6212 eval/flir_rl_full/reward_ssim_std=0.0852
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+
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+
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Pointwise Rewards: dists:  50%|█████     | 3/6 [00:00<00:00,  3.17it/s]
Pointwise Rewards: dists:  67%|██████▋   | 4/6 [00:01<00:00,  3.16it/s]
Pointwise Rewards: dists:  83%|████████▎ | 5/6 [00:01<00:00,  3.16it/s]
Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.25it/s]
Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.21it/s]
+[2026-10-02 01:27:29] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0260 | Epoch 130] train/reward_dists_mean=0.8359 train/reward_dists_std=0.0309 train/reward_ssim_mean=0.6748 train/reward_ssim_std=0.1076 train/reward_dists_group_std_mean=0.0074 train/reward_dists_group_std_max=0.0139 train/reward_dists_group_std_min=0.0037 train/reward_dists_group_mean_std=0.0299 train/reward_ssim_group_std_mean=0.0065 train/reward_ssim_group_std_max=0.0202 train/reward_ssim_group_std_min=0.0027 train/reward_ssim_group_mean_std=0.1073 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.9622 train/adv_max=4.7776 train/adv_abs_mean=1.1509
+
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Epoch 130 Training:   7%|▋         | 7/96 [00:03<00:45,  1.94it/s]
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Epoch 130 Training:   9%|▉         | 9/96 [00:04<00:44,  1.93it/s]
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Epoch 130 Training:  11%|█▏        | 11/96 [00:05<00:44,  1.93it/s]
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Epoch 130 Training:  14%|█▎        | 13/96 [00:06<00:43,  1.93it/s]
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Epoch 130 Training:  15%|█▍        | 14/96 [00:07<00:42,  1.93it/s]
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Epoch 130 Training:  19%|█▉        | 18/96 [00:09<00:40,  1.93it/s]
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Epoch 130 Training:  29%|██▉       | 28/96 [00:14<00:35,  1.93it/s]
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Epoch 130 Training:  39%|███▊      | 37/96 [00:19<00:30,  1.93it/s]
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Epoch 130 Training:  44%|████▍     | 42/96 [00:21<00:28,  1.93it/s]
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Epoch 130 Training:  48%|████▊     | 46/96 [00:23<00:25,  1.93it/s]
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Epoch 130 Training:  49%|████▉     | 47/96 [00:24<00:25,  1.93it/s]
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Epoch 130 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 01:27:54] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0260 | Epoch 130] train/clipped_loss_min=-3.8649 train/clipped_loss_max=3.6907 train/clipped_loss_mean=0.0494 train/clipped_loss_std=1.4944 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-3.8649 train/unclipped_loss_max=3.6907 train/unclipped_loss_mean=0.0494 train/unclipped_loss_std=1.4944 train/policy_loss=0.0494 train/loss=0.0494 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
+
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Epoch 130 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 01:28:19] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0261 | Epoch 130] train/clipped_loss_min=-4.7777 train/clipped_loss_max=4.9623 train/clipped_loss_mean=-0.0494 train/clipped_loss_std=1.4191 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-4.7777 train/unclipped_loss_max=4.9623 train/unclipped_loss_mean=-0.0494 train/unclipped_loss_std=1.4191 train/policy_loss=-0.0494 train/loss=-0.0494 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
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+[2026-10-02 01:28:19] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0262 | Epoch 130] timing/rollout_seconds=179.5569 timing/feedback_seconds=5.2853 timing/optimization_seconds=50.4223 timing/cycle_seconds=233.0448
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+
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+
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Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.16it/s]
+[2026-10-02 01:31:22] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0262 | Epoch 131] train/reward_dists_mean=0.8356 train/reward_dists_std=0.0323 train/reward_ssim_mean=0.6764 train/reward_ssim_std=0.1033 train/reward_dists_group_std_mean=0.0076 train/reward_dists_group_std_max=0.0218 train/reward_dists_group_std_min=0.0038 train/reward_dists_group_mean_std=0.0313 train/reward_ssim_group_std_mean=0.0074 train/reward_ssim_group_std_max=0.0218 train/reward_ssim_group_std_min=0.0030 train/reward_ssim_group_mean_std=0.1030 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.0184 train/adv_max=4.3261 train/adv_abs_mean=1.2178
+
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Epoch 131 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 01:31:47] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0262 | Epoch 131] train/clipped_loss_min=-4.2514 train/clipped_loss_max=4.5503 train/clipped_loss_mean=-0.0444 train/clipped_loss_std=1.5277 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-4.2514 train/unclipped_loss_max=4.5503 train/unclipped_loss_mean=-0.0444 train/unclipped_loss_std=1.5277 train/policy_loss=-0.0444 train/loss=-0.0444 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0005
+
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Epoch 131 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 01:32:12] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0263 | Epoch 131] train/clipped_loss_min=-4.3261 train/clipped_loss_max=5.0000 train/clipped_loss_mean=0.0444 train/clipped_loss_std=1.5206 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-4.3261 train/unclipped_loss_max=5.0000 train/unclipped_loss_mean=0.0444 train/unclipped_loss_std=1.5206 train/policy_loss=0.0444 train/loss=0.0444 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0006
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+[2026-10-02 01:32:12] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0264 | Epoch 131] timing/rollout_seconds=179.5332 timing/feedback_seconds=5.5528 timing/optimization_seconds=50.3798 timing/cycle_seconds=232.9068
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+
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+
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+[2026-10-02 01:35:15] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0264 | Epoch 132] train/reward_dists_mean=0.8459 train/reward_dists_std=0.0240 train/reward_ssim_mean=0.6625 train/reward_ssim_std=0.1277 train/reward_dists_group_std_mean=0.0068 train/reward_dists_group_std_max=0.0135 train/reward_dists_group_std_min=0.0040 train/reward_dists_group_mean_std=0.0229 train/reward_ssim_group_std_mean=0.0071 train/reward_ssim_group_std_max=0.0170 train/reward_ssim_group_std_min=0.0028 train/reward_ssim_group_mean_std=0.1275 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.7177 train/adv_max=4.5820 train/adv_abs_mean=1.2506
+
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Epoch 132 Training:  39%|███▊      | 37/96 [00:19<00:30,  1.93it/s]
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Epoch 132 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 01:35:40] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0264 | Epoch 132] train/clipped_loss_min=-4.5820 train/clipped_loss_max=4.7177 train/clipped_loss_mean=0.0287 train/clipped_loss_std=1.5881 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-4.5820 train/unclipped_loss_max=4.7177 train/unclipped_loss_mean=0.0287 train/unclipped_loss_std=1.5881 train/policy_loss=0.0287 train/loss=0.0287 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0002
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Epoch 132 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 01:36:05] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0265 | Epoch 132] train/clipped_loss_min=-3.6649 train/clipped_loss_max=3.8989 train/clipped_loss_mean=-0.0287 train/clipped_loss_std=1.5047 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0.0001 train/unclipped_loss_min=-3.6649 train/unclipped_loss_max=3.8989 train/unclipped_loss_mean=-0.0287 train/unclipped_loss_std=1.5047 train/policy_loss=-0.0287 train/loss=-0.0287 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0002
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+[2026-10-02 01:36:05] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0266 | Epoch 132] timing/rollout_seconds=179.6524 timing/feedback_seconds=5.6792 timing/optimization_seconds=50.4267 timing/cycle_seconds=233.0603
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Epoch 133 Sampling: 100%|██████████| 96/96 [02:56<00:00,  1.84s/it]
+
Pointwise Rewards: ssim:   0%|          | 0/6 [00:00<?, ?it/s]
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Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:01<00:00,  5.73it/s]
+
Pointwise Rewards: dists:   0%|          | 0/6 [00:00<?, ?it/s]
Pointwise Rewards: dists:  17%|█▋        | 1/6 [00:00<00:01,  3.36it/s]
Pointwise Rewards: dists:  33%|███▎      | 2/6 [00:00<00:01,  3.26it/s]
Pointwise Rewards: dists:  50%|█████     | 3/6 [00:00<00:00,  3.31it/s]
Pointwise Rewards: dists:  67%|██████▋   | 4/6 [00:01<00:00,  3.26it/s]
Pointwise Rewards: dists:  83%|████████▎ | 5/6 [00:01<00:00,  3.23it/s]
Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.32it/s]
Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.29it/s]
+[2026-10-02 01:39:08] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0266 | Epoch 133] train/reward_dists_mean=0.8453 train/reward_dists_std=0.0265 train/reward_ssim_mean=0.6447 train/reward_ssim_std=0.1047 train/reward_dists_group_std_mean=0.0068 train/reward_dists_group_std_max=0.0154 train/reward_dists_group_std_min=0.0034 train/reward_dists_group_mean_std=0.0255 train/reward_ssim_group_std_mean=0.0067 train/reward_ssim_group_std_max=0.0150 train/reward_ssim_group_std_min=0.0021 train/reward_ssim_group_mean_std=0.1044 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.7593 train/adv_max=4.5399 train/adv_abs_mean=1.1866
+
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Epoch 133 Training:   4%|▍         | 4/96 [00:02<00:47,  1.96it/s]
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Epoch 133 Training:   7%|▋         | 7/96 [00:03<00:45,  1.93it/s]
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Epoch 133 Training:   8%|▊         | 8/96 [00:04<00:45,  1.93it/s]
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Epoch 133 Training:   9%|▉         | 9/96 [00:04<00:45,  1.93it/s]
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Epoch 133 Training:  10%|█         | 10/96 [00:05<00:44,  1.93it/s]
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Epoch 133 Training:  11%|█▏        | 11/96 [00:05<00:44,  1.93it/s]
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Epoch 133 Training:  12%|█▎        | 12/96 [00:06<00:43,  1.93it/s]
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Epoch 133 Training:  14%|█▎        | 13/96 [00:06<00:43,  1.93it/s]
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Epoch 133 Training:  15%|█▍        | 14/96 [00:07<00:42,  1.93it/s]
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Epoch 133 Training:  16%|█▌        | 15/96 [00:07<00:41,  1.93it/s]
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Epoch 133 Training:  17%|█▋        | 16/96 [00:08<00:41,  1.93it/s]
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Epoch 133 Training:  18%|█▊        | 17/96 [00:08<00:40,  1.93it/s]
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Epoch 133 Training:  19%|█▉        | 18/96 [00:09<00:40,  1.93it/s]
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Epoch 133 Training:  20%|█▉        | 19/96 [00:09<00:39,  1.93it/s]
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Epoch 133 Training:  24%|██▍       | 23/96 [00:11<00:37,  1.93it/s]
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Epoch 133 Training:  25%|██▌       | 24/96 [00:12<00:37,  1.93it/s]
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Epoch 133 Training:  26%|██▌       | 25/96 [00:12<00:36,  1.93it/s]
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Epoch 133 Training:  27%|██▋       | 26/96 [00:13<00:36,  1.93it/s]
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Epoch 133 Training:  28%|██▊       | 27/96 [00:13<00:35,  1.93it/s]
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Epoch 133 Training:  29%|██▉       | 28/96 [00:14<00:35,  1.93it/s]
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Epoch 133 Training:  30%|███       | 29/96 [00:14<00:34,  1.93it/s]
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Epoch 133 Training:  31%|███▏      | 30/96 [00:15<00:34,  1.93it/s]
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Epoch 133 Training:  32%|███▏      | 31/96 [00:16<00:33,  1.93it/s]
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Epoch 133 Training:  33%|███▎      | 32/96 [00:16<00:33,  1.93it/s]
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Epoch 133 Training:  34%|███▍      | 33/96 [00:17<00:32,  1.93it/s]
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Epoch 133 Training:  35%|███▌      | 34/96 [00:17<00:32,  1.93it/s]
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Epoch 133 Training:  38%|███▊      | 36/96 [00:18<00:31,  1.93it/s]
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Epoch 133 Training:  39%|███▊      | 37/96 [00:19<00:30,  1.93it/s]
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Epoch 133 Training:  40%|███▉      | 38/96 [00:19<00:30,  1.93it/s]
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Epoch 133 Training:  41%|████      | 39/96 [00:20<00:29,  1.93it/s]
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Epoch 133 Training:  44%|████▍     | 42/96 [00:21<00:27,  1.93it/s]
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Epoch 133 Training:  45%|████▍     | 43/96 [00:22<00:27,  1.93it/s]
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Epoch 133 Training:  46%|████▌     | 44/96 [00:22<00:26,  1.93it/s]
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Epoch 133 Training:  47%|████▋     | 45/96 [00:23<00:26,  1.93it/s]
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Epoch 133 Training:  48%|████▊     | 46/96 [00:23<00:25,  1.93it/s]
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Epoch 133 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 01:39:58] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0267 | Epoch 133] train/clipped_loss_min=-4.5399 train/clipped_loss_max=3.5074 train/clipped_loss_mean=-0.0105 train/clipped_loss_std=1.4082 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0.0002 train/unclipped_loss_min=-4.5399 train/unclipped_loss_max=3.5074 train/unclipped_loss_mean=-0.0105 train/unclipped_loss_std=1.4082 train/policy_loss=-0.0105 train/loss=-0.0105 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0002
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+[2026-10-02 01:39:58] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0268 | Epoch 133] timing/rollout_seconds=179.4425 timing/feedback_seconds=5.3798 timing/optimization_seconds=50.3861 timing/cycle_seconds=232.8419
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+
Pointwise Rewards: ssim:   0%|          | 0/6 [00:00<?, ?it/s]
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+
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Pointwise Rewards: dists:  50%|█████     | 3/6 [00:00<00:00,  3.28it/s]
Pointwise Rewards: dists:  67%|██████▋   | 4/6 [00:01<00:00,  3.26it/s]
Pointwise Rewards: dists:  83%|████████▎ | 5/6 [00:01<00:00,  3.20it/s]
Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.18it/s]
Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.22it/s]
+[2026-10-02 01:43:01] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0268 | Epoch 134] train/reward_dists_mean=0.8318 train/reward_dists_std=0.0270 train/reward_ssim_mean=0.6254 train/reward_ssim_std=0.1099 train/reward_dists_group_std_mean=0.0071 train/reward_dists_group_std_max=0.0120 train/reward_dists_group_std_min=0.0035 train/reward_dists_group_mean_std=0.0260 train/reward_ssim_group_std_mean=0.0061 train/reward_ssim_group_std_max=0.0117 train/reward_ssim_group_std_min=0.0024 train/reward_ssim_group_mean_std=0.1097 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.1410 train/adv_max=4.6168 train/adv_abs_mean=1.1976
+
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Epoch 134 Training:   7%|▋         | 7/96 [00:03<00:46,  1.93it/s]
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Epoch 134 Training:   8%|▊         | 8/96 [00:04<00:45,  1.93it/s]
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Epoch 134 Training:   9%|▉         | 9/96 [00:04<00:45,  1.93it/s]
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Epoch 134 Training:  10%|█         | 10/96 [00:05<00:44,  1.93it/s]
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Epoch 134 Training:  11%|█▏        | 11/96 [00:05<00:44,  1.93it/s]
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Epoch 134 Training:  18%|█▊        | 17/96 [00:08<00:41,  1.92it/s]
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Epoch 134 Training:  19%|█▉        | 18/96 [00:09<00:40,  1.92it/s]
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Epoch 134 Training:  26%|██▌       | 25/96 [00:12<00:36,  1.92it/s]
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Epoch 134 Training:  29%|██▉       | 28/96 [00:14<00:35,  1.92it/s]
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Epoch 134 Training:  31%|███▏      | 30/96 [00:15<00:34,  1.92it/s]
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Epoch 134 Training:  32%|███▏      | 31/96 [00:16<00:33,  1.92it/s]
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Epoch 134 Training:  39%|███▊      | 37/96 [00:19<00:30,  1.93it/s]
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Epoch 134 Training:  41%|████      | 39/96 [00:20<00:29,  1.93it/s]
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Epoch 134 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 01:43:26] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0268 | Epoch 134] train/clipped_loss_min=-4.6168 train/clipped_loss_max=4.1410 train/clipped_loss_mean=-0.0312 train/clipped_loss_std=1.4776 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-4.6168 train/unclipped_loss_max=4.1410 train/unclipped_loss_mean=-0.0312 train/unclipped_loss_std=1.4776 train/policy_loss=-0.0312 train/loss=-0.0312 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
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Epoch 134 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 01:43:51] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0269 | Epoch 134] train/clipped_loss_min=-3.9442 train/clipped_loss_max=4.1168 train/clipped_loss_mean=0.0312 train/clipped_loss_std=1.5044 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0.0001 train/unclipped_loss_min=-3.9442 train/unclipped_loss_max=4.1168 train/unclipped_loss_mean=0.0312 train/unclipped_loss_std=1.5044 train/policy_loss=0.0312 train/loss=0.0312 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
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+[2026-10-02 01:43:51] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0270 | Epoch 134] timing/rollout_seconds=179.4365 timing/feedback_seconds=5.4534 timing/optimization_seconds=50.7974 timing/cycle_seconds=233.2557
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+
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+
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Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.25it/s]
+[2026-10-02 01:46:54] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0270 | Epoch 135] train/reward_dists_mean=0.8296 train/reward_dists_std=0.0339 train/reward_ssim_mean=0.6261 train/reward_ssim_std=0.1137 train/reward_dists_group_std_mean=0.0077 train/reward_dists_group_std_max=0.0214 train/reward_dists_group_std_min=0.0035 train/reward_dists_group_mean_std=0.0329 train/reward_ssim_group_std_mean=0.0066 train/reward_ssim_group_std_max=0.0172 train/reward_ssim_group_std_min=0.0023 train/reward_ssim_group_mean_std=0.1134 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.1358 train/adv_max=3.6870 train/adv_abs_mean=1.2334
+
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Epoch 135 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 01:47:19] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0270 | Epoch 135] train/clipped_loss_min=-3.4631 train/clipped_loss_max=4.0263 train/clipped_loss_mean=-0.0824 train/clipped_loss_std=1.5694 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-3.4631 train/unclipped_loss_max=4.0263 train/unclipped_loss_mean=-0.0824 train/unclipped_loss_std=1.5694 train/policy_loss=-0.0824 train/loss=-0.0824 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0006
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Epoch 135 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 01:47:44] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0271 | Epoch 135] train/clipped_loss_min=-3.6869 train/clipped_loss_max=4.1357 train/clipped_loss_mean=0.0824 train/clipped_loss_std=1.4714 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0.0002 train/unclipped_loss_min=-3.6869 train/unclipped_loss_max=4.1357 train/unclipped_loss_mean=0.0824 train/unclipped_loss_std=1.4714 train/policy_loss=0.0824 train/loss=0.0824 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0006
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+[2026-10-02 01:47:44] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0272 | Epoch 135] timing/rollout_seconds=179.4494 timing/feedback_seconds=5.5413 timing/optimization_seconds=50.4325 timing/cycle_seconds=232.9544
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+
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+
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Pointwise Rewards: dists:  83%|████████▎ | 5/6 [00:01<00:00,  3.30it/s]
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Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.29it/s]
+[2026-10-02 01:50:47] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0272 | Epoch 136] train/reward_dists_mean=0.8311 train/reward_dists_std=0.0296 train/reward_ssim_mean=0.6489 train/reward_ssim_std=0.1079 train/reward_dists_group_std_mean=0.0076 train/reward_dists_group_std_max=0.0149 train/reward_dists_group_std_min=0.0029 train/reward_dists_group_mean_std=0.0285 train/reward_ssim_group_std_mean=0.0081 train/reward_ssim_group_std_max=0.0223 train/reward_ssim_group_std_min=0.0033 train/reward_ssim_group_mean_std=0.1075 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.7971 train/adv_max=3.8867 train/adv_abs_mean=1.2028
+
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Epoch 136 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 01:51:12] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0272 | Epoch 136] train/clipped_loss_min=-3.8867 train/clipped_loss_max=4.1966 train/clipped_loss_mean=-0.0660 train/clipped_loss_std=1.4884 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-3.8867 train/unclipped_loss_max=4.1966 train/unclipped_loss_mean=-0.0660 train/unclipped_loss_std=1.4884 train/policy_loss=-0.0660 train/loss=-0.0660 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
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Epoch 136 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 01:51:37] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0273 | Epoch 136] train/clipped_loss_min=-3.8427 train/clipped_loss_max=4.7971 train/clipped_loss_mean=0.0660 train/clipped_loss_std=1.5354 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-3.8427 train/unclipped_loss_max=4.7971 train/unclipped_loss_mean=0.0660 train/unclipped_loss_std=1.5354 train/policy_loss=0.0660 train/loss=0.0660 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
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+[2026-10-02 01:51:37] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0274 | Epoch 136] timing/rollout_seconds=179.4444 timing/feedback_seconds=5.4551 timing/optimization_seconds=50.4458 timing/cycle_seconds=232.9062
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+
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+[2026-10-02 01:54:40] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0274 | Epoch 137] train/reward_dists_mean=0.8270 train/reward_dists_std=0.0344 train/reward_ssim_mean=0.6327 train/reward_ssim_std=0.1058 train/reward_dists_group_std_mean=0.0077 train/reward_dists_group_std_max=0.0170 train/reward_dists_group_std_min=0.0036 train/reward_dists_group_mean_std=0.0334 train/reward_ssim_group_std_mean=0.0064 train/reward_ssim_group_std_max=0.0133 train/reward_ssim_group_std_min=0.0022 train/reward_ssim_group_mean_std=0.1056 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.5937 train/adv_max=3.7292 train/adv_abs_mean=1.2389
+
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Epoch 137 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 01:55:05] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0274 | Epoch 137] train/clipped_loss_min=-3.5360 train/clipped_loss_max=5 train/clipped_loss_mean=-0.0156 train/clipped_loss_std=1.5557 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-3.5360 train/unclipped_loss_max=5 train/unclipped_loss_mean=-0.0156 train/unclipped_loss_std=1.5557 train/policy_loss=-0.0156 train/loss=-0.0156 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0005
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Epoch 137 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 01:55:30] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0275 | Epoch 137] train/clipped_loss_min=-3.7292 train/clipped_loss_max=4.9999 train/clipped_loss_mean=0.0135 train/clipped_loss_std=1.5963 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-3.7292 train/unclipped_loss_max=4.9999 train/unclipped_loss_mean=0.0135 train/unclipped_loss_std=1.5963 train/policy_loss=0.0135 train/loss=0.0135 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0005
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+[2026-10-02 01:55:30] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0276 | Epoch 137] timing/rollout_seconds=179.7001 timing/feedback_seconds=5.2872 timing/optimization_seconds=50.4481 timing/cycle_seconds=233.2090
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+
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+
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Pointwise Rewards: dists:  83%|████████▎ | 5/6 [00:01<00:00,  3.25it/s]
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Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.28it/s]
+[2026-10-02 01:58:33] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0276 | Epoch 138] train/reward_dists_mean=0.8416 train/reward_dists_std=0.0225 train/reward_ssim_mean=0.6692 train/reward_ssim_std=0.1003 train/reward_dists_group_std_mean=0.0074 train/reward_dists_group_std_max=0.0138 train/reward_dists_group_std_min=0.0030 train/reward_dists_group_mean_std=0.0212 train/reward_ssim_group_std_mean=0.0069 train/reward_ssim_group_std_max=0.0232 train/reward_ssim_group_std_min=0.0027 train/reward_ssim_group_mean_std=0.0999 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.2566 train/adv_max=4.7258 train/adv_abs_mean=1.1982
+
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Epoch 138 Training:  48%|████▊     | 46/96 [00:23<00:25,  1.93it/s]
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Epoch 138 Training:  49%|████▉     | 47/96 [00:24<00:25,  1.93it/s]
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Epoch 138 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 01:58:58] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0276 | Epoch 138] train/clipped_loss_min=-4.7258 train/clipped_loss_max=4.5898 train/clipped_loss_mean=0.0083 train/clipped_loss_std=1.5114 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-4.7258 train/unclipped_loss_max=4.5898 train/unclipped_loss_mean=0.0083 train/unclipped_loss_std=1.5114 train/policy_loss=0.0083 train/loss=0.0083 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0005
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Epoch 138 Training:  55%|█████▌    | 53/96 [00:27<00:22,  1.93it/s]
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Epoch 138 Training:  56%|█████▋    | 54/96 [00:27<00:21,  1.93it/s]
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Epoch 138 Training:  57%|█████▋    | 55/96 [00:28<00:21,  1.93it/s]
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Epoch 138 Training:  58%|█████▊    | 56/96 [00:28<00:20,  1.93it/s]
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Epoch 138 Training:  59%|█████▉    | 57/96 [00:29<00:20,  1.93it/s]
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Epoch 138 Training:  60%|██████    | 58/96 [00:30<00:19,  1.93it/s]
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Epoch 138 Training:  61%|██████▏   | 59/96 [00:30<00:19,  1.93it/s]
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Epoch 138 Training:  62%|██████▎   | 60/96 [00:31<00:18,  1.93it/s]
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Epoch 138 Training:  64%|██████▎   | 61/96 [00:31<00:18,  1.93it/s]
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Epoch 138 Training:  65%|██████▍   | 62/96 [00:32<00:17,  1.93it/s]
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Epoch 138 Training:  66%|██████▌   | 63/96 [00:32<00:17,  1.93it/s]
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Epoch 138 Training:  67%|██████▋   | 64/96 [00:33<00:16,  1.93it/s]
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Epoch 138 Training:  68%|██████▊   | 65/96 [00:33<00:16,  1.93it/s]
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Epoch 138 Training:  69%|██████▉   | 66/96 [00:34<00:15,  1.93it/s]
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Epoch 138 Training:  71%|███████   | 68/96 [00:35<00:14,  1.93it/s]
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Epoch 138 Training:  72%|███████▏  | 69/96 [00:35<00:13,  1.93it/s]
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Epoch 138 Training:  73%|███████▎  | 70/96 [00:36<00:13,  1.93it/s]
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Epoch 138 Training:  74%|███████▍  | 71/96 [00:36<00:12,  1.93it/s]
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Epoch 138 Training:  75%|███████▌  | 72/96 [00:37<00:12,  1.93it/s]
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Epoch 138 Training:  76%|███████▌  | 73/96 [00:37<00:11,  1.93it/s]
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Epoch 138 Training:  77%|███████▋  | 74/96 [00:38<00:11,  1.93it/s]
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Epoch 138 Training:  79%|███████▉  | 76/96 [00:39<00:10,  1.93it/s]
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Epoch 138 Training:  80%|████████  | 77/96 [00:39<00:09,  1.93it/s]
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Epoch 138 Training:  81%|████████▏ | 78/96 [00:40<00:09,  1.93it/s]
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Epoch 138 Training:  82%|████████▏ | 79/96 [00:40<00:08,  1.93it/s]
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Epoch 138 Training:  83%|████████▎ | 80/96 [00:41<00:08,  1.93it/s]
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Epoch 138 Training:  84%|████████▍ | 81/96 [00:41<00:07,  1.93it/s]
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Epoch 138 Training:  85%|████████▌ | 82/96 [00:42<00:07,  1.93it/s]
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Epoch 138 Training:  86%|████████▋ | 83/96 [00:42<00:06,  1.93it/s]
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Epoch 138 Training:  88%|████████▊ | 84/96 [00:43<00:06,  1.93it/s]
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Epoch 138 Training:  89%|████████▊ | 85/96 [00:44<00:05,  1.93it/s]
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Epoch 138 Training:  90%|████████▉ | 86/96 [00:44<00:05,  1.93it/s]
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Epoch 138 Training:  98%|█████████▊| 94/96 [00:48<00:01,  1.92it/s]
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Epoch 138 Training:  99%|█████████▉| 95/96 [00:49<00:00,  1.92it/s]
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Epoch 138 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 01:59:23] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0277 | Epoch 138] train/clipped_loss_min=-4.4796 train/clipped_loss_max=5.0000 train/clipped_loss_mean=-0.0090 train/clipped_loss_std=1.5310 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-4.4796 train/unclipped_loss_max=5.0000 train/unclipped_loss_mean=-0.0090 train/unclipped_loss_std=1.5310 train/policy_loss=-0.0090 train/loss=-0.0090 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0005
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Epoch 138 Timestep: 100%|██████████| 1/1 [00:00<00:00,  1.90it/s]
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Epoch 138 Training: 100%|██████████| 96/96 [00:49<00:00,  1.86it/s]
Epoch 138 Training: 100%|██████████| 96/96 [00:49<00:00,  1.92it/s]
+[2026-10-02 01:59:23] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0278 | Epoch 138] timing/rollout_seconds=179.4754 timing/feedback_seconds=5.5720 timing/optimization_seconds=50.5024 timing/cycle_seconds=233.0023
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+
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+
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Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.27it/s]
Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.23it/s]
+[2026-10-02 02:02:26] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0278 | Epoch 139] train/reward_dists_mean=0.8316 train/reward_dists_std=0.0373 train/reward_ssim_mean=0.6587 train/reward_ssim_std=0.1029 train/reward_dists_group_std_mean=0.0086 train/reward_dists_group_std_max=0.0362 train/reward_dists_group_std_min=0.0032 train/reward_dists_group_mean_std=0.0358 train/reward_ssim_group_std_mean=0.0076 train/reward_ssim_group_std_max=0.0192 train/reward_ssim_group_std_min=0.0023 train/reward_ssim_group_mean_std=0.1025 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.8501 train/adv_max=5.0690 train/adv_abs_mean=1.2049
+
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Epoch 139 Training:  49%|████▉     | 47/96 [00:24<00:25,  1.90it/s]
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Epoch 139 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 02:02:51] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0278 | Epoch 139] train/clipped_loss_min=-3.3583 train/clipped_loss_max=4.8501 train/clipped_loss_mean=0.0001 train/clipped_loss_std=1.4757 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-3.3583 train/unclipped_loss_max=4.8501 train/unclipped_loss_mean=0.0001 train/unclipped_loss_std=1.4757 train/policy_loss=0.0001 train/loss=0.0001 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0005
+
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Epoch 139 Training:  66%|██████▌   | 63/96 [00:33<00:17,  1.93it/s]
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Epoch 139 Training:  98%|█████████▊| 94/96 [00:49<00:01,  1.93it/s]
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Epoch 139 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 02:03:16] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0279 | Epoch 139] train/clipped_loss_min=-5.0000 train/clipped_loss_max=4.0084 train/clipped_loss_mean=0.0001 train/clipped_loss_std=1.5184 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-5.0000 train/unclipped_loss_max=4.0084 train/unclipped_loss_mean=0.0001 train/unclipped_loss_std=1.5184 train/policy_loss=0.0001 train/loss=0.0001 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0006
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Epoch 139 Timestep: 100%|██████████| 1/1 [00:00<00:00,  1.89it/s]
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+[2026-10-02 02:03:16] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0280 | Epoch 139] timing/rollout_seconds=179.3687 timing/feedback_seconds=5.3413 timing/optimization_seconds=50.8912 timing/cycle_seconds=233.2387
+[2026-10-02 02:03:16] [Rank 0] [INFO] [flow_factory.models.abc]: Saving LoRA weights for transformer role=base to /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/runs/klein4b_grpo_flir_full_ssim_dists_lr5e-5/saves/klein4b_grpo_flir_full_ssim_dists_lr5e-5/checkpoints/checkpoint-140
+[2026-10-02 02:03:17] [Rank 0] [INFO] [flow_factory.models.abc]: Checkpoint saved successfully to /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/runs/klein4b_grpo_flir_full_ssim_dists_lr5e-5/saves/klein4b_grpo_flir_full_ssim_dists_lr5e-5/checkpoints/checkpoint-140
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+[2026-10-02 02:04:10] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0280 | Epoch 140] eval/flir_rl_full/reward_dists_mean=0.8389 eval/flir_rl_full/reward_dists_std=0.0374 eval/flir_rl_full/reward_psnr_mean=20.3020 eval/flir_rl_full/reward_psnr_std=2.9822 eval/flir_rl_full/reward_ssim_mean=0.6221 eval/flir_rl_full/reward_ssim_std=0.0858
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+[2026-10-02 02:07:13] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0280 | Epoch 140] train/reward_dists_mean=0.8368 train/reward_dists_std=0.0274 train/reward_ssim_mean=0.6442 train/reward_ssim_std=0.1141 train/reward_dists_group_std_mean=0.0075 train/reward_dists_group_std_max=0.0153 train/reward_dists_group_std_min=0.0043 train/reward_dists_group_mean_std=0.0262 train/reward_ssim_group_std_mean=0.0075 train/reward_ssim_group_std_max=0.0184 train/reward_ssim_group_std_min=0.0038 train/reward_ssim_group_mean_std=0.1138 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.7367 train/adv_max=5.7660 train/adv_abs_mean=1.2207
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Epoch 140 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 02:07:38] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0280 | Epoch 140] train/clipped_loss_min=-3.9355 train/clipped_loss_max=4.5562 train/clipped_loss_mean=-0.0321 train/clipped_loss_std=1.5599 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-3.9355 train/unclipped_loss_max=4.5562 train/unclipped_loss_mean=-0.0321 train/unclipped_loss_std=1.5599 train/policy_loss=-0.0321 train/loss=-0.0321 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
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Epoch 140 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 02:08:03] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0281 | Epoch 140] train/clipped_loss_min=-5.0000 train/clipped_loss_max=4.7367 train/clipped_loss_mean=0.0341 train/clipped_loss_std=1.5121 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0.0002 train/unclipped_loss_min=-5.0000 train/unclipped_loss_max=4.7367 train/unclipped_loss_mean=0.0341 train/unclipped_loss_std=1.5121 train/policy_loss=0.0341 train/loss=0.0341 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
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+[2026-10-02 02:08:03] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0282 | Epoch 140] timing/rollout_seconds=179.3262 timing/feedback_seconds=5.3700 timing/optimization_seconds=51.0354 timing/cycle_seconds=233.2330
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+[2026-10-02 02:11:07] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0282 | Epoch 141] train/reward_dists_mean=0.8340 train/reward_dists_std=0.0313 train/reward_ssim_mean=0.6271 train/reward_ssim_std=0.1054 train/reward_dists_group_std_mean=0.0076 train/reward_dists_group_std_max=0.0195 train/reward_dists_group_std_min=0.0028 train/reward_dists_group_mean_std=0.0302 train/reward_ssim_group_std_mean=0.0067 train/reward_ssim_group_std_max=0.0134 train/reward_ssim_group_std_min=0.0029 train/reward_ssim_group_mean_std=0.1052 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.9112 train/adv_max=3.8128 train/adv_abs_mean=1.1664
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Epoch 141 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 02:11:32] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0282 | Epoch 141] train/clipped_loss_min=-3.5588 train/clipped_loss_max=4.4914 train/clipped_loss_mean=0.0079 train/clipped_loss_std=1.4328 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-3.5588 train/unclipped_loss_max=4.4914 train/unclipped_loss_mean=0.0079 train/unclipped_loss_std=1.4328 train/policy_loss=0.0079 train/loss=0.0079 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0002
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Epoch 141 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 02:11:57] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0283 | Epoch 141] train/clipped_loss_min=-3.8128 train/clipped_loss_max=4.9112 train/clipped_loss_mean=-0.0079 train/clipped_loss_std=1.5180 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-3.8128 train/unclipped_loss_max=4.9112 train/unclipped_loss_mean=-0.0079 train/unclipped_loss_std=1.5180 train/policy_loss=-0.0079 train/loss=-0.0079 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0002
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+[2026-10-02 02:11:57] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0284 | Epoch 141] timing/rollout_seconds=179.4063 timing/feedback_seconds=6.1798 timing/optimization_seconds=50.4524 timing/cycle_seconds=233.5054
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+
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+[2026-10-02 02:14:59] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0284 | Epoch 142] train/reward_dists_mean=0.8301 train/reward_dists_std=0.0302 train/reward_ssim_mean=0.6583 train/reward_ssim_std=0.0810 train/reward_dists_group_std_mean=0.0076 train/reward_dists_group_std_max=0.0144 train/reward_dists_group_std_min=0.0029 train/reward_dists_group_mean_std=0.0291 train/reward_ssim_group_std_mean=0.0061 train/reward_ssim_group_std_max=0.0136 train/reward_ssim_group_std_min=0.0027 train/reward_ssim_group_mean_std=0.0808 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.1421 train/adv_max=5.1328 train/adv_abs_mean=1.1503
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Epoch 142 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 02:15:24] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0284 | Epoch 142] train/clipped_loss_min=-3.8211 train/clipped_loss_max=3.9235 train/clipped_loss_mean=0.0702 train/clipped_loss_std=1.3546 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-3.8211 train/unclipped_loss_max=3.9235 train/unclipped_loss_mean=0.0702 train/unclipped_loss_std=1.3546 train/policy_loss=0.0702 train/loss=0.0702 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0005
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Epoch 142 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 02:15:49] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0285 | Epoch 142] train/clipped_loss_min=-4.9999 train/clipped_loss_max=4.9999 train/clipped_loss_mean=-0.0703 train/clipped_loss_std=1.5794 train/ratio_min=0.9999 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0.0002 train/unclipped_loss_min=-4.9999 train/unclipped_loss_max=4.9999 train/unclipped_loss_mean=-0.0703 train/unclipped_loss_std=1.5794 train/policy_loss=-0.0703 train/loss=-0.0703 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0006
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+[2026-10-02 02:15:49] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0286 | Epoch 142] timing/rollout_seconds=179.1542 timing/feedback_seconds=5.0092 timing/optimization_seconds=50.3921 timing/cycle_seconds=232.4751
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+
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+[2026-10-02 02:18:52] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0286 | Epoch 143] train/reward_dists_mean=0.8307 train/reward_dists_std=0.0343 train/reward_ssim_mean=0.6227 train/reward_ssim_std=0.0901 train/reward_dists_group_std_mean=0.0072 train/reward_dists_group_std_max=0.0143 train/reward_dists_group_std_min=0.0036 train/reward_dists_group_mean_std=0.0335 train/reward_ssim_group_std_mean=0.0063 train/reward_ssim_group_std_max=0.0162 train/reward_ssim_group_std_min=0.0022 train/reward_ssim_group_mean_std=0.0898 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.2628 train/adv_max=4.8538 train/adv_abs_mean=1.1552
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Epoch 143 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 02:19:17] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0286 | Epoch 143] train/clipped_loss_min=-4.8538 train/clipped_loss_max=5 train/clipped_loss_mean=-0.0768 train/clipped_loss_std=1.4382 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-4.8538 train/unclipped_loss_max=5 train/unclipped_loss_mean=-0.0768 train/unclipped_loss_std=1.4382 train/policy_loss=-0.0768 train/loss=-0.0768 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
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Epoch 143 Training:  99%|█████████▉| 95/96 [00:49<00:00,  1.93it/s]
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Epoch 143 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 02:19:42] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0287 | Epoch 143] train/clipped_loss_min=-3.4326 train/clipped_loss_max=4.9943 train/clipped_loss_mean=0.0761 train/clipped_loss_std=1.4775 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-3.4326 train/unclipped_loss_max=4.9943 train/unclipped_loss_mean=0.0761 train/unclipped_loss_std=1.4775 train/policy_loss=0.0761 train/loss=0.0761 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
+
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Epoch 143 Training: 100%|██████████| 96/96 [00:49<00:00,  1.93it/s]
+[2026-10-02 02:19:42] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0288 | Epoch 143] timing/rollout_seconds=179.4526 timing/feedback_seconds=5.5274 timing/optimization_seconds=50.3788 timing/cycle_seconds=232.7516
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+
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Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:01<00:00,  5.75it/s]
+
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Pointwise Rewards: dists:  50%|█████     | 3/6 [00:00<00:00,  3.25it/s]
Pointwise Rewards: dists:  67%|██████▋   | 4/6 [00:01<00:00,  3.22it/s]
Pointwise Rewards: dists:  83%|████████▎ | 5/6 [00:01<00:00,  3.31it/s]
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+[2026-10-02 02:22:45] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0288 | Epoch 144] train/reward_dists_mean=0.8324 train/reward_dists_std=0.0378 train/reward_ssim_mean=0.6103 train/reward_ssim_std=0.1191 train/reward_dists_group_std_mean=0.0081 train/reward_dists_group_std_max=0.0196 train/reward_dists_group_std_min=0.0033 train/reward_dists_group_mean_std=0.0367 train/reward_ssim_group_std_mean=0.0063 train/reward_ssim_group_std_max=0.0157 train/reward_ssim_group_std_min=0.0026 train/reward_ssim_group_mean_std=0.1189 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.5077 train/adv_max=4.3582 train/adv_abs_mean=1.1932
+
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Epoch 144 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 02:23:10] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0288 | Epoch 144] train/clipped_loss_min=-3.8576 train/clipped_loss_max=4.5077 train/clipped_loss_mean=-0.0211 train/clipped_loss_std=1.4682 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-3.8576 train/unclipped_loss_max=4.5077 train/unclipped_loss_mean=-0.0211 train/unclipped_loss_std=1.4682 train/policy_loss=-0.0211 train/loss=-0.0211 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
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Epoch 144 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 02:23:34] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0289 | Epoch 144] train/clipped_loss_min=-4.3582 train/clipped_loss_max=3.6511 train/clipped_loss_mean=0.0211 train/clipped_loss_std=1.5076 train/ratio_min=0.9999 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-4.3582 train/unclipped_loss_max=3.6511 train/unclipped_loss_mean=0.0211 train/unclipped_loss_std=1.5076 train/policy_loss=0.0211 train/loss=0.0211 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
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+[2026-10-02 02:23:34] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0290 | Epoch 144] timing/rollout_seconds=179.4202 timing/feedback_seconds=5.5584 timing/optimization_seconds=50.4399 timing/cycle_seconds=232.7304
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+[2026-10-02 02:26:38] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0290 | Epoch 145] train/reward_dists_mean=0.8413 train/reward_dists_std=0.0280 train/reward_ssim_mean=0.6403 train/reward_ssim_std=0.1090 train/reward_dists_group_std_mean=0.0068 train/reward_dists_group_std_max=0.0274 train/reward_dists_group_std_min=0.0023 train/reward_dists_group_mean_std=0.0269 train/reward_ssim_group_std_mean=0.0069 train/reward_ssim_group_std_max=0.0159 train/reward_ssim_group_std_min=0.0020 train/reward_ssim_group_mean_std=0.1088 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-6.0185 train/adv_max=4.6586 train/adv_abs_mean=1.1520
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Epoch 145 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 02:27:03] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0290 | Epoch 145] train/clipped_loss_min=-4.6586 train/clipped_loss_max=4.3841 train/clipped_loss_mean=-0.0065 train/clipped_loss_std=1.5073 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-4.6586 train/unclipped_loss_max=4.3841 train/unclipped_loss_mean=-0.0065 train/unclipped_loss_std=1.5073 train/policy_loss=-0.0065 train/loss=-0.0065 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0002
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Epoch 145 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 02:27:27] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0291 | Epoch 145] train/clipped_loss_min=-3.5908 train/clipped_loss_max=5.0000 train/clipped_loss_mean=0.0038 train/clipped_loss_std=1.3651 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-3.5908 train/unclipped_loss_max=5.0000 train/unclipped_loss_mean=0.0038 train/unclipped_loss_std=1.3651 train/policy_loss=0.0038 train/loss=0.0038 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0002
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+[2026-10-02 02:27:27] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0292 | Epoch 145] timing/rollout_seconds=179.6172 timing/feedback_seconds=5.4413 timing/optimization_seconds=50.4383 timing/cycle_seconds=232.9764
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+[2026-10-02 02:30:31] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0292 | Epoch 146] train/reward_dists_mean=0.8410 train/reward_dists_std=0.0303 train/reward_ssim_mean=0.6303 train/reward_ssim_std=0.1279 train/reward_dists_group_std_mean=0.0072 train/reward_dists_group_std_max=0.0182 train/reward_dists_group_std_min=0.0024 train/reward_dists_group_mean_std=0.0293 train/reward_ssim_group_std_mean=0.0066 train/reward_ssim_group_std_max=0.0140 train/reward_ssim_group_std_min=0.0019 train/reward_ssim_group_mean_std=0.1277 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.8512 train/adv_max=4.4620 train/adv_abs_mean=1.1945
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Epoch 146 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 02:30:56] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0292 | Epoch 146] train/clipped_loss_min=-3.8941 train/clipped_loss_max=4.1234 train/clipped_loss_mean=0.0389 train/clipped_loss_std=1.4285 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-3.8941 train/unclipped_loss_max=4.1234 train/unclipped_loss_mean=0.0389 train/unclipped_loss_std=1.4285 train/policy_loss=0.0389 train/loss=0.0389 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
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Epoch 146 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 02:31:20] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0293 | Epoch 146] train/clipped_loss_min=-4.4620 train/clipped_loss_max=4.8512 train/clipped_loss_mean=-0.0389 train/clipped_loss_std=1.5502 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-4.4620 train/unclipped_loss_max=4.8512 train/unclipped_loss_mean=-0.0389 train/unclipped_loss_std=1.5502 train/policy_loss=-0.0389 train/loss=-0.0389 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0004
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+[2026-10-02 02:31:20] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0294 | Epoch 146] timing/rollout_seconds=179.4507 timing/feedback_seconds=5.2767 timing/optimization_seconds=50.6393 timing/cycle_seconds=233.0106
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+[2026-10-02 02:34:24] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0294 | Epoch 147] train/reward_dists_mean=0.8336 train/reward_dists_std=0.0343 train/reward_ssim_mean=0.6403 train/reward_ssim_std=0.1100 train/reward_dists_group_std_mean=0.0070 train/reward_dists_group_std_max=0.0165 train/reward_dists_group_std_min=0.0030 train/reward_dists_group_mean_std=0.0335 train/reward_ssim_group_std_mean=0.0067 train/reward_ssim_group_std_max=0.0178 train/reward_ssim_group_std_min=0.0029 train/reward_ssim_group_mean_std=0.1097 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.1113 train/adv_max=4.7040 train/adv_abs_mean=1.1592
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Epoch 147 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 02:34:48] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0294 | Epoch 147] train/clipped_loss_min=-3.8131 train/clipped_loss_max=4.3304 train/clipped_loss_mean=0.0036 train/clipped_loss_std=1.4361 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-3.8131 train/unclipped_loss_max=4.3304 train/unclipped_loss_mean=0.0036 train/unclipped_loss_std=1.4361 train/policy_loss=0.0036 train/loss=0.0036 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
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Epoch 147 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 02:35:13] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0295 | Epoch 147] train/clipped_loss_min=-4.7040 train/clipped_loss_max=5.0000 train/clipped_loss_mean=-0.0039 train/clipped_loss_std=1.5202 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0.0001 train/unclipped_loss_min=-4.7040 train/unclipped_loss_max=5.0000 train/unclipped_loss_mean=-0.0039 train/unclipped_loss_std=1.5202 train/policy_loss=-0.0039 train/loss=-0.0039 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0004
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+[2026-10-02 02:35:13] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0296 | Epoch 147] timing/rollout_seconds=179.5229 timing/feedback_seconds=5.5603 timing/optimization_seconds=50.3779 timing/cycle_seconds=232.8335
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+
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+[2026-10-02 02:38:16] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0296 | Epoch 148] train/reward_dists_mean=0.8403 train/reward_dists_std=0.0278 train/reward_ssim_mean=0.6492 train/reward_ssim_std=0.0992 train/reward_dists_group_std_mean=0.0073 train/reward_dists_group_std_max=0.0147 train/reward_dists_group_std_min=0.0036 train/reward_dists_group_mean_std=0.0267 train/reward_ssim_group_std_mean=0.0064 train/reward_ssim_group_std_max=0.0139 train/reward_ssim_group_std_min=0.0027 train/reward_ssim_group_mean_std=0.0990 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-6.0857 train/adv_max=4.2652 train/adv_abs_mean=1.1895
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Epoch 148 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 02:38:41] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0296 | Epoch 148] train/clipped_loss_min=-4.2652 train/clipped_loss_max=5 train/clipped_loss_mean=0.1016 train/clipped_loss_std=1.5134 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-4.2652 train/unclipped_loss_max=5 train/unclipped_loss_mean=0.1016 train/unclipped_loss_std=1.5134 train/policy_loss=0.1016 train/loss=0.1016 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0006
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Epoch 148 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 02:39:06] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0297 | Epoch 148] train/clipped_loss_min=-4.2208 train/clipped_loss_max=5.0000 train/clipped_loss_mean=-0.1045 train/clipped_loss_std=1.4686 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-4.2208 train/unclipped_loss_max=5.0000 train/unclipped_loss_mean=-0.1045 train/unclipped_loss_std=1.4686 train/policy_loss=-0.1045 train/loss=-0.1045 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0006
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+[2026-10-02 02:39:06] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0298 | Epoch 148] timing/rollout_seconds=179.3550 timing/feedback_seconds=5.5672 timing/optimization_seconds=50.7604 timing/cycle_seconds=233.1050
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+[2026-10-02 02:42:10] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0298 | Epoch 149] train/reward_dists_mean=0.8365 train/reward_dists_std=0.0326 train/reward_ssim_mean=0.6424 train/reward_ssim_std=0.1255 train/reward_dists_group_std_mean=0.0087 train/reward_dists_group_std_max=0.0191 train/reward_dists_group_std_min=0.0027 train/reward_dists_group_mean_std=0.0312 train/reward_ssim_group_std_mean=0.0062 train/reward_ssim_group_std_max=0.0201 train/reward_ssim_group_std_min=0.0022 train/reward_ssim_group_mean_std=0.1253 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.9779 train/adv_max=4.0810 train/adv_abs_mean=1.2073
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Epoch 149 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 02:42:35] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0298 | Epoch 149] train/clipped_loss_min=-4.0810 train/clipped_loss_max=4.9779 train/clipped_loss_mean=-0.0189 train/clipped_loss_std=1.5200 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-4.0810 train/unclipped_loss_max=4.9779 train/unclipped_loss_mean=-0.0189 train/unclipped_loss_std=1.5200 train/policy_loss=-0.0189 train/loss=-0.0189 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0002
+
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Epoch 149 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 02:43:00] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0299 | Epoch 149] train/clipped_loss_min=-3.3758 train/clipped_loss_max=4.5268 train/clipped_loss_mean=0.0189 train/clipped_loss_std=1.5121 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-3.3758 train/unclipped_loss_max=4.5268 train/unclipped_loss_mean=0.0189 train/unclipped_loss_std=1.5121 train/policy_loss=0.0189 train/loss=0.0189 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0002
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+[2026-10-02 02:43:00] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0300 | Epoch 149] timing/rollout_seconds=179.6178 timing/feedback_seconds=5.2036 timing/optimization_seconds=50.6073 timing/cycle_seconds=233.1594
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+[2026-10-02 02:43:53] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0300 | Epoch 150] eval/flir_rl_full/reward_dists_mean=0.8392 eval/flir_rl_full/reward_dists_std=0.0374 eval/flir_rl_full/reward_psnr_mean=20.2632 eval/flir_rl_full/reward_psnr_std=2.9196 eval/flir_rl_full/reward_ssim_mean=0.6234 eval/flir_rl_full/reward_ssim_std=0.0855
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+
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+
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Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.27it/s]
+[2026-10-02 02:46:56] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0300 | Epoch 150] train/reward_dists_mean=0.8351 train/reward_dists_std=0.0264 train/reward_ssim_mean=0.6586 train/reward_ssim_std=0.0926 train/reward_dists_group_std_mean=0.0076 train/reward_dists_group_std_max=0.0142 train/reward_dists_group_std_min=0.0038 train/reward_dists_group_mean_std=0.0251 train/reward_ssim_group_std_mean=0.0066 train/reward_ssim_group_std_max=0.0189 train/reward_ssim_group_std_min=0.0025 train/reward_ssim_group_mean_std=0.0923 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.7582 train/adv_max=4.7610 train/adv_abs_mean=1.2125
+
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Epoch 150 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 02:47:21] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0300 | Epoch 150] train/clipped_loss_min=-4.0173 train/clipped_loss_max=4.7582 train/clipped_loss_mean=0.0104 train/clipped_loss_std=1.5107 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-4.0173 train/unclipped_loss_max=4.7582 train/unclipped_loss_mean=0.0104 train/unclipped_loss_std=1.5107 train/policy_loss=0.0104 train/loss=0.0104 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0004
+
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Epoch 150 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 02:47:46] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0301 | Epoch 150] train/clipped_loss_min=-4.7610 train/clipped_loss_max=3.7714 train/clipped_loss_mean=-0.0104 train/clipped_loss_std=1.5541 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-4.7610 train/unclipped_loss_max=3.7714 train/unclipped_loss_mean=-0.0104 train/unclipped_loss_std=1.5541 train/policy_loss=-0.0104 train/loss=-0.0104 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0004
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+[2026-10-02 02:47:46] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0302 | Epoch 150] timing/rollout_seconds=179.4907 timing/feedback_seconds=5.3057 timing/optimization_seconds=50.5323 timing/cycle_seconds=233.0283
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+
Pointwise Rewards: ssim:   0%|          | 0/6 [00:00<?, ?it/s]
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Pointwise Rewards: ssim:  83%|████████▎ | 5/6 [00:00<00:00,  5.90it/s]
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+
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Pointwise Rewards: dists:  50%|█████     | 3/6 [00:00<00:00,  3.31it/s]
Pointwise Rewards: dists:  67%|██████▋   | 4/6 [00:01<00:00,  3.29it/s]
Pointwise Rewards: dists:  83%|████████▎ | 5/6 [00:01<00:00,  3.33it/s]
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Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.33it/s]
+[2026-10-02 02:50:49] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0302 | Epoch 151] train/reward_dists_mean=0.8410 train/reward_dists_std=0.0313 train/reward_ssim_mean=0.6797 train/reward_ssim_std=0.1049 train/reward_dists_group_std_mean=0.0073 train/reward_dists_group_std_max=0.0131 train/reward_dists_group_std_min=0.0036 train/reward_dists_group_mean_std=0.0303 train/reward_ssim_group_std_mean=0.0066 train/reward_ssim_group_std_max=0.0189 train/reward_ssim_group_std_min=0.0026 train/reward_ssim_group_mean_std=0.1047 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.1940 train/adv_max=4.8132 train/adv_abs_mean=1.2038
+
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Epoch 151 Training:   7%|▋         | 7/96 [00:03<00:46,  1.93it/s]
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Epoch 151 Training:   8%|▊         | 8/96 [00:04<00:45,  1.93it/s]
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Epoch 151 Training:   9%|▉         | 9/96 [00:04<00:45,  1.93it/s]
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Epoch 151 Training:  10%|█         | 10/96 [00:05<00:44,  1.93it/s]
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Epoch 151 Training:  11%|█▏        | 11/96 [00:05<00:44,  1.93it/s]
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Epoch 151 Training:  12%|█▎        | 12/96 [00:06<00:43,  1.93it/s]
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Epoch 151 Training:  14%|█▎        | 13/96 [00:06<00:43,  1.93it/s]
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Epoch 151 Training:  15%|█▍        | 14/96 [00:07<00:42,  1.93it/s]
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Epoch 151 Training:  16%|█▌        | 15/96 [00:07<00:42,  1.93it/s]
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Epoch 151 Training:  17%|█▋        | 16/96 [00:08<00:41,  1.93it/s]
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Epoch 151 Training:  18%|█▊        | 17/96 [00:08<00:41,  1.93it/s]
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Epoch 151 Training:  19%|█▉        | 18/96 [00:09<00:40,  1.93it/s]
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Epoch 151 Training:  20%|█▉        | 19/96 [00:09<00:39,  1.93it/s]
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Epoch 151 Training:  21%|██        | 20/96 [00:10<00:39,  1.93it/s]
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Epoch 151 Training:  22%|██▏       | 21/96 [00:10<00:38,  1.93it/s]
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Epoch 151 Training:  23%|██▎       | 22/96 [00:11<00:38,  1.93it/s]
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Epoch 151 Training:  24%|██▍       | 23/96 [00:11<00:37,  1.93it/s]
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Epoch 151 Training:  25%|██▌       | 24/96 [00:12<00:37,  1.93it/s]
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Epoch 151 Training:  26%|██▌       | 25/96 [00:12<00:36,  1.92it/s]
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Epoch 151 Training:  27%|██▋       | 26/96 [00:13<00:36,  1.92it/s]
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Epoch 151 Training:  28%|██▊       | 27/96 [00:13<00:35,  1.92it/s]
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Epoch 151 Training:  29%|██▉       | 28/96 [00:14<00:35,  1.93it/s]
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Epoch 151 Training:  30%|███       | 29/96 [00:15<00:34,  1.93it/s]
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Epoch 151 Training:  31%|███▏      | 30/96 [00:15<00:34,  1.93it/s]
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Epoch 151 Training:  32%|███▏      | 31/96 [00:16<00:33,  1.93it/s]
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Epoch 151 Training:  33%|███▎      | 32/96 [00:16<00:33,  1.93it/s]
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Epoch 151 Training:  34%|███▍      | 33/96 [00:17<00:32,  1.93it/s]
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Epoch 151 Training:  35%|███▌      | 34/96 [00:17<00:32,  1.93it/s]
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Epoch 151 Training:  38%|███▊      | 36/96 [00:18<00:31,  1.93it/s]
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Epoch 151 Training:  39%|███▊      | 37/96 [00:19<00:30,  1.93it/s]
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Epoch 151 Training:  40%|███▉      | 38/96 [00:19<00:30,  1.93it/s]
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Epoch 151 Training:  41%|████      | 39/96 [00:20<00:29,  1.93it/s]
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Epoch 151 Training:  42%|████▏     | 40/96 [00:20<00:29,  1.93it/s]
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Epoch 151 Training:  43%|████▎     | 41/96 [00:21<00:28,  1.93it/s]
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Epoch 151 Training:  44%|████▍     | 42/96 [00:21<00:28,  1.93it/s]
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Epoch 151 Training:  45%|████▍     | 43/96 [00:22<00:27,  1.93it/s]
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Epoch 151 Training:  46%|████▌     | 44/96 [00:22<00:26,  1.93it/s]
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Epoch 151 Training:  47%|████▋     | 45/96 [00:23<00:26,  1.93it/s]
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Epoch 151 Training:  48%|████▊     | 46/96 [00:23<00:25,  1.93it/s]
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Epoch 151 Training:  49%|████▉     | 47/96 [00:24<00:25,  1.93it/s]
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Epoch 151 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 02:51:14] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0302 | Epoch 151] train/clipped_loss_min=-4.0309 train/clipped_loss_max=4.2982 train/clipped_loss_mean=0.0546 train/clipped_loss_std=1.5306 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-4.0309 train/unclipped_loss_max=4.2982 train/unclipped_loss_mean=0.0546 train/unclipped_loss_std=1.5306 train/policy_loss=0.0546 train/loss=0.0546 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0005
+
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Epoch 151 Timestep: 100%|██████████| 1/1 [00:00<00:00,  1.90it/s]
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Epoch 151 Training:  53%|█████▎    | 51/96 [00:26<00:23,  1.93it/s]
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Epoch 151 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 02:51:39] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0303 | Epoch 151] train/clipped_loss_min=-4.8132 train/clipped_loss_max=5.0000 train/clipped_loss_mean=-0.0551 train/clipped_loss_std=1.5034 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0.0001 train/unclipped_loss_min=-4.8132 train/unclipped_loss_max=5.0000 train/unclipped_loss_mean=-0.0551 train/unclipped_loss_std=1.5034 train/policy_loss=-0.0551 train/loss=-0.0551 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0005
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+[2026-10-02 02:51:39] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0304 | Epoch 151] timing/rollout_seconds=179.5241 timing/feedback_seconds=5.6427 timing/optimization_seconds=50.4776 timing/cycle_seconds=233.0361
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+
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+
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Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.25it/s]
+[2026-10-02 02:54:42] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0304 | Epoch 152] train/reward_dists_mean=0.8395 train/reward_dists_std=0.0299 train/reward_ssim_mean=0.6561 train/reward_ssim_std=0.0915 train/reward_dists_group_std_mean=0.0076 train/reward_dists_group_std_max=0.0183 train/reward_dists_group_std_min=0.0027 train/reward_dists_group_mean_std=0.0287 train/reward_ssim_group_std_mean=0.0063 train/reward_ssim_group_std_max=0.0140 train/reward_ssim_group_std_min=0.0029 train/reward_ssim_group_mean_std=0.0913 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.5680 train/adv_max=4.3091 train/adv_abs_mean=1.2496
+
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Epoch 152 Training:  39%|███▊      | 37/96 [00:19<00:30,  1.93it/s]
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Epoch 152 Training:  49%|████▉     | 47/96 [00:24<00:25,  1.92it/s]
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Epoch 152 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 02:55:07] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0304 | Epoch 152] train/clipped_loss_min=-4.3091 train/clipped_loss_max=4.5680 train/clipped_loss_mean=-0.0704 train/clipped_loss_std=1.5398 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-4.3091 train/unclipped_loss_max=4.5680 train/unclipped_loss_mean=-0.0704 train/unclipped_loss_std=1.5398 train/policy_loss=-0.0704 train/loss=-0.0704 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0002
+
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Epoch 152 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 02:55:32] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0305 | Epoch 152] train/clipped_loss_min=-4.1276 train/clipped_loss_max=4.5248 train/clipped_loss_mean=0.0704 train/clipped_loss_std=1.5706 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-4.1276 train/unclipped_loss_max=4.5248 train/unclipped_loss_mean=0.0704 train/unclipped_loss_std=1.5706 train/policy_loss=0.0704 train/loss=0.0704 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0002
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+[2026-10-02 02:55:32] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0306 | Epoch 152] timing/rollout_seconds=179.7303 timing/feedback_seconds=5.8795 timing/optimization_seconds=50.5611 timing/cycle_seconds=233.2382
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+
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+
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+[2026-10-02 02:58:35] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0306 | Epoch 153] train/reward_dists_mean=0.8306 train/reward_dists_std=0.0381 train/reward_ssim_mean=0.6467 train/reward_ssim_std=0.1069 train/reward_dists_group_std_mean=0.0073 train/reward_dists_group_std_max=0.0146 train/reward_dists_group_std_min=0.0028 train/reward_dists_group_mean_std=0.0373 train/reward_ssim_group_std_mean=0.0060 train/reward_ssim_group_std_max=0.0124 train/reward_ssim_group_std_min=0.0030 train/reward_ssim_group_mean_std=0.1068 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.1642 train/adv_max=4.5263 train/adv_abs_mean=1.1860
+
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Epoch 153 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 02:59:00] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0306 | Epoch 153] train/clipped_loss_min=-4.5263 train/clipped_loss_max=5 train/clipped_loss_mean=0.0155 train/clipped_loss_std=1.5567 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-4.5263 train/unclipped_loss_max=5 train/unclipped_loss_mean=0.0155 train/unclipped_loss_std=1.5567 train/policy_loss=0.0155 train/loss=0.0155 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0006
+
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Epoch 153 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 02:59:25] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0307 | Epoch 153] train/clipped_loss_min=-4.3957 train/clipped_loss_max=4.4642 train/clipped_loss_mean=-0.0160 train/clipped_loss_std=1.4359 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0.0001 train/unclipped_loss_min=-4.3957 train/unclipped_loss_max=4.4642 train/unclipped_loss_mean=-0.0160 train/unclipped_loss_std=1.4359 train/policy_loss=-0.0160 train/loss=-0.0160 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0005
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+[2026-10-02 02:59:25] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0308 | Epoch 153] timing/rollout_seconds=179.4285 timing/feedback_seconds=5.6303 timing/optimization_seconds=50.4481 timing/cycle_seconds=232.8590
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+
Pointwise Rewards: ssim:   0%|          | 0/6 [00:00<?, ?it/s]
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Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:01<00:00,  5.47it/s]
+
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Pointwise Rewards: dists:  50%|█████     | 3/6 [00:00<00:00,  3.26it/s]
Pointwise Rewards: dists:  67%|██████▋   | 4/6 [00:01<00:00,  3.25it/s]
Pointwise Rewards: dists:  83%|████████▎ | 5/6 [00:01<00:00,  3.22it/s]
Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.19it/s]
Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.21it/s]
+[2026-10-02 03:02:28] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0308 | Epoch 154] train/reward_dists_mean=0.8415 train/reward_dists_std=0.0294 train/reward_ssim_mean=0.6635 train/reward_ssim_std=0.0948 train/reward_dists_group_std_mean=0.0076 train/reward_dists_group_std_max=0.0196 train/reward_dists_group_std_min=0.0030 train/reward_dists_group_mean_std=0.0282 train/reward_ssim_group_std_mean=0.0074 train/reward_ssim_group_std_max=0.0184 train/reward_ssim_group_std_min=0.0028 train/reward_ssim_group_mean_std=0.0945 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.8982 train/adv_max=4.6551 train/adv_abs_mean=1.2095
+
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Epoch 154 Training:   8%|▊         | 8/96 [00:04<00:45,  1.93it/s]
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Epoch 154 Training:   9%|▉         | 9/96 [00:04<00:45,  1.93it/s]
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Epoch 154 Training:  10%|█         | 10/96 [00:05<00:44,  1.93it/s]
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Epoch 154 Training:  11%|█▏        | 11/96 [00:05<00:44,  1.93it/s]
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Epoch 154 Training:  12%|█▎        | 12/96 [00:06<00:43,  1.93it/s]
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Epoch 154 Training:  14%|█▎        | 13/96 [00:06<00:43,  1.93it/s]
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Epoch 154 Training:  15%|█▍        | 14/96 [00:07<00:42,  1.93it/s]
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Epoch 154 Training:  16%|█▌        | 15/96 [00:07<00:42,  1.93it/s]
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Epoch 154 Training:  19%|█▉        | 18/96 [00:09<00:40,  1.93it/s]
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Epoch 154 Training:  29%|██▉       | 28/96 [00:14<00:35,  1.93it/s]
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Epoch 154 Training:  39%|███▊      | 37/96 [00:19<00:30,  1.93it/s]
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Epoch 154 Training:  44%|████▍     | 42/96 [00:21<00:27,  1.93it/s]
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Epoch 154 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 03:03:18] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0309 | Epoch 154] train/clipped_loss_min=-4.6550 train/clipped_loss_max=4.1999 train/clipped_loss_mean=-0.1166 train/clipped_loss_std=1.4696 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-4.6550 train/unclipped_loss_max=4.1999 train/unclipped_loss_mean=-0.1166 train/unclipped_loss_std=1.4696 train/policy_loss=-0.1166 train/loss=-0.1166 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0005
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+[2026-10-02 03:03:18] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0310 | Epoch 154] timing/rollout_seconds=179.4548 timing/feedback_seconds=5.6066 timing/optimization_seconds=50.5854 timing/cycle_seconds=232.9368
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+
Pointwise Rewards: ssim:   0%|          | 0/6 [00:00<?, ?it/s]
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+
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Pointwise Rewards: dists:  50%|█████     | 3/6 [00:00<00:00,  3.36it/s]
Pointwise Rewards: dists:  67%|██████▋   | 4/6 [00:01<00:00,  3.38it/s]
Pointwise Rewards: dists:  83%|████████▎ | 5/6 [00:01<00:00,  3.35it/s]
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Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.31it/s]
+[2026-10-02 03:06:21] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0310 | Epoch 155] train/reward_dists_mean=0.8441 train/reward_dists_std=0.0266 train/reward_ssim_mean=0.6480 train/reward_ssim_std=0.0950 train/reward_dists_group_std_mean=0.0066 train/reward_dists_group_std_max=0.0127 train/reward_dists_group_std_min=0.0032 train/reward_dists_group_mean_std=0.0257 train/reward_ssim_group_std_mean=0.0088 train/reward_ssim_group_std_max=0.0216 train/reward_ssim_group_std_min=0.0037 train/reward_ssim_group_mean_std=0.0946 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.5500 train/adv_max=4.7984 train/adv_abs_mean=1.2667
+
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Epoch 155 Training:   9%|▉         | 9/96 [00:04<00:44,  1.94it/s]
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Epoch 155 Training:  10%|█         | 10/96 [00:05<00:44,  1.93it/s]
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Epoch 155 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 03:06:46] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0310 | Epoch 155] train/clipped_loss_min=-4.4524 train/clipped_loss_max=5 train/clipped_loss_mean=0.0554 train/clipped_loss_std=1.5969 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-4.4524 train/unclipped_loss_max=5 train/unclipped_loss_mean=0.0554 train/unclipped_loss_std=1.5969 train/policy_loss=0.0554 train/loss=0.0554 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0002
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Epoch 155 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 03:07:11] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0311 | Epoch 155] train/clipped_loss_min=-4.7984 train/clipped_loss_max=4.2387 train/clipped_loss_mean=-0.0569 train/clipped_loss_std=1.5903 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0.0002 train/unclipped_loss_min=-4.7984 train/unclipped_loss_max=4.2387 train/unclipped_loss_mean=-0.0569 train/unclipped_loss_std=1.5903 train/policy_loss=-0.0569 train/loss=-0.0569 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0002
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+[2026-10-02 03:07:11] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0312 | Epoch 155] timing/rollout_seconds=179.3400 timing/feedback_seconds=5.5404 timing/optimization_seconds=50.3810 timing/cycle_seconds=232.7053
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+
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+
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Pointwise Rewards: dists:  67%|██████▋   | 4/6 [00:01<00:00,  3.28it/s]
Pointwise Rewards: dists:  83%|████████▎ | 5/6 [00:01<00:00,  3.23it/s]
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Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.28it/s]
+[2026-10-02 03:10:14] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0312 | Epoch 156] train/reward_dists_mean=0.8501 train/reward_dists_std=0.0237 train/reward_ssim_mean=0.6274 train/reward_ssim_std=0.1040 train/reward_dists_group_std_mean=0.0058 train/reward_dists_group_std_max=0.0133 train/reward_dists_group_std_min=0.0028 train/reward_dists_group_mean_std=0.0229 train/reward_ssim_group_std_mean=0.0076 train/reward_ssim_group_std_max=0.0152 train/reward_ssim_group_std_min=0.0038 train/reward_ssim_group_mean_std=0.1036 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.9547 train/adv_max=4.2911 train/adv_abs_mean=1.1955
+
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Epoch 156 Training:   9%|▉         | 9/96 [00:04<00:44,  1.94it/s]
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Epoch 156 Training:  39%|███▊      | 37/96 [00:19<00:30,  1.92it/s]
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Epoch 156 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 03:10:39] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0312 | Epoch 156] train/clipped_loss_min=-4.2911 train/clipped_loss_max=4.9547 train/clipped_loss_mean=0.0630 train/clipped_loss_std=1.5587 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-4.2911 train/unclipped_loss_max=4.9547 train/unclipped_loss_mean=0.0630 train/unclipped_loss_std=1.5587 train/policy_loss=0.0630 train/loss=0.0630 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0002
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Epoch 156 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 03:11:04] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0313 | Epoch 156] train/clipped_loss_min=-4.1169 train/clipped_loss_max=4.1575 train/clipped_loss_mean=-0.0630 train/clipped_loss_std=1.4288 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0.0001 train/unclipped_loss_min=-4.1169 train/unclipped_loss_max=4.1575 train/unclipped_loss_mean=-0.0630 train/unclipped_loss_std=1.4288 train/policy_loss=-0.0630 train/loss=-0.0630 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0002
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+[2026-10-02 03:11:04] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0314 | Epoch 156] timing/rollout_seconds=179.3618 timing/feedback_seconds=5.4422 timing/optimization_seconds=50.3812 timing/cycle_seconds=232.7203
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+
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+[2026-10-02 03:14:07] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0314 | Epoch 157] train/reward_dists_mean=0.8408 train/reward_dists_std=0.0313 train/reward_ssim_mean=0.6313 train/reward_ssim_std=0.0911 train/reward_dists_group_std_mean=0.0068 train/reward_dists_group_std_max=0.0200 train/reward_dists_group_std_min=0.0026 train/reward_dists_group_mean_std=0.0304 train/reward_ssim_group_std_mean=0.0080 train/reward_ssim_group_std_max=0.0209 train/reward_ssim_group_std_min=0.0033 train/reward_ssim_group_mean_std=0.0907 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.1807 train/adv_max=3.6549 train/adv_abs_mean=1.2140
+
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Epoch 157 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 03:14:57] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0315 | Epoch 157] train/clipped_loss_min=-3.3291 train/clipped_loss_max=4.5650 train/clipped_loss_mean=-0.0195 train/clipped_loss_std=1.4719 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-3.3291 train/unclipped_loss_max=4.5650 train/unclipped_loss_mean=-0.0195 train/unclipped_loss_std=1.4719 train/policy_loss=-0.0195 train/loss=-0.0195 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0002
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+[2026-10-02 03:14:57] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0316 | Epoch 157] timing/rollout_seconds=179.5380 timing/feedback_seconds=5.6427 timing/optimization_seconds=50.4297 timing/cycle_seconds=233.0306
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+
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+
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Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.36it/s]
+[2026-10-02 03:18:00] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0316 | Epoch 158] train/reward_dists_mean=0.8394 train/reward_dists_std=0.0298 train/reward_ssim_mean=0.6433 train/reward_ssim_std=0.0991 train/reward_dists_group_std_mean=0.0062 train/reward_dists_group_std_max=0.0132 train/reward_dists_group_std_min=0.0028 train/reward_dists_group_mean_std=0.0291 train/reward_ssim_group_std_mean=0.0080 train/reward_ssim_group_std_max=0.0152 train/reward_ssim_group_std_min=0.0028 train/reward_ssim_group_mean_std=0.0987 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.8964 train/adv_max=5.1992 train/adv_abs_mean=1.2064
+
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Epoch 158 Training:  49%|████▉     | 47/96 [00:24<00:25,  1.93it/s]
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Epoch 158 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 03:18:25] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0316 | Epoch 158] train/clipped_loss_min=-5 train/clipped_loss_max=4.3431 train/clipped_loss_mean=-0.0585 train/clipped_loss_std=1.5571 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-5 train/unclipped_loss_max=4.3431 train/unclipped_loss_mean=-0.0585 train/unclipped_loss_std=1.5571 train/policy_loss=-0.0585 train/loss=-0.0585 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
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Epoch 158 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 03:18:50] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0317 | Epoch 158] train/clipped_loss_min=-4.4001 train/clipped_loss_max=5.0000 train/clipped_loss_mean=0.0567 train/clipped_loss_std=1.4751 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-4.4001 train/unclipped_loss_max=5.0000 train/unclipped_loss_mean=0.0567 train/unclipped_loss_std=1.4751 train/policy_loss=0.0567 train/loss=0.0567 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
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Epoch 158 Timestep: 100%|██████████| 1/1 [00:00<00:00,  1.90it/s]
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Epoch 158 Training: 100%|██████████| 96/96 [00:49<00:00,  1.93it/s]
+[2026-10-02 03:18:50] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0318 | Epoch 158] timing/rollout_seconds=179.6232 timing/feedback_seconds=5.2996 timing/optimization_seconds=50.3921 timing/cycle_seconds=232.8407
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+
Pointwise Rewards: ssim:   0%|          | 0/6 [00:00<?, ?it/s]
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+
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Pointwise Rewards: dists:  33%|███▎      | 2/6 [00:00<00:01,  3.19it/s]
Pointwise Rewards: dists:  50%|█████     | 3/6 [00:00<00:00,  3.22it/s]
Pointwise Rewards: dists:  67%|██████▋   | 4/6 [00:01<00:00,  3.33it/s]
Pointwise Rewards: dists:  83%|████████▎ | 5/6 [00:01<00:00,  3.33it/s]
Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.35it/s]
Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.29it/s]
+[2026-10-02 03:21:53] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0318 | Epoch 159] train/reward_dists_mean=0.8375 train/reward_dists_std=0.0313 train/reward_ssim_mean=0.6418 train/reward_ssim_std=0.1090 train/reward_dists_group_std_mean=0.0072 train/reward_dists_group_std_max=0.0169 train/reward_dists_group_std_min=0.0020 train/reward_dists_group_mean_std=0.0302 train/reward_ssim_group_std_mean=0.0085 train/reward_ssim_group_std_max=0.0169 train/reward_ssim_group_std_min=0.0031 train/reward_ssim_group_mean_std=0.1087 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.4352 train/adv_max=5.1727 train/adv_abs_mean=1.2022
+
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Epoch 159 Training:   9%|▉         | 9/96 [00:04<00:45,  1.93it/s]
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Epoch 159 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 03:22:42] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0319 | Epoch 159] train/clipped_loss_min=-4.6896 train/clipped_loss_max=4.8414 train/clipped_loss_mean=-0.0148 train/clipped_loss_std=1.4664 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-4.6896 train/unclipped_loss_max=4.8414 train/unclipped_loss_mean=-0.0148 train/unclipped_loss_std=1.4664 train/policy_loss=-0.0148 train/loss=-0.0148 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0005
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+[2026-10-02 03:22:42] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0320 | Epoch 159] timing/rollout_seconds=179.5122 timing/feedback_seconds=5.3183 timing/optimization_seconds=50.4087 timing/cycle_seconds=232.9057
+[2026-10-02 03:22:42] [Rank 0] [INFO] [flow_factory.models.abc]: Saving LoRA weights for transformer role=base to /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/runs/klein4b_grpo_flir_full_ssim_dists_lr5e-5/saves/klein4b_grpo_flir_full_ssim_dists_lr5e-5/checkpoints/checkpoint-160
+[2026-10-02 03:22:43] [Rank 0] [INFO] [flow_factory.models.abc]: Checkpoint saved successfully to /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/runs/klein4b_grpo_flir_full_ssim_dists_lr5e-5/saves/klein4b_grpo_flir_full_ssim_dists_lr5e-5/checkpoints/checkpoint-160
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+[2026-10-02 03:23:36] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0320 | Epoch 160] eval/flir_rl_full/reward_dists_mean=0.8405 eval/flir_rl_full/reward_dists_std=0.0364 eval/flir_rl_full/reward_psnr_mean=20.2885 eval/flir_rl_full/reward_psnr_std=2.8796 eval/flir_rl_full/reward_ssim_mean=0.6245 eval/flir_rl_full/reward_ssim_std=0.0852
+
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+
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+[2026-10-02 03:26:39] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0320 | Epoch 160] train/reward_dists_mean=0.8378 train/reward_dists_std=0.0243 train/reward_ssim_mean=0.6538 train/reward_ssim_std=0.1183 train/reward_dists_group_std_mean=0.0072 train/reward_dists_group_std_max=0.0131 train/reward_dists_group_std_min=0.0032 train/reward_dists_group_mean_std=0.0231 train/reward_ssim_group_std_mean=0.0076 train/reward_ssim_group_std_max=0.0185 train/reward_ssim_group_std_min=0.0033 train/reward_ssim_group_mean_std=0.1180 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.7707 train/adv_max=4.9888 train/adv_abs_mean=1.2627
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Epoch 160 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 03:27:04] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0320 | Epoch 160] train/clipped_loss_min=-3.8815 train/clipped_loss_max=4.0329 train/clipped_loss_mean=0.0206 train/clipped_loss_std=1.5098 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-3.8815 train/unclipped_loss_max=4.0329 train/unclipped_loss_mean=0.0206 train/unclipped_loss_std=1.5098 train/policy_loss=0.0206 train/loss=0.0206 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0005
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Epoch 160 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 03:27:29] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0321 | Epoch 160] train/clipped_loss_min=-4.9888 train/clipped_loss_max=4.7707 train/clipped_loss_mean=-0.0206 train/clipped_loss_std=1.6175 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0.0002 train/unclipped_loss_min=-4.9888 train/unclipped_loss_max=4.7707 train/unclipped_loss_mean=-0.0206 train/unclipped_loss_std=1.6175 train/policy_loss=-0.0206 train/loss=-0.0206 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0005
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+[2026-10-02 03:27:29] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0322 | Epoch 160] timing/rollout_seconds=179.6085 timing/feedback_seconds=5.7981 timing/optimization_seconds=50.5574 timing/cycle_seconds=233.1382
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+
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+[2026-10-02 03:30:32] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0322 | Epoch 161] train/reward_dists_mean=0.8299 train/reward_dists_std=0.0313 train/reward_ssim_mean=0.6593 train/reward_ssim_std=0.1103 train/reward_dists_group_std_mean=0.0072 train/reward_dists_group_std_max=0.0171 train/reward_dists_group_std_min=0.0034 train/reward_dists_group_mean_std=0.0303 train/reward_ssim_group_std_mean=0.0068 train/reward_ssim_group_std_max=0.0156 train/reward_ssim_group_std_min=0.0026 train/reward_ssim_group_mean_std=0.1101 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.9367 train/adv_max=3.8592 train/adv_abs_mean=1.1933
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Epoch 161 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 03:30:57] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0322 | Epoch 161] train/clipped_loss_min=-3.8534 train/clipped_loss_max=4.9367 train/clipped_loss_mean=0.0105 train/clipped_loss_std=1.5144 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-3.8534 train/unclipped_loss_max=4.9367 train/unclipped_loss_mean=0.0105 train/unclipped_loss_std=1.5144 train/policy_loss=0.0105 train/loss=0.0105 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0005
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Epoch 161 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 03:31:22] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0323 | Epoch 161] train/clipped_loss_min=-3.8592 train/clipped_loss_max=3.9913 train/clipped_loss_mean=-0.0105 train/clipped_loss_std=1.4710 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0.0002 train/unclipped_loss_min=-3.8592 train/unclipped_loss_max=3.9913 train/unclipped_loss_mean=-0.0105 train/unclipped_loss_std=1.4710 train/policy_loss=-0.0105 train/loss=-0.0105 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0005
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+[2026-10-02 03:31:22] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0324 | Epoch 161] timing/rollout_seconds=179.4798 timing/feedback_seconds=5.2827 timing/optimization_seconds=50.4171 timing/cycle_seconds=232.7259
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+[2026-10-02 03:34:25] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0324 | Epoch 162] train/reward_dists_mean=0.8297 train/reward_dists_std=0.0307 train/reward_ssim_mean=0.6489 train/reward_ssim_std=0.1082 train/reward_dists_group_std_mean=0.0076 train/reward_dists_group_std_max=0.0192 train/reward_dists_group_std_min=0.0043 train/reward_dists_group_mean_std=0.0296 train/reward_ssim_group_std_mean=0.0067 train/reward_ssim_group_std_max=0.0129 train/reward_ssim_group_std_min=0.0027 train/reward_ssim_group_mean_std=0.1079 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.5382 train/adv_max=4.1087 train/adv_abs_mean=1.1825
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Epoch 162 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 03:34:50] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0324 | Epoch 162] train/clipped_loss_min=-3.2038 train/clipped_loss_max=4.5382 train/clipped_loss_mean=0.0040 train/clipped_loss_std=1.4299 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-3.2038 train/unclipped_loss_max=4.5382 train/unclipped_loss_mean=0.0040 train/unclipped_loss_std=1.4299 train/policy_loss=0.0040 train/loss=0.0040 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0002
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Epoch 162 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 03:35:15] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0325 | Epoch 162] train/clipped_loss_min=-4.1086 train/clipped_loss_max=4.1333 train/clipped_loss_mean=-0.0040 train/clipped_loss_std=1.5121 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0.0002 train/unclipped_loss_min=-4.1086 train/unclipped_loss_max=4.1333 train/unclipped_loss_mean=-0.0040 train/unclipped_loss_std=1.5121 train/policy_loss=-0.0040 train/loss=-0.0040 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0002
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+[2026-10-02 03:35:15] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0326 | Epoch 162] timing/rollout_seconds=179.7010 timing/feedback_seconds=5.8405 timing/optimization_seconds=50.8026 timing/cycle_seconds=233.4294
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+
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+[2026-10-02 03:38:18] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0326 | Epoch 163] train/reward_dists_mean=0.8318 train/reward_dists_std=0.0379 train/reward_ssim_mean=0.6500 train/reward_ssim_std=0.1308 train/reward_dists_group_std_mean=0.0085 train/reward_dists_group_std_max=0.0325 train/reward_dists_group_std_min=0.0031 train/reward_dists_group_mean_std=0.0366 train/reward_ssim_group_std_mean=0.0065 train/reward_ssim_group_std_max=0.0148 train/reward_ssim_group_std_min=0.0029 train/reward_ssim_group_mean_std=0.1306 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.9088 train/adv_max=5.1097 train/adv_abs_mean=1.2197
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Epoch 163 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 03:38:43] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0326 | Epoch 163] train/clipped_loss_min=-3.9105 train/clipped_loss_max=4.9088 train/clipped_loss_mean=0.0306 train/clipped_loss_std=1.4796 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-3.9105 train/unclipped_loss_max=4.9088 train/unclipped_loss_mean=0.0306 train/unclipped_loss_std=1.4796 train/policy_loss=0.0306 train/loss=0.0306 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0002
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Epoch 163 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 03:39:08] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0327 | Epoch 163] train/clipped_loss_min=-5.0000 train/clipped_loss_max=4.0533 train/clipped_loss_mean=-0.0304 train/clipped_loss_std=1.5554 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0.0002 train/unclipped_loss_min=-5.0000 train/unclipped_loss_max=4.0533 train/unclipped_loss_mean=-0.0304 train/unclipped_loss_std=1.5554 train/policy_loss=-0.0304 train/loss=-0.0304 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0002
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+[2026-10-02 03:39:08] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0328 | Epoch 163] timing/rollout_seconds=179.5525 timing/feedback_seconds=5.6666 timing/optimization_seconds=50.3837 timing/cycle_seconds=232.7463
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+
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+[2026-10-02 03:42:11] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0328 | Epoch 164] train/reward_dists_mean=0.8310 train/reward_dists_std=0.0386 train/reward_ssim_mean=0.6639 train/reward_ssim_std=0.1245 train/reward_dists_group_std_mean=0.0074 train/reward_dists_group_std_max=0.0166 train/reward_dists_group_std_min=0.0035 train/reward_dists_group_mean_std=0.0378 train/reward_ssim_group_std_mean=0.0064 train/reward_ssim_group_std_max=0.0133 train/reward_ssim_group_std_min=0.0033 train/reward_ssim_group_mean_std=0.1243 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.8675 train/adv_max=4.8126 train/adv_abs_mean=1.2867
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Epoch 164 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 03:42:36] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0328 | Epoch 164] train/clipped_loss_min=-3.8849 train/clipped_loss_max=4.6517 train/clipped_loss_mean=0.0529 train/clipped_loss_std=1.6432 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-3.8849 train/unclipped_loss_max=4.6517 train/unclipped_loss_mean=0.0529 train/unclipped_loss_std=1.6432 train/policy_loss=0.0529 train/loss=0.0529 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0004
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Epoch 164 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 03:43:01] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0329 | Epoch 164] train/clipped_loss_min=-4.8126 train/clipped_loss_max=4.8675 train/clipped_loss_mean=-0.0529 train/clipped_loss_std=1.5416 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0.0002 train/unclipped_loss_min=-4.8126 train/unclipped_loss_max=4.8675 train/unclipped_loss_mean=-0.0529 train/unclipped_loss_std=1.5416 train/policy_loss=-0.0529 train/loss=-0.0529 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
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+[2026-10-02 03:43:01] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0330 | Epoch 164] timing/rollout_seconds=179.9629 timing/feedback_seconds=5.8699 timing/optimization_seconds=50.4912 timing/cycle_seconds=233.2871
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+[2026-10-02 03:46:06] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0330 | Epoch 165] train/reward_dists_mean=0.8297 train/reward_dists_std=0.0311 train/reward_ssim_mean=0.6394 train/reward_ssim_std=0.1121 train/reward_dists_group_std_mean=0.0078 train/reward_dists_group_std_max=0.0149 train/reward_dists_group_std_min=0.0035 train/reward_dists_group_mean_std=0.0300 train/reward_ssim_group_std_mean=0.0069 train/reward_ssim_group_std_max=0.0203 train/reward_ssim_group_std_min=0.0034 train/reward_ssim_group_mean_std=0.1119 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.4038 train/adv_max=4.7220 train/adv_abs_mean=1.2824
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Epoch 165 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 03:46:31] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0330 | Epoch 165] train/clipped_loss_min=-4.7220 train/clipped_loss_max=5 train/clipped_loss_mean=0.0527 train/clipped_loss_std=1.6461 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-4.7220 train/unclipped_loss_max=5 train/unclipped_loss_mean=0.0527 train/unclipped_loss_std=1.6461 train/policy_loss=0.0527 train/loss=0.0527 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0004
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Epoch 165 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 03:46:56] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0331 | Epoch 165] train/clipped_loss_min=-3.4985 train/clipped_loss_max=4.3845 train/clipped_loss_mean=-0.0537 train/clipped_loss_std=1.5193 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-3.4985 train/unclipped_loss_max=4.3845 train/unclipped_loss_mean=-0.0537 train/unclipped_loss_std=1.5193 train/policy_loss=-0.0537 train/loss=-0.0537 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0004
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+[2026-10-02 03:46:56] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0332 | Epoch 165] timing/rollout_seconds=179.4292 timing/feedback_seconds=6.7556 timing/optimization_seconds=50.4465 timing/cycle_seconds=234.3756
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+[2026-10-02 03:49:59] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0332 | Epoch 166] train/reward_dists_mean=0.8318 train/reward_dists_std=0.0420 train/reward_ssim_mean=0.6635 train/reward_ssim_std=0.1043 train/reward_dists_group_std_mean=0.0073 train/reward_dists_group_std_max=0.0200 train/reward_dists_group_std_min=0.0039 train/reward_dists_group_mean_std=0.0413 train/reward_ssim_group_std_mean=0.0063 train/reward_ssim_group_std_max=0.0111 train/reward_ssim_group_std_min=0.0022 train/reward_ssim_group_mean_std=0.1041 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.1504 train/adv_max=4.6778 train/adv_abs_mean=1.1963
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Epoch 166 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 03:50:23] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0332 | Epoch 166] train/clipped_loss_min=-4.2131 train/clipped_loss_max=5 train/clipped_loss_mean=-0.0054 train/clipped_loss_std=1.5202 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-4.2131 train/unclipped_loss_max=5 train/unclipped_loss_mean=-0.0054 train/unclipped_loss_std=1.5202 train/policy_loss=-0.0054 train/loss=-0.0054 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
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Epoch 166 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 03:50:48] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0333 | Epoch 166] train/clipped_loss_min=-4.6778 train/clipped_loss_max=4.3298 train/clipped_loss_mean=0.0050 train/clipped_loss_std=1.4815 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-4.6778 train/unclipped_loss_max=4.3298 train/unclipped_loss_mean=0.0050 train/unclipped_loss_std=1.4815 train/policy_loss=0.0050 train/loss=0.0050 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
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+[2026-10-02 03:50:48] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0334 | Epoch 166] timing/rollout_seconds=179.3668 timing/feedback_seconds=5.0275 timing/optimization_seconds=50.4172 timing/cycle_seconds=232.5969
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+
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+[2026-10-02 03:53:51] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0334 | Epoch 167] train/reward_dists_mean=0.8367 train/reward_dists_std=0.0351 train/reward_ssim_mean=0.6373 train/reward_ssim_std=0.1038 train/reward_dists_group_std_mean=0.0069 train/reward_dists_group_std_max=0.0201 train/reward_dists_group_std_min=0.0031 train/reward_dists_group_mean_std=0.0342 train/reward_ssim_group_std_mean=0.0074 train/reward_ssim_group_std_max=0.0272 train/reward_ssim_group_std_min=0.0028 train/reward_ssim_group_mean_std=0.1034 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.4710 train/adv_max=4.6905 train/adv_abs_mean=1.1888
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Epoch 167 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 03:54:16] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0334 | Epoch 167] train/clipped_loss_min=-4.4334 train/clipped_loss_max=5 train/clipped_loss_mean=-0.0046 train/clipped_loss_std=1.5231 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-4.4334 train/unclipped_loss_max=5 train/unclipped_loss_mean=-0.0046 train/unclipped_loss_std=1.5231 train/policy_loss=-0.0046 train/loss=-0.0046 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0005
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Epoch 167 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 03:54:41] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0335 | Epoch 167] train/clipped_loss_min=-4.6905 train/clipped_loss_max=5.0000 train/clipped_loss_mean=0.0017 train/clipped_loss_std=1.4894 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-4.6905 train/unclipped_loss_max=5.0000 train/unclipped_loss_mean=0.0017 train/unclipped_loss_std=1.4894 train/policy_loss=0.0017 train/loss=0.0017 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0005
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+[2026-10-02 03:54:41] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0336 | Epoch 167] timing/rollout_seconds=179.3795 timing/feedback_seconds=5.4815 timing/optimization_seconds=50.4010 timing/cycle_seconds=232.6387
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+
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+[2026-10-02 03:57:44] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0336 | Epoch 168] train/reward_dists_mean=0.8435 train/reward_dists_std=0.0295 train/reward_ssim_mean=0.6610 train/reward_ssim_std=0.0995 train/reward_dists_group_std_mean=0.0067 train/reward_dists_group_std_max=0.0111 train/reward_dists_group_std_min=0.0031 train/reward_dists_group_mean_std=0.0287 train/reward_ssim_group_std_mean=0.0090 train/reward_ssim_group_std_max=0.0218 train/reward_ssim_group_std_min=0.0025 train/reward_ssim_group_mean_std=0.0990 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.0689 train/adv_max=4.4008 train/adv_abs_mean=1.2669
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Epoch 168 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 03:58:09] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0336 | Epoch 168] train/clipped_loss_min=-4.4008 train/clipped_loss_max=5 train/clipped_loss_mean=0.0123 train/clipped_loss_std=1.5729 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-4.4008 train/unclipped_loss_max=5 train/unclipped_loss_mean=0.0123 train/unclipped_loss_std=1.5729 train/policy_loss=0.0123 train/loss=0.0123 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0002
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Epoch 168 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 03:58:34] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0337 | Epoch 168] train/clipped_loss_min=-3.8398 train/clipped_loss_max=4.7720 train/clipped_loss_mean=-0.0125 train/clipped_loss_std=1.5732 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-3.8398 train/unclipped_loss_max=4.7720 train/unclipped_loss_mean=-0.0125 train/unclipped_loss_std=1.5732 train/policy_loss=-0.0125 train/loss=-0.0125 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0002
+
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+[2026-10-02 03:58:34] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0338 | Epoch 168] timing/rollout_seconds=179.2804 timing/feedback_seconds=5.5169 timing/optimization_seconds=50.3537 timing/cycle_seconds=232.5774
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+
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+[2026-10-02 04:01:37] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0338 | Epoch 169] train/reward_dists_mean=0.8463 train/reward_dists_std=0.0322 train/reward_ssim_mean=0.6415 train/reward_ssim_std=0.0934 train/reward_dists_group_std_mean=0.0064 train/reward_dists_group_std_max=0.0130 train/reward_dists_group_std_min=0.0031 train/reward_dists_group_mean_std=0.0314 train/reward_ssim_group_std_mean=0.0096 train/reward_ssim_group_std_max=0.0247 train/reward_ssim_group_std_min=0.0039 train/reward_ssim_group_mean_std=0.0928 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.1524 train/adv_max=4.6198 train/adv_abs_mean=1.2190
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Epoch 169 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 04:02:02] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0338 | Epoch 169] train/clipped_loss_min=-4.6198 train/clipped_loss_max=4.5407 train/clipped_loss_mean=0.0226 train/clipped_loss_std=1.5243 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-4.6198 train/unclipped_loss_max=4.5407 train/unclipped_loss_mean=0.0226 train/unclipped_loss_std=1.5243 train/policy_loss=0.0226 train/loss=0.0226 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0002
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Epoch 169 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 04:02:27] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0339 | Epoch 169] train/clipped_loss_min=-4.2815 train/clipped_loss_max=5.0000 train/clipped_loss_mean=-0.0230 train/clipped_loss_std=1.5496 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-4.2815 train/unclipped_loss_max=5.0000 train/unclipped_loss_mean=-0.0230 train/unclipped_loss_std=1.5496 train/policy_loss=-0.0230 train/loss=-0.0230 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0002
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+[2026-10-02 04:02:27] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0340 | Epoch 169] timing/rollout_seconds=179.4971 timing/feedback_seconds=5.7795 timing/optimization_seconds=50.6457 timing/cycle_seconds=233.0632
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+[2026-10-02 04:03:20] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0340 | Epoch 170] eval/flir_rl_full/reward_dists_mean=0.8404 eval/flir_rl_full/reward_dists_std=0.0353 eval/flir_rl_full/reward_psnr_mean=20.2702 eval/flir_rl_full/reward_psnr_std=2.8513 eval/flir_rl_full/reward_ssim_mean=0.6257 eval/flir_rl_full/reward_ssim_std=0.0847
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Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:01<00:00,  5.61it/s]
+
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Pointwise Rewards: dists:  50%|█████     | 3/6 [00:00<00:00,  3.20it/s]
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Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.22it/s]
+[2026-10-02 04:06:23] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0340 | Epoch 170] train/reward_dists_mean=0.8334 train/reward_dists_std=0.0320 train/reward_ssim_mean=0.6051 train/reward_ssim_std=0.1239 train/reward_dists_group_std_mean=0.0076 train/reward_dists_group_std_max=0.0203 train/reward_dists_group_std_min=0.0034 train/reward_dists_group_mean_std=0.0309 train/reward_ssim_group_std_mean=0.0092 train/reward_ssim_group_std_max=0.0246 train/reward_ssim_group_std_min=0.0042 train/reward_ssim_group_mean_std=0.1235 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.6564 train/adv_max=5.8981 train/adv_abs_mean=1.2305
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Epoch 170 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 04:06:48] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0340 | Epoch 170] train/clipped_loss_min=-5 train/clipped_loss_max=4.5609 train/clipped_loss_mean=-0.0528 train/clipped_loss_std=1.5816 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-5 train/unclipped_loss_max=4.5609 train/unclipped_loss_mean=-0.0528 train/unclipped_loss_std=1.5816 train/policy_loss=-0.0528 train/loss=-0.0528 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
+
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Epoch 170 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 04:07:13] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0341 | Epoch 170] train/clipped_loss_min=-4.1231 train/clipped_loss_max=4.6564 train/clipped_loss_mean=0.0552 train/clipped_loss_std=1.5011 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-4.1231 train/unclipped_loss_max=4.6564 train/unclipped_loss_mean=0.0552 train/unclipped_loss_std=1.5011 train/policy_loss=0.0552 train/loss=0.0552 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
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+[2026-10-02 04:07:13] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0342 | Epoch 170] timing/rollout_seconds=179.4378 timing/feedback_seconds=4.7159 timing/optimization_seconds=50.4093 timing/cycle_seconds=232.6850
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Epoch 171 Sampling: 100%|██████████| 96/96 [02:57<00:00,  1.85s/it]
+
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Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:01<00:00,  5.51it/s]
+
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Pointwise Rewards: dists:  33%|███▎      | 2/6 [00:00<00:01,  3.23it/s]
Pointwise Rewards: dists:  50%|█████     | 3/6 [00:00<00:00,  3.21it/s]
Pointwise Rewards: dists:  67%|██████▋   | 4/6 [00:01<00:00,  3.27it/s]
Pointwise Rewards: dists:  83%|████████▎ | 5/6 [00:01<00:00,  3.22it/s]
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Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.22it/s]
+[2026-10-02 04:10:15] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0342 | Epoch 171] train/reward_dists_mean=0.8481 train/reward_dists_std=0.0303 train/reward_ssim_mean=0.6239 train/reward_ssim_std=0.1017 train/reward_dists_group_std_mean=0.0065 train/reward_dists_group_std_max=0.0115 train/reward_dists_group_std_min=0.0027 train/reward_dists_group_mean_std=0.0295 train/reward_ssim_group_std_mean=0.0085 train/reward_ssim_group_std_max=0.0163 train/reward_ssim_group_std_min=0.0044 train/reward_ssim_group_mean_std=0.1013 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.2425 train/adv_max=4.4025 train/adv_abs_mean=1.1597
+
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Epoch 171 Training:   7%|▋         | 7/96 [00:03<00:45,  1.94it/s]
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Epoch 171 Training:   8%|▊         | 8/96 [00:04<00:45,  1.93it/s]
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Epoch 171 Training:   9%|▉         | 9/96 [00:04<00:45,  1.93it/s]
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Epoch 171 Training:  10%|█         | 10/96 [00:05<00:44,  1.93it/s]
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Epoch 171 Training:  11%|█▏        | 11/96 [00:05<00:44,  1.93it/s]
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Epoch 171 Training:  12%|█▎        | 12/96 [00:06<00:43,  1.93it/s]
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Epoch 171 Training:  14%|█▎        | 13/96 [00:06<00:43,  1.93it/s]
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Epoch 171 Training:  15%|█▍        | 14/96 [00:07<00:42,  1.93it/s]
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Epoch 171 Training:  16%|█▌        | 15/96 [00:07<00:41,  1.93it/s]
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Epoch 171 Training:  17%|█▋        | 16/96 [00:08<00:41,  1.93it/s]
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Epoch 171 Training:  18%|█▊        | 17/96 [00:08<00:40,  1.93it/s]
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Epoch 171 Training:  19%|█▉        | 18/96 [00:09<00:40,  1.92it/s]
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Epoch 171 Training:  20%|█▉        | 19/96 [00:09<00:39,  1.93it/s]
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Epoch 171 Training:  21%|██        | 20/96 [00:10<00:39,  1.93it/s]
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Epoch 171 Training:  22%|██▏       | 21/96 [00:10<00:38,  1.93it/s]
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Epoch 171 Training:  23%|██▎       | 22/96 [00:11<00:38,  1.93it/s]
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Epoch 171 Training:  24%|██▍       | 23/96 [00:11<00:37,  1.93it/s]
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Epoch 171 Training:  25%|██▌       | 24/96 [00:12<00:37,  1.93it/s]
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Epoch 171 Training:  26%|██▌       | 25/96 [00:12<00:36,  1.93it/s]
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Epoch 171 Training:  27%|██▋       | 26/96 [00:13<00:36,  1.93it/s]
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Epoch 171 Training:  28%|██▊       | 27/96 [00:13<00:35,  1.93it/s]
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Epoch 171 Training:  29%|██▉       | 28/96 [00:14<00:35,  1.93it/s]
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Epoch 171 Training:  30%|███       | 29/96 [00:14<00:34,  1.93it/s]
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Epoch 171 Training:  31%|███▏      | 30/96 [00:15<00:34,  1.93it/s]
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Epoch 171 Training:  32%|███▏      | 31/96 [00:16<00:33,  1.93it/s]
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Epoch 171 Training:  33%|███▎      | 32/96 [00:16<00:33,  1.93it/s]
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Epoch 171 Training:  34%|███▍      | 33/96 [00:17<00:32,  1.93it/s]
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Epoch 171 Training:  35%|███▌      | 34/96 [00:17<00:32,  1.93it/s]
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Epoch 171 Training:  36%|███▋      | 35/96 [00:18<00:31,  1.93it/s]
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Epoch 171 Training:  38%|███▊      | 36/96 [00:18<00:31,  1.93it/s]
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Epoch 171 Training:  39%|███▊      | 37/96 [00:19<00:30,  1.93it/s]
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Epoch 171 Training:  40%|███▉      | 38/96 [00:19<00:30,  1.93it/s]
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Epoch 171 Training:  41%|████      | 39/96 [00:20<00:29,  1.93it/s]
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Epoch 171 Training:  42%|████▏     | 40/96 [00:20<00:29,  1.93it/s]
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Epoch 171 Training:  43%|████▎     | 41/96 [00:21<00:28,  1.93it/s]
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Epoch 171 Training:  44%|████▍     | 42/96 [00:21<00:27,  1.93it/s]
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Epoch 171 Training:  45%|████▍     | 43/96 [00:22<00:27,  1.93it/s]
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Epoch 171 Training:  46%|████▌     | 44/96 [00:22<00:26,  1.93it/s]
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Epoch 171 Training:  47%|████▋     | 45/96 [00:23<00:26,  1.93it/s]
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Epoch 171 Training:  48%|████▊     | 46/96 [00:23<00:25,  1.93it/s]
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Epoch 171 Training:  49%|████▉     | 47/96 [00:24<00:25,  1.93it/s]
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Epoch 171 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 04:10:40] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0342 | Epoch 171] train/clipped_loss_min=-4.4025 train/clipped_loss_max=5 train/clipped_loss_mean=0.0059 train/clipped_loss_std=1.4079 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-4.4025 train/unclipped_loss_max=5 train/unclipped_loss_mean=0.0059 train/unclipped_loss_std=1.4079 train/policy_loss=0.0059 train/loss=0.0059 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0002
+
+
Epoch 171 Timestep: 100%|██████████| 1/1 [00:00<00:00,  1.90it/s]
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Epoch 171 Training:  51%|█████     | 49/96 [00:25<00:24,  1.94it/s]
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Epoch 171 Training:  53%|█████▎    | 51/96 [00:26<00:23,  1.94it/s]
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Epoch 171 Training:  54%|█████▍    | 52/96 [00:26<00:22,  1.93it/s]
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Epoch 171 Training:  57%|█████▋    | 55/96 [00:28<00:21,  1.93it/s]
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Epoch 171 Training:  58%|█████▊    | 56/96 [00:28<00:20,  1.93it/s]
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Epoch 171 Training:  59%|█████▉    | 57/96 [00:29<00:20,  1.93it/s]
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Epoch 171 Training:  60%|██████    | 58/96 [00:30<00:19,  1.93it/s]
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Epoch 171 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 04:11:05] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0343 | Epoch 171] train/clipped_loss_min=-3.9148 train/clipped_loss_max=4.2294 train/clipped_loss_mean=-0.0065 train/clipped_loss_std=1.5199 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-3.9148 train/unclipped_loss_max=4.2294 train/unclipped_loss_mean=-0.0065 train/unclipped_loss_std=1.5199 train/policy_loss=-0.0065 train/loss=-0.0065 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0002
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+[2026-10-02 04:11:05] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0344 | Epoch 171] timing/rollout_seconds=179.2858 timing/feedback_seconds=5.3258 timing/optimization_seconds=50.3922 timing/cycle_seconds=232.6402
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Epoch 172 Sampling: 100%|██████████| 96/96 [02:57<00:00,  1.85s/it]
+
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Pointwise Rewards: ssim:  83%|████████▎ | 5/6 [00:00<00:00,  5.56it/s]
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Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:01<00:00,  5.54it/s]
+
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Pointwise Rewards: dists:  33%|███▎      | 2/6 [00:00<00:01,  3.29it/s]
Pointwise Rewards: dists:  50%|█████     | 3/6 [00:00<00:00,  3.24it/s]
Pointwise Rewards: dists:  67%|██████▋   | 4/6 [00:01<00:00,  3.26it/s]
Pointwise Rewards: dists:  83%|████████▎ | 5/6 [00:01<00:00,  3.22it/s]
Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.28it/s]
Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.26it/s]
+[2026-10-02 04:14:08] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0344 | Epoch 172] train/reward_dists_mean=0.8441 train/reward_dists_std=0.0312 train/reward_ssim_mean=0.6478 train/reward_ssim_std=0.0976 train/reward_dists_group_std_mean=0.0069 train/reward_dists_group_std_max=0.0135 train/reward_dists_group_std_min=0.0035 train/reward_dists_group_mean_std=0.0303 train/reward_ssim_group_std_mean=0.0078 train/reward_ssim_group_std_max=0.0138 train/reward_ssim_group_std_min=0.0041 train/reward_ssim_group_mean_std=0.0972 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.3427 train/adv_max=4.4484 train/adv_abs_mean=1.1660
+
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Epoch 172 Training:   7%|▋         | 7/96 [00:03<00:46,  1.93it/s]
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Epoch 172 Training:   8%|▊         | 8/96 [00:04<00:45,  1.93it/s]
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Epoch 172 Training:   9%|▉         | 9/96 [00:04<00:45,  1.93it/s]
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Epoch 172 Training:  10%|█         | 10/96 [00:05<00:44,  1.93it/s]
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Epoch 172 Training:  11%|█▏        | 11/96 [00:05<00:44,  1.93it/s]
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Epoch 172 Training:  12%|█▎        | 12/96 [00:06<00:43,  1.92it/s]
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Epoch 172 Training:  14%|█▎        | 13/96 [00:06<00:43,  1.92it/s]
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Epoch 172 Training:  15%|█▍        | 14/96 [00:07<00:42,  1.92it/s]
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Epoch 172 Training:  16%|█▌        | 15/96 [00:07<00:42,  1.93it/s]
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Epoch 172 Training:  17%|█▋        | 16/96 [00:08<00:41,  1.92it/s]
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Epoch 172 Training:  18%|█▊        | 17/96 [00:08<00:41,  1.93it/s]
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Epoch 172 Training:  19%|█▉        | 18/96 [00:09<00:40,  1.92it/s]
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Epoch 172 Training:  20%|█▉        | 19/96 [00:09<00:40,  1.92it/s]
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Epoch 172 Training:  22%|██▏       | 21/96 [00:10<00:38,  1.92it/s]
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Epoch 172 Training:  23%|██▎       | 22/96 [00:11<00:38,  1.92it/s]
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Epoch 172 Training:  24%|██▍       | 23/96 [00:11<00:37,  1.92it/s]
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Epoch 172 Training:  25%|██▌       | 24/96 [00:12<00:37,  1.92it/s]
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Epoch 172 Training:  26%|██▌       | 25/96 [00:12<00:36,  1.92it/s]
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Epoch 172 Training:  27%|██▋       | 26/96 [00:13<00:36,  1.92it/s]
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Epoch 172 Training:  28%|██▊       | 27/96 [00:13<00:35,  1.92it/s]
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Epoch 172 Training:  29%|██▉       | 28/96 [00:14<00:35,  1.92it/s]
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Epoch 172 Training:  30%|███       | 29/96 [00:15<00:34,  1.92it/s]
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Epoch 172 Training:  31%|███▏      | 30/96 [00:15<00:34,  1.92it/s]
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Epoch 172 Training:  32%|███▏      | 31/96 [00:16<00:33,  1.92it/s]
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Epoch 172 Training:  33%|███▎      | 32/96 [00:16<00:33,  1.92it/s]
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Epoch 172 Training:  34%|███▍      | 33/96 [00:17<00:32,  1.92it/s]
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Epoch 172 Training:  35%|███▌      | 34/96 [00:17<00:32,  1.92it/s]
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Epoch 172 Training:  36%|███▋      | 35/96 [00:18<00:31,  1.92it/s]
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Epoch 172 Training:  38%|███▊      | 36/96 [00:18<00:31,  1.92it/s]
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Epoch 172 Training:  39%|███▊      | 37/96 [00:19<00:30,  1.92it/s]
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Epoch 172 Training:  40%|███▉      | 38/96 [00:19<00:30,  1.92it/s]
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Epoch 172 Training:  41%|████      | 39/96 [00:20<00:29,  1.92it/s]
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Epoch 172 Training:  43%|████▎     | 41/96 [00:21<00:28,  1.92it/s]
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Epoch 172 Training:  44%|████▍     | 42/96 [00:21<00:28,  1.92it/s]
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Epoch 172 Training:  45%|████▍     | 43/96 [00:22<00:27,  1.92it/s]
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Epoch 172 Training:  46%|████▌     | 44/96 [00:22<00:27,  1.93it/s]
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Epoch 172 Training:  47%|████▋     | 45/96 [00:23<00:26,  1.93it/s]
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Epoch 172 Training:  48%|████▊     | 46/96 [00:23<00:25,  1.93it/s]
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Epoch 172 Training:  49%|████▉     | 47/96 [00:24<00:25,  1.93it/s]
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Epoch 172 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 04:14:33] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0344 | Epoch 172] train/clipped_loss_min=-4.4484 train/clipped_loss_max=4.3427 train/clipped_loss_mean=-0.0408 train/clipped_loss_std=1.4753 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-4.4484 train/unclipped_loss_max=4.3427 train/unclipped_loss_mean=-0.0408 train/unclipped_loss_std=1.4753 train/policy_loss=-0.0408 train/loss=-0.0408 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0002
+
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Epoch 172 Training:  51%|█████     | 49/96 [00:25<00:24,  1.94it/s]
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Epoch 172 Training:  53%|█████▎    | 51/96 [00:26<00:23,  1.93it/s]
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Epoch 172 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 04:14:59] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0345 | Epoch 172] train/clipped_loss_min=-4.4126 train/clipped_loss_max=3.9386 train/clipped_loss_mean=0.0408 train/clipped_loss_std=1.4550 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0.0002 train/unclipped_loss_min=-4.4126 train/unclipped_loss_max=3.9386 train/unclipped_loss_mean=0.0408 train/unclipped_loss_std=1.4550 train/policy_loss=0.0408 train/loss=0.0408 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0002
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+[2026-10-02 04:14:59] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0346 | Epoch 172] timing/rollout_seconds=179.5513 timing/feedback_seconds=5.2329 timing/optimization_seconds=50.8488 timing/cycle_seconds=233.3349
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Epoch 173 Sampling: 100%|██████████| 96/96 [02:57<00:00,  1.85s/it]
Epoch 173 Sampling: 100%|██████████| 96/96 [02:57<00:00,  1.85s/it]
+
Pointwise Rewards: ssim:   0%|          | 0/6 [00:00<?, ?it/s]
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Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:01<00:00,  5.37it/s]
Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:01<00:00,  5.29it/s]
+
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Pointwise Rewards: dists:  33%|███▎      | 2/6 [00:00<00:01,  3.19it/s]
Pointwise Rewards: dists:  50%|█████     | 3/6 [00:00<00:00,  3.21it/s]
Pointwise Rewards: dists:  67%|██████▋   | 4/6 [00:01<00:00,  3.23it/s]
Pointwise Rewards: dists:  83%|████████▎ | 5/6 [00:01<00:00,  3.18it/s]
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Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.21it/s]
+[2026-10-02 04:18:03] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0346 | Epoch 173] train/reward_dists_mean=0.8377 train/reward_dists_std=0.0300 train/reward_ssim_mean=0.6437 train/reward_ssim_std=0.0875 train/reward_dists_group_std_mean=0.0062 train/reward_dists_group_std_max=0.0118 train/reward_dists_group_std_min=0.0038 train/reward_dists_group_mean_std=0.0293 train/reward_ssim_group_std_mean=0.0063 train/reward_ssim_group_std_max=0.0122 train/reward_ssim_group_std_min=0.0027 train/reward_ssim_group_mean_std=0.0873 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.9037 train/adv_max=4.1767 train/adv_abs_mean=1.1724
+
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Epoch 173 Training:   4%|▍         | 4/96 [00:02<00:47,  1.94it/s]
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Epoch 173 Training:   7%|▋         | 7/96 [00:03<00:46,  1.93it/s]
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Epoch 173 Training:   8%|▊         | 8/96 [00:04<00:45,  1.93it/s]
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Epoch 173 Training:   9%|▉         | 9/96 [00:04<00:45,  1.93it/s]
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Epoch 173 Training:  10%|█         | 10/96 [00:05<00:44,  1.93it/s]
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Epoch 173 Training:  11%|█▏        | 11/96 [00:05<00:44,  1.93it/s]
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Epoch 173 Training:  12%|█▎        | 12/96 [00:06<00:43,  1.93it/s]
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Epoch 173 Training:  14%|█▎        | 13/96 [00:06<00:43,  1.93it/s]
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Epoch 173 Training:  15%|█▍        | 14/96 [00:07<00:42,  1.93it/s]
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Epoch 173 Training:  16%|█▌        | 15/96 [00:07<00:42,  1.92it/s]
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Epoch 173 Training:  17%|█▋        | 16/96 [00:08<00:41,  1.93it/s]
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Epoch 173 Training:  18%|█▊        | 17/96 [00:08<00:41,  1.93it/s]
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Epoch 173 Training:  19%|█▉        | 18/96 [00:09<00:40,  1.92it/s]
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Epoch 173 Training:  20%|█▉        | 19/96 [00:09<00:39,  1.93it/s]
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Epoch 173 Training:  21%|██        | 20/96 [00:10<00:39,  1.93it/s]
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Epoch 173 Training:  22%|██▏       | 21/96 [00:10<00:38,  1.93it/s]
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Epoch 173 Training:  23%|██▎       | 22/96 [00:11<00:38,  1.93it/s]
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Epoch 173 Training:  24%|██▍       | 23/96 [00:11<00:37,  1.93it/s]
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Epoch 173 Training:  25%|██▌       | 24/96 [00:12<00:37,  1.93it/s]
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Epoch 173 Training:  26%|██▌       | 25/96 [00:12<00:36,  1.93it/s]
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Epoch 173 Training:  27%|██▋       | 26/96 [00:13<00:36,  1.93it/s]
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Epoch 173 Training:  28%|██▊       | 27/96 [00:13<00:35,  1.93it/s]
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Epoch 173 Training:  29%|██▉       | 28/96 [00:14<00:35,  1.93it/s]
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Epoch 173 Training:  30%|███       | 29/96 [00:15<00:34,  1.93it/s]
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Epoch 173 Training:  31%|███▏      | 30/96 [00:15<00:34,  1.93it/s]
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Epoch 173 Training:  32%|███▏      | 31/96 [00:16<00:33,  1.93it/s]
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Epoch 173 Training:  33%|███▎      | 32/96 [00:16<00:33,  1.93it/s]
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Epoch 173 Training:  34%|███▍      | 33/96 [00:17<00:32,  1.93it/s]
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Epoch 173 Training:  35%|███▌      | 34/96 [00:17<00:32,  1.93it/s]
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Epoch 173 Training:  36%|███▋      | 35/96 [00:18<00:31,  1.93it/s]
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Epoch 173 Training:  38%|███▊      | 36/96 [00:18<00:31,  1.93it/s]
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Epoch 173 Training:  39%|███▊      | 37/96 [00:19<00:30,  1.93it/s]
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Epoch 173 Training:  40%|███▉      | 38/96 [00:19<00:30,  1.93it/s]
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Epoch 173 Training:  41%|████      | 39/96 [00:20<00:29,  1.93it/s]
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Epoch 173 Training:  42%|████▏     | 40/96 [00:20<00:29,  1.93it/s]
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Epoch 173 Training:  43%|████▎     | 41/96 [00:21<00:28,  1.93it/s]
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Epoch 173 Training:  44%|████▍     | 42/96 [00:21<00:28,  1.93it/s]
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Epoch 173 Training:  45%|████▍     | 43/96 [00:22<00:27,  1.93it/s]
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Epoch 173 Training:  46%|████▌     | 44/96 [00:22<00:26,  1.93it/s]
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Epoch 173 Training:  47%|████▋     | 45/96 [00:23<00:26,  1.93it/s]
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Epoch 173 Training:  48%|████▊     | 46/96 [00:23<00:25,  1.93it/s]
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Epoch 173 Training:  49%|████▉     | 47/96 [00:24<00:25,  1.93it/s]
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Epoch 173 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 04:18:28] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0346 | Epoch 173] train/clipped_loss_min=-3.7234 train/clipped_loss_max=4.9037 train/clipped_loss_mean=0.0909 train/clipped_loss_std=1.4907 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-3.7234 train/unclipped_loss_max=4.9037 train/unclipped_loss_mean=0.0909 train/unclipped_loss_std=1.4907 train/policy_loss=0.0909 train/loss=0.0909 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0005
+
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Epoch 173 Timestep: 100%|██████████| 1/1 [00:00<00:00,  1.90it/s]
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Epoch 173 Training:  51%|█████     | 49/96 [00:25<00:24,  1.94it/s]
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Epoch 173 Training:  53%|█████▎    | 51/96 [00:26<00:23,  1.93it/s]
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Epoch 173 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 04:18:53] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0347 | Epoch 173] train/clipped_loss_min=-4.1767 train/clipped_loss_max=3.8548 train/clipped_loss_mean=-0.0909 train/clipped_loss_std=1.4123 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-4.1767 train/unclipped_loss_max=3.8548 train/unclipped_loss_mean=-0.0909 train/unclipped_loss_std=1.4123 train/policy_loss=-0.0909 train/loss=-0.0909 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0004
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+[2026-10-02 04:18:53] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0348 | Epoch 173] timing/rollout_seconds=179.4208 timing/feedback_seconds=5.0569 timing/optimization_seconds=51.8864 timing/cycle_seconds=234.3208
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Epoch 174 Sampling:  81%|████████▏ | 78/96 [02:23<00:33,  1.84s/it]
Epoch 174 Sampling:  82%|████████▏ | 79/96 [02:25<00:31,  1.84s/it]
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Epoch 174 Sampling:  85%|████████▌ | 82/96 [02:31<00:25,  1.84s/it]
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Epoch 174 Sampling:  88%|████████▊ | 84/96 [02:34<00:22,  1.84s/it]
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Epoch 174 Sampling:  91%|█████████ | 87/96 [02:40<00:16,  1.84s/it]
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Epoch 174 Sampling:  93%|█████████▎| 89/96 [02:44<00:12,  1.84s/it]
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Epoch 174 Sampling:  95%|█████████▍| 91/96 [02:47<00:09,  1.84s/it]
Epoch 174 Sampling:  96%|█████████▌| 92/96 [02:49<00:07,  1.84s/it]
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Epoch 174 Sampling: 100%|██████████| 96/96 [02:56<00:00,  1.84s/it]
Epoch 174 Sampling: 100%|██████████| 96/96 [02:56<00:00,  1.84s/it]
+
Pointwise Rewards: ssim:   0%|          | 0/6 [00:00<?, ?it/s]
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Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:01<00:00,  5.56it/s]
+
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Pointwise Rewards: dists:  17%|█▋        | 1/6 [00:00<00:01,  3.45it/s]
Pointwise Rewards: dists:  33%|███▎      | 2/6 [00:00<00:01,  3.29it/s]
Pointwise Rewards: dists:  50%|█████     | 3/6 [00:00<00:00,  3.33it/s]
Pointwise Rewards: dists:  67%|██████▋   | 4/6 [00:01<00:00,  3.31it/s]
Pointwise Rewards: dists:  83%|████████▎ | 5/6 [00:01<00:00,  3.36it/s]
Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.34it/s]
Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.34it/s]
+[2026-10-02 04:21:56] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0348 | Epoch 174] train/reward_dists_mean=0.8284 train/reward_dists_std=0.0373 train/reward_ssim_mean=0.6147 train/reward_ssim_std=0.1073 train/reward_dists_group_std_mean=0.0076 train/reward_dists_group_std_max=0.0203 train/reward_dists_group_std_min=0.0028 train/reward_dists_group_mean_std=0.0364 train/reward_ssim_group_std_mean=0.0065 train/reward_ssim_group_std_max=0.0146 train/reward_ssim_group_std_min=0.0027 train/reward_ssim_group_mean_std=0.1071 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.0658 train/adv_max=4.5389 train/adv_abs_mean=1.1747
+
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Epoch 174 Training:   1%|          | 1/96 [00:00<00:43,  2.17it/s]
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Epoch 174 Training:   2%|▏         | 2/96 [00:00<00:46,  2.02it/s]
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Epoch 174 Training:   4%|▍         | 4/96 [00:02<00:47,  1.96it/s]
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Epoch 174 Training:   5%|▌         | 5/96 [00:02<00:46,  1.95it/s]
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Epoch 174 Training:   6%|▋         | 6/96 [00:03<00:46,  1.94it/s]
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Epoch 174 Training:   7%|▋         | 7/96 [00:03<00:45,  1.94it/s]
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Epoch 174 Training:   8%|▊         | 8/96 [00:04<00:45,  1.93it/s]
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Epoch 174 Training:   9%|▉         | 9/96 [00:04<00:44,  1.94it/s]
+
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Epoch 174 Training:  10%|█         | 10/96 [00:05<00:44,  1.93it/s]
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Epoch 174 Training:  11%|█▏        | 11/96 [00:05<00:43,  1.93it/s]
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Epoch 174 Training:  12%|█▎        | 12/96 [00:06<00:43,  1.93it/s]
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Epoch 174 Training:  14%|█▎        | 13/96 [00:06<00:42,  1.93it/s]
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+
                                                                 
Epoch 174 Training:  15%|█▍        | 14/96 [00:07<00:42,  1.93it/s]
+
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Epoch 174 Training:  16%|█▌        | 15/96 [00:07<00:41,  1.93it/s]
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Epoch 174 Training:  17%|█▋        | 16/96 [00:08<00:41,  1.93it/s]
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Epoch 174 Training:  18%|█▊        | 17/96 [00:08<00:40,  1.93it/s]
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Epoch 174 Training:  19%|█▉        | 18/96 [00:09<00:40,  1.93it/s]
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Epoch 174 Training:  20%|█▉        | 19/96 [00:09<00:39,  1.93it/s]
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Epoch 174 Training:  21%|██        | 20/96 [00:10<00:39,  1.93it/s]
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Epoch 174 Training:  22%|██▏       | 21/96 [00:10<00:38,  1.93it/s]
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Epoch 174 Training:  23%|██▎       | 22/96 [00:11<00:38,  1.93it/s]
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Epoch 174 Training:  24%|██▍       | 23/96 [00:11<00:37,  1.93it/s]
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Epoch 174 Training:  25%|██▌       | 24/96 [00:12<00:37,  1.93it/s]
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Epoch 174 Training:  26%|██▌       | 25/96 [00:12<00:36,  1.93it/s]
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Epoch 174 Training:  27%|██▋       | 26/96 [00:13<00:36,  1.92it/s]
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Epoch 174 Training:  28%|██▊       | 27/96 [00:13<00:35,  1.93it/s]
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Epoch 174 Training:  29%|██▉       | 28/96 [00:14<00:35,  1.93it/s]
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Epoch 174 Training:  30%|███       | 29/96 [00:14<00:34,  1.93it/s]
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Epoch 174 Timestep: 100%|██████████| 1/1 [00:00<00:00,  2.17it/s]
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Epoch 174 Training:  31%|███▏      | 30/96 [00:15<00:34,  1.93it/s]
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Epoch 174 Training:  32%|███▏      | 31/96 [00:16<00:33,  1.93it/s]
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Epoch 174 Training:  33%|███▎      | 32/96 [00:16<00:33,  1.93it/s]
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Epoch 174 Training:  34%|███▍      | 33/96 [00:17<00:32,  1.93it/s]
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Epoch 174 Training:  35%|███▌      | 34/96 [00:17<00:32,  1.93it/s]
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Epoch 174 Training:  36%|███▋      | 35/96 [00:18<00:31,  1.93it/s]
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Epoch 174 Training:  38%|███▊      | 36/96 [00:18<00:31,  1.93it/s]
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Epoch 174 Training:  39%|███▊      | 37/96 [00:19<00:30,  1.93it/s]
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Epoch 174 Training:  40%|███▉      | 38/96 [00:19<00:30,  1.93it/s]
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Epoch 174 Training:  41%|████      | 39/96 [00:20<00:29,  1.93it/s]
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Epoch 174 Training:  42%|████▏     | 40/96 [00:20<00:29,  1.93it/s]
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Epoch 174 Training:  43%|████▎     | 41/96 [00:21<00:28,  1.93it/s]
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Epoch 174 Training:  44%|████▍     | 42/96 [00:21<00:27,  1.93it/s]
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Epoch 174 Training:  45%|████▍     | 43/96 [00:22<00:27,  1.93it/s]
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Epoch 174 Training:  46%|████▌     | 44/96 [00:22<00:26,  1.93it/s]
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Epoch 174 Training:  47%|████▋     | 45/96 [00:23<00:26,  1.93it/s]
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Epoch 174 Training:  48%|████▊     | 46/96 [00:23<00:25,  1.93it/s]
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Epoch 174 Training:  49%|████▉     | 47/96 [00:24<00:25,  1.93it/s]
+
Epoch 174 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 04:22:21] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0348 | Epoch 174] train/clipped_loss_min=-3.8759 train/clipped_loss_max=5 train/clipped_loss_mean=0.0136 train/clipped_loss_std=1.4717 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-3.8759 train/unclipped_loss_max=5 train/unclipped_loss_mean=0.0136 train/unclipped_loss_std=1.4717 train/policy_loss=0.0136 train/loss=0.0136 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0001
+
+
Epoch 174 Timestep: 100%|██████████| 1/1 [00:00<00:00,  1.90it/s]
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Epoch 174 Training:  51%|█████     | 49/96 [00:25<00:24,  1.95it/s]
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Epoch 174 Training:  52%|█████▏    | 50/96 [00:25<00:23,  1.94it/s]
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Epoch 174 Training:  53%|█████▎    | 51/96 [00:26<00:23,  1.94it/s]
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Epoch 174 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 04:22:46] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0349 | Epoch 174] train/clipped_loss_min=-4.5389 train/clipped_loss_max=3.7085 train/clipped_loss_mean=-0.0137 train/clipped_loss_std=1.4720 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0.0001 train/unclipped_loss_min=-4.5389 train/unclipped_loss_max=3.7085 train/unclipped_loss_mean=-0.0137 train/unclipped_loss_std=1.4720 train/policy_loss=-0.0137 train/loss=-0.0137 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0001
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+[2026-10-02 04:22:46] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0350 | Epoch 174] timing/rollout_seconds=179.3089 timing/feedback_seconds=5.1805 timing/optimization_seconds=50.6538 timing/cycle_seconds=232.7886
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+
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+
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Pointwise Rewards: dists:  67%|██████▋   | 4/6 [00:01<00:00,  3.35it/s]
Pointwise Rewards: dists:  83%|████████▎ | 5/6 [00:01<00:00,  3.29it/s]
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Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.28it/s]
+[2026-10-02 04:25:48] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0350 | Epoch 175] train/reward_dists_mean=0.8399 train/reward_dists_std=0.0252 train/reward_ssim_mean=0.6547 train/reward_ssim_std=0.0892 train/reward_dists_group_std_mean=0.0071 train/reward_dists_group_std_max=0.0127 train/reward_dists_group_std_min=0.0039 train/reward_dists_group_mean_std=0.0241 train/reward_ssim_group_std_mean=0.0071 train/reward_ssim_group_std_max=0.0237 train/reward_ssim_group_std_min=0.0033 train/reward_ssim_group_mean_std=0.0888 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.5867 train/adv_max=5.7254 train/adv_abs_mean=1.1839
+
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Epoch 175 Training:   7%|▋         | 7/96 [00:03<00:45,  1.94it/s]
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Epoch 175 Training:   8%|▊         | 8/96 [00:04<00:45,  1.93it/s]
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Epoch 175 Training:   9%|▉         | 9/96 [00:04<00:44,  1.93it/s]
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Epoch 175 Training:  10%|█         | 10/96 [00:05<00:44,  1.93it/s]
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Epoch 175 Training:  11%|█▏        | 11/96 [00:05<00:43,  1.93it/s]
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Epoch 175 Training:  12%|█▎        | 12/96 [00:06<00:43,  1.93it/s]
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Epoch 175 Training:  14%|█▎        | 13/96 [00:06<00:42,  1.93it/s]
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Epoch 175 Training:  15%|█▍        | 14/96 [00:07<00:42,  1.93it/s]
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Epoch 175 Training:  16%|█▌        | 15/96 [00:07<00:41,  1.93it/s]
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Epoch 175 Training:  17%|█▋        | 16/96 [00:08<00:41,  1.93it/s]
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Epoch 175 Training:  18%|█▊        | 17/96 [00:08<00:40,  1.93it/s]
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Epoch 175 Training:  19%|█▉        | 18/96 [00:09<00:40,  1.93it/s]
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Epoch 175 Training:  20%|█▉        | 19/96 [00:09<00:39,  1.93it/s]
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Epoch 175 Training:  24%|██▍       | 23/96 [00:11<00:37,  1.93it/s]
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Epoch 175 Training:  25%|██▌       | 24/96 [00:12<00:37,  1.93it/s]
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Epoch 175 Training:  26%|██▌       | 25/96 [00:12<00:36,  1.93it/s]
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Epoch 175 Training:  27%|██▋       | 26/96 [00:13<00:36,  1.93it/s]
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Epoch 175 Training:  28%|██▊       | 27/96 [00:13<00:35,  1.93it/s]
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Epoch 175 Training:  29%|██▉       | 28/96 [00:14<00:35,  1.93it/s]
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Epoch 175 Training:  30%|███       | 29/96 [00:14<00:34,  1.93it/s]
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Epoch 175 Training:  31%|███▏      | 30/96 [00:15<00:34,  1.93it/s]
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Epoch 175 Training:  32%|███▏      | 31/96 [00:16<00:33,  1.93it/s]
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Epoch 175 Training:  34%|███▍      | 33/96 [00:17<00:32,  1.93it/s]
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Epoch 175 Training:  35%|███▌      | 34/96 [00:17<00:32,  1.93it/s]
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Epoch 175 Training:  38%|███▊      | 36/96 [00:18<00:31,  1.93it/s]
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Epoch 175 Training:  39%|███▊      | 37/96 [00:19<00:30,  1.93it/s]
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Epoch 175 Training:  40%|███▉      | 38/96 [00:19<00:30,  1.93it/s]
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Epoch 175 Training:  41%|████      | 39/96 [00:20<00:29,  1.93it/s]
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Epoch 175 Training:  44%|████▍     | 42/96 [00:21<00:27,  1.93it/s]
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Epoch 175 Training:  45%|████▍     | 43/96 [00:22<00:27,  1.93it/s]
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Epoch 175 Training:  46%|████▌     | 44/96 [00:22<00:26,  1.93it/s]
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Epoch 175 Training:  47%|████▋     | 45/96 [00:23<00:26,  1.93it/s]
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Epoch 175 Training:  48%|████▊     | 46/96 [00:23<00:25,  1.93it/s]
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Epoch 175 Training:  49%|████▉     | 47/96 [00:24<00:25,  1.93it/s]
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Epoch 175 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 04:26:13] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0350 | Epoch 175] train/clipped_loss_min=-5 train/clipped_loss_max=4.5867 train/clipped_loss_mean=0.0119 train/clipped_loss_std=1.5178 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-5 train/unclipped_loss_max=4.5867 train/unclipped_loss_mean=0.0119 train/unclipped_loss_std=1.5178 train/policy_loss=0.0119 train/loss=0.0119 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0005
+
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Epoch 175 Training:  51%|█████     | 49/96 [00:25<00:24,  1.93it/s]
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Epoch 175 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 04:26:38] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0351 | Epoch 175] train/clipped_loss_min=-3.8747 train/clipped_loss_max=4.3564 train/clipped_loss_mean=-0.0100 train/clipped_loss_std=1.4576 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0.0001 train/unclipped_loss_min=-3.8747 train/unclipped_loss_max=4.3564 train/unclipped_loss_mean=-0.0100 train/unclipped_loss_std=1.4576 train/policy_loss=-0.0100 train/loss=-0.0100 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0005
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+[2026-10-02 04:26:38] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0352 | Epoch 175] timing/rollout_seconds=179.3382 timing/feedback_seconds=5.5533 timing/optimization_seconds=50.5031 timing/cycle_seconds=232.7017
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Epoch 176 Sampling:  85%|████████▌ | 82/96 [02:31<00:25,  1.85s/it]
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Epoch 176 Sampling: 100%|██████████| 96/96 [02:57<00:00,  1.85s/it]
+
Pointwise Rewards: ssim:   0%|          | 0/6 [00:00<?, ?it/s]
Pointwise Rewards: ssim:  17%|█▋        | 1/6 [00:00<00:01,  4.86it/s]
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Pointwise Rewards: ssim:  67%|██████▋   | 4/6 [00:00<00:00,  5.49it/s]
Pointwise Rewards: ssim:  83%|████████▎ | 5/6 [00:00<00:00,  5.63it/s]
Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:01<00:00,  5.59it/s]
Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:01<00:00,  5.52it/s]
+
Pointwise Rewards: dists:   0%|          | 0/6 [00:00<?, ?it/s]
Pointwise Rewards: dists:  17%|█▋        | 1/6 [00:00<00:01,  3.18it/s]
Pointwise Rewards: dists:  33%|███▎      | 2/6 [00:00<00:01,  3.26it/s]
Pointwise Rewards: dists:  50%|█████     | 3/6 [00:00<00:00,  3.22it/s]
Pointwise Rewards: dists:  67%|██████▋   | 4/6 [00:01<00:00,  3.19it/s]
Pointwise Rewards: dists:  83%|████████▎ | 5/6 [00:01<00:00,  3.22it/s]
Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.20it/s]
Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.21it/s]
+[2026-10-02 04:29:41] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0352 | Epoch 176] train/reward_dists_mean=0.8352 train/reward_dists_std=0.0328 train/reward_ssim_mean=0.6314 train/reward_ssim_std=0.0929 train/reward_dists_group_std_mean=0.0073 train/reward_dists_group_std_max=0.0123 train/reward_dists_group_std_min=0.0029 train/reward_dists_group_mean_std=0.0319 train/reward_ssim_group_std_mean=0.0061 train/reward_ssim_group_std_max=0.0157 train/reward_ssim_group_std_min=0.0025 train/reward_ssim_group_mean_std=0.0926 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.4836 train/adv_max=4.1988 train/adv_abs_mean=1.2209
+
Epoch 176 Training:   0%|          | 0/96 [00:00<?, ?it/s]
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+
                                                                 
Epoch 176 Training:   1%|          | 1/96 [00:00<00:43,  2.17it/s]
+
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+
                                                                 
Epoch 176 Training:   2%|▏         | 2/96 [00:00<00:46,  2.01it/s]
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+
                                                                 
Epoch 176 Training:   3%|▎         | 3/96 [00:01<00:47,  1.97it/s]
+
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Epoch 176 Timestep: 100%|██████████| 1/1 [00:00<00:00,  2.16it/s]
+
                                                                 
Epoch 176 Training:   4%|▍         | 4/96 [00:02<00:47,  1.95it/s]
+
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Epoch 176 Timestep: 100%|██████████| 1/1 [00:00<00:00,  2.16it/s]
+
                                                                 
Epoch 176 Training:   5%|▌         | 5/96 [00:02<00:46,  1.95it/s]
+
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Epoch 176 Timestep: 100%|██████████| 1/1 [00:00<00:00,  2.16it/s]
+
                                                                 
Epoch 176 Training:   6%|▋         | 6/96 [00:03<00:46,  1.94it/s]
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Epoch 176 Timestep: 100%|██████████| 1/1 [00:00<00:00,  2.16it/s]
+
                                                                 
Epoch 176 Training:   7%|▋         | 7/96 [00:03<00:46,  1.93it/s]
+
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Epoch 176 Timestep: 100%|██████████| 1/1 [00:00<00:00,  2.16it/s]
+
                                                                 
Epoch 176 Training:   8%|▊         | 8/96 [00:04<00:45,  1.93it/s]
+
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Epoch 176 Timestep: 100%|██████████| 1/1 [00:00<00:00,  2.16it/s]
+
                                                                 
Epoch 176 Training:   9%|▉         | 9/96 [00:04<00:45,  1.93it/s]
+
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Epoch 176 Timestep: 100%|██████████| 1/1 [00:00<00:00,  2.16it/s]
+
                                                                 
Epoch 176 Training:  10%|█         | 10/96 [00:05<00:44,  1.93it/s]
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Epoch 176 Timestep: 100%|██████████| 1/1 [00:00<00:00,  2.16it/s]
+
                                                                 
Epoch 176 Training:  11%|█▏        | 11/96 [00:05<00:44,  1.93it/s]
+
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Epoch 176 Timestep: 100%|██████████| 1/1 [00:00<00:00,  2.16it/s]
+
                                                                 
Epoch 176 Training:  12%|█▎        | 12/96 [00:06<00:43,  1.93it/s]
+
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Epoch 176 Timestep: 100%|██████████| 1/1 [00:00<00:00,  2.16it/s]
+
                                                                 
Epoch 176 Training:  14%|█▎        | 13/96 [00:06<00:43,  1.93it/s]
+
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Epoch 176 Timestep: 100%|██████████| 1/1 [00:00<00:00,  2.16it/s]
+
                                                                 
Epoch 176 Training:  15%|█▍        | 14/96 [00:07<00:42,  1.93it/s]
+
Epoch 176 Timestep:   0%|          | 0/1 [00:00<?, ?it/s]
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Epoch 176 Timestep: 100%|██████████| 1/1 [00:00<00:00,  2.16it/s]
+
                                                                 
Epoch 176 Training:  16%|█▌        | 15/96 [00:07<00:42,  1.92it/s]
+
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Epoch 176 Timestep: 100%|██████████| 1/1 [00:00<00:00,  2.16it/s]
+
                                                                 
Epoch 176 Training:  17%|█▋        | 16/96 [00:08<00:41,  1.92it/s]
+
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Epoch 176 Timestep: 100%|██████████| 1/1 [00:00<00:00,  2.16it/s]
+
                                                                 
Epoch 176 Training:  18%|█▊        | 17/96 [00:08<00:41,  1.92it/s]
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Epoch 176 Timestep: 100%|██████████| 1/1 [00:00<00:00,  2.16it/s]
+
                                                                 
Epoch 176 Training:  19%|█▉        | 18/96 [00:09<00:40,  1.92it/s]
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Epoch 176 Timestep: 100%|██████████| 1/1 [00:00<00:00,  2.16it/s]
+
                                                                 
Epoch 176 Training:  20%|█▉        | 19/96 [00:09<00:40,  1.92it/s]
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Epoch 176 Training:  21%|██        | 20/96 [00:10<00:39,  1.92it/s]
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Epoch 176 Training:  22%|██▏       | 21/96 [00:10<00:38,  1.92it/s]
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Epoch 176 Training:  23%|██▎       | 22/96 [00:11<00:38,  1.93it/s]
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Epoch 176 Training:  24%|██▍       | 23/96 [00:11<00:37,  1.93it/s]
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Epoch 176 Timestep: 100%|██████████| 1/1 [00:00<00:00,  2.16it/s]
+
                                                                 
Epoch 176 Training:  25%|██▌       | 24/96 [00:12<00:37,  1.93it/s]
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Epoch 176 Timestep: 100%|██████████| 1/1 [00:00<00:00,  2.16it/s]
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Epoch 176 Training:  26%|██▌       | 25/96 [00:12<00:36,  1.93it/s]
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Epoch 176 Timestep: 100%|██████████| 1/1 [00:00<00:00,  2.16it/s]
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Epoch 176 Training:  27%|██▋       | 26/96 [00:13<00:36,  1.93it/s]
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Epoch 176 Timestep: 100%|██████████| 1/1 [00:00<00:00,  2.15it/s]
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Epoch 176 Training:  28%|██▊       | 27/96 [00:13<00:35,  1.93it/s]
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Epoch 176 Timestep: 100%|██████████| 1/1 [00:00<00:00,  2.16it/s]
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Epoch 176 Training:  29%|██▉       | 28/96 [00:14<00:35,  1.93it/s]
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Epoch 176 Timestep: 100%|██████████| 1/1 [00:00<00:00,  2.16it/s]
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Epoch 176 Training:  30%|███       | 29/96 [00:15<00:34,  1.92it/s]
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Epoch 176 Timestep: 100%|██████████| 1/1 [00:00<00:00,  2.16it/s]
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Epoch 176 Training:  31%|███▏      | 30/96 [00:15<00:39,  1.66it/s]
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Epoch 176 Training:  32%|███▏      | 31/96 [00:16<00:37,  1.73it/s]
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Epoch 176 Training:  33%|███▎      | 32/96 [00:16<00:35,  1.79it/s]
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Epoch 176 Training:  34%|███▍      | 33/96 [00:17<00:34,  1.83it/s]
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Epoch 176 Training:  35%|███▌      | 34/96 [00:17<00:33,  1.86it/s]
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Epoch 176 Training:  36%|███▋      | 35/96 [00:18<00:32,  1.88it/s]
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Epoch 176 Training:  38%|███▊      | 36/96 [00:18<00:31,  1.89it/s]
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Epoch 176 Training:  39%|███▊      | 37/96 [00:19<00:31,  1.90it/s]
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Epoch 176 Training:  40%|███▉      | 38/96 [00:19<00:30,  1.91it/s]
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Epoch 176 Training:  41%|████      | 39/96 [00:20<00:29,  1.91it/s]
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Epoch 176 Training:  42%|████▏     | 40/96 [00:20<00:29,  1.92it/s]
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Epoch 176 Training:  43%|████▎     | 41/96 [00:21<00:28,  1.92it/s]
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Epoch 176 Training:  44%|████▍     | 42/96 [00:22<00:28,  1.92it/s]
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Epoch 176 Training:  45%|████▍     | 43/96 [00:22<00:27,  1.92it/s]
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Epoch 176 Training:  46%|████▌     | 44/96 [00:23<00:27,  1.92it/s]
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Epoch 176 Training:  47%|████▋     | 45/96 [00:23<00:26,  1.92it/s]
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Epoch 176 Training:  48%|████▊     | 46/96 [00:24<00:25,  1.93it/s]
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Epoch 176 Training:  49%|████▉     | 47/96 [00:24<00:25,  1.93it/s]
+
Epoch 176 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 04:30:07] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0352 | Epoch 176] train/clipped_loss_min=-3.9292 train/clipped_loss_max=4.4836 train/clipped_loss_mean=-0.0380 train/clipped_loss_std=1.5518 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-3.9292 train/unclipped_loss_max=4.4836 train/unclipped_loss_mean=-0.0380 train/unclipped_loss_std=1.5518 train/policy_loss=-0.0380 train/loss=-0.0380 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0005
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Epoch 176 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 04:30:32] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0353 | Epoch 176] train/clipped_loss_min=-4.1988 train/clipped_loss_max=4.3135 train/clipped_loss_mean=0.0380 train/clipped_loss_std=1.4841 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0.0001 train/unclipped_loss_min=-4.1988 train/unclipped_loss_max=4.3135 train/unclipped_loss_mean=0.0380 train/unclipped_loss_std=1.4841 train/policy_loss=0.0380 train/loss=0.0380 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0005
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+[2026-10-02 04:30:32] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0354 | Epoch 176] timing/rollout_seconds=179.4520 timing/feedback_seconds=5.4610 timing/optimization_seconds=50.8162 timing/cycle_seconds=233.2395
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Epoch 177 Sampling: 100%|██████████| 96/96 [02:57<00:00,  1.85s/it]
+
Pointwise Rewards: ssim:   0%|          | 0/6 [00:00<?, ?it/s]
Pointwise Rewards: ssim:  17%|█▋        | 1/6 [00:00<00:01,  4.72it/s]
Pointwise Rewards: ssim:  33%|███▎      | 2/6 [00:00<00:00,  5.16it/s]
Pointwise Rewards: ssim:  50%|█████     | 3/6 [00:00<00:00,  5.46it/s]
Pointwise Rewards: ssim:  67%|██████▋   | 4/6 [00:00<00:00,  5.50it/s]
Pointwise Rewards: ssim:  83%|████████▎ | 5/6 [00:00<00:00,  5.63it/s]
Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:01<00:00,  5.56it/s]
Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:01<00:00,  5.46it/s]
+
Pointwise Rewards: dists:   0%|          | 0/6 [00:00<?, ?it/s]
Pointwise Rewards: dists:  17%|█▋        | 1/6 [00:00<00:01,  3.13it/s]
Pointwise Rewards: dists:  33%|███▎      | 2/6 [00:00<00:01,  3.18it/s]
Pointwise Rewards: dists:  50%|█████     | 3/6 [00:00<00:00,  3.24it/s]
Pointwise Rewards: dists:  67%|██████▋   | 4/6 [00:01<00:00,  3.22it/s]
Pointwise Rewards: dists:  83%|████████▎ | 5/6 [00:01<00:00,  3.27it/s]
Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.24it/s]
Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.23it/s]
+[2026-10-02 04:33:35] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0354 | Epoch 177] train/reward_dists_mean=0.8321 train/reward_dists_std=0.0321 train/reward_ssim_mean=0.6189 train/reward_ssim_std=0.0901 train/reward_dists_group_std_mean=0.0071 train/reward_dists_group_std_max=0.0143 train/reward_dists_group_std_min=0.0025 train/reward_dists_group_mean_std=0.0312 train/reward_ssim_group_std_mean=0.0066 train/reward_ssim_group_std_max=0.0188 train/reward_ssim_group_std_min=0.0029 train/reward_ssim_group_mean_std=0.0898 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.1122 train/adv_max=4.4729 train/adv_abs_mean=1.2149
+
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Epoch 177 Training:   1%|          | 1/96 [00:00<00:43,  2.16it/s]
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Epoch 177 Training:   2%|▏         | 2/96 [00:00<00:46,  2.01it/s]
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Epoch 177 Training:   4%|▍         | 4/96 [00:02<00:47,  1.95it/s]
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Epoch 177 Training:   5%|▌         | 5/96 [00:02<00:46,  1.95it/s]
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Epoch 177 Training:   6%|▋         | 6/96 [00:03<00:46,  1.94it/s]
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Epoch 177 Training:   7%|▋         | 7/96 [00:03<00:45,  1.94it/s]
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Epoch 177 Training:   8%|▊         | 8/96 [00:04<00:45,  1.93it/s]
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Epoch 177 Training:   9%|▉         | 9/96 [00:04<00:45,  1.93it/s]
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Epoch 177 Training:  10%|█         | 10/96 [00:05<00:44,  1.93it/s]
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Epoch 177 Training:  11%|█▏        | 11/96 [00:05<00:44,  1.93it/s]
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Epoch 177 Training:  12%|█▎        | 12/96 [00:06<00:43,  1.93it/s]
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Epoch 177 Training:  14%|█▎        | 13/96 [00:06<00:43,  1.93it/s]
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Epoch 177 Training:  15%|█▍        | 14/96 [00:07<00:42,  1.93it/s]
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Epoch 177 Training:  16%|█▌        | 15/96 [00:07<00:42,  1.93it/s]
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Epoch 177 Training:  17%|█▋        | 16/96 [00:08<00:41,  1.93it/s]
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Epoch 177 Training:  18%|█▊        | 17/96 [00:08<00:40,  1.93it/s]
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Epoch 177 Training:  19%|█▉        | 18/96 [00:09<00:40,  1.92it/s]
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Epoch 177 Training:  20%|█▉        | 19/96 [00:09<00:40,  1.92it/s]
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Epoch 177 Training:  21%|██        | 20/96 [00:10<00:39,  1.93it/s]
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Epoch 177 Training:  22%|██▏       | 21/96 [00:10<00:38,  1.92it/s]
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Epoch 177 Training:  23%|██▎       | 22/96 [00:11<00:38,  1.93it/s]
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Epoch 177 Training:  24%|██▍       | 23/96 [00:11<00:37,  1.93it/s]
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Epoch 177 Training:  25%|██▌       | 24/96 [00:12<00:37,  1.93it/s]
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Epoch 177 Training:  26%|██▌       | 25/96 [00:12<00:36,  1.93it/s]
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Epoch 177 Training:  27%|██▋       | 26/96 [00:13<00:36,  1.93it/s]
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Epoch 177 Training:  28%|██▊       | 27/96 [00:13<00:35,  1.93it/s]
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Epoch 177 Training:  29%|██▉       | 28/96 [00:14<00:35,  1.93it/s]
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Epoch 177 Training:  30%|███       | 29/96 [00:14<00:34,  1.93it/s]
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Epoch 177 Training:  31%|███▏      | 30/96 [00:15<00:34,  1.93it/s]
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Epoch 177 Training:  32%|███▏      | 31/96 [00:16<00:33,  1.93it/s]
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Epoch 177 Training:  33%|███▎      | 32/96 [00:16<00:33,  1.93it/s]
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Epoch 177 Training:  34%|███▍      | 33/96 [00:17<00:32,  1.93it/s]
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Epoch 177 Training:  35%|███▌      | 34/96 [00:17<00:32,  1.93it/s]
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Epoch 177 Training:  36%|███▋      | 35/96 [00:18<00:31,  1.93it/s]
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Epoch 177 Training:  38%|███▊      | 36/96 [00:18<00:31,  1.93it/s]
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Epoch 177 Training:  39%|███▊      | 37/96 [00:19<00:30,  1.93it/s]
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Epoch 177 Training:  40%|███▉      | 38/96 [00:19<00:30,  1.93it/s]
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Epoch 177 Training:  41%|████      | 39/96 [00:20<00:29,  1.93it/s]
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Epoch 177 Training:  42%|████▏     | 40/96 [00:20<00:29,  1.93it/s]
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Epoch 177 Training:  43%|████▎     | 41/96 [00:21<00:28,  1.93it/s]
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Epoch 177 Training:  44%|████▍     | 42/96 [00:21<00:27,  1.93it/s]
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Epoch 177 Training:  45%|████▍     | 43/96 [00:22<00:27,  1.93it/s]
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Epoch 177 Training:  46%|████▌     | 44/96 [00:22<00:26,  1.93it/s]
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Epoch 177 Training:  47%|████▋     | 45/96 [00:23<00:26,  1.93it/s]
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Epoch 177 Training:  48%|████▊     | 46/96 [00:23<00:25,  1.93it/s]
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Epoch 177 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 04:34:00] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0354 | Epoch 177] train/clipped_loss_min=-4.1135 train/clipped_loss_max=4.5820 train/clipped_loss_mean=-0.0546 train/clipped_loss_std=1.4741 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-4.1135 train/unclipped_loss_max=4.5820 train/unclipped_loss_mean=-0.0546 train/unclipped_loss_std=1.4741 train/policy_loss=-0.0546 train/loss=-0.0546 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0005
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Epoch 177 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 04:34:25] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0355 | Epoch 177] train/clipped_loss_min=-4.4728 train/clipped_loss_max=5.0000 train/clipped_loss_mean=0.0543 train/clipped_loss_std=1.5334 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-4.4728 train/unclipped_loss_max=5.0000 train/unclipped_loss_mean=0.0543 train/unclipped_loss_std=1.5334 train/policy_loss=0.0543 train/loss=0.0543 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0005
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+[2026-10-02 04:34:25] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0356 | Epoch 177] timing/rollout_seconds=179.5196 timing/feedback_seconds=5.2840 timing/optimization_seconds=50.4310 timing/cycle_seconds=232.9031
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+
Pointwise Rewards: ssim:   0%|          | 0/6 [00:00<?, ?it/s]
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Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:01<00:00,  5.68it/s]
+
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Pointwise Rewards: dists:  33%|███▎      | 2/6 [00:00<00:01,  3.33it/s]
Pointwise Rewards: dists:  50%|█████     | 3/6 [00:00<00:00,  3.28it/s]
Pointwise Rewards: dists:  67%|██████▋   | 4/6 [00:01<00:00,  3.33it/s]
Pointwise Rewards: dists:  83%|████████▎ | 5/6 [00:01<00:00,  3.33it/s]
Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.26it/s]
Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.29it/s]
+[2026-10-02 04:37:28] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0356 | Epoch 178] train/reward_dists_mean=0.8417 train/reward_dists_std=0.0356 train/reward_ssim_mean=0.5960 train/reward_ssim_std=0.1013 train/reward_dists_group_std_mean=0.0062 train/reward_dists_group_std_max=0.0221 train/reward_dists_group_std_min=0.0036 train/reward_dists_group_mean_std=0.0349 train/reward_ssim_group_std_mean=0.0076 train/reward_ssim_group_std_max=0.0188 train/reward_ssim_group_std_min=0.0033 train/reward_ssim_group_mean_std=0.1010 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.2192 train/adv_max=3.9684 train/adv_abs_mean=1.1605
+
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Epoch 178 Training:   7%|▋         | 7/96 [00:03<00:45,  1.94it/s]
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Epoch 178 Training:   8%|▊         | 8/96 [00:04<00:45,  1.93it/s]
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Epoch 178 Training:   9%|▉         | 9/96 [00:04<00:45,  1.93it/s]
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Epoch 178 Training:  10%|█         | 10/96 [00:05<00:44,  1.93it/s]
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Epoch 178 Training:  11%|█▏        | 11/96 [00:05<00:44,  1.93it/s]
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Epoch 178 Training:  12%|█▎        | 12/96 [00:06<00:43,  1.93it/s]
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Epoch 178 Training:  14%|█▎        | 13/96 [00:06<00:43,  1.93it/s]
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Epoch 178 Training:  15%|█▍        | 14/96 [00:07<00:42,  1.93it/s]
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Epoch 178 Training:  18%|█▊        | 17/96 [00:08<00:41,  1.93it/s]
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Epoch 178 Training:  19%|█▉        | 18/96 [00:09<00:40,  1.93it/s]
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Epoch 178 Training:  26%|██▌       | 25/96 [00:12<00:36,  1.93it/s]
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Epoch 178 Training:  29%|██▉       | 28/96 [00:14<00:35,  1.93it/s]
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Epoch 178 Training:  31%|███▏      | 30/96 [00:15<00:34,  1.93it/s]
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Epoch 178 Training:  39%|███▊      | 37/96 [00:19<00:30,  1.93it/s]
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Epoch 178 Training:  41%|████      | 39/96 [00:20<00:29,  1.93it/s]
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Epoch 178 Training:  44%|████▍     | 42/96 [00:21<00:28,  1.93it/s]
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Epoch 178 Training:  45%|████▍     | 43/96 [00:22<00:27,  1.93it/s]
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Epoch 178 Training:  46%|████▌     | 44/96 [00:22<00:27,  1.93it/s]
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Epoch 178 Training:  49%|████▉     | 47/96 [00:24<00:25,  1.93it/s]
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Epoch 178 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 04:37:53] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0356 | Epoch 178] train/clipped_loss_min=-3.2841 train/clipped_loss_max=3.9501 train/clipped_loss_mean=0.0070 train/clipped_loss_std=1.4321 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-3.2841 train/unclipped_loss_max=3.9501 train/unclipped_loss_mean=0.0070 train/unclipped_loss_std=1.4321 train/policy_loss=0.0070 train/loss=0.0070 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
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Epoch 178 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 04:38:18] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0357 | Epoch 178] train/clipped_loss_min=-3.9684 train/clipped_loss_max=4.2192 train/clipped_loss_mean=-0.0070 train/clipped_loss_std=1.4861 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-3.9684 train/unclipped_loss_max=4.2192 train/unclipped_loss_mean=-0.0070 train/unclipped_loss_std=1.4861 train/policy_loss=-0.0070 train/loss=-0.0070 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
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+[2026-10-02 04:38:18] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0358 | Epoch 178] timing/rollout_seconds=179.4684 timing/feedback_seconds=5.6201 timing/optimization_seconds=50.4886 timing/cycle_seconds=233.0569
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+
Pointwise Rewards: ssim:   0%|          | 0/6 [00:00<?, ?it/s]
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Pointwise Rewards: ssim:  83%|████████▎ | 5/6 [00:00<00:00,  5.71it/s]
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Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:01<00:00,  5.78it/s]
+
Pointwise Rewards: dists:   0%|          | 0/6 [00:00<?, ?it/s]
Pointwise Rewards: dists:  17%|█▋        | 1/6 [00:00<00:01,  3.25it/s]
Pointwise Rewards: dists:  33%|███▎      | 2/6 [00:00<00:01,  3.33it/s]
Pointwise Rewards: dists:  50%|█████     | 3/6 [00:00<00:00,  3.35it/s]
Pointwise Rewards: dists:  67%|██████▋   | 4/6 [00:01<00:00,  3.35it/s]
Pointwise Rewards: dists:  83%|████████▎ | 5/6 [00:01<00:00,  3.30it/s]
Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.35it/s]
Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.34it/s]
+[2026-10-02 04:41:21] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0358 | Epoch 179] train/reward_dists_mean=0.8475 train/reward_dists_std=0.0241 train/reward_ssim_mean=0.6098 train/reward_ssim_std=0.0961 train/reward_dists_group_std_mean=0.0060 train/reward_dists_group_std_max=0.0121 train/reward_dists_group_std_min=0.0031 train/reward_dists_group_mean_std=0.0232 train/reward_ssim_group_std_mean=0.0074 train/reward_ssim_group_std_max=0.0171 train/reward_ssim_group_std_min=0.0038 train/reward_ssim_group_mean_std=0.0957 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.5970 train/adv_max=4.9629 train/adv_abs_mean=1.2017
+
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Epoch 179 Training:   7%|▋         | 7/96 [00:03<00:46,  1.93it/s]
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Epoch 179 Training:   8%|▊         | 8/96 [00:04<00:45,  1.93it/s]
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Epoch 179 Training:   9%|▉         | 9/96 [00:04<00:45,  1.93it/s]
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Epoch 179 Training:  10%|█         | 10/96 [00:05<00:44,  1.93it/s]
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Epoch 179 Training:  11%|█▏        | 11/96 [00:05<00:44,  1.93it/s]
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Epoch 179 Training:  14%|█▎        | 13/96 [00:06<00:43,  1.93it/s]
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Epoch 179 Training:  15%|█▍        | 14/96 [00:07<00:42,  1.93it/s]
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Epoch 179 Training:  18%|█▊        | 17/96 [00:08<00:41,  1.93it/s]
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Epoch 179 Training:  19%|█▉        | 18/96 [00:09<00:40,  1.93it/s]
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Epoch 179 Training:  29%|██▉       | 28/96 [00:14<00:35,  1.93it/s]
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Epoch 179 Training:  39%|███▊      | 37/96 [00:19<00:30,  1.93it/s]
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Epoch 179 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 04:42:11] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0359 | Epoch 179] train/clipped_loss_min=-4.9629 train/clipped_loss_max=4.5970 train/clipped_loss_mean=0.0112 train/clipped_loss_std=1.5013 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-4.9629 train/unclipped_loss_max=4.5970 train/unclipped_loss_mean=0.0112 train/unclipped_loss_std=1.5013 train/policy_loss=0.0112 train/loss=0.0112 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
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+[2026-10-02 04:42:11] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0360 | Epoch 179] timing/rollout_seconds=179.7491 timing/feedback_seconds=5.5076 timing/optimization_seconds=50.5082 timing/cycle_seconds=233.1457
+[2026-10-02 04:42:11] [Rank 0] [INFO] [flow_factory.models.abc]: Saving LoRA weights for transformer role=base to /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/runs/klein4b_grpo_flir_full_ssim_dists_lr5e-5/saves/klein4b_grpo_flir_full_ssim_dists_lr5e-5/checkpoints/checkpoint-180
+[2026-10-02 04:42:11] [Rank 0] [INFO] [flow_factory.models.abc]: Checkpoint saved successfully to /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/runs/klein4b_grpo_flir_full_ssim_dists_lr5e-5/saves/klein4b_grpo_flir_full_ssim_dists_lr5e-5/checkpoints/checkpoint-180
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+[2026-10-02 04:43:04] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0360 | Epoch 180] eval/flir_rl_full/reward_dists_mean=0.8414 eval/flir_rl_full/reward_dists_std=0.0344 eval/flir_rl_full/reward_psnr_mean=20.2459 eval/flir_rl_full/reward_psnr_std=2.8529 eval/flir_rl_full/reward_ssim_mean=0.6239 eval/flir_rl_full/reward_ssim_std=0.0839
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+
Pointwise Rewards: ssim:   0%|          | 0/6 [00:00<?, ?it/s]
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Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:01<00:00,  3.67it/s]
+
Pointwise Rewards: dists:   0%|          | 0/6 [00:00<?, ?it/s]
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Pointwise Rewards: dists:  33%|███▎      | 2/6 [00:00<00:01,  3.20it/s]
Pointwise Rewards: dists:  50%|█████     | 3/6 [00:00<00:00,  3.19it/s]
Pointwise Rewards: dists:  67%|██████▋   | 4/6 [00:01<00:00,  3.25it/s]
Pointwise Rewards: dists:  83%|████████▎ | 5/6 [00:01<00:00,  3.23it/s]
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Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.23it/s]
+[2026-10-02 04:46:07] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0360 | Epoch 180] train/reward_dists_mean=0.8467 train/reward_dists_std=0.0289 train/reward_ssim_mean=0.6029 train/reward_ssim_std=0.1080 train/reward_dists_group_std_mean=0.0060 train/reward_dists_group_std_max=0.0117 train/reward_dists_group_std_min=0.0026 train/reward_dists_group_mean_std=0.0282 train/reward_ssim_group_std_mean=0.0084 train/reward_ssim_group_std_max=0.0206 train/reward_ssim_group_std_min=0.0038 train/reward_ssim_group_mean_std=0.1076 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.6895 train/adv_max=3.9975 train/adv_abs_mean=1.2585
+
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Epoch 180 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 04:46:32] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0360 | Epoch 180] train/clipped_loss_min=-3.9575 train/clipped_loss_max=4.6895 train/clipped_loss_mean=0.0556 train/clipped_loss_std=1.5557 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-3.9575 train/unclipped_loss_max=4.6895 train/unclipped_loss_mean=0.0556 train/unclipped_loss_std=1.5557 train/policy_loss=0.0556 train/loss=0.0556 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0005
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Epoch 180 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 04:46:57] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0361 | Epoch 180] train/clipped_loss_min=-3.9975 train/clipped_loss_max=4.4641 train/clipped_loss_mean=-0.0556 train/clipped_loss_std=1.5603 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-3.9975 train/unclipped_loss_max=4.4641 train/unclipped_loss_mean=-0.0556 train/unclipped_loss_std=1.5603 train/policy_loss=-0.0556 train/loss=-0.0556 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0005
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+
                                                                 
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Epoch 180 Training: 100%|██████████| 96/96 [00:49<00:00,  1.93it/s]
+[2026-10-02 04:46:57] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0362 | Epoch 180] timing/rollout_seconds=179.3985 timing/feedback_seconds=4.8561 timing/optimization_seconds=50.4375 timing/cycle_seconds=232.8094
+
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+
Pointwise Rewards: ssim:   0%|          | 0/6 [00:00<?, ?it/s]
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Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:01<00:00,  5.75it/s]
+
Pointwise Rewards: dists:   0%|          | 0/6 [00:00<?, ?it/s]
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Pointwise Rewards: dists:  33%|███▎      | 2/6 [00:00<00:01,  3.25it/s]
Pointwise Rewards: dists:  50%|█████     | 3/6 [00:00<00:00,  3.25it/s]
Pointwise Rewards: dists:  67%|██████▋   | 4/6 [00:01<00:00,  3.28it/s]
Pointwise Rewards: dists:  83%|████████▎ | 5/6 [00:01<00:00,  3.25it/s]
Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.23it/s]
Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.25it/s]
+[2026-10-02 04:50:00] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0362 | Epoch 181] train/reward_dists_mean=0.8441 train/reward_dists_std=0.0275 train/reward_ssim_mean=0.6154 train/reward_ssim_std=0.1063 train/reward_dists_group_std_mean=0.0059 train/reward_dists_group_std_max=0.0118 train/reward_dists_group_std_min=0.0032 train/reward_dists_group_mean_std=0.0268 train/reward_ssim_group_std_mean=0.0084 train/reward_ssim_group_std_max=0.0143 train/reward_ssim_group_std_min=0.0035 train/reward_ssim_group_mean_std=0.1060 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.9942 train/adv_max=4.4474 train/adv_abs_mean=1.2519
+
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Epoch 181 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 04:50:25] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0362 | Epoch 181] train/clipped_loss_min=-4.4153 train/clipped_loss_max=4.7862 train/clipped_loss_mean=0.0317 train/clipped_loss_std=1.5320 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-4.4153 train/unclipped_loss_max=4.7862 train/unclipped_loss_mean=0.0317 train/unclipped_loss_std=1.5320 train/policy_loss=0.0317 train/loss=0.0317 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0005
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+[2026-10-02 04:50:50] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0364 | Epoch 181] timing/rollout_seconds=179.3495 timing/feedback_seconds=5.2795 timing/optimization_seconds=50.8704 timing/cycle_seconds=233.1563
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Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.25it/s]
+[2026-10-02 04:53:53] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0364 | Epoch 182] train/reward_dists_mean=0.8467 train/reward_dists_std=0.0296 train/reward_ssim_mean=0.6201 train/reward_ssim_std=0.1016 train/reward_dists_group_std_mean=0.0057 train/reward_dists_group_std_max=0.0090 train/reward_dists_group_std_min=0.0028 train/reward_dists_group_mean_std=0.0290 train/reward_ssim_group_std_mean=0.0071 train/reward_ssim_group_std_max=0.0146 train/reward_ssim_group_std_min=0.0031 train/reward_ssim_group_mean_std=0.1014 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.3596 train/adv_max=3.9203 train/adv_abs_mean=1.1981
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Epoch 182 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 04:54:18] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0364 | Epoch 182] train/clipped_loss_min=-3.4816 train/clipped_loss_max=3.9685 train/clipped_loss_mean=0.0122 train/clipped_loss_std=1.4495 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-3.4816 train/unclipped_loss_max=3.9685 train/unclipped_loss_mean=0.0122 train/unclipped_loss_std=1.4495 train/policy_loss=0.0122 train/loss=0.0122 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0004
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Epoch 182 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 04:54:43] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0365 | Epoch 182] train/clipped_loss_min=-3.9203 train/clipped_loss_max=5.0000 train/clipped_loss_mean=-0.0131 train/clipped_loss_std=1.4880 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0.0002 train/unclipped_loss_min=-3.9203 train/unclipped_loss_max=5.0000 train/unclipped_loss_mean=-0.0131 train/unclipped_loss_std=1.4880 train/policy_loss=-0.0131 train/loss=-0.0131 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0004
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+[2026-10-02 04:54:43] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0366 | Epoch 182] timing/rollout_seconds=179.5336 timing/feedback_seconds=5.6839 timing/optimization_seconds=50.4520 timing/cycle_seconds=232.8496
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+[2026-10-02 04:57:46] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0366 | Epoch 183] train/reward_dists_mean=0.8392 train/reward_dists_std=0.0309 train/reward_ssim_mean=0.6281 train/reward_ssim_std=0.0955 train/reward_dists_group_std_mean=0.0083 train/reward_dists_group_std_max=0.0335 train/reward_dists_group_std_min=0.0034 train/reward_dists_group_mean_std=0.0294 train/reward_ssim_group_std_mean=0.0073 train/reward_ssim_group_std_max=0.0128 train/reward_ssim_group_std_min=0.0030 train/reward_ssim_group_mean_std=0.0952 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.4496 train/adv_max=4.3601 train/adv_abs_mean=1.1383
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Epoch 183 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 04:58:11] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0366 | Epoch 183] train/clipped_loss_min=-3.8555 train/clipped_loss_max=4.4496 train/clipped_loss_mean=0.1558 train/clipped_loss_std=1.4493 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-3.8555 train/unclipped_loss_max=4.4496 train/unclipped_loss_mean=0.1558 train/unclipped_loss_std=1.4493 train/policy_loss=0.1558 train/loss=0.1558 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0001
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Epoch 183 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 04:58:36] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0367 | Epoch 183] train/clipped_loss_min=-4.3601 train/clipped_loss_max=3.5873 train/clipped_loss_mean=-0.1558 train/clipped_loss_std=1.3654 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-4.3601 train/unclipped_loss_max=3.5873 train/unclipped_loss_mean=-0.1558 train/unclipped_loss_std=1.3654 train/policy_loss=-0.1558 train/loss=-0.1558 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0001
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+[2026-10-02 04:58:36] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0368 | Epoch 183] timing/rollout_seconds=179.2133 timing/feedback_seconds=4.8579 timing/optimization_seconds=50.5613 timing/cycle_seconds=232.5245
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+
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+[2026-10-02 05:01:39] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0368 | Epoch 184] train/reward_dists_mean=0.8389 train/reward_dists_std=0.0309 train/reward_ssim_mean=0.6307 train/reward_ssim_std=0.1066 train/reward_dists_group_std_mean=0.0069 train/reward_dists_group_std_max=0.0136 train/reward_dists_group_std_min=0.0039 train/reward_dists_group_mean_std=0.0300 train/reward_ssim_group_std_mean=0.0070 train/reward_ssim_group_std_max=0.0181 train/reward_ssim_group_std_min=0.0031 train/reward_ssim_group_mean_std=0.1063 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.7578 train/adv_max=4.0764 train/adv_abs_mean=1.1346
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Epoch 184 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 05:02:04] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0368 | Epoch 184] train/clipped_loss_min=-3.6033 train/clipped_loss_max=4.7578 train/clipped_loss_mean=0.0583 train/clipped_loss_std=1.5081 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-3.6033 train/unclipped_loss_max=4.7578 train/unclipped_loss_mean=0.0583 train/unclipped_loss_std=1.5081 train/policy_loss=0.0583 train/loss=0.0583 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0001
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Epoch 184 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 05:02:29] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0369 | Epoch 184] train/clipped_loss_min=-4.0764 train/clipped_loss_max=3.8389 train/clipped_loss_mean=-0.0583 train/clipped_loss_std=1.3897 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-4.0764 train/unclipped_loss_max=3.8389 train/unclipped_loss_mean=-0.0583 train/unclipped_loss_std=1.3897 train/policy_loss=-0.0583 train/loss=-0.0583 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0001
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+[2026-10-02 05:02:29] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0370 | Epoch 184] timing/rollout_seconds=179.3249 timing/feedback_seconds=5.3324 timing/optimization_seconds=50.6460 timing/cycle_seconds=232.8366
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+
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+[2026-10-02 05:05:31] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0370 | Epoch 185] train/reward_dists_mean=0.8309 train/reward_dists_std=0.0386 train/reward_ssim_mean=0.6420 train/reward_ssim_std=0.1200 train/reward_dists_group_std_mean=0.0082 train/reward_dists_group_std_max=0.0194 train/reward_dists_group_std_min=0.0035 train/reward_dists_group_mean_std=0.0375 train/reward_ssim_group_std_mean=0.0066 train/reward_ssim_group_std_max=0.0153 train/reward_ssim_group_std_min=0.0026 train/reward_ssim_group_mean_std=0.1197 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.6369 train/adv_max=4.6875 train/adv_abs_mean=1.2068
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Epoch 185 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 05:05:56] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0370 | Epoch 185] train/clipped_loss_min=-4.2048 train/clipped_loss_max=4.6369 train/clipped_loss_mean=-0.0351 train/clipped_loss_std=1.4883 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-4.2048 train/unclipped_loss_max=4.6369 train/unclipped_loss_mean=-0.0351 train/unclipped_loss_std=1.4883 train/policy_loss=-0.0351 train/loss=-0.0351 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0004
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Epoch 185 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 05:06:21] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0371 | Epoch 185] train/clipped_loss_min=-4.6876 train/clipped_loss_max=4.0905 train/clipped_loss_mean=0.0351 train/clipped_loss_std=1.5000 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0.0002 train/unclipped_loss_min=-4.6876 train/unclipped_loss_max=4.0905 train/unclipped_loss_mean=0.0351 train/unclipped_loss_std=1.5000 train/policy_loss=0.0351 train/loss=0.0351 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0004
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+[2026-10-02 05:06:21] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0372 | Epoch 185] timing/rollout_seconds=179.3602 timing/feedback_seconds=5.2894 timing/optimization_seconds=50.4266 timing/cycle_seconds=232.7407
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+
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+[2026-10-02 05:09:24] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0372 | Epoch 186] train/reward_dists_mean=0.8278 train/reward_dists_std=0.0396 train/reward_ssim_mean=0.6288 train/reward_ssim_std=0.1070 train/reward_dists_group_std_mean=0.0086 train/reward_dists_group_std_max=0.0178 train/reward_dists_group_std_min=0.0039 train/reward_dists_group_mean_std=0.0385 train/reward_ssim_group_std_mean=0.0065 train/reward_ssim_group_std_max=0.0183 train/reward_ssim_group_std_min=0.0035 train/reward_ssim_group_mean_std=0.1068 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.0643 train/adv_max=4.4434 train/adv_abs_mean=1.1289
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Epoch 186 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 05:09:49] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0372 | Epoch 186] train/clipped_loss_min=-4.4434 train/clipped_loss_max=5 train/clipped_loss_mean=0.0369 train/clipped_loss_std=1.4744 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-4.4434 train/unclipped_loss_max=5 train/unclipped_loss_mean=0.0369 train/unclipped_loss_std=1.4744 train/policy_loss=0.0369 train/loss=0.0369 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0001
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Epoch 186 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 05:10:14] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0373 | Epoch 186] train/clipped_loss_min=-4.2107 train/clipped_loss_max=4.6777 train/clipped_loss_mean=-0.0371 train/clipped_loss_std=1.4394 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-4.2107 train/unclipped_loss_max=4.6777 train/unclipped_loss_mean=-0.0371 train/unclipped_loss_std=1.4394 train/policy_loss=-0.0371 train/loss=-0.0371 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0001
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+[2026-10-02 05:10:14] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0374 | Epoch 186] timing/rollout_seconds=179.4793 timing/feedback_seconds=5.4718 timing/optimization_seconds=50.6286 timing/cycle_seconds=233.1272
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+
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+[2026-10-02 05:13:17] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0374 | Epoch 187] train/reward_dists_mean=0.8339 train/reward_dists_std=0.0358 train/reward_ssim_mean=0.6614 train/reward_ssim_std=0.1317 train/reward_dists_group_std_mean=0.0075 train/reward_dists_group_std_max=0.0164 train/reward_dists_group_std_min=0.0033 train/reward_dists_group_mean_std=0.0349 train/reward_ssim_group_std_mean=0.0062 train/reward_ssim_group_std_max=0.0130 train/reward_ssim_group_std_min=0.0027 train/reward_ssim_group_mean_std=0.1315 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.7073 train/adv_max=4.1811 train/adv_abs_mean=1.2057
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Epoch 187 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 05:13:42] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0374 | Epoch 187] train/clipped_loss_min=-3.6033 train/clipped_loss_max=4.4865 train/clipped_loss_mean=-0.0332 train/clipped_loss_std=1.4786 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-3.6033 train/unclipped_loss_max=4.4865 train/unclipped_loss_mean=-0.0332 train/unclipped_loss_std=1.4786 train/policy_loss=-0.0332 train/loss=-0.0332 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0004
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Epoch 187 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 05:14:07] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0375 | Epoch 187] train/clipped_loss_min=-4.1811 train/clipped_loss_max=4.7073 train/clipped_loss_mean=0.0332 train/clipped_loss_std=1.5167 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-4.1811 train/unclipped_loss_max=4.7073 train/unclipped_loss_mean=0.0332 train/unclipped_loss_std=1.5167 train/policy_loss=0.0332 train/loss=0.0332 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0004
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Epoch 187 Training: 100%|██████████| 96/96 [00:49<00:00,  1.93it/s]
+[2026-10-02 05:14:07] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0376 | Epoch 187] timing/rollout_seconds=179.4168 timing/feedback_seconds=5.1444 timing/optimization_seconds=50.4640 timing/cycle_seconds=232.9124
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+
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+[2026-10-02 05:17:10] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0376 | Epoch 188] train/reward_dists_mean=0.8414 train/reward_dists_std=0.0361 train/reward_ssim_mean=0.6554 train/reward_ssim_std=0.1094 train/reward_dists_group_std_mean=0.0067 train/reward_dists_group_std_max=0.0197 train/reward_dists_group_std_min=0.0034 train/reward_dists_group_mean_std=0.0354 train/reward_ssim_group_std_mean=0.0058 train/reward_ssim_group_std_max=0.0109 train/reward_ssim_group_std_min=0.0034 train/reward_ssim_group_mean_std=0.1092 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.5591 train/adv_max=4.0640 train/adv_abs_mean=1.1449
+
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Epoch 188 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 05:17:35] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0376 | Epoch 188] train/clipped_loss_min=-4.0640 train/clipped_loss_max=5 train/clipped_loss_mean=-0.0273 train/clipped_loss_std=1.4173 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-4.0640 train/unclipped_loss_max=5 train/unclipped_loss_mean=-0.0273 train/unclipped_loss_std=1.4173 train/policy_loss=-0.0273 train/loss=-0.0273 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0004
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Epoch 188 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 05:18:00] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0377 | Epoch 188] train/clipped_loss_min=-3.8892 train/clipped_loss_max=4.8472 train/clipped_loss_mean=0.0258 train/clipped_loss_std=1.4663 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-3.8892 train/unclipped_loss_max=4.8472 train/unclipped_loss_mean=0.0258 train/unclipped_loss_std=1.4663 train/policy_loss=0.0258 train/loss=0.0258 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0004
+
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Epoch 188 Training: 100%|██████████| 96/96 [00:49<00:00,  1.93it/s]
+[2026-10-02 05:18:00] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0378 | Epoch 188] timing/rollout_seconds=179.4908 timing/feedback_seconds=5.6068 timing/optimization_seconds=50.3754 timing/cycle_seconds=232.8219
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+
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Pointwise Rewards: dists:  50%|█████     | 3/6 [00:00<00:00,  3.26it/s]
Pointwise Rewards: dists:  67%|██████▋   | 4/6 [00:01<00:00,  3.30it/s]
Pointwise Rewards: dists:  83%|████████▎ | 5/6 [00:01<00:00,  3.29it/s]
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+[2026-10-02 05:21:03] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0378 | Epoch 189] train/reward_dists_mean=0.8305 train/reward_dists_std=0.0365 train/reward_ssim_mean=0.6284 train/reward_ssim_std=0.1039 train/reward_dists_group_std_mean=0.0081 train/reward_dists_group_std_max=0.0162 train/reward_dists_group_std_min=0.0029 train/reward_dists_group_mean_std=0.0355 train/reward_ssim_group_std_mean=0.0066 train/reward_ssim_group_std_max=0.0151 train/reward_ssim_group_std_min=0.0027 train/reward_ssim_group_mean_std=0.1036 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.2176 train/adv_max=4.0880 train/adv_abs_mean=1.1841
+
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Epoch 189 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 05:21:28] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0378 | Epoch 189] train/clipped_loss_min=-3.9735 train/clipped_loss_max=3.9149 train/clipped_loss_mean=0.0010 train/clipped_loss_std=1.4837 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-3.9735 train/unclipped_loss_max=3.9149 train/unclipped_loss_mean=0.0010 train/unclipped_loss_std=1.4837 train/policy_loss=0.0010 train/loss=0.0010 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
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Epoch 189 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 05:21:53] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0379 | Epoch 189] train/clipped_loss_min=-4.0881 train/clipped_loss_max=4.2176 train/clipped_loss_mean=-0.0010 train/clipped_loss_std=1.4810 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0.0001 train/unclipped_loss_min=-4.0881 train/unclipped_loss_max=4.2176 train/unclipped_loss_mean=-0.0010 train/unclipped_loss_std=1.4810 train/policy_loss=-0.0010 train/loss=-0.0010 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
+
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+[2026-10-02 05:21:53] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0380 | Epoch 189] timing/rollout_seconds=179.6404 timing/feedback_seconds=5.1627 timing/optimization_seconds=50.3710 timing/cycle_seconds=232.9284
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+[2026-10-02 05:22:46] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0380 | Epoch 190] eval/flir_rl_full/reward_dists_mean=0.8412 eval/flir_rl_full/reward_dists_std=0.0334 eval/flir_rl_full/reward_psnr_mean=20.2921 eval/flir_rl_full/reward_psnr_std=2.8563 eval/flir_rl_full/reward_ssim_mean=0.6232 eval/flir_rl_full/reward_ssim_std=0.0839
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+
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+[2026-10-02 05:25:50] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0380 | Epoch 190] train/reward_dists_mean=0.8470 train/reward_dists_std=0.0244 train/reward_ssim_mean=0.6666 train/reward_ssim_std=0.0987 train/reward_dists_group_std_mean=0.0067 train/reward_dists_group_std_max=0.0131 train/reward_dists_group_std_min=0.0033 train/reward_dists_group_mean_std=0.0233 train/reward_ssim_group_std_mean=0.0071 train/reward_ssim_group_std_max=0.0154 train/reward_ssim_group_std_min=0.0025 train/reward_ssim_group_mean_std=0.0984 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.6334 train/adv_max=4.5643 train/adv_abs_mean=1.1866
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Epoch 190 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 05:26:14] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0380 | Epoch 190] train/clipped_loss_min=-4.5643 train/clipped_loss_max=4.0550 train/clipped_loss_mean=-0.0099 train/clipped_loss_std=1.4976 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-4.5643 train/unclipped_loss_max=4.0550 train/unclipped_loss_mean=-0.0099 train/unclipped_loss_std=1.4976 train/policy_loss=-0.0099 train/loss=-0.0099 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0001
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Epoch 190 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 05:26:39] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0381 | Epoch 190] train/clipped_loss_min=-3.7201 train/clipped_loss_max=5.0000 train/clipped_loss_mean=0.0083 train/clipped_loss_std=1.4884 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-3.7201 train/unclipped_loss_max=5.0000 train/unclipped_loss_mean=0.0083 train/unclipped_loss_std=1.4884 train/policy_loss=0.0083 train/loss=0.0083 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0001
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+[2026-10-02 05:26:39] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0382 | Epoch 190] timing/rollout_seconds=179.5346 timing/feedback_seconds=5.0817 timing/optimization_seconds=50.4518 timing/cycle_seconds=233.0162
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+[2026-10-02 05:29:43] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0382 | Epoch 191] train/reward_dists_mean=0.8446 train/reward_dists_std=0.0318 train/reward_ssim_mean=0.6101 train/reward_ssim_std=0.1113 train/reward_dists_group_std_mean=0.0062 train/reward_dists_group_std_max=0.0112 train/reward_dists_group_std_min=0.0035 train/reward_dists_group_mean_std=0.0312 train/reward_ssim_group_std_mean=0.0066 train/reward_ssim_group_std_max=0.0137 train/reward_ssim_group_std_min=0.0040 train/reward_ssim_group_mean_std=0.1111 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.6542 train/adv_max=4.8429 train/adv_abs_mean=1.1735
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Epoch 191 Training:  49%|████▉     | 47/96 [00:24<00:25,  1.93it/s]
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Epoch 191 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 05:30:08] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0382 | Epoch 191] train/clipped_loss_min=-4.5230 train/clipped_loss_max=4.6542 train/clipped_loss_mean=0.1250 train/clipped_loss_std=1.4945 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-4.5230 train/unclipped_loss_max=4.6542 train/unclipped_loss_mean=0.1250 train/unclipped_loss_std=1.4945 train/policy_loss=0.1250 train/loss=0.1250 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0004
+
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Epoch 191 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 05:30:32] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0383 | Epoch 191] train/clipped_loss_min=-4.8428 train/clipped_loss_max=4.4547 train/clipped_loss_mean=-0.1250 train/clipped_loss_std=1.4749 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0.0003 train/unclipped_loss_min=-4.8428 train/unclipped_loss_max=4.4547 train/unclipped_loss_mean=-0.1250 train/unclipped_loss_std=1.4749 train/policy_loss=-0.1250 train/loss=-0.1250 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0004
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+[2026-10-02 05:30:32] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0384 | Epoch 191] timing/rollout_seconds=179.4783 timing/feedback_seconds=5.0382 timing/optimization_seconds=50.5968 timing/cycle_seconds=233.0353
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Pointwise Rewards: dists:  17%|█▋        | 1/6 [00:00<00:01,  3.22it/s]
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Pointwise Rewards: dists:  50%|█████     | 3/6 [00:00<00:00,  3.26it/s]
Pointwise Rewards: dists:  67%|██████▋   | 4/6 [00:01<00:00,  3.31it/s]
Pointwise Rewards: dists:  83%|████████▎ | 5/6 [00:01<00:00,  3.25it/s]
Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.22it/s]
Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.24it/s]
+[2026-10-02 05:33:35] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0384 | Epoch 192] train/reward_dists_mean=0.8417 train/reward_dists_std=0.0283 train/reward_ssim_mean=0.6325 train/reward_ssim_std=0.0977 train/reward_dists_group_std_mean=0.0064 train/reward_dists_group_std_max=0.0118 train/reward_dists_group_std_min=0.0035 train/reward_dists_group_mean_std=0.0275 train/reward_ssim_group_std_mean=0.0065 train/reward_ssim_group_std_max=0.0157 train/reward_ssim_group_std_min=0.0026 train/reward_ssim_group_mean_std=0.0975 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.0041 train/adv_max=4.3327 train/adv_abs_mean=1.1724
+
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Epoch 192 Training:   9%|▉         | 9/96 [00:04<00:45,  1.93it/s]
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Epoch 192 Training:  10%|█         | 10/96 [00:05<00:44,  1.93it/s]
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Epoch 192 Training:  11%|█▏        | 11/96 [00:05<00:44,  1.93it/s]
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Epoch 192 Training:  12%|█▎        | 12/96 [00:06<00:43,  1.93it/s]
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Epoch 192 Training:  14%|█▎        | 13/96 [00:06<00:43,  1.93it/s]
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Epoch 192 Training:  15%|█▍        | 14/96 [00:07<00:42,  1.93it/s]
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Epoch 192 Training:  18%|█▊        | 17/96 [00:08<00:40,  1.93it/s]
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Epoch 192 Training:  19%|█▉        | 18/96 [00:09<00:40,  1.93it/s]
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Epoch 192 Training:  23%|██▎       | 22/96 [00:11<00:38,  1.93it/s]
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Epoch 192 Training:  24%|██▍       | 23/96 [00:11<00:37,  1.93it/s]
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Epoch 192 Training:  25%|██▌       | 24/96 [00:12<00:37,  1.93it/s]
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Epoch 192 Training:  26%|██▌       | 25/96 [00:12<00:36,  1.93it/s]
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Epoch 192 Training:  27%|██▋       | 26/96 [00:13<00:36,  1.93it/s]
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Epoch 192 Training:  28%|██▊       | 27/96 [00:13<00:35,  1.93it/s]
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Epoch 192 Training:  29%|██▉       | 28/96 [00:14<00:35,  1.93it/s]
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Epoch 192 Training:  30%|███       | 29/96 [00:14<00:34,  1.93it/s]
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Epoch 192 Training:  31%|███▏      | 30/96 [00:15<00:34,  1.93it/s]
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Epoch 192 Training:  32%|███▏      | 31/96 [00:16<00:33,  1.93it/s]
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Epoch 192 Training:  33%|███▎      | 32/96 [00:16<00:33,  1.93it/s]
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Epoch 192 Training:  35%|███▌      | 34/96 [00:17<00:32,  1.93it/s]
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Epoch 192 Training:  36%|███▋      | 35/96 [00:18<00:31,  1.93it/s]
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Epoch 192 Training:  38%|███▊      | 36/96 [00:18<00:31,  1.93it/s]
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Epoch 192 Training:  39%|███▊      | 37/96 [00:19<00:30,  1.93it/s]
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Epoch 192 Training:  40%|███▉      | 38/96 [00:19<00:30,  1.93it/s]
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Epoch 192 Training:  41%|████      | 39/96 [00:20<00:29,  1.93it/s]
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Epoch 192 Training:  42%|████▏     | 40/96 [00:20<00:29,  1.93it/s]
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Epoch 192 Training:  43%|████▎     | 41/96 [00:21<00:28,  1.93it/s]
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Epoch 192 Training:  44%|████▍     | 42/96 [00:21<00:28,  1.93it/s]
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Epoch 192 Training:  45%|████▍     | 43/96 [00:22<00:27,  1.93it/s]
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Epoch 192 Training:  46%|████▌     | 44/96 [00:22<00:26,  1.93it/s]
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Epoch 192 Training:  47%|████▋     | 45/96 [00:23<00:26,  1.93it/s]
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Epoch 192 Training:  48%|████▊     | 46/96 [00:23<00:25,  1.93it/s]
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Epoch 192 Training:  49%|████▉     | 47/96 [00:24<00:25,  1.93it/s]
+
Epoch 192 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 05:34:00] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0384 | Epoch 192] train/clipped_loss_min=-4.2533 train/clipped_loss_max=5 train/clipped_loss_mean=-0.0095 train/clipped_loss_std=1.4902 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-4.2533 train/unclipped_loss_max=5 train/unclipped_loss_mean=-0.0095 train/unclipped_loss_std=1.4902 train/policy_loss=-0.0095 train/loss=-0.0095 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
+
+
Epoch 192 Timestep: 100%|██████████| 1/1 [00:00<00:00,  1.89it/s]
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Epoch 192 Training:  50%|█████     | 48/96 [00:24<00:25,  1.86it/s]
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Epoch 192 Training:  51%|█████     | 49/96 [00:25<00:24,  1.94it/s]
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Epoch 192 Training:  52%|█████▏    | 50/96 [00:25<00:23,  1.93it/s]
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Epoch 192 Training:  53%|█████▎    | 51/96 [00:26<00:23,  1.93it/s]
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Epoch 192 Training:  54%|█████▍    | 52/96 [00:26<00:22,  1.93it/s]
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Epoch 192 Training:  58%|█████▊    | 56/96 [00:29<00:20,  1.93it/s]
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Epoch 192 Training:  59%|█████▉    | 57/96 [00:29<00:20,  1.93it/s]
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Epoch 192 Training:  98%|█████████▊| 94/96 [00:48<00:01,  1.93it/s]
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Epoch 192 Training:  99%|█████████▉| 95/96 [00:49<00:00,  1.93it/s]
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Epoch 192 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 05:34:25] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0385 | Epoch 192] train/clipped_loss_min=-4.3327 train/clipped_loss_max=3.7086 train/clipped_loss_mean=0.0095 train/clipped_loss_std=1.4486 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-4.3327 train/unclipped_loss_max=3.7086 train/unclipped_loss_mean=0.0095 train/unclipped_loss_std=1.4486 train/policy_loss=0.0095 train/loss=0.0095 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
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Epoch 192 Timestep: 100%|██████████| 1/1 [00:00<00:00,  1.89it/s]
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Epoch 192 Training: 100%|██████████| 96/96 [00:49<00:00,  1.86it/s]
Epoch 192 Training: 100%|██████████| 96/96 [00:49<00:00,  1.93it/s]
+[2026-10-02 05:34:25] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0386 | Epoch 192] timing/rollout_seconds=179.4124 timing/feedback_seconds=5.5226 timing/optimization_seconds=50.4729 timing/cycle_seconds=232.8137
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+
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+[2026-10-02 05:37:28] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0386 | Epoch 193] train/reward_dists_mean=0.8388 train/reward_dists_std=0.0280 train/reward_ssim_mean=0.6079 train/reward_ssim_std=0.0881 train/reward_dists_group_std_mean=0.0067 train/reward_dists_group_std_max=0.0133 train/reward_dists_group_std_min=0.0035 train/reward_dists_group_mean_std=0.0271 train/reward_ssim_group_std_mean=0.0076 train/reward_ssim_group_std_max=0.0183 train/reward_ssim_group_std_min=0.0026 train/reward_ssim_group_mean_std=0.0877 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.6570 train/adv_max=4.2277 train/adv_abs_mean=1.1830
+
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Epoch 193 Training:  49%|████▉     | 47/96 [00:24<00:25,  1.93it/s]
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Epoch 193 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 05:37:53] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0386 | Epoch 193] train/clipped_loss_min=-4.2277 train/clipped_loss_max=4.6570 train/clipped_loss_mean=-0.0425 train/clipped_loss_std=1.4224 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-4.2277 train/unclipped_loss_max=4.6570 train/unclipped_loss_mean=-0.0425 train/unclipped_loss_std=1.4224 train/policy_loss=-0.0425 train/loss=-0.0425 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0002
+
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Epoch 193 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 05:38:18] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0387 | Epoch 193] train/clipped_loss_min=-4.0923 train/clipped_loss_max=4.0848 train/clipped_loss_mean=0.0425 train/clipped_loss_std=1.5345 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0.0001 train/unclipped_loss_min=-4.0923 train/unclipped_loss_max=4.0848 train/unclipped_loss_mean=0.0425 train/unclipped_loss_std=1.5345 train/policy_loss=0.0425 train/loss=0.0425 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
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+[2026-10-02 05:38:18] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0388 | Epoch 193] timing/rollout_seconds=179.3196 timing/feedback_seconds=4.7265 timing/optimization_seconds=50.7281 timing/cycle_seconds=232.9639
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Epoch 194 Sampling: 100%|██████████| 96/96 [02:56<00:00,  1.84s/it]
+
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Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:01<00:00,  5.90it/s]
+
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Pointwise Rewards: dists:  50%|█████     | 3/6 [00:00<00:00,  3.41it/s]
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Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.35it/s]
+[2026-10-02 05:41:21] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0388 | Epoch 194] train/reward_dists_mean=0.8453 train/reward_dists_std=0.0291 train/reward_ssim_mean=0.6335 train/reward_ssim_std=0.0970 train/reward_dists_group_std_mean=0.0065 train/reward_dists_group_std_max=0.0167 train/reward_dists_group_std_min=0.0032 train/reward_dists_group_mean_std=0.0283 train/reward_ssim_group_std_mean=0.0084 train/reward_ssim_group_std_max=0.0182 train/reward_ssim_group_std_min=0.0020 train/reward_ssim_group_mean_std=0.0966 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.3483 train/adv_max=4.4516 train/adv_abs_mean=1.2042
+
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Epoch 194 Training:  11%|█▏        | 11/96 [00:05<00:44,  1.93it/s]
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Epoch 194 Training:  12%|█▎        | 12/96 [00:06<00:43,  1.93it/s]
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Epoch 194 Training:  14%|█▎        | 13/96 [00:06<00:43,  1.93it/s]
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Epoch 194 Training:  15%|█▍        | 14/96 [00:07<00:42,  1.93it/s]
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Epoch 194 Training:  19%|█▉        | 18/96 [00:09<00:40,  1.93it/s]
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Epoch 194 Training:  25%|██▌       | 24/96 [00:12<00:37,  1.93it/s]
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Epoch 194 Training:  26%|██▌       | 25/96 [00:12<00:36,  1.93it/s]
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Epoch 194 Training:  29%|██▉       | 28/96 [00:14<00:35,  1.93it/s]
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Epoch 194 Training:  39%|███▊      | 37/96 [00:19<00:30,  1.93it/s]
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Epoch 194 Training:  49%|████▉     | 47/96 [00:24<00:25,  1.93it/s]
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Epoch 194 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 05:41:46] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0388 | Epoch 194] train/clipped_loss_min=-4.4516 train/clipped_loss_max=4.3483 train/clipped_loss_mean=-0.0197 train/clipped_loss_std=1.4436 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-4.4516 train/unclipped_loss_max=4.3483 train/unclipped_loss_mean=-0.0197 train/unclipped_loss_std=1.4436 train/policy_loss=-0.0197 train/loss=-0.0197 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0001
+
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Epoch 194 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 05:42:11] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0389 | Epoch 194] train/clipped_loss_min=-4.3276 train/clipped_loss_max=4.3198 train/clipped_loss_mean=0.0197 train/clipped_loss_std=1.5675 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-4.3276 train/unclipped_loss_max=4.3198 train/unclipped_loss_mean=0.0197 train/unclipped_loss_std=1.5675 train/policy_loss=0.0197 train/loss=0.0197 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0001
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+[2026-10-02 05:42:11] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0390 | Epoch 194] timing/rollout_seconds=179.1872 timing/feedback_seconds=5.0937 timing/optimization_seconds=50.4321 timing/cycle_seconds=232.5625
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Epoch 195 Sampling: 100%|██████████| 96/96 [02:56<00:00,  1.84s/it]
+
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Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:01<00:00,  5.53it/s]
+
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Pointwise Rewards: dists:  33%|███▎      | 2/6 [00:00<00:01,  3.20it/s]
Pointwise Rewards: dists:  50%|█████     | 3/6 [00:00<00:00,  3.17it/s]
Pointwise Rewards: dists:  67%|██████▋   | 4/6 [00:01<00:00,  3.20it/s]
Pointwise Rewards: dists:  83%|████████▎ | 5/6 [00:01<00:00,  3.22it/s]
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Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.23it/s]
+[2026-10-02 05:45:14] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0390 | Epoch 195] train/reward_dists_mean=0.8403 train/reward_dists_std=0.0311 train/reward_ssim_mean=0.6277 train/reward_ssim_std=0.1111 train/reward_dists_group_std_mean=0.0067 train/reward_dists_group_std_max=0.0153 train/reward_dists_group_std_min=0.0029 train/reward_dists_group_mean_std=0.0302 train/reward_ssim_group_std_mean=0.0067 train/reward_ssim_group_std_max=0.0166 train/reward_ssim_group_std_min=0.0026 train/reward_ssim_group_mean_std=0.1109 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.5903 train/adv_max=4.1676 train/adv_abs_mean=1.2468
+
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Epoch 195 Training:   9%|▉         | 9/96 [00:04<00:45,  1.93it/s]
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Epoch 195 Training:  11%|█▏        | 11/96 [00:05<00:44,  1.93it/s]
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Epoch 195 Training:  12%|█▎        | 12/96 [00:06<00:43,  1.93it/s]
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Epoch 195 Training:  14%|█▎        | 13/96 [00:06<00:43,  1.93it/s]
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Epoch 195 Training:  15%|█▍        | 14/96 [00:07<00:42,  1.92it/s]
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Epoch 195 Training:  19%|█▉        | 18/96 [00:09<00:40,  1.93it/s]
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Epoch 195 Training:  24%|██▍       | 23/96 [00:11<00:37,  1.93it/s]
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Epoch 195 Training:  25%|██▌       | 24/96 [00:12<00:37,  1.93it/s]
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Epoch 195 Training:  26%|██▌       | 25/96 [00:12<00:36,  1.92it/s]
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Epoch 195 Training:  29%|██▉       | 28/96 [00:14<00:35,  1.93it/s]
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Epoch 195 Training:  39%|███▊      | 37/96 [00:19<00:30,  1.92it/s]
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Epoch 195 Training:  44%|████▍     | 42/96 [00:21<00:28,  1.93it/s]
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Epoch 195 Training:  48%|████▊     | 46/96 [00:23<00:25,  1.93it/s]
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Epoch 195 Training:  49%|████▉     | 47/96 [00:24<00:25,  1.93it/s]
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Epoch 195 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 05:45:39] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0390 | Epoch 195] train/clipped_loss_min=-4.1676 train/clipped_loss_max=3.9373 train/clipped_loss_mean=-0.0222 train/clipped_loss_std=1.5102 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-4.1676 train/unclipped_loss_max=3.9373 train/unclipped_loss_mean=-0.0222 train/unclipped_loss_std=1.5102 train/policy_loss=-0.0222 train/loss=-0.0222 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
+
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Epoch 195 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 05:46:04] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0391 | Epoch 195] train/clipped_loss_min=-3.4223 train/clipped_loss_max=4.5902 train/clipped_loss_mean=0.0222 train/clipped_loss_std=1.5595 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0.0001 train/unclipped_loss_min=-3.4223 train/unclipped_loss_max=4.5902 train/unclipped_loss_mean=0.0222 train/unclipped_loss_std=1.5595 train/policy_loss=0.0222 train/loss=0.0222 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
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+[2026-10-02 05:46:04] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0392 | Epoch 195] timing/rollout_seconds=179.3812 timing/feedback_seconds=5.2891 timing/optimization_seconds=50.6386 timing/cycle_seconds=232.9632
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Epoch 196 Sampling: 100%|██████████| 96/96 [02:56<00:00,  1.84s/it]
+
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+
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Pointwise Rewards: dists:  50%|█████     | 3/6 [00:00<00:00,  3.26it/s]
Pointwise Rewards: dists:  67%|██████▋   | 4/6 [00:01<00:00,  3.21it/s]
Pointwise Rewards: dists:  83%|████████▎ | 5/6 [00:01<00:00,  3.19it/s]
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Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.24it/s]
+[2026-10-02 05:49:07] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0392 | Epoch 196] train/reward_dists_mean=0.8390 train/reward_dists_std=0.0297 train/reward_ssim_mean=0.6338 train/reward_ssim_std=0.1097 train/reward_dists_group_std_mean=0.0069 train/reward_dists_group_std_max=0.0139 train/reward_dists_group_std_min=0.0040 train/reward_dists_group_mean_std=0.0287 train/reward_ssim_group_std_mean=0.0069 train/reward_ssim_group_std_max=0.0134 train/reward_ssim_group_std_min=0.0027 train/reward_ssim_group_mean_std=0.1095 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.6720 train/adv_max=4.1248 train/adv_abs_mean=1.1924
+
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Epoch 196 Training:   5%|▌         | 5/96 [00:02<00:46,  1.95it/s]
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Epoch 196 Training:   6%|▋         | 6/96 [00:03<00:46,  1.94it/s]
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Epoch 196 Training:   7%|▋         | 7/96 [00:03<00:45,  1.93it/s]
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Epoch 196 Training:   8%|▊         | 8/96 [00:04<00:45,  1.93it/s]
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Epoch 196 Training:   9%|▉         | 9/96 [00:04<00:45,  1.93it/s]
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Epoch 196 Training:  10%|█         | 10/96 [00:05<00:44,  1.93it/s]
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Epoch 196 Training:  11%|█▏        | 11/96 [00:05<00:44,  1.93it/s]
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Epoch 196 Training:  12%|█▎        | 12/96 [00:06<00:43,  1.93it/s]
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Epoch 196 Training:  14%|█▎        | 13/96 [00:06<00:43,  1.93it/s]
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Epoch 196 Training:  15%|█▍        | 14/96 [00:07<00:42,  1.93it/s]
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Epoch 196 Training:  16%|█▌        | 15/96 [00:07<00:42,  1.93it/s]
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Epoch 196 Training:  17%|█▋        | 16/96 [00:08<00:41,  1.93it/s]
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Epoch 196 Training:  18%|█▊        | 17/96 [00:08<00:41,  1.93it/s]
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Epoch 196 Training:  19%|█▉        | 18/96 [00:09<00:40,  1.93it/s]
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Epoch 196 Training:  20%|█▉        | 19/96 [00:09<00:39,  1.93it/s]
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Epoch 196 Training:  21%|██        | 20/96 [00:10<00:39,  1.93it/s]
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Epoch 196 Training:  22%|██▏       | 21/96 [00:10<00:38,  1.93it/s]
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Epoch 196 Training:  23%|██▎       | 22/96 [00:11<00:38,  1.93it/s]
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Epoch 196 Training:  24%|██▍       | 23/96 [00:11<00:37,  1.93it/s]
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Epoch 196 Training:  25%|██▌       | 24/96 [00:12<00:37,  1.93it/s]
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Epoch 196 Training:  26%|██▌       | 25/96 [00:12<00:36,  1.93it/s]
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Epoch 196 Training:  27%|██▋       | 26/96 [00:13<00:36,  1.93it/s]
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Epoch 196 Training:  28%|██▊       | 27/96 [00:13<00:35,  1.93it/s]
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Epoch 196 Training:  29%|██▉       | 28/96 [00:14<00:35,  1.93it/s]
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Epoch 196 Training:  30%|███       | 29/96 [00:14<00:34,  1.93it/s]
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Epoch 196 Training:  31%|███▏      | 30/96 [00:15<00:34,  1.93it/s]
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Epoch 196 Training:  32%|███▏      | 31/96 [00:16<00:33,  1.93it/s]
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Epoch 196 Training:  33%|███▎      | 32/96 [00:16<00:33,  1.92it/s]
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Epoch 196 Training:  34%|███▍      | 33/96 [00:17<00:32,  1.93it/s]
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Epoch 196 Training:  35%|███▌      | 34/96 [00:17<00:32,  1.92it/s]
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Epoch 196 Training:  36%|███▋      | 35/96 [00:18<00:31,  1.92it/s]
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Epoch 196 Training:  38%|███▊      | 36/96 [00:18<00:31,  1.93it/s]
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Epoch 196 Training:  39%|███▊      | 37/96 [00:19<00:30,  1.93it/s]
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Epoch 196 Training:  40%|███▉      | 38/96 [00:19<00:30,  1.93it/s]
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Epoch 196 Training:  41%|████      | 39/96 [00:20<00:29,  1.93it/s]
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Epoch 196 Training:  43%|████▎     | 41/96 [00:21<00:28,  1.93it/s]
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Epoch 196 Training:  44%|████▍     | 42/96 [00:21<00:28,  1.93it/s]
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Epoch 196 Training:  45%|████▍     | 43/96 [00:22<00:27,  1.93it/s]
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Epoch 196 Training:  46%|████▌     | 44/96 [00:22<00:26,  1.93it/s]
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Epoch 196 Training:  47%|████▋     | 45/96 [00:23<00:26,  1.93it/s]
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Epoch 196 Training:  48%|████▊     | 46/96 [00:23<00:25,  1.93it/s]
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Epoch 196 Training:  49%|████▉     | 47/96 [00:24<00:25,  1.93it/s]
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Epoch 196 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 05:49:32] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0392 | Epoch 196] train/clipped_loss_min=-3.6990 train/clipped_loss_max=4.6720 train/clipped_loss_mean=-0.0377 train/clipped_loss_std=1.4404 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-3.6990 train/unclipped_loss_max=4.6720 train/unclipped_loss_mean=-0.0377 train/unclipped_loss_std=1.4404 train/policy_loss=-0.0377 train/loss=-0.0377 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0001
+
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Epoch 196 Training:  51%|█████     | 49/96 [00:25<00:24,  1.94it/s]
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Epoch 196 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 05:49:56] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0393 | Epoch 196] train/clipped_loss_min=-4.1248 train/clipped_loss_max=4.5733 train/clipped_loss_mean=0.0377 train/clipped_loss_std=1.5107 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0.0001 train/unclipped_loss_min=-4.1248 train/unclipped_loss_max=4.5733 train/unclipped_loss_mean=0.0377 train/unclipped_loss_std=1.5107 train/policy_loss=0.0377 train/loss=0.0377 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0001
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+[2026-10-02 05:49:56] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0394 | Epoch 196] timing/rollout_seconds=179.3735 timing/feedback_seconds=5.2172 timing/optimization_seconds=50.4624 timing/cycle_seconds=232.6992
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+
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Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:01<00:00,  5.80it/s]
+
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Pointwise Rewards: dists:  50%|█████     | 3/6 [00:00<00:00,  3.22it/s]
Pointwise Rewards: dists:  67%|██████▋   | 4/6 [00:01<00:00,  3.20it/s]
Pointwise Rewards: dists:  83%|████████▎ | 5/6 [00:01<00:00,  3.31it/s]
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Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.31it/s]
+[2026-10-02 05:53:00] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0394 | Epoch 197] train/reward_dists_mean=0.8340 train/reward_dists_std=0.0301 train/reward_ssim_mean=0.6412 train/reward_ssim_std=0.0884 train/reward_dists_group_std_mean=0.0066 train/reward_dists_group_std_max=0.0114 train/reward_dists_group_std_min=0.0030 train/reward_dists_group_mean_std=0.0293 train/reward_ssim_group_std_mean=0.0064 train/reward_ssim_group_std_max=0.0134 train/reward_ssim_group_std_min=0.0030 train/reward_ssim_group_mean_std=0.0881 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.0770 train/adv_max=4.0935 train/adv_abs_mean=1.2066
+
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Epoch 197 Training:   7%|▋         | 7/96 [00:03<00:46,  1.93it/s]
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Epoch 197 Training:   8%|▊         | 8/96 [00:04<00:45,  1.93it/s]
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Epoch 197 Training:   9%|▉         | 9/96 [00:04<00:45,  1.93it/s]
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Epoch 197 Training:  10%|█         | 10/96 [00:05<00:44,  1.93it/s]
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Epoch 197 Training:  11%|█▏        | 11/96 [00:05<00:44,  1.93it/s]
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Epoch 197 Training:  12%|█▎        | 12/96 [00:06<00:43,  1.93it/s]
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Epoch 197 Training:  14%|█▎        | 13/96 [00:06<00:43,  1.93it/s]
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Epoch 197 Training:  15%|█▍        | 14/96 [00:07<00:42,  1.93it/s]
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Epoch 197 Training:  16%|█▌        | 15/96 [00:07<00:42,  1.93it/s]
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Epoch 197 Training:  17%|█▋        | 16/96 [00:08<00:41,  1.93it/s]
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Epoch 197 Training:  18%|█▊        | 17/96 [00:08<00:40,  1.93it/s]
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Epoch 197 Training:  19%|█▉        | 18/96 [00:09<00:40,  1.93it/s]
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Epoch 197 Training:  20%|█▉        | 19/96 [00:09<00:39,  1.93it/s]
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Epoch 197 Training:  24%|██▍       | 23/96 [00:11<00:37,  1.93it/s]
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Epoch 197 Training:  25%|██▌       | 24/96 [00:12<00:37,  1.93it/s]
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Epoch 197 Training:  26%|██▌       | 25/96 [00:12<00:36,  1.93it/s]
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Epoch 197 Training:  27%|██▋       | 26/96 [00:13<00:36,  1.93it/s]
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Epoch 197 Training:  28%|██▊       | 27/96 [00:13<00:35,  1.93it/s]
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Epoch 197 Training:  29%|██▉       | 28/96 [00:14<00:35,  1.93it/s]
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Epoch 197 Training:  30%|███       | 29/96 [00:14<00:34,  1.93it/s]
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Epoch 197 Training:  31%|███▏      | 30/96 [00:15<00:34,  1.93it/s]
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Epoch 197 Training:  32%|███▏      | 31/96 [00:16<00:33,  1.93it/s]
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Epoch 197 Training:  33%|███▎      | 32/96 [00:16<00:33,  1.93it/s]
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Epoch 197 Training:  34%|███▍      | 33/96 [00:17<00:32,  1.92it/s]
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Epoch 197 Training:  35%|███▌      | 34/96 [00:17<00:32,  1.92it/s]
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Epoch 197 Training:  38%|███▊      | 36/96 [00:18<00:31,  1.92it/s]
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Epoch 197 Training:  39%|███▊      | 37/96 [00:19<00:30,  1.92it/s]
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Epoch 197 Training:  40%|███▉      | 38/96 [00:19<00:30,  1.92it/s]
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Epoch 197 Training:  41%|████      | 39/96 [00:20<00:29,  1.93it/s]
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Epoch 197 Training:  44%|████▍     | 42/96 [00:21<00:28,  1.93it/s]
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Epoch 197 Training:  45%|████▍     | 43/96 [00:22<00:27,  1.93it/s]
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Epoch 197 Training:  46%|████▌     | 44/96 [00:22<00:26,  1.93it/s]
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Epoch 197 Training:  47%|████▋     | 45/96 [00:23<00:26,  1.93it/s]
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Epoch 197 Training:  48%|████▊     | 46/96 [00:23<00:25,  1.93it/s]
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Epoch 197 Training:  49%|████▉     | 47/96 [00:24<00:25,  1.93it/s]
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Epoch 197 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 05:53:24] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0394 | Epoch 197] train/clipped_loss_min=-4.0935 train/clipped_loss_max=5 train/clipped_loss_mean=0.0382 train/clipped_loss_std=1.4832 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-4.0935 train/unclipped_loss_max=5 train/unclipped_loss_mean=0.0382 train/unclipped_loss_std=1.4832 train/policy_loss=0.0382 train/loss=0.0382 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0004
+
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Epoch 197 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 05:53:49] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0395 | Epoch 197] train/clipped_loss_min=-4.0356 train/clipped_loss_max=4.7569 train/clipped_loss_mean=-0.0384 train/clipped_loss_std=1.5384 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0.0001 train/unclipped_loss_min=-4.0356 train/unclipped_loss_max=4.7569 train/unclipped_loss_mean=-0.0384 train/unclipped_loss_std=1.5384 train/policy_loss=-0.0384 train/loss=-0.0384 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0005
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+[2026-10-02 05:53:49] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0396 | Epoch 197] timing/rollout_seconds=179.4715 timing/feedback_seconds=5.2183 timing/optimization_seconds=50.4851 timing/cycle_seconds=232.8968
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Epoch 198 Sampling: 100%|██████████| 96/96 [02:56<00:00,  1.84s/it]
+
Pointwise Rewards: ssim:   0%|          | 0/6 [00:00<?, ?it/s]
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Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:01<00:00,  5.90it/s]
+
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Pointwise Rewards: dists:  33%|███▎      | 2/6 [00:00<00:01,  3.29it/s]
Pointwise Rewards: dists:  50%|█████     | 3/6 [00:00<00:00,  3.35it/s]
Pointwise Rewards: dists:  67%|██████▋   | 4/6 [00:01<00:00,  3.28it/s]
Pointwise Rewards: dists:  83%|████████▎ | 5/6 [00:01<00:00,  3.33it/s]
Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.31it/s]
Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.31it/s]
+[2026-10-02 05:56:52] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0396 | Epoch 198] train/reward_dists_mean=0.8390 train/reward_dists_std=0.0338 train/reward_ssim_mean=0.6477 train/reward_ssim_std=0.1175 train/reward_dists_group_std_mean=0.0069 train/reward_dists_group_std_max=0.0146 train/reward_dists_group_std_min=0.0030 train/reward_dists_group_mean_std=0.0330 train/reward_ssim_group_std_mean=0.0067 train/reward_ssim_group_std_max=0.0166 train/reward_ssim_group_std_min=0.0026 train/reward_ssim_group_mean_std=0.1173 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.6032 train/adv_max=3.9963 train/adv_abs_mean=1.2325
+
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Epoch 198 Training:   4%|▍         | 4/96 [00:02<00:47,  1.95it/s]
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Epoch 198 Training:   5%|▌         | 5/96 [00:02<00:46,  1.95it/s]
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Epoch 198 Training:   7%|▋         | 7/96 [00:03<00:52,  1.70it/s]
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Epoch 198 Training:   8%|▊         | 8/96 [00:04<00:49,  1.76it/s]
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Epoch 198 Training:   9%|▉         | 9/96 [00:04<00:48,  1.81it/s]
+
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Epoch 198 Training:  10%|█         | 10/96 [00:05<00:54,  1.59it/s]
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Epoch 198 Training:  11%|█▏        | 11/96 [00:06<00:50,  1.68it/s]
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Epoch 198 Training:  12%|█▎        | 12/96 [00:06<00:48,  1.75it/s]
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Epoch 198 Training:  14%|█▎        | 13/96 [00:07<00:46,  1.80it/s]
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Epoch 198 Training:  15%|█▍        | 14/96 [00:07<00:44,  1.83it/s]
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Epoch 198 Training:  16%|█▌        | 15/96 [00:08<00:43,  1.86it/s]
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Epoch 198 Training:  17%|█▋        | 16/96 [00:08<00:42,  1.87it/s]
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Epoch 198 Training:  18%|█▊        | 17/96 [00:09<00:41,  1.90it/s]
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Epoch 198 Training:  19%|█▉        | 18/96 [00:09<00:40,  1.91it/s]
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Epoch 198 Training:  20%|█▉        | 19/96 [00:10<00:40,  1.91it/s]
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Epoch 198 Training:  24%|██▍       | 23/96 [00:12<00:37,  1.92it/s]
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Epoch 198 Training:  25%|██▌       | 24/96 [00:12<00:37,  1.92it/s]
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Epoch 198 Training:  26%|██▌       | 25/96 [00:13<00:36,  1.93it/s]
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Epoch 198 Training:  27%|██▋       | 26/96 [00:14<00:36,  1.93it/s]
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Epoch 198 Training:  28%|██▊       | 27/96 [00:14<00:35,  1.93it/s]
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Epoch 198 Training:  29%|██▉       | 28/96 [00:15<00:35,  1.93it/s]
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Epoch 198 Training:  30%|███       | 29/96 [00:15<00:34,  1.93it/s]
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Epoch 198 Training:  31%|███▏      | 30/96 [00:16<00:34,  1.93it/s]
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Epoch 198 Training:  32%|███▏      | 31/96 [00:16<00:33,  1.93it/s]
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Epoch 198 Training:  33%|███▎      | 32/96 [00:17<00:33,  1.93it/s]
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Epoch 198 Training:  34%|███▍      | 33/96 [00:17<00:32,  1.93it/s]
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Epoch 198 Training:  35%|███▌      | 34/96 [00:18<00:32,  1.93it/s]
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Epoch 198 Training:  36%|███▋      | 35/96 [00:18<00:31,  1.93it/s]
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Epoch 198 Training:  38%|███▊      | 36/96 [00:19<00:31,  1.93it/s]
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Epoch 198 Training:  39%|███▊      | 37/96 [00:19<00:30,  1.93it/s]
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Epoch 198 Training:  40%|███▉      | 38/96 [00:20<00:30,  1.93it/s]
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Epoch 198 Training:  41%|████      | 39/96 [00:20<00:29,  1.93it/s]
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Epoch 198 Training:  42%|████▏     | 40/96 [00:21<00:29,  1.93it/s]
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Epoch 198 Training:  43%|████▎     | 41/96 [00:21<00:28,  1.93it/s]
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Epoch 198 Training:  44%|████▍     | 42/96 [00:22<00:28,  1.93it/s]
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Epoch 198 Training:  45%|████▍     | 43/96 [00:22<00:27,  1.93it/s]
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Epoch 198 Training:  46%|████▌     | 44/96 [00:23<00:26,  1.93it/s]
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Epoch 198 Training:  47%|████▋     | 45/96 [00:23<00:26,  1.93it/s]
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Epoch 198 Training:  48%|████▊     | 46/96 [00:24<00:25,  1.93it/s]
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Epoch 198 Training:  49%|████▉     | 47/96 [00:24<00:25,  1.93it/s]
+
Epoch 198 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 05:57:18] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0396 | Epoch 198] train/clipped_loss_min=-3.9963 train/clipped_loss_max=4.3071 train/clipped_loss_mean=0.0507 train/clipped_loss_std=1.5303 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-3.9963 train/unclipped_loss_max=4.3071 train/unclipped_loss_mean=0.0507 train/unclipped_loss_std=1.5303 train/policy_loss=0.0507 train/loss=0.0507 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0001
+
+
Epoch 198 Timestep: 100%|██████████| 1/1 [00:00<00:00,  1.90it/s]
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Epoch 198 Training:  50%|█████     | 48/96 [00:25<00:25,  1.86it/s]
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Epoch 198 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 05:57:43] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0397 | Epoch 198] train/clipped_loss_min=-3.9452 train/clipped_loss_max=4.6031 train/clipped_loss_mean=-0.0507 train/clipped_loss_std=1.5240 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-3.9452 train/unclipped_loss_max=4.6031 train/unclipped_loss_mean=-0.0507 train/unclipped_loss_std=1.5240 train/policy_loss=-0.0507 train/loss=-0.0507 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0001
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+[2026-10-02 05:57:43] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0398 | Epoch 198] timing/rollout_seconds=179.2796 timing/feedback_seconds=5.2655 timing/optimization_seconds=51.0343 timing/cycle_seconds=233.2190
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Epoch 199 Sampling:  76%|███████▌  | 73/96 [02:14<00:42,  1.84s/it]
Epoch 199 Sampling:  77%|███████▋  | 74/96 [02:16<00:40,  1.84s/it]
Epoch 199 Sampling:  78%|███████▊  | 75/96 [02:18<00:38,  1.84s/it]
Epoch 199 Sampling:  79%|███████▉  | 76/96 [02:20<00:36,  1.84s/it]
Epoch 199 Sampling:  80%|████████  | 77/96 [02:21<00:34,  1.84s/it]
Epoch 199 Sampling:  81%|████████▏ | 78/96 [02:23<00:33,  1.84s/it]
Epoch 199 Sampling:  82%|████████▏ | 79/96 [02:25<00:31,  1.84s/it]
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Epoch 199 Sampling:  84%|████████▍ | 81/96 [02:29<00:27,  1.84s/it]
Epoch 199 Sampling:  85%|████████▌ | 82/96 [02:31<00:25,  1.84s/it]
Epoch 199 Sampling:  86%|████████▋ | 83/96 [02:33<00:23,  1.84s/it]
Epoch 199 Sampling:  88%|████████▊ | 84/96 [02:34<00:22,  1.84s/it]
Epoch 199 Sampling:  89%|████████▊ | 85/96 [02:36<00:20,  1.84s/it]
Epoch 199 Sampling:  90%|████████▉ | 86/96 [02:38<00:18,  1.84s/it]
Epoch 199 Sampling:  91%|█████████ | 87/96 [02:40<00:16,  1.84s/it]
Epoch 199 Sampling:  92%|█████████▏| 88/96 [02:42<00:14,  1.84s/it]
Epoch 199 Sampling:  93%|█████████▎| 89/96 [02:44<00:12,  1.84s/it]
Epoch 199 Sampling:  94%|█████████▍| 90/96 [02:45<00:11,  1.84s/it]
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Epoch 199 Sampling: 100%|██████████| 96/96 [02:56<00:00,  1.84s/it]
+
Pointwise Rewards: ssim:   0%|          | 0/6 [00:00<?, ?it/s]
Pointwise Rewards: ssim:  17%|█▋        | 1/6 [00:00<00:00,  5.03it/s]
Pointwise Rewards: ssim:  33%|███▎      | 2/6 [00:00<00:00,  5.60it/s]
Pointwise Rewards: ssim:  50%|█████     | 3/6 [00:00<00:00,  5.64it/s]
Pointwise Rewards: ssim:  67%|██████▋   | 4/6 [00:00<00:00,  5.55it/s]
Pointwise Rewards: ssim:  83%|████████▎ | 5/6 [00:00<00:00,  5.42it/s]
Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:01<00:00,  5.47it/s]
Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:01<00:00,  5.47it/s]
+
Pointwise Rewards: dists:   0%|          | 0/6 [00:00<?, ?it/s]
Pointwise Rewards: dists:  17%|█▋        | 1/6 [00:00<00:01,  3.27it/s]
Pointwise Rewards: dists:  33%|███▎      | 2/6 [00:00<00:01,  3.34it/s]
Pointwise Rewards: dists:  50%|█████     | 3/6 [00:00<00:00,  3.27it/s]
Pointwise Rewards: dists:  67%|██████▋   | 4/6 [00:01<00:00,  3.23it/s]
Pointwise Rewards: dists:  83%|████████▎ | 5/6 [00:01<00:00,  3.18it/s]
Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.22it/s]
Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.23it/s]
+[2026-10-02 06:00:46] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0398 | Epoch 199] train/reward_dists_mean=0.8369 train/reward_dists_std=0.0328 train/reward_ssim_mean=0.6237 train/reward_ssim_std=0.1069 train/reward_dists_group_std_mean=0.0065 train/reward_dists_group_std_max=0.0153 train/reward_dists_group_std_min=0.0034 train/reward_dists_group_mean_std=0.0321 train/reward_ssim_group_std_mean=0.0067 train/reward_ssim_group_std_max=0.0136 train/reward_ssim_group_std_min=0.0030 train/reward_ssim_group_mean_std=0.1067 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.6501 train/adv_max=4.1526 train/adv_abs_mean=1.2032
+
Epoch 199 Training:   0%|          | 0/96 [00:00<?, ?it/s]
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Epoch 199 Timestep: 100%|██████████| 1/1 [00:00<00:00,  2.19it/s]
+
                                                                 
Epoch 199 Training:   1%|          | 1/96 [00:00<00:43,  2.17it/s]
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Epoch 199 Timestep: 100%|██████████| 1/1 [00:00<00:00,  2.16it/s]
+
                                                                 
Epoch 199 Training:   2%|▏         | 2/96 [00:00<00:46,  2.02it/s]
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Epoch 199 Timestep: 100%|██████████| 1/1 [00:00<00:00,  2.17it/s]
+
                                                                 
Epoch 199 Training:   3%|▎         | 3/96 [00:01<00:47,  1.98it/s]
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Epoch 199 Timestep: 100%|██████████| 1/1 [00:00<00:00,  2.16it/s]
+
                                                                 
Epoch 199 Training:   4%|▍         | 4/96 [00:02<00:47,  1.96it/s]
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Epoch 199 Timestep: 100%|██████████| 1/1 [00:00<00:00,  2.17it/s]
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Epoch 199 Training:   5%|▌         | 5/96 [00:02<00:46,  1.95it/s]
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Epoch 199 Timestep: 100%|██████████| 1/1 [00:00<00:00,  2.16it/s]
+
                                                                 
Epoch 199 Training:   6%|▋         | 6/96 [00:03<00:46,  1.94it/s]
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Epoch 199 Timestep: 100%|██████████| 1/1 [00:00<00:00,  2.17it/s]
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Epoch 199 Training:   7%|▋         | 7/96 [00:03<00:45,  1.94it/s]
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Epoch 199 Timestep:   0%|          | 0/1 [00:00<?, ?it/s]
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Epoch 199 Timestep: 100%|██████████| 1/1 [00:00<00:00,  2.16it/s]
+
                                                                 
Epoch 199 Training:   8%|▊         | 8/96 [00:04<00:45,  1.93it/s]
+
Epoch 199 Timestep:   0%|          | 0/1 [00:00<?, ?it/s]
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Epoch 199 Timestep: 100%|██████████| 1/1 [00:00<00:00,  2.16it/s]
+
                                                                 
Epoch 199 Training:   9%|▉         | 9/96 [00:04<00:45,  1.93it/s]
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Epoch 199 Timestep:   0%|          | 0/1 [00:00<?, ?it/s]
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Epoch 199 Timestep: 100%|██████████| 1/1 [00:00<00:00,  2.16it/s]
+
                                                                 
Epoch 199 Training:  10%|█         | 10/96 [00:05<00:44,  1.93it/s]
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Epoch 199 Timestep:   0%|          | 0/1 [00:00<?, ?it/s]
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Epoch 199 Timestep: 100%|██████████| 1/1 [00:00<00:00,  2.16it/s]
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Epoch 199 Training:  11%|█▏        | 11/96 [00:05<00:44,  1.93it/s]
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Epoch 199 Timestep: 100%|██████████| 1/1 [00:00<00:00,  2.16it/s]
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Epoch 199 Training:  12%|█▎        | 12/96 [00:06<00:43,  1.93it/s]
+
Epoch 199 Timestep:   0%|          | 0/1 [00:00<?, ?it/s]
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Epoch 199 Timestep: 100%|██████████| 1/1 [00:00<00:00,  2.16it/s]
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Epoch 199 Training:  14%|█▎        | 13/96 [00:06<00:43,  1.93it/s]
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Epoch 199 Timestep:   0%|          | 0/1 [00:00<?, ?it/s]
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Epoch 199 Timestep: 100%|██████████| 1/1 [00:00<00:00,  2.16it/s]
+
                                                                 
Epoch 199 Training:  15%|█▍        | 14/96 [00:07<00:42,  1.93it/s]
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Epoch 199 Timestep:   0%|          | 0/1 [00:00<?, ?it/s]
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Epoch 199 Timestep: 100%|██████████| 1/1 [00:00<00:00,  2.15it/s]
+
                                                                 
Epoch 199 Training:  16%|█▌        | 15/96 [00:07<00:42,  1.93it/s]
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Epoch 199 Timestep:   0%|          | 0/1 [00:00<?, ?it/s]
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Epoch 199 Timestep: 100%|██████████| 1/1 [00:00<00:00,  2.16it/s]
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Epoch 199 Training:  17%|█▋        | 16/96 [00:08<00:41,  1.93it/s]
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Epoch 199 Timestep:   0%|          | 0/1 [00:00<?, ?it/s]
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Epoch 199 Timestep: 100%|██████████| 1/1 [00:00<00:00,  2.16it/s]
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Epoch 199 Training:  18%|█▊        | 17/96 [00:08<00:40,  1.93it/s]
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Epoch 199 Timestep: 100%|██████████| 1/1 [00:00<00:00,  2.16it/s]
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Epoch 199 Training:  19%|█▉        | 18/96 [00:09<00:40,  1.93it/s]
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Epoch 199 Timestep:   0%|          | 0/1 [00:00<?, ?it/s]
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Epoch 199 Timestep: 100%|██████████| 1/1 [00:00<00:00,  2.16it/s]
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Epoch 199 Training:  20%|█▉        | 19/96 [00:09<00:39,  1.93it/s]
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Epoch 199 Timestep: 100%|██████████| 1/1 [00:00<00:00,  2.16it/s]
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Epoch 199 Training:  21%|██        | 20/96 [00:10<00:39,  1.93it/s]
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Epoch 199 Timestep: 100%|██████████| 1/1 [00:00<00:00,  2.16it/s]
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Epoch 199 Training:  22%|██▏       | 21/96 [00:10<00:38,  1.93it/s]
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Epoch 199 Timestep: 100%|██████████| 1/1 [00:00<00:00,  2.16it/s]
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Epoch 199 Training:  23%|██▎       | 22/96 [00:11<00:38,  1.93it/s]
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Epoch 199 Timestep: 100%|██████████| 1/1 [00:00<00:00,  2.16it/s]
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Epoch 199 Training:  24%|██▍       | 23/96 [00:11<00:37,  1.93it/s]
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Epoch 199 Timestep: 100%|██████████| 1/1 [00:00<00:00,  2.16it/s]
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Epoch 199 Training:  25%|██▌       | 24/96 [00:12<00:37,  1.93it/s]
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Epoch 199 Timestep: 100%|██████████| 1/1 [00:00<00:00,  2.16it/s]
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Epoch 199 Training:  26%|██▌       | 25/96 [00:12<00:36,  1.93it/s]
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Epoch 199 Timestep: 100%|██████████| 1/1 [00:00<00:00,  2.16it/s]
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Epoch 199 Training:  27%|██▋       | 26/96 [00:13<00:36,  1.93it/s]
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Epoch 199 Timestep: 100%|██████████| 1/1 [00:00<00:00,  2.16it/s]
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Epoch 199 Training:  28%|██▊       | 27/96 [00:13<00:35,  1.93it/s]
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Epoch 199 Timestep: 100%|██████████| 1/1 [00:00<00:00,  2.16it/s]
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Epoch 199 Training:  29%|██▉       | 28/96 [00:14<00:35,  1.93it/s]
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Epoch 199 Timestep: 100%|██████████| 1/1 [00:00<00:00,  2.16it/s]
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Epoch 199 Training:  30%|███       | 29/96 [00:14<00:34,  1.93it/s]
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Epoch 199 Timestep: 100%|██████████| 1/1 [00:00<00:00,  2.16it/s]
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Epoch 199 Training:  31%|███▏      | 30/96 [00:15<00:34,  1.93it/s]
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Epoch 199 Timestep: 100%|██████████| 1/1 [00:00<00:00,  2.16it/s]
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Epoch 199 Training:  32%|███▏      | 31/96 [00:16<00:33,  1.93it/s]
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Epoch 199 Timestep: 100%|██████████| 1/1 [00:00<00:00,  2.16it/s]
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Epoch 199 Training:  33%|███▎      | 32/96 [00:16<00:33,  1.93it/s]
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Epoch 199 Timestep: 100%|██████████| 1/1 [00:00<00:00,  2.16it/s]
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Epoch 199 Training:  34%|███▍      | 33/96 [00:17<00:32,  1.93it/s]
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Epoch 199 Timestep: 100%|██████████| 1/1 [00:00<00:00,  2.16it/s]
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Epoch 199 Training:  35%|███▌      | 34/96 [00:17<00:32,  1.93it/s]
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Epoch 199 Timestep: 100%|██████████| 1/1 [00:00<00:00,  2.16it/s]
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Epoch 199 Training:  36%|███▋      | 35/96 [00:18<00:31,  1.93it/s]
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Epoch 199 Timestep: 100%|██████████| 1/1 [00:00<00:00,  2.16it/s]
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Epoch 199 Training:  38%|███▊      | 36/96 [00:18<00:31,  1.93it/s]
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Epoch 199 Timestep: 100%|██████████| 1/1 [00:00<00:00,  2.16it/s]
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Epoch 199 Training:  39%|███▊      | 37/96 [00:19<00:30,  1.93it/s]
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Epoch 199 Timestep: 100%|██████████| 1/1 [00:00<00:00,  2.16it/s]
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Epoch 199 Training:  40%|███▉      | 38/96 [00:19<00:30,  1.93it/s]
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Epoch 199 Timestep: 100%|██████████| 1/1 [00:00<00:00,  2.16it/s]
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Epoch 199 Training:  41%|████      | 39/96 [00:20<00:29,  1.92it/s]
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Epoch 199 Training:  42%|████▏     | 40/96 [00:20<00:28,  1.93it/s]
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Epoch 199 Training:  43%|████▎     | 41/96 [00:21<00:28,  1.93it/s]
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Epoch 199 Timestep: 100%|██████████| 1/1 [00:00<00:00,  2.16it/s]
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Epoch 199 Training:  44%|████▍     | 42/96 [00:21<00:27,  1.93it/s]
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Epoch 199 Training:  45%|████▍     | 43/96 [00:22<00:27,  1.93it/s]
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Epoch 199 Timestep: 100%|██████████| 1/1 [00:00<00:00,  2.16it/s]
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Epoch 199 Training:  46%|████▌     | 44/96 [00:22<00:26,  1.93it/s]
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Epoch 199 Training:  47%|████▋     | 45/96 [00:23<00:26,  1.93it/s]
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Epoch 199 Training:  48%|████▊     | 46/96 [00:23<00:25,  1.93it/s]
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Epoch 199 Training:  49%|████▉     | 47/96 [00:24<00:25,  1.93it/s]
+
Epoch 199 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 06:01:10] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0398 | Epoch 199] train/clipped_loss_min=-4.1526 train/clipped_loss_max=3.7612 train/clipped_loss_mean=0.0804 train/clipped_loss_std=1.4844 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-4.1526 train/unclipped_loss_max=3.7612 train/unclipped_loss_mean=0.0804 train/unclipped_loss_std=1.4844 train/policy_loss=0.0804 train/loss=0.0804 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0002
+
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+
Epoch 199 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-02 06:01:35] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0399 | Epoch 199] train/clipped_loss_min=-4.0869 train/clipped_loss_max=4.6501 train/clipped_loss_mean=-0.0804 train/clipped_loss_std=1.5221 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0.0002 train/unclipped_loss_min=-4.0869 train/unclipped_loss_max=4.6501 train/unclipped_loss_mean=-0.0804 train/unclipped_loss_std=1.5221 train/policy_loss=-0.0804 train/loss=-0.0804 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
+
+
Epoch 199 Timestep: 100%|██████████| 1/1 [00:00<00:00,  1.90it/s]
+
                                                                 
Epoch 199 Training: 100%|██████████| 96/96 [00:49<00:00,  1.86it/s]
Epoch 199 Training: 100%|██████████| 96/96 [00:49<00:00,  1.93it/s]
+[2026-10-02 06:01:35] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0400 | Epoch 199] timing/rollout_seconds=179.4914 timing/feedback_seconds=5.6014 timing/optimization_seconds=50.3866 timing/cycle_seconds=232.7792
+[2026-10-02 06:01:35,854] [INFO] [flow_factory.train]: Training completed successfully
+wandb: 
+wandb: 🚀 View run klein4b_grpo_flir_full_ssim_dists_lr5e-5 at: https://wandb.ai/315229706-xi-an-jiaotong-university-/IR-Engine/runs/juvo095a
+wandb: Find logs at: ../../../ir_engine/runs/klein4b_grpo_flir_full_ssim_dists_lr5e-5/wandb/run-20261001_164700-juvo095a/logs
diff --git a/runs/klein4b_grpo_vedai_full_ssim_dists_psnr/train.log b/runs/klein4b_grpo_vedai_full_ssim_dists_psnr/train.log
new file mode 100644
index 0000000000000000000000000000000000000000..d9de8025d18f1891c6f508a216a65166b174ac88
--- /dev/null
+++ b/runs/klein4b_grpo_vedai_full_ssim_dists_psnr/train.log
@@ -0,0 +1,3736 @@
+[2026-10-01 16:05:36,375] [INFO] [flow_factory.cli]: ============================================================
+[2026-10-01 16:05:36,375] [INFO] [flow_factory.cli]: Flow-Factory Launch Configuration
+[2026-10-01 16:05:36,375] [INFO] [flow_factory.cli]:   Num machines:       1
+[2026-10-01 16:05:36,375] [INFO] [flow_factory.cli]:   Machine rank:       0
+[2026-10-01 16:05:36,376] [INFO] [flow_factory.cli]:   Num processes:      8
+[2026-10-01 16:05:36,376] [INFO] [flow_factory.cli]:   Master node:        localhost:29513
+[2026-10-01 16:05:36,376] [INFO] [flow_factory.cli]:   Mixed precision:    bf16
+[2026-10-01 16:05:36,376] [INFO] [flow_factory.cli]:   Accelerate config:  config/deepspeed/deepspeed_zero2.yaml
+[2026-10-01 16:05:36,376] [INFO] [flow_factory.cli]: ============================================================
+W1001 16:05:44.660000 4069794 site-packages/torch/distributed/run.py:852] 
+W1001 16:05:44.660000 4069794 site-packages/torch/distributed/run.py:852] *****************************************
+W1001 16:05:44.660000 4069794 site-packages/torch/distributed/run.py:852] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
+W1001 16:05:44.660000 4069794 site-packages/torch/distributed/run.py:852] *****************************************
+[2026-10-01 16:05:51] [Rank 0] [WARNING] [flow_factory.hparams.abc]: GRPOTrainingArguments.from_dict captured 1 unknown key(s) into extra_kwargs: ['condition_image_size']. Verify these are intentional (e.g., adapter-specific kwargs); typos against declared fields will be silently accepted otherwise.
+[2026-10-01 16:05:51] [Rank 0] [INFO] [flow_factory.hparams.training_args._base]: World Size: 8
+[2026-10-01 16:05:51] [Rank 0] [WARNING] [flow_factory.hparams.abc]: EvaluationArguments.from_dict captured 1 unknown key(s) into extra_kwargs: ['condition_image_size']. Verify these are intentional (e.g., adapter-specific kwargs); typos against declared fields will be silently accepted otherwise.
+[2026-10-01 16:05:51,540] [INFO] [flow_factory.train]: ====================================================================================================
+[2026-10-01 16:05:51,540] [INFO] [flow_factory.train]: Flow-Factory Training Initialized
+[2026-10-01 16:05:51,540] [INFO] [flow_factory.train]: World Size: 8
+[2026-10-01 16:05:51,540] [INFO] [flow_factory.train]: ====================================================================================================
+[2026-10-01 16:05:51,540] [INFO] [flow_factory.train]: Config: /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/runs/klein4b_grpo_vedai_full_ssim_dists_psnr/train.yaml
+[2026-10-01 16:05:51,547] [INFO] [flow_factory.train]: 
+launcher: accelerate
+config_file: config/deepspeed/deepspeed_zero2.yaml
+num_processes: 8
+main_process_port: 29513
+mixed_precision: bf16
+data:
+  dataset_dir: data
+  cache_dir: ~/.cache/flow_factory/datasets
+  image_dir: null
+  video_dir: null
+  audio_dir: null
+  preprocessing_batch_size: 8
+  dataloader_num_workers: 8
+  enable_preprocess: true
+  force_reprocess: false
+  max_dataset_size: null
+  preprocess_parallelism: local
+  sampler_type: group_contiguous
+  sampler_subgroup_size: null
+  datasets:
+  - extra_kwargs: {}
+    name: vedai_rl_full
+    dataset_dir: /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/vedai_rl_full
+    image_dir: null
+    video_dir: null
+    audio_dir: null
+    train:
+      extra_kwargs: {}
+      enabled: true
+      split: train
+      weight: 1
+      max_dataset_size: null
+      unique_sample_num_per_epoch: 48
+      num_batches_per_epoch: null
+    eval:
+      extra_kwargs: {}
+      enabled: true
+      split: test
+      max_dataset_size: null
+      resolution: null
+      num_inference_steps: null
+      guidance_scale: null
+    source_id: 0
+model:
+  model_name_or_path: /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/flow_factory_gpu_gate_models/FLUX.2-klein-base-4B
+  finetune_type: lora
+  trainable_parameters_dtype: bfloat16
+  component_load_dtypes: null
+  frozen_parameters_dtype: null
+  target_components:
+  - transformer
+  target_modules: default
+  model_type: flux2-klein
+  lora_rank: 64
+  lora_alpha: 128
+  resume_path: /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/runs/klein4b_sft_vedai_full_r64a128_lr1e-4/saves/klein4b_sft_vedai_full_r64a128_lr1e-4/checkpoints/checkpoint-48
+  resume_type: lora
+scheduler:
+  dynamics_type: Flow-SDE
+  noise_level: 0.7
+  num_sde_steps: 1
+  sde_steps:
+  - 1
+  - 2
+  - 3
+  seed: 42
+training:
+  trainer_type: grpo
+  resolution:
+  - 512
+  - 512
+  height: 512
+  width: 512
+  num_frames: null
+  frame_rate: null
+  max_epochs: 200
+  per_device_batch_size: 1
+  gradient_step_per_epoch: 2
+  max_grad_norm: 1.0
+  num_batches_per_epoch: 96
+  gradient_accumulation_steps: 48
+  num_inner_epochs: 1
+  group_size: 16
+  unique_sample_num_per_epoch: 48
+  num_inference_steps: 8
+  guidance_scale: 1.0
+  ref_param_device: cuda
+  seed: 42
+  learning_rate: 1.0e-05
+  adam_weight_decay: 0.0001
+  adam_betas:
+  - 0.9
+  - 0.999
+  adam_epsilon: 1.0e-08
+  enable_gradient_checkpointing: false
+  offload_samples_to_cpu: false
+  ema_decay: 0.9
+  ema_update_interval: 4
+  ema_device: cuda
+  ema_decay_schedule: power
+  latent_storage_dtype: fp16
+  shuffle_samples: true
+  reward_optimization_overlap: false
+  reward_optimization_overlap_mode: ready
+  reward_optimization_overlap_poll_interval: 0.05
+  global_std: false
+  advantage_aggregation: gdpo
+  clip_range:
+  - -0.0001
+  - 0.0001
+  adv_clip_range:
+  - -5.0
+  - 5.0
+  kl_type: v-based
+  kl_beta: 0.0
+  condition_image_size: 1024
+eval:
+  resolution:
+  - 512
+  - 512
+  height: 512
+  width: 512
+  num_frames: null
+  frame_rate: null
+  per_device_batch_size: 1
+  seed: 42
+  guidance_scale: 1.0
+  num_inference_steps: 28
+  eval_freq: 10
+  condition_image_size: 1024
+log:
+  run_name: klein4b_grpo_vedai_full_ssim_dists_psnr
+  project: IR-Engine
+  logging_backend: wandb
+  save_dir: /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/runs/klein4b_grpo_vedai_full_ssim_dists_psnr/saves
+  save_freq: 20
+  save_model_only: true
+  verbose: true
+acceleration:
+  shared: []
+  rollout: []
+reward:
+  reward_0:
+    name: ssim
+    reward_model: ir_rewards.fidelity.SSIMReward
+    weight:
+      vedai_rl_full: 1.0
+    dtype: float32
+    device: cuda
+    batch_size: 16
+    async_reward: false
+    num_workers: 1
+    applicable_datasets:
+    - vedai_rl_full
+  reward_1:
+    name: dists
+    reward_model: ir_rewards.fidelity.DISTSReward
+    weight:
+      vedai_rl_full: 1.0
+    dtype: float32
+    device: cuda
+    batch_size: 16
+    async_reward: false
+    num_workers: 1
+    applicable_datasets:
+    - vedai_rl_full
+  reward_2:
+    name: psnr
+    reward_model: ir_rewards.fidelity.PSNRReward
+    weight:
+      vedai_rl_full: 1.0
+    dtype: float32
+    device: cuda
+    batch_size: 16
+    async_reward: false
+    num_workers: 1
+    applicable_datasets:
+    - vedai_rl_full
+eval_reward:
+  reward_0:
+    name: ssim
+    reward_model: ir_rewards.fidelity.SSIMReward
+    weight:
+      vedai_rl_full: 1.0
+    dtype: float32
+    device: cuda
+    batch_size: 16
+    async_reward: false
+    num_workers: 1
+    applicable_datasets:
+    - vedai_rl_full
+  reward_1:
+    name: psnr
+    reward_model: ir_rewards.fidelity.PSNRReward
+    weight:
+      vedai_rl_full: 1.0
+    dtype: float32
+    device: cuda
+    batch_size: 16
+    async_reward: false
+    num_workers: 1
+    applicable_datasets:
+    - vedai_rl_full
+  reward_2:
+    name: dists
+    reward_model: ir_rewards.fidelity.DISTSReward
+    weight:
+      vedai_rl_full: 1.0
+    dtype: float32
+    device: cuda
+    batch_size: 16
+    async_reward: false
+    num_workers: 1
+    applicable_datasets:
+    - vedai_rl_full
+optimizer:
+  optimizer_configs:
+  - extra_kwargs: {}
+    name: default
+    optimizer: adamw
+    learning_rate: 0.0003
+    weight_decay: 0.0001
+    max_grad_norm: 1.0
+    update_frequency: 1
+    betas:
+    - 0.9
+    - 0.999
+    eps: 1.0e-08
+
+[2026-10-01 16:05:51,548] [INFO] [flow_factory.train]: ====================================================================================================
+[2026-10-01 16:06:00] [Rank 1] [INFO] [flow_factory.models.loader]: Loading model architecture: flux2-klein...
+[2026-10-01 16:06:01] [Rank 5] [INFO] [flow_factory.models.loader]: Loading model architecture: flux2-klein...
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+[2026-10-01 16:06:01] [Rank 7] [INFO] [flow_factory.models.loader]: Loading model architecture: flux2-klein...
+[2026-10-01 16:06:01,040] [WARNING] [accelerate.utils.other]: [RANK 0] Detected kernel version 5.4.241, which is below the recommended minimum of 5.5.0; this can cause the process to hang. It is recommended to upgrade the kernel to the minimum version or higher.
+[2026-10-01 16:06:01] [Rank 0] [INFO] [flow_factory.models.loader]: Loading model architecture: flux2-klein...
+
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+
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+[2026-10-01 16:06:23] [Rank 3] [INFO] [flow_factory.scheduler.loader]: Loaded SDE scheduler: FlowMatchEulerDiscreteSDEScheduler
+
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+
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+[2026-10-01 16:06:24] [Rank 6] [INFO] [flow_factory.scheduler.loader]: Loaded SDE scheduler: FlowMatchEulerDiscreteSDEScheduler
+
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+[2026-10-01 16:06:24] [Rank 0] [INFO] [flow_factory.scheduler.loader]: Loaded SDE scheduler: FlowMatchEulerDiscreteSDEScheduler
+
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+
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+
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+[2026-10-01 16:06:24] [Rank 7] [INFO] [flow_factory.scheduler.loader]: Loaded SDE scheduler: FlowMatchEulerDiscreteSDEScheduler
+[2026-10-01 16:06:25] [Rank 0] [INFO] [flow_factory.models.abc]: LoRA adapter loaded for transformer role=base from /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/runs/klein4b_sft_vedai_full_r64a128_lr1e-4/saves/klein4b_sft_vedai_full_r64a128_lr1e-4/checkpoints/checkpoint-48
+[2026-10-01 16:06:26] [Rank 0] [INFO] [flow_factory.models.abc]: Checkpoint loaded successfully from /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/runs/klein4b_sft_vedai_full_r64a128_lr1e-4/saves/klein4b_sft_vedai_full_r64a128_lr1e-4/checkpoints/checkpoint-48 (type=lora)
+[2026-10-01 16:06:26] [Rank 7] [INFO] [flow_factory.models.abc]: Froze ALL transformer parameters
+[2026-10-01 16:06:26] [Rank 4] [INFO] [flow_factory.models.abc]: Froze ALL transformer parameters
+[2026-10-01 16:06:26] [Rank 7] [INFO] [flow_factory.models.abc]: Component transformer already has 'default' adapter. Skipping initialization but enabling gradients.
+[2026-10-01 16:06:26] [Rank 1] [INFO] [flow_factory.models.abc]: Froze ALL transformer parameters
+[2026-10-01 16:06:26] [Rank 6] [INFO] [flow_factory.models.abc]: Froze ALL transformer parameters
+[2026-10-01 16:06:26] [Rank 1] [INFO] [flow_factory.models.abc]: Component transformer already has 'default' adapter. Skipping initialization but enabling gradients.
+[2026-10-01 16:06:26] [Rank 4] [INFO] [flow_factory.models.abc]: Component transformer already has 'default' adapter. Skipping initialization but enabling gradients.
+[2026-10-01 16:06:26] [Rank 6] [INFO] [flow_factory.models.abc]: Component transformer already has 'default' adapter. Skipping initialization but enabling gradients.
+[2026-10-01 16:06:26] [Rank 2] [INFO] [flow_factory.models.abc]: Froze ALL transformer parameters
+[2026-10-01 16:06:26] [Rank 2] [INFO] [flow_factory.models.abc]: Component transformer already has 'default' adapter. Skipping initialization but enabling gradients.
+[2026-10-01 16:06:26] [Rank 0] [INFO] [flow_factory.models.abc]: Froze ALL transformer parameters
+[2026-10-01 16:06:26] [Rank 3] [INFO] [flow_factory.models.abc]: Froze ALL transformer parameters
+[2026-10-01 16:06:26] [Rank 0] [INFO] [flow_factory.models.abc]: Component transformer already has 'default' adapter. Skipping initialization but enabling gradients.
+[2026-10-01 16:06:26] [Rank 3] [INFO] [flow_factory.models.abc]: Component transformer already has 'default' adapter. Skipping initialization but enabling gradients.
+[2026-10-01 16:06:26] [Rank 5] [INFO] [flow_factory.models.abc]: Froze ALL transformer parameters
+[2026-10-01 16:06:26] [Rank 5] [INFO] [flow_factory.models.abc]: Component transformer already has 'default' adapter. Skipping initialization but enabling gradients.
+[2026-10-01 16:06:26] [Rank 7] [INFO] [flow_factory.models.abc]: Set 160 trainable parameters to torch.bfloat16; frozen component policy -> {'text_encoder': None, 'transformer': None, 'vae': None}
+[2026-10-01 16:06:26] [Rank 7] [INFO] [flow_factory.models.loader]: Successfully loaded Flux2KleinAdapter
+[2026-10-01 16:06:26] [Rank 6] [INFO] [flow_factory.models.abc]: Set 160 trainable parameters to torch.bfloat16; frozen component policy -> {'text_encoder': None, 'transformer': None, 'vae': None}
+[2026-10-01 16:06:26] [Rank 4] [INFO] [flow_factory.models.abc]: Set 160 trainable parameters to torch.bfloat16; frozen component policy -> {'text_encoder': None, 'transformer': None, 'vae': None}
+[2026-10-01 16:06:26] [Rank 1] [INFO] [flow_factory.models.abc]: Set 160 trainable parameters to torch.bfloat16; frozen component policy -> {'text_encoder': None, 'transformer': None, 'vae': None}
+[2026-10-01 16:06:26] [Rank 6] [INFO] [flow_factory.models.loader]: Successfully loaded Flux2KleinAdapter
+[2026-10-01 16:06:26] [Rank 4] [INFO] [flow_factory.models.loader]: Successfully loaded Flux2KleinAdapter
+[2026-10-01 16:06:26] [Rank 1] [INFO] [flow_factory.models.loader]: Successfully loaded Flux2KleinAdapter
+[2026-10-01 16:06:26] [Rank 2] [INFO] [flow_factory.models.abc]: Set 160 trainable parameters to torch.bfloat16; frozen component policy -> {'text_encoder': None, 'transformer': None, 'vae': None}
+[2026-10-01 16:06:26] [Rank 2] [INFO] [flow_factory.models.loader]: Successfully loaded Flux2KleinAdapter
+[2026-10-01 16:06:26] [Rank 5] [INFO] [flow_factory.models.abc]: Set 160 trainable parameters to torch.bfloat16; frozen component policy -> {'text_encoder': None, 'transformer': None, 'vae': None}
+[2026-10-01 16:06:26] [Rank 5] [INFO] [flow_factory.models.loader]: Successfully loaded Flux2KleinAdapter
+[2026-10-01 16:06:26] [Rank 3] [INFO] [flow_factory.models.abc]: Set 160 trainable parameters to torch.bfloat16; frozen component policy -> {'text_encoder': None, 'transformer': None, 'vae': None}
+[2026-10-01 16:06:26] [Rank 3] [INFO] [flow_factory.models.loader]: Successfully loaded Flux2KleinAdapter
+[2026-10-01 16:06:26] [Rank 0] [INFO] [flow_factory.models.abc]: Set 160 trainable parameters to torch.bfloat16; frozen component policy -> {'text_encoder': None, 'transformer': None, 'vae': None}
+[2026-10-01 16:06:26] [Rank 0] [INFO] [flow_factory.models.abc]: Precision inventory stage=initialize component=text_encoder {'trainable': {}, 'frozen': {'torch.bfloat16': 4022468096}}
+[2026-10-01 16:06:26] [Rank 0] [INFO] [flow_factory.models.abc]: Precision inventory stage=initialize component=transformer {'trainable': {'torch.bfloat16': 76677120}, 'frozen': {'torch.float32': 3875544576}}
+[2026-10-01 16:06:26] [Rank 0] [INFO] [flow_factory.models.abc]: Precision inventory stage=initialize component=vae {'trainable': {}, 'frozen': {'torch.float32': 84046115}}
+[2026-10-01 16:06:26] [Rank 0] [INFO] [flow_factory.models.loader]: Successfully loaded Flux2KleinAdapter
+[2026-10-01 16:06:50] [Rank 7] [INFO] [flow_factory.data_utils.loader]: Preprocessing train dataset shard 0007/0007 -> /root/.cache/flow_factory/datasets/1e68492a08871f0f6ebe621929ff234a.tmp/_parts/rank_0007_of_0008/cache-1e68492a08871f0f6ebe621929ff234a_shard0007of0007.arrow
+[2026-10-01 16:06:50] [Rank 6] [INFO] [flow_factory.data_utils.loader]: Preprocessing train dataset shard 0006/0007 -> /root/.cache/flow_factory/datasets/1e68492a08871f0f6ebe621929ff234a.tmp/_parts/rank_0006_of_0008/cache-1e68492a08871f0f6ebe621929ff234a_shard0006of0007.arrow
+[2026-10-01 16:06:50] [Rank 5] [INFO] [flow_factory.data_utils.loader]: Preprocessing train dataset shard 0005/0007 -> /root/.cache/flow_factory/datasets/1e68492a08871f0f6ebe621929ff234a.tmp/_parts/rank_0005_of_0008/cache-1e68492a08871f0f6ebe621929ff234a_shard0005of0007.arrow
+[2026-10-01 16:06:50] [Rank 1] [INFO] [flow_factory.data_utils.loader]: Preprocessing train dataset shard 0001/0007 -> /root/.cache/flow_factory/datasets/1e68492a08871f0f6ebe621929ff234a.tmp/_parts/rank_0001_of_0008/cache-1e68492a08871f0f6ebe621929ff234a_shard0001of0007.arrow
+[2026-10-01 16:06:50] [Rank 0] [INFO] [flow_factory.data_utils.loader]: Preprocessing train dataset shard 0000/0007 -> /root/.cache/flow_factory/datasets/1e68492a08871f0f6ebe621929ff234a.tmp/_parts/rank_0000_of_0008/cache-1e68492a08871f0f6ebe621929ff234a_shard0000of0007.arrow
+[2026-10-01 16:06:50] [Rank 2] [INFO] [flow_factory.data_utils.loader]: Preprocessing train dataset shard 0002/0007 -> /root/.cache/flow_factory/datasets/1e68492a08871f0f6ebe621929ff234a.tmp/_parts/rank_0002_of_0008/cache-1e68492a08871f0f6ebe621929ff234a_shard0002of0007.arrow
+[2026-10-01 16:06:50] [Rank 3] [INFO] [flow_factory.data_utils.loader]: Preprocessing train dataset shard 0003/0007 -> /root/.cache/flow_factory/datasets/1e68492a08871f0f6ebe621929ff234a.tmp/_parts/rank_0003_of_0008/cache-1e68492a08871f0f6ebe621929ff234a_shard0003of0007.arrow
+[2026-10-01 16:06:50] [Rank 4] [INFO] [flow_factory.data_utils.loader]: Preprocessing train dataset shard 0004/0007 -> /root/.cache/flow_factory/datasets/1e68492a08871f0f6ebe621929ff234a.tmp/_parts/rank_0004_of_0008/cache-1e68492a08871f0f6ebe621929ff234a_shard0004of0007.arrow
+/opt/conda/envs/ff-qi21/lib/python3.10/contextlib.py:135: UserWarning: CUDA reports that you have 8 available devices, and you have used fork_rng without explicitly specifying which devices are being used. For safety, we initialize *every* CUDA device by default, which can be quite slow if you have a lot of CUDAs. If you know that you are only making use of a few CUDA devices, set the environment variable CUDA_VISIBLE_DEVICES or the 'devices' keyword argument of fork_rng with the set of devices you are actually using. For example, if you are using CPU only, set device.upper()_VISIBLE_DEVICES= or devices=[]; if you are using device 0 only, set CUDA_VISIBLE_DEVICES=0 or devices=[0].  To initialize all devices and suppress this warning, set the 'devices' keyword argument to `range(torch.cuda.device_count())`.
+  return next(self.gen)
+/opt/conda/envs/ff-qi21/lib/python3.10/contextlib.py:135: UserWarning: CUDA reports that you have 8 available devices, and you have used fork_rng without explicitly specifying which devices are being used. For safety, we initialize *every* CUDA device by default, which can be quite slow if you have a lot of CUDAs. If you know that you are only making use of a few CUDA devices, set the environment variable CUDA_VISIBLE_DEVICES or the 'devices' keyword argument of fork_rng with the set of devices you are actually using. For example, if you are using CPU only, set device.upper()_VISIBLE_DEVICES= or devices=[]; if you are using device 0 only, set CUDA_VISIBLE_DEVICES=0 or devices=[0].  To initialize all devices and suppress this warning, set the 'devices' keyword argument to `range(torch.cuda.device_count())`.
+  return next(self.gen)
+/opt/conda/envs/ff-qi21/lib/python3.10/contextlib.py:135: UserWarning: CUDA reports that you have 8 available devices, and you have used fork_rng without explicitly specifying which devices are being used. For safety, we initialize *every* CUDA device by default, which can be quite slow if you have a lot of CUDAs. If you know that you are only making use of a few CUDA devices, set the environment variable CUDA_VISIBLE_DEVICES or the 'devices' keyword argument of fork_rng with the set of devices you are actually using. For example, if you are using CPU only, set device.upper()_VISIBLE_DEVICES= or devices=[]; if you are using device 0 only, set CUDA_VISIBLE_DEVICES=0 or devices=[0].  To initialize all devices and suppress this warning, set the 'devices' keyword argument to `range(torch.cuda.device_count())`.
+  return next(self.gen)
+/opt/conda/envs/ff-qi21/lib/python3.10/contextlib.py:135: UserWarning: CUDA reports that you have 8 available devices, and you have used fork_rng without explicitly specifying which devices are being used. For safety, we initialize *every* CUDA device by default, which can be quite slow if you have a lot of CUDAs. If you know that you are only making use of a few CUDA devices, set the environment variable CUDA_VISIBLE_DEVICES or the 'devices' keyword argument of fork_rng with the set of devices you are actually using. For example, if you are using CPU only, set device.upper()_VISIBLE_DEVICES= or devices=[]; if you are using device 0 only, set CUDA_VISIBLE_DEVICES=0 or devices=[0].  To initialize all devices and suppress this warning, set the 'devices' keyword argument to `range(torch.cuda.device_count())`.
+  return next(self.gen)
+/opt/conda/envs/ff-qi21/lib/python3.10/contextlib.py:135: UserWarning: CUDA reports that you have 8 available devices, and you have used fork_rng without explicitly specifying which devices are being used. For safety, we initialize *every* CUDA device by default, which can be quite slow if you have a lot of CUDAs. If you know that you are only making use of a few CUDA devices, set the environment variable CUDA_VISIBLE_DEVICES or the 'devices' keyword argument of fork_rng with the set of devices you are actually using. For example, if you are using CPU only, set device.upper()_VISIBLE_DEVICES= or devices=[]; if you are using device 0 only, set CUDA_VISIBLE_DEVICES=0 or devices=[0].  To initialize all devices and suppress this warning, set the 'devices' keyword argument to `range(torch.cuda.device_count())`.
+  return next(self.gen)
+/opt/conda/envs/ff-qi21/lib/python3.10/contextlib.py:135: UserWarning: CUDA reports that you have 8 available devices, and you have used fork_rng without explicitly specifying which devices are being used. For safety, we initialize *every* CUDA device by default, which can be quite slow if you have a lot of CUDAs. If you know that you are only making use of a few CUDA devices, set the environment variable CUDA_VISIBLE_DEVICES or the 'devices' keyword argument of fork_rng with the set of devices you are actually using. For example, if you are using CPU only, set device.upper()_VISIBLE_DEVICES= or devices=[]; if you are using device 0 only, set CUDA_VISIBLE_DEVICES=0 or devices=[0].  To initialize all devices and suppress this warning, set the 'devices' keyword argument to `range(torch.cuda.device_count())`.
+  return next(self.gen)
+/opt/conda/envs/ff-qi21/lib/python3.10/contextlib.py:135: UserWarning: CUDA reports that you have 8 available devices, and you have used fork_rng without explicitly specifying which devices are being used. For safety, we initialize *every* CUDA device by default, which can be quite slow if you have a lot of CUDAs. If you know that you are only making use of a few CUDA devices, set the environment variable CUDA_VISIBLE_DEVICES or the 'devices' keyword argument of fork_rng with the set of devices you are actually using. For example, if you are using CPU only, set device.upper()_VISIBLE_DEVICES= or devices=[]; if you are using device 0 only, set CUDA_VISIBLE_DEVICES=0 or devices=[0].  To initialize all devices and suppress this warning, set the 'devices' keyword argument to `range(torch.cuda.device_count())`.
+  return next(self.gen)
+/opt/conda/envs/ff-qi21/lib/python3.10/contextlib.py:135: UserWarning: CUDA reports that you have 8 available devices, and you have used fork_rng without explicitly specifying which devices are being used. For safety, we initialize *every* CUDA device by default, which can be quite slow if you have a lot of CUDAs. If you know that you are only making use of a few CUDA devices, set the environment variable CUDA_VISIBLE_DEVICES or the 'devices' keyword argument of fork_rng with the set of devices you are actually using. For example, if you are using CPU only, set device.upper()_VISIBLE_DEVICES= or devices=[]; if you are using device 0 only, set CUDA_VISIBLE_DEVICES=0 or devices=[0].  To initialize all devices and suppress this warning, set the 'devices' keyword argument to `range(torch.cuda.device_count())`.
+  return next(self.gen)
+
[Preprocessing train dataset] Shard 0000/0007:   0%|          | 0/109 [00:00<?, ? examples/s]
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[Preprocessing train dataset] Shard 0000/0007:  81%|████████  | 88/109 [00:24<00:06,  3.45 examples/s]
[Preprocessing train dataset] Shard 0000/0007:  88%|████████▊ | 96/109 [00:27<00:03,  3.55 examples/s]
[Preprocessing train dataset] Shard 0000/0007:  95%|█████████▌| 104/109 [00:28<00:01,  3.80 examples/s]
[Preprocessing train dataset] Shard 0000/0007: 100%|██████████| 109/109 [00:29<00:00,  3.93 examples/s]
[Preprocessing train dataset] Shard 0000/0007: 100%|██████████| 109/109 [00:29<00:00,  3.65 examples/s]
+[2026-10-01 16:07:26] [Rank 0] [INFO] [flow_factory.data_utils.loader]: [local] Consolidated 8 part(s) for train split -> /root/.cache/flow_factory/datasets/1e68492a08871f0f6ebe621929ff234a
+[2026-10-01 16:07:26] [Rank 0] [INFO] [flow_factory.data_utils.loader]: Multi-source: built DataLoader for 'vedai_rl_full' (dataset_size=872, unique_sample_num_per_epoch=48, num_batches_per_epoch=96)
+[2026-10-01 16:07:26] [Rank 5] [INFO] [flow_factory.data_utils.loader]: Multi-source: built DataLoader for 'vedai_rl_full' (dataset_size=872, unique_sample_num_per_epoch=48, num_batches_per_epoch=96)
+[2026-10-01 16:07:26] [Rank 6] [INFO] [flow_factory.data_utils.loader]: Multi-source: built DataLoader for 'vedai_rl_full' (dataset_size=872, unique_sample_num_per_epoch=48, num_batches_per_epoch=96)
+[2026-10-01 16:07:26] [Rank 2] [INFO] [flow_factory.data_utils.loader]: Multi-source: built DataLoader for 'vedai_rl_full' (dataset_size=872, unique_sample_num_per_epoch=48, num_batches_per_epoch=96)
+[2026-10-01 16:07:26] [Rank 4] [INFO] [flow_factory.data_utils.loader]: Multi-source: built DataLoader for 'vedai_rl_full' (dataset_size=872, unique_sample_num_per_epoch=48, num_batches_per_epoch=96)
+[2026-10-01 16:07:26] [Rank 1] [INFO] [flow_factory.data_utils.loader]: Multi-source: built DataLoader for 'vedai_rl_full' (dataset_size=872, unique_sample_num_per_epoch=48, num_batches_per_epoch=96)
+[2026-10-01 16:07:26] [Rank 3] [INFO] [flow_factory.data_utils.loader]: Multi-source: built DataLoader for 'vedai_rl_full' (dataset_size=872, unique_sample_num_per_epoch=48, num_batches_per_epoch=96)
+[2026-10-01 16:07:26] [Rank 7] [INFO] [flow_factory.data_utils.loader]: Multi-source: built DataLoader for 'vedai_rl_full' (dataset_size=872, unique_sample_num_per_epoch=48, num_batches_per_epoch=96)
+[2026-10-01 16:07:26] [Rank 0] [INFO] [flow_factory.data_utils.loader]: Preprocessing test dataset shard 0000/0007 -> /root/.cache/flow_factory/datasets/c624060e1ad3d97604e0883a86585d87.tmp/_parts/rank_0000_of_0008/cache-c624060e1ad3d97604e0883a86585d87_shard0000of0007.arrow
+[2026-10-01 16:07:26] [Rank 3] [INFO] [flow_factory.data_utils.loader]: Preprocessing test dataset shard 0003/0007 -> /root/.cache/flow_factory/datasets/c624060e1ad3d97604e0883a86585d87.tmp/_parts/rank_0003_of_0008/cache-c624060e1ad3d97604e0883a86585d87_shard0003of0007.arrow
+[2026-10-01 16:07:26] [Rank 7] [INFO] [flow_factory.data_utils.loader]: Preprocessing test dataset shard 0007/0007 -> /root/.cache/flow_factory/datasets/c624060e1ad3d97604e0883a86585d87.tmp/_parts/rank_0007_of_0008/cache-c624060e1ad3d97604e0883a86585d87_shard0007of0007.arrow
+[2026-10-01 16:07:26] [Rank 6] [INFO] [flow_factory.data_utils.loader]: Preprocessing test dataset shard 0006/0007 -> /root/.cache/flow_factory/datasets/c624060e1ad3d97604e0883a86585d87.tmp/_parts/rank_0006_of_0008/cache-c624060e1ad3d97604e0883a86585d87_shard0006of0007.arrow
+[2026-10-01 16:07:26] [Rank 2] [INFO] [flow_factory.data_utils.loader]: Preprocessing test dataset shard 0002/0007 -> /root/.cache/flow_factory/datasets/c624060e1ad3d97604e0883a86585d87.tmp/_parts/rank_0002_of_0008/cache-c624060e1ad3d97604e0883a86585d87_shard0002of0007.arrow
+[2026-10-01 16:07:26] [Rank 5] [INFO] [flow_factory.data_utils.loader]: Preprocessing test dataset shard 0005/0007 -> /root/.cache/flow_factory/datasets/c624060e1ad3d97604e0883a86585d87.tmp/_parts/rank_0005_of_0008/cache-c624060e1ad3d97604e0883a86585d87_shard0005of0007.arrow
+[2026-10-01 16:07:26] [Rank 1] [INFO] [flow_factory.data_utils.loader]: Preprocessing test dataset shard 0001/0007 -> /root/.cache/flow_factory/datasets/c624060e1ad3d97604e0883a86585d87.tmp/_parts/rank_0001_of_0008/cache-c624060e1ad3d97604e0883a86585d87_shard0001of0007.arrow
+[2026-10-01 16:07:26] [Rank 4] [INFO] [flow_factory.data_utils.loader]: Preprocessing test dataset shard 0004/0007 -> /root/.cache/flow_factory/datasets/c624060e1ad3d97604e0883a86585d87.tmp/_parts/rank_0004_of_0008/cache-c624060e1ad3d97604e0883a86585d87_shard0004of0007.arrow
+
Generating train split: 0 examples [00:00, ? examples/s]
Generating train split: 64 examples [00:00, 33701.88 examples/s]
+
[Preprocessing test dataset] Shard 0000/0007:   0%|          | 0/8 [00:00<?, ? examples/s]
[Preprocessing test dataset] Shard 0000/0007: 100%|██████████| 8/8 [00:01<00:00,  4.37 examples/s]
[Preprocessing test dataset] Shard 0000/0007: 100%|██████████| 8/8 [00:01<00:00,  4.36 examples/s]
+[2026-10-01 16:07:31] [Rank 0] [INFO] [flow_factory.data_utils.loader]: [local] Consolidated 8 part(s) for test split -> /root/.cache/flow_factory/datasets/c624060e1ad3d97604e0883a86585d87
+/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.
+  warnings.warn(
+/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.
+  warnings.warn(
+/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.
+  warnings.warn(
+/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.
+  warnings.warn(
+/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.
+  warnings.warn(
+/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.
+  warnings.warn(
+/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.
+  warnings.warn(
+/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.
+  warnings.warn(
+/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=VGG16_Weights.IMAGENET1K_V1`. You can also use `weights=VGG16_Weights.DEFAULT` to get the most up-to-date weights.
+  warnings.warn(msg)
+/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=VGG16_Weights.IMAGENET1K_V1`. You can also use `weights=VGG16_Weights.DEFAULT` to get the most up-to-date weights.
+  warnings.warn(msg)
+/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=VGG16_Weights.IMAGENET1K_V1`. You can also use `weights=VGG16_Weights.DEFAULT` to get the most up-to-date weights.
+  warnings.warn(msg)
+/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=VGG16_Weights.IMAGENET1K_V1`. You can also use `weights=VGG16_Weights.DEFAULT` to get the most up-to-date weights.
+  warnings.warn(msg)
+/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=VGG16_Weights.IMAGENET1K_V1`. You can also use `weights=VGG16_Weights.DEFAULT` to get the most up-to-date weights.
+  warnings.warn(msg)
+/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=VGG16_Weights.IMAGENET1K_V1`. You can also use `weights=VGG16_Weights.DEFAULT` to get the most up-to-date weights.
+  warnings.warn(msg)
+/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=VGG16_Weights.IMAGENET1K_V1`. You can also use `weights=VGG16_Weights.DEFAULT` to get the most up-to-date weights.
+  warnings.warn(msg)
+/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=VGG16_Weights.IMAGENET1K_V1`. You can also use `weights=VGG16_Weights.DEFAULT` to get the most up-to-date weights.
+  warnings.warn(msg)
+wandb: [wandb.login()] Loaded credentials for https://api.wandb.ai from /root/.netrc.
+wandb: Currently logged in as: 315229706 (315229706-xi-an-jiaotong-university-) to https://api.wandb.ai. Use `wandb login --relogin` to force relogin
+wandb: setting up run kxhnixio
+wandb: Tracking run with wandb version 0.28.2
+wandb: Run data is saved locally in /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/runs/klein4b_grpo_vedai_full_ssim_dists_psnr/wandb/run-20261001_160749-kxhnixio
+wandb: Run `wandb offline` to turn off syncing.
+wandb: Syncing run klein4b_grpo_vedai_full_ssim_dists_psnr
+wandb: ⭐️ View project at https://wandb.ai/315229706-xi-an-jiaotong-university-/IR-Engine
+wandb: 🚀 View run at https://wandb.ai/315229706-xi-an-jiaotong-university-/IR-Engine/runs/kxhnixio
+[2026-10-01 16:07:50] [Rank 0] [INFO] [flow_factory.models.abc]: ======================================================================
+[2026-10-01 16:07:50] [Rank 0] [INFO] [flow_factory.models.abc]: Transformer Trainable Parameters:
+[2026-10-01 16:07:50] [Rank 0] [INFO] [flow_factory.models.abc]:   Total:        3,952,221,696 (  7.36 GB)
+[2026-10-01 16:07:50] [Rank 0] [INFO] [flow_factory.models.abc]:   Trainable:       76,677,120 (  0.14 GB)
+[2026-10-01 16:07:50] [Rank 0] [INFO] [flow_factory.models.abc]:   Percentage:           1.94%
+[2026-10-01 16:07:50] [Rank 0] [INFO] [flow_factory.models.abc]: ======================================================================
+[2026-10-01 16:07:50] [Rank 0] [INFO] [flow_factory.models.abc]: Saving LoRA weights for transformer role=base to /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/runs/klein4b_grpo_vedai_full_ssim_dists_psnr/saves/klein4b_grpo_vedai_full_ssim_dists_psnr/checkpoints/checkpoint-0
+[2026-10-01 16:07:50] [Rank 0] [INFO] [flow_factory.models.abc]: Checkpoint saved successfully to /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/runs/klein4b_grpo_vedai_full_ssim_dists_psnr/saves/klein4b_grpo_vedai_full_ssim_dists_psnr/checkpoints/checkpoint-0
+
Eval/vedai_rl_full:   0%|          | 0/8 [00:00<?, ?it/s][2026-10-01 16:07:51] [Rank 6] [WARNING] [flow_factory.models.flux.flux2_klein]: FLUX.2 does not support batch inference with varying condition images per sample. Falling back to single-sample inference. This warning will only appear once.
+[2026-10-01 16:07:51] [Rank 0] [WARNING] [flow_factory.models.flux.flux2_klein]: FLUX.2 does not support batch inference with varying condition images per sample. Falling back to single-sample inference. This warning will only appear once.
+[2026-10-01 16:07:51] [Rank 5] [WARNING] [flow_factory.models.flux.flux2_klein]: FLUX.2 does not support batch inference with varying condition images per sample. Falling back to single-sample inference. This warning will only appear once.
+[2026-10-01 16:07:51] [Rank 2] [WARNING] [flow_factory.models.flux.flux2_klein]: FLUX.2 does not support batch inference with varying condition images per sample. Falling back to single-sample inference. This warning will only appear once.
+[2026-10-01 16:07:51] [Rank 4] [WARNING] [flow_factory.models.flux.flux2_klein]: FLUX.2 does not support batch inference with varying condition images per sample. Falling back to single-sample inference. This warning will only appear once.
+[2026-10-01 16:07:51] [Rank 3] [WARNING] [flow_factory.models.flux.flux2_klein]: FLUX.2 does not support batch inference with varying condition images per sample. Falling back to single-sample inference. This warning will only appear once.
+[2026-10-01 16:07:51] [Rank 1] [WARNING] [flow_factory.models.flux.flux2_klein]: FLUX.2 does not support batch inference with varying condition images per sample. Falling back to single-sample inference. This warning will only appear once.
+[2026-10-01 16:07:51] [Rank 7] [WARNING] [flow_factory.models.flux.flux2_klein]: FLUX.2 does not support batch inference with varying condition images per sample. Falling back to single-sample inference. This warning will only appear once.
+
Eval/vedai_rl_full:  12%|█▎        | 1/8 [00:06<00:44,  6.29s/it]
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+
Pointwise Rewards: ssim:   0%|          | 0/1 [00:00<?, ?it/s]
Pointwise Rewards: ssim: 100%|██████████| 1/1 [00:00<00:00,  7.00it/s]
Pointwise Rewards: ssim: 100%|██████████| 1/1 [00:00<00:00,  6.97it/s]
+
Pointwise Rewards: psnr:   0%|          | 0/1 [00:00<?, ?it/s]
Pointwise Rewards: psnr: 100%|██████████| 1/1 [00:00<00:00, 12.61it/s]
+
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Pointwise Rewards: dists: 100%|██████████| 1/1 [00:00<00:00,  8.07it/s]
+[2026-10-01 16:08:34] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0000 | Epoch 000] eval/vedai_rl_full/reward_dists_mean=0.9292 eval/vedai_rl_full/reward_dists_std=0.0183 eval/vedai_rl_full/reward_psnr_mean=29.0025 eval/vedai_rl_full/reward_psnr_std=2.6570 eval/vedai_rl_full/reward_ssim_mean=0.9194 eval/vedai_rl_full/reward_ssim_std=0.0282
+
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+
Pointwise Rewards: ssim:   0%|          | 0/6 [00:00<?, ?it/s]
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Pointwise Rewards: ssim:  50%|█████     | 3/6 [00:00<00:00,  6.25it/s]
Pointwise Rewards: ssim:  67%|██████▋   | 4/6 [00:02<00:01,  1.17it/s]
Pointwise Rewards: ssim:  83%|████████▎ | 5/6 [00:02<00:00,  1.67it/s]
Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:02<00:00,  2.25it/s]
Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:02<00:00,  2.23it/s]
+
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Pointwise Rewards: dists:  17%|█▋        | 1/6 [00:00<00:01,  3.54it/s]
Pointwise Rewards: dists:  33%|███▎      | 2/6 [00:00<00:01,  3.59it/s]
Pointwise Rewards: dists:  50%|█████     | 3/6 [00:00<00:00,  3.61it/s]
Pointwise Rewards: dists:  67%|██████▋   | 4/6 [00:01<00:00,  3.65it/s]
Pointwise Rewards: dists:  83%|████████▎ | 5/6 [00:01<00:00,  3.67it/s]
Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.71it/s]
Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.67it/s]
+
Pointwise Rewards: psnr:   0%|          | 0/6 [00:00<?, ?it/s]
Pointwise Rewards: psnr:  17%|█▋        | 1/6 [00:00<00:00,  6.86it/s]
Pointwise Rewards: psnr:  33%|███▎      | 2/6 [00:00<00:00,  6.86it/s]
Pointwise Rewards: psnr:  50%|█████     | 3/6 [00:00<00:00,  6.84it/s]
Pointwise Rewards: psnr:  67%|██████▋   | 4/6 [00:00<00:00,  6.90it/s]
Pointwise Rewards: psnr:  83%|████████▎ | 5/6 [00:00<00:00,  6.90it/s]
Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  7.09it/s]
Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.98it/s]
+[2026-10-01 16:11:06] [Rank 6] [WARNING] [flow_factory.models.flux.flux2_klein]: Flux.2: Ragged I2I detected (varying condition image sizes). Processing samples individually (warning shown once).
+[2026-10-01 16:11:06] [Rank 4] [WARNING] [flow_factory.models.flux.flux2_klein]: Flux.2: Ragged I2I detected (varying condition image sizes). Processing samples individually (warning shown once).
+[2026-10-01 16:11:06] [Rank 5] [WARNING] [flow_factory.models.flux.flux2_klein]: Flux.2: Ragged I2I detected (varying condition image sizes). Processing samples individually (warning shown once).
+[2026-10-01 16:11:06] [Rank 3] [WARNING] [flow_factory.models.flux.flux2_klein]: Flux.2: Ragged I2I detected (varying condition image sizes). Processing samples individually (warning shown once).
+[2026-10-01 16:11:06] [Rank 7] [WARNING] [flow_factory.models.flux.flux2_klein]: Flux.2: Ragged I2I detected (varying condition image sizes). Processing samples individually (warning shown once).
+[2026-10-01 16:11:06] [Rank 2] [WARNING] [flow_factory.models.flux.flux2_klein]: Flux.2: Ragged I2I detected (varying condition image sizes). Processing samples individually (warning shown once).
+[2026-10-01 16:11:06] [Rank 1] [WARNING] [flow_factory.models.flux.flux2_klein]: Flux.2: Ragged I2I detected (varying condition image sizes). Processing samples individually (warning shown once).
+[2026-10-01 16:11:07] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0000 | Epoch 000] train/reward_dists_mean=0.9199 train/reward_dists_std=0.0249 train/reward_psnr_mean=29.1590 train/reward_psnr_std=2.3156 train/reward_ssim_mean=0.9314 train/reward_ssim_std=0.0212 train/reward_dists_group_std_mean=0.0048 train/reward_dists_group_std_max=0.0089 train/reward_dists_group_std_min=0.0021 train/reward_dists_group_mean_std=0.0244 train/reward_psnr_group_std_mean=1.6716 train/reward_psnr_group_std_max=3.4153 train/reward_psnr_group_std_min=0.4119 train/reward_psnr_group_mean_std=1.4624 train/reward_ssim_group_std_mean=0.0023 train/reward_ssim_group_std_max=0.0310 train/reward_ssim_group_std_min=0.0006 train/reward_ssim_group_mean_std=0.0206 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-9.4127 train/adv_max=4.9454 train/adv_abs_mean=1.7852
+
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+
Epoch 0 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 16:11:07] [Rank 0] [WARNING] [flow_factory.models.flux.flux2_klein]: Flux.2: Ragged I2I detected (varying condition image sizes). Processing samples individually (warning shown once).
+
+
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Epoch 0 Training:   6%|▋         | 6/96 [00:02<00:37,  2.38it/s]
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Epoch 0 Training:   9%|▉         | 9/96 [00:03<00:36,  2.38it/s]
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Epoch 0 Training:  21%|██        | 20/96 [00:08<00:31,  2.38it/s]
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Epoch 0 Training:  26%|██▌       | 25/96 [00:10<00:29,  2.38it/s]
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Epoch 0 Training:  27%|██▋       | 26/96 [00:10<00:29,  2.38it/s]
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Epoch 0 Training:  28%|██▊       | 27/96 [00:11<00:28,  2.38it/s]
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Epoch 0 Training:  29%|██▉       | 28/96 [00:11<00:28,  2.38it/s]
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Epoch 0 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 16:11:27] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0000 | Epoch 000] train/clipped_loss_min=-4.8210 train/clipped_loss_max=5 train/clipped_loss_mean=-0.0460 train/clipped_loss_std=2.1477 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-4.8210 train/unclipped_loss_max=5 train/unclipped_loss_mean=-0.0460 train/unclipped_loss_std=2.1477 train/policy_loss=-0.0460 train/loss=-0.0460 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
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Epoch 0 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 16:11:47] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0001 | Epoch 000] train/clipped_loss_min=-4.9449 train/clipped_loss_max=4.9995 train/clipped_loss_mean=-0.0051 train/clipped_loss_std=2.1740 train/ratio_min=0.9996 train/ratio_max=1.0001 train/ratio_mean=0.9998 train/ratio_std=0 train/unclipped_loss_min=-4.9444 train/unclipped_loss_max=4.9994 train/unclipped_loss_mean=-0.0051 train/unclipped_loss_std=2.1739 train/policy_loss=-0.0050 train/loss=-0.0050 train/clip_frac_high=0 train/clip_frac_low=0.8542 train/clip_frac_total=0.8542 train/grad_norm=0.0010
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+[2026-10-01 16:11:47] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0002 | Epoch 000] timing/rollout_seconds=146.6514 timing/feedback_seconds=6.2532 timing/optimization_seconds=40.9617 timing/cycle_seconds=192.8190
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+
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+[2026-10-01 16:14:18] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0002 | Epoch 001] train/reward_dists_mean=0.9009 train/reward_dists_std=0.0254 train/reward_psnr_mean=23.7939 train/reward_psnr_std=2.6889 train/reward_ssim_mean=0.8796 train/reward_ssim_std=0.0558 train/reward_dists_group_std_mean=0.0056 train/reward_dists_group_std_max=0.0252 train/reward_dists_group_std_min=0.0029 train/reward_dists_group_mean_std=0.0245 train/reward_psnr_group_std_mean=1.0884 train/reward_psnr_group_std_max=2.6153 train/reward_psnr_group_std_min=0.3699 train/reward_psnr_group_mean_std=2.4020 train/reward_ssim_group_std_mean=0.0081 train/reward_ssim_group_std_max=0.0451 train/reward_ssim_group_std_min=0.0016 train/reward_ssim_group_mean_std=0.0546 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-8.4288 train/adv_max=6.3874 train/adv_abs_mean=1.8957
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Epoch 1 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 16:14:38] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0002 | Epoch 001] train/clipped_loss_min=-5 train/clipped_loss_max=5 train/clipped_loss_mean=0.0051 train/clipped_loss_std=2.2358 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-5 train/unclipped_loss_max=5 train/unclipped_loss_mean=0.0051 train/unclipped_loss_std=2.2358 train/policy_loss=0.0051 train/loss=0.0051 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
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Epoch 1 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 16:14:59] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0003 | Epoch 001] train/clipped_loss_min=-4.9996 train/clipped_loss_max=4.9999 train/clipped_loss_mean=-0.0300 train/clipped_loss_std=2.3358 train/ratio_min=0.9997 train/ratio_max=1.0000 train/ratio_mean=0.9998 train/ratio_std=0 train/unclipped_loss_min=-4.9996 train/unclipped_loss_max=4.9999 train/unclipped_loss_mean=-0.0300 train/unclipped_loss_std=2.3357 train/policy_loss=-0.0299 train/loss=-0.0299 train/clip_frac_high=0 train/clip_frac_low=0.8594 train/clip_frac_total=0.8594 train/grad_norm=0.0019
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+[2026-10-01 16:14:59] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0004 | Epoch 001] timing/rollout_seconds=146.6490 timing/feedback_seconds=5.3535 timing/optimization_seconds=41.3239 timing/cycle_seconds=191.4263
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+
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+[2026-10-01 16:17:29] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0004 | Epoch 002] train/reward_dists_mean=0.8647 train/reward_dists_std=0.0503 train/reward_psnr_mean=20.5114 train/reward_psnr_std=4.8049 train/reward_ssim_mean=0.8429 train/reward_ssim_std=0.0707 train/reward_dists_group_std_mean=0.0038 train/reward_dists_group_std_max=0.0117 train/reward_dists_group_std_min=0.0020 train/reward_dists_group_mean_std=0.0501 train/reward_psnr_group_std_mean=0.7635 train/reward_psnr_group_std_max=1.6512 train/reward_psnr_group_std_min=0.3787 train/reward_psnr_group_mean_std=4.7361 train/reward_ssim_group_std_mean=0.0059 train/reward_ssim_group_std_max=0.0284 train/reward_ssim_group_std_min=0.0014 train/reward_ssim_group_mean_std=0.0702 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-7.7256 train/adv_max=7.3713 train/adv_abs_mean=2.1194
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Epoch 2 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 16:17:49] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0004 | Epoch 002] train/clipped_loss_min=-5 train/clipped_loss_max=5 train/clipped_loss_mean=-0.0946 train/clipped_loss_std=2.5529 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-5 train/unclipped_loss_max=5 train/unclipped_loss_mean=-0.0946 train/unclipped_loss_std=2.5529 train/policy_loss=-0.0946 train/loss=-0.0946 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0002
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Epoch 2 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 16:18:10] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0005 | Epoch 002] train/clipped_loss_min=-5.0000 train/clipped_loss_max=5.0001 train/clipped_loss_mean=0.0872 train/clipped_loss_std=2.5107 train/ratio_min=0.9999 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0.0001 train/unclipped_loss_min=-5.0000 train/unclipped_loss_max=5.0001 train/unclipped_loss_mean=0.0872 train/unclipped_loss_std=2.5107 train/policy_loss=0.0872 train/loss=0.0872 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0002
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+[2026-10-01 16:18:10] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0006 | Epoch 002] timing/rollout_seconds=146.4347 timing/feedback_seconds=4.6264 timing/optimization_seconds=41.1699 timing/cycle_seconds=191.0181
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+[2026-10-01 16:20:41] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0006 | Epoch 003] train/reward_dists_mean=0.8502 train/reward_dists_std=0.0395 train/reward_psnr_mean=19.7250 train/reward_psnr_std=4.0393 train/reward_ssim_mean=0.8081 train/reward_ssim_std=0.0631 train/reward_dists_group_std_mean=0.0027 train/reward_dists_group_std_max=0.0106 train/reward_dists_group_std_min=0.0014 train/reward_dists_group_mean_std=0.0394 train/reward_psnr_group_std_mean=0.4225 train/reward_psnr_group_std_max=2.4476 train/reward_psnr_group_std_min=0.1233 train/reward_psnr_group_mean_std=4.0029 train/reward_ssim_group_std_mean=0.0033 train/reward_ssim_group_std_max=0.0106 train/reward_ssim_group_std_min=0.0012 train/reward_ssim_group_mean_std=0.0630 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-6.6302 train/adv_max=7.2935 train/adv_abs_mean=1.9826
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Epoch 3 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 16:21:02] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0006 | Epoch 003] train/clipped_loss_min=-5 train/clipped_loss_max=5 train/clipped_loss_mean=-0.0733 train/clipped_loss_std=2.3856 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-5 train/unclipped_loss_max=5 train/unclipped_loss_mean=-0.0733 train/unclipped_loss_std=2.3856 train/policy_loss=-0.0733 train/loss=-0.0733 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
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Epoch 3 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 16:21:22] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0007 | Epoch 003] train/clipped_loss_min=-5.0000 train/clipped_loss_max=5.0000 train/clipped_loss_mean=0.0647 train/clipped_loss_std=2.3890 train/ratio_min=0.9999 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-5.0000 train/unclipped_loss_max=5.0000 train/unclipped_loss_mean=0.0647 train/unclipped_loss_std=2.3890 train/policy_loss=0.0647 train/loss=0.0647 train/clip_frac_high=0 train/clip_frac_low=0.0026 train/clip_frac_total=0.0026 train/grad_norm=0.0006
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+[2026-10-01 16:21:22] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0008 | Epoch 003] timing/rollout_seconds=147.5001 timing/feedback_seconds=5.7789 timing/optimization_seconds=41.1310 timing/cycle_seconds=192.2099
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+
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+[2026-10-01 16:23:53] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0008 | Epoch 004] train/reward_dists_mean=0.8315 train/reward_dists_std=0.0262 train/reward_psnr_mean=17.2192 train/reward_psnr_std=2.3701 train/reward_ssim_mean=0.7965 train/reward_ssim_std=0.0603 train/reward_dists_group_std_mean=0.0043 train/reward_dists_group_std_max=0.0071 train/reward_dists_group_std_min=0.0024 train/reward_dists_group_mean_std=0.0258 train/reward_psnr_group_std_mean=0.4571 train/reward_psnr_group_std_max=0.9266 train/reward_psnr_group_std_min=0.2159 train/reward_psnr_group_mean_std=2.3212 train/reward_ssim_group_std_mean=0.0038 train/reward_ssim_group_std_max=0.0089 train/reward_ssim_group_std_min=0.0014 train/reward_ssim_group_mean_std=0.0602 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-7.3710 train/adv_max=7.4620 train/adv_abs_mean=2.1441
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Epoch 4 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 16:24:13] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0008 | Epoch 004] train/clipped_loss_min=-5 train/clipped_loss_max=5 train/clipped_loss_mean=0.1105 train/clipped_loss_std=2.3989 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-5 train/unclipped_loss_max=5 train/unclipped_loss_mean=0.1105 train/unclipped_loss_std=2.3989 train/policy_loss=0.1105 train/loss=0.1105 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0002
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Epoch 4 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 16:24:34] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0009 | Epoch 004] train/clipped_loss_min=-5.0000 train/clipped_loss_max=5.0001 train/clipped_loss_mean=-0.1229 train/clipped_loss_std=2.6664 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-5.0000 train/unclipped_loss_max=5.0001 train/unclipped_loss_mean=-0.1229 train/unclipped_loss_std=2.6664 train/policy_loss=-0.1229 train/loss=-0.1229 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0002
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+[2026-10-01 16:24:34] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0010 | Epoch 004] timing/rollout_seconds=147.3405 timing/feedback_seconds=5.5581 timing/optimization_seconds=40.9637 timing/cycle_seconds=191.7631
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+[2026-10-01 16:27:04] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0010 | Epoch 005] train/reward_dists_mean=0.8217 train/reward_dists_std=0.0307 train/reward_psnr_mean=17.4346 train/reward_psnr_std=3.0014 train/reward_ssim_mean=0.7924 train/reward_ssim_std=0.0549 train/reward_dists_group_std_mean=0.0044 train/reward_dists_group_std_max=0.0086 train/reward_dists_group_std_min=0.0023 train/reward_dists_group_mean_std=0.0304 train/reward_psnr_group_std_mean=0.4158 train/reward_psnr_group_std_max=0.8602 train/reward_psnr_group_std_min=0.2342 train/reward_psnr_group_mean_std=2.9687 train/reward_ssim_group_std_mean=0.0037 train/reward_ssim_group_std_max=0.0098 train/reward_ssim_group_std_min=0.0013 train/reward_ssim_group_mean_std=0.0548 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-8.1505 train/adv_max=7.4334 train/adv_abs_mean=2.0952
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Epoch 5 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 16:27:45] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0011 | Epoch 005] train/clipped_loss_min=-4.9999 train/clipped_loss_max=5.0000 train/clipped_loss_mean=-0.0423 train/clipped_loss_std=2.4823 train/ratio_min=0.9999 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0.0001 train/unclipped_loss_min=-4.9999 train/unclipped_loss_max=5.0000 train/unclipped_loss_mean=-0.0423 train/unclipped_loss_std=2.4823 train/policy_loss=-0.0423 train/loss=-0.0423 train/clip_frac_high=0 train/clip_frac_low=0.0026 train/clip_frac_total=0.0026 train/grad_norm=0.0003
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+[2026-10-01 16:27:45] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0012 | Epoch 005] timing/rollout_seconds=146.3587 timing/feedback_seconds=4.6397 timing/optimization_seconds=41.1524 timing/cycle_seconds=191.1330
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Pointwise Rewards: psnr:  83%|████████▎ | 5/6 [00:00<00:00,  6.93it/s]
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Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.87it/s]
+[2026-10-01 16:30:16] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0012 | Epoch 006] train/reward_dists_mean=0.8492 train/reward_dists_std=0.0358 train/reward_psnr_mean=21.6388 train/reward_psnr_std=3.1355 train/reward_ssim_mean=0.8124 train/reward_ssim_std=0.0591 train/reward_dists_group_std_mean=0.0034 train/reward_dists_group_std_max=0.0068 train/reward_dists_group_std_min=0.0017 train/reward_dists_group_mean_std=0.0356 train/reward_psnr_group_std_mean=0.4677 train/reward_psnr_group_std_max=1.2401 train/reward_psnr_group_std_min=0.1180 train/reward_psnr_group_mean_std=3.0919 train/reward_ssim_group_std_mean=0.0030 train/reward_ssim_group_std_max=0.0078 train/reward_ssim_group_std_min=0.0006 train/reward_ssim_group_mean_std=0.0590 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-6.8322 train/adv_max=6.9563 train/adv_abs_mean=1.7702
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Epoch 6 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 16:30:38] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0012 | Epoch 006] train/clipped_loss_min=-5 train/clipped_loss_max=5 train/clipped_loss_mean=-0.0596 train/clipped_loss_std=2.2661 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-5 train/unclipped_loss_max=5 train/unclipped_loss_mean=-0.0596 train/unclipped_loss_std=2.2661 train/policy_loss=-0.0596 train/loss=-0.0596 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0004
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Epoch 6 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 16:30:58] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0013 | Epoch 006] train/clipped_loss_min=-4.9997 train/clipped_loss_max=4.9997 train/clipped_loss_mean=0.0654 train/clipped_loss_std=2.0594 train/ratio_min=0.9998 train/ratio_max=1.0000 train/ratio_mean=0.9999 train/ratio_std=0.0001 train/unclipped_loss_min=-4.9997 train/unclipped_loss_max=4.9997 train/unclipped_loss_mean=0.0654 train/unclipped_loss_std=2.0594 train/policy_loss=0.0654 train/loss=0.0654 train/clip_frac_high=0 train/clip_frac_low=0.1719 train/clip_frac_total=0.1719 train/grad_norm=0.0005
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+[2026-10-01 16:30:58] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0014 | Epoch 006] timing/rollout_seconds=147.4459 timing/feedback_seconds=5.6939 timing/optimization_seconds=42.6616 timing/cycle_seconds=193.6361
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+
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+[2026-10-01 16:33:30] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0014 | Epoch 007] train/reward_dists_mean=0.8592 train/reward_dists_std=0.0383 train/reward_psnr_mean=19.2337 train/reward_psnr_std=3.6801 train/reward_ssim_mean=0.8075 train/reward_ssim_std=0.0737 train/reward_dists_group_std_mean=0.0031 train/reward_dists_group_std_max=0.0070 train/reward_dists_group_std_min=0.0014 train/reward_dists_group_mean_std=0.0382 train/reward_psnr_group_std_mean=0.5040 train/reward_psnr_group_std_max=1.0225 train/reward_psnr_group_std_min=0.2364 train/reward_psnr_group_mean_std=3.6401 train/reward_ssim_group_std_mean=0.0037 train/reward_ssim_group_std_max=0.0113 train/reward_ssim_group_std_min=0.0009 train/reward_ssim_group_mean_std=0.0736 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-8.0584 train/adv_max=6.6943 train/adv_abs_mean=1.9919
+
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Epoch 7 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 16:33:50] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0014 | Epoch 007] train/clipped_loss_min=-5 train/clipped_loss_max=5 train/clipped_loss_mean=0.0255 train/clipped_loss_std=2.3721 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-5 train/unclipped_loss_max=5 train/unclipped_loss_mean=0.0255 train/unclipped_loss_std=2.3721 train/policy_loss=0.0255 train/loss=0.0255 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0004
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Epoch 7 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 16:34:10] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0015 | Epoch 007] train/clipped_loss_min=-4.9999 train/clipped_loss_max=5.0000 train/clipped_loss_mean=-0.0589 train/clipped_loss_std=2.4080 train/ratio_min=0.9999 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-4.9999 train/unclipped_loss_max=5.0000 train/unclipped_loss_mean=-0.0589 train/unclipped_loss_std=2.4080 train/policy_loss=-0.0589 train/loss=-0.0589 train/clip_frac_high=0 train/clip_frac_low=0.0130 train/clip_frac_total=0.0130 train/grad_norm=0.0004
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+[2026-10-01 16:34:10] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0016 | Epoch 007] timing/rollout_seconds=147.3871 timing/feedback_seconds=5.7203 timing/optimization_seconds=41.0457 timing/cycle_seconds=191.9843
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+
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Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:00<00:00,  6.54it/s]
+
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Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.72it/s]
+
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Pointwise Rewards: psnr:  33%|███▎      | 2/6 [00:00<00:00,  6.93it/s]
Pointwise Rewards: psnr:  50%|█████     | 3/6 [00:00<00:00,  6.96it/s]
Pointwise Rewards: psnr:  67%|██████▋   | 4/6 [00:00<00:00,  6.88it/s]
Pointwise Rewards: psnr:  83%|████████▎ | 5/6 [00:00<00:00,  7.01it/s]
Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.95it/s]
Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.94it/s]
+[2026-10-01 16:36:42] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0016 | Epoch 008] train/reward_dists_mean=0.8787 train/reward_dists_std=0.0264 train/reward_psnr_mean=22.1641 train/reward_psnr_std=3.1263 train/reward_ssim_mean=0.8291 train/reward_ssim_std=0.0494 train/reward_dists_group_std_mean=0.0028 train/reward_dists_group_std_max=0.0080 train/reward_dists_group_std_min=0.0012 train/reward_dists_group_mean_std=0.0262 train/reward_psnr_group_std_mean=0.3968 train/reward_psnr_group_std_max=0.8983 train/reward_psnr_group_std_min=0.1039 train/reward_psnr_group_mean_std=3.0945 train/reward_ssim_group_std_mean=0.0025 train/reward_ssim_group_std_max=0.0097 train/reward_ssim_group_std_min=0.0008 train/reward_ssim_group_mean_std=0.0493 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-7.0055 train/adv_max=6.6703 train/adv_abs_mean=1.9737
+
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Epoch 8 Training:  14%|█▎        | 13/96 [00:05<00:35,  2.37it/s]
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Epoch 8 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 16:37:22] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0017 | Epoch 008] train/clipped_loss_min=-5.0000 train/clipped_loss_max=5.0001 train/clipped_loss_mean=0.1127 train/clipped_loss_std=2.3467 train/ratio_min=0.9999 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-5.0000 train/unclipped_loss_max=5.0001 train/unclipped_loss_mean=0.1127 train/unclipped_loss_std=2.3467 train/policy_loss=0.1127 train/loss=0.1127 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0002
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+[2026-10-01 16:37:22] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0018 | Epoch 008] timing/rollout_seconds=147.2536 timing/feedback_seconds=5.5649 timing/optimization_seconds=41.0604 timing/cycle_seconds=191.9245
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+
Pointwise Rewards: ssim:   0%|          | 0/6 [00:00<?, ?it/s]
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Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:00<00:00,  6.62it/s]
+
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Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.72it/s]
+
Pointwise Rewards: psnr:   0%|          | 0/6 [00:00<?, ?it/s]
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Pointwise Rewards: psnr:  33%|███▎      | 2/6 [00:00<00:00,  6.98it/s]
Pointwise Rewards: psnr:  50%|█████     | 3/6 [00:00<00:00,  6.83it/s]
Pointwise Rewards: psnr:  67%|██████▋   | 4/6 [00:00<00:00,  6.85it/s]
Pointwise Rewards: psnr:  83%|████████▎ | 5/6 [00:00<00:00,  7.01it/s]
Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.83it/s]
Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.86it/s]
+[2026-10-01 16:39:54] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0018 | Epoch 009] train/reward_dists_mean=0.8695 train/reward_dists_std=0.0225 train/reward_psnr_mean=20.1512 train/reward_psnr_std=2.1699 train/reward_ssim_mean=0.8225 train/reward_ssim_std=0.0571 train/reward_dists_group_std_mean=0.0028 train/reward_dists_group_std_max=0.0046 train/reward_dists_group_std_min=0.0016 train/reward_dists_group_mean_std=0.0224 train/reward_psnr_group_std_mean=0.3683 train/reward_psnr_group_std_max=0.6400 train/reward_psnr_group_std_min=0.1906 train/reward_psnr_group_mean_std=2.1355 train/reward_ssim_group_std_mean=0.0023 train/reward_ssim_group_std_max=0.0085 train/reward_ssim_group_std_min=0.0009 train/reward_ssim_group_mean_std=0.0570 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-7.9011 train/adv_max=7.6311 train/adv_abs_mean=1.9756
+
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Epoch 9 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 16:40:15] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0018 | Epoch 009] train/clipped_loss_min=-5 train/clipped_loss_max=5 train/clipped_loss_mean=0.0186 train/clipped_loss_std=2.2991 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-5 train/unclipped_loss_max=5 train/unclipped_loss_mean=0.0186 train/unclipped_loss_std=2.2991 train/policy_loss=0.0186 train/loss=0.0186 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
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Epoch 9 Training:  99%|█████████▉| 95/96 [00:40<00:00,  2.37it/s]
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Epoch 9 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 16:40:35] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0019 | Epoch 009] train/clipped_loss_min=-5.0000 train/clipped_loss_max=5.0000 train/clipped_loss_mean=0.0033 train/clipped_loss_std=2.4123 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-5.0000 train/unclipped_loss_max=5.0000 train/unclipped_loss_mean=0.0033 train/unclipped_loss_std=2.4123 train/policy_loss=0.0033 train/loss=0.0033 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
+
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Epoch 9 Timestep: 100%|██████████| 1/1 [00:00<00:00,  2.32it/s]
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Epoch 9 Training: 100%|██████████| 96/96 [00:40<00:00,  2.28it/s]
Epoch 9 Training: 100%|██████████| 96/96 [00:40<00:00,  2.37it/s]
+[2026-10-01 16:40:35] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0020 | Epoch 009] timing/rollout_seconds=147.7880 timing/feedback_seconds=6.3070 timing/optimization_seconds=41.0813 timing/cycle_seconds=192.5250
+
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Eval/vedai_rl_full:  25%|██▌       | 2/8 [00:11<00:33,  5.60s/it]
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Eval/vedai_rl_full:  50%|█████     | 4/8 [00:21<00:21,  5.30s/it]
Eval/vedai_rl_full:  62%|██████▎   | 5/8 [00:26<00:15,  5.25s/it]
Eval/vedai_rl_full:  75%|███████▌  | 6/8 [00:31<00:10,  5.21s/it]
Eval/vedai_rl_full:  88%|████████▊ | 7/8 [00:37<00:05,  5.19s/it]
Eval/vedai_rl_full: 100%|██████████| 8/8 [00:42<00:00,  5.28s/it]
Eval/vedai_rl_full: 100%|██████████| 8/8 [00:42<00:00,  5.33s/it]
+
Pointwise Rewards: ssim:   0%|          | 0/1 [00:00<?, ?it/s]
Pointwise Rewards: ssim: 100%|██████████| 1/1 [00:00<00:00,  7.06it/s]
Pointwise Rewards: ssim: 100%|██████████| 1/1 [00:00<00:00,  7.03it/s]
+
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Pointwise Rewards: psnr: 100%|██████████| 1/1 [00:00<00:00, 12.17it/s]
+
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Pointwise Rewards: dists: 100%|██████████| 1/1 [00:00<00:00,  8.69it/s]
+[2026-10-01 16:41:19] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0020 | Epoch 010] eval/vedai_rl_full/reward_dists_mean=0.8708 eval/vedai_rl_full/reward_dists_std=0.0373 eval/vedai_rl_full/reward_psnr_mean=20.6659 eval/vedai_rl_full/reward_psnr_std=3.0051 eval/vedai_rl_full/reward_ssim_mean=0.8263 eval/vedai_rl_full/reward_ssim_std=0.0600
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+
Pointwise Rewards: ssim:   0%|          | 0/6 [00:00<?, ?it/s]
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Pointwise Rewards: ssim:  83%|████████▎ | 5/6 [00:00<00:00,  6.36it/s]
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Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:00<00:00,  6.28it/s]
+
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Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.64it/s]
Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.64it/s]
+
Pointwise Rewards: psnr:   0%|          | 0/6 [00:00<?, ?it/s]
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Pointwise Rewards: psnr:  33%|███▎      | 2/6 [00:00<00:00,  6.79it/s]
Pointwise Rewards: psnr:  50%|█████     | 3/6 [00:00<00:00,  6.84it/s]
Pointwise Rewards: psnr:  67%|██████▋   | 4/6 [00:00<00:00,  6.78it/s]
Pointwise Rewards: psnr:  83%|████████▎ | 5/6 [00:00<00:00,  6.75it/s]
Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.65it/s]
Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.71it/s]
+[2026-10-01 16:43:50] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0020 | Epoch 010] train/reward_dists_mean=0.8650 train/reward_dists_std=0.0304 train/reward_psnr_mean=21.5869 train/reward_psnr_std=2.8135 train/reward_ssim_mean=0.8286 train/reward_ssim_std=0.0591 train/reward_dists_group_std_mean=0.0029 train/reward_dists_group_std_max=0.0050 train/reward_dists_group_std_min=0.0016 train/reward_dists_group_mean_std=0.0302 train/reward_psnr_group_std_mean=0.3866 train/reward_psnr_group_std_max=0.8703 train/reward_psnr_group_std_min=0.1211 train/reward_psnr_group_mean_std=2.7827 train/reward_ssim_group_std_mean=0.0025 train/reward_ssim_group_std_max=0.0064 train/reward_ssim_group_std_min=0.0008 train/reward_ssim_group_mean_std=0.0590 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-7.2469 train/adv_max=6.5262 train/adv_abs_mean=1.8973
+
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Epoch 10 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 16:44:10] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0020 | Epoch 010] train/clipped_loss_min=-5 train/clipped_loss_max=5 train/clipped_loss_mean=-0.1052 train/clipped_loss_std=2.2693 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-5 train/unclipped_loss_max=5 train/unclipped_loss_mean=-0.1052 train/unclipped_loss_std=2.2693 train/policy_loss=-0.1052 train/loss=-0.1052 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0002
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+
                                                                
Epoch 10 Training:  99%|█████████▉| 95/96 [00:39<00:00,  2.37it/s]
+
Epoch 10 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 16:44:30] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0021 | Epoch 010] train/clipped_loss_min=-5.0001 train/clipped_loss_max=5.0000 train/clipped_loss_mean=0.1124 train/clipped_loss_std=2.3404 train/ratio_min=0.9999 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-5.0001 train/unclipped_loss_max=5.0000 train/unclipped_loss_mean=0.1124 train/unclipped_loss_std=2.3404 train/policy_loss=0.1124 train/loss=0.1124 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0004
+
+
Epoch 10 Timestep: 100%|██████████| 1/1 [00:00<00:00,  2.32it/s]
+
                                                                
Epoch 10 Training: 100%|██████████| 96/96 [00:40<00:00,  2.28it/s]
Epoch 10 Training: 100%|██████████| 96/96 [00:40<00:00,  2.37it/s]
+[2026-10-01 16:44:30] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0022 | Epoch 010] timing/rollout_seconds=146.8736 timing/feedback_seconds=4.2342 timing/optimization_seconds=41.0488 timing/cycle_seconds=191.2864
+
Epoch 11 Sampling:   0%|          | 0/96 [00:00<?, ?it/s]
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Epoch 11 Sampling:  34%|███▍      | 33/96 [00:50<01:34,  1.51s/it]
Epoch 11 Sampling:  35%|███▌      | 34/96 [00:51<01:33,  1.51s/it]
Epoch 11 Sampling:  36%|███▋      | 35/96 [00:53<01:31,  1.51s/it]W1001 16:45:25.606000 4069794 site-packages/torch/distributed/elastic/agent/server/api.py:739] Received Signals.SIGTERM death signal, shutting down workers
+W1001 16:45:25.653000 4069794 site-packages/torch/distributed/elastic/multiprocessing/api.py:1010] Sending process 4069979 closing signal SIGTERM
+W1001 16:45:25.654000 4069794 site-packages/torch/distributed/elastic/multiprocessing/api.py:1010] Sending process 4069980 closing signal SIGTERM
+W1001 16:45:25.654000 4069794 site-packages/torch/distributed/elastic/multiprocessing/api.py:1010] Sending process 4069981 closing signal SIGTERM
+W1001 16:45:25.655000 4069794 site-packages/torch/distributed/elastic/multiprocessing/api.py:1010] Sending process 4069982 closing signal SIGTERM
+W1001 16:45:25.655000 4069794 site-packages/torch/distributed/elastic/multiprocessing/api.py:1010] Sending process 4069983 closing signal SIGTERM
+W1001 16:45:25.655000 4069794 site-packages/torch/distributed/elastic/multiprocessing/api.py:1010] Sending process 4069984 closing signal SIGTERM
+W1001 16:45:25.655000 4069794 site-packages/torch/distributed/elastic/multiprocessing/api.py:1010] Sending process 4069985 closing signal SIGTERM
+W1001 16:45:25.655000 4069794 site-packages/torch/distributed/elastic/multiprocessing/api.py:1010] Sending process 4069986 closing signal SIGTERM
+Traceback (most recent call last):
+  File "/opt/conda/envs/ff-qi21/bin/accelerate", line 6, in <module>
+    sys.exit(main())
+  File "/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/accelerate/commands/accelerate_cli.py", line 50, in main
+    args.func(args)
+  File "/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/accelerate/commands/launch.py", line 1392, in launch_command
+    deepspeed_launcher(args)
+  File "/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/accelerate/commands/launch.py", line 1076, in deepspeed_launcher
+    distrib_run.run(args)
+  File "/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torch/distributed/run.py", line 982, in run
+    elastic_launch(
+  File "/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torch/distributed/launcher/api.py", line 170, in __call__
+    return launch_agent(self._config, self._entrypoint, list(args))
+  File "/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torch/distributed/launcher/api.py", line 308, in launch_agent
+    result = agent.run()
+  File "/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torch/distributed/elastic/metrics/api.py", line 134, in wrapper
+    result = f(*args, **kwargs)
+  File "/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torch/distributed/elastic/agent/server/api.py", line 731, in run
+    result = self._invoke_run(role)
+  File "/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torch/distributed/elastic/agent/server/api.py", line 908, in _invoke_run
+    time.sleep(monitor_interval)
+  File "/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torch/distributed/elastic/multiprocessing/api.py", line 86, in _terminate_process_handler
+    raise SignalException(f"Process {os.getpid()} got signal: {sigval}", sigval=sigval)
+torch.distributed.elastic.multiprocessing.api.SignalException: Process 4069794 got signal: 15
diff --git a/runs/klein4b_grpo_vedai_full_ssim_dists_psnr_lr5e-5/saves/klein4b_grpo_vedai_full_ssim_dists_psnr_lr5e-5/checkpoints/checkpoint-100/README.md b/runs/klein4b_grpo_vedai_full_ssim_dists_psnr_lr5e-5/saves/klein4b_grpo_vedai_full_ssim_dists_psnr_lr5e-5/checkpoints/checkpoint-100/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..2b78a7a995873d5db3090fca56885fe89cdf97e1
--- /dev/null
+++ b/runs/klein4b_grpo_vedai_full_ssim_dists_psnr_lr5e-5/saves/klein4b_grpo_vedai_full_ssim_dists_psnr_lr5e-5/checkpoints/checkpoint-100/README.md
@@ -0,0 +1,204 @@
+---
+base_model: /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/flow_factory_gpu_gate_models/FLUX.2-klein-base-4B/transformer
+library_name: peft
+tags:
+- lora
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]
+### Framework versions
+
+- PEFT 0.20.0
\ No newline at end of file
diff --git a/runs/klein4b_grpo_vedai_full_ssim_dists_psnr_lr5e-5/saves/klein4b_grpo_vedai_full_ssim_dists_psnr_lr5e-5/checkpoints/checkpoint-100/adapter_config.json b/runs/klein4b_grpo_vedai_full_ssim_dists_psnr_lr5e-5/saves/klein4b_grpo_vedai_full_ssim_dists_psnr_lr5e-5/checkpoints/checkpoint-100/adapter_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..402dfbae2fc34ebff81b3bf7660d2199d5239505
--- /dev/null
+++ b/runs/klein4b_grpo_vedai_full_ssim_dists_psnr_lr5e-5/saves/klein4b_grpo_vedai_full_ssim_dists_psnr_lr5e-5/checkpoints/checkpoint-100/adapter_config.json
@@ -0,0 +1,59 @@
+{
+  "alora_invocation_tokens": null,
+  "alpha_pattern": {},
+  "arrow_config": null,
+  "auto_mapping": {
+    "base_model_class": "Flux2Transformer2DModel",
+    "parent_library": "diffusers.models.transformers.transformer_flux2"
+  },
+  "base_model_name_or_path": null,
+  "bias": "none",
+  "corda_config": null,
+  "ensure_weight_tying": false,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": "gaussian",
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 128,
+  "lora_bias": false,
+  "lora_dropout": 0.0,
+  "lora_ga_config": null,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "monteclora_config": null,
+  "peft_type": "LORA",
+  "peft_version": "0.20.0",
+  "qalora_group_size": 16,
+  "r": 64,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "ff.linear_out",
+    "attn.to_k",
+    "ff_context.linear_in",
+    "attn.add_k_proj",
+    "ff.linear_in",
+    "attn.add_v_proj",
+    "ff_context.linear_out",
+    "attn.to_out.0",
+    "attn.add_q_proj",
+    "attn.to_qkv_mlp_proj",
+    "attn.to_add_out",
+    "attn.to_q",
+    "attn.to_v"
+  ],
+  "target_parameters": null,
+  "task_type": null,
+  "trainable_token_indices": null,
+  "use_bdlora": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false,
+  "velora_config": null
+}
\ No newline at end of file
diff --git a/runs/klein4b_grpo_vedai_full_ssim_dists_psnr_lr5e-5/saves/klein4b_grpo_vedai_full_ssim_dists_psnr_lr5e-5/checkpoints/checkpoint-100/manifest.json b/runs/klein4b_grpo_vedai_full_ssim_dists_psnr_lr5e-5/saves/klein4b_grpo_vedai_full_ssim_dists_psnr_lr5e-5/checkpoints/checkpoint-100/manifest.json
new file mode 100644
index 0000000000000000000000000000000000000000..4443fd995e0f6f3bd9b7e3dacea93df8d345ecfb
--- /dev/null
+++ b/runs/klein4b_grpo_vedai_full_ssim_dists_psnr_lr5e-5/saves/klein4b_grpo_vedai_full_ssim_dists_psnr_lr5e-5/checkpoints/checkpoint-100/manifest.json
@@ -0,0 +1,16 @@
+{
+  "base_role": "base",
+  "entries": [
+    {
+      "component": "transformer",
+      "path": ".",
+      "role": "base"
+    }
+  ],
+  "finetune_type": "lora",
+  "format_version": 1,
+  "primary": {
+    "component": "transformer",
+    "path": "."
+  }
+}
diff --git a/runs/klein4b_grpo_vedai_full_ssim_dists_psnr_lr5e-5/train.log b/runs/klein4b_grpo_vedai_full_ssim_dists_psnr_lr5e-5/train.log
new file mode 100644
index 0000000000000000000000000000000000000000..5879a141d7ff7ebc6a6d85d8a20c44a2b16c2f52
--- /dev/null
+++ b/runs/klein4b_grpo_vedai_full_ssim_dists_psnr_lr5e-5/train.log
@@ -0,0 +1,30189 @@
+[2026-10-01 16:45:33,704] [INFO] [flow_factory.cli]: ============================================================
+[2026-10-01 16:45:33,705] [INFO] [flow_factory.cli]: Flow-Factory Launch Configuration
+[2026-10-01 16:45:33,705] [INFO] [flow_factory.cli]:   Num machines:       1
+[2026-10-01 16:45:33,705] [INFO] [flow_factory.cli]:   Machine rank:       0
+[2026-10-01 16:45:33,705] [INFO] [flow_factory.cli]:   Num processes:      8
+[2026-10-01 16:45:33,705] [INFO] [flow_factory.cli]:   Master node:        localhost:29513
+[2026-10-01 16:45:33,705] [INFO] [flow_factory.cli]:   Mixed precision:    bf16
+[2026-10-01 16:45:33,705] [INFO] [flow_factory.cli]:   Accelerate config:  config/deepspeed/deepspeed_zero2.yaml
+[2026-10-01 16:45:33,706] [INFO] [flow_factory.cli]: ============================================================
+W1001 16:45:41.788000 4096482 site-packages/torch/distributed/run.py:852] 
+W1001 16:45:41.788000 4096482 site-packages/torch/distributed/run.py:852] *****************************************
+W1001 16:45:41.788000 4096482 site-packages/torch/distributed/run.py:852] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
+W1001 16:45:41.788000 4096482 site-packages/torch/distributed/run.py:852] *****************************************
+[2026-10-01 16:45:49] [Rank 0] [WARNING] [flow_factory.hparams.abc]: GRPOTrainingArguments.from_dict captured 1 unknown key(s) into extra_kwargs: ['condition_image_size']. Verify these are intentional (e.g., adapter-specific kwargs); typos against declared fields will be silently accepted otherwise.
+[2026-10-01 16:45:49] [Rank 0] [INFO] [flow_factory.hparams.training_args._base]: World Size: 8
+[2026-10-01 16:45:49] [Rank 0] [WARNING] [flow_factory.hparams.abc]: EvaluationArguments.from_dict captured 1 unknown key(s) into extra_kwargs: ['condition_image_size']. Verify these are intentional (e.g., adapter-specific kwargs); typos against declared fields will be silently accepted otherwise.
+[2026-10-01 16:45:49,855] [INFO] [flow_factory.train]: ====================================================================================================
+[2026-10-01 16:45:49,855] [INFO] [flow_factory.train]: Flow-Factory Training Initialized
+[2026-10-01 16:45:49,855] [INFO] [flow_factory.train]: World Size: 8
+[2026-10-01 16:45:49,855] [INFO] [flow_factory.train]: ====================================================================================================
+[2026-10-01 16:45:49,856] [INFO] [flow_factory.train]: Config: /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/runs/klein4b_grpo_vedai_full_ssim_dists_psnr_lr5e-5/train.yaml
+[2026-10-01 16:45:49,863] [INFO] [flow_factory.train]: 
+launcher: accelerate
+config_file: config/deepspeed/deepspeed_zero2.yaml
+num_processes: 8
+main_process_port: 29513
+mixed_precision: bf16
+data:
+  dataset_dir: data
+  cache_dir: ~/.cache/flow_factory/datasets
+  image_dir: null
+  video_dir: null
+  audio_dir: null
+  preprocessing_batch_size: 8
+  dataloader_num_workers: 8
+  enable_preprocess: true
+  force_reprocess: false
+  max_dataset_size: null
+  preprocess_parallelism: local
+  sampler_type: group_contiguous
+  sampler_subgroup_size: null
+  datasets:
+  - extra_kwargs: {}
+    name: vedai_rl_full
+    dataset_dir: /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/vedai_rl_full
+    image_dir: null
+    video_dir: null
+    audio_dir: null
+    train:
+      extra_kwargs: {}
+      enabled: true
+      split: train
+      weight: 1
+      max_dataset_size: null
+      unique_sample_num_per_epoch: 48
+      num_batches_per_epoch: null
+    eval:
+      extra_kwargs: {}
+      enabled: true
+      split: test
+      max_dataset_size: null
+      resolution: null
+      num_inference_steps: null
+      guidance_scale: null
+    source_id: 0
+model:
+  model_name_or_path: /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/flow_factory_gpu_gate_models/FLUX.2-klein-base-4B
+  finetune_type: lora
+  trainable_parameters_dtype: bfloat16
+  component_load_dtypes: null
+  frozen_parameters_dtype: null
+  target_components:
+  - transformer
+  target_modules: default
+  model_type: flux2-klein
+  lora_rank: 64
+  lora_alpha: 128
+  resume_path: /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/runs/klein4b_sft_vedai_full_r64a128_lr1e-4/saves/klein4b_sft_vedai_full_r64a128_lr1e-4/checkpoints/checkpoint-48
+  resume_type: lora
+scheduler:
+  dynamics_type: Flow-SDE
+  noise_level: 0.7
+  num_sde_steps: 1
+  sde_steps:
+  - 1
+  - 2
+  - 3
+  seed: 42
+training:
+  trainer_type: grpo
+  resolution:
+  - 512
+  - 512
+  height: 512
+  width: 512
+  num_frames: null
+  frame_rate: null
+  max_epochs: 200
+  per_device_batch_size: 1
+  gradient_step_per_epoch: 2
+  max_grad_norm: 1.0
+  num_batches_per_epoch: 96
+  gradient_accumulation_steps: 48
+  num_inner_epochs: 1
+  group_size: 16
+  unique_sample_num_per_epoch: 48
+  num_inference_steps: 8
+  guidance_scale: 1.0
+  ref_param_device: cuda
+  seed: 42
+  learning_rate: 1.0e-05
+  adam_weight_decay: 0.0001
+  adam_betas:
+  - 0.9
+  - 0.999
+  adam_epsilon: 1.0e-08
+  enable_gradient_checkpointing: false
+  offload_samples_to_cpu: false
+  ema_decay: 0.9
+  ema_update_interval: 4
+  ema_device: cuda
+  ema_decay_schedule: power
+  latent_storage_dtype: fp16
+  shuffle_samples: true
+  reward_optimization_overlap: false
+  reward_optimization_overlap_mode: ready
+  reward_optimization_overlap_poll_interval: 0.05
+  global_std: false
+  advantage_aggregation: gdpo
+  clip_range:
+  - -0.0001
+  - 0.0001
+  adv_clip_range:
+  - -5.0
+  - 5.0
+  kl_type: v-based
+  kl_beta: 0.0
+  condition_image_size: 1024
+eval:
+  resolution:
+  - 512
+  - 512
+  height: 512
+  width: 512
+  num_frames: null
+  frame_rate: null
+  per_device_batch_size: 1
+  seed: 42
+  guidance_scale: 1.0
+  num_inference_steps: 28
+  eval_freq: 10
+  condition_image_size: 1024
+log:
+  run_name: klein4b_grpo_vedai_full_ssim_dists_psnr_lr5e-5
+  project: IR-Engine
+  logging_backend: wandb
+  save_dir: /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/runs/klein4b_grpo_vedai_full_ssim_dists_psnr_lr5e-5/saves
+  save_freq: 20
+  save_model_only: true
+  verbose: true
+acceleration:
+  shared: []
+  rollout: []
+reward:
+  reward_0:
+    name: ssim
+    reward_model: ir_rewards.fidelity.SSIMReward
+    weight:
+      vedai_rl_full: 1.0
+    dtype: float32
+    device: cuda
+    batch_size: 16
+    async_reward: false
+    num_workers: 1
+    applicable_datasets:
+    - vedai_rl_full
+  reward_1:
+    name: dists
+    reward_model: ir_rewards.fidelity.DISTSReward
+    weight:
+      vedai_rl_full: 1.0
+    dtype: float32
+    device: cuda
+    batch_size: 16
+    async_reward: false
+    num_workers: 1
+    applicable_datasets:
+    - vedai_rl_full
+  reward_2:
+    name: psnr
+    reward_model: ir_rewards.fidelity.PSNRReward
+    weight:
+      vedai_rl_full: 1.0
+    dtype: float32
+    device: cuda
+    batch_size: 16
+    async_reward: false
+    num_workers: 1
+    applicable_datasets:
+    - vedai_rl_full
+eval_reward:
+  reward_0:
+    name: ssim
+    reward_model: ir_rewards.fidelity.SSIMReward
+    weight:
+      vedai_rl_full: 1.0
+    dtype: float32
+    device: cuda
+    batch_size: 16
+    async_reward: false
+    num_workers: 1
+    applicable_datasets:
+    - vedai_rl_full
+  reward_1:
+    name: psnr
+    reward_model: ir_rewards.fidelity.PSNRReward
+    weight:
+      vedai_rl_full: 1.0
+    dtype: float32
+    device: cuda
+    batch_size: 16
+    async_reward: false
+    num_workers: 1
+    applicable_datasets:
+    - vedai_rl_full
+  reward_2:
+    name: dists
+    reward_model: ir_rewards.fidelity.DISTSReward
+    weight:
+      vedai_rl_full: 1.0
+    dtype: float32
+    device: cuda
+    batch_size: 16
+    async_reward: false
+    num_workers: 1
+    applicable_datasets:
+    - vedai_rl_full
+optimizer:
+  optimizer_configs:
+  - extra_kwargs: {}
+    name: default
+    optimizer: adamw
+    learning_rate: 5.0e-05
+    weight_decay: 0.0001
+    max_grad_norm: 1.0
+    update_frequency: 1
+    betas:
+    - 0.9
+    - 0.999
+    eps: 1.0e-08
+
+[2026-10-01 16:45:49,863] [INFO] [flow_factory.train]: ====================================================================================================
+[2026-10-01 16:45:59] [Rank 7] [INFO] [flow_factory.models.loader]: Loading model architecture: flux2-klein...
+[2026-10-01 16:45:59] [Rank 6] [INFO] [flow_factory.models.loader]: Loading model architecture: flux2-klein...
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+[2026-10-01 16:45:59] [Rank 1] [INFO] [flow_factory.models.loader]: Loading model architecture: flux2-klein...
+[2026-10-01 16:45:59,432] [WARNING] [accelerate.utils.other]: [RANK 0] Detected kernel version 5.4.241, which is below the recommended minimum of 5.5.0; this can cause the process to hang. It is recommended to upgrade the kernel to the minimum version or higher.
+[2026-10-01 16:45:59] [Rank 0] [INFO] [flow_factory.models.loader]: Loading model architecture: flux2-klein...
+
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+
+
+
+
Loading pipeline components...:  20%|██        | 1/5 [00:23<01:32, 23.16s/it][2026-10-01 16:46:22] [Rank 7] [INFO] [flow_factory.scheduler.loader]: Loaded SDE scheduler: FlowMatchEulerDiscreteSDEScheduler
+[2026-10-01 16:46:22] [Rank 1] [INFO] [flow_factory.scheduler.loader]: Loaded SDE scheduler: FlowMatchEulerDiscreteSDEScheduler
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+
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+
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+[2026-10-01 16:46:23] [Rank 4] [INFO] [flow_factory.scheduler.loader]: Loaded SDE scheduler: FlowMatchEulerDiscreteSDEScheduler
+
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+[2026-10-01 16:46:23] [Rank 0] [INFO] [flow_factory.scheduler.loader]: Loaded SDE scheduler: FlowMatchEulerDiscreteSDEScheduler
+[2026-10-01 16:46:23] [Rank 2] [INFO] [flow_factory.scheduler.loader]: Loaded SDE scheduler: FlowMatchEulerDiscreteSDEScheduler
+[2026-10-01 16:46:24] [Rank 0] [INFO] [flow_factory.models.abc]: LoRA adapter loaded for transformer role=base from /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/runs/klein4b_sft_vedai_full_r64a128_lr1e-4/saves/klein4b_sft_vedai_full_r64a128_lr1e-4/checkpoints/checkpoint-48
+[2026-10-01 16:46:24] [Rank 0] [INFO] [flow_factory.models.abc]: Checkpoint loaded successfully from /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/runs/klein4b_sft_vedai_full_r64a128_lr1e-4/saves/klein4b_sft_vedai_full_r64a128_lr1e-4/checkpoints/checkpoint-48 (type=lora)
+[2026-10-01 16:46:24] [Rank 2] [INFO] [flow_factory.models.abc]: Froze ALL transformer parameters
+[2026-10-01 16:46:24] [Rank 6] [INFO] [flow_factory.models.abc]: Froze ALL transformer parameters
+[2026-10-01 16:46:24] [Rank 4] [INFO] [flow_factory.models.abc]: Froze ALL transformer parameters
+[2026-10-01 16:46:24] [Rank 5] [INFO] [flow_factory.models.abc]: Froze ALL transformer parameters
+[2026-10-01 16:46:24] [Rank 3] [INFO] [flow_factory.models.abc]: Froze ALL transformer parameters
+[2026-10-01 16:46:24] [Rank 7] [INFO] [flow_factory.models.abc]: Froze ALL transformer parameters
+[2026-10-01 16:46:24] [Rank 2] [INFO] [flow_factory.models.abc]: Component transformer already has 'default' adapter. Skipping initialization but enabling gradients.
+[2026-10-01 16:46:24] [Rank 6] [INFO] [flow_factory.models.abc]: Component transformer already has 'default' adapter. Skipping initialization but enabling gradients.
+[2026-10-01 16:46:24] [Rank 0] [INFO] [flow_factory.models.abc]: Froze ALL transformer parameters
+[2026-10-01 16:46:24] [Rank 1] [INFO] [flow_factory.models.abc]: Froze ALL transformer parameters
+[2026-10-01 16:46:24] [Rank 4] [INFO] [flow_factory.models.abc]: Component transformer already has 'default' adapter. Skipping initialization but enabling gradients.
+[2026-10-01 16:46:24] [Rank 5] [INFO] [flow_factory.models.abc]: Component transformer already has 'default' adapter. Skipping initialization but enabling gradients.
+[2026-10-01 16:46:24] [Rank 3] [INFO] [flow_factory.models.abc]: Component transformer already has 'default' adapter. Skipping initialization but enabling gradients.
+[2026-10-01 16:46:24] [Rank 7] [INFO] [flow_factory.models.abc]: Component transformer already has 'default' adapter. Skipping initialization but enabling gradients.
+[2026-10-01 16:46:24] [Rank 0] [INFO] [flow_factory.models.abc]: Component transformer already has 'default' adapter. Skipping initialization but enabling gradients.
+[2026-10-01 16:46:24] [Rank 1] [INFO] [flow_factory.models.abc]: Component transformer already has 'default' adapter. Skipping initialization but enabling gradients.
+[2026-10-01 16:46:24] [Rank 2] [INFO] [flow_factory.models.abc]: Set 160 trainable parameters to torch.bfloat16; frozen component policy -> {'text_encoder': None, 'transformer': None, 'vae': None}
+[2026-10-01 16:46:24] [Rank 2] [INFO] [flow_factory.models.loader]: Successfully loaded Flux2KleinAdapter
+[2026-10-01 16:46:24] [Rank 6] [INFO] [flow_factory.models.abc]: Set 160 trainable parameters to torch.bfloat16; frozen component policy -> {'text_encoder': None, 'transformer': None, 'vae': None}
+[2026-10-01 16:46:24] [Rank 6] [INFO] [flow_factory.models.loader]: Successfully loaded Flux2KleinAdapter
+[2026-10-01 16:46:24] [Rank 4] [INFO] [flow_factory.models.abc]: Set 160 trainable parameters to torch.bfloat16; frozen component policy -> {'text_encoder': None, 'transformer': None, 'vae': None}
+[2026-10-01 16:46:24] [Rank 5] [INFO] [flow_factory.models.abc]: Set 160 trainable parameters to torch.bfloat16; frozen component policy -> {'text_encoder': None, 'transformer': None, 'vae': None}
+[2026-10-01 16:46:24] [Rank 4] [INFO] [flow_factory.models.loader]: Successfully loaded Flux2KleinAdapter
+[2026-10-01 16:46:24] [Rank 7] [INFO] [flow_factory.models.abc]: Set 160 trainable parameters to torch.bfloat16; frozen component policy -> {'text_encoder': None, 'transformer': None, 'vae': None}
+[2026-10-01 16:46:24] [Rank 3] [INFO] [flow_factory.models.abc]: Set 160 trainable parameters to torch.bfloat16; frozen component policy -> {'text_encoder': None, 'transformer': None, 'vae': None}
+[2026-10-01 16:46:24] [Rank 0] [INFO] [flow_factory.models.abc]: Set 160 trainable parameters to torch.bfloat16; frozen component policy -> {'text_encoder': None, 'transformer': None, 'vae': None}
+[2026-10-01 16:46:24] [Rank 5] [INFO] [flow_factory.models.loader]: Successfully loaded Flux2KleinAdapter
+[2026-10-01 16:46:24] [Rank 1] [INFO] [flow_factory.models.abc]: Set 160 trainable parameters to torch.bfloat16; frozen component policy -> {'text_encoder': None, 'transformer': None, 'vae': None}
+[2026-10-01 16:46:24] [Rank 7] [INFO] [flow_factory.models.loader]: Successfully loaded Flux2KleinAdapter
+[2026-10-01 16:46:24] [Rank 3] [INFO] [flow_factory.models.loader]: Successfully loaded Flux2KleinAdapter
+[2026-10-01 16:46:24] [Rank 0] [INFO] [flow_factory.models.abc]: Precision inventory stage=initialize component=text_encoder {'trainable': {}, 'frozen': {'torch.bfloat16': 4022468096}}
+[2026-10-01 16:46:24] [Rank 1] [INFO] [flow_factory.models.loader]: Successfully loaded Flux2KleinAdapter
+[2026-10-01 16:46:24] [Rank 0] [INFO] [flow_factory.models.abc]: Precision inventory stage=initialize component=transformer {'trainable': {'torch.bfloat16': 76677120}, 'frozen': {'torch.float32': 3875544576}}
+[2026-10-01 16:46:24] [Rank 0] [INFO] [flow_factory.models.abc]: Precision inventory stage=initialize component=vae {'trainable': {}, 'frozen': {'torch.float32': 84046115}}
+[2026-10-01 16:46:24] [Rank 0] [INFO] [flow_factory.models.loader]: Successfully loaded Flux2KleinAdapter
+[2026-10-01 16:46:49] [Rank 4] [INFO] [flow_factory.data_utils.loader]: Multi-source: built DataLoader for 'vedai_rl_full' (dataset_size=872, unique_sample_num_per_epoch=48, num_batches_per_epoch=96)
+[2026-10-01 16:46:49] [Rank 6] [INFO] [flow_factory.data_utils.loader]: Multi-source: built DataLoader for 'vedai_rl_full' (dataset_size=872, unique_sample_num_per_epoch=48, num_batches_per_epoch=96)
+[2026-10-01 16:46:49] [Rank 2] [INFO] [flow_factory.data_utils.loader]: Multi-source: built DataLoader for 'vedai_rl_full' (dataset_size=872, unique_sample_num_per_epoch=48, num_batches_per_epoch=96)
+[2026-10-01 16:46:49] [Rank 0] [INFO] [flow_factory.data_utils.loader]: Loading train dataset from merged cache: /root/.cache/flow_factory/datasets/1e68492a08871f0f6ebe621929ff234a
+[2026-10-01 16:46:49] [Rank 0] [INFO] [flow_factory.data_utils.loader]: Multi-source: built DataLoader for 'vedai_rl_full' (dataset_size=872, unique_sample_num_per_epoch=48, num_batches_per_epoch=96)
+[2026-10-01 16:46:49] [Rank 7] [INFO] [flow_factory.data_utils.loader]: Multi-source: built DataLoader for 'vedai_rl_full' (dataset_size=872, unique_sample_num_per_epoch=48, num_batches_per_epoch=96)
+[2026-10-01 16:46:49] [Rank 3] [INFO] [flow_factory.data_utils.loader]: Multi-source: built DataLoader for 'vedai_rl_full' (dataset_size=872, unique_sample_num_per_epoch=48, num_batches_per_epoch=96)
+[2026-10-01 16:46:49] [Rank 0] [INFO] [flow_factory.data_utils.loader]: Loading test dataset from merged cache: /root/.cache/flow_factory/datasets/c624060e1ad3d97604e0883a86585d87
+[2026-10-01 16:46:49] [Rank 5] [INFO] [flow_factory.data_utils.loader]: Multi-source: built DataLoader for 'vedai_rl_full' (dataset_size=872, unique_sample_num_per_epoch=48, num_batches_per_epoch=96)
+[2026-10-01 16:46:49] [Rank 1] [INFO] [flow_factory.data_utils.loader]: Multi-source: built DataLoader for 'vedai_rl_full' (dataset_size=872, unique_sample_num_per_epoch=48, num_batches_per_epoch=96)
+/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.
+  warnings.warn(
+/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.
+  warnings.warn(
+/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.
+  warnings.warn(
+/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.
+  warnings.warn(
+/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.
+  warnings.warn(
+/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.
+  warnings.warn(
+/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.
+  warnings.warn(
+/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.
+  warnings.warn(
+/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=VGG16_Weights.IMAGENET1K_V1`. You can also use `weights=VGG16_Weights.DEFAULT` to get the most up-to-date weights.
+  warnings.warn(msg)
+/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=VGG16_Weights.IMAGENET1K_V1`. You can also use `weights=VGG16_Weights.DEFAULT` to get the most up-to-date weights.
+  warnings.warn(msg)
+/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=VGG16_Weights.IMAGENET1K_V1`. You can also use `weights=VGG16_Weights.DEFAULT` to get the most up-to-date weights.
+  warnings.warn(msg)
+/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=VGG16_Weights.IMAGENET1K_V1`. You can also use `weights=VGG16_Weights.DEFAULT` to get the most up-to-date weights.
+  warnings.warn(msg)
+/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=VGG16_Weights.IMAGENET1K_V1`. You can also use `weights=VGG16_Weights.DEFAULT` to get the most up-to-date weights.
+  warnings.warn(msg)
+/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=VGG16_Weights.IMAGENET1K_V1`. You can also use `weights=VGG16_Weights.DEFAULT` to get the most up-to-date weights.
+  warnings.warn(msg)
+/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=VGG16_Weights.IMAGENET1K_V1`. You can also use `weights=VGG16_Weights.DEFAULT` to get the most up-to-date weights.
+  warnings.warn(msg)
+/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=VGG16_Weights.IMAGENET1K_V1`. You can also use `weights=VGG16_Weights.DEFAULT` to get the most up-to-date weights.
+  warnings.warn(msg)
+wandb: [wandb.login()] Loaded credentials for https://api.wandb.ai from /root/.netrc.
+wandb: Currently logged in as: 315229706 (315229706-xi-an-jiaotong-university-) to https://api.wandb.ai. Use `wandb login --relogin` to force relogin
+wandb: setting up run itgk39n6
+wandb: Tracking run with wandb version 0.28.2
+wandb: Run data is saved locally in /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/runs/klein4b_grpo_vedai_full_ssim_dists_psnr_lr5e-5/wandb/run-20261001_164708-itgk39n6
+wandb: Run `wandb offline` to turn off syncing.
+wandb: Syncing run klein4b_grpo_vedai_full_ssim_dists_psnr_lr5e-5
+wandb: ⭐️ View project at https://wandb.ai/315229706-xi-an-jiaotong-university-/IR-Engine
+wandb: 🚀 View run at https://wandb.ai/315229706-xi-an-jiaotong-university-/IR-Engine/runs/itgk39n6
+[2026-10-01 16:47:09] [Rank 0] [INFO] [flow_factory.models.abc]: ======================================================================
+[2026-10-01 16:47:09] [Rank 0] [INFO] [flow_factory.models.abc]: Transformer Trainable Parameters:
+[2026-10-01 16:47:09] [Rank 0] [INFO] [flow_factory.models.abc]:   Total:        3,952,221,696 (  7.36 GB)
+[2026-10-01 16:47:09] [Rank 0] [INFO] [flow_factory.models.abc]:   Trainable:       76,677,120 (  0.14 GB)
+[2026-10-01 16:47:09] [Rank 0] [INFO] [flow_factory.models.abc]:   Percentage:           1.94%
+[2026-10-01 16:47:09] [Rank 0] [INFO] [flow_factory.models.abc]: ======================================================================
+[2026-10-01 16:47:09] [Rank 0] [INFO] [flow_factory.models.abc]: Saving LoRA weights for transformer role=base to /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/runs/klein4b_grpo_vedai_full_ssim_dists_psnr_lr5e-5/saves/klein4b_grpo_vedai_full_ssim_dists_psnr_lr5e-5/checkpoints/checkpoint-0
+[2026-10-01 16:47:10] [Rank 0] [INFO] [flow_factory.models.abc]: Checkpoint saved successfully to /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/runs/klein4b_grpo_vedai_full_ssim_dists_psnr_lr5e-5/saves/klein4b_grpo_vedai_full_ssim_dists_psnr_lr5e-5/checkpoints/checkpoint-0
+
Eval/vedai_rl_full:   0%|          | 0/8 [00:00<?, ?it/s][2026-10-01 16:47:11] [Rank 2] [WARNING] [flow_factory.models.flux.flux2_klein]: FLUX.2 does not support batch inference with varying condition images per sample. Falling back to single-sample inference. This warning will only appear once.
+[2026-10-01 16:47:11] [Rank 4] [WARNING] [flow_factory.models.flux.flux2_klein]: FLUX.2 does not support batch inference with varying condition images per sample. Falling back to single-sample inference. This warning will only appear once.
+[2026-10-01 16:47:11] [Rank 0] [WARNING] [flow_factory.models.flux.flux2_klein]: FLUX.2 does not support batch inference with varying condition images per sample. Falling back to single-sample inference. This warning will only appear once.
+[2026-10-01 16:47:11] [Rank 7] [WARNING] [flow_factory.models.flux.flux2_klein]: FLUX.2 does not support batch inference with varying condition images per sample. Falling back to single-sample inference. This warning will only appear once.
+[2026-10-01 16:47:11] [Rank 5] [WARNING] [flow_factory.models.flux.flux2_klein]: FLUX.2 does not support batch inference with varying condition images per sample. Falling back to single-sample inference. This warning will only appear once.
+[2026-10-01 16:47:11] [Rank 1] [WARNING] [flow_factory.models.flux.flux2_klein]: FLUX.2 does not support batch inference with varying condition images per sample. Falling back to single-sample inference. This warning will only appear once.
+[2026-10-01 16:47:11] [Rank 6] [WARNING] [flow_factory.models.flux.flux2_klein]: FLUX.2 does not support batch inference with varying condition images per sample. Falling back to single-sample inference. This warning will only appear once.
+[2026-10-01 16:47:11] [Rank 3] [WARNING] [flow_factory.models.flux.flux2_klein]: FLUX.2 does not support batch inference with varying condition images per sample. Falling back to single-sample inference. This warning will only appear once.
+
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+
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+
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+
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+[2026-10-01 16:47:54] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0000 | Epoch 000] eval/vedai_rl_full/reward_dists_mean=0.9292 eval/vedai_rl_full/reward_dists_std=0.0183 eval/vedai_rl_full/reward_psnr_mean=29.0025 eval/vedai_rl_full/reward_psnr_std=2.6570 eval/vedai_rl_full/reward_ssim_mean=0.9194 eval/vedai_rl_full/reward_ssim_std=0.0282
+
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+
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Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:00<00:00,  6.19it/s]
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+
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Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.72it/s]
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+
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Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.93it/s]
Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.81it/s]
+[2026-10-01 16:50:25] [Rank 6] [WARNING] [flow_factory.models.flux.flux2_klein]: Flux.2: Ragged I2I detected (varying condition image sizes). Processing samples individually (warning shown once).
+[2026-10-01 16:50:25] [Rank 7] [WARNING] [flow_factory.models.flux.flux2_klein]: Flux.2: Ragged I2I detected (varying condition image sizes). Processing samples individually (warning shown once).
+[2026-10-01 16:50:25] [Rank 4] [WARNING] [flow_factory.models.flux.flux2_klein]: Flux.2: Ragged I2I detected (varying condition image sizes). Processing samples individually (warning shown once).
+[2026-10-01 16:50:25] [Rank 2] [WARNING] [flow_factory.models.flux.flux2_klein]: Flux.2: Ragged I2I detected (varying condition image sizes). Processing samples individually (warning shown once).
+[2026-10-01 16:50:25] [Rank 1] [WARNING] [flow_factory.models.flux.flux2_klein]: Flux.2: Ragged I2I detected (varying condition image sizes). Processing samples individually (warning shown once).
+[2026-10-01 16:50:25] [Rank 5] [WARNING] [flow_factory.models.flux.flux2_klein]: Flux.2: Ragged I2I detected (varying condition image sizes). Processing samples individually (warning shown once).
+[2026-10-01 16:50:25] [Rank 3] [WARNING] [flow_factory.models.flux.flux2_klein]: Flux.2: Ragged I2I detected (varying condition image sizes). Processing samples individually (warning shown once).
+[2026-10-01 16:50:25] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0000 | Epoch 000] train/reward_dists_mean=0.9199 train/reward_dists_std=0.0249 train/reward_psnr_mean=29.1590 train/reward_psnr_std=2.3156 train/reward_ssim_mean=0.9314 train/reward_ssim_std=0.0212 train/reward_dists_group_std_mean=0.0048 train/reward_dists_group_std_max=0.0089 train/reward_dists_group_std_min=0.0021 train/reward_dists_group_mean_std=0.0244 train/reward_psnr_group_std_mean=1.6716 train/reward_psnr_group_std_max=3.4153 train/reward_psnr_group_std_min=0.4119 train/reward_psnr_group_mean_std=1.4624 train/reward_ssim_group_std_mean=0.0023 train/reward_ssim_group_std_max=0.0310 train/reward_ssim_group_std_min=0.0006 train/reward_ssim_group_mean_std=0.0206 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-9.4127 train/adv_max=4.9454 train/adv_abs_mean=1.7852
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Epoch 0 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 16:50:45] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0000 | Epoch 000] train/clipped_loss_min=-4.8210 train/clipped_loss_max=5 train/clipped_loss_mean=-0.0460 train/clipped_loss_std=2.1477 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-4.8210 train/unclipped_loss_max=5 train/unclipped_loss_mean=-0.0460 train/unclipped_loss_std=2.1477 train/policy_loss=-0.0460 train/loss=-0.0460 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
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Epoch 0 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 16:51:05] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0001 | Epoch 000] train/clipped_loss_min=-4.9453 train/clipped_loss_max=5.0000 train/clipped_loss_mean=-0.0051 train/clipped_loss_std=2.1742 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-4.9453 train/unclipped_loss_max=5.0000 train/unclipped_loss_mean=-0.0051 train/unclipped_loss_std=2.1742 train/policy_loss=-0.0051 train/loss=-0.0051 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
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Epoch 0 Training: 100%|██████████| 96/96 [00:40<00:00,  2.38it/s]
+[2026-10-01 16:51:05] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0002 | Epoch 000] timing/rollout_seconds=146.8566 timing/feedback_seconds=5.0707 timing/optimization_seconds=40.9119 timing/cycle_seconds=191.2952
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Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:00<00:00,  6.18it/s]
+
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+
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Pointwise Rewards: psnr:  50%|█████     | 3/6 [00:00<00:00,  6.41it/s]
Pointwise Rewards: psnr:  67%|██████▋   | 4/6 [00:00<00:00,  6.34it/s]
Pointwise Rewards: psnr:  83%|████████▎ | 5/6 [00:00<00:00,  6.30it/s]
Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.27it/s]
Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.34it/s]
+[2026-10-01 16:53:36] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0002 | Epoch 001] train/reward_dists_mean=0.9279 train/reward_dists_std=0.0185 train/reward_psnr_mean=26.6784 train/reward_psnr_std=2.7934 train/reward_ssim_mean=0.9307 train/reward_ssim_std=0.0213 train/reward_dists_group_std_mean=0.0051 train/reward_dists_group_std_max=0.0140 train/reward_dists_group_std_min=0.0020 train/reward_dists_group_mean_std=0.0175 train/reward_psnr_group_std_mean=1.6738 train/reward_psnr_group_std_max=5.6917 train/reward_psnr_group_std_min=0.7312 train/reward_psnr_group_mean_std=2.1069 train/reward_ssim_group_std_mean=0.0036 train/reward_ssim_group_std_max=0.0226 train/reward_ssim_group_std_min=0.0007 train/reward_ssim_group_mean_std=0.0208 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-6.8407 train/adv_max=6.3463 train/adv_abs_mean=1.7516
+
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Epoch 1 Training:  49%|████▉     | 47/96 [00:19<00:20,  2.38it/s]
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Epoch 1 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 16:53:57] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0002 | Epoch 001] train/clipped_loss_min=-5 train/clipped_loss_max=5 train/clipped_loss_mean=-0.0018 train/clipped_loss_std=2.1428 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-5 train/unclipped_loss_max=5 train/unclipped_loss_mean=-0.0018 train/unclipped_loss_std=2.1428 train/policy_loss=-0.0018 train/loss=-0.0018 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0005
+
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Epoch 1 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 16:54:17] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0003 | Epoch 001] train/clipped_loss_min=-5.0000 train/clipped_loss_max=5.0001 train/clipped_loss_mean=-0.0155 train/clipped_loss_std=2.1761 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0.0001 train/unclipped_loss_min=-5.0000 train/unclipped_loss_max=5.0001 train/unclipped_loss_mean=-0.0155 train/unclipped_loss_std=2.1761 train/policy_loss=-0.0155 train/loss=-0.0155 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0005
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+[2026-10-01 16:54:17] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0004 | Epoch 001] timing/rollout_seconds=146.8094 timing/feedback_seconds=5.1724 timing/optimization_seconds=40.9296 timing/cycle_seconds=191.2732
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+
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+
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Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.57it/s]
+[2026-10-01 16:56:48] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0004 | Epoch 002] train/reward_dists_mean=0.9256 train/reward_dists_std=0.0303 train/reward_psnr_mean=28.3878 train/reward_psnr_std=3.2176 train/reward_ssim_mean=0.9221 train/reward_ssim_std=0.0319 train/reward_dists_group_std_mean=0.0047 train/reward_dists_group_std_max=0.0329 train/reward_dists_group_std_min=0.0013 train/reward_dists_group_mean_std=0.0296 train/reward_psnr_group_std_mean=1.3227 train/reward_psnr_group_std_max=4.7342 train/reward_psnr_group_std_min=0.3621 train/reward_psnr_group_mean_std=2.8352 train/reward_ssim_group_std_mean=0.0050 train/reward_ssim_group_std_max=0.0541 train/reward_ssim_group_std_min=0.0007 train/reward_ssim_group_mean_std=0.0305 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-8.2428 train/adv_max=6.3928 train/adv_abs_mean=1.9221
+
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Epoch 2 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 16:57:08] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0004 | Epoch 002] train/clipped_loss_min=-5 train/clipped_loss_max=5 train/clipped_loss_mean=-0.0946 train/clipped_loss_std=2.3614 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-5 train/unclipped_loss_max=5 train/unclipped_loss_mean=-0.0946 train/unclipped_loss_std=2.3614 train/policy_loss=-0.0946 train/loss=-0.0946 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0004
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Epoch 2 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 16:57:28] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0005 | Epoch 002] train/clipped_loss_min=-5.0000 train/clipped_loss_max=5.0000 train/clipped_loss_mean=0.0580 train/clipped_loss_std=2.2813 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-5.0000 train/unclipped_loss_max=5.0000 train/unclipped_loss_mean=0.0580 train/unclipped_loss_std=2.2813 train/policy_loss=0.0580 train/loss=0.0580 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0005
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+[2026-10-01 16:57:28] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0006 | Epoch 002] timing/rollout_seconds=146.8394 timing/feedback_seconds=5.1271 timing/optimization_seconds=40.9379 timing/cycle_seconds=191.2610
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+
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+
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Pointwise Rewards: psnr:  83%|████████▎ | 5/6 [00:00<00:00,  6.93it/s]
Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  7.18it/s]
Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  7.00it/s]
+[2026-10-01 16:59:59] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0006 | Epoch 003] train/reward_dists_mean=0.9293 train/reward_dists_std=0.0168 train/reward_psnr_mean=28.4634 train/reward_psnr_std=2.3815 train/reward_ssim_mean=0.9285 train/reward_ssim_std=0.0202 train/reward_dists_group_std_mean=0.0040 train/reward_dists_group_std_max=0.0094 train/reward_dists_group_std_min=0.0015 train/reward_dists_group_mean_std=0.0162 train/reward_psnr_group_std_mean=1.0226 train/reward_psnr_group_std_max=3.5603 train/reward_psnr_group_std_min=0.3138 train/reward_psnr_group_mean_std=2.0545 train/reward_ssim_group_std_mean=0.0024 train/reward_ssim_group_std_max=0.0109 train/reward_ssim_group_std_min=0.0008 train/reward_ssim_group_mean_std=0.0199 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-9.3170 train/adv_max=6.0129 train/adv_abs_mean=1.8938
+
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Epoch 3 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 17:00:40] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0007 | Epoch 003] train/clipped_loss_min=-5.0000 train/clipped_loss_max=5.0000 train/clipped_loss_mean=-0.0740 train/clipped_loss_std=2.2203 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0.0001 train/unclipped_loss_min=-5.0000 train/unclipped_loss_max=5.0000 train/unclipped_loss_mean=-0.0740 train/unclipped_loss_std=2.2203 train/policy_loss=-0.0740 train/loss=-0.0740 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0006
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+[2026-10-01 17:00:40] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0008 | Epoch 003] timing/rollout_seconds=146.8255 timing/feedback_seconds=5.2458 timing/optimization_seconds=41.2522 timing/cycle_seconds=191.7273
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+
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+
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+
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Pointwise Rewards: psnr:  50%|█████     | 3/6 [00:00<00:00,  7.07it/s]
Pointwise Rewards: psnr:  67%|██████▋   | 4/6 [00:00<00:00,  6.98it/s]
Pointwise Rewards: psnr:  83%|████████▎ | 5/6 [00:00<00:00,  6.96it/s]
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Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.97it/s]
+[2026-10-01 17:03:11] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0008 | Epoch 004] train/reward_dists_mean=0.9285 train/reward_dists_std=0.0180 train/reward_psnr_mean=28.4772 train/reward_psnr_std=2.7711 train/reward_ssim_mean=0.9252 train/reward_ssim_std=0.0232 train/reward_dists_group_std_mean=0.0047 train/reward_dists_group_std_max=0.0083 train/reward_dists_group_std_min=0.0017 train/reward_dists_group_mean_std=0.0174 train/reward_psnr_group_std_mean=1.3817 train/reward_psnr_group_std_max=2.8450 train/reward_psnr_group_std_min=0.2687 train/reward_psnr_group_mean_std=2.3519 train/reward_ssim_group_std_mean=0.0023 train/reward_ssim_group_std_max=0.0062 train/reward_ssim_group_std_min=0.0008 train/reward_ssim_group_mean_std=0.0231 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-8.6113 train/adv_max=6.5969 train/adv_abs_mean=2.0001
+
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Epoch 4 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 17:03:52] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0009 | Epoch 004] train/clipped_loss_min=-5.0000 train/clipped_loss_max=5.0000 train/clipped_loss_mean=-0.2167 train/clipped_loss_std=2.5199 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-5.0000 train/unclipped_loss_max=5.0000 train/unclipped_loss_mean=-0.2167 train/unclipped_loss_std=2.5199 train/policy_loss=-0.2167 train/loss=-0.2167 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
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+[2026-10-01 17:03:52] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0010 | Epoch 004] timing/rollout_seconds=146.9231 timing/feedback_seconds=5.7124 timing/optimization_seconds=41.1227 timing/cycle_seconds=192.1613
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+
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+
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+[2026-10-01 17:06:23] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0010 | Epoch 005] train/reward_dists_mean=0.9291 train/reward_dists_std=0.0219 train/reward_psnr_mean=28.9797 train/reward_psnr_std=2.5214 train/reward_ssim_mean=0.9275 train/reward_ssim_std=0.0241 train/reward_dists_group_std_mean=0.0046 train/reward_dists_group_std_max=0.0096 train/reward_dists_group_std_min=0.0023 train/reward_dists_group_mean_std=0.0214 train/reward_psnr_group_std_mean=1.2991 train/reward_psnr_group_std_max=2.7803 train/reward_psnr_group_std_min=0.3977 train/reward_psnr_group_mean_std=2.0915 train/reward_ssim_group_std_mean=0.0026 train/reward_ssim_group_std_max=0.0078 train/reward_ssim_group_std_min=0.0006 train/reward_ssim_group_mean_std=0.0240 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-7.5334 train/adv_max=7.4056 train/adv_abs_mean=2.0099
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Epoch 5 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 17:07:04] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0011 | Epoch 005] train/clipped_loss_min=-5.0000 train/clipped_loss_max=5.0000 train/clipped_loss_mean=0.0011 train/clipped_loss_std=2.3941 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0.0001 train/unclipped_loss_min=-5.0000 train/unclipped_loss_max=5.0000 train/unclipped_loss_mean=0.0011 train/unclipped_loss_std=2.3941 train/policy_loss=0.0011 train/loss=0.0011 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0005
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+[2026-10-01 17:07:04] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0012 | Epoch 005] timing/rollout_seconds=147.2397 timing/feedback_seconds=5.7402 timing/optimization_seconds=41.1473 timing/cycle_seconds=192.1284
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+
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Pointwise Rewards: psnr:  67%|██████▋   | 4/6 [00:00<00:00,  6.50it/s]
Pointwise Rewards: psnr:  83%|████████▎ | 5/6 [00:00<00:00,  6.57it/s]
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+[2026-10-01 17:09:36] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0012 | Epoch 006] train/reward_dists_mean=0.9320 train/reward_dists_std=0.0172 train/reward_psnr_mean=26.6165 train/reward_psnr_std=2.5664 train/reward_ssim_mean=0.9327 train/reward_ssim_std=0.0192 train/reward_dists_group_std_mean=0.0036 train/reward_dists_group_std_max=0.0098 train/reward_dists_group_std_min=0.0017 train/reward_dists_group_mean_std=0.0167 train/reward_psnr_group_std_mean=1.4202 train/reward_psnr_group_std_max=3.2447 train/reward_psnr_group_std_min=0.5221 train/reward_psnr_group_mean_std=2.0753 train/reward_ssim_group_std_mean=0.0017 train/reward_ssim_group_std_max=0.0045 train/reward_ssim_group_std_min=0.0008 train/reward_ssim_group_mean_std=0.0191 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-6.9619 train/adv_max=6.9522 train/adv_abs_mean=1.8787
+
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Epoch 6 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 17:09:56] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0012 | Epoch 006] train/clipped_loss_min=-5 train/clipped_loss_max=5 train/clipped_loss_mean=-0.1093 train/clipped_loss_std=2.3717 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-5 train/unclipped_loss_max=5 train/unclipped_loss_mean=-0.1093 train/unclipped_loss_std=2.3717 train/policy_loss=-0.1093 train/loss=-0.1093 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0007
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+[2026-10-01 17:10:16] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0014 | Epoch 006] timing/rollout_seconds=147.2717 timing/feedback_seconds=5.5839 timing/optimization_seconds=41.0903 timing/cycle_seconds=191.9488
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+[2026-10-01 17:12:47] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0014 | Epoch 007] train/reward_dists_mean=0.9349 train/reward_dists_std=0.0145 train/reward_psnr_mean=25.9087 train/reward_psnr_std=2.3430 train/reward_ssim_mean=0.9337 train/reward_ssim_std=0.0160 train/reward_dists_group_std_mean=0.0035 train/reward_dists_group_std_max=0.0068 train/reward_dists_group_std_min=0.0014 train/reward_dists_group_mean_std=0.0140 train/reward_psnr_group_std_mean=1.2642 train/reward_psnr_group_std_max=2.1674 train/reward_psnr_group_std_min=0.5653 train/reward_psnr_group_mean_std=1.9395 train/reward_ssim_group_std_mean=0.0025 train/reward_ssim_group_std_max=0.0092 train/reward_ssim_group_std_min=0.0008 train/reward_ssim_group_mean_std=0.0158 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-8.2089 train/adv_max=6.8692 train/adv_abs_mean=1.9985
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Epoch 7 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 17:13:08] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0014 | Epoch 007] train/clipped_loss_min=-5 train/clipped_loss_max=5 train/clipped_loss_mean=-0.0025 train/clipped_loss_std=2.4878 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-5 train/unclipped_loss_max=5 train/unclipped_loss_mean=-0.0025 train/unclipped_loss_std=2.4878 train/policy_loss=-0.0025 train/loss=-0.0025 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0007
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Epoch 7 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 17:13:28] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0015 | Epoch 007] train/clipped_loss_min=-5.0000 train/clipped_loss_max=5.0000 train/clipped_loss_mean=-0.0597 train/clipped_loss_std=2.2974 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0.0002 train/unclipped_loss_min=-5.0000 train/unclipped_loss_max=5.0000 train/unclipped_loss_mean=-0.0597 train/unclipped_loss_std=2.2974 train/policy_loss=-0.0597 train/loss=-0.0597 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0007
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+[2026-10-01 17:13:28] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0016 | Epoch 007] timing/rollout_seconds=146.8951 timing/feedback_seconds=4.9262 timing/optimization_seconds=41.2910 timing/cycle_seconds=191.9955
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+
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+[2026-10-01 17:15:59] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0016 | Epoch 008] train/reward_dists_mean=0.9371 train/reward_dists_std=0.0128 train/reward_psnr_mean=28.6961 train/reward_psnr_std=2.2974 train/reward_ssim_mean=0.9325 train/reward_ssim_std=0.0216 train/reward_dists_group_std_mean=0.0032 train/reward_dists_group_std_max=0.0055 train/reward_dists_group_std_min=0.0011 train/reward_dists_group_mean_std=0.0123 train/reward_psnr_group_std_mean=1.1978 train/reward_psnr_group_std_max=3.1320 train/reward_psnr_group_std_min=0.4281 train/reward_psnr_group_mean_std=1.8770 train/reward_ssim_group_std_mean=0.0015 train/reward_ssim_group_std_max=0.0047 train/reward_ssim_group_std_min=0.0006 train/reward_ssim_group_mean_std=0.0215 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-8.0578 train/adv_max=6.0043 train/adv_abs_mean=1.8769
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Epoch 8 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 17:16:19] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0016 | Epoch 008] train/clipped_loss_min=-5 train/clipped_loss_max=5 train/clipped_loss_mean=-0.1047 train/clipped_loss_std=2.3797 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-5 train/unclipped_loss_max=5 train/unclipped_loss_mean=-0.1047 train/unclipped_loss_std=2.3797 train/policy_loss=-0.1047 train/loss=-0.1047 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0004
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Epoch 8 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 17:16:40] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0017 | Epoch 008] train/clipped_loss_min=-5.0000 train/clipped_loss_max=5.0001 train/clipped_loss_mean=0.0650 train/clipped_loss_std=2.1582 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-5.0000 train/unclipped_loss_max=5.0001 train/unclipped_loss_mean=0.0650 train/unclipped_loss_std=2.1582 train/policy_loss=0.0650 train/loss=0.0650 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0004
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+[2026-10-01 17:16:40] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0018 | Epoch 008] timing/rollout_seconds=146.9607 timing/feedback_seconds=5.0516 timing/optimization_seconds=41.1754 timing/cycle_seconds=191.7949
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+
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+
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+[2026-10-01 17:19:11] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0018 | Epoch 009] train/reward_dists_mean=0.9386 train/reward_dists_std=0.0106 train/reward_psnr_mean=27.1399 train/reward_psnr_std=2.9907 train/reward_ssim_mean=0.9314 train/reward_ssim_std=0.0220 train/reward_dists_group_std_mean=0.0034 train/reward_dists_group_std_max=0.0068 train/reward_dists_group_std_min=0.0017 train/reward_dists_group_mean_std=0.0100 train/reward_psnr_group_std_mean=1.4463 train/reward_psnr_group_std_max=2.5547 train/reward_psnr_group_std_min=0.5467 train/reward_psnr_group_mean_std=2.5868 train/reward_ssim_group_std_mean=0.0015 train/reward_ssim_group_std_max=0.0085 train/reward_ssim_group_std_min=0.0006 train/reward_ssim_group_mean_std=0.0219 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-7.6326 train/adv_max=6.4462 train/adv_abs_mean=2.0181
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Epoch 9 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 17:19:32] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0018 | Epoch 009] train/clipped_loss_min=-5 train/clipped_loss_max=5 train/clipped_loss_mean=-0.0209 train/clipped_loss_std=2.3308 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-5 train/unclipped_loss_max=5 train/unclipped_loss_mean=-0.0209 train/unclipped_loss_std=2.3308 train/policy_loss=-0.0209 train/loss=-0.0209 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0004
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Epoch 9 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 17:19:52] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0019 | Epoch 009] train/clipped_loss_min=-5.0000 train/clipped_loss_max=5.0000 train/clipped_loss_mean=-0.0051 train/clipped_loss_std=2.4879 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0.0001 train/unclipped_loss_min=-5.0000 train/unclipped_loss_max=5.0000 train/unclipped_loss_mean=-0.0051 train/unclipped_loss_std=2.4879 train/policy_loss=-0.0051 train/loss=-0.0051 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0005
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+[2026-10-01 17:19:52] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0020 | Epoch 009] timing/rollout_seconds=147.2910 timing/feedback_seconds=5.0687 timing/optimization_seconds=41.4858 timing/cycle_seconds=192.3352
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+[2026-10-01 17:20:36] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0020 | Epoch 010] eval/vedai_rl_full/reward_dists_mean=0.9340 eval/vedai_rl_full/reward_dists_std=0.0164 eval/vedai_rl_full/reward_psnr_mean=28.5551 eval/vedai_rl_full/reward_psnr_std=2.5768 eval/vedai_rl_full/reward_ssim_mean=0.9200 eval/vedai_rl_full/reward_ssim_std=0.0277
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Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.83it/s]
+[2026-10-01 17:23:08] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0020 | Epoch 010] train/reward_dists_mean=0.9340 train/reward_dists_std=0.0164 train/reward_psnr_mean=27.4359 train/reward_psnr_std=2.9708 train/reward_ssim_mean=0.9307 train/reward_ssim_std=0.0212 train/reward_dists_group_std_mean=0.0036 train/reward_dists_group_std_max=0.0146 train/reward_dists_group_std_min=0.0016 train/reward_dists_group_mean_std=0.0159 train/reward_psnr_group_std_mean=1.4209 train/reward_psnr_group_std_max=2.6140 train/reward_psnr_group_std_min=0.3617 train/reward_psnr_group_mean_std=2.5690 train/reward_ssim_group_std_mean=0.0016 train/reward_ssim_group_std_max=0.0051 train/reward_ssim_group_std_min=0.0004 train/reward_ssim_group_mean_std=0.0211 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-8.8540 train/adv_max=7.5130 train/adv_abs_mean=1.9920
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Epoch 10 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 17:23:28] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0020 | Epoch 010] train/clipped_loss_min=-5 train/clipped_loss_max=5 train/clipped_loss_mean=-0.0521 train/clipped_loss_std=2.3567 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-5 train/unclipped_loss_max=5 train/unclipped_loss_mean=-0.0521 train/unclipped_loss_std=2.3567 train/policy_loss=-0.0521 train/loss=-0.0521 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
+
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Epoch 10 Training:  99%|█████████▉| 95/96 [00:40<00:00,  2.37it/s]
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Epoch 10 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 17:23:48] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0021 | Epoch 010] train/clipped_loss_min=-5.0000 train/clipped_loss_max=5.0000 train/clipped_loss_mean=0.0224 train/clipped_loss_std=2.4250 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0.0002 train/unclipped_loss_min=-5.0000 train/unclipped_loss_max=5.0000 train/unclipped_loss_mean=0.0224 train/unclipped_loss_std=2.4250 train/policy_loss=0.0224 train/loss=0.0224 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
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Epoch 10 Training: 100%|██████████| 96/96 [00:40<00:00,  2.29it/s]
Epoch 10 Training: 100%|██████████| 96/96 [00:40<00:00,  2.37it/s]
+[2026-10-01 17:23:48] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0022 | Epoch 010] timing/rollout_seconds=147.4621 timing/feedback_seconds=5.3472 timing/optimization_seconds=41.2011 timing/cycle_seconds=192.2786
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Pointwise Rewards: ssim:  67%|██████▋   | 4/6 [00:00<00:00,  6.62it/s]
Pointwise Rewards: ssim:  83%|████████▎ | 5/6 [00:00<00:00,  6.53it/s]
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Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:00<00:00,  6.43it/s]
+
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+
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Pointwise Rewards: psnr:  50%|█████     | 3/6 [00:00<00:00,  6.99it/s]
Pointwise Rewards: psnr:  67%|██████▋   | 4/6 [00:00<00:00,  7.00it/s]
Pointwise Rewards: psnr:  83%|████████▎ | 5/6 [00:00<00:00,  6.93it/s]
Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.84it/s]
Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.91it/s]
+[2026-10-01 17:26:20] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0022 | Epoch 011] train/reward_dists_mean=0.9345 train/reward_dists_std=0.0142 train/reward_psnr_mean=28.6337 train/reward_psnr_std=2.5708 train/reward_ssim_mean=0.9321 train/reward_ssim_std=0.0242 train/reward_dists_group_std_mean=0.0033 train/reward_dists_group_std_max=0.0078 train/reward_dists_group_std_min=0.0012 train/reward_dists_group_mean_std=0.0138 train/reward_psnr_group_std_mean=1.2881 train/reward_psnr_group_std_max=2.9558 train/reward_psnr_group_std_min=0.2869 train/reward_psnr_group_mean_std=2.1607 train/reward_ssim_group_std_mean=0.0015 train/reward_ssim_group_std_max=0.0106 train/reward_ssim_group_std_min=0.0005 train/reward_ssim_group_mean_std=0.0241 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-8.0372 train/adv_max=5.8936 train/adv_abs_mean=1.9124
+
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Epoch 11 Training:  11%|█▏        | 11/96 [00:04<00:35,  2.37it/s]
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Epoch 11 Training:  14%|█▎        | 13/96 [00:05<00:34,  2.38it/s]
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Epoch 11 Training:  15%|█▍        | 14/96 [00:05<00:34,  2.38it/s]
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Epoch 11 Training:  26%|██▌       | 25/96 [00:10<00:29,  2.37it/s]
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Epoch 11 Training:  33%|███▎      | 32/96 [00:13<00:26,  2.37it/s]
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Epoch 11 Training:  34%|███▍      | 33/96 [00:13<00:26,  2.37it/s]
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Epoch 11 Training:  39%|███▊      | 37/96 [00:15<00:24,  2.37it/s]
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Epoch 11 Training:  41%|████      | 39/96 [00:16<00:24,  2.37it/s]
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Epoch 11 Training:  43%|████▎     | 41/96 [00:17<00:23,  2.36it/s]
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Epoch 11 Training:  44%|████▍     | 42/96 [00:17<00:22,  2.36it/s]
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Epoch 11 Training:  46%|████▌     | 44/96 [00:18<00:21,  2.37it/s]
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Epoch 11 Training:  47%|████▋     | 45/96 [00:18<00:21,  2.37it/s]
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Epoch 11 Training:  48%|████▊     | 46/96 [00:19<00:21,  2.37it/s]
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Epoch 11 Training:  49%|████▉     | 47/96 [00:19<00:20,  2.37it/s]
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Epoch 11 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 17:26:40] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0022 | Epoch 011] train/clipped_loss_min=-5 train/clipped_loss_max=5 train/clipped_loss_mean=0.0134 train/clipped_loss_std=2.3406 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-5 train/unclipped_loss_max=5 train/unclipped_loss_mean=0.0134 train/unclipped_loss_std=2.3406 train/policy_loss=0.0134 train/loss=0.0134 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0006
+
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Epoch 11 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 17:27:00] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0023 | Epoch 011] train/clipped_loss_min=-5.0000 train/clipped_loss_max=5.0000 train/clipped_loss_mean=-0.0492 train/clipped_loss_std=2.3322 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0.0002 train/unclipped_loss_min=-5.0000 train/unclipped_loss_max=5.0000 train/unclipped_loss_mean=-0.0492 train/unclipped_loss_std=2.3322 train/policy_loss=-0.0492 train/loss=-0.0492 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0006
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+[2026-10-01 17:27:00] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0024 | Epoch 011] timing/rollout_seconds=147.0893 timing/feedback_seconds=4.8606 timing/optimization_seconds=41.1726 timing/cycle_seconds=191.8966
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Epoch 12 Sampling:  93%|█████████▎| 89/96 [02:14<00:10,  1.51s/it]
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Epoch 12 Sampling:  95%|█████████▍| 91/96 [02:17<00:07,  1.51s/it]
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Epoch 12 Sampling:  97%|█████████▋| 93/96 [02:20<00:04,  1.51s/it]
Epoch 12 Sampling:  98%|█████████▊| 94/96 [02:21<00:03,  1.51s/it]
Epoch 12 Sampling:  99%|█████████▉| 95/96 [02:23<00:01,  1.51s/it]
Epoch 12 Sampling: 100%|██████████| 96/96 [02:24<00:00,  1.51s/it]
Epoch 12 Sampling: 100%|██████████| 96/96 [02:24<00:00,  1.51s/it]
+
Pointwise Rewards: ssim:   0%|          | 0/6 [00:00<?, ?it/s]
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Pointwise Rewards: ssim:  67%|██████▋   | 4/6 [00:00<00:00,  6.49it/s]
Pointwise Rewards: ssim:  83%|████████▎ | 5/6 [00:00<00:00,  6.75it/s]
Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:00<00:00,  6.96it/s]
Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:00<00:00,  6.64it/s]
+
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Pointwise Rewards: dists:  33%|███▎      | 2/6 [00:00<00:01,  3.68it/s]
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Pointwise Rewards: dists:  67%|██████▋   | 4/6 [00:01<00:00,  3.73it/s]
Pointwise Rewards: dists:  83%|████████▎ | 5/6 [00:01<00:00,  3.78it/s]
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Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.79it/s]
+
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Pointwise Rewards: psnr:  33%|███▎      | 2/6 [00:00<00:00,  6.82it/s]
Pointwise Rewards: psnr:  50%|█████     | 3/6 [00:00<00:00,  7.15it/s]
Pointwise Rewards: psnr:  67%|██████▋   | 4/6 [00:00<00:00,  7.07it/s]
Pointwise Rewards: psnr:  83%|████████▎ | 5/6 [00:00<00:00,  7.20it/s]
Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  7.50it/s]
Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  7.26it/s]
+[2026-10-01 17:29:31] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0024 | Epoch 012] train/reward_dists_mean=0.9314 train/reward_dists_std=0.0192 train/reward_psnr_mean=28.9418 train/reward_psnr_std=3.4192 train/reward_ssim_mean=0.9328 train/reward_ssim_std=0.0239 train/reward_dists_group_std_mean=0.0032 train/reward_dists_group_std_max=0.0085 train/reward_dists_group_std_min=0.0013 train/reward_dists_group_mean_std=0.0188 train/reward_psnr_group_std_mean=1.1790 train/reward_psnr_group_std_max=2.2170 train/reward_psnr_group_std_min=0.3757 train/reward_psnr_group_mean_std=3.1785 train/reward_ssim_group_std_mean=0.0017 train/reward_ssim_group_std_max=0.0071 train/reward_ssim_group_std_min=0.0003 train/reward_ssim_group_mean_std=0.0238 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-8.4697 train/adv_max=7.7979 train/adv_abs_mean=1.9736
+
Epoch 12 Training:   0%|          | 0/96 [00:00<?, ?it/s]
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Epoch 12 Training:   1%|          | 1/96 [00:00<00:35,  2.66it/s]
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Epoch 12 Training:   2%|▏         | 2/96 [00:00<00:37,  2.48it/s]
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Epoch 12 Training:   3%|▎         | 3/96 [00:01<00:38,  2.43it/s]
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Epoch 12 Training:   4%|▍         | 4/96 [00:01<00:38,  2.41it/s]
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Epoch 12 Training:   5%|▌         | 5/96 [00:02<00:37,  2.40it/s]
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Epoch 12 Training:   6%|▋         | 6/96 [00:02<00:37,  2.39it/s]
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Epoch 12 Training:   7%|▋         | 7/96 [00:02<00:37,  2.39it/s]
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Epoch 12 Training:   8%|▊         | 8/96 [00:03<00:36,  2.38it/s]
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Epoch 12 Training:   9%|▉         | 9/96 [00:03<00:36,  2.38it/s]
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Epoch 12 Training:  10%|█         | 10/96 [00:04<00:36,  2.37it/s]
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Epoch 12 Training:  11%|█▏        | 11/96 [00:04<00:35,  2.38it/s]
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Epoch 12 Training:  12%|█▎        | 12/96 [00:05<00:35,  2.38it/s]
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Epoch 12 Timestep: 100%|██████████| 1/1 [00:00<00:00,  2.67it/s]
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Epoch 12 Training:  14%|█▎        | 13/96 [00:05<00:34,  2.38it/s]
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Epoch 12 Training:  15%|█▍        | 14/96 [00:05<00:34,  2.37it/s]
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Epoch 12 Training:  16%|█▌        | 15/96 [00:06<00:34,  2.37it/s]
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Epoch 12 Training:  17%|█▋        | 16/96 [00:06<00:33,  2.37it/s]
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Epoch 12 Training:  18%|█▊        | 17/96 [00:07<00:33,  2.37it/s]
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Epoch 12 Training:  19%|█▉        | 18/96 [00:07<00:32,  2.37it/s]
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Epoch 12 Training:  20%|█▉        | 19/96 [00:07<00:32,  2.37it/s]
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Epoch 12 Training:  21%|██        | 20/96 [00:08<00:32,  2.37it/s]
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Epoch 12 Training:  22%|██▏       | 21/96 [00:08<00:31,  2.37it/s]
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Epoch 12 Training:  23%|██▎       | 22/96 [00:09<00:31,  2.37it/s]
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Epoch 12 Training:  24%|██▍       | 23/96 [00:09<00:30,  2.37it/s]
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Epoch 12 Training:  25%|██▌       | 24/96 [00:10<00:30,  2.37it/s]
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Epoch 12 Training:  26%|██▌       | 25/96 [00:10<00:29,  2.37it/s]
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Epoch 12 Training:  27%|██▋       | 26/96 [00:10<00:29,  2.37it/s]
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Epoch 12 Training:  28%|██▊       | 27/96 [00:11<00:29,  2.37it/s]
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Epoch 12 Training:  29%|██▉       | 28/96 [00:11<00:28,  2.37it/s]
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Epoch 12 Training:  30%|███       | 29/96 [00:12<00:28,  2.37it/s]
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Epoch 12 Training:  31%|███▏      | 30/96 [00:12<00:27,  2.37it/s]
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Epoch 12 Training:  32%|███▏      | 31/96 [00:13<00:27,  2.37it/s]
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Epoch 12 Training:  33%|███▎      | 32/96 [00:13<00:26,  2.37it/s]
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Epoch 12 Training:  34%|███▍      | 33/96 [00:13<00:26,  2.37it/s]
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Epoch 12 Training:  35%|███▌      | 34/96 [00:14<00:26,  2.37it/s]
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Epoch 12 Training:  36%|███▋      | 35/96 [00:14<00:25,  2.37it/s]
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Epoch 12 Training:  38%|███▊      | 36/96 [00:15<00:25,  2.37it/s]
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Epoch 12 Training:  39%|███▊      | 37/96 [00:15<00:24,  2.38it/s]
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Epoch 12 Training:  40%|███▉      | 38/96 [00:15<00:24,  2.38it/s]
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Epoch 12 Training:  41%|████      | 39/96 [00:16<00:23,  2.38it/s]
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Epoch 12 Training:  42%|████▏     | 40/96 [00:16<00:23,  2.37it/s]
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Epoch 12 Training:  43%|████▎     | 41/96 [00:17<00:23,  2.37it/s]
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Epoch 12 Training:  44%|████▍     | 42/96 [00:17<00:22,  2.37it/s]
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Epoch 12 Training:  45%|████▍     | 43/96 [00:18<00:22,  2.38it/s]
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Epoch 12 Training:  46%|████▌     | 44/96 [00:18<00:21,  2.37it/s]
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Epoch 12 Training:  47%|████▋     | 45/96 [00:18<00:21,  2.37it/s]
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Epoch 12 Training:  48%|████▊     | 46/96 [00:19<00:21,  2.37it/s]
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Epoch 12 Training:  49%|████▉     | 47/96 [00:19<00:20,  2.37it/s]
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Epoch 12 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 17:29:52] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0024 | Epoch 012] train/clipped_loss_min=-5 train/clipped_loss_max=5 train/clipped_loss_mean=0.0502 train/clipped_loss_std=2.1790 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-5 train/unclipped_loss_max=5 train/unclipped_loss_mean=0.0502 train/unclipped_loss_std=2.1790 train/policy_loss=0.0502 train/loss=0.0502 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0004
+
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Epoch 12 Timestep: 100%|██████████| 1/1 [00:00<00:00,  2.34it/s]
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Epoch 12 Training:  50%|█████     | 48/96 [00:20<00:20,  2.29it/s]
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Epoch 12 Training:  51%|█████     | 49/96 [00:20<00:19,  2.39it/s]
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Epoch 12 Training:  52%|█████▏    | 50/96 [00:21<00:19,  2.39it/s]
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Epoch 12 Training:  53%|█████▎    | 51/96 [00:21<00:18,  2.38it/s]
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Epoch 12 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 17:30:12] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0025 | Epoch 012] train/clipped_loss_min=-5.0000 train/clipped_loss_max=5.0000 train/clipped_loss_mean=-0.1179 train/clipped_loss_std=2.4858 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-5.0000 train/unclipped_loss_max=5.0000 train/unclipped_loss_mean=-0.1179 train/unclipped_loss_std=2.4858 train/policy_loss=-0.1179 train/loss=-0.1179 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0004
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+[2026-10-01 17:30:12] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0026 | Epoch 012] timing/rollout_seconds=147.2165 timing/feedback_seconds=4.8149 timing/optimization_seconds=41.0678 timing/cycle_seconds=191.8801
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+
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+[2026-10-01 17:32:43] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0026 | Epoch 013] train/reward_dists_mean=0.9319 train/reward_dists_std=0.0170 train/reward_psnr_mean=26.5939 train/reward_psnr_std=3.3108 train/reward_ssim_mean=0.9296 train/reward_ssim_std=0.0218 train/reward_dists_group_std_mean=0.0033 train/reward_dists_group_std_max=0.0088 train/reward_dists_group_std_min=0.0016 train/reward_dists_group_mean_std=0.0166 train/reward_psnr_group_std_mean=1.3196 train/reward_psnr_group_std_max=2.4434 train/reward_psnr_group_std_min=0.6168 train/reward_psnr_group_mean_std=3.0049 train/reward_ssim_group_std_mean=0.0024 train/reward_ssim_group_std_max=0.0088 train/reward_ssim_group_std_min=0.0006 train/reward_ssim_group_mean_std=0.0216 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-7.2817 train/adv_max=6.7221 train/adv_abs_mean=2.0126
+
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Epoch 13 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 17:33:23] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0027 | Epoch 013] train/clipped_loss_min=-5.0000 train/clipped_loss_max=5.0000 train/clipped_loss_mean=0.1388 train/clipped_loss_std=2.4032 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-5.0000 train/unclipped_loss_max=5.0000 train/unclipped_loss_mean=0.1388 train/unclipped_loss_std=2.4032 train/policy_loss=0.1388 train/loss=0.1388 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0004
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+[2026-10-01 17:33:23] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0028 | Epoch 013] timing/rollout_seconds=146.8694 timing/feedback_seconds=4.4423 timing/optimization_seconds=41.1039 timing/cycle_seconds=191.5381
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+
Pointwise Rewards: ssim:   0%|          | 0/6 [00:00<?, ?it/s]
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Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:00<00:00,  6.57it/s]
+
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Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.72it/s]
+
Pointwise Rewards: psnr:   0%|          | 0/6 [00:00<?, ?it/s]
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Pointwise Rewards: psnr:  33%|███▎      | 2/6 [00:00<00:00,  6.84it/s]
Pointwise Rewards: psnr:  50%|█████     | 3/6 [00:00<00:00,  6.90it/s]
Pointwise Rewards: psnr:  67%|██████▋   | 4/6 [00:00<00:00,  7.09it/s]
Pointwise Rewards: psnr:  83%|████████▎ | 5/6 [00:00<00:00,  6.92it/s]
Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.99it/s]
Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.95it/s]
+[2026-10-01 17:35:55] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0028 | Epoch 014] train/reward_dists_mean=0.9332 train/reward_dists_std=0.0172 train/reward_psnr_mean=27.2554 train/reward_psnr_std=2.7674 train/reward_ssim_mean=0.9297 train/reward_ssim_std=0.0214 train/reward_dists_group_std_mean=0.0031 train/reward_dists_group_std_max=0.0063 train/reward_dists_group_std_min=0.0011 train/reward_dists_group_mean_std=0.0169 train/reward_psnr_group_std_mean=1.4393 train/reward_psnr_group_std_max=2.8338 train/reward_psnr_group_std_min=0.6104 train/reward_psnr_group_mean_std=2.3021 train/reward_ssim_group_std_mean=0.0019 train/reward_ssim_group_std_max=0.0088 train/reward_ssim_group_std_min=0.0007 train/reward_ssim_group_mean_std=0.0213 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-7.5249 train/adv_max=6.2683 train/adv_abs_mean=1.9795
+
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Epoch 14 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 17:36:15] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0028 | Epoch 014] train/clipped_loss_min=-5 train/clipped_loss_max=5 train/clipped_loss_mean=0.0615 train/clipped_loss_std=2.3912 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-5 train/unclipped_loss_max=5 train/unclipped_loss_mean=0.0615 train/unclipped_loss_std=2.3912 train/policy_loss=0.0615 train/loss=0.0615 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0002
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Epoch 14 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 17:36:35] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0029 | Epoch 014] train/clipped_loss_min=-5.0000 train/clipped_loss_max=5.0000 train/clipped_loss_mean=-0.0862 train/clipped_loss_std=2.3938 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0.0001 train/unclipped_loss_min=-5.0000 train/unclipped_loss_max=5.0000 train/unclipped_loss_mean=-0.0862 train/unclipped_loss_std=2.3938 train/policy_loss=-0.0862 train/loss=-0.0862 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
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+[2026-10-01 17:36:35] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0030 | Epoch 014] timing/rollout_seconds=147.1631 timing/feedback_seconds=4.7430 timing/optimization_seconds=41.0907 timing/cycle_seconds=191.8111
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+
Pointwise Rewards: ssim:   0%|          | 0/6 [00:00<?, ?it/s]
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Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:02<00:00,  3.70it/s]
Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:02<00:00,  2.50it/s]
+
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+
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Pointwise Rewards: psnr:  33%|███▎      | 2/6 [00:00<00:00,  7.09it/s]
Pointwise Rewards: psnr:  50%|█████     | 3/6 [00:00<00:00,  7.16it/s]
Pointwise Rewards: psnr:  67%|██████▋   | 4/6 [00:00<00:00,  6.90it/s]
Pointwise Rewards: psnr:  83%|████████▎ | 5/6 [00:00<00:00,  6.90it/s]
Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  7.04it/s]
Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  7.01it/s]
+[2026-10-01 17:39:08] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0030 | Epoch 015] train/reward_dists_mean=0.9407 train/reward_dists_std=0.0185 train/reward_psnr_mean=29.7278 train/reward_psnr_std=2.5455 train/reward_ssim_mean=0.9365 train/reward_ssim_std=0.0223 train/reward_dists_group_std_mean=0.0031 train/reward_dists_group_std_max=0.0117 train/reward_dists_group_std_min=0.0014 train/reward_dists_group_mean_std=0.0181 train/reward_psnr_group_std_mean=1.0308 train/reward_psnr_group_std_max=2.2073 train/reward_psnr_group_std_min=0.2719 train/reward_psnr_group_mean_std=2.2896 train/reward_ssim_group_std_mean=0.0011 train/reward_ssim_group_std_max=0.0033 train/reward_ssim_group_std_min=0.0004 train/reward_ssim_group_mean_std=0.0223 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-7.5953 train/adv_max=5.6480 train/adv_abs_mean=1.8982
+
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Epoch 15 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 17:39:28] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0030 | Epoch 015] train/clipped_loss_min=-5 train/clipped_loss_max=5 train/clipped_loss_mean=-0.0892 train/clipped_loss_std=2.2368 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-5 train/unclipped_loss_max=5 train/unclipped_loss_mean=-0.0892 train/unclipped_loss_std=2.2368 train/policy_loss=-0.0892 train/loss=-0.0892 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
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Epoch 15 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 17:39:48] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0031 | Epoch 015] train/clipped_loss_min=-4.9831 train/clipped_loss_max=5.0000 train/clipped_loss_mean=0.0526 train/clipped_loss_std=2.3626 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-4.9831 train/unclipped_loss_max=5.0000 train/unclipped_loss_mean=0.0526 train/unclipped_loss_std=2.3626 train/policy_loss=0.0526 train/loss=0.0526 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0004
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Epoch 15 Training: 100%|██████████| 96/96 [00:40<00:00,  2.36it/s]
+[2026-10-01 17:39:48] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0032 | Epoch 015] timing/rollout_seconds=147.5335 timing/feedback_seconds=5.8720 timing/optimization_seconds=41.2588 timing/cycle_seconds=193.0131
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+
Pointwise Rewards: ssim:   0%|          | 0/6 [00:00<?, ?it/s]
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Pointwise Rewards: ssim:  50%|█████     | 3/6 [00:00<00:00,  6.42it/s]
Pointwise Rewards: ssim:  67%|██████▋   | 4/6 [00:00<00:00,  6.40it/s]
Pointwise Rewards: ssim:  83%|████████▎ | 5/6 [00:00<00:00,  6.63it/s]
Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:00<00:00,  6.65it/s]
Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:00<00:00,  6.51it/s]
+
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Pointwise Rewards: dists:  50%|█████     | 3/6 [00:00<00:00,  3.79it/s]
Pointwise Rewards: dists:  67%|██████▋   | 4/6 [00:01<00:00,  3.73it/s]
Pointwise Rewards: dists:  83%|████████▎ | 5/6 [00:01<00:00,  3.78it/s]
Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.75it/s]
Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.76it/s]
+
Pointwise Rewards: psnr:   0%|          | 0/6 [00:00<?, ?it/s]
Pointwise Rewards: psnr:  17%|█▋        | 1/6 [00:00<00:00,  6.68it/s]
Pointwise Rewards: psnr:  33%|███▎      | 2/6 [00:00<00:00,  7.66it/s]
Pointwise Rewards: psnr:  50%|█████     | 3/6 [00:00<00:00,  7.21it/s]
Pointwise Rewards: psnr:  67%|██████▋   | 4/6 [00:00<00:00,  6.96it/s]
Pointwise Rewards: psnr:  83%|████████▎ | 5/6 [00:00<00:00,  7.11it/s]
Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.99it/s]
Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  7.06it/s]
+[2026-10-01 17:42:19] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0032 | Epoch 016] train/reward_dists_mean=0.9338 train/reward_dists_std=0.0183 train/reward_psnr_mean=28.0602 train/reward_psnr_std=2.5441 train/reward_ssim_mean=0.9309 train/reward_ssim_std=0.0213 train/reward_dists_group_std_mean=0.0034 train/reward_dists_group_std_max=0.0074 train/reward_dists_group_std_min=0.0012 train/reward_dists_group_mean_std=0.0180 train/reward_psnr_group_std_mean=1.2308 train/reward_psnr_group_std_max=2.1569 train/reward_psnr_group_std_min=0.4475 train/reward_psnr_group_mean_std=2.1803 train/reward_ssim_group_std_mean=0.0017 train/reward_ssim_group_std_max=0.0112 train/reward_ssim_group_std_min=0.0005 train/reward_ssim_group_mean_std=0.0212 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-8.6416 train/adv_max=6.5985 train/adv_abs_mean=1.9715
+
Epoch 16 Training:   0%|          | 0/96 [00:00<?, ?it/s]
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Epoch 16 Training:  28%|██▊       | 27/96 [00:11<00:29,  2.38it/s]
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Epoch 16 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 17:42:39] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0032 | Epoch 016] train/clipped_loss_min=-5 train/clipped_loss_max=5 train/clipped_loss_mean=-0.1168 train/clipped_loss_std=2.3413 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-5 train/unclipped_loss_max=5 train/unclipped_loss_mean=-0.1168 train/unclipped_loss_std=2.3413 train/policy_loss=-0.1168 train/loss=-0.1168 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0002
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+[2026-10-01 17:43:00] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0034 | Epoch 016] timing/rollout_seconds=146.9143 timing/feedback_seconds=5.0027 timing/optimization_seconds=41.0058 timing/cycle_seconds=191.4208
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+[2026-10-01 17:45:31] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0034 | Epoch 017] train/reward_dists_mean=0.9298 train/reward_dists_std=0.0238 train/reward_psnr_mean=27.2162 train/reward_psnr_std=2.9647 train/reward_ssim_mean=0.9286 train/reward_ssim_std=0.0240 train/reward_dists_group_std_mean=0.0040 train/reward_dists_group_std_max=0.0275 train/reward_dists_group_std_min=0.0013 train/reward_dists_group_mean_std=0.0231 train/reward_psnr_group_std_mean=1.5486 train/reward_psnr_group_std_max=6.5825 train/reward_psnr_group_std_min=0.4903 train/reward_psnr_group_mean_std=2.3641 train/reward_ssim_group_std_mean=0.0027 train/reward_ssim_group_std_max=0.0248 train/reward_ssim_group_std_min=0.0003 train/reward_ssim_group_mean_std=0.0234 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-7.8425 train/adv_max=6.9690 train/adv_abs_mean=2.0799
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Epoch 17 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 17:45:51] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0034 | Epoch 017] train/clipped_loss_min=-5 train/clipped_loss_max=5 train/clipped_loss_mean=-0.1005 train/clipped_loss_std=2.4368 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-5 train/unclipped_loss_max=5 train/unclipped_loss_mean=-0.1005 train/unclipped_loss_std=2.4368 train/policy_loss=-0.1005 train/loss=-0.1005 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0006
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Epoch 17 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 17:46:11] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0035 | Epoch 017] train/clipped_loss_min=-5.0000 train/clipped_loss_max=5.0000 train/clipped_loss_mean=0.0403 train/clipped_loss_std=2.5062 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0.0001 train/unclipped_loss_min=-5.0000 train/unclipped_loss_max=5.0000 train/unclipped_loss_mean=0.0403 train/unclipped_loss_std=2.5062 train/policy_loss=0.0403 train/loss=0.0403 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0007
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+[2026-10-01 17:46:11] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0036 | Epoch 017] timing/rollout_seconds=147.0196 timing/feedback_seconds=5.2036 timing/optimization_seconds=41.0392 timing/cycle_seconds=191.5417
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+
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+[2026-10-01 17:48:42] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0036 | Epoch 018] train/reward_dists_mean=0.9371 train/reward_dists_std=0.0152 train/reward_psnr_mean=29.0078 train/reward_psnr_std=2.2348 train/reward_ssim_mean=0.9285 train/reward_ssim_std=0.0215 train/reward_dists_group_std_mean=0.0031 train/reward_dists_group_std_max=0.0059 train/reward_dists_group_std_min=0.0013 train/reward_dists_group_mean_std=0.0149 train/reward_psnr_group_std_mean=1.0211 train/reward_psnr_group_std_max=1.8496 train/reward_psnr_group_std_min=0.3105 train/reward_psnr_group_mean_std=1.9508 train/reward_ssim_group_std_mean=0.0012 train/reward_ssim_group_std_max=0.0027 train/reward_ssim_group_std_min=0.0006 train/reward_ssim_group_mean_std=0.0214 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-7.4764 train/adv_max=6.0401 train/adv_abs_mean=1.8156
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Epoch 18 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 17:49:03] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0036 | Epoch 018] train/clipped_loss_min=-5 train/clipped_loss_max=5 train/clipped_loss_mean=-0.2238 train/clipped_loss_std=2.2919 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-5 train/unclipped_loss_max=5 train/unclipped_loss_mean=-0.2238 train/unclipped_loss_std=2.2919 train/policy_loss=-0.2238 train/loss=-0.2238 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0006
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Epoch 18 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 17:49:23] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0037 | Epoch 018] train/clipped_loss_min=-5.0000 train/clipped_loss_max=5.0000 train/clipped_loss_mean=0.1861 train/clipped_loss_std=2.1452 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-5.0000 train/unclipped_loss_max=5.0000 train/unclipped_loss_mean=0.1861 train/unclipped_loss_std=2.1452 train/policy_loss=0.1861 train/loss=0.1861 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0006
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+[2026-10-01 17:49:23] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0038 | Epoch 018] timing/rollout_seconds=146.8699 timing/feedback_seconds=4.5697 timing/optimization_seconds=41.3101 timing/cycle_seconds=191.6808
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+[2026-10-01 17:51:54] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0038 | Epoch 019] train/reward_dists_mean=0.9370 train/reward_dists_std=0.0217 train/reward_psnr_mean=27.4187 train/reward_psnr_std=2.6541 train/reward_ssim_mean=0.9305 train/reward_ssim_std=0.0203 train/reward_dists_group_std_mean=0.0030 train/reward_dists_group_std_max=0.0103 train/reward_dists_group_std_min=0.0015 train/reward_dists_group_mean_std=0.0214 train/reward_psnr_group_std_mean=1.1546 train/reward_psnr_group_std_max=2.7769 train/reward_psnr_group_std_min=0.2255 train/reward_psnr_group_mean_std=2.3497 train/reward_ssim_group_std_mean=0.0030 train/reward_ssim_group_std_max=0.0580 train/reward_ssim_group_std_min=0.0007 train/reward_ssim_group_mean_std=0.0184 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-8.3570 train/adv_max=6.2265 train/adv_abs_mean=1.9576
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Epoch 19 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 17:52:14] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0038 | Epoch 019] train/clipped_loss_min=-5 train/clipped_loss_max=5 train/clipped_loss_mean=0.0506 train/clipped_loss_std=2.3166 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-5 train/unclipped_loss_max=5 train/unclipped_loss_mean=0.0506 train/unclipped_loss_std=2.3166 train/policy_loss=0.0506 train/loss=0.0506 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0004
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Epoch 19 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 17:52:34] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0039 | Epoch 019] train/clipped_loss_min=-5.0000 train/clipped_loss_max=5.0000 train/clipped_loss_mean=-0.0951 train/clipped_loss_std=2.3804 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-5.0000 train/unclipped_loss_max=5.0000 train/unclipped_loss_mean=-0.0951 train/unclipped_loss_std=2.3804 train/policy_loss=-0.0951 train/loss=-0.0951 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0004
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+[2026-10-01 17:52:34] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0040 | Epoch 019] timing/rollout_seconds=146.9310 timing/feedback_seconds=5.0736 timing/optimization_seconds=40.9794 timing/cycle_seconds=191.4805
+[2026-10-01 17:52:35] [Rank 0] [INFO] [flow_factory.models.abc]: Saving LoRA weights for transformer role=base to /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/runs/klein4b_grpo_vedai_full_ssim_dists_psnr_lr5e-5/saves/klein4b_grpo_vedai_full_ssim_dists_psnr_lr5e-5/checkpoints/checkpoint-20
+[2026-10-01 17:52:35] [Rank 0] [INFO] [flow_factory.models.abc]: Checkpoint saved successfully to /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/runs/klein4b_grpo_vedai_full_ssim_dists_psnr_lr5e-5/saves/klein4b_grpo_vedai_full_ssim_dists_psnr_lr5e-5/checkpoints/checkpoint-20
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+[2026-10-01 17:53:19] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0040 | Epoch 020] eval/vedai_rl_full/reward_dists_mean=0.9331 eval/vedai_rl_full/reward_dists_std=0.0187 eval/vedai_rl_full/reward_psnr_mean=28.7097 eval/vedai_rl_full/reward_psnr_std=2.7462 eval/vedai_rl_full/reward_ssim_mean=0.9186 eval/vedai_rl_full/reward_ssim_std=0.0276
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Pointwise Rewards: psnr:  67%|██████▋   | 4/6 [00:00<00:00,  6.81it/s]
Pointwise Rewards: psnr:  83%|████████▎ | 5/6 [00:00<00:00,  7.13it/s]
Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.97it/s]
Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.90it/s]
+[2026-10-01 17:55:50] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0040 | Epoch 020] train/reward_dists_mean=0.9305 train/reward_dists_std=0.0173 train/reward_psnr_mean=24.4266 train/reward_psnr_std=2.5787 train/reward_ssim_mean=0.9181 train/reward_ssim_std=0.0179 train/reward_dists_group_std_mean=0.0037 train/reward_dists_group_std_max=0.0083 train/reward_dists_group_std_min=0.0019 train/reward_dists_group_mean_std=0.0169 train/reward_psnr_group_std_mean=1.1461 train/reward_psnr_group_std_max=1.8687 train/reward_psnr_group_std_min=0.4906 train/reward_psnr_group_mean_std=2.2891 train/reward_ssim_group_std_mean=0.0031 train/reward_ssim_group_std_max=0.0133 train/reward_ssim_group_std_min=0.0012 train/reward_ssim_group_mean_std=0.0175 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-8.9510 train/adv_max=6.5870 train/adv_abs_mean=2.0704
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Epoch 20 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 17:56:10] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0040 | Epoch 020] train/clipped_loss_min=-5 train/clipped_loss_max=5 train/clipped_loss_mean=-0.0221 train/clipped_loss_std=2.5290 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-5 train/unclipped_loss_max=5 train/unclipped_loss_mean=-0.0221 train/unclipped_loss_std=2.5290 train/policy_loss=-0.0221 train/loss=-0.0221 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
+
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Epoch 20 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 17:56:30] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0041 | Epoch 020] train/clipped_loss_min=-5.0000 train/clipped_loss_max=5.0000 train/clipped_loss_mean=-0.0372 train/clipped_loss_std=2.3769 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-5.0000 train/unclipped_loss_max=5.0000 train/unclipped_loss_mean=-0.0372 train/unclipped_loss_std=2.3769 train/policy_loss=-0.0372 train/loss=-0.0372 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
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Epoch 20 Training: 100%|██████████| 96/96 [00:40<00:00,  2.38it/s]
+[2026-10-01 17:56:30] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0042 | Epoch 020] timing/rollout_seconds=146.9344 timing/feedback_seconds=4.7038 timing/optimization_seconds=41.0048 timing/cycle_seconds=191.4479
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+[2026-10-01 17:59:01] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0042 | Epoch 021] train/reward_dists_mean=0.9375 train/reward_dists_std=0.0169 train/reward_psnr_mean=25.6873 train/reward_psnr_std=2.9426 train/reward_ssim_mean=0.9273 train/reward_ssim_std=0.0233 train/reward_dists_group_std_mean=0.0031 train/reward_dists_group_std_max=0.0056 train/reward_dists_group_std_min=0.0013 train/reward_dists_group_mean_std=0.0166 train/reward_psnr_group_std_mean=1.3497 train/reward_psnr_group_std_max=2.4627 train/reward_psnr_group_std_min=0.7554 train/reward_psnr_group_mean_std=2.5893 train/reward_ssim_group_std_mean=0.0025 train/reward_ssim_group_std_max=0.0082 train/reward_ssim_group_std_min=0.0008 train/reward_ssim_group_mean_std=0.0232 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-8.4739 train/adv_max=5.9291 train/adv_abs_mean=2.0498
+
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Epoch 21 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 17:59:21] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0042 | Epoch 021] train/clipped_loss_min=-5 train/clipped_loss_max=5 train/clipped_loss_mean=-0.1045 train/clipped_loss_std=2.4607 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-5 train/unclipped_loss_max=5 train/unclipped_loss_mean=-0.1045 train/unclipped_loss_std=2.4607 train/policy_loss=-0.1045 train/loss=-0.1045 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0005
+
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Epoch 21 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 17:59:42] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0043 | Epoch 021] train/clipped_loss_min=-5.0000 train/clipped_loss_max=5.0000 train/clipped_loss_mean=0.0679 train/clipped_loss_std=2.4085 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-5.0000 train/unclipped_loss_max=5.0000 train/unclipped_loss_mean=0.0679 train/unclipped_loss_std=2.4085 train/policy_loss=0.0679 train/loss=0.0679 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0005
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+[2026-10-01 17:59:42] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0044 | Epoch 021] timing/rollout_seconds=146.9545 timing/feedback_seconds=4.6853 timing/optimization_seconds=41.2359 timing/cycle_seconds=191.7002
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Epoch 22 Sampling:  91%|█████████ | 87/96 [02:11<00:13,  1.51s/it]
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Epoch 22 Sampling:  99%|█████████▉| 95/96 [02:23<00:01,  1.51s/it]
Epoch 22 Sampling: 100%|██████████| 96/96 [02:24<00:00,  1.51s/it]
Epoch 22 Sampling: 100%|██████████| 96/96 [02:24<00:00,  1.51s/it]
+
Pointwise Rewards: ssim:   0%|          | 0/6 [00:00<?, ?it/s]
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Pointwise Rewards: ssim:  50%|█████     | 3/6 [00:00<00:00,  6.79it/s]
Pointwise Rewards: ssim:  67%|██████▋   | 4/6 [00:00<00:00,  6.71it/s]
Pointwise Rewards: ssim:  83%|████████▎ | 5/6 [00:00<00:00,  6.80it/s]
Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:00<00:00,  6.61it/s]
Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:00<00:00,  6.60it/s]
+
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Pointwise Rewards: dists:  33%|███▎      | 2/6 [00:00<00:01,  3.69it/s]
Pointwise Rewards: dists:  50%|█████     | 3/6 [00:00<00:00,  3.84it/s]
Pointwise Rewards: dists:  67%|██████▋   | 4/6 [00:01<00:00,  3.78it/s]
Pointwise Rewards: dists:  83%|████████▎ | 5/6 [00:01<00:00,  3.76it/s]
Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.73it/s]
Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.75it/s]
+
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Pointwise Rewards: psnr:  17%|█▋        | 1/6 [00:00<00:00,  7.15it/s]
Pointwise Rewards: psnr:  33%|███▎      | 2/6 [00:00<00:00,  6.81it/s]
Pointwise Rewards: psnr:  50%|█████     | 3/6 [00:00<00:00,  7.37it/s]
Pointwise Rewards: psnr:  67%|██████▋   | 4/6 [00:00<00:00,  7.14it/s]
Pointwise Rewards: psnr:  83%|████████▎ | 5/6 [00:00<00:00,  6.97it/s]
Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.90it/s]
Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.99it/s]
+[2026-10-01 18:02:13] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0044 | Epoch 022] train/reward_dists_mean=0.9378 train/reward_dists_std=0.0145 train/reward_psnr_mean=27.5383 train/reward_psnr_std=3.2342 train/reward_ssim_mean=0.9333 train/reward_ssim_std=0.0186 train/reward_dists_group_std_mean=0.0032 train/reward_dists_group_std_max=0.0062 train/reward_dists_group_std_min=0.0015 train/reward_dists_group_mean_std=0.0141 train/reward_psnr_group_std_mean=1.1648 train/reward_psnr_group_std_max=2.2138 train/reward_psnr_group_std_min=0.3453 train/reward_psnr_group_mean_std=2.9869 train/reward_ssim_group_std_mean=0.0019 train/reward_ssim_group_std_max=0.0072 train/reward_ssim_group_std_min=0.0006 train/reward_ssim_group_mean_std=0.0185 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-8.9507 train/adv_max=6.8388 train/adv_abs_mean=1.9670
+
Epoch 22 Training:   0%|          | 0/96 [00:00<?, ?it/s]
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Epoch 22 Training:   1%|          | 1/96 [00:00<00:35,  2.66it/s]
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Epoch 22 Training:   2%|▏         | 2/96 [00:00<00:37,  2.48it/s]
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Epoch 22 Training:   3%|▎         | 3/96 [00:01<00:38,  2.44it/s]
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Epoch 22 Timestep: 100%|██████████| 1/1 [00:00<00:00,  2.66it/s]
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Epoch 22 Training:   4%|▍         | 4/96 [00:01<00:38,  2.41it/s]
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Epoch 22 Training:   5%|▌         | 5/96 [00:02<00:37,  2.40it/s]
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Epoch 22 Training:   6%|▋         | 6/96 [00:02<00:37,  2.39it/s]
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Epoch 22 Training:   7%|▋         | 7/96 [00:02<00:37,  2.39it/s]
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Epoch 22 Training:   8%|▊         | 8/96 [00:03<00:36,  2.38it/s]
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Epoch 22 Training:   9%|▉         | 9/96 [00:03<00:36,  2.38it/s]
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Epoch 22 Training:  10%|█         | 10/96 [00:04<00:36,  2.38it/s]
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Epoch 22 Training:  11%|█▏        | 11/96 [00:04<00:35,  2.39it/s]
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Epoch 22 Training:  12%|█▎        | 12/96 [00:04<00:35,  2.38it/s]
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Epoch 22 Timestep: 100%|██████████| 1/1 [00:00<00:00,  2.67it/s]
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Epoch 22 Training:  14%|█▎        | 13/96 [00:05<00:34,  2.38it/s]
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Epoch 22 Timestep: 100%|██████████| 1/1 [00:00<00:00,  2.67it/s]
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Epoch 22 Training:  15%|█▍        | 14/96 [00:05<00:34,  2.38it/s]
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Epoch 22 Timestep: 100%|██████████| 1/1 [00:00<00:00,  2.67it/s]
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Epoch 22 Training:  16%|█▌        | 15/96 [00:06<00:33,  2.38it/s]
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Epoch 22 Timestep: 100%|██████████| 1/1 [00:00<00:00,  2.66it/s]
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Epoch 22 Training:  17%|█▋        | 16/96 [00:06<00:33,  2.38it/s]
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Epoch 22 Training:  18%|█▊        | 17/96 [00:07<00:33,  2.37it/s]
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Epoch 22 Training:  19%|█▉        | 18/96 [00:07<00:32,  2.38it/s]
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Epoch 22 Training:  20%|█▉        | 19/96 [00:07<00:32,  2.38it/s]
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Epoch 22 Timestep: 100%|██████████| 1/1 [00:00<00:00,  2.67it/s]
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Epoch 22 Training:  21%|██        | 20/96 [00:08<00:31,  2.38it/s]
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Epoch 22 Training:  22%|██▏       | 21/96 [00:08<00:31,  2.38it/s]
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Epoch 22 Training:  23%|██▎       | 22/96 [00:09<00:31,  2.38it/s]
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Epoch 22 Training:  24%|██▍       | 23/96 [00:09<00:30,  2.38it/s]
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Epoch 22 Training:  25%|██▌       | 24/96 [00:10<00:30,  2.38it/s]
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Epoch 22 Training:  26%|██▌       | 25/96 [00:10<00:29,  2.38it/s]
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Epoch 22 Training:  27%|██▋       | 26/96 [00:10<00:29,  2.38it/s]
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Epoch 22 Training:  28%|██▊       | 27/96 [00:11<00:29,  2.38it/s]
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Epoch 22 Training:  29%|██▉       | 28/96 [00:11<00:28,  2.38it/s]
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Epoch 22 Training:  30%|███       | 29/96 [00:12<00:28,  2.38it/s]
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Epoch 22 Training:  31%|███▏      | 30/96 [00:12<00:27,  2.38it/s]
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Epoch 22 Training:  32%|███▏      | 31/96 [00:12<00:27,  2.38it/s]
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Epoch 22 Training:  33%|███▎      | 32/96 [00:13<00:26,  2.38it/s]
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Epoch 22 Training:  34%|███▍      | 33/96 [00:13<00:26,  2.38it/s]
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Epoch 22 Training:  35%|███▌      | 34/96 [00:14<00:26,  2.38it/s]
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Epoch 22 Training:  36%|███▋      | 35/96 [00:14<00:25,  2.38it/s]
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Epoch 22 Training:  38%|███▊      | 36/96 [00:15<00:25,  2.38it/s]
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Epoch 22 Training:  39%|███▊      | 37/96 [00:15<00:24,  2.37it/s]
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Epoch 22 Training:  40%|███▉      | 38/96 [00:15<00:24,  2.38it/s]
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Epoch 22 Training:  41%|████      | 39/96 [00:16<00:23,  2.38it/s]
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Epoch 22 Training:  42%|████▏     | 40/96 [00:16<00:23,  2.36it/s]
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Epoch 22 Training:  43%|████▎     | 41/96 [00:17<00:23,  2.38it/s]
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Epoch 22 Training:  44%|████▍     | 42/96 [00:17<00:22,  2.37it/s]
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Epoch 22 Training:  45%|████▍     | 43/96 [00:18<00:22,  2.38it/s]
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Epoch 22 Training:  46%|████▌     | 44/96 [00:18<00:21,  2.38it/s]
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Epoch 22 Training:  47%|████▋     | 45/96 [00:18<00:21,  2.38it/s]
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Epoch 22 Training:  48%|████▊     | 46/96 [00:19<00:21,  2.38it/s]
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Epoch 22 Training:  49%|████▉     | 47/96 [00:19<00:20,  2.38it/s]
+
Epoch 22 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 18:02:33] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0044 | Epoch 022] train/clipped_loss_min=-5 train/clipped_loss_max=5 train/clipped_loss_mean=-0.1385 train/clipped_loss_std=2.4162 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-5 train/unclipped_loss_max=5 train/unclipped_loss_mean=-0.1385 train/unclipped_loss_std=2.4162 train/policy_loss=-0.1385 train/loss=-0.1385 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0007
+
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Epoch 22 Timestep: 100%|██████████| 1/1 [00:00<00:00,  2.33it/s]
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Epoch 22 Training:  50%|█████     | 48/96 [00:20<00:20,  2.29it/s]
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Epoch 22 Training:  51%|█████     | 49/96 [00:20<00:19,  2.39it/s]
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Epoch 22 Training:  52%|█████▏    | 50/96 [00:20<00:19,  2.38it/s]
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Epoch 22 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 18:02:53] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0045 | Epoch 022] train/clipped_loss_min=-5.0000 train/clipped_loss_max=5.0001 train/clipped_loss_mean=0.0919 train/clipped_loss_std=2.2896 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-5.0000 train/unclipped_loss_max=5.0001 train/unclipped_loss_mean=0.0919 train/unclipped_loss_std=2.2896 train/policy_loss=0.0919 train/loss=0.0919 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0007
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+[2026-10-01 18:02:53] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0046 | Epoch 022] timing/rollout_seconds=146.8375 timing/feedback_seconds=4.6274 timing/optimization_seconds=40.9700 timing/cycle_seconds=191.3030
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+
Pointwise Rewards: ssim:   0%|          | 0/6 [00:00<?, ?it/s]
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Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:00<00:00,  6.61it/s]
+
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Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.74it/s]
+
Pointwise Rewards: psnr:   0%|          | 0/6 [00:00<?, ?it/s]
Pointwise Rewards: psnr:  17%|█▋        | 1/6 [00:00<00:00,  7.17it/s]
Pointwise Rewards: psnr:  33%|███▎      | 2/6 [00:00<00:00,  7.04it/s]
Pointwise Rewards: psnr:  50%|█████     | 3/6 [00:00<00:00,  6.93it/s]
Pointwise Rewards: psnr:  67%|██████▋   | 4/6 [00:00<00:00,  7.07it/s]
Pointwise Rewards: psnr:  83%|████████▎ | 5/6 [00:00<00:00,  7.11it/s]
Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  7.06it/s]
Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  7.06it/s]
+[2026-10-01 18:05:24] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0046 | Epoch 023] train/reward_dists_mean=0.9359 train/reward_dists_std=0.0142 train/reward_psnr_mean=28.3514 train/reward_psnr_std=2.6379 train/reward_ssim_mean=0.9284 train/reward_ssim_std=0.0216 train/reward_dists_group_std_mean=0.0035 train/reward_dists_group_std_max=0.0098 train/reward_dists_group_std_min=0.0015 train/reward_dists_group_mean_std=0.0137 train/reward_psnr_group_std_mean=1.1411 train/reward_psnr_group_std_max=2.3298 train/reward_psnr_group_std_min=0.2962 train/reward_psnr_group_mean_std=2.3408 train/reward_ssim_group_std_mean=0.0017 train/reward_ssim_group_std_max=0.0068 train/reward_ssim_group_std_min=0.0004 train/reward_ssim_group_mean_std=0.0215 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-8.8484 train/adv_max=7.4206 train/adv_abs_mean=1.9721
+
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Epoch 23 Training:   4%|▍         | 4/96 [00:01<00:38,  2.41it/s]
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Epoch 23 Training:   5%|▌         | 5/96 [00:02<00:37,  2.40it/s]
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Epoch 23 Training:   6%|▋         | 6/96 [00:02<00:37,  2.39it/s]
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Epoch 23 Training:   7%|▋         | 7/96 [00:02<00:37,  2.39it/s]
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Epoch 23 Training:   8%|▊         | 8/96 [00:03<00:36,  2.39it/s]
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Epoch 23 Training:   9%|▉         | 9/96 [00:03<00:36,  2.39it/s]
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Epoch 23 Training:  10%|█         | 10/96 [00:04<00:36,  2.38it/s]
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Epoch 23 Training:  11%|█▏        | 11/96 [00:04<00:35,  2.38it/s]
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Epoch 23 Training:  12%|█▎        | 12/96 [00:05<00:35,  2.37it/s]
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Epoch 23 Training:  14%|█▎        | 13/96 [00:05<00:34,  2.37it/s]
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Epoch 23 Training:  15%|█▍        | 14/96 [00:05<00:34,  2.37it/s]
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Epoch 23 Training:  16%|█▌        | 15/96 [00:06<00:34,  2.38it/s]
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Epoch 23 Training:  23%|██▎       | 22/96 [00:09<00:31,  2.38it/s]
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Epoch 23 Training:  25%|██▌       | 24/96 [00:10<00:30,  2.38it/s]
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Epoch 23 Training:  26%|██▌       | 25/96 [00:10<00:29,  2.38it/s]
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Epoch 23 Training:  27%|██▋       | 26/96 [00:10<00:29,  2.38it/s]
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Epoch 23 Training:  28%|██▊       | 27/96 [00:11<00:29,  2.38it/s]
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Epoch 23 Training:  29%|██▉       | 28/96 [00:11<00:28,  2.37it/s]
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Epoch 23 Training:  33%|███▎      | 32/96 [00:13<00:26,  2.38it/s]
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Epoch 23 Training:  34%|███▍      | 33/96 [00:13<00:26,  2.38it/s]
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Epoch 23 Training:  35%|███▌      | 34/96 [00:14<00:26,  2.38it/s]
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Epoch 23 Training:  39%|███▊      | 37/96 [00:15<00:24,  2.37it/s]
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Epoch 23 Training:  42%|████▏     | 40/96 [00:16<00:23,  2.38it/s]
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Epoch 23 Training:  43%|████▎     | 41/96 [00:17<00:23,  2.38it/s]
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Epoch 23 Training:  44%|████▍     | 42/96 [00:17<00:22,  2.38it/s]
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Epoch 23 Training:  45%|████▍     | 43/96 [00:18<00:22,  2.38it/s]
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Epoch 23 Training:  46%|████▌     | 44/96 [00:18<00:21,  2.38it/s]
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Epoch 23 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 18:06:04] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0047 | Epoch 023] train/clipped_loss_min=-5 train/clipped_loss_max=5.0000 train/clipped_loss_mean=-0.1800 train/clipped_loss_std=2.2695 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0.0001 train/unclipped_loss_min=-5 train/unclipped_loss_max=5.0000 train/unclipped_loss_mean=-0.1800 train/unclipped_loss_std=2.2695 train/policy_loss=-0.1800 train/loss=-0.1800 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0002
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+[2026-10-01 18:06:04] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0048 | Epoch 023] timing/rollout_seconds=146.8390 timing/feedback_seconds=4.5944 timing/optimization_seconds=40.9133 timing/cycle_seconds=191.2636
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+
Pointwise Rewards: ssim:   0%|          | 0/6 [00:00<?, ?it/s]
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Pointwise Rewards: ssim:  83%|████████▎ | 5/6 [00:00<00:00,  6.67it/s]
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Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:00<00:00,  6.55it/s]
+
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Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.73it/s]
+
Pointwise Rewards: psnr:   0%|          | 0/6 [00:00<?, ?it/s]
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Pointwise Rewards: psnr:  33%|███▎      | 2/6 [00:00<00:00,  6.74it/s]
Pointwise Rewards: psnr:  50%|█████     | 3/6 [00:00<00:00,  6.97it/s]
Pointwise Rewards: psnr:  67%|██████▋   | 4/6 [00:00<00:00,  7.23it/s]
Pointwise Rewards: psnr:  83%|████████▎ | 5/6 [00:00<00:00,  7.04it/s]
Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.97it/s]
Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.98it/s]
+[2026-10-01 18:08:35] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0048 | Epoch 024] train/reward_dists_mean=0.9259 train/reward_dists_std=0.0153 train/reward_psnr_mean=25.4272 train/reward_psnr_std=2.4948 train/reward_ssim_mean=0.9225 train/reward_ssim_std=0.0221 train/reward_dists_group_std_mean=0.0045 train/reward_dists_group_std_max=0.0092 train/reward_dists_group_std_min=0.0024 train/reward_dists_group_mean_std=0.0146 train/reward_psnr_group_std_mean=1.2147 train/reward_psnr_group_std_max=2.5258 train/reward_psnr_group_std_min=0.4347 train/reward_psnr_group_mean_std=2.1450 train/reward_ssim_group_std_mean=0.0026 train/reward_ssim_group_std_max=0.0062 train/reward_ssim_group_std_min=0.0007 train/reward_ssim_group_mean_std=0.0219 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-9.2058 train/adv_max=7.8606 train/adv_abs_mean=2.1735
+
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Epoch 24 Training:   6%|▋         | 6/96 [00:02<00:37,  2.39it/s]
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Epoch 24 Training:   9%|▉         | 9/96 [00:03<00:36,  2.38it/s]
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Epoch 24 Training:  11%|█▏        | 11/96 [00:04<00:35,  2.38it/s]
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Epoch 24 Training:  14%|█▎        | 13/96 [00:05<00:34,  2.38it/s]
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Epoch 24 Training:  15%|█▍        | 14/96 [00:05<00:34,  2.38it/s]
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Epoch 24 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 18:09:16] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0049 | Epoch 024] train/clipped_loss_min=-5.0000 train/clipped_loss_max=5.0000 train/clipped_loss_mean=-0.1346 train/clipped_loss_std=2.4967 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-5.0000 train/unclipped_loss_max=5.0000 train/unclipped_loss_mean=-0.1346 train/unclipped_loss_std=2.4967 train/policy_loss=-0.1346 train/loss=-0.1346 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0005
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+[2026-10-01 18:09:16] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0050 | Epoch 024] timing/rollout_seconds=146.9192 timing/feedback_seconds=4.2914 timing/optimization_seconds=40.9366 timing/cycle_seconds=191.3810
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+
Pointwise Rewards: ssim:   0%|          | 0/6 [00:00<?, ?it/s]
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Pointwise Rewards: ssim:  67%|██████▋   | 4/6 [00:00<00:00,  5.79it/s]
Pointwise Rewards: ssim:  83%|████████▎ | 5/6 [00:00<00:00,  6.31it/s]
Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:00<00:00,  6.29it/s]
Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:00<00:00,  6.21it/s]
+
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Pointwise Rewards: dists:  50%|█████     | 3/6 [00:00<00:00,  3.69it/s]
Pointwise Rewards: dists:  67%|██████▋   | 4/6 [00:01<00:00,  3.68it/s]
Pointwise Rewards: dists:  83%|████████▎ | 5/6 [00:01<00:00,  3.75it/s]
Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.76it/s]
Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.73it/s]
+
Pointwise Rewards: psnr:   0%|          | 0/6 [00:00<?, ?it/s]
Pointwise Rewards: psnr:  17%|█▋        | 1/6 [00:00<00:00,  7.21it/s]
Pointwise Rewards: psnr:  33%|███▎      | 2/6 [00:00<00:00,  6.97it/s]
Pointwise Rewards: psnr:  50%|█████     | 3/6 [00:00<00:00,  6.86it/s]
Pointwise Rewards: psnr:  67%|██████▋   | 4/6 [00:00<00:00,  6.76it/s]
Pointwise Rewards: psnr:  83%|████████▎ | 5/6 [00:00<00:00,  7.05it/s]
Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  7.06it/s]
Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.99it/s]
+[2026-10-01 18:11:47] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0050 | Epoch 025] train/reward_dists_mean=0.9242 train/reward_dists_std=0.0226 train/reward_psnr_mean=23.9984 train/reward_psnr_std=3.0085 train/reward_ssim_mean=0.9192 train/reward_ssim_std=0.0255 train/reward_dists_group_std_mean=0.0050 train/reward_dists_group_std_max=0.0303 train/reward_dists_group_std_min=0.0025 train/reward_dists_group_mean_std=0.0217 train/reward_psnr_group_std_mean=1.1651 train/reward_psnr_group_std_max=3.5376 train/reward_psnr_group_std_min=0.5816 train/reward_psnr_group_mean_std=2.7339 train/reward_ssim_group_std_mean=0.0040 train/reward_ssim_group_std_max=0.0499 train/reward_ssim_group_std_min=0.0009 train/reward_ssim_group_mean_std=0.0242 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-7.6350 train/adv_max=7.0046 train/adv_abs_mean=2.1623
+
Epoch 25 Training:   0%|          | 0/96 [00:00<?, ?it/s]
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Epoch 25 Training:   4%|▍         | 4/96 [00:01<00:38,  2.40it/s]
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Epoch 25 Training:   6%|▋         | 6/96 [00:02<00:37,  2.38it/s]
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Epoch 25 Training:   7%|▋         | 7/96 [00:02<00:37,  2.38it/s]
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Epoch 25 Training:   8%|▊         | 8/96 [00:03<00:37,  2.38it/s]
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Epoch 25 Training:   9%|▉         | 9/96 [00:03<00:36,  2.38it/s]
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Epoch 25 Training:  10%|█         | 10/96 [00:04<00:36,  2.38it/s]
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Epoch 25 Training:  11%|█▏        | 11/96 [00:04<00:35,  2.37it/s]
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Epoch 25 Training:  14%|█▎        | 13/96 [00:05<00:34,  2.37it/s]
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Epoch 25 Training:  15%|█▍        | 14/96 [00:05<00:34,  2.37it/s]
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Epoch 25 Training:  25%|██▌       | 24/96 [00:10<00:30,  2.38it/s]
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Epoch 25 Training:  34%|███▍      | 33/96 [00:13<00:26,  2.38it/s]
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Epoch 25 Training:  35%|███▌      | 34/96 [00:14<00:26,  2.38it/s]
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Epoch 25 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 18:12:07] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0050 | Epoch 025] train/clipped_loss_min=-5 train/clipped_loss_max=5 train/clipped_loss_mean=-0.1851 train/clipped_loss_std=2.4808 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-5 train/unclipped_loss_max=5 train/unclipped_loss_mean=-0.1851 train/unclipped_loss_std=2.4808 train/policy_loss=-0.1851 train/loss=-0.1851 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0007
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Epoch 25 Training:  77%|███████▋  | 74/96 [00:31<00:09,  2.37it/s]
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Epoch 25 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 18:12:27] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0051 | Epoch 025] train/clipped_loss_min=-5.0000 train/clipped_loss_max=5.0000 train/clipped_loss_mean=0.1546 train/clipped_loss_std=2.5903 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0.0002 train/unclipped_loss_min=-5.0000 train/unclipped_loss_max=5.0000 train/unclipped_loss_mean=0.1546 train/unclipped_loss_std=2.5903 train/policy_loss=0.1546 train/loss=0.1546 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0007
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Epoch 25 Training: 100%|██████████| 96/96 [00:40<00:00,  2.37it/s]
+[2026-10-01 18:12:27] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0052 | Epoch 025] timing/rollout_seconds=147.0350 timing/feedback_seconds=4.5845 timing/optimization_seconds=41.0197 timing/cycle_seconds=191.5786
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+
Pointwise Rewards: ssim:   0%|          | 0/6 [00:00<?, ?it/s]
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Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:00<00:00,  6.29it/s]
+
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Pointwise Rewards: dists:  83%|████████▎ | 5/6 [00:01<00:00,  3.59it/s]
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Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.63it/s]
+
Pointwise Rewards: psnr:   0%|          | 0/6 [00:00<?, ?it/s]
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Pointwise Rewards: psnr:  33%|███▎      | 2/6 [00:00<00:00,  6.71it/s]
Pointwise Rewards: psnr:  50%|█████     | 3/6 [00:00<00:00,  6.69it/s]
Pointwise Rewards: psnr:  67%|██████▋   | 4/6 [00:00<00:00,  6.75it/s]
Pointwise Rewards: psnr:  83%|████████▎ | 5/6 [00:00<00:00,  6.77it/s]
Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.83it/s]
Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.77it/s]
+[2026-10-01 18:14:59] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0052 | Epoch 026] train/reward_dists_mean=0.9330 train/reward_dists_std=0.0152 train/reward_psnr_mean=27.1820 train/reward_psnr_std=2.4992 train/reward_ssim_mean=0.9250 train/reward_ssim_std=0.0234 train/reward_dists_group_std_mean=0.0033 train/reward_dists_group_std_max=0.0077 train/reward_dists_group_std_min=0.0021 train/reward_dists_group_mean_std=0.0148 train/reward_psnr_group_std_mean=1.1564 train/reward_psnr_group_std_max=2.3157 train/reward_psnr_group_std_min=0.3698 train/reward_psnr_group_mean_std=2.1815 train/reward_ssim_group_std_mean=0.0014 train/reward_ssim_group_std_max=0.0027 train/reward_ssim_group_std_min=0.0005 train/reward_ssim_group_mean_std=0.0233 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-8.6846 train/adv_max=6.5148 train/adv_abs_mean=1.9665
+
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Epoch 26 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 18:15:19] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0052 | Epoch 026] train/clipped_loss_min=-5 train/clipped_loss_max=5 train/clipped_loss_mean=-0.0720 train/clipped_loss_std=2.3705 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-5 train/unclipped_loss_max=5 train/unclipped_loss_mean=-0.0720 train/unclipped_loss_std=2.3705 train/policy_loss=-0.0720 train/loss=-0.0720 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0004
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Epoch 26 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 18:15:39] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0053 | Epoch 026] train/clipped_loss_min=-5.0001 train/clipped_loss_max=5.0000 train/clipped_loss_mean=0.0334 train/clipped_loss_std=2.4173 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0.0001 train/unclipped_loss_min=-5.0001 train/unclipped_loss_max=5.0000 train/unclipped_loss_mean=0.0334 train/unclipped_loss_std=2.4173 train/policy_loss=0.0334 train/loss=0.0334 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0005
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+[2026-10-01 18:15:39] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0054 | Epoch 026] timing/rollout_seconds=147.1296 timing/feedback_seconds=5.0838 timing/optimization_seconds=41.1325 timing/cycle_seconds=191.9201
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+
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+
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+[2026-10-01 18:18:11] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0054 | Epoch 027] train/reward_dists_mean=0.9316 train/reward_dists_std=0.0194 train/reward_psnr_mean=28.5353 train/reward_psnr_std=2.5114 train/reward_ssim_mean=0.9257 train/reward_ssim_std=0.0273 train/reward_dists_group_std_mean=0.0031 train/reward_dists_group_std_max=0.0081 train/reward_dists_group_std_min=0.0017 train/reward_dists_group_mean_std=0.0191 train/reward_psnr_group_std_mean=0.9840 train/reward_psnr_group_std_max=2.5338 train/reward_psnr_group_std_min=0.2163 train/reward_psnr_group_mean_std=2.2553 train/reward_ssim_group_std_mean=0.0017 train/reward_ssim_group_std_max=0.0074 train/reward_ssim_group_std_min=0.0006 train/reward_ssim_group_mean_std=0.0272 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-8.0278 train/adv_max=6.8951 train/adv_abs_mean=1.8982
+
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Epoch 27 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 18:18:31] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0054 | Epoch 027] train/clipped_loss_min=-5 train/clipped_loss_max=5 train/clipped_loss_mean=0.1063 train/clipped_loss_std=2.3135 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-5 train/unclipped_loss_max=5 train/unclipped_loss_mean=0.1063 train/unclipped_loss_std=2.3135 train/policy_loss=0.1063 train/loss=0.1063 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0004
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Epoch 27 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 18:18:51] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0055 | Epoch 027] train/clipped_loss_min=-5.0000 train/clipped_loss_max=5.0000 train/clipped_loss_mean=-0.1260 train/clipped_loss_std=2.2820 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0.0001 train/unclipped_loss_min=-5.0000 train/unclipped_loss_max=5.0000 train/unclipped_loss_mean=-0.1260 train/unclipped_loss_std=2.2820 train/policy_loss=-0.1260 train/loss=-0.1260 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0004
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+[2026-10-01 18:18:51] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0056 | Epoch 027] timing/rollout_seconds=147.2028 timing/feedback_seconds=5.1676 timing/optimization_seconds=41.4720 timing/cycle_seconds=192.3372
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+
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+[2026-10-01 18:21:23] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0056 | Epoch 028] train/reward_dists_mean=0.9309 train/reward_dists_std=0.0220 train/reward_psnr_mean=26.5464 train/reward_psnr_std=3.2998 train/reward_ssim_mean=0.9213 train/reward_ssim_std=0.0297 train/reward_dists_group_std_mean=0.0036 train/reward_dists_group_std_max=0.0115 train/reward_dists_group_std_min=0.0014 train/reward_dists_group_mean_std=0.0216 train/reward_psnr_group_std_mean=1.1349 train/reward_psnr_group_std_max=2.2635 train/reward_psnr_group_std_min=0.4282 train/reward_psnr_group_mean_std=3.0698 train/reward_ssim_group_std_mean=0.0026 train/reward_ssim_group_std_max=0.0145 train/reward_ssim_group_std_min=0.0006 train/reward_ssim_group_mean_std=0.0296 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-8.7620 train/adv_max=6.1289 train/adv_abs_mean=1.9684
+
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Epoch 28 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 18:21:43] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0056 | Epoch 028] train/clipped_loss_min=-5 train/clipped_loss_max=5 train/clipped_loss_mean=-0.2128 train/clipped_loss_std=2.3362 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-5 train/unclipped_loss_max=5 train/unclipped_loss_mean=-0.2128 train/unclipped_loss_std=2.3362 train/policy_loss=-0.2128 train/loss=-0.2128 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0002
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Epoch 28 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 18:22:03] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0057 | Epoch 028] train/clipped_loss_min=-5.0000 train/clipped_loss_max=5.0000 train/clipped_loss_mean=0.1767 train/clipped_loss_std=2.3630 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-5.0000 train/unclipped_loss_max=5.0000 train/unclipped_loss_mean=0.1767 train/unclipped_loss_std=2.3630 train/policy_loss=0.1767 train/loss=0.1767 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0002
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+[2026-10-01 18:22:03] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0058 | Epoch 028] timing/rollout_seconds=147.2226 timing/feedback_seconds=5.0693 timing/optimization_seconds=41.0760 timing/cycle_seconds=191.8269
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+
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+
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+
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+[2026-10-01 18:24:35] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0058 | Epoch 029] train/reward_dists_mean=0.9271 train/reward_dists_std=0.0224 train/reward_psnr_mean=24.8731 train/reward_psnr_std=3.5491 train/reward_ssim_mean=0.9178 train/reward_ssim_std=0.0264 train/reward_dists_group_std_mean=0.0035 train/reward_dists_group_std_max=0.0085 train/reward_dists_group_std_min=0.0011 train/reward_dists_group_mean_std=0.0221 train/reward_psnr_group_std_mean=1.0499 train/reward_psnr_group_std_max=1.9947 train/reward_psnr_group_std_min=0.4918 train/reward_psnr_group_mean_std=3.3735 train/reward_ssim_group_std_mean=0.0027 train/reward_ssim_group_std_max=0.0069 train/reward_ssim_group_std_min=0.0007 train/reward_ssim_group_mean_std=0.0262 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-8.3348 train/adv_max=6.5214 train/adv_abs_mean=2.0174
+
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Epoch 29 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 18:24:55] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0058 | Epoch 029] train/clipped_loss_min=-5 train/clipped_loss_max=5 train/clipped_loss_mean=-0.0115 train/clipped_loss_std=2.4003 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-5 train/unclipped_loss_max=5 train/unclipped_loss_mean=-0.0115 train/unclipped_loss_std=2.4003 train/policy_loss=-0.0115 train/loss=-0.0115 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0006
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Epoch 29 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 18:25:15] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0059 | Epoch 029] train/clipped_loss_min=-5.0000 train/clipped_loss_max=5.0000 train/clipped_loss_mean=-0.0550 train/clipped_loss_std=2.4495 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-5.0000 train/unclipped_loss_max=5.0000 train/unclipped_loss_mean=-0.0550 train/unclipped_loss_std=2.4495 train/policy_loss=-0.0550 train/loss=-0.0550 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0007
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+[2026-10-01 18:25:15] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0060 | Epoch 029] timing/rollout_seconds=147.1903 timing/feedback_seconds=5.0769 timing/optimization_seconds=41.3712 timing/cycle_seconds=192.1296
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+[2026-10-01 18:25:59] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0060 | Epoch 030] eval/vedai_rl_full/reward_dists_mean=0.9324 eval/vedai_rl_full/reward_dists_std=0.0192 eval/vedai_rl_full/reward_psnr_mean=28.6361 eval/vedai_rl_full/reward_psnr_std=2.3223 eval/vedai_rl_full/reward_ssim_mean=0.9184 eval/vedai_rl_full/reward_ssim_std=0.0274
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Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.94it/s]
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+[2026-10-01 18:28:31] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0060 | Epoch 030] train/reward_dists_mean=0.9338 train/reward_dists_std=0.0160 train/reward_psnr_mean=26.6901 train/reward_psnr_std=2.9875 train/reward_ssim_mean=0.9246 train/reward_ssim_std=0.0280 train/reward_dists_group_std_mean=0.0033 train/reward_dists_group_std_max=0.0067 train/reward_dists_group_std_min=0.0017 train/reward_dists_group_mean_std=0.0156 train/reward_psnr_group_std_mean=1.1733 train/reward_psnr_group_std_max=2.3758 train/reward_psnr_group_std_min=0.3690 train/reward_psnr_group_mean_std=2.7213 train/reward_ssim_group_std_mean=0.0021 train/reward_ssim_group_std_max=0.0076 train/reward_ssim_group_std_min=0.0008 train/reward_ssim_group_mean_std=0.0279 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-7.2349 train/adv_max=7.7886 train/adv_abs_mean=2.0263
+
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Epoch 30 Training:  11%|█▏        | 11/96 [00:04<00:35,  2.37it/s]
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Epoch 30 Training:  43%|████▎     | 41/96 [00:17<00:23,  2.37it/s]
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Epoch 30 Training:  44%|████▍     | 42/96 [00:17<00:22,  2.37it/s]
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Epoch 30 Training:  46%|████▌     | 44/96 [00:18<00:21,  2.37it/s]
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Epoch 30 Training:  47%|████▋     | 45/96 [00:18<00:21,  2.37it/s]
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Epoch 30 Training:  48%|████▊     | 46/96 [00:19<00:21,  2.37it/s]
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Epoch 30 Training:  49%|████▉     | 47/96 [00:19<00:20,  2.37it/s]
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Epoch 30 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 18:28:51] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0060 | Epoch 030] train/clipped_loss_min=-5 train/clipped_loss_max=5 train/clipped_loss_mean=0.0952 train/clipped_loss_std=2.4192 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-5 train/unclipped_loss_max=5 train/unclipped_loss_mean=0.0952 train/unclipped_loss_std=2.4192 train/policy_loss=0.0952 train/loss=0.0952 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0002
+
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Epoch 30 Timestep: 100%|██████████| 1/1 [00:00<00:00,  1.69it/s]
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Epoch 30 Training:  50%|█████     | 48/96 [00:20<00:23,  2.05it/s]
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Epoch 30 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 18:29:12] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0061 | Epoch 030] train/clipped_loss_min=-5.0000 train/clipped_loss_max=5.0000 train/clipped_loss_mean=-0.1093 train/clipped_loss_std=2.4120 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-5.0000 train/unclipped_loss_max=5.0000 train/unclipped_loss_mean=-0.1093 train/unclipped_loss_std=2.4120 train/policy_loss=-0.1093 train/loss=-0.1093 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0002
+
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Epoch 30 Training: 100%|██████████| 96/96 [00:41<00:00,  2.29it/s]
Epoch 30 Training: 100%|██████████| 96/96 [00:41<00:00,  2.33it/s]
+[2026-10-01 18:29:12] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0062 | Epoch 030] timing/rollout_seconds=147.2242 timing/feedback_seconds=5.2122 timing/optimization_seconds=41.7085 timing/cycle_seconds=192.5025
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Pointwise Rewards: ssim:  67%|██████▋   | 4/6 [00:00<00:00,  6.24it/s]
Pointwise Rewards: ssim:  83%|████████▎ | 5/6 [00:00<00:00,  6.38it/s]
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Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:00<00:00,  6.15it/s]
+
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Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.69it/s]
+
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Pointwise Rewards: psnr:  50%|█████     | 3/6 [00:00<00:00,  6.88it/s]
Pointwise Rewards: psnr:  67%|██████▋   | 4/6 [00:00<00:00,  6.87it/s]
Pointwise Rewards: psnr:  83%|████████▎ | 5/6 [00:00<00:00,  6.85it/s]
Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.84it/s]
Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.87it/s]
+[2026-10-01 18:31:43] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0062 | Epoch 031] train/reward_dists_mean=0.9340 train/reward_dists_std=0.0157 train/reward_psnr_mean=28.3896 train/reward_psnr_std=2.6623 train/reward_ssim_mean=0.9251 train/reward_ssim_std=0.0186 train/reward_dists_group_std_mean=0.0033 train/reward_dists_group_std_max=0.0067 train/reward_dists_group_std_min=0.0018 train/reward_dists_group_mean_std=0.0154 train/reward_psnr_group_std_mean=1.0385 train/reward_psnr_group_std_max=2.3258 train/reward_psnr_group_std_min=0.3210 train/reward_psnr_group_mean_std=2.4087 train/reward_ssim_group_std_mean=0.0016 train/reward_ssim_group_std_max=0.0059 train/reward_ssim_group_std_min=0.0005 train/reward_ssim_group_mean_std=0.0185 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-6.3445 train/adv_max=6.1634 train/adv_abs_mean=1.8697
+
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Epoch 31 Training:  11%|█▏        | 11/96 [00:04<00:35,  2.37it/s]
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Epoch 31 Training:  15%|█▍        | 14/96 [00:05<00:34,  2.37it/s]
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Epoch 31 Training:  26%|██▌       | 25/96 [00:10<00:29,  2.37it/s]
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Epoch 31 Training:  33%|███▎      | 32/96 [00:13<00:27,  2.37it/s]
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Epoch 31 Training:  39%|███▊      | 37/96 [00:15<00:24,  2.37it/s]
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Epoch 31 Training:  44%|████▍     | 42/96 [00:17<00:22,  2.37it/s]
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Epoch 31 Training:  46%|████▌     | 44/96 [00:18<00:21,  2.37it/s]
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Epoch 31 Training:  47%|████▋     | 45/96 [00:18<00:21,  2.37it/s]
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Epoch 31 Training:  48%|████▊     | 46/96 [00:19<00:21,  2.37it/s]
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Epoch 31 Training:  49%|████▉     | 47/96 [00:19<00:20,  2.37it/s]
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Epoch 31 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 18:32:03] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0062 | Epoch 031] train/clipped_loss_min=-5 train/clipped_loss_max=5 train/clipped_loss_mean=-0.0717 train/clipped_loss_std=2.2523 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-5 train/unclipped_loss_max=5 train/unclipped_loss_mean=-0.0717 train/unclipped_loss_std=2.2523 train/policy_loss=-0.0717 train/loss=-0.0717 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0004
+
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Epoch 31 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 18:32:23] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0063 | Epoch 031] train/clipped_loss_min=-5.0000 train/clipped_loss_max=5.0000 train/clipped_loss_mean=0.0595 train/clipped_loss_std=2.2868 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0.0001 train/unclipped_loss_min=-5.0000 train/unclipped_loss_max=5.0000 train/unclipped_loss_mean=0.0595 train/unclipped_loss_std=2.2868 train/policy_loss=0.0595 train/loss=0.0595 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0004
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+[2026-10-01 18:32:23] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0064 | Epoch 031] timing/rollout_seconds=147.1250 timing/feedback_seconds=4.8427 timing/optimization_seconds=41.1107 timing/cycle_seconds=191.7604
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+
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+
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+
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Pointwise Rewards: psnr:  83%|████████▎ | 5/6 [00:00<00:00,  6.89it/s]
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Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.96it/s]
+[2026-10-01 18:34:55] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0064 | Epoch 032] train/reward_dists_mean=0.9298 train/reward_dists_std=0.0158 train/reward_psnr_mean=28.9946 train/reward_psnr_std=2.4177 train/reward_ssim_mean=0.9259 train/reward_ssim_std=0.0199 train/reward_dists_group_std_mean=0.0034 train/reward_dists_group_std_max=0.0076 train/reward_dists_group_std_min=0.0018 train/reward_dists_group_mean_std=0.0154 train/reward_psnr_group_std_mean=1.0189 train/reward_psnr_group_std_max=3.6031 train/reward_psnr_group_std_min=0.3656 train/reward_psnr_group_mean_std=2.1311 train/reward_ssim_group_std_mean=0.0013 train/reward_ssim_group_std_max=0.0040 train/reward_ssim_group_std_min=0.0005 train/reward_ssim_group_mean_std=0.0198 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-7.9108 train/adv_max=5.6470 train/adv_abs_mean=1.8708
+
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Epoch 32 Training:  49%|████▉     | 47/96 [00:19<00:20,  2.37it/s]
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Epoch 32 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 18:35:15] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0064 | Epoch 032] train/clipped_loss_min=-5 train/clipped_loss_max=5 train/clipped_loss_mean=-0.0279 train/clipped_loss_std=2.2754 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-5 train/unclipped_loss_max=5 train/unclipped_loss_mean=-0.0279 train/unclipped_loss_std=2.2754 train/policy_loss=-0.0279 train/loss=-0.0279 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0006
+
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Epoch 32 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 18:35:36] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0065 | Epoch 032] train/clipped_loss_min=-5.0000 train/clipped_loss_max=5.0001 train/clipped_loss_mean=-0.0182 train/clipped_loss_std=2.2766 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-5.0000 train/unclipped_loss_max=5.0001 train/unclipped_loss_mean=-0.0182 train/unclipped_loss_std=2.2766 train/policy_loss=-0.0182 train/loss=-0.0182 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0006
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+[2026-10-01 18:35:36] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0066 | Epoch 032] timing/rollout_seconds=147.2077 timing/feedback_seconds=4.8437 timing/optimization_seconds=41.4457 timing/cycle_seconds=192.2673
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+
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+
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Pointwise Rewards: psnr:  83%|████████▎ | 5/6 [00:00<00:00,  6.92it/s]
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Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.96it/s]
+[2026-10-01 18:38:08] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0066 | Epoch 033] train/reward_dists_mean=0.9274 train/reward_dists_std=0.0165 train/reward_psnr_mean=27.6782 train/reward_psnr_std=2.6676 train/reward_ssim_mean=0.9182 train/reward_ssim_std=0.0219 train/reward_dists_group_std_mean=0.0037 train/reward_dists_group_std_max=0.0075 train/reward_dists_group_std_min=0.0010 train/reward_dists_group_mean_std=0.0161 train/reward_psnr_group_std_mean=1.2503 train/reward_psnr_group_std_max=2.3915 train/reward_psnr_group_std_min=0.3162 train/reward_psnr_group_mean_std=2.3206 train/reward_ssim_group_std_mean=0.0017 train/reward_ssim_group_std_max=0.0043 train/reward_ssim_group_std_min=0.0002 train/reward_ssim_group_mean_std=0.0218 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-9.0720 train/adv_max=6.7969 train/adv_abs_mean=2.0310
+
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Epoch 33 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 18:38:48] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0067 | Epoch 033] train/clipped_loss_min=-5.0000 train/clipped_loss_max=5.0000 train/clipped_loss_mean=-0.0231 train/clipped_loss_std=2.4689 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-5.0000 train/unclipped_loss_max=5.0000 train/unclipped_loss_mean=-0.0231 train/unclipped_loss_std=2.4689 train/policy_loss=-0.0231 train/loss=-0.0231 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0007
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+[2026-10-01 18:38:48] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0068 | Epoch 033] timing/rollout_seconds=147.7578 timing/feedback_seconds=5.1196 timing/optimization_seconds=41.0624 timing/cycle_seconds=192.3872
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+
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+
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+
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Pointwise Rewards: psnr:  67%|██████▋   | 4/6 [00:00<00:00,  6.78it/s]
Pointwise Rewards: psnr:  83%|████████▎ | 5/6 [00:00<00:00,  6.83it/s]
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Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.82it/s]
+[2026-10-01 18:41:19] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0068 | Epoch 034] train/reward_dists_mean=0.9309 train/reward_dists_std=0.0167 train/reward_psnr_mean=29.4289 train/reward_psnr_std=2.4239 train/reward_ssim_mean=0.9246 train/reward_ssim_std=0.0253 train/reward_dists_group_std_mean=0.0032 train/reward_dists_group_std_max=0.0059 train/reward_dists_group_std_min=0.0012 train/reward_dists_group_mean_std=0.0163 train/reward_psnr_group_std_mean=1.0797 train/reward_psnr_group_std_max=2.3515 train/reward_psnr_group_std_min=0.3471 train/reward_psnr_group_mean_std=2.1230 train/reward_ssim_group_std_mean=0.0017 train/reward_ssim_group_std_max=0.0165 train/reward_ssim_group_std_min=0.0003 train/reward_ssim_group_mean_std=0.0251 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-7.5220 train/adv_max=6.3959 train/adv_abs_mean=1.8827
+
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Epoch 34 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 18:41:40] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0068 | Epoch 034] train/clipped_loss_min=-5 train/clipped_loss_max=5 train/clipped_loss_mean=-0.0972 train/clipped_loss_std=2.3212 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-5 train/unclipped_loss_max=5 train/unclipped_loss_mean=-0.0972 train/unclipped_loss_std=2.3212 train/policy_loss=-0.0972 train/loss=-0.0972 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0006
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Epoch 34 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 18:42:00] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0069 | Epoch 034] train/clipped_loss_min=-5.0000 train/clipped_loss_max=5.0000 train/clipped_loss_mean=0.0609 train/clipped_loss_std=2.2471 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0.0002 train/unclipped_loss_min=-5.0000 train/unclipped_loss_max=5.0000 train/unclipped_loss_mean=0.0609 train/unclipped_loss_std=2.2471 train/policy_loss=0.0609 train/loss=0.0609 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0006
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+[2026-10-01 18:42:00] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0070 | Epoch 034] timing/rollout_seconds=147.2210 timing/feedback_seconds=4.7064 timing/optimization_seconds=41.0662 timing/cycle_seconds=191.8297
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+
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Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:00<00:00,  6.72it/s]
+
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+
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Pointwise Rewards: psnr:  50%|█████     | 3/6 [00:00<00:00,  7.15it/s]
Pointwise Rewards: psnr:  67%|██████▋   | 4/6 [00:00<00:00,  7.01it/s]
Pointwise Rewards: psnr:  83%|████████▎ | 5/6 [00:00<00:00,  7.15it/s]
Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  7.08it/s]
Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  7.15it/s]
+[2026-10-01 18:44:31] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0070 | Epoch 035] train/reward_dists_mean=0.9314 train/reward_dists_std=0.0230 train/reward_psnr_mean=28.8103 train/reward_psnr_std=2.9816 train/reward_ssim_mean=0.9270 train/reward_ssim_std=0.0192 train/reward_dists_group_std_mean=0.0032 train/reward_dists_group_std_max=0.0081 train/reward_dists_group_std_min=0.0013 train/reward_dists_group_mean_std=0.0227 train/reward_psnr_group_std_mean=0.9987 train/reward_psnr_group_std_max=2.5555 train/reward_psnr_group_std_min=0.3164 train/reward_psnr_group_mean_std=2.7759 train/reward_ssim_group_std_mean=0.0015 train/reward_ssim_group_std_max=0.0120 train/reward_ssim_group_std_min=0.0005 train/reward_ssim_group_mean_std=0.0191 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-6.7316 train/adv_max=5.1290 train/adv_abs_mean=1.8367
+
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Epoch 35 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 18:44:52] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0070 | Epoch 035] train/clipped_loss_min=-5 train/clipped_loss_max=5 train/clipped_loss_mean=-0.0950 train/clipped_loss_std=2.2233 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-5 train/unclipped_loss_max=5 train/unclipped_loss_mean=-0.0950 train/unclipped_loss_std=2.2233 train/policy_loss=-0.0950 train/loss=-0.0950 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0006
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Epoch 35 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 18:45:12] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0071 | Epoch 035] train/clipped_loss_min=-5.0000 train/clipped_loss_max=5.0000 train/clipped_loss_mean=0.0606 train/clipped_loss_std=2.2307 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-5.0000 train/unclipped_loss_max=5.0000 train/unclipped_loss_mean=0.0606 train/unclipped_loss_std=2.2307 train/policy_loss=0.0606 train/loss=0.0606 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0006
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+[2026-10-01 18:45:12] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0072 | Epoch 035] timing/rollout_seconds=147.3300 timing/feedback_seconds=4.8995 timing/optimization_seconds=41.1048 timing/cycle_seconds=191.9551
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+
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+
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Pointwise Rewards: psnr:  83%|████████▎ | 5/6 [00:00<00:00,  6.63it/s]
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+[2026-10-01 18:47:43] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0072 | Epoch 036] train/reward_dists_mean=0.9362 train/reward_dists_std=0.0141 train/reward_psnr_mean=27.7925 train/reward_psnr_std=2.3705 train/reward_ssim_mean=0.9233 train/reward_ssim_std=0.0240 train/reward_dists_group_std_mean=0.0026 train/reward_dists_group_std_max=0.0049 train/reward_dists_group_std_min=0.0011 train/reward_dists_group_mean_std=0.0138 train/reward_psnr_group_std_mean=0.9933 train/reward_psnr_group_std_max=2.6766 train/reward_psnr_group_std_min=0.2432 train/reward_psnr_group_mean_std=2.1142 train/reward_ssim_group_std_mean=0.0016 train/reward_ssim_group_std_max=0.0143 train/reward_ssim_group_std_min=0.0005 train/reward_ssim_group_mean_std=0.0239 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-7.3354 train/adv_max=5.5739 train/adv_abs_mean=1.8879
+
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Epoch 36 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 18:48:03] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0072 | Epoch 036] train/clipped_loss_min=-5 train/clipped_loss_max=5 train/clipped_loss_mean=0.0488 train/clipped_loss_std=2.3212 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-5 train/unclipped_loss_max=5 train/unclipped_loss_mean=0.0488 train/unclipped_loss_std=2.3212 train/policy_loss=0.0488 train/loss=0.0488 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0004
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+[2026-10-01 18:48:24] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0074 | Epoch 036] timing/rollout_seconds=147.2171 timing/feedback_seconds=4.7606 timing/optimization_seconds=41.0724 timing/cycle_seconds=191.8356
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+[2026-10-01 18:50:55] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0074 | Epoch 037] train/reward_dists_mean=0.9355 train/reward_dists_std=0.0167 train/reward_psnr_mean=27.6865 train/reward_psnr_std=3.1252 train/reward_ssim_mean=0.9216 train/reward_ssim_std=0.0258 train/reward_dists_group_std_mean=0.0027 train/reward_dists_group_std_max=0.0054 train/reward_dists_group_std_min=0.0012 train/reward_dists_group_mean_std=0.0165 train/reward_psnr_group_std_mean=0.9656 train/reward_psnr_group_std_max=2.0453 train/reward_psnr_group_std_min=0.2515 train/reward_psnr_group_mean_std=2.9517 train/reward_ssim_group_std_mean=0.0016 train/reward_ssim_group_std_max=0.0050 train/reward_ssim_group_std_min=0.0003 train/reward_ssim_group_mean_std=0.0258 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-6.1995 train/adv_max=7.7679 train/adv_abs_mean=1.9019
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Epoch 37 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 18:51:15] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0074 | Epoch 037] train/clipped_loss_min=-5 train/clipped_loss_max=5 train/clipped_loss_mean=-0.1883 train/clipped_loss_std=2.3142 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-5 train/unclipped_loss_max=5 train/unclipped_loss_mean=-0.1883 train/unclipped_loss_std=2.3142 train/policy_loss=-0.1883 train/loss=-0.1883 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0002
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Epoch 37 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 18:51:35] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0075 | Epoch 037] train/clipped_loss_min=-5.0000 train/clipped_loss_max=5.0000 train/clipped_loss_mean=0.1984 train/clipped_loss_std=2.2547 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-5.0000 train/unclipped_loss_max=5.0000 train/unclipped_loss_mean=0.1984 train/unclipped_loss_std=2.2547 train/policy_loss=0.1984 train/loss=0.1984 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0002
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+[2026-10-01 18:51:35] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0076 | Epoch 037] timing/rollout_seconds=146.9421 timing/feedback_seconds=4.5066 timing/optimization_seconds=41.0588 timing/cycle_seconds=191.5180
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+[2026-10-01 18:54:08] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0076 | Epoch 038] train/reward_dists_mean=0.9321 train/reward_dists_std=0.0171 train/reward_psnr_mean=28.1848 train/reward_psnr_std=2.4379 train/reward_ssim_mean=0.9241 train/reward_ssim_std=0.0220 train/reward_dists_group_std_mean=0.0028 train/reward_dists_group_std_max=0.0079 train/reward_dists_group_std_min=0.0012 train/reward_dists_group_mean_std=0.0169 train/reward_psnr_group_std_mean=0.8717 train/reward_psnr_group_std_max=1.6341 train/reward_psnr_group_std_min=0.2113 train/reward_psnr_group_mean_std=2.2475 train/reward_ssim_group_std_mean=0.0015 train/reward_ssim_group_std_max=0.0059 train/reward_ssim_group_std_min=0.0006 train/reward_ssim_group_mean_std=0.0220 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-8.7018 train/adv_max=6.0130 train/adv_abs_mean=1.9028
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Epoch 38 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 18:54:28] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0076 | Epoch 038] train/clipped_loss_min=-5 train/clipped_loss_max=5 train/clipped_loss_mean=-0.1020 train/clipped_loss_std=2.2744 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-5 train/unclipped_loss_max=5 train/unclipped_loss_mean=-0.1020 train/unclipped_loss_std=2.2744 train/policy_loss=-0.1020 train/loss=-0.1020 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0004
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Epoch 38 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 18:54:48] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0077 | Epoch 038] train/clipped_loss_min=-5.0000 train/clipped_loss_max=5.0000 train/clipped_loss_mean=0.0418 train/clipped_loss_std=2.2982 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-5.0000 train/unclipped_loss_max=5.0000 train/unclipped_loss_mean=0.0418 train/unclipped_loss_std=2.2982 train/policy_loss=0.0418 train/loss=0.0418 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0004
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+[2026-10-01 18:54:48] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0078 | Epoch 038] timing/rollout_seconds=147.2500 timing/feedback_seconds=5.7039 timing/optimization_seconds=41.1205 timing/cycle_seconds=192.8676
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+[2026-10-01 18:57:19] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0078 | Epoch 039] train/reward_dists_mean=0.9299 train/reward_dists_std=0.0182 train/reward_psnr_mean=28.5933 train/reward_psnr_std=2.1473 train/reward_ssim_mean=0.9163 train/reward_ssim_std=0.0283 train/reward_dists_group_std_mean=0.0028 train/reward_dists_group_std_max=0.0067 train/reward_dists_group_std_min=0.0014 train/reward_dists_group_mean_std=0.0180 train/reward_psnr_group_std_mean=0.6970 train/reward_psnr_group_std_max=1.4971 train/reward_psnr_group_std_min=0.2649 train/reward_psnr_group_mean_std=2.0141 train/reward_ssim_group_std_mean=0.0014 train/reward_ssim_group_std_max=0.0089 train/reward_ssim_group_std_min=0.0004 train/reward_ssim_group_mean_std=0.0282 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-7.0029 train/adv_max=7.7282 train/adv_abs_mean=1.8217
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Epoch 39 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 18:57:40] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0078 | Epoch 039] train/clipped_loss_min=-5 train/clipped_loss_max=5 train/clipped_loss_mean=-0.1141 train/clipped_loss_std=2.2669 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-5 train/unclipped_loss_max=5 train/unclipped_loss_mean=-0.1141 train/unclipped_loss_std=2.2669 train/policy_loss=-0.1141 train/loss=-0.1141 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
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Epoch 39 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 18:58:00] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0079 | Epoch 039] train/clipped_loss_min=-5.0000 train/clipped_loss_max=5.0000 train/clipped_loss_mean=0.1042 train/clipped_loss_std=2.1633 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0.0002 train/unclipped_loss_min=-5.0000 train/unclipped_loss_max=5.0000 train/unclipped_loss_mean=0.1042 train/unclipped_loss_std=2.1633 train/policy_loss=0.1042 train/loss=0.1042 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0002
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+[2026-10-01 18:58:00] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0080 | Epoch 039] timing/rollout_seconds=147.2457 timing/feedback_seconds=4.8615 timing/optimization_seconds=41.3055 timing/cycle_seconds=192.0099
+[2026-10-01 18:58:00] [Rank 0] [INFO] [flow_factory.models.abc]: Saving LoRA weights for transformer role=base to /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/runs/klein4b_grpo_vedai_full_ssim_dists_psnr_lr5e-5/saves/klein4b_grpo_vedai_full_ssim_dists_psnr_lr5e-5/checkpoints/checkpoint-40
+[2026-10-01 18:58:00] [Rank 0] [INFO] [flow_factory.models.abc]: Checkpoint saved successfully to /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/runs/klein4b_grpo_vedai_full_ssim_dists_psnr_lr5e-5/saves/klein4b_grpo_vedai_full_ssim_dists_psnr_lr5e-5/checkpoints/checkpoint-40
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+[2026-10-01 18:58:44] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0080 | Epoch 040] eval/vedai_rl_full/reward_dists_mean=0.9323 eval/vedai_rl_full/reward_dists_std=0.0193 eval/vedai_rl_full/reward_psnr_mean=28.5073 eval/vedai_rl_full/reward_psnr_std=2.4246 eval/vedai_rl_full/reward_ssim_mean=0.9181 eval/vedai_rl_full/reward_ssim_std=0.0274
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Pointwise Rewards: psnr:  67%|██████▋   | 4/6 [00:00<00:00,  7.19it/s]
Pointwise Rewards: psnr:  83%|████████▎ | 5/6 [00:00<00:00,  7.14it/s]
Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  7.04it/s]
Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  7.01it/s]
+[2026-10-01 19:01:15] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0080 | Epoch 040] train/reward_dists_mean=0.9333 train/reward_dists_std=0.0162 train/reward_psnr_mean=28.3896 train/reward_psnr_std=2.8695 train/reward_ssim_mean=0.9276 train/reward_ssim_std=0.0240 train/reward_dists_group_std_mean=0.0027 train/reward_dists_group_std_max=0.0078 train/reward_dists_group_std_min=0.0013 train/reward_dists_group_mean_std=0.0160 train/reward_psnr_group_std_mean=0.9919 train/reward_psnr_group_std_max=1.8451 train/reward_psnr_group_std_min=0.3323 train/reward_psnr_group_mean_std=2.6684 train/reward_ssim_group_std_mean=0.0012 train/reward_ssim_group_std_max=0.0033 train/reward_ssim_group_std_min=0.0004 train/reward_ssim_group_mean_std=0.0239 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-7.2764 train/adv_max=6.6733 train/adv_abs_mean=1.8113
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Epoch 40 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 19:01:36] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0080 | Epoch 040] train/clipped_loss_min=-5 train/clipped_loss_max=5 train/clipped_loss_mean=-0.1289 train/clipped_loss_std=2.3149 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-5 train/unclipped_loss_max=5 train/unclipped_loss_mean=-0.1289 train/unclipped_loss_std=2.3149 train/policy_loss=-0.1289 train/loss=-0.1289 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
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Epoch 40 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 19:01:56] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0081 | Epoch 040] train/clipped_loss_min=-5.0000 train/clipped_loss_max=5.0000 train/clipped_loss_mean=0.1227 train/clipped_loss_std=2.1687 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-5.0000 train/unclipped_loss_max=5.0000 train/unclipped_loss_mean=0.1227 train/unclipped_loss_std=2.1687 train/policy_loss=0.1227 train/loss=0.1227 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0002
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+[2026-10-01 19:01:56] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0082 | Epoch 040] timing/rollout_seconds=147.2392 timing/feedback_seconds=5.2307 timing/optimization_seconds=41.0590 timing/cycle_seconds=191.7986
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+[2026-10-01 19:04:27] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0082 | Epoch 041] train/reward_dists_mean=0.9254 train/reward_dists_std=0.0324 train/reward_psnr_mean=27.0087 train/reward_psnr_std=2.5063 train/reward_ssim_mean=0.9157 train/reward_ssim_std=0.0226 train/reward_dists_group_std_mean=0.0031 train/reward_dists_group_std_max=0.0085 train/reward_dists_group_std_min=0.0013 train/reward_dists_group_mean_std=0.0322 train/reward_psnr_group_std_mean=1.1929 train/reward_psnr_group_std_max=6.0131 train/reward_psnr_group_std_min=0.3902 train/reward_psnr_group_mean_std=2.0632 train/reward_ssim_group_std_mean=0.0024 train/reward_ssim_group_std_max=0.0517 train/reward_ssim_group_std_min=0.0006 train/reward_ssim_group_mean_std=0.0213 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-8.1255 train/adv_max=6.7151 train/adv_abs_mean=1.9672
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Epoch 41 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 19:04:48] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0082 | Epoch 041] train/clipped_loss_min=-5 train/clipped_loss_max=5 train/clipped_loss_mean=0.0367 train/clipped_loss_std=2.3648 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-5 train/unclipped_loss_max=5 train/unclipped_loss_mean=0.0367 train/unclipped_loss_std=2.3648 train/policy_loss=0.0367 train/loss=0.0367 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0007
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Epoch 41 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 19:05:08] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0083 | Epoch 041] train/clipped_loss_min=-5.0000 train/clipped_loss_max=5.0000 train/clipped_loss_mean=-0.0883 train/clipped_loss_std=2.3535 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0.0001 train/unclipped_loss_min=-5.0000 train/unclipped_loss_max=5.0000 train/unclipped_loss_mean=-0.0883 train/unclipped_loss_std=2.3535 train/policy_loss=-0.0883 train/loss=-0.0883 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0007
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+[2026-10-01 19:05:08] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0084 | Epoch 041] timing/rollout_seconds=147.3327 timing/feedback_seconds=5.5901 timing/optimization_seconds=41.3406 timing/cycle_seconds=192.2249
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+
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+
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Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.86it/s]
+[2026-10-01 19:07:39] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0084 | Epoch 042] train/reward_dists_mean=0.9373 train/reward_dists_std=0.0143 train/reward_psnr_mean=28.5010 train/reward_psnr_std=2.5597 train/reward_ssim_mean=0.9273 train/reward_ssim_std=0.0228 train/reward_dists_group_std_mean=0.0025 train/reward_dists_group_std_max=0.0044 train/reward_dists_group_std_min=0.0014 train/reward_dists_group_mean_std=0.0141 train/reward_psnr_group_std_mean=0.9550 train/reward_psnr_group_std_max=1.8073 train/reward_psnr_group_std_min=0.2953 train/reward_psnr_group_mean_std=2.3419 train/reward_ssim_group_std_mean=0.0011 train/reward_ssim_group_std_max=0.0079 train/reward_ssim_group_std_min=0.0003 train/reward_ssim_group_mean_std=0.0227 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-8.1593 train/adv_max=6.1443 train/adv_abs_mean=1.9253
+
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Epoch 42 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 19:08:00] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0084 | Epoch 042] train/clipped_loss_min=-5 train/clipped_loss_max=5 train/clipped_loss_mean=-0.0761 train/clipped_loss_std=2.2749 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-5 train/unclipped_loss_max=5 train/unclipped_loss_mean=-0.0761 train/unclipped_loss_std=2.2749 train/policy_loss=-0.0761 train/loss=-0.0761 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0004
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Epoch 42 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 19:08:20] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0085 | Epoch 042] train/clipped_loss_min=-5.0000 train/clipped_loss_max=5.0000 train/clipped_loss_mean=0.0434 train/clipped_loss_std=2.3182 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-5.0000 train/unclipped_loss_max=5.0000 train/unclipped_loss_mean=0.0434 train/unclipped_loss_std=2.3182 train/policy_loss=0.0434 train/loss=0.0434 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0005
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+[2026-10-01 19:08:20] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0086 | Epoch 042] timing/rollout_seconds=147.1735 timing/feedback_seconds=5.4078 timing/optimization_seconds=41.5283 timing/cycle_seconds=192.2788
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+
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+
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Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.90it/s]
Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.90it/s]
+[2026-10-01 19:10:52] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0086 | Epoch 043] train/reward_dists_mean=0.9407 train/reward_dists_std=0.0133 train/reward_psnr_mean=29.6717 train/reward_psnr_std=2.2701 train/reward_ssim_mean=0.9292 train/reward_ssim_std=0.0262 train/reward_dists_group_std_mean=0.0025 train/reward_dists_group_std_max=0.0046 train/reward_dists_group_std_min=0.0012 train/reward_dists_group_mean_std=0.0131 train/reward_psnr_group_std_mean=0.8670 train/reward_psnr_group_std_max=2.0702 train/reward_psnr_group_std_min=0.2861 train/reward_psnr_group_mean_std=2.0499 train/reward_ssim_group_std_mean=0.0010 train/reward_ssim_group_std_max=0.0028 train/reward_ssim_group_std_min=0.0003 train/reward_ssim_group_mean_std=0.0262 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-7.4521 train/adv_max=5.6036 train/adv_abs_mean=1.8251
+
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Epoch 43 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 19:11:32] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0087 | Epoch 043] train/clipped_loss_min=-5.0000 train/clipped_loss_max=5.0000 train/clipped_loss_mean=0.0609 train/clipped_loss_std=2.2249 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-5.0000 train/unclipped_loss_max=5.0000 train/unclipped_loss_mean=0.0609 train/unclipped_loss_std=2.2249 train/policy_loss=0.0609 train/loss=0.0609 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0002
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+[2026-10-01 19:11:32] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0088 | Epoch 043] timing/rollout_seconds=147.2073 timing/feedback_seconds=5.0864 timing/optimization_seconds=41.0635 timing/cycle_seconds=191.8171
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+
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+
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+
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Pointwise Rewards: psnr:  67%|██████▋   | 4/6 [00:00<00:00,  6.68it/s]
Pointwise Rewards: psnr:  83%|████████▎ | 5/6 [00:00<00:00,  7.04it/s]
Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.96it/s]
Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.86it/s]
+[2026-10-01 19:14:04] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0088 | Epoch 044] train/reward_dists_mean=0.9380 train/reward_dists_std=0.0132 train/reward_psnr_mean=28.5931 train/reward_psnr_std=2.2286 train/reward_ssim_mean=0.9276 train/reward_ssim_std=0.0210 train/reward_dists_group_std_mean=0.0026 train/reward_dists_group_std_max=0.0061 train/reward_dists_group_std_min=0.0012 train/reward_dists_group_mean_std=0.0129 train/reward_psnr_group_std_mean=0.9623 train/reward_psnr_group_std_max=1.8403 train/reward_psnr_group_std_min=0.3724 train/reward_psnr_group_mean_std=1.9759 train/reward_ssim_group_std_mean=0.0010 train/reward_ssim_group_std_max=0.0029 train/reward_ssim_group_std_min=0.0004 train/reward_ssim_group_mean_std=0.0209 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-6.1485 train/adv_max=5.5507 train/adv_abs_mean=1.8343
+
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Epoch 44 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 19:14:45] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0089 | Epoch 044] train/clipped_loss_min=-5.0000 train/clipped_loss_max=5.0000 train/clipped_loss_mean=0.0934 train/clipped_loss_std=2.2739 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-5.0000 train/unclipped_loss_max=5.0000 train/unclipped_loss_mean=0.0934 train/unclipped_loss_std=2.2739 train/policy_loss=0.0934 train/loss=0.0934 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0004
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+[2026-10-01 19:14:45] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0090 | Epoch 044] timing/rollout_seconds=147.1784 timing/feedback_seconds=5.0724 timing/optimization_seconds=42.1804 timing/cycle_seconds=192.9623
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+
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+
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+[2026-10-01 19:17:17] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0090 | Epoch 045] train/reward_dists_mean=0.9323 train/reward_dists_std=0.0347 train/reward_psnr_mean=27.9958 train/reward_psnr_std=2.3461 train/reward_ssim_mean=0.9296 train/reward_ssim_std=0.0218 train/reward_dists_group_std_mean=0.0031 train/reward_dists_group_std_max=0.0162 train/reward_dists_group_std_min=0.0010 train/reward_dists_group_mean_std=0.0345 train/reward_psnr_group_std_mean=1.0784 train/reward_psnr_group_std_max=3.4236 train/reward_psnr_group_std_min=0.2876 train/reward_psnr_group_mean_std=1.9907 train/reward_ssim_group_std_mean=0.0020 train/reward_ssim_group_std_max=0.0228 train/reward_ssim_group_std_min=0.0003 train/reward_ssim_group_mean_std=0.0213 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-7.3219 train/adv_max=7.5842 train/adv_abs_mean=1.9237
+
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Epoch 45 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 19:17:57] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0091 | Epoch 045] train/clipped_loss_min=-5.0001 train/clipped_loss_max=5.0000 train/clipped_loss_mean=-0.0394 train/clipped_loss_std=2.3076 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0.0001 train/unclipped_loss_min=-5.0001 train/unclipped_loss_max=5.0000 train/unclipped_loss_mean=-0.0394 train/unclipped_loss_std=2.3076 train/policy_loss=-0.0394 train/loss=-0.0394 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0007
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+[2026-10-01 19:17:57] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0092 | Epoch 045] timing/rollout_seconds=147.2608 timing/feedback_seconds=5.1170 timing/optimization_seconds=41.0046 timing/cycle_seconds=191.8750
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+
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+
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+[2026-10-01 19:20:29] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0092 | Epoch 046] train/reward_dists_mean=0.9374 train/reward_dists_std=0.0181 train/reward_psnr_mean=29.7855 train/reward_psnr_std=2.3287 train/reward_ssim_mean=0.9307 train/reward_ssim_std=0.0246 train/reward_dists_group_std_mean=0.0023 train/reward_dists_group_std_max=0.0061 train/reward_dists_group_std_min=0.0012 train/reward_dists_group_mean_std=0.0179 train/reward_psnr_group_std_mean=0.9040 train/reward_psnr_group_std_max=3.2187 train/reward_psnr_group_std_min=0.1706 train/reward_psnr_group_mean_std=2.0718 train/reward_ssim_group_std_mean=0.0014 train/reward_ssim_group_std_max=0.0145 train/reward_ssim_group_std_min=0.0003 train/reward_ssim_group_mean_std=0.0245 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-8.8731 train/adv_max=5.5270 train/adv_abs_mean=1.7802
+
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Epoch 46 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 19:20:49] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0092 | Epoch 046] train/clipped_loss_min=-5 train/clipped_loss_max=5 train/clipped_loss_mean=0.0099 train/clipped_loss_std=2.1577 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-5 train/unclipped_loss_max=5 train/unclipped_loss_mean=0.0099 train/unclipped_loss_std=2.1577 train/policy_loss=0.0099 train/loss=0.0099 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0006
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+[2026-10-01 19:21:09] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0094 | Epoch 046] timing/rollout_seconds=147.3870 timing/feedback_seconds=5.0039 timing/optimization_seconds=40.9991 timing/cycle_seconds=191.9082
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+[2026-10-01 19:23:41] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0094 | Epoch 047] train/reward_dists_mean=0.9323 train/reward_dists_std=0.0217 train/reward_psnr_mean=29.4316 train/reward_psnr_std=2.5242 train/reward_ssim_mean=0.9278 train/reward_ssim_std=0.0302 train/reward_dists_group_std_mean=0.0026 train/reward_dists_group_std_max=0.0060 train/reward_dists_group_std_min=0.0015 train/reward_dists_group_mean_std=0.0215 train/reward_psnr_group_std_mean=0.8801 train/reward_psnr_group_std_max=2.0559 train/reward_psnr_group_std_min=0.2489 train/reward_psnr_group_mean_std=2.3330 train/reward_ssim_group_std_mean=0.0009 train/reward_ssim_group_std_max=0.0024 train/reward_ssim_group_std_min=0.0003 train/reward_ssim_group_mean_std=0.0302 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-7.0692 train/adv_max=6.5968 train/adv_abs_mean=1.8227
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Epoch 47 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 19:24:01] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0094 | Epoch 047] train/clipped_loss_min=-5 train/clipped_loss_max=5 train/clipped_loss_mean=-0.1303 train/clipped_loss_std=2.2625 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-5 train/unclipped_loss_max=5 train/unclipped_loss_mean=-0.1303 train/unclipped_loss_std=2.2625 train/policy_loss=-0.1303 train/loss=-0.1303 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
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Epoch 47 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 19:24:21] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0095 | Epoch 047] train/clipped_loss_min=-5.0000 train/clipped_loss_max=5.0000 train/clipped_loss_mean=0.1003 train/clipped_loss_std=2.2161 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0.0002 train/unclipped_loss_min=-5.0000 train/unclipped_loss_max=5.0000 train/unclipped_loss_mean=0.1003 train/unclipped_loss_std=2.2161 train/policy_loss=0.1003 train/loss=0.1003 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0002
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+[2026-10-01 19:24:21] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0096 | Epoch 047] timing/rollout_seconds=147.4301 timing/feedback_seconds=5.1494 timing/optimization_seconds=41.0517 timing/cycle_seconds=192.0536
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+
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+[2026-10-01 19:26:53] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0096 | Epoch 048] train/reward_dists_mean=0.9290 train/reward_dists_std=0.0178 train/reward_psnr_mean=27.8269 train/reward_psnr_std=2.4276 train/reward_ssim_mean=0.9222 train/reward_ssim_std=0.0243 train/reward_dists_group_std_mean=0.0027 train/reward_dists_group_std_max=0.0061 train/reward_dists_group_std_min=0.0012 train/reward_dists_group_mean_std=0.0175 train/reward_psnr_group_std_mean=0.9687 train/reward_psnr_group_std_max=1.6696 train/reward_psnr_group_std_min=0.4134 train/reward_psnr_group_mean_std=2.2036 train/reward_ssim_group_std_mean=0.0012 train/reward_ssim_group_std_max=0.0048 train/reward_ssim_group_std_min=0.0004 train/reward_ssim_group_mean_std=0.0243 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-7.6220 train/adv_max=7.0673 train/adv_abs_mean=1.9049
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Epoch 48 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 19:27:13] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0096 | Epoch 048] train/clipped_loss_min=-5 train/clipped_loss_max=5 train/clipped_loss_mean=0.0453 train/clipped_loss_std=2.2538 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-5 train/unclipped_loss_max=5 train/unclipped_loss_mean=0.0453 train/unclipped_loss_std=2.2538 train/policy_loss=0.0453 train/loss=0.0453 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0004
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Epoch 48 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 19:27:33] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0097 | Epoch 048] train/clipped_loss_min=-5.0001 train/clipped_loss_max=5.0001 train/clipped_loss_mean=-0.0608 train/clipped_loss_std=2.3861 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-5.0001 train/unclipped_loss_max=5.0001 train/unclipped_loss_mean=-0.0608 train/unclipped_loss_std=2.3861 train/policy_loss=-0.0608 train/loss=-0.0608 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0004
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+[2026-10-01 19:27:33] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0098 | Epoch 048] timing/rollout_seconds=147.2756 timing/feedback_seconds=5.4965 timing/optimization_seconds=41.1064 timing/cycle_seconds=191.9494
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+
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+[2026-10-01 19:30:04] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0098 | Epoch 049] train/reward_dists_mean=0.9320 train/reward_dists_std=0.0131 train/reward_psnr_mean=28.1285 train/reward_psnr_std=2.1804 train/reward_ssim_mean=0.9223 train/reward_ssim_std=0.0232 train/reward_dists_group_std_mean=0.0023 train/reward_dists_group_std_max=0.0045 train/reward_dists_group_std_min=0.0011 train/reward_dists_group_mean_std=0.0129 train/reward_psnr_group_std_mean=0.9435 train/reward_psnr_group_std_max=1.7651 train/reward_psnr_group_std_min=0.3337 train/reward_psnr_group_mean_std=1.9370 train/reward_ssim_group_std_mean=0.0010 train/reward_ssim_group_std_max=0.0035 train/reward_ssim_group_std_min=0.0004 train/reward_ssim_group_mean_std=0.0231 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-7.1124 train/adv_max=6.0840 train/adv_abs_mean=1.9075
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Epoch 49 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 19:30:25] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0098 | Epoch 049] train/clipped_loss_min=-5 train/clipped_loss_max=5 train/clipped_loss_mean=-0.0962 train/clipped_loss_std=2.1631 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-5 train/unclipped_loss_max=5 train/unclipped_loss_mean=-0.0962 train/unclipped_loss_std=2.1631 train/policy_loss=-0.0962 train/loss=-0.0962 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0006
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Epoch 49 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 19:30:45] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0099 | Epoch 049] train/clipped_loss_min=-5.0000 train/clipped_loss_max=5.0000 train/clipped_loss_mean=0.0626 train/clipped_loss_std=2.4242 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-5.0000 train/unclipped_loss_max=5.0000 train/unclipped_loss_mean=0.0626 train/unclipped_loss_std=2.4242 train/policy_loss=0.0626 train/loss=0.0626 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0007
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+[2026-10-01 19:30:45] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0100 | Epoch 049] timing/rollout_seconds=147.2055 timing/feedback_seconds=5.4717 timing/optimization_seconds=41.0480 timing/cycle_seconds=191.8708
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+[2026-10-01 19:31:29] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0100 | Epoch 050] eval/vedai_rl_full/reward_dists_mean=0.9325 eval/vedai_rl_full/reward_dists_std=0.0195 eval/vedai_rl_full/reward_psnr_mean=28.5616 eval/vedai_rl_full/reward_psnr_std=2.4869 eval/vedai_rl_full/reward_ssim_mean=0.9188 eval/vedai_rl_full/reward_ssim_std=0.0273
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+[2026-10-01 19:34:00] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0100 | Epoch 050] train/reward_dists_mean=0.9276 train/reward_dists_std=0.0179 train/reward_psnr_mean=28.2966 train/reward_psnr_std=2.5577 train/reward_ssim_mean=0.9178 train/reward_ssim_std=0.0255 train/reward_dists_group_std_mean=0.0025 train/reward_dists_group_std_max=0.0052 train/reward_dists_group_std_min=0.0014 train/reward_dists_group_mean_std=0.0177 train/reward_psnr_group_std_mean=0.7719 train/reward_psnr_group_std_max=1.4910 train/reward_psnr_group_std_min=0.2426 train/reward_psnr_group_mean_std=2.4136 train/reward_ssim_group_std_mean=0.0015 train/reward_ssim_group_std_max=0.0082 train/reward_ssim_group_std_min=0.0003 train/reward_ssim_group_mean_std=0.0254 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-7.0172 train/adv_max=7.1870 train/adv_abs_mean=1.9392
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Epoch 50 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 19:34:20] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0100 | Epoch 050] train/clipped_loss_min=-5 train/clipped_loss_max=5 train/clipped_loss_mean=0.0422 train/clipped_loss_std=2.2399 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-5 train/unclipped_loss_max=5 train/unclipped_loss_mean=0.0422 train/unclipped_loss_std=2.2399 train/policy_loss=0.0422 train/loss=0.0422 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0004
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Epoch 50 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 19:34:40] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0101 | Epoch 050] train/clipped_loss_min=-5.0000 train/clipped_loss_max=5.0001 train/clipped_loss_mean=-0.0757 train/clipped_loss_std=2.4522 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-5.0000 train/unclipped_loss_max=5.0001 train/unclipped_loss_mean=-0.0757 train/unclipped_loss_std=2.4522 train/policy_loss=-0.0757 train/loss=-0.0757 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0005
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Epoch 50 Training: 100%|██████████| 96/96 [00:40<00:00,  2.37it/s]
+[2026-10-01 19:34:40] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0102 | Epoch 050] timing/rollout_seconds=147.1638 timing/feedback_seconds=4.5570 timing/optimization_seconds=41.0127 timing/cycle_seconds=191.6855
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+
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Pointwise Rewards: psnr:  67%|██████▋   | 4/6 [00:00<00:00,  6.95it/s]
Pointwise Rewards: psnr:  83%|████████▎ | 5/6 [00:00<00:00,  6.86it/s]
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Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.91it/s]
+[2026-10-01 19:37:11] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0102 | Epoch 051] train/reward_dists_mean=0.9344 train/reward_dists_std=0.0167 train/reward_psnr_mean=27.3909 train/reward_psnr_std=2.6273 train/reward_ssim_mean=0.9224 train/reward_ssim_std=0.0235 train/reward_dists_group_std_mean=0.0025 train/reward_dists_group_std_max=0.0056 train/reward_dists_group_std_min=0.0010 train/reward_dists_group_mean_std=0.0165 train/reward_psnr_group_std_mean=0.8954 train/reward_psnr_group_std_max=1.4650 train/reward_psnr_group_std_min=0.2619 train/reward_psnr_group_mean_std=2.4498 train/reward_ssim_group_std_mean=0.0015 train/reward_ssim_group_std_max=0.0074 train/reward_ssim_group_std_min=0.0006 train/reward_ssim_group_mean_std=0.0235 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-6.8298 train/adv_max=6.7007 train/adv_abs_mean=1.9755
+
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Epoch 51 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 19:37:32] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0102 | Epoch 051] train/clipped_loss_min=-5 train/clipped_loss_max=5 train/clipped_loss_mean=0.0553 train/clipped_loss_std=2.4065 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-5 train/unclipped_loss_max=5 train/unclipped_loss_mean=0.0553 train/unclipped_loss_std=2.4065 train/policy_loss=0.0553 train/loss=0.0553 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
+
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Epoch 51 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 19:37:52] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0103 | Epoch 051] train/clipped_loss_min=-5.0000 train/clipped_loss_max=5.0000 train/clipped_loss_mean=-0.0698 train/clipped_loss_std=2.3160 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0.0002 train/unclipped_loss_min=-5.0000 train/unclipped_loss_max=5.0000 train/unclipped_loss_mean=-0.0698 train/unclipped_loss_std=2.3160 train/policy_loss=-0.0698 train/loss=-0.0698 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
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+[2026-10-01 19:37:52] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0104 | Epoch 051] timing/rollout_seconds=147.0098 timing/feedback_seconds=4.7534 timing/optimization_seconds=40.9859 timing/cycle_seconds=191.4574
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+
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+
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+
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Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.79it/s]
Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.79it/s]
+[2026-10-01 19:40:23] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0104 | Epoch 052] train/reward_dists_mean=0.9284 train/reward_dists_std=0.0157 train/reward_psnr_mean=27.8545 train/reward_psnr_std=3.0737 train/reward_ssim_mean=0.9232 train/reward_ssim_std=0.0225 train/reward_dists_group_std_mean=0.0028 train/reward_dists_group_std_max=0.0058 train/reward_dists_group_std_min=0.0013 train/reward_dists_group_mean_std=0.0155 train/reward_psnr_group_std_mean=1.0371 train/reward_psnr_group_std_max=2.2426 train/reward_psnr_group_std_min=0.3578 train/reward_psnr_group_mean_std=2.8640 train/reward_ssim_group_std_mean=0.0016 train/reward_ssim_group_std_max=0.0062 train/reward_ssim_group_std_min=0.0005 train/reward_ssim_group_mean_std=0.0224 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-9.0906 train/adv_max=7.0424 train/adv_abs_mean=1.8790
+
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Epoch 52 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 19:40:43] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0104 | Epoch 052] train/clipped_loss_min=-5 train/clipped_loss_max=5 train/clipped_loss_mean=-0.0196 train/clipped_loss_std=2.2621 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-5 train/unclipped_loss_max=5 train/unclipped_loss_mean=-0.0196 train/unclipped_loss_std=2.2621 train/policy_loss=-0.0196 train/loss=-0.0196 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0002
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Epoch 52 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 19:41:03] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0105 | Epoch 052] train/clipped_loss_min=-5.0000 train/clipped_loss_max=5.0000 train/clipped_loss_mean=-0.0000 train/clipped_loss_std=2.2909 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0.0002 train/unclipped_loss_min=-5.0000 train/unclipped_loss_max=5.0000 train/unclipped_loss_mean=-0.0000 train/unclipped_loss_std=2.2909 train/policy_loss=-0.0000 train/loss=-0.0000 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0002
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+[2026-10-01 19:41:03] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0106 | Epoch 052] timing/rollout_seconds=146.8438 timing/feedback_seconds=4.5075 timing/optimization_seconds=41.0109 timing/cycle_seconds=191.3820
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+
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+
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+
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Pointwise Rewards: psnr:  83%|████████▎ | 5/6 [00:00<00:00,  6.95it/s]
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Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.94it/s]
+[2026-10-01 19:43:34] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0106 | Epoch 053] train/reward_dists_mean=0.9318 train/reward_dists_std=0.0138 train/reward_psnr_mean=28.7928 train/reward_psnr_std=2.2519 train/reward_ssim_mean=0.9247 train/reward_ssim_std=0.0241 train/reward_dists_group_std_mean=0.0027 train/reward_dists_group_std_max=0.0081 train/reward_dists_group_std_min=0.0012 train/reward_dists_group_mean_std=0.0135 train/reward_psnr_group_std_mean=0.9700 train/reward_psnr_group_std_max=2.1327 train/reward_psnr_group_std_min=0.1685 train/reward_psnr_group_mean_std=1.9763 train/reward_ssim_group_std_mean=0.0013 train/reward_ssim_group_std_max=0.0046 train/reward_ssim_group_std_min=0.0003 train/reward_ssim_group_mean_std=0.0241 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-8.0361 train/adv_max=6.9890 train/adv_abs_mean=1.8654
+
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Epoch 53 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 19:44:15] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0107 | Epoch 053] train/clipped_loss_min=-5.0001 train/clipped_loss_max=5.0000 train/clipped_loss_mean=-0.0462 train/clipped_loss_std=2.2700 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-5.0001 train/unclipped_loss_max=5.0000 train/unclipped_loss_mean=-0.0462 train/unclipped_loss_std=2.2700 train/policy_loss=-0.0462 train/loss=-0.0462 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0006
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+[2026-10-01 19:44:15] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0108 | Epoch 053] timing/rollout_seconds=146.9413 timing/feedback_seconds=4.6719 timing/optimization_seconds=41.6296 timing/cycle_seconds=192.0134
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+
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+
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+
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Pointwise Rewards: psnr:  67%|██████▋   | 4/6 [00:00<00:00,  6.85it/s]
Pointwise Rewards: psnr:  83%|████████▎ | 5/6 [00:00<00:00,  6.91it/s]
Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.94it/s]
Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.86it/s]
+[2026-10-01 19:46:46] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0108 | Epoch 054] train/reward_dists_mean=0.9275 train/reward_dists_std=0.0273 train/reward_psnr_mean=28.3123 train/reward_psnr_std=3.1455 train/reward_ssim_mean=0.9242 train/reward_ssim_std=0.0285 train/reward_dists_group_std_mean=0.0033 train/reward_dists_group_std_max=0.0096 train/reward_dists_group_std_min=0.0012 train/reward_dists_group_mean_std=0.0271 train/reward_psnr_group_std_mean=1.0358 train/reward_psnr_group_std_max=3.1758 train/reward_psnr_group_std_min=0.3667 train/reward_psnr_group_mean_std=2.9185 train/reward_ssim_group_std_mean=0.0025 train/reward_ssim_group_std_max=0.0391 train/reward_ssim_group_std_min=0.0003 train/reward_ssim_group_mean_std=0.0276 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-6.9488 train/adv_max=7.1912 train/adv_abs_mean=1.9413
+
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Epoch 54 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 19:47:06] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0108 | Epoch 054] train/clipped_loss_min=-5 train/clipped_loss_max=5 train/clipped_loss_mean=0.1012 train/clipped_loss_std=2.3818 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-5 train/unclipped_loss_max=5 train/unclipped_loss_mean=0.1012 train/unclipped_loss_std=2.3818 train/policy_loss=0.1012 train/loss=0.1012 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0002
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Epoch 54 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 19:47:27] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0109 | Epoch 054] train/clipped_loss_min=-5.0000 train/clipped_loss_max=5.0000 train/clipped_loss_mean=-0.1378 train/clipped_loss_std=2.3068 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-5.0000 train/unclipped_loss_max=5.0000 train/unclipped_loss_mean=-0.1378 train/unclipped_loss_std=2.3068 train/policy_loss=-0.1378 train/loss=-0.1378 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0002
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+[2026-10-01 19:47:27] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0110 | Epoch 054] timing/rollout_seconds=146.9349 timing/feedback_seconds=4.5579 timing/optimization_seconds=40.9692 timing/cycle_seconds=191.3602
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+
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Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:00<00:00,  6.53it/s]
+
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+
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Pointwise Rewards: psnr:  50%|█████     | 3/6 [00:00<00:00,  6.92it/s]
Pointwise Rewards: psnr:  67%|██████▋   | 4/6 [00:00<00:00,  6.84it/s]
Pointwise Rewards: psnr:  83%|████████▎ | 5/6 [00:00<00:00,  6.78it/s]
Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.79it/s]
Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.81it/s]
+[2026-10-01 19:49:58] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0110 | Epoch 055] train/reward_dists_mean=0.9243 train/reward_dists_std=0.0135 train/reward_psnr_mean=26.4648 train/reward_psnr_std=2.8993 train/reward_ssim_mean=0.9185 train/reward_ssim_std=0.0229 train/reward_dists_group_std_mean=0.0032 train/reward_dists_group_std_max=0.0083 train/reward_dists_group_std_min=0.0014 train/reward_dists_group_mean_std=0.0131 train/reward_psnr_group_std_mean=0.9492 train/reward_psnr_group_std_max=1.7365 train/reward_psnr_group_std_min=0.3520 train/reward_psnr_group_mean_std=2.7253 train/reward_ssim_group_std_mean=0.0015 train/reward_ssim_group_std_max=0.0040 train/reward_ssim_group_std_min=0.0006 train/reward_ssim_group_mean_std=0.0228 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-7.8583 train/adv_max=6.4832 train/adv_abs_mean=2.0430
+
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Epoch 55 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 19:50:38] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0111 | Epoch 055] train/clipped_loss_min=-5.0000 train/clipped_loss_max=5.0000 train/clipped_loss_mean=-0.1273 train/clipped_loss_std=2.4445 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0.0002 train/unclipped_loss_min=-5.0000 train/unclipped_loss_max=5.0000 train/unclipped_loss_mean=-0.1273 train/unclipped_loss_std=2.4445 train/policy_loss=-0.1273 train/loss=-0.1273 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0007
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+[2026-10-01 19:50:38] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0112 | Epoch 055] timing/rollout_seconds=146.9568 timing/feedback_seconds=4.8302 timing/optimization_seconds=41.0738 timing/cycle_seconds=191.5744
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+
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+
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+
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Pointwise Rewards: psnr:  50%|█████     | 3/6 [00:00<00:00,  7.14it/s]
Pointwise Rewards: psnr:  67%|██████▋   | 4/6 [00:00<00:00,  7.06it/s]
Pointwise Rewards: psnr:  83%|████████▎ | 5/6 [00:00<00:00,  7.10it/s]
Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.97it/s]
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+[2026-10-01 19:53:10] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0112 | Epoch 056] train/reward_dists_mean=0.9283 train/reward_dists_std=0.0176 train/reward_psnr_mean=27.0810 train/reward_psnr_std=3.0526 train/reward_ssim_mean=0.9192 train/reward_ssim_std=0.0250 train/reward_dists_group_std_mean=0.0028 train/reward_dists_group_std_max=0.0059 train/reward_dists_group_std_min=0.0012 train/reward_dists_group_mean_std=0.0173 train/reward_psnr_group_std_mean=1.0653 train/reward_psnr_group_std_max=2.0137 train/reward_psnr_group_std_min=0.4130 train/reward_psnr_group_mean_std=2.8309 train/reward_ssim_group_std_mean=0.0015 train/reward_ssim_group_std_max=0.0092 train/reward_ssim_group_std_min=0.0002 train/reward_ssim_group_mean_std=0.0249 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-7.2928 train/adv_max=7.2253 train/adv_abs_mean=2.0080
+
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Epoch 56 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 19:53:30] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0112 | Epoch 056] train/clipped_loss_min=-5 train/clipped_loss_max=5 train/clipped_loss_mean=0.0218 train/clipped_loss_std=2.4193 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-5 train/unclipped_loss_max=5 train/unclipped_loss_mean=0.0218 train/unclipped_loss_std=2.4193 train/policy_loss=0.0218 train/loss=0.0218 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0007
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Epoch 56 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 19:53:50] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0113 | Epoch 056] train/clipped_loss_min=-5.0000 train/clipped_loss_max=5.0000 train/clipped_loss_mean=-0.0330 train/clipped_loss_std=2.4679 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0.0001 train/unclipped_loss_min=-5.0000 train/unclipped_loss_max=5.0000 train/unclipped_loss_mean=-0.0330 train/unclipped_loss_std=2.4679 train/policy_loss=-0.0330 train/loss=-0.0330 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0007
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+[2026-10-01 19:53:50] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0114 | Epoch 056] timing/rollout_seconds=147.3298 timing/feedback_seconds=4.9193 timing/optimization_seconds=41.3627 timing/cycle_seconds=192.1627
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+[2026-10-01 19:56:22] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0114 | Epoch 057] train/reward_dists_mean=0.9313 train/reward_dists_std=0.0164 train/reward_psnr_mean=29.3209 train/reward_psnr_std=2.4187 train/reward_ssim_mean=0.9245 train/reward_ssim_std=0.0245 train/reward_dists_group_std_mean=0.0030 train/reward_dists_group_std_max=0.0135 train/reward_dists_group_std_min=0.0010 train/reward_dists_group_mean_std=0.0161 train/reward_psnr_group_std_mean=0.9024 train/reward_psnr_group_std_max=2.1893 train/reward_psnr_group_std_min=0.2393 train/reward_psnr_group_mean_std=2.1926 train/reward_ssim_group_std_mean=0.0012 train/reward_ssim_group_std_max=0.0045 train/reward_ssim_group_std_min=0.0004 train/reward_ssim_group_mean_std=0.0245 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-8.6429 train/adv_max=6.0588 train/adv_abs_mean=1.8665
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Epoch 57 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 19:56:42] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0114 | Epoch 057] train/clipped_loss_min=-4.6970 train/clipped_loss_max=5 train/clipped_loss_mean=0.1026 train/clipped_loss_std=2.2750 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-4.6970 train/unclipped_loss_max=5 train/unclipped_loss_mean=0.1026 train/unclipped_loss_std=2.2750 train/policy_loss=0.1026 train/loss=0.1026 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0002
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Epoch 57 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 19:57:02] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0115 | Epoch 057] train/clipped_loss_min=-5.0000 train/clipped_loss_max=5.0000 train/clipped_loss_mean=-0.1358 train/clipped_loss_std=2.3114 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0.0001 train/unclipped_loss_min=-5.0000 train/unclipped_loss_max=5.0000 train/unclipped_loss_mean=-0.1358 train/unclipped_loss_std=2.3114 train/policy_loss=-0.1358 train/loss=-0.1358 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0002
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+[2026-10-01 19:57:02] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0116 | Epoch 057] timing/rollout_seconds=147.1263 timing/feedback_seconds=4.8078 timing/optimization_seconds=41.1208 timing/cycle_seconds=191.7554
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+[2026-10-01 19:59:34] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0116 | Epoch 058] train/reward_dists_mean=0.9306 train/reward_dists_std=0.0169 train/reward_psnr_mean=27.4418 train/reward_psnr_std=2.9980 train/reward_ssim_mean=0.9206 train/reward_ssim_std=0.0272 train/reward_dists_group_std_mean=0.0026 train/reward_dists_group_std_max=0.0102 train/reward_dists_group_std_min=0.0012 train/reward_dists_group_mean_std=0.0166 train/reward_psnr_group_std_mean=0.9765 train/reward_psnr_group_std_max=1.9711 train/reward_psnr_group_std_min=0.3211 train/reward_psnr_group_mean_std=2.8141 train/reward_ssim_group_std_mean=0.0013 train/reward_ssim_group_std_max=0.0036 train/reward_ssim_group_std_min=0.0003 train/reward_ssim_group_mean_std=0.0271 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-6.6546 train/adv_max=6.8541 train/adv_abs_mean=1.8910
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Epoch 58 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 19:59:54] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0116 | Epoch 058] train/clipped_loss_min=-5 train/clipped_loss_max=5 train/clipped_loss_mean=-0.0028 train/clipped_loss_std=2.2987 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-5 train/unclipped_loss_max=5 train/unclipped_loss_mean=-0.0028 train/unclipped_loss_std=2.2987 train/policy_loss=-0.0028 train/loss=-0.0028 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0004
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Epoch 58 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 20:00:14] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0117 | Epoch 058] train/clipped_loss_min=-5.0000 train/clipped_loss_max=5.0000 train/clipped_loss_mean=-0.0069 train/clipped_loss_std=2.2716 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-5.0000 train/unclipped_loss_max=5.0000 train/unclipped_loss_mean=-0.0069 train/unclipped_loss_std=2.2716 train/policy_loss=-0.0069 train/loss=-0.0069 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0005
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+[2026-10-01 20:00:14] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0118 | Epoch 058] timing/rollout_seconds=147.3201 timing/feedback_seconds=5.1137 timing/optimization_seconds=41.0478 timing/cycle_seconds=191.9590
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+
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+
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+[2026-10-01 20:02:46] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0118 | Epoch 059] train/reward_dists_mean=0.9248 train/reward_dists_std=0.0176 train/reward_psnr_mean=25.2560 train/reward_psnr_std=3.0018 train/reward_ssim_mean=0.9173 train/reward_ssim_std=0.0225 train/reward_dists_group_std_mean=0.0029 train/reward_dists_group_std_max=0.0068 train/reward_dists_group_std_min=0.0010 train/reward_dists_group_mean_std=0.0173 train/reward_psnr_group_std_mean=0.9370 train/reward_psnr_group_std_max=1.9810 train/reward_psnr_group_std_min=0.4919 train/reward_psnr_group_mean_std=2.8376 train/reward_ssim_group_std_mean=0.0018 train/reward_ssim_group_std_max=0.0038 train/reward_ssim_group_std_min=0.0004 train/reward_ssim_group_mean_std=0.0224 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-7.0809 train/adv_max=6.8977 train/adv_abs_mean=1.9787
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Epoch 59 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 20:03:06] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0118 | Epoch 059] train/clipped_loss_min=-5 train/clipped_loss_max=5 train/clipped_loss_mean=-0.0817 train/clipped_loss_std=2.3642 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-5 train/unclipped_loss_max=5 train/unclipped_loss_mean=-0.0817 train/unclipped_loss_std=2.3642 train/policy_loss=-0.0817 train/loss=-0.0817 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
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Epoch 59 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 20:03:26] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0119 | Epoch 059] train/clipped_loss_min=-5.0000 train/clipped_loss_max=5.0000 train/clipped_loss_mean=0.0695 train/clipped_loss_std=2.4088 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-5.0000 train/unclipped_loss_max=5.0000 train/unclipped_loss_mean=0.0695 train/unclipped_loss_std=2.4088 train/policy_loss=0.0695 train/loss=0.0695 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
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+[2026-10-01 20:03:26] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0120 | Epoch 059] timing/rollout_seconds=147.3253 timing/feedback_seconds=5.3786 timing/optimization_seconds=41.0455 timing/cycle_seconds=191.9359
+[2026-10-01 20:03:26] [Rank 0] [INFO] [flow_factory.models.abc]: Saving LoRA weights for transformer role=base to /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/runs/klein4b_grpo_vedai_full_ssim_dists_psnr_lr5e-5/saves/klein4b_grpo_vedai_full_ssim_dists_psnr_lr5e-5/checkpoints/checkpoint-60
+[2026-10-01 20:03:26] [Rank 0] [INFO] [flow_factory.models.abc]: Checkpoint saved successfully to /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/runs/klein4b_grpo_vedai_full_ssim_dists_psnr_lr5e-5/saves/klein4b_grpo_vedai_full_ssim_dists_psnr_lr5e-5/checkpoints/checkpoint-60
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+[2026-10-01 20:04:10] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0120 | Epoch 060] eval/vedai_rl_full/reward_dists_mean=0.9317 eval/vedai_rl_full/reward_dists_std=0.0209 eval/vedai_rl_full/reward_psnr_mean=28.3990 eval/vedai_rl_full/reward_psnr_std=2.7280 eval/vedai_rl_full/reward_ssim_mean=0.9187 eval/vedai_rl_full/reward_ssim_std=0.0272
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Pointwise Rewards: psnr:  83%|████████▎ | 5/6 [00:00<00:00,  6.97it/s]
Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.88it/s]
Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.86it/s]
+[2026-10-01 20:06:42] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0120 | Epoch 060] train/reward_dists_mean=0.9244 train/reward_dists_std=0.0224 train/reward_psnr_mean=26.2871 train/reward_psnr_std=3.2255 train/reward_ssim_mean=0.9146 train/reward_ssim_std=0.0307 train/reward_dists_group_std_mean=0.0026 train/reward_dists_group_std_max=0.0063 train/reward_dists_group_std_min=0.0012 train/reward_dists_group_mean_std=0.0222 train/reward_psnr_group_std_mean=0.8697 train/reward_psnr_group_std_max=1.6046 train/reward_psnr_group_std_min=0.4172 train/reward_psnr_group_mean_std=3.0958 train/reward_ssim_group_std_mean=0.0016 train/reward_ssim_group_std_max=0.0047 train/reward_ssim_group_std_min=0.0003 train/reward_ssim_group_mean_std=0.0306 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-7.1541 train/adv_max=6.0865 train/adv_abs_mean=1.9513
+
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Epoch 60 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 20:07:02] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0120 | Epoch 060] train/clipped_loss_min=-5 train/clipped_loss_max=5 train/clipped_loss_mean=0.0279 train/clipped_loss_std=2.4309 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-5 train/unclipped_loss_max=5 train/unclipped_loss_mean=0.0279 train/unclipped_loss_std=2.4309 train/policy_loss=0.0279 train/loss=0.0279 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0007
+
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Epoch 60 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 20:07:22] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0121 | Epoch 060] train/clipped_loss_min=-5.0000 train/clipped_loss_max=5.0000 train/clipped_loss_mean=-0.0540 train/clipped_loss_std=2.2687 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0.0002 train/unclipped_loss_min=-5.0000 train/unclipped_loss_max=5.0000 train/unclipped_loss_mean=-0.0540 train/unclipped_loss_std=2.2687 train/policy_loss=-0.0540 train/loss=-0.0540 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0007
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Epoch 60 Training: 100%|██████████| 96/96 [00:40<00:00,  2.37it/s]
+[2026-10-01 20:07:22] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0122 | Epoch 060] timing/rollout_seconds=147.2390 timing/feedback_seconds=5.1577 timing/optimization_seconds=41.0890 timing/cycle_seconds=191.8700
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Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  7.06it/s]
+[2026-10-01 20:09:53] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0122 | Epoch 061] train/reward_dists_mean=0.9315 train/reward_dists_std=0.0184 train/reward_psnr_mean=28.9381 train/reward_psnr_std=2.8382 train/reward_ssim_mean=0.9239 train/reward_ssim_std=0.0261 train/reward_dists_group_std_mean=0.0023 train/reward_dists_group_std_max=0.0047 train/reward_dists_group_std_min=0.0010 train/reward_dists_group_mean_std=0.0182 train/reward_psnr_group_std_mean=0.7998 train/reward_psnr_group_std_max=1.5806 train/reward_psnr_group_std_min=0.2163 train/reward_psnr_group_mean_std=2.7040 train/reward_ssim_group_std_mean=0.0012 train/reward_ssim_group_std_max=0.0049 train/reward_ssim_group_std_min=0.0003 train/reward_ssim_group_mean_std=0.0260 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-8.1830 train/adv_max=6.1728 train/adv_abs_mean=1.8834
+
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Epoch 61 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 20:10:14] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0122 | Epoch 061] train/clipped_loss_min=-5 train/clipped_loss_max=5 train/clipped_loss_mean=-0.1142 train/clipped_loss_std=2.3105 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-5 train/unclipped_loss_max=5 train/unclipped_loss_mean=-0.1142 train/unclipped_loss_std=2.3105 train/policy_loss=-0.1142 train/loss=-0.1142 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0004
+
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Epoch 61 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 20:10:34] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0123 | Epoch 061] train/clipped_loss_min=-5.0000 train/clipped_loss_max=5.0000 train/clipped_loss_mean=0.0759 train/clipped_loss_std=2.2806 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-5.0000 train/unclipped_loss_max=5.0000 train/unclipped_loss_mean=0.0759 train/unclipped_loss_std=2.2806 train/policy_loss=0.0759 train/loss=0.0759 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0004
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+[2026-10-01 20:10:34] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0124 | Epoch 061] timing/rollout_seconds=147.1611 timing/feedback_seconds=5.0825 timing/optimization_seconds=41.1707 timing/cycle_seconds=191.8805
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+
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+
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+[2026-10-01 20:13:06] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0124 | Epoch 062] train/reward_dists_mean=0.9259 train/reward_dists_std=0.0256 train/reward_psnr_mean=27.1884 train/reward_psnr_std=3.5210 train/reward_ssim_mean=0.9202 train/reward_ssim_std=0.0340 train/reward_dists_group_std_mean=0.0035 train/reward_dists_group_std_max=0.0286 train/reward_dists_group_std_min=0.0011 train/reward_dists_group_mean_std=0.0250 train/reward_psnr_group_std_mean=1.0602 train/reward_psnr_group_std_max=2.6242 train/reward_psnr_group_std_min=0.2821 train/reward_psnr_group_mean_std=3.3226 train/reward_ssim_group_std_mean=0.0021 train/reward_ssim_group_std_max=0.0359 train/reward_ssim_group_std_min=0.0005 train/reward_ssim_group_mean_std=0.0336 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-7.6207 train/adv_max=6.4092 train/adv_abs_mean=2.0400
+
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Epoch 62 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 20:13:26] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0124 | Epoch 062] train/clipped_loss_min=-5 train/clipped_loss_max=5 train/clipped_loss_mean=-0.0202 train/clipped_loss_std=2.4562 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-5 train/unclipped_loss_max=5 train/unclipped_loss_mean=-0.0202 train/unclipped_loss_std=2.4562 train/policy_loss=-0.0202 train/loss=-0.0202 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
+
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Epoch 62 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 20:13:47] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0125 | Epoch 062] train/clipped_loss_min=-5.0000 train/clipped_loss_max=5.0000 train/clipped_loss_mean=-0.0250 train/clipped_loss_std=2.4300 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-5.0000 train/unclipped_loss_max=5.0000 train/unclipped_loss_mean=-0.0250 train/unclipped_loss_std=2.4300 train/policy_loss=-0.0250 train/loss=-0.0250 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
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+[2026-10-01 20:13:47] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0126 | Epoch 062] timing/rollout_seconds=147.1459 timing/feedback_seconds=5.7997 timing/optimization_seconds=41.2745 timing/cycle_seconds=192.7965
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+
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+
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Pointwise Rewards: psnr:  83%|████████▎ | 5/6 [00:00<00:00,  7.19it/s]
Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  7.08it/s]
Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  7.14it/s]
+[2026-10-01 20:16:18] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0126 | Epoch 063] train/reward_dists_mean=0.9203 train/reward_dists_std=0.0175 train/reward_psnr_mean=25.1448 train/reward_psnr_std=2.2482 train/reward_ssim_mean=0.9114 train/reward_ssim_std=0.0224 train/reward_dists_group_std_mean=0.0035 train/reward_dists_group_std_max=0.0080 train/reward_dists_group_std_min=0.0014 train/reward_dists_group_mean_std=0.0171 train/reward_psnr_group_std_mean=1.0021 train/reward_psnr_group_std_max=1.6342 train/reward_psnr_group_std_min=0.5195 train/reward_psnr_group_mean_std=1.9977 train/reward_ssim_group_std_mean=0.0020 train/reward_ssim_group_std_max=0.0137 train/reward_ssim_group_std_min=0.0005 train/reward_ssim_group_mean_std=0.0222 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-8.5336 train/adv_max=6.8174 train/adv_abs_mean=2.1314
+
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Epoch 63 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 20:16:59] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0127 | Epoch 063] train/clipped_loss_min=-5.0000 train/clipped_loss_max=5.0000 train/clipped_loss_mean=-0.0267 train/clipped_loss_std=2.5029 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-5.0000 train/unclipped_loss_max=5.0000 train/unclipped_loss_mean=-0.0267 train/unclipped_loss_std=2.5029 train/policy_loss=-0.0267 train/loss=-0.0267 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0005
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+[2026-10-01 20:16:59] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0128 | Epoch 063] timing/rollout_seconds=147.2119 timing/feedback_seconds=5.0941 timing/optimization_seconds=41.0336 timing/cycle_seconds=191.8160
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+
Pointwise Rewards: ssim:   0%|          | 0/6 [00:00<?, ?it/s]
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Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:00<00:00,  6.37it/s]
+
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Pointwise Rewards: dists:  83%|████████▎ | 5/6 [00:01<00:00,  3.75it/s]
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Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.72it/s]
+
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Pointwise Rewards: psnr:  33%|███▎      | 2/6 [00:00<00:00,  6.60it/s]
Pointwise Rewards: psnr:  50%|█████     | 3/6 [00:00<00:00,  6.68it/s]
Pointwise Rewards: psnr:  67%|██████▋   | 4/6 [00:00<00:00,  6.69it/s]
Pointwise Rewards: psnr:  83%|████████▎ | 5/6 [00:00<00:00,  6.93it/s]
Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.96it/s]
Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.86it/s]
+[2026-10-01 20:19:30] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0128 | Epoch 064] train/reward_dists_mean=0.9225 train/reward_dists_std=0.0346 train/reward_psnr_mean=25.1213 train/reward_psnr_std=3.9140 train/reward_ssim_mean=0.9123 train/reward_ssim_std=0.0547 train/reward_dists_group_std_mean=0.0040 train/reward_dists_group_std_max=0.0481 train/reward_dists_group_std_min=0.0016 train/reward_dists_group_mean_std=0.0338 train/reward_psnr_group_std_mean=0.9491 train/reward_psnr_group_std_max=1.6786 train/reward_psnr_group_std_min=0.5083 train/reward_psnr_group_mean_std=3.7887 train/reward_ssim_group_std_mean=0.0029 train/reward_ssim_group_std_max=0.0636 train/reward_ssim_group_std_min=0.0007 train/reward_ssim_group_mean_std=0.0539 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-8.5926 train/adv_max=7.4268 train/adv_abs_mean=2.0844
+
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Epoch 64 Training:   6%|▋         | 6/96 [00:02<00:37,  2.39it/s]
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Epoch 64 Training:  14%|█▎        | 13/96 [00:05<00:34,  2.38it/s]
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Epoch 64 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 20:19:50] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0128 | Epoch 064] train/clipped_loss_min=-5 train/clipped_loss_max=5 train/clipped_loss_mean=0.0708 train/clipped_loss_std=2.4551 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-5 train/unclipped_loss_max=5 train/unclipped_loss_mean=0.0708 train/unclipped_loss_std=2.4551 train/policy_loss=0.0708 train/loss=0.0708 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0005
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Epoch 64 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 20:20:11] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0129 | Epoch 064] train/clipped_loss_min=-5.0000 train/clipped_loss_max=5.0000 train/clipped_loss_mean=-0.1477 train/clipped_loss_std=2.4548 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0.0002 train/unclipped_loss_min=-5.0000 train/unclipped_loss_max=5.0000 train/unclipped_loss_mean=-0.1477 train/unclipped_loss_std=2.4548 train/policy_loss=-0.1477 train/loss=-0.1477 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0006
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+[2026-10-01 20:20:11] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0130 | Epoch 064] timing/rollout_seconds=147.2947 timing/feedback_seconds=5.1456 timing/optimization_seconds=41.2190 timing/cycle_seconds=192.0670
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+
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+
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+
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Pointwise Rewards: psnr:  83%|████████▎ | 5/6 [00:00<00:00,  6.79it/s]
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+[2026-10-01 20:22:42] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0130 | Epoch 065] train/reward_dists_mean=0.9267 train/reward_dists_std=0.0168 train/reward_psnr_mean=27.7256 train/reward_psnr_std=2.5157 train/reward_ssim_mean=0.9164 train/reward_ssim_std=0.0263 train/reward_dists_group_std_mean=0.0026 train/reward_dists_group_std_max=0.0052 train/reward_dists_group_std_min=0.0011 train/reward_dists_group_mean_std=0.0166 train/reward_psnr_group_std_mean=0.9052 train/reward_psnr_group_std_max=2.1011 train/reward_psnr_group_std_min=0.1703 train/reward_psnr_group_mean_std=2.3209 train/reward_ssim_group_std_mean=0.0011 train/reward_ssim_group_std_max=0.0035 train/reward_ssim_group_std_min=0.0005 train/reward_ssim_group_mean_std=0.0263 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-7.6802 train/adv_max=5.6867 train/adv_abs_mean=1.8532
+
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Epoch 65 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 20:23:03] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0130 | Epoch 065] train/clipped_loss_min=-5 train/clipped_loss_max=5 train/clipped_loss_mean=-0.0152 train/clipped_loss_std=2.3532 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-5 train/unclipped_loss_max=5 train/unclipped_loss_mean=-0.0152 train/unclipped_loss_std=2.3532 train/policy_loss=-0.0152 train/loss=-0.0152 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0007
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Epoch 65 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 20:23:23] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0131 | Epoch 065] train/clipped_loss_min=-5.0000 train/clipped_loss_max=5.0000 train/clipped_loss_mean=-0.0099 train/clipped_loss_std=2.1596 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-5.0000 train/unclipped_loss_max=5.0000 train/unclipped_loss_mean=-0.0099 train/unclipped_loss_std=2.1596 train/policy_loss=-0.0099 train/loss=-0.0099 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0007
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+[2026-10-01 20:23:23] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0132 | Epoch 065] timing/rollout_seconds=147.1927 timing/feedback_seconds=5.2430 timing/optimization_seconds=41.5240 timing/cycle_seconds=192.3136
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+
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+
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Pointwise Rewards: psnr:  83%|████████▎ | 5/6 [00:00<00:00,  7.05it/s]
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Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.97it/s]
+[2026-10-01 20:25:54] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0132 | Epoch 066] train/reward_dists_mean=0.9244 train/reward_dists_std=0.0249 train/reward_psnr_mean=27.9269 train/reward_psnr_std=3.4052 train/reward_ssim_mean=0.9083 train/reward_ssim_std=0.0341 train/reward_dists_group_std_mean=0.0038 train/reward_dists_group_std_max=0.0484 train/reward_dists_group_std_min=0.0012 train/reward_dists_group_mean_std=0.0237 train/reward_psnr_group_std_mean=0.8596 train/reward_psnr_group_std_max=2.9547 train/reward_psnr_group_std_min=0.1801 train/reward_psnr_group_mean_std=3.2616 train/reward_ssim_group_std_mean=0.0034 train/reward_ssim_group_std_max=0.0872 train/reward_ssim_group_std_min=0.0005 train/reward_ssim_group_mean_std=0.0316 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-6.9133 train/adv_max=6.2959 train/adv_abs_mean=1.8843
+
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Epoch 66 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 20:26:15] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0132 | Epoch 066] train/clipped_loss_min=-5 train/clipped_loss_max=5 train/clipped_loss_mean=0.0556 train/clipped_loss_std=2.2671 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-5 train/unclipped_loss_max=5 train/unclipped_loss_mean=0.0556 train/unclipped_loss_std=2.2671 train/policy_loss=0.0556 train/loss=0.0556 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
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+[2026-10-01 20:26:35] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0134 | Epoch 066] timing/rollout_seconds=147.1748 timing/feedback_seconds=5.1383 timing/optimization_seconds=41.1247 timing/cycle_seconds=191.8150
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+[2026-10-01 20:29:07] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0134 | Epoch 067] train/reward_dists_mean=0.9223 train/reward_dists_std=0.0152 train/reward_psnr_mean=25.6039 train/reward_psnr_std=2.4712 train/reward_ssim_mean=0.9086 train/reward_ssim_std=0.0235 train/reward_dists_group_std_mean=0.0032 train/reward_dists_group_std_max=0.0094 train/reward_dists_group_std_min=0.0017 train/reward_dists_group_mean_std=0.0148 train/reward_psnr_group_std_mean=0.9288 train/reward_psnr_group_std_max=1.4648 train/reward_psnr_group_std_min=0.6032 train/reward_psnr_group_mean_std=2.2810 train/reward_ssim_group_std_mean=0.0020 train/reward_ssim_group_std_max=0.0104 train/reward_ssim_group_std_min=0.0008 train/reward_ssim_group_mean_std=0.0233 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-6.6587 train/adv_max=6.1099 train/adv_abs_mean=1.9130
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Epoch 67 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 20:29:27] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0134 | Epoch 067] train/clipped_loss_min=-5 train/clipped_loss_max=5 train/clipped_loss_mean=-0.1045 train/clipped_loss_std=2.4184 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-5 train/unclipped_loss_max=5 train/unclipped_loss_mean=-0.1045 train/unclipped_loss_std=2.4184 train/policy_loss=-0.1045 train/loss=-0.1045 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
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Epoch 67 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 20:29:48] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0135 | Epoch 067] train/clipped_loss_min=-5.0000 train/clipped_loss_max=5.0000 train/clipped_loss_mean=0.0916 train/clipped_loss_std=2.2436 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-5.0000 train/unclipped_loss_max=5.0000 train/unclipped_loss_mean=0.0916 train/unclipped_loss_std=2.2436 train/policy_loss=0.0916 train/loss=0.0916 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
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+[2026-10-01 20:29:48] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0136 | Epoch 067] timing/rollout_seconds=147.3158 timing/feedback_seconds=5.6011 timing/optimization_seconds=41.2834 timing/cycle_seconds=192.7743
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+
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+[2026-10-01 20:32:19] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0136 | Epoch 068] train/reward_dists_mean=0.9206 train/reward_dists_std=0.0173 train/reward_psnr_mean=24.9381 train/reward_psnr_std=2.7383 train/reward_ssim_mean=0.9151 train/reward_ssim_std=0.0250 train/reward_dists_group_std_mean=0.0033 train/reward_dists_group_std_max=0.0064 train/reward_dists_group_std_min=0.0013 train/reward_dists_group_mean_std=0.0170 train/reward_psnr_group_std_mean=0.9493 train/reward_psnr_group_std_max=1.4792 train/reward_psnr_group_std_min=0.3484 train/reward_psnr_group_mean_std=2.5550 train/reward_ssim_group_std_mean=0.0022 train/reward_ssim_group_std_max=0.0061 train/reward_ssim_group_std_min=0.0009 train/reward_ssim_group_mean_std=0.0248 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-7.0376 train/adv_max=6.5548 train/adv_abs_mean=2.1079
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Epoch 68 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 20:32:39] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0136 | Epoch 068] train/clipped_loss_min=-5 train/clipped_loss_max=5 train/clipped_loss_mean=-0.1863 train/clipped_loss_std=2.4315 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-5 train/unclipped_loss_max=5 train/unclipped_loss_mean=-0.1863 train/unclipped_loss_std=2.4315 train/policy_loss=-0.1863 train/loss=-0.1863 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0008
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Epoch 68 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 20:33:00] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0137 | Epoch 068] train/clipped_loss_min=-5.0001 train/clipped_loss_max=5.0000 train/clipped_loss_mean=0.1656 train/clipped_loss_std=2.5268 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0.0002 train/unclipped_loss_min=-5.0001 train/unclipped_loss_max=5.0000 train/unclipped_loss_mean=0.1656 train/unclipped_loss_std=2.5268 train/policy_loss=0.1656 train/loss=0.1656 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0008
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+[2026-10-01 20:33:00] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0138 | Epoch 068] timing/rollout_seconds=147.5724 timing/feedback_seconds=5.1385 timing/optimization_seconds=41.0246 timing/cycle_seconds=192.1334
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+
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+[2026-10-01 20:35:31] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0138 | Epoch 069] train/reward_dists_mean=0.9243 train/reward_dists_std=0.0203 train/reward_psnr_mean=27.6648 train/reward_psnr_std=3.3446 train/reward_ssim_mean=0.9176 train/reward_ssim_std=0.0253 train/reward_dists_group_std_mean=0.0028 train/reward_dists_group_std_max=0.0152 train/reward_dists_group_std_min=0.0011 train/reward_dists_group_mean_std=0.0200 train/reward_psnr_group_std_mean=0.8642 train/reward_psnr_group_std_max=2.9289 train/reward_psnr_group_std_min=0.2366 train/reward_psnr_group_mean_std=3.1928 train/reward_ssim_group_std_mean=0.0022 train/reward_ssim_group_std_max=0.0403 train/reward_ssim_group_std_min=0.0005 train/reward_ssim_group_mean_std=0.0245 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-9.9939 train/adv_max=5.8865 train/adv_abs_mean=1.8240
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Epoch 69 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 20:35:51] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0138 | Epoch 069] train/clipped_loss_min=-5 train/clipped_loss_max=5 train/clipped_loss_mean=-0.0348 train/clipped_loss_std=2.2258 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-5 train/unclipped_loss_max=5 train/unclipped_loss_mean=-0.0348 train/unclipped_loss_std=2.2258 train/policy_loss=-0.0348 train/loss=-0.0348 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0007
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Epoch 69 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 20:36:12] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0139 | Epoch 069] train/clipped_loss_min=-5.0000 train/clipped_loss_max=5.0000 train/clipped_loss_mean=-0.0222 train/clipped_loss_std=2.2021 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0.0001 train/unclipped_loss_min=-5.0000 train/unclipped_loss_max=5.0000 train/unclipped_loss_mean=-0.0222 train/unclipped_loss_std=2.2021 train/policy_loss=-0.0222 train/loss=-0.0222 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0007
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+[2026-10-01 20:36:12] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0140 | Epoch 069] timing/rollout_seconds=147.3130 timing/feedback_seconds=4.7046 timing/optimization_seconds=41.0507 timing/cycle_seconds=191.7976
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+[2026-10-01 20:36:55] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0140 | Epoch 070] eval/vedai_rl_full/reward_dists_mean=0.9309 eval/vedai_rl_full/reward_dists_std=0.0216 eval/vedai_rl_full/reward_psnr_mean=28.3036 eval/vedai_rl_full/reward_psnr_std=2.8555 eval/vedai_rl_full/reward_ssim_mean=0.9182 eval/vedai_rl_full/reward_ssim_std=0.0272
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+[2026-10-01 20:39:27] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0140 | Epoch 070] train/reward_dists_mean=0.9292 train/reward_dists_std=0.0142 train/reward_psnr_mean=29.5673 train/reward_psnr_std=2.0304 train/reward_ssim_mean=0.9266 train/reward_ssim_std=0.0226 train/reward_dists_group_std_mean=0.0025 train/reward_dists_group_std_max=0.0050 train/reward_dists_group_std_min=0.0010 train/reward_dists_group_mean_std=0.0140 train/reward_psnr_group_std_mean=0.7855 train/reward_psnr_group_std_max=2.2602 train/reward_psnr_group_std_min=0.0950 train/reward_psnr_group_mean_std=1.8230 train/reward_ssim_group_std_mean=0.0008 train/reward_ssim_group_std_max=0.0016 train/reward_ssim_group_std_min=0.0003 train/reward_ssim_group_mean_std=0.0226 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-8.6269 train/adv_max=6.0657 train/adv_abs_mean=1.7910
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Epoch 70 Training:  49%|████▉     | 47/96 [00:19<00:20,  2.37it/s]
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Epoch 70 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 20:39:47] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0140 | Epoch 070] train/clipped_loss_min=-5 train/clipped_loss_max=5 train/clipped_loss_mean=-0.1075 train/clipped_loss_std=2.1513 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-5 train/unclipped_loss_max=5 train/unclipped_loss_mean=-0.1075 train/unclipped_loss_std=2.1513 train/policy_loss=-0.1075 train/loss=-0.1075 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0006
+
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Epoch 70 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 20:40:07] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0141 | Epoch 070] train/clipped_loss_min=-4.9999 train/clipped_loss_max=5.0000 train/clipped_loss_mean=0.0621 train/clipped_loss_std=2.1809 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0.0001 train/unclipped_loss_min=-4.9999 train/unclipped_loss_max=5.0000 train/unclipped_loss_mean=0.0621 train/unclipped_loss_std=2.1809 train/policy_loss=0.0621 train/loss=0.0621 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0007
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Epoch 70 Training: 100%|██████████| 96/96 [00:40<00:00,  2.37it/s]
+[2026-10-01 20:40:07] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0142 | Epoch 070] timing/rollout_seconds=147.3789 timing/feedback_seconds=4.6466 timing/optimization_seconds=41.0730 timing/cycle_seconds=191.9796
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Pointwise Rewards: ssim:  83%|████████▎ | 5/6 [00:00<00:00,  6.71it/s]
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Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:00<00:00,  6.67it/s]
+
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Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.66it/s]
+
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Pointwise Rewards: psnr:  50%|█████     | 3/6 [00:00<00:00,  6.89it/s]
Pointwise Rewards: psnr:  67%|██████▋   | 4/6 [00:00<00:00,  6.58it/s]
Pointwise Rewards: psnr:  83%|████████▎ | 5/6 [00:00<00:00,  6.54it/s]
Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.63it/s]
Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.70it/s]
+[2026-10-01 20:42:39] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0142 | Epoch 071] train/reward_dists_mean=0.9203 train/reward_dists_std=0.0245 train/reward_psnr_mean=26.3299 train/reward_psnr_std=3.0470 train/reward_ssim_mean=0.9174 train/reward_ssim_std=0.0274 train/reward_dists_group_std_mean=0.0029 train/reward_dists_group_std_max=0.0133 train/reward_dists_group_std_min=0.0011 train/reward_dists_group_mean_std=0.0243 train/reward_psnr_group_std_mean=0.9270 train/reward_psnr_group_std_max=1.3279 train/reward_psnr_group_std_min=0.5214 train/reward_psnr_group_mean_std=2.8933 train/reward_ssim_group_std_mean=0.0019 train/reward_ssim_group_std_max=0.0218 train/reward_ssim_group_std_min=0.0005 train/reward_ssim_group_mean_std=0.0272 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-9.0828 train/adv_max=6.9140 train/adv_abs_mean=2.0170
+
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Epoch 71 Training:  11%|█▏        | 11/96 [00:04<00:35,  2.38it/s]
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Epoch 71 Training:  26%|██▌       | 25/96 [00:10<00:29,  2.37it/s]
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Epoch 71 Training:  39%|███▊      | 37/96 [00:15<00:24,  2.37it/s]
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Epoch 71 Training:  44%|████▍     | 42/96 [00:17<00:22,  2.37it/s]
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Epoch 71 Training:  46%|████▌     | 44/96 [00:18<00:21,  2.37it/s]
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Epoch 71 Training:  47%|████▋     | 45/96 [00:18<00:21,  2.37it/s]
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Epoch 71 Training:  48%|████▊     | 46/96 [00:19<00:21,  2.37it/s]
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Epoch 71 Training:  49%|████▉     | 47/96 [00:19<00:20,  2.37it/s]
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Epoch 71 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 20:42:59] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0142 | Epoch 071] train/clipped_loss_min=-5 train/clipped_loss_max=5 train/clipped_loss_mean=-0.0381 train/clipped_loss_std=2.4393 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-5 train/unclipped_loss_max=5 train/unclipped_loss_mean=-0.0381 train/unclipped_loss_std=2.4393 train/policy_loss=-0.0381 train/loss=-0.0381 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
+
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Epoch 71 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 20:43:19] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0143 | Epoch 071] train/clipped_loss_min=-5.0000 train/clipped_loss_max=5.0000 train/clipped_loss_mean=0.0243 train/clipped_loss_std=2.3829 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0.0002 train/unclipped_loss_min=-5.0000 train/unclipped_loss_max=5.0000 train/unclipped_loss_mean=0.0243 train/unclipped_loss_std=2.3829 train/policy_loss=0.0243 train/loss=0.0243 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
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+[2026-10-01 20:43:19] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0144 | Epoch 071] timing/rollout_seconds=147.1937 timing/feedback_seconds=4.7027 timing/optimization_seconds=41.0558 timing/cycle_seconds=191.7664
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+
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+
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+
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Pointwise Rewards: psnr:  83%|████████▎ | 5/6 [00:00<00:00,  6.91it/s]
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Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.94it/s]
+[2026-10-01 20:45:50] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0144 | Epoch 072] train/reward_dists_mean=0.9108 train/reward_dists_std=0.0296 train/reward_psnr_mean=23.8641 train/reward_psnr_std=3.1400 train/reward_ssim_mean=0.9086 train/reward_ssim_std=0.0301 train/reward_dists_group_std_mean=0.0034 train/reward_dists_group_std_max=0.0129 train/reward_dists_group_std_min=0.0016 train/reward_dists_group_mean_std=0.0294 train/reward_psnr_group_std_mean=0.8393 train/reward_psnr_group_std_max=1.8128 train/reward_psnr_group_std_min=0.4188 train/reward_psnr_group_mean_std=3.0122 train/reward_ssim_group_std_mean=0.0025 train/reward_ssim_group_std_max=0.0274 train/reward_ssim_group_std_min=0.0007 train/reward_ssim_group_mean_std=0.0298 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-7.3246 train/adv_max=6.4824 train/adv_abs_mean=2.1087
+
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Epoch 72 Training:   8%|▊         | 8/96 [00:03<00:36,  2.38it/s]
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Epoch 72 Training:   9%|▉         | 9/96 [00:03<00:36,  2.38it/s]
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Epoch 72 Training:  11%|█▏        | 11/96 [00:04<00:35,  2.37it/s]
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Epoch 72 Training:  14%|█▎        | 13/96 [00:05<00:34,  2.37it/s]
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Epoch 72 Training:  15%|█▍        | 14/96 [00:05<00:34,  2.37it/s]
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Epoch 72 Training:  16%|█▌        | 15/96 [00:06<00:34,  2.37it/s]
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Epoch 72 Training:  23%|██▎       | 22/96 [00:09<00:31,  2.37it/s]
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Epoch 72 Training:  25%|██▌       | 24/96 [00:10<00:30,  2.37it/s]
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Epoch 72 Training:  26%|██▌       | 25/96 [00:10<00:29,  2.37it/s]
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Epoch 72 Training:  28%|██▊       | 27/96 [00:11<00:29,  2.37it/s]
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Epoch 72 Training:  29%|██▉       | 28/96 [00:11<00:28,  2.37it/s]
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Epoch 72 Training:  33%|███▎      | 32/96 [00:13<00:26,  2.38it/s]
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Epoch 72 Training:  34%|███▍      | 33/96 [00:13<00:26,  2.37it/s]
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Epoch 72 Training:  35%|███▌      | 34/96 [00:14<00:26,  2.37it/s]
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Epoch 72 Training:  39%|███▊      | 37/96 [00:15<00:24,  2.38it/s]
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Epoch 72 Training:  47%|████▋     | 45/96 [00:18<00:21,  2.38it/s]
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Epoch 72 Training:  48%|████▊     | 46/96 [00:19<00:21,  2.38it/s]
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Epoch 72 Training:  49%|████▉     | 47/96 [00:19<00:20,  2.37it/s]
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Epoch 72 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 20:46:11] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0144 | Epoch 072] train/clipped_loss_min=-5 train/clipped_loss_max=5 train/clipped_loss_mean=-0.0640 train/clipped_loss_std=2.4848 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-5 train/unclipped_loss_max=5 train/unclipped_loss_mean=-0.0640 train/unclipped_loss_std=2.4848 train/policy_loss=-0.0640 train/loss=-0.0640 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
+
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Epoch 72 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 20:46:31] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0145 | Epoch 072] train/clipped_loss_min=-5.0000 train/clipped_loss_max=5.0000 train/clipped_loss_mean=0.0199 train/clipped_loss_std=2.5258 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0.0001 train/unclipped_loss_min=-5.0000 train/unclipped_loss_max=5.0000 train/unclipped_loss_mean=0.0199 train/unclipped_loss_std=2.5258 train/policy_loss=0.0199 train/loss=0.0199 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
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+[2026-10-01 20:46:31] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0146 | Epoch 072] timing/rollout_seconds=147.2803 timing/feedback_seconds=4.8492 timing/optimization_seconds=41.0214 timing/cycle_seconds=191.8349
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+
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+
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+
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Pointwise Rewards: psnr:  33%|███▎      | 2/6 [00:00<00:00,  7.29it/s]
Pointwise Rewards: psnr:  50%|█████     | 3/6 [00:00<00:00,  7.21it/s]
Pointwise Rewards: psnr:  67%|██████▋   | 4/6 [00:00<00:00,  7.21it/s]
Pointwise Rewards: psnr:  83%|████████▎ | 5/6 [00:00<00:00,  7.06it/s]
Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.99it/s]
Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  7.09it/s]
+[2026-10-01 20:49:03] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0146 | Epoch 073] train/reward_dists_mean=0.9194 train/reward_dists_std=0.0134 train/reward_psnr_mean=25.0806 train/reward_psnr_std=2.6866 train/reward_ssim_mean=0.9095 train/reward_ssim_std=0.0281 train/reward_dists_group_std_mean=0.0028 train/reward_dists_group_std_max=0.0049 train/reward_dists_group_std_min=0.0013 train/reward_dists_group_mean_std=0.0131 train/reward_psnr_group_std_mean=0.8354 train/reward_psnr_group_std_max=2.1396 train/reward_psnr_group_std_min=0.3713 train/reward_psnr_group_mean_std=2.5311 train/reward_ssim_group_std_mean=0.0020 train/reward_ssim_group_std_max=0.0054 train/reward_ssim_group_std_min=0.0007 train/reward_ssim_group_mean_std=0.0280 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-7.3940 train/adv_max=6.4019 train/adv_abs_mean=2.1072
+
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Epoch 73 Training:   9%|▉         | 9/96 [00:03<00:36,  2.38it/s]
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Epoch 73 Training:  11%|█▏        | 11/96 [00:04<00:35,  2.38it/s]
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Epoch 73 Training:  14%|█▎        | 13/96 [00:05<00:34,  2.38it/s]
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Epoch 73 Training:  15%|█▍        | 14/96 [00:05<00:34,  2.38it/s]
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Epoch 73 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 20:49:43] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0147 | Epoch 073] train/clipped_loss_min=-5.0000 train/clipped_loss_max=5.0000 train/clipped_loss_mean=-0.0272 train/clipped_loss_std=2.4664 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0.0001 train/unclipped_loss_min=-5.0000 train/unclipped_loss_max=5.0000 train/unclipped_loss_mean=-0.0272 train/unclipped_loss_std=2.4664 train/policy_loss=-0.0272 train/loss=-0.0272 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0008
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+[2026-10-01 20:49:43] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0148 | Epoch 073] timing/rollout_seconds=147.2991 timing/feedback_seconds=5.3915 timing/optimization_seconds=41.5754 timing/cycle_seconds=192.4182
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+
Pointwise Rewards: ssim:   0%|          | 0/6 [00:00<?, ?it/s]
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Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:00<00:00,  6.38it/s]
+
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Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.65it/s]
Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.65it/s]
+
Pointwise Rewards: psnr:   0%|          | 0/6 [00:00<?, ?it/s]
Pointwise Rewards: psnr:  17%|█▋        | 1/6 [00:00<00:00,  6.74it/s]
Pointwise Rewards: psnr:  33%|███▎      | 2/6 [00:00<00:00,  6.62it/s]
Pointwise Rewards: psnr:  50%|█████     | 3/6 [00:00<00:00,  6.66it/s]
Pointwise Rewards: psnr:  67%|██████▋   | 4/6 [00:00<00:00,  6.54it/s]
Pointwise Rewards: psnr:  83%|████████▎ | 5/6 [00:00<00:00,  6.65it/s]
Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.67it/s]
Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.64it/s]
+[2026-10-01 20:52:15] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0148 | Epoch 074] train/reward_dists_mean=0.9235 train/reward_dists_std=0.0183 train/reward_psnr_mean=26.1976 train/reward_psnr_std=2.3962 train/reward_ssim_mean=0.9103 train/reward_ssim_std=0.0277 train/reward_dists_group_std_mean=0.0025 train/reward_dists_group_std_max=0.0083 train/reward_dists_group_std_min=0.0009 train/reward_dists_group_mean_std=0.0181 train/reward_psnr_group_std_mean=0.8552 train/reward_psnr_group_std_max=1.5539 train/reward_psnr_group_std_min=0.4208 train/reward_psnr_group_mean_std=2.2249 train/reward_ssim_group_std_mean=0.0018 train/reward_ssim_group_std_max=0.0098 train/reward_ssim_group_std_min=0.0006 train/reward_ssim_group_mean_std=0.0276 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-6.6422 train/adv_max=5.9554 train/adv_abs_mean=1.9320
+
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Epoch 74 Training:   6%|▋         | 6/96 [00:02<00:37,  2.39it/s]
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Epoch 74 Training:   7%|▋         | 7/96 [00:02<00:37,  2.38it/s]
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Epoch 74 Training:   8%|▊         | 8/96 [00:03<00:37,  2.38it/s]
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Epoch 74 Training:   9%|▉         | 9/96 [00:03<00:36,  2.38it/s]
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Epoch 74 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 20:52:35] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0148 | Epoch 074] train/clipped_loss_min=-5 train/clipped_loss_max=5 train/clipped_loss_mean=-0.1257 train/clipped_loss_std=2.4216 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-5 train/unclipped_loss_max=5 train/unclipped_loss_mean=-0.1257 train/unclipped_loss_std=2.4216 train/policy_loss=-0.1257 train/loss=-0.1257 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0008
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Epoch 74 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 20:52:56] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0149 | Epoch 074] train/clipped_loss_min=-5.0000 train/clipped_loss_max=4.9999 train/clipped_loss_mean=0.1006 train/clipped_loss_std=2.2576 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-5.0000 train/unclipped_loss_max=4.9999 train/unclipped_loss_mean=0.1006 train/unclipped_loss_std=2.2576 train/policy_loss=0.1006 train/loss=0.1006 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0007
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+[2026-10-01 20:52:56] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0150 | Epoch 074] timing/rollout_seconds=147.3358 timing/feedback_seconds=4.8380 timing/optimization_seconds=41.4137 timing/cycle_seconds=192.2910
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+
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Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:00<00:00,  6.57it/s]
+
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+
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Pointwise Rewards: psnr:  50%|█████     | 3/6 [00:00<00:00,  6.69it/s]
Pointwise Rewards: psnr:  67%|██████▋   | 4/6 [00:00<00:00,  6.80it/s]
Pointwise Rewards: psnr:  83%|████████▎ | 5/6 [00:00<00:00,  6.81it/s]
Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.93it/s]
Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.86it/s]
+[2026-10-01 20:55:27] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0150 | Epoch 075] train/reward_dists_mean=0.9283 train/reward_dists_std=0.0259 train/reward_psnr_mean=28.3409 train/reward_psnr_std=2.3949 train/reward_ssim_mean=0.9151 train/reward_ssim_std=0.0288 train/reward_dists_group_std_mean=0.0022 train/reward_dists_group_std_max=0.0058 train/reward_dists_group_std_min=0.0008 train/reward_dists_group_mean_std=0.0258 train/reward_psnr_group_std_mean=0.6884 train/reward_psnr_group_std_max=1.6855 train/reward_psnr_group_std_min=0.1332 train/reward_psnr_group_mean_std=2.2606 train/reward_ssim_group_std_mean=0.0014 train/reward_ssim_group_std_max=0.0066 train/reward_ssim_group_std_min=0.0004 train/reward_ssim_group_mean_std=0.0287 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-7.3048 train/adv_max=6.1509 train/adv_abs_mean=1.8011
+
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Epoch 75 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 20:55:47] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0150 | Epoch 075] train/clipped_loss_min=-5 train/clipped_loss_max=5 train/clipped_loss_mean=0.1577 train/clipped_loss_std=2.1793 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-5 train/unclipped_loss_max=5 train/unclipped_loss_mean=0.1577 train/unclipped_loss_std=2.1793 train/policy_loss=0.1577 train/loss=0.1577 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
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Epoch 75 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 20:56:08] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0151 | Epoch 075] train/clipped_loss_min=-5.0000 train/clipped_loss_max=5.0000 train/clipped_loss_mean=-0.1773 train/clipped_loss_std=2.2183 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-5.0000 train/unclipped_loss_max=5.0000 train/unclipped_loss_mean=-0.1773 train/unclipped_loss_std=2.2183 train/policy_loss=-0.1773 train/loss=-0.1773 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
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+[2026-10-01 20:56:08] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0152 | Epoch 075] timing/rollout_seconds=147.2828 timing/feedback_seconds=5.3621 timing/optimization_seconds=41.1109 timing/cycle_seconds=191.9115
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+
Pointwise Rewards: ssim:   0%|          | 0/6 [00:00<?, ?it/s]
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Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:00<00:00,  6.64it/s]
+
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Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.75it/s]
+
Pointwise Rewards: psnr:   0%|          | 0/6 [00:00<?, ?it/s]
Pointwise Rewards: psnr:  17%|█▋        | 1/6 [00:00<00:00,  6.97it/s]
Pointwise Rewards: psnr:  33%|███▎      | 2/6 [00:00<00:00,  7.18it/s]
Pointwise Rewards: psnr:  50%|█████     | 3/6 [00:00<00:00,  7.08it/s]
Pointwise Rewards: psnr:  67%|██████▋   | 4/6 [00:00<00:00,  7.30it/s]
Pointwise Rewards: psnr:  83%|████████▎ | 5/6 [00:00<00:00,  7.05it/s]
Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.97it/s]
Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  7.04it/s]
+[2026-10-01 20:58:39] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0152 | Epoch 076] train/reward_dists_mean=0.9206 train/reward_dists_std=0.0169 train/reward_psnr_mean=24.9683 train/reward_psnr_std=2.1380 train/reward_ssim_mean=0.8995 train/reward_ssim_std=0.0244 train/reward_dists_group_std_mean=0.0027 train/reward_dists_group_std_max=0.0106 train/reward_dists_group_std_min=0.0013 train/reward_dists_group_mean_std=0.0166 train/reward_psnr_group_std_mean=0.8752 train/reward_psnr_group_std_max=2.1942 train/reward_psnr_group_std_min=0.3762 train/reward_psnr_group_mean_std=1.9206 train/reward_ssim_group_std_mean=0.0027 train/reward_ssim_group_std_max=0.0128 train/reward_ssim_group_std_min=0.0008 train/reward_ssim_group_mean_std=0.0242 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-7.9775 train/adv_max=6.7671 train/adv_abs_mean=2.0249
+
Epoch 76 Training:   0%|          | 0/96 [00:00<?, ?it/s]
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Epoch 76 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 20:58:59] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0152 | Epoch 076] train/clipped_loss_min=-5 train/clipped_loss_max=5 train/clipped_loss_mean=0.0354 train/clipped_loss_std=2.4420 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-5 train/unclipped_loss_max=5 train/unclipped_loss_mean=0.0354 train/unclipped_loss_std=2.4420 train/policy_loss=0.0354 train/loss=0.0354 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0008
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+[2026-10-01 20:59:19] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0154 | Epoch 076] timing/rollout_seconds=147.2694 timing/feedback_seconds=5.3454 timing/optimization_seconds=41.1862 timing/cycle_seconds=191.9782
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+[2026-10-01 21:01:51] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0154 | Epoch 077] train/reward_dists_mean=0.9189 train/reward_dists_std=0.0195 train/reward_psnr_mean=23.5959 train/reward_psnr_std=2.6462 train/reward_ssim_mean=0.8989 train/reward_ssim_std=0.0297 train/reward_dists_group_std_mean=0.0025 train/reward_dists_group_std_max=0.0066 train/reward_dists_group_std_min=0.0014 train/reward_dists_group_mean_std=0.0193 train/reward_psnr_group_std_mean=0.6917 train/reward_psnr_group_std_max=1.1908 train/reward_psnr_group_std_min=0.4168 train/reward_psnr_group_mean_std=2.5481 train/reward_ssim_group_std_mean=0.0025 train/reward_ssim_group_std_max=0.0072 train/reward_ssim_group_std_min=0.0008 train/reward_ssim_group_mean_std=0.0295 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-7.0252 train/adv_max=7.6986 train/adv_abs_mean=2.0055
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Epoch 77 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 21:02:11] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0154 | Epoch 077] train/clipped_loss_min=-5 train/clipped_loss_max=5 train/clipped_loss_mean=-0.0861 train/clipped_loss_std=2.4766 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-5 train/unclipped_loss_max=5 train/unclipped_loss_mean=-0.0861 train/unclipped_loss_std=2.4766 train/policy_loss=-0.0861 train/loss=-0.0861 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0005
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Epoch 77 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 21:02:31] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0155 | Epoch 077] train/clipped_loss_min=-5.0000 train/clipped_loss_max=5.0000 train/clipped_loss_mean=0.0761 train/clipped_loss_std=2.3719 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0.0001 train/unclipped_loss_min=-5.0000 train/unclipped_loss_max=5.0000 train/unclipped_loss_mean=0.0761 train/unclipped_loss_std=2.3719 train/policy_loss=0.0761 train/loss=0.0761 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0005
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+[2026-10-01 21:02:31] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0156 | Epoch 077] timing/rollout_seconds=147.3590 timing/feedback_seconds=5.4767 timing/optimization_seconds=41.0248 timing/cycle_seconds=191.9193
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+[2026-10-01 21:05:03] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0156 | Epoch 078] train/reward_dists_mean=0.9246 train/reward_dists_std=0.0171 train/reward_psnr_mean=25.0279 train/reward_psnr_std=2.5477 train/reward_ssim_mean=0.9005 train/reward_ssim_std=0.0310 train/reward_dists_group_std_mean=0.0025 train/reward_dists_group_std_max=0.0061 train/reward_dists_group_std_min=0.0014 train/reward_dists_group_mean_std=0.0169 train/reward_psnr_group_std_mean=0.6740 train/reward_psnr_group_std_max=1.0944 train/reward_psnr_group_std_min=0.2598 train/reward_psnr_group_mean_std=2.4485 train/reward_ssim_group_std_mean=0.0022 train/reward_ssim_group_std_max=0.0090 train/reward_ssim_group_std_min=0.0009 train/reward_ssim_group_mean_std=0.0309 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-7.8719 train/adv_max=7.4402 train/adv_abs_mean=2.0301
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Epoch 78 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 21:05:23] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0156 | Epoch 078] train/clipped_loss_min=-5 train/clipped_loss_max=5 train/clipped_loss_mean=-0.0001 train/clipped_loss_std=2.4054 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-5 train/unclipped_loss_max=5 train/unclipped_loss_mean=-0.0001 train/unclipped_loss_std=2.4054 train/policy_loss=-0.0001 train/loss=-0.0001 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0008
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Epoch 78 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 21:05:44] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0157 | Epoch 078] train/clipped_loss_min=-5.0001 train/clipped_loss_max=5.0000 train/clipped_loss_mean=0.0002 train/clipped_loss_std=2.4573 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-5.0001 train/unclipped_loss_max=5.0000 train/unclipped_loss_mean=0.0002 train/unclipped_loss_std=2.4573 train/policy_loss=0.0002 train/loss=0.0002 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0008
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+[2026-10-01 21:05:44] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0158 | Epoch 078] timing/rollout_seconds=147.1708 timing/feedback_seconds=5.5555 timing/optimization_seconds=41.5365 timing/cycle_seconds=192.2586
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+
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+
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+[2026-10-01 21:08:15] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0158 | Epoch 079] train/reward_dists_mean=0.9254 train/reward_dists_std=0.0171 train/reward_psnr_mean=27.3379 train/reward_psnr_std=2.1514 train/reward_ssim_mean=0.9063 train/reward_ssim_std=0.0250 train/reward_dists_group_std_mean=0.0027 train/reward_dists_group_std_max=0.0219 train/reward_dists_group_std_min=0.0011 train/reward_dists_group_mean_std=0.0166 train/reward_psnr_group_std_mean=0.6998 train/reward_psnr_group_std_max=4.7303 train/reward_psnr_group_std_min=0.1955 train/reward_psnr_group_mean_std=1.9280 train/reward_ssim_group_std_mean=0.0015 train/reward_ssim_group_std_max=0.0180 train/reward_ssim_group_std_min=0.0005 train/reward_ssim_group_mean_std=0.0248 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-7.0704 train/adv_max=5.8192 train/adv_abs_mean=1.8825
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Epoch 79 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 21:08:36] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0158 | Epoch 079] train/clipped_loss_min=-5 train/clipped_loss_max=5 train/clipped_loss_mean=0.0492 train/clipped_loss_std=2.2489 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-5 train/unclipped_loss_max=5 train/unclipped_loss_mean=0.0492 train/unclipped_loss_std=2.2489 train/policy_loss=0.0492 train/loss=0.0492 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
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Epoch 79 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 21:08:56] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0159 | Epoch 079] train/clipped_loss_min=-5.0000 train/clipped_loss_max=5.0000 train/clipped_loss_mean=-0.0607 train/clipped_loss_std=2.3215 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0.0001 train/unclipped_loss_min=-5.0000 train/unclipped_loss_max=5.0000 train/unclipped_loss_mean=-0.0607 train/unclipped_loss_std=2.3215 train/policy_loss=-0.0607 train/loss=-0.0607 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
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+[2026-10-01 21:08:56] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0160 | Epoch 079] timing/rollout_seconds=147.2798 timing/feedback_seconds=5.3406 timing/optimization_seconds=41.6662 timing/cycle_seconds=192.4484
+[2026-10-01 21:08:56] [Rank 0] [INFO] [flow_factory.models.abc]: Saving LoRA weights for transformer role=base to /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/runs/klein4b_grpo_vedai_full_ssim_dists_psnr_lr5e-5/saves/klein4b_grpo_vedai_full_ssim_dists_psnr_lr5e-5/checkpoints/checkpoint-80
+[2026-10-01 21:08:56] [Rank 0] [INFO] [flow_factory.models.abc]: Checkpoint saved successfully to /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/runs/klein4b_grpo_vedai_full_ssim_dists_psnr_lr5e-5/saves/klein4b_grpo_vedai_full_ssim_dists_psnr_lr5e-5/checkpoints/checkpoint-80
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+[2026-10-01 21:09:40] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0160 | Epoch 080] eval/vedai_rl_full/reward_dists_mean=0.9301 eval/vedai_rl_full/reward_dists_std=0.0218 eval/vedai_rl_full/reward_psnr_mean=28.3649 eval/vedai_rl_full/reward_psnr_std=2.8975 eval/vedai_rl_full/reward_ssim_mean=0.9181 eval/vedai_rl_full/reward_ssim_std=0.0272
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Pointwise Rewards: psnr:  83%|████████▎ | 5/6 [00:00<00:00,  6.88it/s]
Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.95it/s]
Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.99it/s]
+[2026-10-01 21:12:12] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0160 | Epoch 080] train/reward_dists_mean=0.9191 train/reward_dists_std=0.0147 train/reward_psnr_mean=26.6905 train/reward_psnr_std=2.7557 train/reward_ssim_mean=0.9021 train/reward_ssim_std=0.0264 train/reward_dists_group_std_mean=0.0028 train/reward_dists_group_std_max=0.0077 train/reward_dists_group_std_min=0.0015 train/reward_dists_group_mean_std=0.0144 train/reward_psnr_group_std_mean=0.7460 train/reward_psnr_group_std_max=1.2740 train/reward_psnr_group_std_min=0.2084 train/reward_psnr_group_mean_std=2.6419 train/reward_ssim_group_std_mean=0.0014 train/reward_ssim_group_std_max=0.0058 train/reward_ssim_group_std_min=0.0003 train/reward_ssim_group_mean_std=0.0263 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-6.9938 train/adv_max=6.8620 train/adv_abs_mean=1.9401
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Epoch 80 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 21:12:32] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0160 | Epoch 080] train/clipped_loss_min=-5 train/clipped_loss_max=5 train/clipped_loss_mean=-0.0390 train/clipped_loss_std=2.3553 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-5 train/unclipped_loss_max=5 train/unclipped_loss_mean=-0.0390 train/unclipped_loss_std=2.3553 train/policy_loss=-0.0390 train/loss=-0.0390 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0005
+
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Epoch 80 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 21:12:52] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0161 | Epoch 080] train/clipped_loss_min=-5.0000 train/clipped_loss_max=5.0000 train/clipped_loss_mean=0.0125 train/clipped_loss_std=2.3248 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0.0002 train/unclipped_loss_min=-5.0000 train/unclipped_loss_max=5.0000 train/unclipped_loss_mean=0.0125 train/unclipped_loss_std=2.3248 train/policy_loss=0.0125 train/loss=0.0125 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0005
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+[2026-10-01 21:12:52] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0162 | Epoch 080] timing/rollout_seconds=147.2428 timing/feedback_seconds=5.3033 timing/optimization_seconds=41.0226 timing/cycle_seconds=191.7654
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+[2026-10-01 21:15:23] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0162 | Epoch 081] train/reward_dists_mean=0.9163 train/reward_dists_std=0.0176 train/reward_psnr_mean=25.3663 train/reward_psnr_std=3.2692 train/reward_ssim_mean=0.8999 train/reward_ssim_std=0.0328 train/reward_dists_group_std_mean=0.0028 train/reward_dists_group_std_max=0.0048 train/reward_dists_group_std_min=0.0009 train/reward_dists_group_mean_std=0.0174 train/reward_psnr_group_std_mean=0.6778 train/reward_psnr_group_std_max=1.4013 train/reward_psnr_group_std_min=0.2191 train/reward_psnr_group_mean_std=3.1898 train/reward_ssim_group_std_mean=0.0016 train/reward_ssim_group_std_max=0.0030 train/reward_ssim_group_std_min=0.0002 train/reward_ssim_group_mean_std=0.0328 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-8.3931 train/adv_max=7.3861 train/adv_abs_mean=1.9908
+
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Epoch 81 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 21:15:44] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0162 | Epoch 081] train/clipped_loss_min=-5 train/clipped_loss_max=5 train/clipped_loss_mean=0.0738 train/clipped_loss_std=2.4383 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-5 train/unclipped_loss_max=5 train/unclipped_loss_mean=0.0738 train/unclipped_loss_std=2.4383 train/policy_loss=0.0738 train/loss=0.0738 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0005
+
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Epoch 81 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 21:16:04] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0163 | Epoch 081] train/clipped_loss_min=-5.0000 train/clipped_loss_max=5.0000 train/clipped_loss_mean=-0.0865 train/clipped_loss_std=2.3397 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-5.0000 train/unclipped_loss_max=5.0000 train/unclipped_loss_mean=-0.0865 train/unclipped_loss_std=2.3397 train/policy_loss=-0.0865 train/loss=-0.0865 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0005
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+[2026-10-01 21:16:04] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0164 | Epoch 081] timing/rollout_seconds=147.2136 timing/feedback_seconds=5.4751 timing/optimization_seconds=41.0119 timing/cycle_seconds=191.7602
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+
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+
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+
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Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.94it/s]
+[2026-10-01 21:18:35] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0164 | Epoch 082] train/reward_dists_mean=0.9173 train/reward_dists_std=0.0183 train/reward_psnr_mean=26.2216 train/reward_psnr_std=3.1572 train/reward_ssim_mean=0.9031 train/reward_ssim_std=0.0290 train/reward_dists_group_std_mean=0.0030 train/reward_dists_group_std_max=0.0067 train/reward_dists_group_std_min=0.0012 train/reward_dists_group_mean_std=0.0181 train/reward_psnr_group_std_mean=0.7448 train/reward_psnr_group_std_max=1.3216 train/reward_psnr_group_std_min=0.3542 train/reward_psnr_group_mean_std=3.0594 train/reward_ssim_group_std_mean=0.0017 train/reward_ssim_group_std_max=0.0047 train/reward_ssim_group_std_min=0.0003 train/reward_ssim_group_mean_std=0.0290 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-7.3963 train/adv_max=7.2392 train/adv_abs_mean=2.0353
+
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Epoch 82 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 21:18:55] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0164 | Epoch 082] train/clipped_loss_min=-5 train/clipped_loss_max=5 train/clipped_loss_mean=0.0726 train/clipped_loss_std=2.4890 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-5 train/unclipped_loss_max=5 train/unclipped_loss_mean=0.0726 train/unclipped_loss_std=2.4890 train/policy_loss=0.0726 train/loss=0.0726 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0005
+
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Epoch 82 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 21:19:16] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0165 | Epoch 082] train/clipped_loss_min=-5.0000 train/clipped_loss_max=5.0000 train/clipped_loss_mean=-0.0986 train/clipped_loss_std=2.4095 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-5.0000 train/unclipped_loss_max=5.0000 train/unclipped_loss_mean=-0.0986 train/unclipped_loss_std=2.4095 train/policy_loss=-0.0986 train/loss=-0.0986 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0005
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+[2026-10-01 21:19:16] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0166 | Epoch 082] timing/rollout_seconds=147.2113 timing/feedback_seconds=5.3230 timing/optimization_seconds=41.0251 timing/cycle_seconds=191.7182
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+
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+
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Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.71it/s]
+[2026-10-01 21:21:47] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0166 | Epoch 083] train/reward_dists_mean=0.9186 train/reward_dists_std=0.0193 train/reward_psnr_mean=26.6865 train/reward_psnr_std=2.6490 train/reward_ssim_mean=0.9036 train/reward_ssim_std=0.0264 train/reward_dists_group_std_mean=0.0029 train/reward_dists_group_std_max=0.0077 train/reward_dists_group_std_min=0.0011 train/reward_dists_group_mean_std=0.0190 train/reward_psnr_group_std_mean=0.7451 train/reward_psnr_group_std_max=4.0358 train/reward_psnr_group_std_min=0.2170 train/reward_psnr_group_mean_std=2.4850 train/reward_ssim_group_std_mean=0.0020 train/reward_ssim_group_std_max=0.0291 train/reward_ssim_group_std_min=0.0006 train/reward_ssim_group_mean_std=0.0260 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-8.7448 train/adv_max=7.4486 train/adv_abs_mean=1.9524
+
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Epoch 83 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 21:22:27] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0167 | Epoch 083] train/clipped_loss_min=-5.0000 train/clipped_loss_max=5.0000 train/clipped_loss_mean=0.0009 train/clipped_loss_std=2.3440 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-5.0000 train/unclipped_loss_max=5.0000 train/unclipped_loss_mean=0.0009 train/unclipped_loss_std=2.3440 train/policy_loss=0.0009 train/loss=0.0009 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
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+[2026-10-01 21:22:27] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0168 | Epoch 083] timing/rollout_seconds=147.2939 timing/feedback_seconds=5.0647 timing/optimization_seconds=41.0530 timing/cycle_seconds=191.8703
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+
Pointwise Rewards: ssim:   0%|          | 0/6 [00:00<?, ?it/s]
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Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:00<00:00,  6.75it/s]
Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:00<00:00,  6.65it/s]
+
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Pointwise Rewards: dists:  67%|██████▋   | 4/6 [00:01<00:00,  3.66it/s]
Pointwise Rewards: dists:  83%|████████▎ | 5/6 [00:01<00:00,  3.66it/s]
Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.68it/s]
Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.68it/s]
+
Pointwise Rewards: psnr:   0%|          | 0/6 [00:00<?, ?it/s]
Pointwise Rewards: psnr:  17%|█▋        | 1/6 [00:00<00:00,  6.86it/s]
Pointwise Rewards: psnr:  33%|███▎      | 2/6 [00:00<00:00,  6.68it/s]
Pointwise Rewards: psnr:  50%|█████     | 3/6 [00:00<00:00,  6.83it/s]
Pointwise Rewards: psnr:  67%|██████▋   | 4/6 [00:00<00:00,  6.74it/s]
Pointwise Rewards: psnr:  83%|████████▎ | 5/6 [00:00<00:00,  6.79it/s]
Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.91it/s]
Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.83it/s]
+[2026-10-01 21:24:59] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0168 | Epoch 084] train/reward_dists_mean=0.9168 train/reward_dists_std=0.0233 train/reward_psnr_mean=28.2913 train/reward_psnr_std=2.3123 train/reward_ssim_mean=0.9008 train/reward_ssim_std=0.0301 train/reward_dists_group_std_mean=0.0027 train/reward_dists_group_std_max=0.0067 train/reward_dists_group_std_min=0.0014 train/reward_dists_group_mean_std=0.0232 train/reward_psnr_group_std_mean=0.6109 train/reward_psnr_group_std_max=2.1634 train/reward_psnr_group_std_min=0.1094 train/reward_psnr_group_mean_std=2.1952 train/reward_ssim_group_std_mean=0.0013 train/reward_ssim_group_std_max=0.0081 train/reward_ssim_group_std_min=0.0003 train/reward_ssim_group_mean_std=0.0300 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-8.1201 train/adv_max=7.3046 train/adv_abs_mean=1.8106
+
Epoch 84 Training:   0%|          | 0/96 [00:00<?, ?it/s]
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Epoch 84 Training:   4%|▍         | 4/96 [00:01<00:38,  2.41it/s]
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Epoch 84 Training:   5%|▌         | 5/96 [00:02<00:38,  2.39it/s]
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Epoch 84 Training:   6%|▋         | 6/96 [00:02<00:37,  2.39it/s]
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Epoch 84 Training:   7%|▋         | 7/96 [00:02<00:37,  2.38it/s]
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Epoch 84 Training:   8%|▊         | 8/96 [00:03<00:36,  2.38it/s]
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Epoch 84 Training:   9%|▉         | 9/96 [00:03<00:36,  2.38it/s]
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Epoch 84 Training:  10%|█         | 10/96 [00:04<00:36,  2.38it/s]
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Epoch 84 Training:  11%|█▏        | 11/96 [00:04<00:35,  2.37it/s]
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Epoch 84 Training:  12%|█▎        | 12/96 [00:05<00:35,  2.37it/s]
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Epoch 84 Training:  14%|█▎        | 13/96 [00:05<00:34,  2.37it/s]
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Epoch 84 Training:  15%|█▍        | 14/96 [00:05<00:34,  2.37it/s]
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Epoch 84 Training:  16%|█▌        | 15/96 [00:06<00:34,  2.37it/s]
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Epoch 84 Training:  17%|█▋        | 16/96 [00:06<00:33,  2.37it/s]
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Epoch 84 Training:  18%|█▊        | 17/96 [00:07<00:33,  2.37it/s]
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Epoch 84 Training:  23%|██▎       | 22/96 [00:09<00:31,  2.38it/s]
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Epoch 84 Training:  25%|██▌       | 24/96 [00:10<00:30,  2.37it/s]
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Epoch 84 Training:  27%|██▋       | 26/96 [00:10<00:29,  2.38it/s]
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Epoch 84 Training:  28%|██▊       | 27/96 [00:11<00:29,  2.38it/s]
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Epoch 84 Training:  29%|██▉       | 28/96 [00:11<00:28,  2.37it/s]
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Epoch 84 Training:  33%|███▎      | 32/96 [00:13<00:27,  2.37it/s]
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Epoch 84 Training:  34%|███▍      | 33/96 [00:13<00:26,  2.37it/s]
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Epoch 84 Training:  35%|███▌      | 34/96 [00:14<00:26,  2.37it/s]
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Epoch 84 Training:  39%|███▊      | 37/96 [00:15<00:24,  2.37it/s]
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Epoch 84 Training:  40%|███▉      | 38/96 [00:15<00:24,  2.37it/s]
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Epoch 84 Training:  41%|████      | 39/96 [00:16<00:24,  2.37it/s]
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Epoch 84 Training:  42%|████▏     | 40/96 [00:16<00:23,  2.37it/s]
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Epoch 84 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 21:25:19] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0168 | Epoch 084] train/clipped_loss_min=-5 train/clipped_loss_max=5 train/clipped_loss_mean=0.0210 train/clipped_loss_std=2.1246 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-5 train/unclipped_loss_max=5 train/unclipped_loss_mean=0.0210 train/unclipped_loss_std=2.1246 train/policy_loss=0.0210 train/loss=0.0210 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0002
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Epoch 84 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 21:25:39] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0169 | Epoch 084] train/clipped_loss_min=-5.0000 train/clipped_loss_max=5.0000 train/clipped_loss_mean=-0.0522 train/clipped_loss_std=2.2674 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-5.0000 train/unclipped_loss_max=5.0000 train/unclipped_loss_mean=-0.0522 train/unclipped_loss_std=2.2674 train/policy_loss=-0.0522 train/loss=-0.0522 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0003
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+[2026-10-01 21:25:39] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0170 | Epoch 084] timing/rollout_seconds=147.1739 timing/feedback_seconds=5.3360 timing/optimization_seconds=41.0235 timing/cycle_seconds=191.7505
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+
Pointwise Rewards: ssim:   0%|          | 0/6 [00:00<?, ?it/s]
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Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:00<00:00,  6.55it/s]
+
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+
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Pointwise Rewards: psnr:  33%|███▎      | 2/6 [00:00<00:00,  6.76it/s]
Pointwise Rewards: psnr:  50%|█████     | 3/6 [00:00<00:00,  6.86it/s]
Pointwise Rewards: psnr:  67%|██████▋   | 4/6 [00:00<00:00,  6.87it/s]
Pointwise Rewards: psnr:  83%|████████▎ | 5/6 [00:00<00:00,  6.95it/s]
Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.83it/s]
Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.85it/s]
+[2026-10-01 21:28:11] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0170 | Epoch 085] train/reward_dists_mean=0.9252 train/reward_dists_std=0.0189 train/reward_psnr_mean=28.0185 train/reward_psnr_std=2.1573 train/reward_ssim_mean=0.9085 train/reward_ssim_std=0.0312 train/reward_dists_group_std_mean=0.0025 train/reward_dists_group_std_max=0.0067 train/reward_dists_group_std_min=0.0013 train/reward_dists_group_mean_std=0.0187 train/reward_psnr_group_std_mean=0.7051 train/reward_psnr_group_std_max=1.7420 train/reward_psnr_group_std_min=0.0948 train/reward_psnr_group_mean_std=2.0006 train/reward_ssim_group_std_mean=0.0013 train/reward_ssim_group_std_max=0.0100 train/reward_ssim_group_std_min=0.0002 train/reward_ssim_group_mean_std=0.0312 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-7.5719 train/adv_max=6.4214 train/adv_abs_mean=1.8702
+
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Epoch 85 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 21:28:31] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0170 | Epoch 085] train/clipped_loss_min=-5 train/clipped_loss_max=5 train/clipped_loss_mean=-0.1241 train/clipped_loss_std=2.2297 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-5 train/unclipped_loss_max=5 train/unclipped_loss_mean=-0.1241 train/unclipped_loss_std=2.2297 train/policy_loss=-0.1241 train/loss=-0.1241 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0002
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Epoch 85 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 21:28:51] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0171 | Epoch 085] train/clipped_loss_min=-5.0000 train/clipped_loss_max=5.0000 train/clipped_loss_mean=0.1030 train/clipped_loss_std=2.3133 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0.0002 train/unclipped_loss_min=-5.0000 train/unclipped_loss_max=5.0000 train/unclipped_loss_mean=0.1030 train/unclipped_loss_std=2.3133 train/policy_loss=0.1030 train/loss=0.1030 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0002
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+[2026-10-01 21:28:51] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0172 | Epoch 085] timing/rollout_seconds=147.4108 timing/feedback_seconds=5.6402 timing/optimization_seconds=41.0281 timing/cycle_seconds=191.9939
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+
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+
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Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.17it/s]
+
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Pointwise Rewards: psnr:  67%|██████▋   | 4/6 [00:00<00:00,  7.10it/s]
Pointwise Rewards: psnr:  83%|████████▎ | 5/6 [00:00<00:00,  6.93it/s]
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Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.96it/s]
+[2026-10-01 21:31:22] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0172 | Epoch 086] train/reward_dists_mean=0.9225 train/reward_dists_std=0.0236 train/reward_psnr_mean=26.7910 train/reward_psnr_std=3.1330 train/reward_ssim_mean=0.9074 train/reward_ssim_std=0.0304 train/reward_dists_group_std_mean=0.0025 train/reward_dists_group_std_max=0.0054 train/reward_dists_group_std_min=0.0012 train/reward_dists_group_mean_std=0.0235 train/reward_psnr_group_std_mean=0.7882 train/reward_psnr_group_std_max=1.5716 train/reward_psnr_group_std_min=0.3056 train/reward_psnr_group_mean_std=3.0172 train/reward_ssim_group_std_mean=0.0017 train/reward_ssim_group_std_max=0.0053 train/reward_ssim_group_std_min=0.0005 train/reward_ssim_group_mean_std=0.0304 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-7.4920 train/adv_max=6.5938 train/adv_abs_mean=1.9208
+
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Epoch 86 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 21:31:43] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0172 | Epoch 086] train/clipped_loss_min=-5 train/clipped_loss_max=5 train/clipped_loss_mean=-0.0350 train/clipped_loss_std=2.2361 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-5 train/unclipped_loss_max=5 train/unclipped_loss_mean=-0.0350 train/unclipped_loss_std=2.2361 train/policy_loss=-0.0350 train/loss=-0.0350 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0004
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+[2026-10-01 21:32:03] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0174 | Epoch 086] timing/rollout_seconds=147.2000 timing/feedback_seconds=5.0759 timing/optimization_seconds=41.0758 timing/cycle_seconds=191.7909
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+[2026-10-01 21:34:34] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0174 | Epoch 087] train/reward_dists_mean=0.9270 train/reward_dists_std=0.0181 train/reward_psnr_mean=27.4321 train/reward_psnr_std=2.2329 train/reward_ssim_mean=0.9053 train/reward_ssim_std=0.0276 train/reward_dists_group_std_mean=0.0024 train/reward_dists_group_std_max=0.0048 train/reward_dists_group_std_min=0.0012 train/reward_dists_group_mean_std=0.0179 train/reward_psnr_group_std_mean=0.6850 train/reward_psnr_group_std_max=1.6357 train/reward_psnr_group_std_min=0.1486 train/reward_psnr_group_mean_std=2.1062 train/reward_ssim_group_std_mean=0.0014 train/reward_ssim_group_std_max=0.0031 train/reward_ssim_group_std_min=0.0004 train/reward_ssim_group_mean_std=0.0276 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-7.1821 train/adv_max=6.0025 train/adv_abs_mean=1.8813
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Epoch 87 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 21:34:54] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0174 | Epoch 087] train/clipped_loss_min=-5 train/clipped_loss_max=5 train/clipped_loss_mean=0.0471 train/clipped_loss_std=2.3416 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-5 train/unclipped_loss_max=5 train/unclipped_loss_mean=0.0471 train/unclipped_loss_std=2.3416 train/policy_loss=0.0471 train/loss=0.0471 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0007
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Epoch 87 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 21:35:15] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0175 | Epoch 087] train/clipped_loss_min=-5.0000 train/clipped_loss_max=5.0000 train/clipped_loss_mean=-0.0591 train/clipped_loss_std=2.2260 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0.0002 train/unclipped_loss_min=-5.0000 train/unclipped_loss_max=5.0000 train/unclipped_loss_mean=-0.0591 train/unclipped_loss_std=2.2260 train/policy_loss=-0.0591 train/loss=-0.0591 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0007
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+[2026-10-01 21:35:15] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0176 | Epoch 087] timing/rollout_seconds=146.9570 timing/feedback_seconds=5.0257 timing/optimization_seconds=41.3103 timing/cycle_seconds=191.7516
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+
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+[2026-10-01 21:37:46] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0176 | Epoch 088] train/reward_dists_mean=0.9268 train/reward_dists_std=0.0149 train/reward_psnr_mean=28.5468 train/reward_psnr_std=2.0897 train/reward_ssim_mean=0.9074 train/reward_ssim_std=0.0272 train/reward_dists_group_std_mean=0.0025 train/reward_dists_group_std_max=0.0044 train/reward_dists_group_std_min=0.0009 train/reward_dists_group_mean_std=0.0147 train/reward_psnr_group_std_mean=0.6775 train/reward_psnr_group_std_max=1.6858 train/reward_psnr_group_std_min=0.1223 train/reward_psnr_group_mean_std=1.9524 train/reward_ssim_group_std_mean=0.0013 train/reward_ssim_group_std_max=0.0052 train/reward_ssim_group_std_min=0.0004 train/reward_ssim_group_mean_std=0.0272 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-7.0893 train/adv_max=7.2079 train/adv_abs_mean=1.8288
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Epoch 88 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 21:38:07] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0176 | Epoch 088] train/clipped_loss_min=-5 train/clipped_loss_max=5 train/clipped_loss_mean=0.0823 train/clipped_loss_std=2.2298 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-5 train/unclipped_loss_max=5 train/unclipped_loss_mean=0.0823 train/unclipped_loss_std=2.2298 train/policy_loss=0.0823 train/loss=0.0823 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0007
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Epoch 88 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 21:38:27] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0177 | Epoch 088] train/clipped_loss_min=-5.0000 train/clipped_loss_max=5.0000 train/clipped_loss_mean=-0.1019 train/clipped_loss_std=2.1669 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0.0002 train/unclipped_loss_min=-5.0000 train/unclipped_loss_max=5.0000 train/unclipped_loss_mean=-0.1019 train/unclipped_loss_std=2.1669 train/policy_loss=-0.1019 train/loss=-0.1019 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0007
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+[2026-10-01 21:38:27] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0178 | Epoch 088] timing/rollout_seconds=147.1336 timing/feedback_seconds=4.8822 timing/optimization_seconds=41.3822 timing/cycle_seconds=192.1642
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+[2026-10-01 21:40:58] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0178 | Epoch 089] train/reward_dists_mean=0.9235 train/reward_dists_std=0.0355 train/reward_psnr_mean=28.2710 train/reward_psnr_std=2.5749 train/reward_ssim_mean=0.9139 train/reward_ssim_std=0.0268 train/reward_dists_group_std_mean=0.0027 train/reward_dists_group_std_max=0.0069 train/reward_dists_group_std_min=0.0011 train/reward_dists_group_mean_std=0.0354 train/reward_psnr_group_std_mean=0.8176 train/reward_psnr_group_std_max=5.3698 train/reward_psnr_group_std_min=0.1614 train/reward_psnr_group_mean_std=2.3278 train/reward_ssim_group_std_mean=0.0016 train/reward_ssim_group_std_max=0.0297 train/reward_ssim_group_std_min=0.0003 train/reward_ssim_group_mean_std=0.0264 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-6.6835 train/adv_max=5.4273 train/adv_abs_mean=1.8175
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Epoch 89 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 21:41:18] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0178 | Epoch 089] train/clipped_loss_min=-5 train/clipped_loss_max=5 train/clipped_loss_mean=-0.0085 train/clipped_loss_std=2.1822 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-5 train/unclipped_loss_max=5 train/unclipped_loss_mean=-0.0085 train/unclipped_loss_std=2.1822 train/policy_loss=-0.0085 train/loss=-0.0085 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0006
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Epoch 89 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 21:41:38] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0179 | Epoch 089] train/clipped_loss_min=-5.0000 train/clipped_loss_max=5.0001 train/clipped_loss_mean=-0.0143 train/clipped_loss_std=2.2442 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0.0001 train/unclipped_loss_min=-5.0000 train/unclipped_loss_max=5.0001 train/unclipped_loss_mean=-0.0143 train/unclipped_loss_std=2.2442 train/policy_loss=-0.0143 train/loss=-0.0143 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0006
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+[2026-10-01 21:41:38] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0180 | Epoch 089] timing/rollout_seconds=146.9227 timing/feedback_seconds=4.4589 timing/optimization_seconds=40.9871 timing/cycle_seconds=191.3947
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+[2026-10-01 21:42:22] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0180 | Epoch 090] eval/vedai_rl_full/reward_dists_mean=0.9296 eval/vedai_rl_full/reward_dists_std=0.0216 eval/vedai_rl_full/reward_psnr_mean=28.3006 eval/vedai_rl_full/reward_psnr_std=2.8803 eval/vedai_rl_full/reward_ssim_mean=0.9175 eval/vedai_rl_full/reward_ssim_std=0.0275
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+[2026-10-01 21:44:53] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0180 | Epoch 090] train/reward_dists_mean=0.9299 train/reward_dists_std=0.0133 train/reward_psnr_mean=26.7898 train/reward_psnr_std=2.1795 train/reward_ssim_mean=0.9095 train/reward_ssim_std=0.0244 train/reward_dists_group_std_mean=0.0024 train/reward_dists_group_std_max=0.0050 train/reward_dists_group_std_min=0.0014 train/reward_dists_group_mean_std=0.0131 train/reward_psnr_group_std_mean=0.7386 train/reward_psnr_group_std_max=1.6629 train/reward_psnr_group_std_min=0.2222 train/reward_psnr_group_mean_std=2.0328 train/reward_ssim_group_std_mean=0.0011 train/reward_ssim_group_std_max=0.0024 train/reward_ssim_group_std_min=0.0004 train/reward_ssim_group_mean_std=0.0244 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-7.3720 train/adv_max=5.8281 train/adv_abs_mean=1.8688
+
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Epoch 90 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 21:45:14] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0180 | Epoch 090] train/clipped_loss_min=-5 train/clipped_loss_max=5 train/clipped_loss_mean=0.1141 train/clipped_loss_std=2.3424 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-5 train/unclipped_loss_max=5 train/unclipped_loss_mean=0.1141 train/unclipped_loss_std=2.3424 train/policy_loss=0.1141 train/loss=0.1141 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0005
+
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Epoch 90 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 21:45:34] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0181 | Epoch 090] train/clipped_loss_min=-5.0000 train/clipped_loss_max=5.0000 train/clipped_loss_mean=-0.1552 train/clipped_loss_std=2.1830 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-5.0000 train/unclipped_loss_max=5.0000 train/unclipped_loss_mean=-0.1552 train/unclipped_loss_std=2.1830 train/policy_loss=-0.1552 train/loss=-0.1552 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0004
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Epoch 90 Training: 100%|██████████| 96/96 [00:40<00:00,  2.35it/s]
+[2026-10-01 21:45:34] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0182 | Epoch 090] timing/rollout_seconds=147.2499 timing/feedback_seconds=5.4957 timing/optimization_seconds=41.4162 timing/cycle_seconds=192.2243
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+
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Pointwise Rewards: psnr:  67%|██████▋   | 4/6 [00:00<00:00,  6.76it/s]
Pointwise Rewards: psnr:  83%|████████▎ | 5/6 [00:00<00:00,  6.78it/s]
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Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.82it/s]
+[2026-10-01 21:48:06] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0182 | Epoch 091] train/reward_dists_mean=0.9187 train/reward_dists_std=0.0368 train/reward_psnr_mean=27.3910 train/reward_psnr_std=2.8805 train/reward_ssim_mean=0.9155 train/reward_ssim_std=0.0304 train/reward_dists_group_std_mean=0.0029 train/reward_dists_group_std_max=0.0090 train/reward_dists_group_std_min=0.0013 train/reward_dists_group_mean_std=0.0366 train/reward_psnr_group_std_mean=0.8582 train/reward_psnr_group_std_max=4.7118 train/reward_psnr_group_std_min=0.2477 train/reward_psnr_group_mean_std=2.6718 train/reward_ssim_group_std_mean=0.0018 train/reward_ssim_group_std_max=0.0286 train/reward_ssim_group_std_min=0.0003 train/reward_ssim_group_mean_std=0.0301 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-7.8054 train/adv_max=6.5400 train/adv_abs_mean=1.8670
+
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Epoch 91 Training:  49%|████▉     | 47/96 [00:19<00:20,  2.37it/s]
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Epoch 91 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 21:48:26] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0182 | Epoch 091] train/clipped_loss_min=-5 train/clipped_loss_max=5 train/clipped_loss_mean=-0.2884 train/clipped_loss_std=2.2758 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-5 train/unclipped_loss_max=5 train/unclipped_loss_mean=-0.2884 train/unclipped_loss_std=2.2758 train/policy_loss=-0.2884 train/loss=-0.2884 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0007
+
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Epoch 91 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 21:48:46] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0183 | Epoch 091] train/clipped_loss_min=-5.0000 train/clipped_loss_max=5.0000 train/clipped_loss_mean=0.2591 train/clipped_loss_std=2.2419 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-5.0000 train/unclipped_loss_max=5.0000 train/unclipped_loss_mean=0.2591 train/unclipped_loss_std=2.2419 train/policy_loss=0.2591 train/loss=0.2591 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0007
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+[2026-10-01 21:48:46] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0184 | Epoch 091] timing/rollout_seconds=147.6419 timing/feedback_seconds=5.8134 timing/optimization_seconds=41.0802 timing/cycle_seconds=192.2676
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+
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Pointwise Rewards: ssim:  83%|████████▎ | 5/6 [00:00<00:00,  6.67it/s]
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Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:00<00:00,  6.56it/s]
+
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+
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Pointwise Rewards: psnr:  67%|██████▋   | 4/6 [00:00<00:00,  7.13it/s]
Pointwise Rewards: psnr:  83%|████████▎ | 5/6 [00:00<00:00,  7.05it/s]
Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.92it/s]
Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  7.00it/s]
+[2026-10-01 21:51:18] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0184 | Epoch 092] train/reward_dists_mean=0.9272 train/reward_dists_std=0.0177 train/reward_psnr_mean=28.6718 train/reward_psnr_std=2.6793 train/reward_ssim_mean=0.9173 train/reward_ssim_std=0.0236 train/reward_dists_group_std_mean=0.0024 train/reward_dists_group_std_max=0.0060 train/reward_dists_group_std_min=0.0010 train/reward_dists_group_mean_std=0.0175 train/reward_psnr_group_std_mean=0.6014 train/reward_psnr_group_std_max=1.4733 train/reward_psnr_group_std_min=0.1879 train/reward_psnr_group_mean_std=2.5936 train/reward_ssim_group_std_mean=0.0011 train/reward_ssim_group_std_max=0.0072 train/reward_ssim_group_std_min=0.0002 train/reward_ssim_group_mean_std=0.0235 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-7.4149 train/adv_max=6.4547 train/adv_abs_mean=1.7469
+
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Epoch 92 Training:  11%|█▏        | 11/96 [00:04<00:35,  2.37it/s]
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Epoch 92 Training:  14%|█▎        | 13/96 [00:05<00:35,  2.37it/s]
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Epoch 92 Training:  15%|█▍        | 14/96 [00:05<00:34,  2.37it/s]
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Epoch 92 Training:  16%|█▌        | 15/96 [00:06<00:34,  2.37it/s]
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Epoch 92 Training:  25%|██▌       | 24/96 [00:10<00:30,  2.37it/s]
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Epoch 92 Training:  26%|██▌       | 25/96 [00:10<00:29,  2.37it/s]
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Epoch 92 Training:  28%|██▊       | 27/96 [00:11<00:29,  2.37it/s]
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Epoch 92 Training:  29%|██▉       | 28/96 [00:11<00:28,  2.37it/s]
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Epoch 92 Training:  30%|███       | 29/96 [00:12<00:28,  2.37it/s]
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Epoch 92 Training:  32%|███▏      | 31/96 [00:13<00:27,  2.37it/s]
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Epoch 92 Training:  33%|███▎      | 32/96 [00:13<00:27,  2.37it/s]
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Epoch 92 Training:  34%|███▍      | 33/96 [00:13<00:26,  2.37it/s]
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Epoch 92 Training:  39%|███▊      | 37/96 [00:15<00:24,  2.37it/s]
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Epoch 92 Training:  49%|████▉     | 47/96 [00:19<00:20,  2.37it/s]
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Epoch 92 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 21:51:38] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0184 | Epoch 092] train/clipped_loss_min=-5 train/clipped_loss_max=5 train/clipped_loss_mean=-0.0354 train/clipped_loss_std=2.1494 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-5 train/unclipped_loss_max=5 train/unclipped_loss_mean=-0.0354 train/unclipped_loss_std=2.1494 train/policy_loss=-0.0354 train/loss=-0.0354 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0004
+
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Epoch 92 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 21:51:59] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0185 | Epoch 092] train/clipped_loss_min=-5.0000 train/clipped_loss_max=5.0000 train/clipped_loss_mean=0.0147 train/clipped_loss_std=2.1396 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0.0001 train/unclipped_loss_min=-5.0000 train/unclipped_loss_max=5.0000 train/unclipped_loss_mean=0.0147 train/unclipped_loss_std=2.1396 train/policy_loss=0.0147 train/loss=0.0147 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0004
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+[2026-10-01 21:51:59] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0186 | Epoch 092] timing/rollout_seconds=147.3844 timing/feedback_seconds=5.4934 timing/optimization_seconds=41.1108 timing/cycle_seconds=192.0581
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+
Pointwise Rewards: ssim:   0%|          | 0/6 [00:00<?, ?it/s]
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+
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Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.72it/s]
+
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Pointwise Rewards: psnr:  33%|███▎      | 2/6 [00:00<00:00,  6.74it/s]
Pointwise Rewards: psnr:  50%|█████     | 3/6 [00:00<00:00,  6.79it/s]
Pointwise Rewards: psnr:  67%|██████▋   | 4/6 [00:00<00:00,  6.67it/s]
Pointwise Rewards: psnr:  83%|████████▎ | 5/6 [00:00<00:00,  6.79it/s]
Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.91it/s]
Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.83it/s]
+[2026-10-01 21:54:30] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0186 | Epoch 093] train/reward_dists_mean=0.9251 train/reward_dists_std=0.0178 train/reward_psnr_mean=28.8251 train/reward_psnr_std=2.2718 train/reward_ssim_mean=0.9144 train/reward_ssim_std=0.0276 train/reward_dists_group_std_mean=0.0024 train/reward_dists_group_std_max=0.0051 train/reward_dists_group_std_min=0.0010 train/reward_dists_group_mean_std=0.0177 train/reward_psnr_group_std_mean=0.5477 train/reward_psnr_group_std_max=1.2077 train/reward_psnr_group_std_min=0.1059 train/reward_psnr_group_mean_std=2.1863 train/reward_ssim_group_std_mean=0.0008 train/reward_ssim_group_std_max=0.0022 train/reward_ssim_group_std_min=0.0002 train/reward_ssim_group_mean_std=0.0276 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-6.6747 train/adv_max=6.0832 train/adv_abs_mean=1.8012
+
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Epoch 93 Training:   9%|▉         | 9/96 [00:03<00:36,  2.38it/s]
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Epoch 93 Training:  11%|█▏        | 11/96 [00:04<00:35,  2.38it/s]
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Epoch 93 Training:  14%|█▎        | 13/96 [00:05<00:34,  2.38it/s]
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Epoch 93 Training:  15%|█▍        | 14/96 [00:05<00:34,  2.37it/s]
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Epoch 93 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 21:55:10] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0187 | Epoch 093] train/clipped_loss_min=-4.9999 train/clipped_loss_max=5.0000 train/clipped_loss_mean=0.0216 train/clipped_loss_std=2.1830 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0.0001 train/unclipped_loss_min=-4.9999 train/unclipped_loss_max=5.0000 train/unclipped_loss_mean=0.0216 train/unclipped_loss_std=2.1830 train/policy_loss=0.0216 train/loss=0.0216 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0004
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+[2026-10-01 21:55:10] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0188 | Epoch 093] timing/rollout_seconds=147.3132 timing/feedback_seconds=5.5075 timing/optimization_seconds=41.0400 timing/cycle_seconds=191.8813
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+
Pointwise Rewards: ssim:   0%|          | 0/6 [00:00<?, ?it/s]
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Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:00<00:00,  6.52it/s]
+
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Pointwise Rewards: dists:  83%|████████▎ | 5/6 [00:01<00:00,  3.73it/s]
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Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.72it/s]
+
Pointwise Rewards: psnr:   0%|          | 0/6 [00:00<?, ?it/s]
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Pointwise Rewards: psnr:  33%|███▎      | 2/6 [00:00<00:00,  6.94it/s]
Pointwise Rewards: psnr:  50%|█████     | 3/6 [00:00<00:00,  6.85it/s]
Pointwise Rewards: psnr:  67%|██████▋   | 4/6 [00:00<00:00,  6.99it/s]
Pointwise Rewards: psnr:  83%|████████▎ | 5/6 [00:00<00:00,  6.91it/s]
Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.93it/s]
Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.92it/s]
+[2026-10-01 21:57:42] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0188 | Epoch 094] train/reward_dists_mean=0.9274 train/reward_dists_std=0.0161 train/reward_psnr_mean=28.0721 train/reward_psnr_std=2.5046 train/reward_ssim_mean=0.9208 train/reward_ssim_std=0.0246 train/reward_dists_group_std_mean=0.0026 train/reward_dists_group_std_max=0.0062 train/reward_dists_group_std_min=0.0008 train/reward_dists_group_mean_std=0.0159 train/reward_psnr_group_std_mean=0.7082 train/reward_psnr_group_std_max=1.4053 train/reward_psnr_group_std_min=0.2257 train/reward_psnr_group_mean_std=2.3871 train/reward_ssim_group_std_mean=0.0010 train/reward_ssim_group_std_max=0.0042 train/reward_ssim_group_std_min=0.0004 train/reward_ssim_group_mean_std=0.0245 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-6.9649 train/adv_max=6.0647 train/adv_abs_mean=1.8900
+
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Epoch 94 Training:   5%|▌         | 5/96 [00:02<00:37,  2.40it/s]
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Epoch 94 Training:   6%|▋         | 6/96 [00:02<00:37,  2.39it/s]
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Epoch 94 Training:   7%|▋         | 7/96 [00:02<00:37,  2.38it/s]
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Epoch 94 Training:   8%|▊         | 8/96 [00:03<00:36,  2.38it/s]
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Epoch 94 Training:   9%|▉         | 9/96 [00:03<00:36,  2.38it/s]
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Epoch 94 Training:  11%|█▏        | 11/96 [00:04<00:35,  2.38it/s]
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Epoch 94 Training:  12%|█▎        | 12/96 [00:05<00:35,  2.38it/s]
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Epoch 94 Training:  14%|█▎        | 13/96 [00:05<00:34,  2.37it/s]
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Epoch 94 Training:  15%|█▍        | 14/96 [00:05<00:34,  2.37it/s]
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Epoch 94 Training:  16%|█▌        | 15/96 [00:06<00:34,  2.38it/s]
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Epoch 94 Training:  17%|█▋        | 16/96 [00:06<00:33,  2.37it/s]
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Epoch 94 Training:  22%|██▏       | 21/96 [00:08<00:31,  2.37it/s]
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Epoch 94 Training:  26%|██▌       | 25/96 [00:10<00:29,  2.37it/s]
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Epoch 94 Training:  27%|██▋       | 26/96 [00:10<00:29,  2.37it/s]
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Epoch 94 Training:  28%|██▊       | 27/96 [00:11<00:29,  2.37it/s]
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Epoch 94 Training:  29%|██▉       | 28/96 [00:11<00:28,  2.37it/s]
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Epoch 94 Training:  34%|███▍      | 33/96 [00:13<00:26,  2.37it/s]
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Epoch 94 Training:  39%|███▊      | 37/96 [00:15<00:24,  2.37it/s]
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Epoch 94 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 21:58:02] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0188 | Epoch 094] train/clipped_loss_min=-5 train/clipped_loss_max=5 train/clipped_loss_mean=0.0166 train/clipped_loss_std=2.2969 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-5 train/unclipped_loss_max=5 train/unclipped_loss_mean=0.0166 train/unclipped_loss_std=2.2969 train/policy_loss=0.0166 train/loss=0.0166 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0006
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Epoch 94 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 21:58:22] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0189 | Epoch 094] train/clipped_loss_min=-5.0000 train/clipped_loss_max=5.0000 train/clipped_loss_mean=-0.0370 train/clipped_loss_std=2.2564 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-5.0000 train/unclipped_loss_max=5.0000 train/unclipped_loss_mean=-0.0370 train/unclipped_loss_std=2.2564 train/policy_loss=-0.0370 train/loss=-0.0370 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0006
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+[2026-10-01 21:58:22] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0190 | Epoch 094] timing/rollout_seconds=147.1834 timing/feedback_seconds=5.2268 timing/optimization_seconds=41.0086 timing/cycle_seconds=191.7228
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+
Pointwise Rewards: ssim:   0%|          | 0/6 [00:00<?, ?it/s]
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Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:00<00:00,  6.43it/s]
+
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Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.73it/s]
+
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Pointwise Rewards: psnr:  33%|███▎      | 2/6 [00:00<00:00,  6.70it/s]
Pointwise Rewards: psnr:  50%|█████     | 3/6 [00:00<00:00,  6.83it/s]
Pointwise Rewards: psnr:  67%|██████▋   | 4/6 [00:00<00:00,  6.86it/s]
Pointwise Rewards: psnr:  83%|████████▎ | 5/6 [00:00<00:00,  6.94it/s]
Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.87it/s]
Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.85it/s]
+[2026-10-01 22:00:53] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0190 | Epoch 095] train/reward_dists_mean=0.9210 train/reward_dists_std=0.0190 train/reward_psnr_mean=28.0531 train/reward_psnr_std=2.3582 train/reward_ssim_mean=0.9159 train/reward_ssim_std=0.0253 train/reward_dists_group_std_mean=0.0027 train/reward_dists_group_std_max=0.0065 train/reward_dists_group_std_min=0.0010 train/reward_dists_group_mean_std=0.0188 train/reward_psnr_group_std_mean=0.7884 train/reward_psnr_group_std_max=1.5296 train/reward_psnr_group_std_min=0.4084 train/reward_psnr_group_mean_std=2.2076 train/reward_ssim_group_std_mean=0.0013 train/reward_ssim_group_std_max=0.0109 train/reward_ssim_group_std_min=0.0003 train/reward_ssim_group_mean_std=0.0252 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-6.8285 train/adv_max=8.5234 train/adv_abs_mean=1.8119
+
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Epoch 95 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 22:01:14] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0190 | Epoch 095] train/clipped_loss_min=-5 train/clipped_loss_max=5 train/clipped_loss_mean=0.0531 train/clipped_loss_std=2.2778 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-5 train/unclipped_loss_max=5 train/unclipped_loss_mean=0.0531 train/unclipped_loss_std=2.2778 train/policy_loss=0.0531 train/loss=0.0531 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0007
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Epoch 95 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 22:01:34] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0191 | Epoch 095] train/clipped_loss_min=-5.0000 train/clipped_loss_max=5.0000 train/clipped_loss_mean=-0.0424 train/clipped_loss_std=2.1606 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-5.0000 train/unclipped_loss_max=5.0000 train/unclipped_loss_mean=-0.0424 train/unclipped_loss_std=2.1606 train/policy_loss=-0.0424 train/loss=-0.0424 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0006
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+[2026-10-01 22:01:34] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0192 | Epoch 095] timing/rollout_seconds=147.1785 timing/feedback_seconds=5.3072 timing/optimization_seconds=41.0126 timing/cycle_seconds=191.7096
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+
Pointwise Rewards: ssim:   0%|          | 0/6 [00:00<?, ?it/s]
Pointwise Rewards: ssim:  17%|█▋        | 1/6 [00:00<00:00,  6.74it/s]
Pointwise Rewards: ssim:  33%|███▎      | 2/6 [00:00<00:00,  6.78it/s]
Pointwise Rewards: ssim:  50%|█████     | 3/6 [00:00<00:00,  6.74it/s]
Pointwise Rewards: ssim:  67%|██████▋   | 4/6 [00:00<00:00,  6.79it/s]
Pointwise Rewards: ssim:  83%|████████▎ | 5/6 [00:00<00:00,  6.68it/s]
Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:00<00:00,  6.72it/s]
Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:00<00:00,  6.73it/s]
+
Pointwise Rewards: dists:   0%|          | 0/6 [00:00<?, ?it/s]
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Pointwise Rewards: dists:  33%|███▎      | 2/6 [00:00<00:01,  3.79it/s]
Pointwise Rewards: dists:  50%|█████     | 3/6 [00:00<00:00,  3.76it/s]
Pointwise Rewards: dists:  67%|██████▋   | 4/6 [00:01<00:00,  3.74it/s]
Pointwise Rewards: dists:  83%|████████▎ | 5/6 [00:01<00:00,  3.73it/s]
Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.72it/s]
Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.74it/s]
+
Pointwise Rewards: psnr:   0%|          | 0/6 [00:00<?, ?it/s]
Pointwise Rewards: psnr:  17%|█▋        | 1/6 [00:00<00:00,  7.26it/s]
Pointwise Rewards: psnr:  33%|███▎      | 2/6 [00:00<00:00,  7.19it/s]
Pointwise Rewards: psnr:  50%|█████     | 3/6 [00:00<00:00,  7.05it/s]
Pointwise Rewards: psnr:  67%|██████▋   | 4/6 [00:00<00:00,  6.97it/s]
Pointwise Rewards: psnr:  83%|████████▎ | 5/6 [00:00<00:00,  6.93it/s]
Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.86it/s]
Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.94it/s]
+[2026-10-01 22:04:05] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0192 | Epoch 096] train/reward_dists_mean=0.9268 train/reward_dists_std=0.0200 train/reward_psnr_mean=28.7839 train/reward_psnr_std=3.1629 train/reward_ssim_mean=0.9181 train/reward_ssim_std=0.0289 train/reward_dists_group_std_mean=0.0024 train/reward_dists_group_std_max=0.0054 train/reward_dists_group_std_min=0.0008 train/reward_dists_group_mean_std=0.0199 train/reward_psnr_group_std_mean=0.6085 train/reward_psnr_group_std_max=1.5047 train/reward_psnr_group_std_min=0.1195 train/reward_psnr_group_mean_std=3.0906 train/reward_ssim_group_std_mean=0.0009 train/reward_ssim_group_std_max=0.0031 train/reward_ssim_group_std_min=0.0003 train/reward_ssim_group_mean_std=0.0288 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-7.3334 train/adv_max=6.4551 train/adv_abs_mean=1.8045
+
Epoch 96 Training:   0%|          | 0/96 [00:00<?, ?it/s]
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Epoch 96 Training:   1%|          | 1/96 [00:00<00:35,  2.67it/s]
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Epoch 96 Training:   3%|▎         | 3/96 [00:01<00:38,  2.43it/s]
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Epoch 96 Training:   4%|▍         | 4/96 [00:01<00:38,  2.41it/s]
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Epoch 96 Training:   5%|▌         | 5/96 [00:02<00:37,  2.40it/s]
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Epoch 96 Training:   6%|▋         | 6/96 [00:02<00:38,  2.36it/s]
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Epoch 96 Training:   7%|▋         | 7/96 [00:02<00:37,  2.39it/s]
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Epoch 96 Training:   8%|▊         | 8/96 [00:03<00:36,  2.39it/s]
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Epoch 96 Training:   9%|▉         | 9/96 [00:03<00:36,  2.38it/s]
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Epoch 96 Training:  10%|█         | 10/96 [00:04<00:36,  2.37it/s]
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Epoch 96 Training:  11%|█▏        | 11/96 [00:04<00:35,  2.38it/s]
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Epoch 96 Training:  12%|█▎        | 12/96 [00:05<00:35,  2.38it/s]
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Epoch 96 Training:  14%|█▎        | 13/96 [00:05<00:34,  2.37it/s]
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Epoch 96 Training:  15%|█▍        | 14/96 [00:05<00:34,  2.37it/s]
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Epoch 96 Training:  16%|█▌        | 15/96 [00:06<00:34,  2.37it/s]
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Epoch 96 Training:  18%|█▊        | 17/96 [00:07<00:33,  2.37it/s]
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Epoch 96 Training:  21%|██        | 20/96 [00:08<00:32,  2.37it/s]
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Epoch 96 Training:  22%|██▏       | 21/96 [00:08<00:31,  2.37it/s]
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Epoch 96 Training:  25%|██▌       | 24/96 [00:10<00:30,  2.37it/s]
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Epoch 96 Training:  27%|██▋       | 26/96 [00:10<00:29,  2.37it/s]
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Epoch 96 Training:  28%|██▊       | 27/96 [00:11<00:29,  2.37it/s]
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Epoch 96 Training:  29%|██▉       | 28/96 [00:11<00:28,  2.37it/s]
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Epoch 96 Training:  30%|███       | 29/96 [00:12<00:28,  2.37it/s]
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Epoch 96 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 22:04:25] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0192 | Epoch 096] train/clipped_loss_min=-5 train/clipped_loss_max=5 train/clipped_loss_mean=0.0704 train/clipped_loss_std=2.2025 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-5 train/unclipped_loss_max=5 train/unclipped_loss_mean=0.0704 train/unclipped_loss_std=2.2025 train/policy_loss=0.0704 train/loss=0.0704 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0006
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Epoch 96 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 22:04:46] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0193 | Epoch 096] train/clipped_loss_min=-5.0000 train/clipped_loss_max=5.0000 train/clipped_loss_mean=-0.0803 train/clipped_loss_std=2.1586 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-5.0000 train/unclipped_loss_max=5.0000 train/unclipped_loss_mean=-0.0803 train/unclipped_loss_std=2.1586 train/policy_loss=-0.0803 train/loss=-0.0803 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0006
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+[2026-10-01 22:04:46] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0194 | Epoch 096] timing/rollout_seconds=147.2686 timing/feedback_seconds=5.5583 timing/optimization_seconds=41.0299 timing/cycle_seconds=191.8735
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+[2026-10-01 22:07:17] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0194 | Epoch 097] train/reward_dists_mean=0.9263 train/reward_dists_std=0.0189 train/reward_psnr_mean=29.2983 train/reward_psnr_std=1.7843 train/reward_ssim_mean=0.9181 train/reward_ssim_std=0.0240 train/reward_dists_group_std_mean=0.0024 train/reward_dists_group_std_max=0.0049 train/reward_dists_group_std_min=0.0011 train/reward_dists_group_mean_std=0.0187 train/reward_psnr_group_std_mean=0.7135 train/reward_psnr_group_std_max=1.5752 train/reward_psnr_group_std_min=0.1740 train/reward_psnr_group_mean_std=1.5925 train/reward_ssim_group_std_mean=0.0008 train/reward_ssim_group_std_max=0.0023 train/reward_ssim_group_std_min=0.0003 train/reward_ssim_group_mean_std=0.0240 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-6.9271 train/adv_max=5.5202 train/adv_abs_mean=1.7820
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Epoch 97 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 22:07:37] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0194 | Epoch 097] train/clipped_loss_min=-5 train/clipped_loss_max=5 train/clipped_loss_mean=0.0440 train/clipped_loss_std=2.2332 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-5 train/unclipped_loss_max=5 train/unclipped_loss_mean=0.0440 train/unclipped_loss_std=2.2332 train/policy_loss=0.0440 train/loss=0.0440 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0002
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Epoch 97 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 22:07:58] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0195 | Epoch 097] train/clipped_loss_min=-5.0000 train/clipped_loss_max=5.0000 train/clipped_loss_mean=-0.0601 train/clipped_loss_std=2.1319 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0 train/unclipped_loss_min=-5.0000 train/unclipped_loss_max=5.0000 train/unclipped_loss_mean=-0.0601 train/unclipped_loss_std=2.1319 train/policy_loss=-0.0601 train/loss=-0.0601 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0002
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+[2026-10-01 22:07:58] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0196 | Epoch 097] timing/rollout_seconds=147.2570 timing/feedback_seconds=5.4072 timing/optimization_seconds=41.0752 timing/cycle_seconds=191.9515
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+[2026-10-01 22:10:29] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0196 | Epoch 098] train/reward_dists_mean=0.9283 train/reward_dists_std=0.0179 train/reward_psnr_mean=28.0277 train/reward_psnr_std=2.2180 train/reward_ssim_mean=0.9152 train/reward_ssim_std=0.0212 train/reward_dists_group_std_mean=0.0025 train/reward_dists_group_std_max=0.0064 train/reward_dists_group_std_min=0.0014 train/reward_dists_group_mean_std=0.0177 train/reward_psnr_group_std_mean=0.7511 train/reward_psnr_group_std_max=3.2718 train/reward_psnr_group_std_min=0.2144 train/reward_psnr_group_mean_std=2.0384 train/reward_ssim_group_std_mean=0.0010 train/reward_ssim_group_std_max=0.0067 train/reward_ssim_group_std_min=0.0003 train/reward_ssim_group_mean_std=0.0211 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-6.7933 train/adv_max=5.5380 train/adv_abs_mean=1.9106
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Epoch 98 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 22:10:49] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0196 | Epoch 098] train/clipped_loss_min=-5 train/clipped_loss_max=5 train/clipped_loss_mean=-0.0207 train/clipped_loss_std=2.2864 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-5 train/unclipped_loss_max=5 train/unclipped_loss_mean=-0.0207 train/unclipped_loss_std=2.2864 train/policy_loss=-0.0207 train/loss=-0.0207 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0004
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Epoch 98 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 22:11:10] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0197 | Epoch 098] train/clipped_loss_min=-5.0000 train/clipped_loss_max=5.0000 train/clipped_loss_mean=-0.0135 train/clipped_loss_std=2.3033 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0.0002 train/unclipped_loss_min=-5.0000 train/unclipped_loss_max=5.0000 train/unclipped_loss_mean=-0.0135 train/unclipped_loss_std=2.3033 train/policy_loss=-0.0135 train/loss=-0.0135 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0005
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+[2026-10-01 22:11:10] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0198 | Epoch 098] timing/rollout_seconds=147.1717 timing/feedback_seconds=5.1695 timing/optimization_seconds=41.0814 timing/cycle_seconds=191.8318
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+
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+[2026-10-01 22:13:41] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0198 | Epoch 099] train/reward_dists_mean=0.9221 train/reward_dists_std=0.0288 train/reward_psnr_mean=28.2928 train/reward_psnr_std=2.8154 train/reward_ssim_mean=0.9172 train/reward_ssim_std=0.0244 train/reward_dists_group_std_mean=0.0030 train/reward_dists_group_std_max=0.0243 train/reward_dists_group_std_min=0.0012 train/reward_dists_group_mean_std=0.0285 train/reward_psnr_group_std_mean=0.8601 train/reward_psnr_group_std_max=5.4151 train/reward_psnr_group_std_min=0.1879 train/reward_psnr_group_mean_std=2.5766 train/reward_ssim_group_std_mean=0.0023 train/reward_ssim_group_std_max=0.0663 train/reward_ssim_group_std_min=0.0004 train/reward_ssim_group_mean_std=0.0224 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-6.6981 train/adv_max=7.2042 train/adv_abs_mean=1.8911
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Epoch 99 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 22:14:01] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0198 | Epoch 099] train/clipped_loss_min=-5 train/clipped_loss_max=5 train/clipped_loss_mean=-0.0254 train/clipped_loss_std=2.2318 train/ratio_min=1 train/ratio_max=1 train/ratio_mean=1 train/ratio_std=0 train/unclipped_loss_min=-5 train/unclipped_loss_max=5 train/unclipped_loss_mean=-0.0254 train/unclipped_loss_std=2.2318 train/policy_loss=-0.0254 train/loss=-0.0254 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0007
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+
Epoch 99 Timestep:   0%|          | 0/1 [00:00<?, ?it/s][2026-10-01 22:14:21] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0199 | Epoch 099] train/clipped_loss_min=-5.0000 train/clipped_loss_max=5.0000 train/clipped_loss_mean=0.0146 train/clipped_loss_std=2.3281 train/ratio_min=1.0000 train/ratio_max=1.0000 train/ratio_mean=1.0000 train/ratio_std=0.0001 train/unclipped_loss_min=-5.0000 train/unclipped_loss_max=5.0000 train/unclipped_loss_mean=0.0146 train/unclipped_loss_std=2.3281 train/policy_loss=0.0146 train/loss=0.0146 train/clip_frac_high=0 train/clip_frac_low=0 train/clip_frac_total=0 train/grad_norm=0.0007
+
+
Epoch 99 Timestep: 100%|██████████| 1/1 [00:00<00:00,  2.33it/s]
+
                                                                
Epoch 99 Training: 100%|██████████| 96/96 [00:40<00:00,  2.29it/s]
Epoch 99 Training: 100%|██████████| 96/96 [00:40<00:00,  2.36it/s]
+[2026-10-01 22:14:21] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0200 | Epoch 099] timing/rollout_seconds=147.0699 timing/feedback_seconds=5.2891 timing/optimization_seconds=41.3157 timing/cycle_seconds=191.9568
+[2026-10-01 22:14:22] [Rank 0] [INFO] [flow_factory.models.abc]: Saving LoRA weights for transformer role=base to /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/runs/klein4b_grpo_vedai_full_ssim_dists_psnr_lr5e-5/saves/klein4b_grpo_vedai_full_ssim_dists_psnr_lr5e-5/checkpoints/checkpoint-100
+[2026-10-01 22:14:22] [Rank 0] [INFO] [flow_factory.models.abc]: Checkpoint saved successfully to /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/runs/klein4b_grpo_vedai_full_ssim_dists_psnr_lr5e-5/saves/klein4b_grpo_vedai_full_ssim_dists_psnr_lr5e-5/checkpoints/checkpoint-100
+
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+
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+
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Pointwise Rewards: psnr: 100%|██████████| 1/1 [00:00<00:00, 14.06it/s]
+
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Pointwise Rewards: dists: 100%|██████████| 1/1 [00:00<00:00,  8.74it/s]
+[2026-10-01 22:15:06] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0200 | Epoch 100] eval/vedai_rl_full/reward_dists_mean=0.9290 eval/vedai_rl_full/reward_dists_std=0.0209 eval/vedai_rl_full/reward_psnr_mean=28.2724 eval/vedai_rl_full/reward_psnr_std=2.6374 eval/vedai_rl_full/reward_ssim_mean=0.9176 eval/vedai_rl_full/reward_ssim_std=0.0275
+
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Epoch 100 Sampling:  51%|█████     | 49/96 [01:14<01:10,  1.51s/it]W1001 22:16:21.876000 4096482 site-packages/torch/distributed/elastic/agent/server/api.py:739] Received Signals.SIGTERM death signal, shutting down workers
+W1001 22:16:21.903000 4096482 site-packages/torch/distributed/elastic/multiprocessing/api.py:1010] Sending process 4096667 closing signal SIGTERM
+W1001 22:16:21.905000 4096482 site-packages/torch/distributed/elastic/multiprocessing/api.py:1010] Sending process 4096668 closing signal SIGTERM
+W1001 22:16:21.906000 4096482 site-packages/torch/distributed/elastic/multiprocessing/api.py:1010] Sending process 4096669 closing signal SIGTERM
+W1001 22:16:21.906000 4096482 site-packages/torch/distributed/elastic/multiprocessing/api.py:1010] Sending process 4096670 closing signal SIGTERM
+W1001 22:16:21.906000 4096482 site-packages/torch/distributed/elastic/multiprocessing/api.py:1010] Sending process 4096671 closing signal SIGTERM
+W1001 22:16:21.907000 4096482 site-packages/torch/distributed/elastic/multiprocessing/api.py:1010] Sending process 4096672 closing signal SIGTERM
+W1001 22:16:21.907000 4096482 site-packages/torch/distributed/elastic/multiprocessing/api.py:1010] Sending process 4096673 closing signal SIGTERM
+W1001 22:16:21.907000 4096482 site-packages/torch/distributed/elastic/multiprocessing/api.py:1010] Sending process 4096674 closing signal SIGTERM
+Traceback (most recent call last):
+  File "/opt/conda/envs/ff-qi21/bin/accelerate", line 6, in <module>
+    sys.exit(main())
+  File "/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/accelerate/commands/accelerate_cli.py", line 50, in main
+    args.func(args)
+  File "/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/accelerate/commands/launch.py", line 1392, in launch_command
+    deepspeed_launcher(args)
+  File "/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/accelerate/commands/launch.py", line 1076, in deepspeed_launcher
+    distrib_run.run(args)
+  File "/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torch/distributed/run.py", line 982, in run
+    elastic_launch(
+  File "/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torch/distributed/launcher/api.py", line 170, in __call__
+    return launch_agent(self._config, self._entrypoint, list(args))
+  File "/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torch/distributed/launcher/api.py", line 308, in launch_agent
+    result = agent.run()
+  File "/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torch/distributed/elastic/metrics/api.py", line 134, in wrapper
+    result = f(*args, **kwargs)
+  File "/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torch/distributed/elastic/agent/server/api.py", line 731, in run
+    result = self._invoke_run(role)
+  File "/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torch/distributed/elastic/agent/server/api.py", line 908, in _invoke_run
+    time.sleep(monitor_interval)
+  File "/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torch/distributed/elastic/multiprocessing/api.py", line 86, in _terminate_process_handler
+    raise SignalException(f"Process {os.getpid()} got signal: {sigval}", sigval=sigval)
+torch.distributed.elastic.multiprocessing.api.SignalException: Process 4096482 got signal: 15
diff --git a/runs/klein4b_nft_detector_clean_from_ep72/saves/klein4b_nft_detector_clean_from_ep72/checkpoints/checkpoint-10/README.md b/runs/klein4b_nft_detector_clean_from_ep72/saves/klein4b_nft_detector_clean_from_ep72/checkpoints/checkpoint-10/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..2b78a7a995873d5db3090fca56885fe89cdf97e1
--- /dev/null
+++ b/runs/klein4b_nft_detector_clean_from_ep72/saves/klein4b_nft_detector_clean_from_ep72/checkpoints/checkpoint-10/README.md
@@ -0,0 +1,204 @@
+---
+base_model: /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/flow_factory_gpu_gate_models/FLUX.2-klein-base-4B/transformer
+library_name: peft
+tags:
+- lora
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]
+### Framework versions
+
+- PEFT 0.20.0
\ No newline at end of file
diff --git a/runs/klein4b_nft_detector_clean_from_ep72/saves/klein4b_nft_detector_clean_from_ep72/checkpoints/checkpoint-10/adapter_config.json b/runs/klein4b_nft_detector_clean_from_ep72/saves/klein4b_nft_detector_clean_from_ep72/checkpoints/checkpoint-10/adapter_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..1685005433dab09d6549197e3b83e9c74e2a35c4
--- /dev/null
+++ b/runs/klein4b_nft_detector_clean_from_ep72/saves/klein4b_nft_detector_clean_from_ep72/checkpoints/checkpoint-10/adapter_config.json
@@ -0,0 +1,59 @@
+{
+  "alora_invocation_tokens": null,
+  "alpha_pattern": {},
+  "arrow_config": null,
+  "auto_mapping": {
+    "base_model_class": "Flux2Transformer2DModel",
+    "parent_library": "diffusers.models.transformers.transformer_flux2"
+  },
+  "base_model_name_or_path": null,
+  "bias": "none",
+  "corda_config": null,
+  "ensure_weight_tying": false,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": "gaussian",
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 128,
+  "lora_bias": false,
+  "lora_dropout": 0.0,
+  "lora_ga_config": null,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "monteclora_config": null,
+  "peft_type": "LORA",
+  "peft_version": "0.20.0",
+  "qalora_group_size": 16,
+  "r": 64,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "attn.to_v",
+    "ff_context.linear_out",
+    "attn.to_add_out",
+    "attn.add_k_proj",
+    "attn.add_q_proj",
+    "attn.add_v_proj",
+    "ff.linear_in",
+    "ff_context.linear_in",
+    "attn.to_k",
+    "attn.to_qkv_mlp_proj",
+    "attn.to_q",
+    "ff.linear_out",
+    "attn.to_out.0"
+  ],
+  "target_parameters": null,
+  "task_type": null,
+  "trainable_token_indices": null,
+  "use_bdlora": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false,
+  "velora_config": null
+}
\ No newline at end of file
diff --git a/runs/klein4b_nft_detector_clean_from_ep72/saves/klein4b_nft_detector_clean_from_ep72/checkpoints/checkpoint-10/manifest.json b/runs/klein4b_nft_detector_clean_from_ep72/saves/klein4b_nft_detector_clean_from_ep72/checkpoints/checkpoint-10/manifest.json
new file mode 100644
index 0000000000000000000000000000000000000000..4443fd995e0f6f3bd9b7e3dacea93df8d345ecfb
--- /dev/null
+++ b/runs/klein4b_nft_detector_clean_from_ep72/saves/klein4b_nft_detector_clean_from_ep72/checkpoints/checkpoint-10/manifest.json
@@ -0,0 +1,16 @@
+{
+  "base_role": "base",
+  "entries": [
+    {
+      "component": "transformer",
+      "path": ".",
+      "role": "base"
+    }
+  ],
+  "finetune_type": "lora",
+  "format_version": 1,
+  "primary": {
+    "component": "transformer",
+    "path": "."
+  }
+}
diff --git a/runs/klein4b_nft_detector_hardneg_from_ep72/saves/klein4b_nft_detector_hardneg_from_ep72/checkpoints/checkpoint-10/README.md b/runs/klein4b_nft_detector_hardneg_from_ep72/saves/klein4b_nft_detector_hardneg_from_ep72/checkpoints/checkpoint-10/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..2b78a7a995873d5db3090fca56885fe89cdf97e1
--- /dev/null
+++ b/runs/klein4b_nft_detector_hardneg_from_ep72/saves/klein4b_nft_detector_hardneg_from_ep72/checkpoints/checkpoint-10/README.md
@@ -0,0 +1,204 @@
+---
+base_model: /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/flow_factory_gpu_gate_models/FLUX.2-klein-base-4B/transformer
+library_name: peft
+tags:
+- lora
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]
+### Framework versions
+
+- PEFT 0.20.0
\ No newline at end of file
diff --git a/runs/klein4b_nft_detector_hardneg_from_ep72/saves/klein4b_nft_detector_hardneg_from_ep72/checkpoints/checkpoint-10/adapter_config.json b/runs/klein4b_nft_detector_hardneg_from_ep72/saves/klein4b_nft_detector_hardneg_from_ep72/checkpoints/checkpoint-10/adapter_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..e136dd93badd88860dbe554bbfe1ef3ac44aaccf
--- /dev/null
+++ b/runs/klein4b_nft_detector_hardneg_from_ep72/saves/klein4b_nft_detector_hardneg_from_ep72/checkpoints/checkpoint-10/adapter_config.json
@@ -0,0 +1,59 @@
+{
+  "alora_invocation_tokens": null,
+  "alpha_pattern": {},
+  "arrow_config": null,
+  "auto_mapping": {
+    "base_model_class": "Flux2Transformer2DModel",
+    "parent_library": "diffusers.models.transformers.transformer_flux2"
+  },
+  "base_model_name_or_path": null,
+  "bias": "none",
+  "corda_config": null,
+  "ensure_weight_tying": false,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": "gaussian",
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 128,
+  "lora_bias": false,
+  "lora_dropout": 0.0,
+  "lora_ga_config": null,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "monteclora_config": null,
+  "peft_type": "LORA",
+  "peft_version": "0.20.0",
+  "qalora_group_size": 16,
+  "r": 64,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "attn.add_k_proj",
+    "attn.add_v_proj",
+    "attn.to_out.0",
+    "attn.add_q_proj",
+    "ff_context.linear_out",
+    "attn.to_v",
+    "ff_context.linear_in",
+    "attn.to_q",
+    "ff.linear_out",
+    "ff.linear_in",
+    "attn.to_add_out",
+    "attn.to_qkv_mlp_proj",
+    "attn.to_k"
+  ],
+  "target_parameters": null,
+  "task_type": null,
+  "trainable_token_indices": null,
+  "use_bdlora": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false,
+  "velora_config": null
+}
\ No newline at end of file
diff --git a/runs/klein4b_nft_detector_hardneg_from_ep72/saves/klein4b_nft_detector_hardneg_from_ep72/checkpoints/checkpoint-10/manifest.json b/runs/klein4b_nft_detector_hardneg_from_ep72/saves/klein4b_nft_detector_hardneg_from_ep72/checkpoints/checkpoint-10/manifest.json
new file mode 100644
index 0000000000000000000000000000000000000000..4443fd995e0f6f3bd9b7e3dacea93df8d345ecfb
--- /dev/null
+++ b/runs/klein4b_nft_detector_hardneg_from_ep72/saves/klein4b_nft_detector_hardneg_from_ep72/checkpoints/checkpoint-10/manifest.json
@@ -0,0 +1,16 @@
+{
+  "base_role": "base",
+  "entries": [
+    {
+      "component": "transformer",
+      "path": ".",
+      "role": "base"
+    }
+  ],
+  "finetune_type": "lora",
+  "format_version": 1,
+  "primary": {
+    "component": "transformer",
+    "path": "."
+  }
+}
diff --git a/runs/klein4b_nft_domain_sirst/saves/klein4b_nft_domain_sirst/checkpoints/checkpoint-10/README.md b/runs/klein4b_nft_domain_sirst/saves/klein4b_nft_domain_sirst/checkpoints/checkpoint-10/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..2b78a7a995873d5db3090fca56885fe89cdf97e1
--- /dev/null
+++ b/runs/klein4b_nft_domain_sirst/saves/klein4b_nft_domain_sirst/checkpoints/checkpoint-10/README.md
@@ -0,0 +1,204 @@
+---
+base_model: /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/flow_factory_gpu_gate_models/FLUX.2-klein-base-4B/transformer
+library_name: peft
+tags:
+- lora
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]
+### Framework versions
+
+- PEFT 0.20.0
\ No newline at end of file
diff --git a/runs/klein4b_nft_domain_sirst/saves/klein4b_nft_domain_sirst/checkpoints/checkpoint-10/adapter_config.json b/runs/klein4b_nft_domain_sirst/saves/klein4b_nft_domain_sirst/checkpoints/checkpoint-10/adapter_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..cf1a48bd7e8230366a2e0092f4a443dcf98c140e
--- /dev/null
+++ b/runs/klein4b_nft_domain_sirst/saves/klein4b_nft_domain_sirst/checkpoints/checkpoint-10/adapter_config.json
@@ -0,0 +1,59 @@
+{
+  "alora_invocation_tokens": null,
+  "alpha_pattern": {},
+  "arrow_config": null,
+  "auto_mapping": {
+    "base_model_class": "Flux2Transformer2DModel",
+    "parent_library": "diffusers.models.transformers.transformer_flux2"
+  },
+  "base_model_name_or_path": null,
+  "bias": "none",
+  "corda_config": null,
+  "ensure_weight_tying": false,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": "gaussian",
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 128,
+  "lora_bias": false,
+  "lora_dropout": 0.0,
+  "lora_ga_config": null,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "monteclora_config": null,
+  "peft_type": "LORA",
+  "peft_version": "0.20.0",
+  "qalora_group_size": 16,
+  "r": 64,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "attn.add_k_proj",
+    "attn.add_v_proj",
+    "attn.to_q",
+    "attn.to_v",
+    "attn.add_q_proj",
+    "attn.to_out.0",
+    "attn.to_k",
+    "ff_context.linear_out",
+    "attn.to_add_out",
+    "ff.linear_out",
+    "attn.to_qkv_mlp_proj",
+    "ff_context.linear_in",
+    "ff.linear_in"
+  ],
+  "target_parameters": null,
+  "task_type": null,
+  "trainable_token_indices": null,
+  "use_bdlora": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false,
+  "velora_config": null
+}
\ No newline at end of file
diff --git a/runs/klein4b_nft_domain_sirst/saves/klein4b_nft_domain_sirst/checkpoints/checkpoint-10/manifest.json b/runs/klein4b_nft_domain_sirst/saves/klein4b_nft_domain_sirst/checkpoints/checkpoint-10/manifest.json
new file mode 100644
index 0000000000000000000000000000000000000000..4443fd995e0f6f3bd9b7e3dacea93df8d345ecfb
--- /dev/null
+++ b/runs/klein4b_nft_domain_sirst/saves/klein4b_nft_domain_sirst/checkpoints/checkpoint-10/manifest.json
@@ -0,0 +1,16 @@
+{
+  "base_role": "base",
+  "entries": [
+    {
+      "component": "transformer",
+      "path": ".",
+      "role": "base"
+    }
+  ],
+  "finetune_type": "lora",
+  "format_version": 1,
+  "primary": {
+    "component": "transformer",
+    "path": "."
+  }
+}
diff --git a/runs/klein4b_nft_domain_sirst/train.log b/runs/klein4b_nft_domain_sirst/train.log
new file mode 100644
index 0000000000000000000000000000000000000000..3cd01b8ac89b9827eda7a87686f4736a37387d5e
--- /dev/null
+++ b/runs/klein4b_nft_domain_sirst/train.log
@@ -0,0 +1,12168 @@
+[2026-10-02 07:24:44,725] [INFO] [flow_factory.cli]: ============================================================
+[2026-10-02 07:24:44,726] [INFO] [flow_factory.cli]: Flow-Factory Launch Configuration
+[2026-10-02 07:24:44,726] [INFO] [flow_factory.cli]:   Num machines:       1
+[2026-10-02 07:24:44,726] [INFO] [flow_factory.cli]:   Machine rank:       0
+[2026-10-02 07:24:44,726] [INFO] [flow_factory.cli]:   Num processes:      8
+[2026-10-02 07:24:44,727] [INFO] [flow_factory.cli]:   Master node:        localhost:29514
+[2026-10-02 07:24:44,727] [INFO] [flow_factory.cli]:   Mixed precision:    bf16
+[2026-10-02 07:24:44,727] [INFO] [flow_factory.cli]:   Accelerate config:  config/deepspeed/deepspeed_zero2.yaml
+[2026-10-02 07:24:44,727] [INFO] [flow_factory.cli]: ============================================================
+W1002 07:24:53.147000 3112123 site-packages/torch/distributed/run.py:852] 
+W1002 07:24:53.147000 3112123 site-packages/torch/distributed/run.py:852] *****************************************
+W1002 07:24:53.147000 3112123 site-packages/torch/distributed/run.py:852] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
+W1002 07:24:53.147000 3112123 site-packages/torch/distributed/run.py:852] *****************************************
+[2026-10-02 07:24:59] [Rank 0] [WARNING] [flow_factory.hparams.abc]: NFTTrainingArguments.from_dict captured 3 unknown key(s) into extra_kwargs: ['condition_image_size', 'flat_steps', 'ramp_rate']. Verify these are intentional (e.g., adapter-specific kwargs); typos against declared fields will be silently accepted otherwise.
+[2026-10-02 07:24:59] [Rank 0] [INFO] [flow_factory.hparams.training_args._base]: World Size: 8
+[2026-10-02 07:24:59] [Rank 0] [WARNING] [flow_factory.hparams.abc]: EvaluationArguments.from_dict captured 1 unknown key(s) into extra_kwargs: ['condition_image_size']. Verify these are intentional (e.g., adapter-specific kwargs); typos against declared fields will be silently accepted otherwise.
+[2026-10-02 07:24:59] [Rank 0] [WARNING] [flow_factory.hparams.abc]: RewardArguments.from_dict captured 2 unknown key(s) into extra_kwargs: ['encoder', 'probe']. Verify these are intentional (e.g., adapter-specific kwargs); typos against declared fields will be silently accepted otherwise.
+[2026-10-02 07:24:59] [Rank 0] [WARNING] [flow_factory.hparams.abc]: RewardArguments.from_dict captured 2 unknown key(s) into extra_kwargs: ['encoder', 'probe']. Verify these are intentional (e.g., adapter-specific kwargs); typos against declared fields will be silently accepted otherwise.
+[2026-10-02 07:24:59,803] [INFO] [flow_factory.train]: ====================================================================================================
+[2026-10-02 07:24:59,803] [INFO] [flow_factory.train]: Flow-Factory Training Initialized
+[2026-10-02 07:24:59,803] [INFO] [flow_factory.train]: World Size: 8
+[2026-10-02 07:24:59,803] [INFO] [flow_factory.train]: ====================================================================================================
+[2026-10-02 07:24:59,803] [INFO] [flow_factory.train]: Config: /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/runs/klein4b_nft_domain_sirst/train.yaml
+[2026-10-02 07:24:59,811] [INFO] [flow_factory.train]: 
+launcher: accelerate
+config_file: config/deepspeed/deepspeed_zero2.yaml
+num_processes: 8
+main_process_port: 29514
+mixed_precision: bf16
+data:
+  dataset_dir: data
+  cache_dir: ~/.cache/flow_factory/datasets
+  image_dir: null
+  video_dir: null
+  audio_dir: null
+  preprocessing_batch_size: 8
+  dataloader_num_workers: 8
+  enable_preprocess: true
+  force_reprocess: false
+  max_dataset_size: null
+  preprocess_parallelism: local
+  sampler_type: group_contiguous
+  sampler_subgroup_size: null
+  datasets:
+  - extra_kwargs: {}
+    name: places_domain_rl
+    dataset_dir: /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/places_domain_rl
+    image_dir: null
+    video_dir: null
+    audio_dir: null
+    train:
+      extra_kwargs: {}
+      enabled: true
+      split: train
+      weight: 1
+      max_dataset_size: null
+      unique_sample_num_per_epoch: 48
+      num_batches_per_epoch: null
+    eval:
+      extra_kwargs: {}
+      enabled: true
+      split: test
+      max_dataset_size: null
+      resolution: null
+      num_inference_steps: null
+      guidance_scale: null
+    source_id: 0
+model:
+  model_name_or_path: /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/flow_factory_gpu_gate_models/FLUX.2-klein-base-4B
+  finetune_type: lora
+  trainable_parameters_dtype: bfloat16
+  component_load_dtypes: null
+  frozen_parameters_dtype: null
+  target_components:
+  - transformer
+  target_modules: default
+  model_type: flux2-klein
+  lora_rank: 64
+  lora_alpha: 128
+  resume_path: /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/runs/klein4b_sft_flir_full_r64a128_lr1e-4/saves/klein4b_sft_flir_full_r64a128_lr1e-4/checkpoints/checkpoint-12
+  resume_type: lora
+scheduler:
+  dynamics_type: ODE
+  noise_level: 0.0
+  num_sde_steps: 0
+  sde_steps: []
+  seed: 42
+training:
+  trainer_type: nft
+  resolution:
+  - 512
+  - 640
+  height: 512
+  width: 640
+  num_frames: null
+  frame_rate: null
+  max_epochs: 30
+  per_device_batch_size: 1
+  gradient_step_per_epoch: 1
+  max_grad_norm: 1.0
+  num_batches_per_epoch: 96
+  gradient_accumulation_steps: 192
+  num_inner_epochs: 1
+  group_size: 16
+  unique_sample_num_per_epoch: 48
+  num_inference_steps: 8
+  guidance_scale: 1.0
+  ref_param_device: cuda
+  seed: 42
+  learning_rate: 1.0e-05
+  adam_weight_decay: 0.0001
+  adam_betas:
+  - 0.9
+  - 0.999
+  adam_epsilon: 1.0e-08
+  enable_gradient_checkpointing: false
+  offload_samples_to_cpu: false
+  ema_decay: 0.5
+  ema_update_interval: 1
+  ema_device: cuda
+  ema_decay_schedule: piecewise_linear
+  latent_storage_dtype: fp16
+  shuffle_samples: true
+  reward_optimization_overlap: false
+  reward_optimization_overlap_mode: ready
+  reward_optimization_overlap_poll_interval: 0.05
+  global_std: false
+  advantage_aggregation: gdpo
+  nft_beta: 0.1
+  off_policy: true
+  adv_clip_range:
+  - -5.0
+  - 5.0
+  kl_type: v-based
+  kl_beta: 0.0
+  num_train_timesteps: 2
+  time_sampling_strategy: discrete
+  time_shift: 3.0
+  timestep_range:
+  - 0.0
+  - 0.5
+  flat_steps: 0
+  ramp_rate: 0.001
+  condition_image_size: 1024
+eval:
+  resolution:
+  - 512
+  - 640
+  height: 512
+  width: 640
+  num_frames: null
+  frame_rate: null
+  per_device_batch_size: 1
+  seed: 42
+  guidance_scale: 1.0
+  num_inference_steps: 8
+  eval_freq: 5
+  condition_image_size: 1024
+log:
+  run_name: klein4b_nft_domain_sirst
+  project: IR-Engine
+  logging_backend: wandb
+  save_dir: /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/runs/klein4b_nft_domain_sirst/saves
+  save_freq: 10
+  save_model_only: true
+  verbose: true
+acceleration:
+  shared: []
+  rollout: []
+reward:
+  reward_0:
+    name: domain
+    reward_model: ir_rewards.domain.DomainProbeReward
+    weight:
+      places_domain_rl: 1.0
+    dtype: float32
+    device: cuda
+    batch_size: 16
+    async_reward: false
+    num_workers: 1
+    applicable_datasets:
+    - places_domain_rl
+    probe: /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/domain_probe.npz
+    encoder: /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/cache/dinov2-small
+  reward_1:
+    name: structure
+    reward_model: ir_rewards.fidelity.CoarseStructureReward
+    weight:
+      places_domain_rl: 1.0
+    dtype: float32
+    device: cuda
+    batch_size: 16
+    async_reward: false
+    num_workers: 1
+    applicable_datasets:
+    - places_domain_rl
+eval_reward:
+  reward_0:
+    name: domain
+    reward_model: ir_rewards.domain.DomainProbeReward
+    weight:
+      places_domain_rl: 1.0
+    dtype: float32
+    device: cuda
+    batch_size: 16
+    async_reward: false
+    num_workers: 1
+    applicable_datasets:
+    - places_domain_rl
+    probe: /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/domain_probe.npz
+    encoder: /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/cache/dinov2-small
+  reward_1:
+    name: structure
+    reward_model: ir_rewards.fidelity.CoarseStructureReward
+    weight:
+      places_domain_rl: 1.0
+    dtype: float32
+    device: cuda
+    batch_size: 16
+    async_reward: false
+    num_workers: 1
+    applicable_datasets:
+    - places_domain_rl
+  reward_2:
+    name: ssim_to_stage1
+    reward_model: ir_rewards.fidelity.SSIMReward
+    weight:
+      places_domain_rl: 1.0
+    dtype: float32
+    device: cuda
+    batch_size: 16
+    async_reward: false
+    num_workers: 1
+    applicable_datasets:
+    - places_domain_rl
+optimizer:
+  optimizer_configs:
+  - extra_kwargs: {}
+    name: default
+    optimizer: adamw
+    learning_rate: 5.0e-05
+    weight_decay: 0.0001
+    max_grad_norm: 1.0
+    update_frequency: 1
+    betas:
+    - 0.9
+    - 0.999
+    eps: 1.0e-08
+
+[2026-10-02 07:24:59,811] [INFO] [flow_factory.train]: ====================================================================================================
+[2026-10-02 07:25:09] [Rank 4] [INFO] [flow_factory.models.loader]: Loading model architecture: flux2-klein...
+[2026-10-02 07:25:09] [Rank 5] [INFO] [flow_factory.models.loader]: Loading model architecture: flux2-klein...
+[2026-10-02 07:25:09] [Rank 1] [INFO] [flow_factory.models.loader]: Loading model architecture: flux2-klein...
+[2026-10-02 07:25:09] [Rank 7] [INFO] [flow_factory.models.loader]: Loading model architecture: flux2-klein...
+[2026-10-02 07:25:09] [Rank 3] [INFO] [flow_factory.models.loader]: Loading model architecture: flux2-klein...
+[2026-10-02 07:25:09] [Rank 6] [INFO] [flow_factory.models.loader]: Loading model architecture: flux2-klein...
+[2026-10-02 07:25:09,911] [WARNING] [accelerate.utils.other]: [RANK 0] Detected kernel version 5.4.241, which is below the recommended minimum of 5.5.0; this can cause the process to hang. It is recommended to upgrade the kernel to the minimum version or higher.
+[2026-10-02 07:25:09] [Rank 2] [INFO] [flow_factory.models.loader]: Loading model architecture: flux2-klein...
+[2026-10-02 07:25:09] [Rank 0] [INFO] [flow_factory.models.loader]: Loading model architecture: flux2-klein...
+
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+[2026-10-02 07:25:36] [Rank 0] [INFO] [flow_factory.scheduler.loader]: Loaded SDE scheduler: FlowMatchEulerDiscreteSDEScheduler
+[2026-10-02 07:25:36] [Rank 7] [INFO] [flow_factory.scheduler.loader]: Loaded SDE scheduler: FlowMatchEulerDiscreteSDEScheduler
+
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+[2026-10-02 07:25:37] [Rank 5] [INFO] [flow_factory.scheduler.loader]: Loaded SDE scheduler: FlowMatchEulerDiscreteSDEScheduler
+
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+[2026-10-02 07:25:37] [Rank 3] [INFO] [flow_factory.scheduler.loader]: Loaded SDE scheduler: FlowMatchEulerDiscreteSDEScheduler
+
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+
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+
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+[2026-10-02 07:25:37] [Rank 1] [INFO] [flow_factory.scheduler.loader]: Loaded SDE scheduler: FlowMatchEulerDiscreteSDEScheduler
+[2026-10-02 07:25:37] [Rank 4] [INFO] [flow_factory.scheduler.loader]: Loaded SDE scheduler: FlowMatchEulerDiscreteSDEScheduler
+[2026-10-02 07:25:37] [Rank 2] [INFO] [flow_factory.scheduler.loader]: Loaded SDE scheduler: FlowMatchEulerDiscreteSDEScheduler
+[2026-10-02 07:25:37] [Rank 6] [INFO] [flow_factory.scheduler.loader]: Loaded SDE scheduler: FlowMatchEulerDiscreteSDEScheduler
+[2026-10-02 07:25:38] [Rank 0] [INFO] [flow_factory.models.abc]: Checkpoint loaded successfully from /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/runs/klein4b_sft_flir_full_r64a128_lr1e-4/saves/klein4b_sft_flir_full_r64a128_lr1e-4/checkpoints/checkpoint-12 (type=lora)
+[2026-10-02 07:25:38] [Rank 6] [INFO] [flow_factory.models.abc]: Froze ALL transformer parameters
+[2026-10-02 07:25:38] [Rank 4] [INFO] [flow_factory.models.abc]: Froze ALL transformer parameters
+[2026-10-02 07:25:38] [Rank 6] [INFO] [flow_factory.models.abc]: Component transformer already has 'default' adapter. Skipping initialization but enabling gradients.
+[2026-10-02 07:25:38] [Rank 4] [INFO] [flow_factory.models.abc]: Component transformer already has 'default' adapter. Skipping initialization but enabling gradients.
+[2026-10-02 07:25:38] [Rank 2] [INFO] [flow_factory.models.abc]: Froze ALL transformer parameters
+[2026-10-02 07:25:38] [Rank 0] [INFO] [flow_factory.models.abc]: Froze ALL transformer parameters
+[2026-10-02 07:25:38] [Rank 2] [INFO] [flow_factory.models.abc]: Component transformer already has 'default' adapter. Skipping initialization but enabling gradients.
+[2026-10-02 07:25:38] [Rank 0] [INFO] [flow_factory.models.abc]: Component transformer already has 'default' adapter. Skipping initialization but enabling gradients.
+[2026-10-02 07:25:38] [Rank 3] [INFO] [flow_factory.models.abc]: Froze ALL transformer parameters
+[2026-10-02 07:25:38] [Rank 7] [INFO] [flow_factory.models.abc]: Froze ALL transformer parameters
+[2026-10-02 07:25:38] [Rank 5] [INFO] [flow_factory.models.abc]: Froze ALL transformer parameters
+[2026-10-02 07:25:38] [Rank 3] [INFO] [flow_factory.models.abc]: Component transformer already has 'default' adapter. Skipping initialization but enabling gradients.
+[2026-10-02 07:25:38] [Rank 7] [INFO] [flow_factory.models.abc]: Component transformer already has 'default' adapter. Skipping initialization but enabling gradients.
+[2026-10-02 07:25:38] [Rank 5] [INFO] [flow_factory.models.abc]: Component transformer already has 'default' adapter. Skipping initialization but enabling gradients.
+[2026-10-02 07:25:38] [Rank 1] [INFO] [flow_factory.models.abc]: Froze ALL transformer parameters
+[2026-10-02 07:25:38] [Rank 1] [INFO] [flow_factory.models.abc]: Component transformer already has 'default' adapter. Skipping initialization but enabling gradients.
+[2026-10-02 07:25:38] [Rank 4] [INFO] [flow_factory.models.abc]: Set 160 trainable parameters to torch.bfloat16; frozen component policy -> {'text_encoder': None, 'transformer': None, 'vae': None}
+[2026-10-02 07:25:38] [Rank 4] [INFO] [flow_factory.models.loader]: Successfully loaded Flux2KleinAdapter
+[2026-10-02 07:25:38] [Rank 2] [INFO] [flow_factory.models.abc]: Set 160 trainable parameters to torch.bfloat16; frozen component policy -> {'text_encoder': None, 'transformer': None, 'vae': None}
+[2026-10-02 07:25:38] [Rank 7] [INFO] [flow_factory.models.abc]: Set 160 trainable parameters to torch.bfloat16; frozen component policy -> {'text_encoder': None, 'transformer': None, 'vae': None}
+[2026-10-02 07:25:38] [Rank 6] [INFO] [flow_factory.models.abc]: Set 160 trainable parameters to torch.bfloat16; frozen component policy -> {'text_encoder': None, 'transformer': None, 'vae': None}
+[2026-10-02 07:25:38] [Rank 2] [INFO] [flow_factory.models.loader]: Successfully loaded Flux2KleinAdapter
+[2026-10-02 07:25:38] [Rank 7] [INFO] [flow_factory.models.loader]: Successfully loaded Flux2KleinAdapter
+[2026-10-02 07:25:38] [Rank 6] [INFO] [flow_factory.models.loader]: Successfully loaded Flux2KleinAdapter
+[2026-10-02 07:25:38] [Rank 0] [INFO] [flow_factory.models.abc]: Set 160 trainable parameters to torch.bfloat16; frozen component policy -> {'text_encoder': None, 'transformer': None, 'vae': None}
+[2026-10-02 07:25:38] [Rank 1] [INFO] [flow_factory.models.abc]: Set 160 trainable parameters to torch.bfloat16; frozen component policy -> {'text_encoder': None, 'transformer': None, 'vae': None}
+[2026-10-02 07:25:38] [Rank 5] [INFO] [flow_factory.models.abc]: Set 160 trainable parameters to torch.bfloat16; frozen component policy -> {'text_encoder': None, 'transformer': None, 'vae': None}
+[2026-10-02 07:25:38] [Rank 1] [INFO] [flow_factory.models.loader]: Successfully loaded Flux2KleinAdapter
+[2026-10-02 07:25:38] [Rank 5] [INFO] [flow_factory.models.loader]: Successfully loaded Flux2KleinAdapter
+[2026-10-02 07:25:38] [Rank 0] [INFO] [flow_factory.models.abc]: Precision inventory stage=initialize component=text_encoder {'trainable': {}, 'frozen': {'torch.bfloat16': 4022468096}}
+[2026-10-02 07:25:38] [Rank 3] [INFO] [flow_factory.models.abc]: Set 160 trainable parameters to torch.bfloat16; frozen component policy -> {'text_encoder': None, 'transformer': None, 'vae': None}
+[2026-10-02 07:25:38] [Rank 3] [INFO] [flow_factory.models.loader]: Successfully loaded Flux2KleinAdapter
+[2026-10-02 07:25:38] [Rank 0] [INFO] [flow_factory.models.abc]: Precision inventory stage=initialize component=transformer {'trainable': {'torch.bfloat16': 76677120}, 'frozen': {'torch.float32': 3875544576}}
+[2026-10-02 07:25:38] [Rank 0] [INFO] [flow_factory.models.abc]: Precision inventory stage=initialize component=vae {'trainable': {}, 'frozen': {'torch.float32': 84046115}}
+[2026-10-02 07:25:38] [Rank 0] [INFO] [flow_factory.models.loader]: Successfully loaded Flux2KleinAdapter
+[2026-10-02 07:26:04] [Rank 1] [INFO] [flow_factory.data_utils.loader]: Preprocessing train dataset shard 0001/0007 -> /root/.cache/flow_factory/datasets/82135bc75fb8475b64b8342a3ef5f714.tmp/_parts/rank_0001_of_0008/cache-82135bc75fb8475b64b8342a3ef5f714_shard0001of0007.arrow
+[2026-10-02 07:26:04] [Rank 5] [INFO] [flow_factory.data_utils.loader]: Preprocessing train dataset shard 0005/0007 -> /root/.cache/flow_factory/datasets/82135bc75fb8475b64b8342a3ef5f714.tmp/_parts/rank_0005_of_0008/cache-82135bc75fb8475b64b8342a3ef5f714_shard0005of0007.arrow
+[2026-10-02 07:26:04] [Rank 0] [INFO] [flow_factory.data_utils.loader]: Preprocessing train dataset shard 0000/0007 -> /root/.cache/flow_factory/datasets/82135bc75fb8475b64b8342a3ef5f714.tmp/_parts/rank_0000_of_0008/cache-82135bc75fb8475b64b8342a3ef5f714_shard0000of0007.arrow
+[2026-10-02 07:26:04] [Rank 3] [INFO] [flow_factory.data_utils.loader]: Preprocessing train dataset shard 0003/0007 -> /root/.cache/flow_factory/datasets/82135bc75fb8475b64b8342a3ef5f714.tmp/_parts/rank_0003_of_0008/cache-82135bc75fb8475b64b8342a3ef5f714_shard0003of0007.arrow
+[2026-10-02 07:26:04] [Rank 4] [INFO] [flow_factory.data_utils.loader]: Preprocessing train dataset shard 0004/0007 -> /root/.cache/flow_factory/datasets/82135bc75fb8475b64b8342a3ef5f714.tmp/_parts/rank_0004_of_0008/cache-82135bc75fb8475b64b8342a3ef5f714_shard0004of0007.arrow
+[2026-10-02 07:26:04] [Rank 6] [INFO] [flow_factory.data_utils.loader]: Preprocessing train dataset shard 0006/0007 -> /root/.cache/flow_factory/datasets/82135bc75fb8475b64b8342a3ef5f714.tmp/_parts/rank_0006_of_0008/cache-82135bc75fb8475b64b8342a3ef5f714_shard0006of0007.arrow
+[2026-10-02 07:26:04] [Rank 7] [INFO] [flow_factory.data_utils.loader]: Preprocessing train dataset shard 0007/0007 -> /root/.cache/flow_factory/datasets/82135bc75fb8475b64b8342a3ef5f714.tmp/_parts/rank_0007_of_0008/cache-82135bc75fb8475b64b8342a3ef5f714_shard0007of0007.arrow
+[2026-10-02 07:26:04] [Rank 2] [INFO] [flow_factory.data_utils.loader]: Preprocessing train dataset shard 0002/0007 -> /root/.cache/flow_factory/datasets/82135bc75fb8475b64b8342a3ef5f714.tmp/_parts/rank_0002_of_0008/cache-82135bc75fb8475b64b8342a3ef5f714_shard0002of0007.arrow
+/opt/conda/envs/ff-qi21/lib/python3.10/contextlib.py:135: UserWarning: CUDA reports that you have 8 available devices, and you have used fork_rng without explicitly specifying which devices are being used. For safety, we initialize *every* CUDA device by default, which can be quite slow if you have a lot of CUDAs. If you know that you are only making use of a few CUDA devices, set the environment variable CUDA_VISIBLE_DEVICES or the 'devices' keyword argument of fork_rng with the set of devices you are actually using. For example, if you are using CPU only, set device.upper()_VISIBLE_DEVICES= or devices=[]; if you are using device 0 only, set CUDA_VISIBLE_DEVICES=0 or devices=[0].  To initialize all devices and suppress this warning, set the 'devices' keyword argument to `range(torch.cuda.device_count())`.
+  return next(self.gen)
+/opt/conda/envs/ff-qi21/lib/python3.10/contextlib.py:135: UserWarning: CUDA reports that you have 8 available devices, and you have used fork_rng without explicitly specifying which devices are being used. For safety, we initialize *every* CUDA device by default, which can be quite slow if you have a lot of CUDAs. If you know that you are only making use of a few CUDA devices, set the environment variable CUDA_VISIBLE_DEVICES or the 'devices' keyword argument of fork_rng with the set of devices you are actually using. For example, if you are using CPU only, set device.upper()_VISIBLE_DEVICES= or devices=[]; if you are using device 0 only, set CUDA_VISIBLE_DEVICES=0 or devices=[0].  To initialize all devices and suppress this warning, set the 'devices' keyword argument to `range(torch.cuda.device_count())`.
+  return next(self.gen)
+/opt/conda/envs/ff-qi21/lib/python3.10/contextlib.py:135: UserWarning: CUDA reports that you have 8 available devices, and you have used fork_rng without explicitly specifying which devices are being used. For safety, we initialize *every* CUDA device by default, which can be quite slow if you have a lot of CUDAs. If you know that you are only making use of a few CUDA devices, set the environment variable CUDA_VISIBLE_DEVICES or the 'devices' keyword argument of fork_rng with the set of devices you are actually using. For example, if you are using CPU only, set device.upper()_VISIBLE_DEVICES= or devices=[]; if you are using device 0 only, set CUDA_VISIBLE_DEVICES=0 or devices=[0].  To initialize all devices and suppress this warning, set the 'devices' keyword argument to `range(torch.cuda.device_count())`.
+  return next(self.gen)
+/opt/conda/envs/ff-qi21/lib/python3.10/contextlib.py:135: UserWarning: CUDA reports that you have 8 available devices, and you have used fork_rng without explicitly specifying which devices are being used. For safety, we initialize *every* CUDA device by default, which can be quite slow if you have a lot of CUDAs. If you know that you are only making use of a few CUDA devices, set the environment variable CUDA_VISIBLE_DEVICES or the 'devices' keyword argument of fork_rng with the set of devices you are actually using. For example, if you are using CPU only, set device.upper()_VISIBLE_DEVICES= or devices=[]; if you are using device 0 only, set CUDA_VISIBLE_DEVICES=0 or devices=[0].  To initialize all devices and suppress this warning, set the 'devices' keyword argument to `range(torch.cuda.device_count())`.
+  return next(self.gen)
+/opt/conda/envs/ff-qi21/lib/python3.10/contextlib.py:135: UserWarning: CUDA reports that you have 8 available devices, and you have used fork_rng without explicitly specifying which devices are being used. For safety, we initialize *every* CUDA device by default, which can be quite slow if you have a lot of CUDAs. If you know that you are only making use of a few CUDA devices, set the environment variable CUDA_VISIBLE_DEVICES or the 'devices' keyword argument of fork_rng with the set of devices you are actually using. For example, if you are using CPU only, set device.upper()_VISIBLE_DEVICES= or devices=[]; if you are using device 0 only, set CUDA_VISIBLE_DEVICES=0 or devices=[0].  To initialize all devices and suppress this warning, set the 'devices' keyword argument to `range(torch.cuda.device_count())`.
+  return next(self.gen)
+/opt/conda/envs/ff-qi21/lib/python3.10/contextlib.py:135: UserWarning: CUDA reports that you have 8 available devices, and you have used fork_rng without explicitly specifying which devices are being used. For safety, we initialize *every* CUDA device by default, which can be quite slow if you have a lot of CUDAs. If you know that you are only making use of a few CUDA devices, set the environment variable CUDA_VISIBLE_DEVICES or the 'devices' keyword argument of fork_rng with the set of devices you are actually using. For example, if you are using CPU only, set device.upper()_VISIBLE_DEVICES= or devices=[]; if you are using device 0 only, set CUDA_VISIBLE_DEVICES=0 or devices=[0].  To initialize all devices and suppress this warning, set the 'devices' keyword argument to `range(torch.cuda.device_count())`.
+  return next(self.gen)
+/opt/conda/envs/ff-qi21/lib/python3.10/contextlib.py:135: UserWarning: CUDA reports that you have 8 available devices, and you have used fork_rng without explicitly specifying which devices are being used. For safety, we initialize *every* CUDA device by default, which can be quite slow if you have a lot of CUDAs. If you know that you are only making use of a few CUDA devices, set the environment variable CUDA_VISIBLE_DEVICES or the 'devices' keyword argument of fork_rng with the set of devices you are actually using. For example, if you are using CPU only, set device.upper()_VISIBLE_DEVICES= or devices=[]; if you are using device 0 only, set CUDA_VISIBLE_DEVICES=0 or devices=[0].  To initialize all devices and suppress this warning, set the 'devices' keyword argument to `range(torch.cuda.device_count())`.
+  return next(self.gen)
+/opt/conda/envs/ff-qi21/lib/python3.10/contextlib.py:135: UserWarning: CUDA reports that you have 8 available devices, and you have used fork_rng without explicitly specifying which devices are being used. For safety, we initialize *every* CUDA device by default, which can be quite slow if you have a lot of CUDAs. If you know that you are only making use of a few CUDA devices, set the environment variable CUDA_VISIBLE_DEVICES or the 'devices' keyword argument of fork_rng with the set of devices you are actually using. For example, if you are using CPU only, set device.upper()_VISIBLE_DEVICES= or devices=[]; if you are using device 0 only, set CUDA_VISIBLE_DEVICES=0 or devices=[0].  To initialize all devices and suppress this warning, set the 'devices' keyword argument to `range(torch.cuda.device_count())`.
+  return next(self.gen)
+
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+[2026-10-02 07:28:30] [Rank 0] [INFO] [flow_factory.data_utils.loader]: [local] Consolidated 8 part(s) for train split -> /root/.cache/flow_factory/datasets/82135bc75fb8475b64b8342a3ef5f714
+[2026-10-02 07:28:30] [Rank 1] [INFO] [flow_factory.data_utils.loader]: Multi-source: built DataLoader for 'places_domain_rl' (dataset_size=3600, unique_sample_num_per_epoch=48, num_batches_per_epoch=96)
+[2026-10-02 07:28:30] [Rank 5] [INFO] [flow_factory.data_utils.loader]: Multi-source: built DataLoader for 'places_domain_rl' (dataset_size=3600, unique_sample_num_per_epoch=48, num_batches_per_epoch=96)
+[2026-10-02 07:28:30] [Rank 0] [INFO] [flow_factory.data_utils.loader]: Multi-source: built DataLoader for 'places_domain_rl' (dataset_size=3600, unique_sample_num_per_epoch=48, num_batches_per_epoch=96)
+[2026-10-02 07:28:30] [Rank 2] [INFO] [flow_factory.data_utils.loader]: Multi-source: built DataLoader for 'places_domain_rl' (dataset_size=3600, unique_sample_num_per_epoch=48, num_batches_per_epoch=96)
+[2026-10-02 07:28:30] [Rank 4] [INFO] [flow_factory.data_utils.loader]: Multi-source: built DataLoader for 'places_domain_rl' (dataset_size=3600, unique_sample_num_per_epoch=48, num_batches_per_epoch=96)
+[2026-10-02 07:28:30] [Rank 3] [INFO] [flow_factory.data_utils.loader]: Multi-source: built DataLoader for 'places_domain_rl' (dataset_size=3600, unique_sample_num_per_epoch=48, num_batches_per_epoch=96)
+[2026-10-02 07:28:30] [Rank 6] [INFO] [flow_factory.data_utils.loader]: Multi-source: built DataLoader for 'places_domain_rl' (dataset_size=3600, unique_sample_num_per_epoch=48, num_batches_per_epoch=96)
+[2026-10-02 07:28:30] [Rank 7] [INFO] [flow_factory.data_utils.loader]: Multi-source: built DataLoader for 'places_domain_rl' (dataset_size=3600, unique_sample_num_per_epoch=48, num_batches_per_epoch=96)
+[2026-10-02 07:28:30] [Rank 0] [INFO] [flow_factory.data_utils.loader]: Preprocessing test dataset shard 0000/0007 -> /root/.cache/flow_factory/datasets/5208a8ea74fd47a93c48d046f8731b58.tmp/_parts/rank_0000_of_0008/cache-5208a8ea74fd47a93c48d046f8731b58_shard0000of0007.arrow
+[2026-10-02 07:28:30] [Rank 5] [INFO] [flow_factory.data_utils.loader]: Preprocessing test dataset shard 0005/0007 -> /root/.cache/flow_factory/datasets/5208a8ea74fd47a93c48d046f8731b58.tmp/_parts/rank_0005_of_0008/cache-5208a8ea74fd47a93c48d046f8731b58_shard0005of0007.arrow
+[2026-10-02 07:28:30] [Rank 1] [INFO] [flow_factory.data_utils.loader]: Preprocessing test dataset shard 0001/0007 -> /root/.cache/flow_factory/datasets/5208a8ea74fd47a93c48d046f8731b58.tmp/_parts/rank_0001_of_0008/cache-5208a8ea74fd47a93c48d046f8731b58_shard0001of0007.arrow
+[2026-10-02 07:28:30] [Rank 2] [INFO] [flow_factory.data_utils.loader]: Preprocessing test dataset shard 0002/0007 -> /root/.cache/flow_factory/datasets/5208a8ea74fd47a93c48d046f8731b58.tmp/_parts/rank_0002_of_0008/cache-5208a8ea74fd47a93c48d046f8731b58_shard0002of0007.arrow
+[2026-10-02 07:28:30] [Rank 3] [INFO] [flow_factory.data_utils.loader]: Preprocessing test dataset shard 0003/0007 -> /root/.cache/flow_factory/datasets/5208a8ea74fd47a93c48d046f8731b58.tmp/_parts/rank_0003_of_0008/cache-5208a8ea74fd47a93c48d046f8731b58_shard0003of0007.arrow
+[2026-10-02 07:28:30] [Rank 7] [INFO] [flow_factory.data_utils.loader]: Preprocessing test dataset shard 0007/0007 -> /root/.cache/flow_factory/datasets/5208a8ea74fd47a93c48d046f8731b58.tmp/_parts/rank_0007_of_0008/cache-5208a8ea74fd47a93c48d046f8731b58_shard0007of0007.arrow
+[2026-10-02 07:28:30] [Rank 4] [INFO] [flow_factory.data_utils.loader]: Preprocessing test dataset shard 0004/0007 -> /root/.cache/flow_factory/datasets/5208a8ea74fd47a93c48d046f8731b58.tmp/_parts/rank_0004_of_0008/cache-5208a8ea74fd47a93c48d046f8731b58_shard0004of0007.arrow
+[2026-10-02 07:28:30] [Rank 6] [INFO] [flow_factory.data_utils.loader]: Preprocessing test dataset shard 0006/0007 -> /root/.cache/flow_factory/datasets/5208a8ea74fd47a93c48d046f8731b58.tmp/_parts/rank_0006_of_0008/cache-5208a8ea74fd47a93c48d046f8731b58_shard0006of0007.arrow
+
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+
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+[2026-10-02 07:28:35] [Rank 0] [INFO] [flow_factory.data_utils.loader]: [local] Consolidated 8 part(s) for test split -> /root/.cache/flow_factory/datasets/5208a8ea74fd47a93c48d046f8731b58
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+wandb: [wandb.login()] Loaded credentials for https://api.wandb.ai from /root/.netrc.
+wandb: Currently logged in as: 315229706 (315229706-xi-an-jiaotong-university-) to https://api.wandb.ai. Use `wandb login --relogin` to force relogin
+wandb: setting up run gklyn30y
+wandb: Tracking run with wandb version 0.28.2
+wandb: Run data is saved locally in /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/runs/klein4b_nft_domain_sirst/wandb/run-20261002_072850-gklyn30y
+wandb: Run `wandb offline` to turn off syncing.
+wandb: Syncing run klein4b_nft_domain_sirst
+wandb: ⭐️ View project at https://wandb.ai/315229706-xi-an-jiaotong-university-/IR-Engine
+wandb: 🚀 View run at https://wandb.ai/315229706-xi-an-jiaotong-university-/IR-Engine/runs/gklyn30y
+[2026-10-02 07:28:52] [Rank 0] [INFO] [flow_factory.models.abc]: ======================================================================
+[2026-10-02 07:28:52] [Rank 0] [INFO] [flow_factory.models.abc]: Transformer Trainable Parameters:
+[2026-10-02 07:28:52] [Rank 0] [INFO] [flow_factory.models.abc]:   Total:        3,952,221,696 (  7.36 GB)
+[2026-10-02 07:28:52] [Rank 0] [INFO] [flow_factory.models.abc]:   Trainable:       76,677,120 (  0.14 GB)
+[2026-10-02 07:28:52] [Rank 0] [INFO] [flow_factory.models.abc]:   Percentage:           1.94%
+[2026-10-02 07:28:52] [Rank 0] [INFO] [flow_factory.models.abc]: ======================================================================
+[2026-10-02 07:28:52] [Rank 0] [INFO] [flow_factory.models.abc]: Saving LoRA weights for transformer role=base to /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/runs/klein4b_nft_domain_sirst/saves/klein4b_nft_domain_sirst/checkpoints/checkpoint-0
+[2026-10-02 07:28:52] [Rank 0] [INFO] [flow_factory.models.abc]: Checkpoint saved successfully to /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/runs/klein4b_nft_domain_sirst/saves/klein4b_nft_domain_sirst/checkpoints/checkpoint-0
+
Eval/places_domain_rl:   0%|          | 0/8 [00:00<?, ?it/s][2026-10-02 07:28:53] [Rank 3] [WARNING] [flow_factory.models.flux.flux2_klein]: FLUX.2 does not support batch inference with varying condition images per sample. Falling back to single-sample inference. This warning will only appear once.
+[2026-10-02 07:28:53] [Rank 2] [WARNING] [flow_factory.models.flux.flux2_klein]: FLUX.2 does not support batch inference with varying condition images per sample. Falling back to single-sample inference. This warning will only appear once.
+[2026-10-02 07:28:53] [Rank 0] [WARNING] [flow_factory.models.flux.flux2_klein]: FLUX.2 does not support batch inference with varying condition images per sample. Falling back to single-sample inference. This warning will only appear once.
+[2026-10-02 07:28:53] [Rank 5] [WARNING] [flow_factory.models.flux.flux2_klein]: FLUX.2 does not support batch inference with varying condition images per sample. Falling back to single-sample inference. This warning will only appear once.
+[2026-10-02 07:28:53] [Rank 7] [WARNING] [flow_factory.models.flux.flux2_klein]: FLUX.2 does not support batch inference with varying condition images per sample. Falling back to single-sample inference. This warning will only appear once.
+[2026-10-02 07:28:53] [Rank 1] [WARNING] [flow_factory.models.flux.flux2_klein]: FLUX.2 does not support batch inference with varying condition images per sample. Falling back to single-sample inference. This warning will only appear once.
+[2026-10-02 07:28:53] [Rank 4] [WARNING] [flow_factory.models.flux.flux2_klein]: FLUX.2 does not support batch inference with varying condition images per sample. Falling back to single-sample inference. This warning will only appear once.
+[2026-10-02 07:28:53] [Rank 6] [WARNING] [flow_factory.models.flux.flux2_klein]: FLUX.2 does not support batch inference with varying condition images per sample. Falling back to single-sample inference. This warning will only appear once.
+
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+
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+
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Pointwise Rewards: structure: 100%|██████████| 1/1 [00:00<00:00,  8.34it/s]
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+
Pointwise Rewards: ssim_to_stage1:   0%|          | 0/1 [00:00<?, ?it/s]
Pointwise Rewards: ssim_to_stage1: 100%|██████████| 1/1 [00:00<00:00,  9.95it/s]
Pointwise Rewards: ssim_to_stage1: 100%|██████████| 1/1 [00:00<00:00,  9.91it/s]
+[2026-10-02 07:29:10] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0000 | Epoch 000] eval/places_domain_rl/reward_domain_mean=-0.0524 eval/places_domain_rl/reward_domain_std=0.1177 eval/places_domain_rl/reward_ssim_to_stage1_mean=0.5735 eval/places_domain_rl/reward_ssim_to_stage1_std=0.1248 eval/places_domain_rl/reward_structure_mean=0.6991 eval/places_domain_rl/reward_structure_std=0.1363
+
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+
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Pointwise Rewards: structure:  83%|████████▎ | 5/6 [00:00<00:00,  5.34it/s]
Pointwise Rewards: structure: 100%|██████████| 6/6 [00:01<00:00,  5.55it/s]
Pointwise Rewards: structure: 100%|██████████| 6/6 [00:01<00:00,  5.38it/s]
+[2026-10-02 07:32:12] [Rank 5] [WARNING] [flow_factory.models.flux.flux2_klein]: Flux.2: Ragged I2I detected (varying condition image sizes). Processing samples individually (warning shown once).
+[2026-10-02 07:32:12] [Rank 3] [WARNING] [flow_factory.models.flux.flux2_klein]: Flux.2: Ragged I2I detected (varying condition image sizes). Processing samples individually (warning shown once).
+[2026-10-02 07:32:12] [Rank 1] [WARNING] [flow_factory.models.flux.flux2_klein]: Flux.2: Ragged I2I detected (varying condition image sizes). Processing samples individually (warning shown once).
+[2026-10-02 07:32:12] [Rank 4] [WARNING] [flow_factory.models.flux.flux2_klein]: Flux.2: Ragged I2I detected (varying condition image sizes). Processing samples individually (warning shown once).
+[2026-10-02 07:32:12] [Rank 6] [WARNING] [flow_factory.models.flux.flux2_klein]: Flux.2: Ragged I2I detected (varying condition image sizes). Processing samples individually (warning shown once).
+[2026-10-02 07:32:12] [Rank 7] [WARNING] [flow_factory.models.flux.flux2_klein]: Flux.2: Ragged I2I detected (varying condition image sizes). Processing samples individually (warning shown once).
+[2026-10-02 07:32:12] [Rank 2] [WARNING] [flow_factory.models.flux.flux2_klein]: Flux.2: Ragged I2I detected (varying condition image sizes). Processing samples individually (warning shown once).
+[2026-10-02 07:32:13] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0000 | Epoch 000] train/reward_domain_mean=-0.0656 train/reward_domain_std=0.1100 train/reward_structure_mean=0.7306 train/reward_structure_std=0.1053 train/reward_domain_group_std_mean=0.0335 train/reward_domain_group_std_max=0.0672 train/reward_domain_group_std_min=0.0087 train/reward_domain_group_mean_std=0.1037 train/reward_structure_group_std_mean=0.0356 train/reward_structure_group_std_max=0.1273 train/reward_structure_group_std_min=0.0107 train/reward_structure_group_mean_std=0.0963 train/reward_domain_zero_std_ratio=0 train/reward_structure_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.0873 train/adv_max=4.2158 train/adv_abs_mean=1.1825
+
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+
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Epoch 0 Training:   7%|▋         | 7/96 [00:11<02:20,  1.58s/it]
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Epoch 0 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 0 Training:   8%|▊         | 8/96 [00:12<02:18,  1.58s/it]
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Epoch 0 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 0 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.04it/s]
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Epoch 0 Training:   9%|▉         | 9/96 [00:14<02:17,  1.58s/it]
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Epoch 0 Training:  10%|█         | 10/96 [00:15<02:15,  1.58s/it]
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Epoch 0 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 0 Training:  11%|█▏        | 11/96 [00:17<02:14,  1.58s/it]
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Epoch 0 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 0 Training:  12%|█▎        | 12/96 [00:18<02:12,  1.58s/it]
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Epoch 0 Training:  14%|█▎        | 13/96 [00:20<02:10,  1.58s/it]
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Epoch 0 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 0 Training:  15%|█▍        | 14/96 [00:22<02:09,  1.58s/it]
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Epoch 0 Training:  16%|█▌        | 15/96 [00:23<02:07,  1.58s/it]
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Epoch 0 Training:  17%|█▋        | 16/96 [00:25<02:06,  1.58s/it]
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Epoch 0 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 0 Training:  18%|█▊        | 17/96 [00:26<02:04,  1.58s/it]
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Epoch 0 Training:  19%|█▉        | 18/96 [00:28<02:02,  1.58s/it]
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Epoch 0 Training:  20%|█▉        | 19/96 [00:29<02:01,  1.58s/it]
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Epoch 0 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 0 Training:  21%|██        | 20/96 [00:31<01:59,  1.58s/it]
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Epoch 0 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 0 Training:  22%|██▏       | 21/96 [00:33<01:58,  1.58s/it]
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Epoch 0 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 0 Training:  23%|██▎       | 22/96 [00:34<01:56,  1.58s/it]
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Epoch 0 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 0 Training:  24%|██▍       | 23/96 [00:36<01:55,  1.58s/it]
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Epoch 0 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 0 Training:  25%|██▌       | 24/96 [00:37<01:53,  1.58s/it]
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Epoch 0 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.04it/s]
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Epoch 0 Training:  26%|██▌       | 25/96 [00:39<01:52,  1.58s/it]
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Epoch 0 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 0 Training:  27%|██▋       | 26/96 [00:40<01:50,  1.58s/it]
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Epoch 0 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.04it/s]
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Epoch 0 Training:  28%|██▊       | 27/96 [00:42<01:48,  1.58s/it]
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Epoch 0 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.04it/s]
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Epoch 0 Training:  29%|██▉       | 28/96 [00:44<01:47,  1.58s/it]
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Epoch 0 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 0 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.04it/s]
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Epoch 0 Training:  30%|███       | 29/96 [00:45<01:45,  1.58s/it]
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Epoch 0 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.04it/s]
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Epoch 0 Training:  31%|███▏      | 30/96 [00:47<01:44,  1.58s/it]
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Epoch 0 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 0 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.04it/s]
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Epoch 0 Training:  32%|███▏      | 31/96 [00:48<01:42,  1.58s/it]
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Epoch 0 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 0 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.04it/s]
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Epoch 0 Training:  33%|███▎      | 32/96 [00:50<01:41,  1.58s/it]
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Epoch 0 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 0 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 0 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.04it/s]
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Epoch 0 Training:  34%|███▍      | 33/96 [00:52<01:39,  1.58s/it]
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Epoch 0 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 0 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.04it/s]
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Epoch 0 Training:  35%|███▌      | 34/96 [00:53<01:37,  1.58s/it]
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Epoch 0 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.04it/s]
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Epoch 0 Training:  36%|███▋      | 35/96 [00:55<01:36,  1.58s/it]
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Epoch 0 Training:  38%|███▊      | 36/96 [00:56<01:34,  1.58s/it]
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Epoch 0 Training:  39%|███▊      | 37/96 [00:58<01:33,  1.58s/it]
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Epoch 0 Training:  40%|███▉      | 38/96 [00:59<01:31,  1.58s/it]
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Epoch 0 Training:  41%|████      | 39/96 [01:01<01:30,  1.58s/it]
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Epoch 0 Training:  42%|████▏     | 40/96 [01:03<01:28,  1.58s/it]
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Epoch 0 Training:  43%|████▎     | 41/96 [01:04<01:26,  1.58s/it]
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Epoch 0 Training:  44%|████▍     | 42/96 [01:06<01:25,  1.58s/it]
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Epoch 0 Training:  45%|████▍     | 43/96 [01:07<01:23,  1.58s/it]
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Epoch 0 Training:  46%|████▌     | 44/96 [01:09<01:22,  1.58s/it]
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Epoch 0 Training:  47%|████▋     | 45/96 [01:11<01:20,  1.58s/it]
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Epoch 0 Training:  48%|████▊     | 46/96 [01:12<01:19,  1.58s/it]
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Epoch 0 Training:  49%|████▉     | 47/96 [01:14<01:17,  1.58s/it]
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Epoch 0 Training:  50%|█████     | 48/96 [01:15<01:15,  1.58s/it]
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Epoch 0 Training:  51%|█████     | 49/96 [01:17<01:14,  1.58s/it]
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Epoch 0 Training:  52%|█████▏    | 50/96 [01:18<01:12,  1.58s/it]
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Epoch 0 Training:  53%|█████▎    | 51/96 [01:20<01:11,  1.58s/it]
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Epoch 0 Training:  54%|█████▍    | 52/96 [01:22<01:09,  1.58s/it]
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Epoch 0 Training:  55%|█████▌    | 53/96 [01:23<01:08,  1.58s/it]
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Epoch 0 Training:  56%|█████▋    | 54/96 [01:25<01:06,  1.58s/it]
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Epoch 0 Training:  57%|█████▋    | 55/96 [01:26<01:04,  1.58s/it]
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Epoch 0 Training:  58%|█████▊    | 56/96 [01:28<01:03,  1.58s/it]
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Epoch 0 Training:  59%|█████▉    | 57/96 [01:29<01:01,  1.58s/it]
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Epoch 0 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s][2026-10-02 07:34:45] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0000 | Epoch 000] train/policy_loss=27.4727 train/unweighted_policy_loss=5.4945 train/loss=27.4727 train/grad_norm=0.1096
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+[2026-10-02 07:34:45] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0001 | Epoch 000] timing/rollout_seconds=179.5899 timing/feedback_seconds=5.7797 timing/optimization_seconds=152.2960 timing/cycle_seconds=334.9938
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+
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+
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Pointwise Rewards: structure: 100%|██████████| 6/6 [00:01<00:00,  5.48it/s]
Pointwise Rewards: structure: 100%|██████████| 6/6 [00:01<00:00,  5.52it/s]
+[2026-10-02 07:37:47] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0001 | Epoch 001] train/reward_domain_mean=0.1327 train/reward_domain_std=0.0981 train/reward_structure_mean=0.6958 train/reward_structure_std=0.1533 train/reward_domain_group_std_mean=0.0569 train/reward_domain_group_std_max=0.1047 train/reward_domain_group_std_min=0.0279 train/reward_domain_group_mean_std=0.0780 train/reward_structure_group_std_mean=0.0211 train/reward_structure_group_std_max=0.0443 train/reward_structure_group_std_min=0.0069 train/reward_structure_group_mean_std=0.1515 train/reward_domain_zero_std_ratio=0 train/reward_structure_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.0323 train/adv_max=4.0180 train/adv_abs_mean=1.0146
+
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Epoch 1 Training:  35%|███▌      | 34/96 [00:52<01:35,  1.55s/it]
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Epoch 1 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 1 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.15it/s]
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Epoch 1 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.98it/s]
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Epoch 1 Training:  36%|███▋      | 35/96 [00:54<01:34,  1.55s/it]
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Epoch 1 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 1 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.04it/s]
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Epoch 1 Training:  38%|███▊      | 36/96 [00:55<01:32,  1.55s/it]
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Epoch 1 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 1 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.97it/s]
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Epoch 1 Training:  39%|███▊      | 37/96 [00:57<01:31,  1.55s/it]
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Epoch 1 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 1 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.98it/s]
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Epoch 1 Training:  40%|███▉      | 38/96 [00:58<01:29,  1.54s/it]
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Epoch 1 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 1 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.04it/s]
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Epoch 1 Training:  41%|████      | 39/96 [01:00<01:28,  1.55s/it]
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Epoch 1 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 1 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.98it/s]
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Epoch 1 Training:  42%|████▏     | 40/96 [01:01<01:26,  1.55s/it]
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Epoch 1 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 1 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.14it/s]
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Epoch 1 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.97it/s]
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Epoch 1 Training:  43%|████▎     | 41/96 [01:03<01:25,  1.55s/it]
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Epoch 1 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 1 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.15it/s]
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Epoch 1 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 1 Training:  44%|████▍     | 42/96 [01:05<01:23,  1.55s/it]
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Epoch 1 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 1 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 1 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.02it/s]
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Epoch 1 Training:  45%|████▍     | 43/96 [01:06<01:21,  1.55s/it]
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Epoch 1 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 1 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 1 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 1 Training:  46%|████▌     | 44/96 [01:08<01:20,  1.55s/it]
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Epoch 1 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 1 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.98it/s]
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Epoch 1 Training:  47%|████▋     | 45/96 [01:09<01:18,  1.55s/it]
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Epoch 1 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 1 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 1 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 1 Training:  48%|████▊     | 46/96 [01:11<01:17,  1.55s/it]
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Epoch 1 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 1 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 1 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 1 Training:  49%|████▉     | 47/96 [01:12<01:15,  1.55s/it]
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Epoch 1 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 1 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 1 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 1 Training:  50%|█████     | 48/96 [01:14<01:14,  1.55s/it]
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Epoch 1 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 1 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 1 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.97it/s]
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Epoch 1 Training:  51%|█████     | 49/96 [01:15<01:12,  1.55s/it]
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Epoch 1 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 1 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 1 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.98it/s]
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Epoch 1 Training:  52%|█████▏    | 50/96 [01:17<01:11,  1.55s/it]
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Epoch 1 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 1 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 1 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.98it/s]
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Epoch 1 Training:  53%|█████▎    | 51/96 [01:18<01:09,  1.55s/it]
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Epoch 1 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 1 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 1 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.97it/s]
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Epoch 1 Training:  54%|█████▍    | 52/96 [01:20<01:07,  1.54s/it]
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Epoch 1 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 1 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 1 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.97it/s]
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Epoch 1 Training:  55%|█████▌    | 53/96 [01:22<01:06,  1.55s/it]
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Epoch 1 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 1 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 1 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.97it/s]
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Epoch 1 Training:  56%|█████▋    | 54/96 [01:23<01:04,  1.55s/it]
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Epoch 1 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 1 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 1 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.97it/s]
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Epoch 1 Training:  57%|█████▋    | 55/96 [01:25<01:03,  1.54s/it]
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Epoch 1 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 1 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 1 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.98it/s]
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Epoch 1 Training:  58%|█████▊    | 56/96 [01:26<01:01,  1.54s/it]
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Epoch 1 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 1 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 1 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.97it/s]
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Epoch 1 Training:  59%|█████▉    | 57/96 [01:28<01:00,  1.54s/it]
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Epoch 1 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 1 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 1 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.98it/s]
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Epoch 1 Training:  60%|██████    | 58/96 [01:29<00:58,  1.54s/it]
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Epoch 1 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 1 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 1 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.97it/s]
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Epoch 1 Training:  61%|██████▏   | 59/96 [01:31<00:57,  1.54s/it]
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Epoch 1 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 1 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.98it/s]
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Epoch 1 Training:  62%|██████▎   | 60/96 [01:32<00:55,  1.54s/it]
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Epoch 1 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 1 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 1 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.98it/s]
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Epoch 1 Training:  64%|██████▎   | 61/96 [01:34<00:54,  1.54s/it]
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Epoch 1 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 1 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 1 Training:  65%|██████▍   | 62/96 [01:35<00:52,  1.54s/it]
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Epoch 1 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 1 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 1 Training:  66%|██████▌   | 63/96 [01:37<00:50,  1.54s/it]
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Epoch 1 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 1 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 1 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 1 Training:  67%|██████▋   | 64/96 [01:39<00:49,  1.54s/it]
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Epoch 1 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 1 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.98it/s]
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Epoch 1 Training:  68%|██████▊   | 65/96 [01:40<00:47,  1.54s/it]
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Epoch 1 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 1 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.97it/s]
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Epoch 1 Training:  69%|██████▉   | 66/96 [01:42<00:46,  1.54s/it]
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Epoch 1 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 1 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 1 Training:  70%|██████▉   | 67/96 [01:43<00:44,  1.54s/it]
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Epoch 1 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 1 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.97it/s]
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Epoch 1 Training:  71%|███████   | 68/96 [01:45<00:43,  1.55s/it]
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Epoch 1 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 1 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.97it/s]
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Epoch 1 Training:  72%|███████▏  | 69/96 [01:46<00:41,  1.55s/it]
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Epoch 1 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 1 Training:  73%|███████▎  | 70/96 [01:48<00:40,  1.55s/it]
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Epoch 1 Training:  74%|███████▍  | 71/96 [01:49<00:38,  1.54s/it]
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Epoch 1 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.96it/s]
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Epoch 1 Training:  75%|███████▌  | 72/96 [01:51<00:37,  1.55s/it]
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Epoch 1 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.97it/s]
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Epoch 1 Training:  76%|███████▌  | 73/96 [01:52<00:35,  1.55s/it]
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Epoch 1 Training:  77%|███████▋  | 74/96 [01:54<00:34,  1.55s/it]
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Epoch 1 Training:  78%|███████▊  | 75/96 [01:56<00:32,  1.55s/it]
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Epoch 1 Training:  79%|███████▉  | 76/96 [01:57<00:30,  1.55s/it]
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Epoch 1 Training:  80%|████████  | 77/96 [01:59<00:29,  1.55s/it]
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Epoch 1 Training:  81%|████████▏ | 78/96 [02:00<00:27,  1.55s/it]
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Epoch 1 Training:  82%|████████▏ | 79/96 [02:02<00:26,  1.55s/it]
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Epoch 1 Training:  83%|████████▎ | 80/96 [02:03<00:24,  1.55s/it]
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Epoch 1 Training:  84%|████████▍ | 81/96 [02:05<00:23,  1.55s/it]
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Epoch 1 Training:  85%|████████▌ | 82/96 [02:06<00:21,  1.55s/it]
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Epoch 1 Training:  86%|████████▋ | 83/96 [02:08<00:20,  1.55s/it]
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Epoch 1 Training:  88%|████████▊ | 84/96 [02:09<00:18,  1.55s/it]
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Epoch 1 Training:  89%|████████▊ | 85/96 [02:11<00:17,  1.55s/it]
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Epoch 1 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s][2026-10-02 07:40:16] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0001 | Epoch 001] train/policy_loss=26.2910 train/unweighted_policy_loss=5.2582 train/loss=26.2910 train/grad_norm=0.1183
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+[2026-10-02 07:40:16] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0002 | Epoch 001] timing/rollout_seconds=179.3234 timing/feedback_seconds=5.7410 timing/optimization_seconds=149.1232 timing/cycle_seconds=331.2455
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+[2026-10-02 07:43:19] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0002 | Epoch 002] train/reward_domain_mean=0.3043 train/reward_domain_std=0.0562 train/reward_structure_mean=0.6286 train/reward_structure_std=0.1709 train/reward_domain_group_std_mean=0.0398 train/reward_domain_group_std_max=0.0782 train/reward_domain_group_std_min=0.0194 train/reward_domain_group_mean_std=0.0378 train/reward_structure_group_std_mean=0.0177 train/reward_structure_group_std_max=0.0402 train/reward_structure_group_std_min=0.0065 train/reward_structure_group_mean_std=0.1697 train/reward_domain_zero_std_ratio=0 train/reward_structure_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.5223 train/adv_max=3.1787 train/adv_abs_mean=1.0015
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Epoch 2 Training:  11%|█▏        | 11/96 [00:17<02:12,  1.56s/it]
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Epoch 2 Training:  12%|█▎        | 12/96 [00:18<02:11,  1.56s/it]
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Epoch 2 Training:  14%|█▎        | 13/96 [00:20<02:09,  1.57s/it]
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Epoch 2 Training:  15%|█▍        | 14/96 [00:21<02:08,  1.57s/it]
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Epoch 2 Training:  16%|█▌        | 15/96 [00:23<02:07,  1.57s/it]
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Epoch 2 Training:  17%|█▋        | 16/96 [00:24<02:05,  1.56s/it]
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Epoch 2 Training:  18%|█▊        | 17/96 [00:26<02:03,  1.56s/it]
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Epoch 2 Training:  19%|█▉        | 18/96 [00:28<02:00,  1.55s/it]
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Epoch 2 Training:  20%|█▉        | 19/96 [00:29<01:58,  1.54s/it]
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Epoch 2 Training:  21%|██        | 20/96 [00:31<01:57,  1.54s/it]
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Epoch 2 Training:  22%|██▏       | 21/96 [00:32<01:56,  1.55s/it]
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Epoch 2 Training:  23%|██▎       | 22/96 [00:34<01:55,  1.56s/it]
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Epoch 2 Training:  24%|██▍       | 23/96 [00:35<01:53,  1.56s/it]
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Epoch 2 Training:  25%|██▌       | 24/96 [00:37<01:52,  1.56s/it]
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Epoch 2 Training:  26%|██▌       | 25/96 [00:38<01:51,  1.57s/it]
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Epoch 2 Training:  27%|██▋       | 26/96 [00:40<01:49,  1.57s/it]
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Epoch 2 Training:  28%|██▊       | 27/96 [00:42<01:48,  1.57s/it]
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Epoch 2 Training:  29%|██▉       | 28/96 [00:43<01:46,  1.57s/it]
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Epoch 2 Training:  30%|███       | 29/96 [00:45<01:45,  1.57s/it]
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Epoch 2 Training:  31%|███▏      | 30/96 [00:46<01:43,  1.57s/it]
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Epoch 2 Training:  32%|███▏      | 31/96 [00:48<01:42,  1.57s/it]
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Epoch 2 Training:  33%|███▎      | 32/96 [00:49<01:40,  1.57s/it]
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Epoch 2 Training:  34%|███▍      | 33/96 [00:51<01:39,  1.57s/it]
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Epoch 2 Training:  35%|███▌      | 34/96 [00:53<01:37,  1.57s/it]
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Epoch 2 Training:  36%|███▋      | 35/96 [00:54<01:35,  1.57s/it]
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Epoch 2 Training:  38%|███▊      | 36/96 [00:56<01:34,  1.57s/it]
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Epoch 2 Training:  39%|███▊      | 37/96 [00:57<01:32,  1.57s/it]
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Epoch 2 Training:  40%|███▉      | 38/96 [00:59<01:31,  1.57s/it]
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Epoch 2 Training:  41%|████      | 39/96 [01:00<01:29,  1.57s/it]
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Epoch 2 Training:  42%|████▏     | 40/96 [01:02<01:27,  1.57s/it]
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Epoch 2 Training:  43%|████▎     | 41/96 [01:04<01:26,  1.57s/it]
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Epoch 2 Training:  44%|████▍     | 42/96 [01:05<01:24,  1.57s/it]
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Epoch 2 Training:  45%|████▍     | 43/96 [01:07<01:23,  1.57s/it]
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Epoch 2 Training:  46%|████▌     | 44/96 [01:08<01:21,  1.57s/it]
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Epoch 2 Training:  47%|████▋     | 45/96 [01:10<01:20,  1.57s/it]
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Epoch 2 Training:  48%|████▊     | 46/96 [01:11<01:18,  1.57s/it]
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Epoch 2 Training:  49%|████▉     | 47/96 [01:13<01:17,  1.57s/it]
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Epoch 2 Training:  50%|█████     | 48/96 [01:15<01:15,  1.57s/it]
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Epoch 2 Training:  51%|█████     | 49/96 [01:16<01:13,  1.57s/it]
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Epoch 2 Training:  52%|█████▏    | 50/96 [01:18<01:12,  1.57s/it]
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Epoch 2 Training:  53%|█████▎    | 51/96 [01:19<01:10,  1.57s/it]
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Epoch 2 Training:  54%|█████▍    | 52/96 [01:21<01:09,  1.57s/it]
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Epoch 2 Training:  55%|█████▌    | 53/96 [01:22<01:07,  1.57s/it]
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Epoch 2 Training:  56%|█████▋    | 54/96 [01:24<01:06,  1.57s/it]
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Epoch 2 Training:  57%|█████▋    | 55/96 [01:26<01:04,  1.57s/it]
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Epoch 2 Training:  59%|█████▉    | 57/96 [01:29<01:01,  1.57s/it]
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Epoch 2 Training:  60%|██████    | 58/96 [01:30<00:59,  1.57s/it]
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Epoch 2 Training:  61%|██████▏   | 59/96 [01:32<00:58,  1.57s/it]
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Epoch 2 Training:  64%|██████▎   | 61/96 [01:35<00:54,  1.57s/it]
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Epoch 2 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.18it/s][2026-10-02 07:45:49] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0002 | Epoch 002] train/policy_loss=24.8995 train/unweighted_policy_loss=4.9799 train/loss=24.8995 train/grad_norm=0.0803
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+[2026-10-02 07:45:49] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0003 | Epoch 002] timing/rollout_seconds=179.2793 timing/feedback_seconds=6.0809 timing/optimization_seconds=151.1625 timing/cycle_seconds=333.2657
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+
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+[2026-10-02 07:48:52] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0003 | Epoch 003] train/reward_domain_mean=0.3285 train/reward_domain_std=0.0468 train/reward_structure_mean=0.6366 train/reward_structure_std=0.1523 train/reward_domain_group_std_mean=0.0306 train/reward_domain_group_std_max=0.0583 train/reward_domain_group_std_min=0.0145 train/reward_domain_group_mean_std=0.0338 train/reward_structure_group_std_mean=0.0143 train/reward_structure_group_std_max=0.0635 train/reward_structure_group_std_min=0.0041 train/reward_structure_group_mean_std=0.1512 train/reward_domain_zero_std_ratio=0 train/reward_structure_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-6.5265 train/adv_max=3.1019 train/adv_abs_mean=0.9954
+
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Epoch 3 Training:  40%|███▉      | 38/96 [00:59<01:31,  1.57s/it]
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Epoch 3 Training:  41%|████      | 39/96 [01:01<01:29,  1.57s/it]
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Epoch 3 Training:  42%|████▏     | 40/96 [01:02<01:27,  1.57s/it]
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Epoch 3 Training:  43%|████▎     | 41/96 [01:04<01:26,  1.57s/it]
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Epoch 3 Training:  44%|████▍     | 42/96 [01:05<01:24,  1.57s/it]
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Epoch 3 Training:  45%|████▍     | 43/96 [01:07<01:23,  1.57s/it]
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Epoch 3 Training:  46%|████▌     | 44/96 [01:09<01:21,  1.57s/it]
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Epoch 3 Training:  47%|████▋     | 45/96 [01:10<01:20,  1.57s/it]
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Epoch 3 Training:  48%|████▊     | 46/96 [01:12<01:18,  1.57s/it]
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Epoch 3 Training:  49%|████▉     | 47/96 [01:13<01:17,  1.57s/it]
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Epoch 3 Training:  51%|█████     | 49/96 [01:16<01:13,  1.57s/it]
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Epoch 3 Training:  52%|█████▏    | 50/96 [01:18<01:12,  1.57s/it]
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Epoch 3 Training:  53%|█████▎    | 51/96 [01:20<01:10,  1.57s/it]
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Epoch 3 Training:  54%|█████▍    | 52/96 [01:21<01:09,  1.57s/it]
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Epoch 3 Training:  55%|█████▌    | 53/96 [01:23<01:07,  1.57s/it]
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Epoch 3 Training:  56%|█████▋    | 54/96 [01:24<01:06,  1.57s/it]
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Epoch 3 Training:  57%|█████▋    | 55/96 [01:26<01:04,  1.57s/it]
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Epoch 3 Training:  58%|█████▊    | 56/96 [01:27<01:02,  1.57s/it]
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Epoch 3 Training:  59%|█████▉    | 57/96 [01:29<01:01,  1.57s/it]
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Epoch 3 Training:  60%|██████    | 58/96 [01:31<00:59,  1.57s/it]
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Epoch 3 Training:  61%|██████▏   | 59/96 [01:32<00:58,  1.57s/it]
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Epoch 3 Training:  62%|██████▎   | 60/96 [01:34<00:56,  1.57s/it]
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Epoch 3 Training:  64%|██████▎   | 61/96 [01:35<00:54,  1.57s/it]
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Epoch 3 Training:  65%|██████▍   | 62/96 [01:37<00:53,  1.57s/it]
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Epoch 3 Training:  66%|██████▌   | 63/96 [01:38<00:51,  1.57s/it]
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Epoch 3 Training:  67%|██████▋   | 64/96 [01:40<00:50,  1.57s/it]
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Epoch 3 Training:  68%|██████▊   | 65/96 [01:42<00:48,  1.57s/it]
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Epoch 3 Training:  69%|██████▉   | 66/96 [01:43<00:47,  1.57s/it]
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Epoch 3 Training:  70%|██████▉   | 67/96 [01:45<00:45,  1.57s/it]
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Epoch 3 Training:  71%|███████   | 68/96 [01:46<00:43,  1.57s/it]
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Epoch 3 Training:  72%|███████▏  | 69/96 [01:48<00:42,  1.57s/it]
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Epoch 3 Training:  73%|███████▎  | 70/96 [01:49<00:40,  1.57s/it]
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Epoch 3 Training:  74%|███████▍  | 71/96 [01:51<00:39,  1.57s/it]
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Epoch 3 Training:  75%|███████▌  | 72/96 [01:53<00:37,  1.57s/it]
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Epoch 3 Training:  76%|███████▌  | 73/96 [01:54<00:36,  1.57s/it]
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Epoch 3 Training:  77%|███████▋  | 74/96 [01:56<00:34,  1.57s/it]
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Epoch 3 Training:  78%|███████▊  | 75/96 [01:57<00:32,  1.57s/it]
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Epoch 3 Training:  79%|███████▉  | 76/96 [01:59<00:31,  1.57s/it]
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Epoch 3 Training:  80%|████████  | 77/96 [02:00<00:29,  1.57s/it]
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Epoch 3 Training:  81%|████████▏ | 78/96 [02:02<00:28,  1.57s/it]
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Epoch 3 Training:  82%|████████▏ | 79/96 [02:04<00:26,  1.57s/it]
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Epoch 3 Training:  83%|████████▎ | 80/96 [02:05<00:25,  1.57s/it]
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Epoch 3 Training:  84%|████████▍ | 81/96 [02:07<00:23,  1.57s/it]
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Epoch 3 Training:  85%|████████▌ | 82/96 [02:08<00:22,  1.57s/it]
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Epoch 3 Training:  86%|████████▋ | 83/96 [02:10<00:20,  1.57s/it]
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Epoch 3 Training:  88%|████████▊ | 84/96 [02:11<00:18,  1.57s/it]
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Epoch 3 Training:  89%|████████▊ | 85/96 [02:13<00:17,  1.57s/it]
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Epoch 3 Training:  90%|████████▉ | 86/96 [02:15<00:15,  1.57s/it]
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Epoch 3 Training:  91%|█████████ | 87/96 [02:16<00:14,  1.57s/it]
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Epoch 3 Training:  92%|█████████▏| 88/96 [02:18<00:12,  1.57s/it]
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Epoch 3 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.18it/s][2026-10-02 07:51:22] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0003 | Epoch 003] train/policy_loss=23.3935 train/unweighted_policy_loss=4.6787 train/loss=23.3935 train/grad_norm=0.0838
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+[2026-10-02 07:51:22] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0004 | Epoch 003] timing/rollout_seconds=179.2279 timing/feedback_seconds=6.0579 timing/optimization_seconds=151.3698 timing/cycle_seconds=333.3449
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+[2026-10-02 07:54:25] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0004 | Epoch 004] train/reward_domain_mean=0.3544 train/reward_domain_std=0.0367 train/reward_structure_mean=0.6322 train/reward_structure_std=0.1904 train/reward_domain_group_std_mean=0.0218 train/reward_domain_group_std_max=0.0487 train/reward_domain_group_std_min=0.0089 train/reward_domain_group_mean_std=0.0284 train/reward_structure_group_std_mean=0.0100 train/reward_structure_group_std_max=0.0251 train/reward_structure_group_std_min=0.0014 train/reward_structure_group_mean_std=0.1901 train/reward_domain_zero_std_ratio=0 train/reward_structure_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.9274 train/adv_max=4.1803 train/adv_abs_mean=0.9639
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Epoch 4 Training:  15%|█▍        | 14/96 [00:21<02:05,  1.53s/it]
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Epoch 4 Training:  16%|█▌        | 15/96 [00:23<02:04,  1.53s/it]
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Epoch 4 Training:  17%|█▋        | 16/96 [00:24<02:02,  1.53s/it]
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Epoch 4 Training:  18%|█▊        | 17/96 [00:26<02:00,  1.53s/it]
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Epoch 4 Training:  19%|█▉        | 18/96 [00:27<01:59,  1.53s/it]
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Epoch 4 Training:  20%|█▉        | 19/96 [00:29<01:57,  1.53s/it]
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Epoch 4 Training:  21%|██        | 20/96 [00:30<01:56,  1.53s/it]
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Epoch 4 Training:  22%|██▏       | 21/96 [00:32<01:54,  1.53s/it]
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Epoch 4 Training:  23%|██▎       | 22/96 [00:34<01:53,  1.53s/it]
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Epoch 4 Training:  24%|██▍       | 23/96 [00:35<01:51,  1.53s/it]
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Epoch 4 Training:  25%|██▌       | 24/96 [00:37<01:50,  1.53s/it]
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Epoch 4 Training:  26%|██▌       | 25/96 [00:38<01:48,  1.53s/it]
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Epoch 4 Training:  27%|██▋       | 26/96 [00:40<01:47,  1.53s/it]
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Epoch 4 Training:  28%|██▊       | 27/96 [00:41<01:45,  1.53s/it]
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Epoch 4 Training:  29%|██▉       | 28/96 [00:43<01:44,  1.53s/it]
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Epoch 4 Training:  30%|███       | 29/96 [00:44<01:42,  1.53s/it]
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Epoch 4 Training:  31%|███▏      | 30/96 [00:46<01:41,  1.53s/it]
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Epoch 4 Training:  32%|███▏      | 31/96 [00:47<01:39,  1.53s/it]
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Epoch 4 Training:  33%|███▎      | 32/96 [00:49<01:37,  1.53s/it]
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Epoch 4 Training:  34%|███▍      | 33/96 [00:50<01:36,  1.53s/it]
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Epoch 4 Training:  35%|███▌      | 34/96 [00:52<01:34,  1.53s/it]
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Epoch 4 Training:  36%|███▋      | 35/96 [00:53<01:33,  1.53s/it]
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Epoch 4 Training:  38%|███▊      | 36/96 [00:55<01:31,  1.53s/it]
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Epoch 4 Training:  39%|███▊      | 37/96 [00:56<01:30,  1.53s/it]
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Epoch 4 Training:  40%|███▉      | 38/96 [00:58<01:28,  1.53s/it]
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Epoch 4 Training:  41%|████      | 39/96 [01:00<01:27,  1.53s/it]
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Epoch 4 Training:  42%|████▏     | 40/96 [01:01<01:25,  1.53s/it]
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Epoch 4 Training:  43%|████▎     | 41/96 [01:03<01:24,  1.53s/it]
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Epoch 4 Training:  44%|████▍     | 42/96 [01:04<01:22,  1.53s/it]
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Epoch 4 Training:  45%|████▍     | 43/96 [01:06<01:21,  1.53s/it]
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Epoch 4 Training:  46%|████▌     | 44/96 [01:07<01:19,  1.53s/it]
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Epoch 4 Training:  47%|████▋     | 45/96 [01:09<01:18,  1.53s/it]
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Epoch 4 Training:  48%|████▊     | 46/96 [01:10<01:16,  1.53s/it]
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Epoch 4 Training:  49%|████▉     | 47/96 [01:12<01:15,  1.53s/it]
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Epoch 4 Training:  50%|█████     | 48/96 [01:13<01:13,  1.53s/it]
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Epoch 4 Training:  51%|█████     | 49/96 [01:15<01:11,  1.53s/it]
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Epoch 4 Training:  52%|█████▏    | 50/96 [01:16<01:10,  1.53s/it]
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Epoch 4 Training:  53%|█████▎    | 51/96 [01:18<01:08,  1.53s/it]
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Epoch 4 Training:  54%|█████▍    | 52/96 [01:19<01:07,  1.53s/it]
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Epoch 4 Training:  55%|█████▌    | 53/96 [01:21<01:05,  1.53s/it]
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Epoch 4 Training:  56%|█████▋    | 54/96 [01:23<01:04,  1.53s/it]
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Epoch 4 Training:  57%|█████▋    | 55/96 [01:24<01:02,  1.53s/it]
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Epoch 4 Training:  58%|█████▊    | 56/96 [01:26<01:01,  1.53s/it]
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Epoch 4 Training:  59%|█████▉    | 57/96 [01:27<01:00,  1.56s/it]
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Epoch 4 Training:  60%|██████    | 58/96 [01:29<00:58,  1.55s/it]
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Epoch 4 Training:  61%|██████▏   | 59/96 [01:30<00:57,  1.54s/it]
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Epoch 4 Training:  62%|██████▎   | 60/96 [01:32<00:55,  1.54s/it]
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Epoch 4 Training:  64%|██████▎   | 61/96 [01:33<00:53,  1.54s/it]
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Epoch 4 Training:  65%|██████▍   | 62/96 [01:35<00:52,  1.53s/it]
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Epoch 4 Training:  66%|██████▌   | 63/96 [01:36<00:50,  1.53s/it]
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Epoch 4 Training:  67%|██████▋   | 64/96 [01:38<00:49,  1.53s/it]
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Epoch 4 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s][2026-10-02 07:56:53] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0004 | Epoch 004] train/policy_loss=22.5891 train/unweighted_policy_loss=4.5178 train/loss=22.5891 train/grad_norm=0.0969
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+[2026-10-02 07:56:53] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0005 | Epoch 004] timing/rollout_seconds=179.2126 timing/feedback_seconds=6.3032 timing/optimization_seconds=148.1254 timing/cycle_seconds=330.1405
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+[2026-10-02 07:57:09] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0005 | Epoch 005] eval/places_domain_rl/reward_domain_mean=0.3404 eval/places_domain_rl/reward_domain_std=0.0453 eval/places_domain_rl/reward_ssim_to_stage1_mean=0.5620 eval/places_domain_rl/reward_ssim_to_stage1_std=0.1457 eval/places_domain_rl/reward_structure_mean=0.6616 eval/places_domain_rl/reward_structure_std=0.1530
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+
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+
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+[2026-10-02 08:00:12] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0005 | Epoch 005] train/reward_domain_mean=0.3412 train/reward_domain_std=0.0518 train/reward_structure_mean=0.6025 train/reward_structure_std=0.1924 train/reward_domain_group_std_mean=0.0224 train/reward_domain_group_std_max=0.0405 train/reward_domain_group_std_min=0.0087 train/reward_domain_group_mean_std=0.0459 train/reward_structure_group_std_mean=0.0075 train/reward_structure_group_std_max=0.0246 train/reward_structure_group_std_min=0.0020 train/reward_structure_group_mean_std=0.1922 train/reward_domain_zero_std_ratio=0 train/reward_structure_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.2864 train/adv_max=3.6913 train/adv_abs_mean=0.9143
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Epoch 5 Training:  38%|███▊      | 36/96 [00:55<01:32,  1.54s/it]
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Epoch 5 Training:  39%|███▊      | 37/96 [00:56<01:30,  1.54s/it]
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Epoch 5 Training:  40%|███▉      | 38/96 [00:58<01:29,  1.54s/it]
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Epoch 5 Training:  41%|████      | 39/96 [00:59<01:27,  1.54s/it]
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Epoch 5 Training:  42%|████▏     | 40/96 [01:01<01:26,  1.54s/it]
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Epoch 5 Training:  43%|████▎     | 41/96 [01:02<01:24,  1.54s/it]
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Epoch 5 Training:  44%|████▍     | 42/96 [01:04<01:22,  1.54s/it]
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Epoch 5 Training:  45%|████▍     | 43/96 [01:05<01:21,  1.54s/it]
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Epoch 5 Training:  46%|████▌     | 44/96 [01:07<01:19,  1.54s/it]
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Epoch 5 Training:  47%|████▋     | 45/96 [01:09<01:18,  1.54s/it]
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Epoch 5 Training:  48%|████▊     | 46/96 [01:10<01:16,  1.54s/it]
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Epoch 5 Training:  49%|████▉     | 47/96 [01:12<01:15,  1.54s/it]
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Epoch 5 Training:  50%|█████     | 48/96 [01:13<01:13,  1.54s/it]
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Epoch 5 Training:  51%|█████     | 49/96 [01:15<01:12,  1.54s/it]
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Epoch 5 Training:  52%|█████▏    | 50/96 [01:16<01:10,  1.54s/it]
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Epoch 5 Training:  53%|█████▎    | 51/96 [01:18<01:09,  1.54s/it]
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Epoch 5 Training:  54%|█████▍    | 52/96 [01:19<01:07,  1.54s/it]
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Epoch 5 Training:  55%|█████▌    | 53/96 [01:21<01:06,  1.54s/it]
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Epoch 5 Training:  56%|█████▋    | 54/96 [01:22<01:04,  1.54s/it]
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Epoch 5 Training:  57%|█████▋    | 55/96 [01:24<01:03,  1.54s/it]
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Epoch 5 Training:  58%|█████▊    | 56/96 [01:25<01:01,  1.54s/it]
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Epoch 5 Training:  59%|█████▉    | 57/96 [01:27<00:59,  1.54s/it]
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Epoch 5 Training:  60%|██████    | 58/96 [01:29<00:58,  1.54s/it]
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Epoch 5 Training:  61%|██████▏   | 59/96 [01:30<00:56,  1.54s/it]
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Epoch 5 Training:  62%|██████▎   | 60/96 [01:32<00:55,  1.54s/it]
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Epoch 5 Training:  64%|██████▎   | 61/96 [01:33<00:53,  1.54s/it]
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Epoch 5 Training:  65%|██████▍   | 62/96 [01:35<00:52,  1.54s/it]
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Epoch 5 Training:  66%|██████▌   | 63/96 [01:36<00:50,  1.54s/it]
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Epoch 5 Training:  67%|██████▋   | 64/96 [01:38<00:49,  1.54s/it]
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Epoch 5 Training:  68%|██████▊   | 65/96 [01:39<00:47,  1.54s/it]
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Epoch 5 Training:  69%|██████▉   | 66/96 [01:41<00:46,  1.54s/it]
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Epoch 5 Training:  70%|██████▉   | 67/96 [01:42<00:44,  1.54s/it]
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Epoch 5 Training:  71%|███████   | 68/96 [01:44<00:43,  1.54s/it]
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Epoch 5 Training:  72%|███████▏  | 69/96 [01:45<00:41,  1.54s/it]
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Epoch 5 Training:  73%|███████▎  | 70/96 [01:47<00:40,  1.54s/it]
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Epoch 5 Training:  74%|███████▍  | 71/96 [01:49<00:38,  1.54s/it]
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Epoch 5 Training:  75%|███████▌  | 72/96 [01:50<00:36,  1.54s/it]
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Epoch 5 Training:  76%|███████▌  | 73/96 [01:52<00:35,  1.54s/it]
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Epoch 5 Training:  77%|███████▋  | 74/96 [01:53<00:33,  1.54s/it]
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Epoch 5 Training:  78%|███████▊  | 75/96 [01:55<00:32,  1.54s/it]
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Epoch 5 Training:  79%|███████▉  | 76/96 [01:56<00:30,  1.54s/it]
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Epoch 5 Training:  80%|████████  | 77/96 [01:58<00:29,  1.54s/it]
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Epoch 5 Training:  81%|████████▏ | 78/96 [01:59<00:27,  1.54s/it]
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Epoch 5 Training:  82%|████████▏ | 79/96 [02:01<00:26,  1.54s/it]
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Epoch 5 Training:  83%|████████▎ | 80/96 [02:02<00:24,  1.54s/it]
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Epoch 5 Training:  84%|████████▍ | 81/96 [02:04<00:23,  1.54s/it]
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Epoch 5 Training:  85%|████████▌ | 82/96 [02:05<00:21,  1.54s/it]
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Epoch 5 Training:  86%|████████▋ | 83/96 [02:07<00:19,  1.54s/it]
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Epoch 5 Training:  88%|████████▊ | 84/96 [02:09<00:18,  1.54s/it]
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Epoch 5 Training:  89%|████████▊ | 85/96 [02:10<00:16,  1.54s/it]
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Epoch 5 Training:  90%|████████▉ | 86/96 [02:12<00:15,  1.54s/it]
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Epoch 5 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s][2026-10-02 08:02:40] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0005 | Epoch 005] train/policy_loss=22.3802 train/unweighted_policy_loss=4.4760 train/loss=22.3802 train/grad_norm=0.0457
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+[2026-10-02 08:02:40] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0006 | Epoch 005] timing/rollout_seconds=179.2199 timing/feedback_seconds=5.6550 timing/optimization_seconds=148.1436 timing/cycle_seconds=330.1751
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+[2026-10-02 08:05:42] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0006 | Epoch 006] train/reward_domain_mean=0.3148 train/reward_domain_std=0.0682 train/reward_structure_mean=0.6463 train/reward_structure_std=0.1436 train/reward_domain_group_std_mean=0.0244 train/reward_domain_group_std_max=0.0489 train/reward_domain_group_std_min=0.0095 train/reward_domain_group_mean_std=0.0630 train/reward_structure_group_std_mean=0.0090 train/reward_structure_group_std_max=0.0292 train/reward_structure_group_std_min=0.0017 train/reward_structure_group_mean_std=0.1433 train/reward_domain_zero_std_ratio=0 train/reward_structure_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.3168 train/adv_max=3.7098 train/adv_abs_mean=0.9053
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Epoch 6 Training:  12%|█▎        | 12/96 [00:18<02:08,  1.54s/it]
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Epoch 6 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 6 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 6 Training:  14%|█▎        | 13/96 [00:19<02:07,  1.54s/it]
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Epoch 6 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 6 Training:  15%|█▍        | 14/96 [00:21<02:05,  1.54s/it]
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Epoch 6 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 6 Training:  16%|█▌        | 15/96 [00:22<02:04,  1.54s/it]
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Epoch 6 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 6 Training:  17%|█▋        | 16/96 [00:24<02:02,  1.54s/it]
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Epoch 6 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 6 Training:  18%|█▊        | 17/96 [00:26<02:01,  1.54s/it]
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Epoch 6 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 6 Training:  19%|█▉        | 18/96 [00:27<01:59,  1.54s/it]
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Epoch 6 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 6 Training:  20%|█▉        | 19/96 [00:29<01:58,  1.54s/it]
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Epoch 6 Training:  21%|██        | 20/96 [00:30<01:56,  1.54s/it]
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Epoch 6 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 6 Training:  22%|██▏       | 21/96 [00:32<01:55,  1.54s/it]
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Epoch 6 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 6 Training:  23%|██▎       | 22/96 [00:33<01:53,  1.54s/it]
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Epoch 6 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 6 Training:  24%|██▍       | 23/96 [00:35<01:52,  1.54s/it]
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Epoch 6 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 6 Training:  25%|██▌       | 24/96 [00:36<01:50,  1.53s/it]
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Epoch 6 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 6 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 6 Training:  26%|██▌       | 25/96 [00:38<01:48,  1.53s/it]
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Epoch 6 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 6 Training:  27%|██▋       | 26/96 [00:39<01:47,  1.53s/it]
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Epoch 6 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 6 Training:  28%|██▊       | 27/96 [00:41<01:45,  1.53s/it]
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Epoch 6 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 6 Training:  29%|██▉       | 28/96 [00:42<01:44,  1.53s/it]
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Epoch 6 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 6 Training:  30%|███       | 29/96 [00:44<01:42,  1.53s/it]
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Epoch 6 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 6 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 6 Training:  31%|███▏      | 30/96 [00:46<01:41,  1.53s/it]
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Epoch 6 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 6 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 6 Training:  32%|███▏      | 31/96 [00:47<01:39,  1.54s/it]
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Epoch 6 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 6 Training:  33%|███▎      | 32/96 [00:49<01:38,  1.53s/it]
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Epoch 6 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 6 Training:  34%|███▍      | 33/96 [00:50<01:36,  1.53s/it]
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Epoch 6 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 6 Training:  35%|███▌      | 34/96 [00:52<01:35,  1.53s/it]
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Epoch 6 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 6 Training:  36%|███▋      | 35/96 [00:53<01:35,  1.57s/it]
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Epoch 6 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 6 Training:  38%|███▊      | 36/96 [00:55<01:33,  1.56s/it]
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Epoch 6 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 6 Training:  39%|███▊      | 37/96 [00:56<01:31,  1.56s/it]
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Epoch 6 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 6 Training:  40%|███▉      | 38/96 [00:58<01:30,  1.55s/it]
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Epoch 6 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 6 Training:  41%|████      | 39/96 [00:59<01:28,  1.55s/it]
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Epoch 6 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 6 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 6 Training:  42%|████▏     | 40/96 [01:01<01:26,  1.54s/it]
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Epoch 6 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 6 Training:  43%|████▎     | 41/96 [01:03<01:24,  1.54s/it]
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Epoch 6 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 6 Training:  44%|████▍     | 42/96 [01:04<01:23,  1.54s/it]
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Epoch 6 Training:  45%|████▍     | 43/96 [01:06<01:21,  1.54s/it]
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Epoch 6 Training:  46%|████▌     | 44/96 [01:07<01:19,  1.54s/it]
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Epoch 6 Training:  47%|████▋     | 45/96 [01:09<01:18,  1.54s/it]
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Epoch 6 Training:  48%|████▊     | 46/96 [01:10<01:16,  1.54s/it]
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Epoch 6 Training:  49%|████▉     | 47/96 [01:12<01:15,  1.54s/it]
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Epoch 6 Training:  50%|█████     | 48/96 [01:13<01:13,  1.54s/it]
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Epoch 6 Training:  51%|█████     | 49/96 [01:15<01:12,  1.54s/it]
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Epoch 6 Training:  52%|█████▏    | 50/96 [01:16<01:10,  1.54s/it]
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Epoch 6 Training:  53%|█████▎    | 51/96 [01:18<01:09,  1.54s/it]
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Epoch 6 Training:  54%|█████▍    | 52/96 [01:19<01:07,  1.54s/it]
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Epoch 6 Training:  55%|█████▌    | 53/96 [01:21<01:06,  1.54s/it]
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Epoch 6 Training:  56%|█████▋    | 54/96 [01:22<01:04,  1.54s/it]
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Epoch 6 Training:  57%|█████▋    | 55/96 [01:24<01:02,  1.54s/it]
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Epoch 6 Training:  58%|█████▊    | 56/96 [01:26<01:01,  1.53s/it]
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Epoch 6 Training:  59%|█████▉    | 57/96 [01:27<00:59,  1.54s/it]
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Epoch 6 Training:  60%|██████    | 58/96 [01:29<00:58,  1.54s/it]
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Epoch 6 Training:  61%|██████▏   | 59/96 [01:30<00:56,  1.53s/it]
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Epoch 6 Training:  62%|██████▎   | 60/96 [01:32<00:55,  1.53s/it]
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Epoch 6 Training:  64%|██████▎   | 61/96 [01:33<00:53,  1.53s/it]
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Epoch 6 Training:  65%|██████▍   | 62/96 [01:35<00:52,  1.53s/it]
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Epoch 6 Training:  66%|██████▌   | 63/96 [01:36<00:50,  1.53s/it]
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Epoch 6 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s][2026-10-02 08:08:10] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0006 | Epoch 006] train/policy_loss=22.4765 train/unweighted_policy_loss=4.4953 train/loss=22.4765 train/grad_norm=0.0478
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+[2026-10-02 08:08:10] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0007 | Epoch 006] timing/rollout_seconds=179.1685 timing/feedback_seconds=5.6973 timing/optimization_seconds=148.2238 timing/cycle_seconds=330.1833
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+[2026-10-02 08:11:12] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0007 | Epoch 007] train/reward_domain_mean=0.2951 train/reward_domain_std=0.0637 train/reward_structure_mean=0.6552 train/reward_structure_std=0.1581 train/reward_domain_group_std_mean=0.0261 train/reward_domain_group_std_max=0.0483 train/reward_domain_group_std_min=0.0086 train/reward_domain_group_mean_std=0.0575 train/reward_structure_group_std_mean=0.0116 train/reward_structure_group_std_max=0.0461 train/reward_structure_group_std_min=0.0037 train/reward_structure_group_mean_std=0.1575 train/reward_domain_zero_std_ratio=0 train/reward_structure_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.2640 train/adv_max=3.2760 train/adv_abs_mean=0.8465
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Epoch 7 Training:  42%|████▏     | 40/96 [01:01<01:26,  1.54s/it]
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Epoch 7 Training:  44%|████▍     | 42/96 [01:04<01:22,  1.54s/it]
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Epoch 7 Training:  47%|████▋     | 45/96 [01:09<01:18,  1.54s/it]
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Epoch 7 Training:  48%|████▊     | 46/96 [01:10<01:16,  1.54s/it]
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Epoch 7 Training:  49%|████▉     | 47/96 [01:12<01:15,  1.54s/it]
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Epoch 7 Training:  51%|█████     | 49/96 [01:15<01:12,  1.53s/it]
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Epoch 7 Training:  53%|█████▎    | 51/96 [01:18<01:09,  1.53s/it]
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Epoch 7 Training:  54%|█████▍    | 52/96 [01:19<01:07,  1.53s/it]
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Epoch 7 Training:  55%|█████▌    | 53/96 [01:21<01:06,  1.54s/it]
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Epoch 7 Training:  56%|█████▋    | 54/96 [01:22<01:04,  1.54s/it]
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Epoch 7 Training:  57%|█████▋    | 55/96 [01:24<01:02,  1.53s/it]
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Epoch 7 Training:  58%|█████▊    | 56/96 [01:25<01:01,  1.54s/it]
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Epoch 7 Training:  59%|█████▉    | 57/96 [01:27<00:59,  1.54s/it]
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Epoch 7 Training:  60%|██████    | 58/96 [01:29<00:58,  1.54s/it]
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Epoch 7 Training:  61%|██████▏   | 59/96 [01:30<00:56,  1.54s/it]
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Epoch 7 Training:  62%|██████▎   | 60/96 [01:32<00:55,  1.54s/it]
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Epoch 7 Training:  64%|██████▎   | 61/96 [01:33<00:54,  1.55s/it]
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Epoch 7 Training:  65%|██████▍   | 62/96 [01:35<00:52,  1.55s/it]
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Epoch 7 Training:  66%|██████▌   | 63/96 [01:36<00:50,  1.54s/it]
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Epoch 7 Training:  67%|██████▋   | 64/96 [01:38<00:49,  1.54s/it]
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Epoch 7 Training:  68%|██████▊   | 65/96 [01:39<00:47,  1.54s/it]
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Epoch 7 Training:  69%|██████▉   | 66/96 [01:41<00:46,  1.54s/it]
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Epoch 7 Training:  70%|██████▉   | 67/96 [01:42<00:44,  1.54s/it]
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Epoch 7 Training:  71%|███████   | 68/96 [01:44<00:43,  1.54s/it]
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Epoch 7 Training:  72%|███████▏  | 69/96 [01:45<00:41,  1.54s/it]
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Epoch 7 Training:  73%|███████▎  | 70/96 [01:47<00:39,  1.53s/it]
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Epoch 7 Training:  74%|███████▍  | 71/96 [01:49<00:38,  1.53s/it]
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Epoch 7 Training:  75%|███████▌  | 72/96 [01:50<00:36,  1.53s/it]
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Epoch 7 Training:  76%|███████▌  | 73/96 [01:52<00:35,  1.53s/it]
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Epoch 7 Training:  77%|███████▋  | 74/96 [01:53<00:33,  1.54s/it]
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Epoch 7 Training:  78%|███████▊  | 75/96 [01:55<00:32,  1.53s/it]
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Epoch 7 Training:  79%|███████▉  | 76/96 [01:56<00:30,  1.54s/it]
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Epoch 7 Training:  80%|████████  | 77/96 [01:58<00:29,  1.54s/it]
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Epoch 7 Training:  81%|████████▏ | 78/96 [01:59<00:27,  1.53s/it]
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Epoch 7 Training:  82%|████████▏ | 79/96 [02:01<00:26,  1.53s/it]
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Epoch 7 Training:  83%|████████▎ | 80/96 [02:02<00:24,  1.53s/it]
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Epoch 7 Training:  84%|████████▍ | 81/96 [02:04<00:23,  1.53s/it]
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Epoch 7 Training:  85%|████████▌ | 82/96 [02:05<00:21,  1.53s/it]
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Epoch 7 Training:  86%|████████▋ | 83/96 [02:07<00:19,  1.53s/it]
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Epoch 7 Training:  88%|████████▊ | 84/96 [02:09<00:18,  1.53s/it]
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Epoch 7 Training:  89%|████████▊ | 85/96 [02:10<00:16,  1.53s/it]
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Epoch 7 Training:  90%|████████▉ | 86/96 [02:12<00:15,  1.54s/it]
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Epoch 7 Training:  91%|█████████ | 87/96 [02:13<00:13,  1.54s/it]
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Epoch 7 Training:  92%|█████████▏| 88/96 [02:15<00:12,  1.54s/it]
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Epoch 7 Training:  93%|█████████▎| 89/96 [02:16<00:10,  1.54s/it]
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Epoch 7 Training:  94%|█████████▍| 90/96 [02:18<00:09,  1.54s/it]
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Epoch 7 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s][2026-10-02 08:13:40] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0007 | Epoch 007] train/policy_loss=22.6291 train/unweighted_policy_loss=4.5258 train/loss=22.6291 train/grad_norm=0.0461
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+[2026-10-02 08:13:40] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0008 | Epoch 007] timing/rollout_seconds=178.9489 timing/feedback_seconds=5.4031 timing/optimization_seconds=148.1401 timing/cycle_seconds=329.8990
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+
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+[2026-10-02 08:16:42] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0008 | Epoch 008] train/reward_domain_mean=0.2806 train/reward_domain_std=0.0753 train/reward_structure_mean=0.7056 train/reward_structure_std=0.1618 train/reward_domain_group_std_mean=0.0241 train/reward_domain_group_std_max=0.0520 train/reward_domain_group_std_min=0.0103 train/reward_domain_group_mean_std=0.0709 train/reward_structure_group_std_mean=0.0094 train/reward_structure_group_std_max=0.0192 train/reward_structure_group_std_min=0.0024 train/reward_structure_group_mean_std=0.1614 train/reward_domain_zero_std_ratio=0 train/reward_structure_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-3.6834 train/adv_max=3.5667 train/adv_abs_mean=0.9416
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Epoch 8 Training:  16%|█▌        | 15/96 [00:23<02:05,  1.54s/it]
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Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.97it/s]
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Epoch 8 Training:  17%|█▋        | 16/96 [00:24<02:03,  1.54s/it]
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Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.98it/s]
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Epoch 8 Training:  18%|█▊        | 17/96 [00:26<02:01,  1.54s/it]
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Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.91it/s]
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Epoch 8 Training:  19%|█▉        | 18/96 [00:27<02:01,  1.55s/it]
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Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.95it/s]
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Epoch 8 Training:  20%|█▉        | 19/96 [00:29<01:59,  1.55s/it]
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Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Training:  21%|██        | 20/96 [00:30<01:57,  1.55s/it]
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Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.98it/s]
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Epoch 8 Training:  22%|██▏       | 21/96 [00:32<01:56,  1.55s/it]
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Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.98it/s]
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Epoch 8 Training:  23%|██▎       | 22/96 [00:33<01:54,  1.55s/it]
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Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.94it/s]
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Epoch 8 Training:  24%|██▍       | 23/96 [00:35<01:53,  1.55s/it]
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Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.98it/s]
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Epoch 8 Training:  25%|██▌       | 24/96 [00:37<01:51,  1.55s/it]
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Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.98it/s]
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Epoch 8 Training:  26%|██▌       | 25/96 [00:38<01:49,  1.55s/it]
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Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.98it/s]
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Epoch 8 Training:  27%|██▋       | 26/96 [00:40<01:48,  1.55s/it]
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Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.98it/s]
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Epoch 8 Training:  28%|██▊       | 27/96 [00:41<01:46,  1.55s/it]
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Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.98it/s]
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Epoch 8 Training:  29%|██▉       | 28/96 [00:43<01:45,  1.55s/it]
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Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.95it/s]
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Epoch 8 Training:  30%|███       | 29/96 [00:44<01:43,  1.55s/it]
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Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.95it/s]
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Epoch 8 Training:  31%|███▏      | 30/96 [00:46<01:42,  1.55s/it]
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Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.98it/s]
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Epoch 8 Training:  32%|███▏      | 31/96 [00:47<01:40,  1.55s/it]
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Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.95it/s]
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Epoch 8 Training:  33%|███▎      | 32/96 [00:49<01:39,  1.55s/it]
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Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.95it/s]
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Epoch 8 Training:  34%|███▍      | 33/96 [00:50<01:37,  1.55s/it]
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Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.98it/s]
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Epoch 8 Training:  35%|███▌      | 34/96 [00:52<01:36,  1.55s/it]
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Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.98it/s]
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Epoch 8 Training:  36%|███▋      | 35/96 [00:54<01:34,  1.55s/it]
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Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.98it/s]
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Epoch 8 Training:  38%|███▊      | 36/96 [00:55<01:32,  1.55s/it]
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Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.94it/s]
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Epoch 8 Training:  39%|███▊      | 37/96 [00:57<01:31,  1.55s/it]
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Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.95it/s]
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Epoch 8 Training:  40%|███▉      | 38/96 [00:58<01:30,  1.55s/it]
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Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.98it/s]
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Epoch 8 Training:  41%|████      | 39/96 [01:00<01:28,  1.55s/it]
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Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.98it/s]
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Epoch 8 Training:  42%|████▏     | 40/96 [01:01<01:26,  1.55s/it]
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Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.98it/s]
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Epoch 8 Training:  43%|████▎     | 41/96 [01:03<01:25,  1.55s/it]
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Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.95it/s]
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Epoch 8 Training:  44%|████▍     | 42/96 [01:04<01:23,  1.55s/it]
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Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.95it/s]
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Epoch 8 Training:  45%|████▍     | 43/96 [01:06<01:22,  1.55s/it]
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Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 8 Training:  46%|████▌     | 44/96 [01:08<01:20,  1.55s/it]
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Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.97it/s]
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Epoch 8 Training:  47%|████▋     | 45/96 [01:09<01:18,  1.55s/it]
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Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 8 Training:  48%|████▊     | 46/96 [01:11<01:17,  1.55s/it]
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Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 8 Training:  49%|████▉     | 47/96 [01:12<01:16,  1.55s/it]
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Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
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Epoch 8 Training:  50%|█████     | 48/96 [01:14<01:14,  1.56s/it]
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Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Training:  51%|█████     | 49/96 [01:15<01:13,  1.56s/it]
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Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Training:  52%|█████▏    | 50/96 [01:17<01:11,  1.56s/it]
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Epoch 8 Training:  53%|█████▎    | 51/96 [01:18<01:09,  1.55s/it]
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Epoch 8 Training:  54%|█████▍    | 52/96 [01:20<01:08,  1.56s/it]
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Epoch 8 Training:  55%|█████▌    | 53/96 [01:22<01:06,  1.55s/it]
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Epoch 8 Training:  56%|█████▋    | 54/96 [01:23<01:05,  1.55s/it]
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Epoch 8 Training:  57%|█████▋    | 55/96 [01:25<01:03,  1.55s/it]
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Epoch 8 Training:  58%|█████▊    | 56/96 [01:26<01:01,  1.55s/it]
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Epoch 8 Training:  59%|█████▉    | 57/96 [01:28<01:00,  1.55s/it]
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Epoch 8 Training:  60%|██████    | 58/96 [01:29<00:58,  1.55s/it]
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Epoch 8 Training:  61%|██████▏   | 59/96 [01:31<00:57,  1.55s/it]
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Epoch 8 Training:  62%|██████▎   | 60/96 [01:32<00:55,  1.55s/it]
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Epoch 8 Training:  64%|██████▎   | 61/96 [01:34<00:54,  1.55s/it]
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Epoch 8 Training:  65%|██████▍   | 62/96 [01:35<00:52,  1.56s/it]
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Epoch 8 Training:  66%|██████▌   | 63/96 [01:37<00:51,  1.55s/it]
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Epoch 8 Training:  67%|██████▋   | 64/96 [01:39<00:49,  1.55s/it]
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Epoch 8 Training:  68%|██████▊   | 65/96 [01:40<00:48,  1.55s/it]
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Epoch 8 Training:  69%|██████▉   | 66/96 [01:42<00:46,  1.55s/it]
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Epoch 8 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.15it/s][2026-10-02 08:19:11] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0008 | Epoch 008] train/policy_loss=22.4038 train/unweighted_policy_loss=4.4808 train/loss=22.4038 train/grad_norm=0.0512
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+[2026-10-02 08:19:11] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0009 | Epoch 008] timing/rollout_seconds=178.9302 timing/feedback_seconds=5.2378 timing/optimization_seconds=149.3376 timing/cycle_seconds=331.1844
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+
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+
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Pointwise Rewards: structure: 100%|██████████| 6/6 [00:01<00:00,  5.16it/s]
Pointwise Rewards: structure: 100%|██████████| 6/6 [00:01<00:00,  5.24it/s]
+[2026-10-02 08:22:14] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0009 | Epoch 009] train/reward_domain_mean=0.2567 train/reward_domain_std=0.0825 train/reward_structure_mean=0.6563 train/reward_structure_std=0.1654 train/reward_domain_group_std_mean=0.0258 train/reward_domain_group_std_max=0.0497 train/reward_domain_group_std_min=0.0071 train/reward_domain_group_mean_std=0.0779 train/reward_structure_group_std_mean=0.0137 train/reward_structure_group_std_max=0.0365 train/reward_structure_group_std_min=0.0041 train/reward_structure_group_mean_std=0.1647 train/reward_domain_zero_std_ratio=0 train/reward_structure_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.0341 train/adv_max=3.4625 train/adv_abs_mean=0.8718
+
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Epoch 9 Training:  45%|████▍     | 43/96 [01:06<01:21,  1.54s/it]
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Epoch 9 Training:  46%|████▌     | 44/96 [01:07<01:20,  1.54s/it]
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Epoch 9 Training:  47%|████▋     | 45/96 [01:09<01:18,  1.54s/it]
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Epoch 9 Training:  48%|████▊     | 46/96 [01:11<01:16,  1.54s/it]
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Epoch 9 Training:  49%|████▉     | 47/96 [01:12<01:15,  1.53s/it]
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Epoch 9 Training:  50%|█████     | 48/96 [01:14<01:14,  1.54s/it]
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Epoch 9 Training:  51%|█████     | 49/96 [01:15<01:12,  1.55s/it]
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Epoch 9 Training:  52%|█████▏    | 50/96 [01:17<01:11,  1.55s/it]
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Epoch 9 Training:  53%|█████▎    | 51/96 [01:18<01:09,  1.55s/it]
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Epoch 9 Training:  54%|█████▍    | 52/96 [01:20<01:08,  1.55s/it]
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Epoch 9 Training:  55%|█████▌    | 53/96 [01:21<01:06,  1.55s/it]
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Epoch 9 Training:  56%|█████▋    | 54/96 [01:23<01:05,  1.55s/it]
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Epoch 9 Training:  57%|█████▋    | 55/96 [01:25<01:03,  1.56s/it]
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Epoch 9 Training:  58%|█████▊    | 56/96 [01:26<01:02,  1.56s/it]
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Epoch 9 Training:  59%|█████▉    | 57/96 [01:28<01:00,  1.55s/it]
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Epoch 9 Training:  60%|██████    | 58/96 [01:29<00:58,  1.55s/it]
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Epoch 9 Training:  61%|██████▏   | 59/96 [01:31<00:57,  1.55s/it]
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Epoch 9 Training:  62%|██████▎   | 60/96 [01:32<00:55,  1.54s/it]
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Epoch 9 Training:  64%|██████▎   | 61/96 [01:34<00:54,  1.54s/it]
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Epoch 9 Training:  65%|██████▍   | 62/96 [01:35<00:52,  1.54s/it]
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Epoch 9 Training:  66%|██████▌   | 63/96 [01:37<00:50,  1.54s/it]
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Epoch 9 Training:  67%|██████▋   | 64/96 [01:38<00:49,  1.54s/it]
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Epoch 9 Training:  68%|██████▊   | 65/96 [01:40<00:47,  1.54s/it]
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Epoch 9 Training:  69%|██████▉   | 66/96 [01:41<00:46,  1.53s/it]
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Epoch 9 Training:  70%|██████▉   | 67/96 [01:43<00:44,  1.53s/it]
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Epoch 9 Training:  71%|███████   | 68/96 [01:45<00:42,  1.53s/it]
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Epoch 9 Training:  72%|███████▏  | 69/96 [01:46<00:41,  1.53s/it]
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Epoch 9 Training:  73%|███████▎  | 70/96 [01:48<00:39,  1.53s/it]
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Epoch 9 Training:  74%|███████▍  | 71/96 [01:49<00:38,  1.53s/it]
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Epoch 9 Training:  75%|███████▌  | 72/96 [01:51<00:36,  1.54s/it]
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Epoch 9 Training:  76%|███████▌  | 73/96 [01:52<00:35,  1.54s/it]
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Epoch 9 Training:  77%|███████▋  | 74/96 [01:54<00:33,  1.53s/it]
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Epoch 9 Training:  78%|███████▊  | 75/96 [01:55<00:32,  1.53s/it]
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Epoch 9 Training:  79%|███████▉  | 76/96 [01:57<00:30,  1.53s/it]
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Epoch 9 Training:  80%|████████  | 77/96 [01:58<00:29,  1.53s/it]
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Epoch 9 Training:  81%|████████▏ | 78/96 [02:00<00:27,  1.53s/it]
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Epoch 9 Training:  82%|████████▏ | 79/96 [02:01<00:26,  1.53s/it]
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Epoch 9 Training:  83%|████████▎ | 80/96 [02:03<00:24,  1.53s/it]
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Epoch 9 Training:  84%|████████▍ | 81/96 [02:04<00:22,  1.53s/it]
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Epoch 9 Training:  85%|████████▌ | 82/96 [02:06<00:21,  1.53s/it]
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Epoch 9 Training:  86%|████████▋ | 83/96 [02:07<00:19,  1.53s/it]
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Epoch 9 Training:  88%|████████▊ | 84/96 [02:09<00:18,  1.53s/it]
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Epoch 9 Training:  89%|████████▊ | 85/96 [02:11<00:16,  1.53s/it]
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Epoch 9 Training:  90%|████████▉ | 86/96 [02:12<00:15,  1.53s/it]
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Epoch 9 Training:  91%|█████████ | 87/96 [02:14<00:13,  1.53s/it]
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Epoch 9 Training:  92%|█████████▏| 88/96 [02:15<00:12,  1.53s/it]
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Epoch 9 Training:  93%|█████████▎| 89/96 [02:17<00:10,  1.53s/it]
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Epoch 9 Training:  94%|█████████▍| 90/96 [02:18<00:09,  1.53s/it]
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Epoch 9 Training:  95%|█████████▍| 91/96 [02:20<00:07,  1.53s/it]
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Epoch 9 Training:  96%|█████████▌| 92/96 [02:21<00:06,  1.53s/it]
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Epoch 9 Training:  97%|█████████▋| 93/96 [02:23<00:04,  1.53s/it]
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Epoch 9 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s][2026-10-02 08:24:42] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0009 | Epoch 009] train/policy_loss=22.4527 train/unweighted_policy_loss=4.4905 train/loss=22.4527 train/grad_norm=0.0553
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+[2026-10-02 08:24:42] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0010 | Epoch 009] timing/rollout_seconds=179.4868 timing/feedback_seconds=5.7620 timing/optimization_seconds=148.6117 timing/cycle_seconds=330.9611
+[2026-10-02 08:24:42] [Rank 0] [INFO] [flow_factory.models.abc]: Saving LoRA weights for transformer role=base to /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/runs/klein4b_nft_domain_sirst/saves/klein4b_nft_domain_sirst/checkpoints/checkpoint-10
+[2026-10-02 08:24:42] [Rank 0] [INFO] [flow_factory.models.abc]: Checkpoint saved successfully to /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/runs/klein4b_nft_domain_sirst/saves/klein4b_nft_domain_sirst/checkpoints/checkpoint-10
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+[2026-10-02 08:24:59] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0010 | Epoch 010] eval/places_domain_rl/reward_domain_mean=0.2728 eval/places_domain_rl/reward_domain_std=0.0611 eval/places_domain_rl/reward_ssim_to_stage1_mean=0.5817 eval/places_domain_rl/reward_ssim_to_stage1_std=0.1336 eval/places_domain_rl/reward_structure_mean=0.7055 eval/places_domain_rl/reward_structure_std=0.1400
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+[2026-10-02 08:28:03] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0010 | Epoch 010] train/reward_domain_mean=0.2521 train/reward_domain_std=0.0795 train/reward_structure_mean=0.6731 train/reward_structure_std=0.1545 train/reward_domain_group_std_mean=0.0262 train/reward_domain_group_std_max=0.0472 train/reward_domain_group_std_min=0.0114 train/reward_domain_group_mean_std=0.0746 train/reward_structure_group_std_mean=0.0117 train/reward_structure_group_std_max=0.0270 train/reward_structure_group_std_min=0.0034 train/reward_structure_group_mean_std=0.1539 train/reward_domain_zero_std_ratio=0 train/reward_structure_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.1296 train/adv_max=3.6682 train/adv_abs_mean=0.9567
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Epoch 10 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 10 Training:  12%|█▎        | 12/96 [00:18<02:08,  1.53s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Training:  14%|█▎        | 13/96 [00:19<02:07,  1.53s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Training:  15%|█▍        | 14/96 [00:21<02:05,  1.53s/it]
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Epoch 10 Training:  16%|█▌        | 15/96 [00:22<02:03,  1.53s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Training:  17%|█▋        | 16/96 [00:24<02:02,  1.53s/it]
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Epoch 10 Training:  18%|█▊        | 17/96 [00:25<02:00,  1.53s/it]
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Epoch 10 Training:  19%|█▉        | 18/96 [00:27<01:59,  1.53s/it]
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Epoch 10 Training:  20%|█▉        | 19/96 [00:29<01:57,  1.53s/it]
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Epoch 10 Training:  21%|██        | 20/96 [00:30<01:56,  1.53s/it]
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Epoch 10 Training:  22%|██▏       | 21/96 [00:32<01:54,  1.53s/it]
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Epoch 10 Training:  23%|██▎       | 22/96 [00:33<01:53,  1.53s/it]
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Epoch 10 Training:  24%|██▍       | 23/96 [00:35<01:51,  1.53s/it]
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Epoch 10 Training:  25%|██▌       | 24/96 [00:36<01:50,  1.53s/it]
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Epoch 10 Training:  26%|██▌       | 25/96 [00:38<01:48,  1.53s/it]
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Epoch 10 Training:  27%|██▋       | 26/96 [00:39<01:47,  1.53s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Training:  28%|██▊       | 27/96 [00:41<01:45,  1.53s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Training:  29%|██▉       | 28/96 [00:42<01:44,  1.53s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Training:  30%|███       | 29/96 [00:44<01:42,  1.53s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Training:  31%|███▏      | 30/96 [00:45<01:40,  1.53s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Training:  32%|███▏      | 31/96 [00:47<01:39,  1.53s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Training:  33%|███▎      | 32/96 [00:48<01:37,  1.53s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Training:  34%|███▍      | 33/96 [00:50<01:36,  1.53s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Training:  35%|███▌      | 34/96 [00:51<01:34,  1.53s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Training:  36%|███▋      | 35/96 [00:53<01:33,  1.53s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Training:  38%|███▊      | 36/96 [00:55<01:31,  1.53s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Training:  39%|███▊      | 37/96 [00:56<01:30,  1.53s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Training:  40%|███▉      | 38/96 [00:58<01:28,  1.53s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Training:  41%|████      | 39/96 [00:59<01:27,  1.53s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Training:  42%|████▏     | 40/96 [01:01<01:25,  1.53s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Training:  43%|████▎     | 41/96 [01:02<01:24,  1.53s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Training:  44%|████▍     | 42/96 [01:04<01:22,  1.53s/it]
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Epoch 10 Training:  45%|████▍     | 43/96 [01:05<01:21,  1.53s/it]
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Epoch 10 Training:  46%|████▌     | 44/96 [01:07<01:19,  1.53s/it]
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Epoch 10 Training:  47%|████▋     | 45/96 [01:08<01:18,  1.53s/it]
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Epoch 10 Training:  48%|████▊     | 46/96 [01:10<01:16,  1.53s/it]
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Epoch 10 Training:  49%|████▉     | 47/96 [01:11<01:15,  1.54s/it]
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Epoch 10 Training:  50%|█████     | 48/96 [01:13<01:13,  1.54s/it]
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Epoch 10 Training:  51%|█████     | 49/96 [01:14<01:12,  1.54s/it]
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Epoch 10 Training:  52%|█████▏    | 50/96 [01:16<01:10,  1.53s/it]
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Epoch 10 Training:  53%|█████▎    | 51/96 [01:18<01:08,  1.53s/it]
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Epoch 10 Training:  54%|█████▍    | 52/96 [01:19<01:07,  1.53s/it]
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Epoch 10 Training:  55%|█████▌    | 53/96 [01:21<01:05,  1.53s/it]
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Epoch 10 Training:  56%|█████▋    | 54/96 [01:22<01:04,  1.53s/it]
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Epoch 10 Training:  57%|█████▋    | 55/96 [01:24<01:02,  1.53s/it]
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Epoch 10 Training:  58%|█████▊    | 56/96 [01:25<01:01,  1.53s/it]
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Epoch 10 Training:  59%|█████▉    | 57/96 [01:27<00:59,  1.53s/it]
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Epoch 10 Training:  60%|██████    | 58/96 [01:28<00:58,  1.53s/it]
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Epoch 10 Training:  61%|██████▏   | 59/96 [01:30<00:56,  1.53s/it]
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Epoch 10 Training:  62%|██████▎   | 60/96 [01:31<00:55,  1.53s/it]
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Epoch 10 Training:  64%|██████▎   | 61/96 [01:33<00:53,  1.53s/it]
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Epoch 10 Training:  65%|██████▍   | 62/96 [01:34<00:52,  1.53s/it]
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Epoch 10 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s][2026-10-02 08:30:30] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0010 | Epoch 010] train/policy_loss=22.4747 train/unweighted_policy_loss=4.4949 train/loss=22.4747 train/grad_norm=0.1030
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+[2026-10-02 08:30:30] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0011 | Epoch 010] timing/rollout_seconds=179.3842 timing/feedback_seconds=6.6126 timing/optimization_seconds=149.3181 timing/cycle_seconds=331.5483
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+
Pointwise Rewards: domain:   0%|          | 0/6 [00:00<?, ?it/s]
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Pointwise Rewards: domain:  83%|████████▎ | 5/6 [00:01<00:00,  4.24it/s]
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Pointwise Rewards: domain: 100%|██████████| 6/6 [00:01<00:00,  4.19it/s]
+
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Pointwise Rewards: structure:  33%|███▎      | 2/6 [00:00<00:00,  5.34it/s]
Pointwise Rewards: structure:  50%|█████     | 3/6 [00:00<00:00,  5.30it/s]
Pointwise Rewards: structure:  67%|██████▋   | 4/6 [00:00<00:00,  5.21it/s]
Pointwise Rewards: structure:  83%|████████▎ | 5/6 [00:00<00:00,  5.16it/s]
Pointwise Rewards: structure: 100%|██████████| 6/6 [00:01<00:00,  5.15it/s]
Pointwise Rewards: structure: 100%|██████████| 6/6 [00:01<00:00,  5.23it/s]
+[2026-10-02 08:33:33] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0011 | Epoch 011] train/reward_domain_mean=0.2503 train/reward_domain_std=0.0820 train/reward_structure_mean=0.7187 train/reward_structure_std=0.1599 train/reward_domain_group_std_mean=0.0234 train/reward_domain_group_std_max=0.0589 train/reward_domain_group_std_min=0.0091 train/reward_domain_group_mean_std=0.0780 train/reward_structure_group_std_mean=0.0112 train/reward_structure_group_std_max=0.0369 train/reward_structure_group_std_min=0.0021 train/reward_structure_group_mean_std=0.1593 train/reward_domain_zero_std_ratio=0 train/reward_structure_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.2292 train/adv_max=4.0940 train/adv_abs_mean=0.9711
+
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Epoch 11 Training:   2%|▏         | 2/96 [00:03<02:21,  1.51s/it]
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Epoch 11 Training:   3%|▎         | 3/96 [00:04<02:21,  1.52s/it]
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Epoch 11 Training:   4%|▍         | 4/96 [00:06<02:20,  1.53s/it]
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Epoch 11 Training:   5%|▌         | 5/96 [00:07<02:19,  1.53s/it]
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Epoch 11 Training:   6%|▋         | 6/96 [00:09<02:18,  1.53s/it]
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Epoch 11 Training:   7%|▋         | 7/96 [00:10<02:16,  1.53s/it]
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Epoch 11 Training:   8%|▊         | 8/96 [00:12<02:14,  1.53s/it]
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Epoch 11 Training:   9%|▉         | 9/96 [00:13<02:13,  1.53s/it]
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Epoch 11 Training:  10%|█         | 10/96 [00:15<02:11,  1.53s/it]
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Epoch 11 Training:  11%|█▏        | 11/96 [00:16<02:10,  1.53s/it]
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Epoch 11 Training:  12%|█▎        | 12/96 [00:18<02:08,  1.53s/it]
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Epoch 11 Training:  14%|█▎        | 13/96 [00:19<02:07,  1.53s/it]
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Epoch 11 Training:  15%|█▍        | 14/96 [00:21<02:05,  1.53s/it]
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Epoch 11 Training:  16%|█▌        | 15/96 [00:22<02:04,  1.53s/it]
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Epoch 11 Training:  17%|█▋        | 16/96 [00:24<02:02,  1.53s/it]
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Epoch 11 Training:  18%|█▊        | 17/96 [00:26<02:01,  1.53s/it]
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Epoch 11 Training:  19%|█▉        | 18/96 [00:27<01:59,  1.53s/it]
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Epoch 11 Training:  22%|██▏       | 21/96 [00:32<01:55,  1.53s/it]
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Epoch 11 Training:  23%|██▎       | 22/96 [00:33<01:53,  1.53s/it]
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Epoch 11 Training:  24%|██▍       | 23/96 [00:35<01:51,  1.53s/it]
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Epoch 11 Training:  25%|██▌       | 24/96 [00:36<01:50,  1.53s/it]
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Epoch 11 Training:  26%|██▌       | 25/96 [00:38<01:48,  1.53s/it]
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Epoch 11 Training:  27%|██▋       | 26/96 [00:39<01:47,  1.53s/it]
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Epoch 11 Training:  28%|██▊       | 27/96 [00:41<01:45,  1.53s/it]
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Epoch 11 Training:  29%|██▉       | 28/96 [00:42<01:44,  1.53s/it]
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Epoch 11 Training:  33%|███▎      | 32/96 [00:49<01:38,  1.53s/it]
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Epoch 11 Training:  34%|███▍      | 33/96 [00:50<01:36,  1.53s/it]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.02it/s]
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Epoch 11 Training:  41%|████      | 39/96 [00:59<01:27,  1.54s/it]
+
Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 11 Training:  42%|████▏     | 40/96 [01:01<01:26,  1.54s/it]
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Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 11 Training:  43%|████▎     | 41/96 [01:02<01:24,  1.54s/it]
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Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.98it/s]
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Epoch 11 Training:  44%|████▍     | 42/96 [01:04<01:23,  1.54s/it]
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Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 11 Training:  45%|████▍     | 43/96 [01:05<01:21,  1.54s/it]
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Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 11 Training:  46%|████▌     | 44/96 [01:07<01:20,  1.54s/it]
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Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 11 Training:  47%|████▋     | 45/96 [01:09<01:18,  1.54s/it]
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Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 11 Training:  48%|████▊     | 46/96 [01:10<01:17,  1.54s/it]
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Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 11 Training:  49%|████▉     | 47/96 [01:12<01:15,  1.54s/it]
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Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 11 Training:  50%|█████     | 48/96 [01:13<01:13,  1.54s/it]
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Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 11 Training:  51%|█████     | 49/96 [01:15<01:12,  1.54s/it]
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Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 11 Training:  52%|█████▏    | 50/96 [01:16<01:10,  1.54s/it]
+
Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 11 Training:  53%|█████▎    | 51/96 [01:18<01:09,  1.54s/it]
+
Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 11 Training:  54%|█████▍    | 52/96 [01:19<01:07,  1.54s/it]
+
Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 11 Training:  55%|█████▌    | 53/96 [01:21<01:06,  1.54s/it]
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Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.02it/s]
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Epoch 11 Training:  56%|█████▋    | 54/96 [01:22<01:04,  1.53s/it]
+
Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 11 Training:  57%|█████▋    | 55/96 [01:24<01:02,  1.53s/it]
+
Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 11 Training:  58%|█████▊    | 56/96 [01:25<01:01,  1.53s/it]
+
Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 11 Training:  59%|█████▉    | 57/96 [01:27<00:59,  1.53s/it]
+
Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 11 Training:  60%|██████    | 58/96 [01:29<00:58,  1.53s/it]
+
Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 11 Training:  61%|██████▏   | 59/96 [01:30<00:56,  1.54s/it]
+
Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 11 Training:  62%|██████▎   | 60/96 [01:32<00:55,  1.53s/it]
+
Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 11 Training:  64%|██████▎   | 61/96 [01:33<00:53,  1.53s/it]
+
Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 11 Training:  65%|██████▍   | 62/96 [01:35<00:52,  1.53s/it]
+
Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 11 Training:  66%|██████▌   | 63/96 [01:36<00:50,  1.53s/it]
+
Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 11 Training:  67%|██████▋   | 64/96 [01:38<00:49,  1.53s/it]
+
Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 11 Training:  68%|██████▊   | 65/96 [01:39<00:47,  1.53s/it]
+
Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 11 Training:  69%|██████▉   | 66/96 [01:41<00:46,  1.53s/it]
+
Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 11 Training:  70%|██████▉   | 67/96 [01:42<00:44,  1.53s/it]
+
Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 11 Training:  71%|███████   | 68/96 [01:44<00:42,  1.53s/it]
+
Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 11 Training:  72%|███████▏  | 69/96 [01:45<00:41,  1.53s/it]
+
Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 11 Training:  73%|███████▎  | 70/96 [01:47<00:39,  1.53s/it]
+
Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 11 Training:  74%|███████▍  | 71/96 [01:48<00:38,  1.53s/it]
+
Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 11 Training:  75%|███████▌  | 72/96 [01:50<00:36,  1.53s/it]
+
Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 11 Training:  76%|███████▌  | 73/96 [01:52<00:35,  1.53s/it]
+
Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.97it/s]
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Epoch 11 Training:  77%|███████▋  | 74/96 [01:53<00:33,  1.54s/it]
+
Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 11 Training:  78%|███████▊  | 75/96 [01:55<00:32,  1.54s/it]
+
Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 11 Training:  79%|███████▉  | 76/96 [01:56<00:30,  1.54s/it]
+
Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 11 Training:  80%|████████  | 77/96 [01:58<00:29,  1.53s/it]
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Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Training:  81%|████████▏ | 78/96 [01:59<00:27,  1.53s/it]
+
Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Training:  82%|████████▏ | 79/96 [02:01<00:26,  1.53s/it]
+
Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Training:  83%|████████▎ | 80/96 [02:02<00:24,  1.53s/it]
+
Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Training:  84%|████████▍ | 81/96 [02:04<00:23,  1.53s/it]
+
Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Training:  85%|████████▌ | 82/96 [02:05<00:21,  1.53s/it]
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Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Training:  86%|████████▋ | 83/96 [02:07<00:19,  1.53s/it]
+
Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Training:  88%|████████▊ | 84/96 [02:08<00:18,  1.53s/it]
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Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Training:  89%|████████▊ | 85/96 [02:10<00:16,  1.53s/it]
+
Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 11 Training:  90%|████████▉ | 86/96 [02:11<00:15,  1.53s/it]
+
Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Training:  91%|█████████ | 87/96 [02:13<00:13,  1.53s/it]
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Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Training:  92%|█████████▏| 88/96 [02:15<00:12,  1.53s/it]
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Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Training:  93%|█████████▎| 89/96 [02:16<00:10,  1.53s/it]
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Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Training:  94%|█████████▍| 90/96 [02:18<00:09,  1.53s/it]
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Epoch 11 Training:  99%|█████████▉| 95/96 [02:25<00:01,  1.54s/it]
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Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s][2026-10-02 08:36:01] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0011 | Epoch 011] train/policy_loss=22.0495 train/unweighted_policy_loss=4.4099 train/loss=22.0495 train/grad_norm=0.0646
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Epoch 11 Training: 100%|██████████| 96/96 [02:27<00:00,  1.55s/it]
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+[2026-10-02 08:36:01] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0012 | Epoch 011] timing/rollout_seconds=179.4951 timing/feedback_seconds=5.7930 timing/optimization_seconds=147.9829 timing/cycle_seconds=330.3157
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Pointwise Rewards: structure: 100%|██████████| 6/6 [00:01<00:00,  5.41it/s]
+[2026-10-02 08:39:04] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0012 | Epoch 012] train/reward_domain_mean=0.2756 train/reward_domain_std=0.0669 train/reward_structure_mean=0.6944 train/reward_structure_std=0.1661 train/reward_domain_group_std_mean=0.0222 train/reward_domain_group_std_max=0.0439 train/reward_domain_group_std_min=0.0113 train/reward_domain_group_mean_std=0.0629 train/reward_structure_group_std_mean=0.0105 train/reward_structure_group_std_max=0.0335 train/reward_structure_group_std_min=0.0027 train/reward_structure_group_mean_std=0.1657 train/reward_domain_zero_std_ratio=0 train/reward_structure_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.0669 train/adv_max=3.6571 train/adv_abs_mean=0.9765
+
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 12 Training:  16%|█▌        | 15/96 [00:23<02:11,  1.62s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 12 Training:  17%|█▋        | 16/96 [00:25<02:07,  1.59s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 12 Training:  18%|█▊        | 17/96 [00:26<02:04,  1.58s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.98it/s]
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Epoch 12 Training:  19%|█▉        | 18/96 [00:28<02:02,  1.56s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 12 Training:  20%|█▉        | 19/96 [00:29<01:59,  1.56s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 12 Training:  21%|██        | 20/96 [00:31<01:57,  1.55s/it]
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Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 12 Training:  22%|██▏       | 21/96 [00:32<01:55,  1.54s/it]
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Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 12 Training:  23%|██▎       | 22/96 [00:34<01:53,  1.54s/it]
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Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 12 Training:  24%|██▍       | 23/96 [00:35<01:52,  1.54s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.79it/s]
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Epoch 12 Training:  25%|██▌       | 24/96 [00:37<01:52,  1.57s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 12 Training:  26%|██▌       | 25/96 [00:38<01:50,  1.56s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
+
                                                                
Epoch 12 Training:  27%|██▋       | 26/96 [00:40<01:48,  1.55s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
+
                                                                
Epoch 12 Training:  28%|██▊       | 27/96 [00:41<01:46,  1.54s/it]
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Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
+
                                                                
Epoch 12 Training:  29%|██▉       | 28/96 [00:43<01:44,  1.54s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 12 Training:  30%|███       | 29/96 [00:45<01:43,  1.54s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
+
                                                                
Epoch 12 Training:  31%|███▏      | 30/96 [00:46<01:41,  1.54s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
+
                                                                
Epoch 12 Training:  32%|███▏      | 31/96 [00:48<01:39,  1.53s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
+
                                                                
Epoch 12 Training:  33%|███▎      | 32/96 [00:49<01:38,  1.53s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
+
                                                                
Epoch 12 Training:  34%|███▍      | 33/96 [00:51<01:36,  1.53s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 12 Training:  35%|███▌      | 34/96 [00:52<01:35,  1.53s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
+
                                                                
Epoch 12 Training:  36%|███▋      | 35/96 [00:54<01:33,  1.53s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 12 Training:  38%|███▊      | 36/96 [00:55<01:31,  1.53s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 12 Training:  39%|███▊      | 37/96 [00:57<01:30,  1.53s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
+
                                                                
Epoch 12 Training:  40%|███▉      | 38/96 [00:58<01:28,  1.53s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 12 Training:  41%|████      | 39/96 [01:00<01:27,  1.53s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
+
                                                                
Epoch 12 Training:  42%|████▏     | 40/96 [01:01<01:25,  1.53s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
+
                                                                
Epoch 12 Training:  43%|████▎     | 41/96 [01:03<01:24,  1.53s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 12 Training:  44%|████▍     | 42/96 [01:05<01:23,  1.55s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 12 Training:  45%|████▍     | 43/96 [01:06<01:21,  1.54s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 12 Training:  46%|████▌     | 44/96 [01:08<01:20,  1.54s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 12 Training:  47%|████▋     | 45/96 [01:09<01:18,  1.54s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 12 Training:  48%|████▊     | 46/96 [01:11<01:16,  1.54s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 12 Training:  49%|████▉     | 47/96 [01:12<01:15,  1.54s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 12 Training:  50%|█████     | 48/96 [01:14<01:13,  1.54s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 12 Training:  51%|█████     | 49/96 [01:15<01:12,  1.53s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 12 Training:  52%|█████▏    | 50/96 [01:17<01:10,  1.53s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 12 Training:  53%|█████▎    | 51/96 [01:18<01:08,  1.53s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 12 Training:  54%|█████▍    | 52/96 [01:20<01:07,  1.53s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 12 Training:  55%|█████▌    | 53/96 [01:21<01:06,  1.54s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 12 Training:  56%|█████▋    | 54/96 [01:23<01:04,  1.54s/it]
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Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.98it/s]
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Epoch 12 Training:  57%|█████▋    | 55/96 [01:24<01:03,  1.54s/it]
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Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 12 Training:  58%|█████▊    | 56/96 [01:26<01:01,  1.54s/it]
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Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Training:  59%|█████▉    | 57/96 [01:28<00:59,  1.54s/it]
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Epoch 12 Training:  60%|██████    | 58/96 [01:29<00:58,  1.54s/it]
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Epoch 12 Training:  61%|██████▏   | 59/96 [01:31<00:56,  1.54s/it]
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Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Training:  62%|██████▎   | 60/96 [01:32<00:55,  1.54s/it]
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Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Training:  64%|██████▎   | 61/96 [01:34<00:53,  1.53s/it]
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Epoch 12 Training:  65%|██████▍   | 62/96 [01:35<00:52,  1.53s/it]
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Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Training:  66%|██████▌   | 63/96 [01:37<00:50,  1.53s/it]
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Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Training:  67%|██████▋   | 64/96 [01:38<00:49,  1.53s/it]
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Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Training:  68%|██████▊   | 65/96 [01:40<00:47,  1.53s/it]
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Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Training:  69%|██████▉   | 66/96 [01:41<00:45,  1.53s/it]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s][2026-10-02 08:41:32] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0012 | Epoch 012] train/policy_loss=22.0349 train/unweighted_policy_loss=4.4070 train/loss=22.0349 train/grad_norm=0.0715
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+[2026-10-02 08:41:32] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0013 | Epoch 012] timing/rollout_seconds=179.4338 timing/feedback_seconds=5.4402 timing/optimization_seconds=148.6712 timing/cycle_seconds=330.9461
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+[2026-10-02 08:44:34] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0013 | Epoch 013] train/reward_domain_mean=0.2721 train/reward_domain_std=0.0727 train/reward_structure_mean=0.7323 train/reward_structure_std=0.1097 train/reward_domain_group_std_mean=0.0232 train/reward_domain_group_std_max=0.0421 train/reward_domain_group_std_min=0.0113 train/reward_domain_group_mean_std=0.0685 train/reward_structure_group_std_mean=0.0091 train/reward_structure_group_std_max=0.0279 train/reward_structure_group_std_min=0.0033 train/reward_structure_group_mean_std=0.1092 train/reward_domain_zero_std_ratio=0 train/reward_structure_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.5665 train/adv_max=3.6778 train/adv_abs_mean=0.9661
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Epoch 13 Training:  45%|████▍     | 43/96 [01:06<01:21,  1.54s/it]
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Epoch 13 Training:  46%|████▌     | 44/96 [01:07<01:20,  1.54s/it]
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Epoch 13 Training:  47%|████▋     | 45/96 [01:09<01:18,  1.54s/it]
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Epoch 13 Training:  48%|████▊     | 46/96 [01:10<01:16,  1.54s/it]
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Epoch 13 Training:  49%|████▉     | 47/96 [01:12<01:15,  1.54s/it]
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Epoch 13 Training:  50%|█████     | 48/96 [01:13<01:13,  1.54s/it]
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Epoch 13 Training:  51%|█████     | 49/96 [01:15<01:12,  1.54s/it]
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Epoch 13 Training:  52%|█████▏    | 50/96 [01:16<01:10,  1.54s/it]
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Epoch 13 Training:  53%|█████▎    | 51/96 [01:18<01:09,  1.54s/it]
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Epoch 13 Training:  54%|█████▍    | 52/96 [01:19<01:07,  1.54s/it]
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Epoch 13 Training:  55%|█████▌    | 53/96 [01:21<01:06,  1.54s/it]
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Epoch 13 Training:  56%|█████▋    | 54/96 [01:23<01:04,  1.54s/it]
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Epoch 13 Training:  57%|█████▋    | 55/96 [01:24<01:03,  1.54s/it]
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Epoch 13 Training:  58%|█████▊    | 56/96 [01:26<01:01,  1.54s/it]
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Epoch 13 Training:  59%|█████▉    | 57/96 [01:27<01:00,  1.54s/it]
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Epoch 13 Training:  60%|██████    | 58/96 [01:29<00:58,  1.54s/it]
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Epoch 13 Training:  61%|██████▏   | 59/96 [01:30<00:56,  1.54s/it]
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Epoch 13 Training:  62%|██████▎   | 60/96 [01:32<00:55,  1.54s/it]
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Epoch 13 Training:  64%|██████▎   | 61/96 [01:33<00:53,  1.54s/it]
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Epoch 13 Training:  65%|██████▍   | 62/96 [01:35<00:52,  1.54s/it]
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Epoch 13 Training:  66%|██████▌   | 63/96 [01:36<00:50,  1.54s/it]
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Epoch 13 Training:  67%|██████▋   | 64/96 [01:38<00:49,  1.54s/it]
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Epoch 13 Training:  68%|██████▊   | 65/96 [01:40<00:47,  1.54s/it]
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Epoch 13 Training:  69%|██████▉   | 66/96 [01:41<00:46,  1.54s/it]
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Epoch 13 Training:  70%|██████▉   | 67/96 [01:43<00:44,  1.54s/it]
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Epoch 13 Training:  71%|███████   | 68/96 [01:44<00:43,  1.54s/it]
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Epoch 13 Training:  72%|███████▏  | 69/96 [01:46<00:41,  1.54s/it]
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Epoch 13 Training:  73%|███████▎  | 70/96 [01:47<00:39,  1.54s/it]
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Epoch 13 Training:  74%|███████▍  | 71/96 [01:49<00:38,  1.54s/it]
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Epoch 13 Training:  75%|███████▌  | 72/96 [01:50<00:36,  1.54s/it]
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Epoch 13 Training:  76%|███████▌  | 73/96 [01:52<00:35,  1.54s/it]
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Epoch 13 Training:  77%|███████▋  | 74/96 [01:53<00:33,  1.54s/it]
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Epoch 13 Training:  78%|███████▊  | 75/96 [01:55<00:32,  1.54s/it]
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Epoch 13 Training:  79%|███████▉  | 76/96 [01:56<00:30,  1.54s/it]
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Epoch 13 Training:  80%|████████  | 77/96 [01:58<00:29,  1.55s/it]
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Epoch 13 Training:  81%|████████▏ | 78/96 [02:00<00:27,  1.54s/it]
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Epoch 13 Training:  82%|████████▏ | 79/96 [02:01<00:26,  1.54s/it]
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Epoch 13 Training:  83%|████████▎ | 80/96 [02:03<00:24,  1.54s/it]
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Epoch 13 Training:  84%|████████▍ | 81/96 [02:04<00:23,  1.54s/it]
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Epoch 13 Training:  85%|████████▌ | 82/96 [02:06<00:21,  1.54s/it]
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Epoch 13 Training:  86%|████████▋ | 83/96 [02:07<00:20,  1.54s/it]
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Epoch 13 Training:  88%|████████▊ | 84/96 [02:09<00:18,  1.54s/it]
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Epoch 13 Training:  89%|████████▊ | 85/96 [02:10<00:16,  1.54s/it]
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Epoch 13 Training:  90%|████████▉ | 86/96 [02:12<00:15,  1.54s/it]
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Epoch 13 Training:  91%|█████████ | 87/96 [02:13<00:13,  1.54s/it]
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Epoch 13 Training:  92%|█████████▏| 88/96 [02:15<00:12,  1.54s/it]
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Epoch 13 Training:  93%|█████████▎| 89/96 [02:16<00:10,  1.54s/it]
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Epoch 13 Training:  94%|█████████▍| 90/96 [02:18<00:09,  1.54s/it]
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Epoch 13 Training:  95%|█████████▍| 91/96 [02:20<00:07,  1.54s/it]
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Epoch 13 Training:  96%|█████████▌| 92/96 [02:21<00:06,  1.54s/it]
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Epoch 13 Training:  97%|█████████▋| 93/96 [02:23<00:04,  1.54s/it]
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Epoch 13 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s][2026-10-02 08:47:02] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0013 | Epoch 013] train/policy_loss=21.8680 train/unweighted_policy_loss=4.3736 train/loss=21.8680 train/grad_norm=0.0650
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+[2026-10-02 08:47:02] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0014 | Epoch 013] timing/rollout_seconds=179.4079 timing/feedback_seconds=5.4169 timing/optimization_seconds=148.3965 timing/cycle_seconds=330.6247
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+[2026-10-02 08:50:05] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0014 | Epoch 014] train/reward_domain_mean=0.3003 train/reward_domain_std=0.0654 train/reward_structure_mean=0.7095 train/reward_structure_std=0.1430 train/reward_domain_group_std_mean=0.0214 train/reward_domain_group_std_max=0.0407 train/reward_domain_group_std_min=0.0080 train/reward_domain_group_mean_std=0.0614 train/reward_structure_group_std_mean=0.0090 train/reward_structure_group_std_max=0.0206 train/reward_structure_group_std_min=0.0033 train/reward_structure_group_mean_std=0.1426 train/reward_domain_zero_std_ratio=0 train/reward_structure_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-3.9672 train/adv_max=3.7896 train/adv_abs_mean=0.9552
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Epoch 14 Training:  20%|█▉        | 19/96 [00:29<01:58,  1.55s/it]
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Epoch 14 Training:  21%|██        | 20/96 [00:30<01:57,  1.54s/it]
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Epoch 14 Training:  22%|██▏       | 21/96 [00:32<01:55,  1.54s/it]
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Epoch 14 Training:  23%|██▎       | 22/96 [00:34<01:53,  1.54s/it]
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Epoch 14 Training:  24%|██▍       | 23/96 [00:35<01:58,  1.62s/it]
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Epoch 14 Training:  25%|██▌       | 24/96 [00:37<01:54,  1.59s/it]
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Epoch 14 Training:  26%|██▌       | 25/96 [00:38<01:51,  1.58s/it]
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Epoch 14 Training:  27%|██▋       | 26/96 [00:40<01:49,  1.56s/it]
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Epoch 14 Training:  28%|██▊       | 27/96 [00:41<01:47,  1.55s/it]
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Epoch 14 Training:  29%|██▉       | 28/96 [00:43<01:45,  1.55s/it]
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Epoch 14 Training:  30%|███       | 29/96 [00:45<01:43,  1.54s/it]
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Epoch 14 Training:  31%|███▏      | 30/96 [00:46<01:41,  1.54s/it]
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Epoch 14 Training:  32%|███▏      | 31/96 [00:48<01:40,  1.54s/it]
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Epoch 14 Training:  33%|███▎      | 32/96 [00:49<01:38,  1.54s/it]
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Epoch 14 Training:  34%|███▍      | 33/96 [00:51<01:36,  1.54s/it]
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Epoch 14 Training:  35%|███▌      | 34/96 [00:52<01:35,  1.54s/it]
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Epoch 14 Training:  36%|███▋      | 35/96 [00:54<01:34,  1.55s/it]
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Epoch 14 Training:  38%|███▊      | 36/96 [00:55<01:32,  1.54s/it]
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Epoch 14 Training:  39%|███▊      | 37/96 [00:57<01:30,  1.54s/it]
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Epoch 14 Training:  40%|███▉      | 38/96 [00:58<01:29,  1.54s/it]
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Epoch 14 Training:  41%|████      | 39/96 [01:00<01:27,  1.54s/it]
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Epoch 14 Training:  42%|████▏     | 40/96 [01:01<01:26,  1.54s/it]
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Epoch 14 Training:  43%|████▎     | 41/96 [01:03<01:24,  1.54s/it]
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Epoch 14 Training:  44%|████▍     | 42/96 [01:05<01:22,  1.54s/it]
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Epoch 14 Training:  45%|████▍     | 43/96 [01:06<01:21,  1.54s/it]
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Epoch 14 Training:  46%|████▌     | 44/96 [01:08<01:19,  1.54s/it]
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Epoch 14 Training:  47%|████▋     | 45/96 [01:09<01:18,  1.54s/it]
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Epoch 14 Training:  48%|████▊     | 46/96 [01:11<01:16,  1.54s/it]
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Epoch 14 Training:  49%|████▉     | 47/96 [01:12<01:15,  1.54s/it]
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Epoch 14 Training:  50%|█████     | 48/96 [01:14<01:13,  1.53s/it]
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Epoch 14 Training:  51%|█████     | 49/96 [01:15<01:12,  1.53s/it]
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Epoch 14 Training:  52%|█████▏    | 50/96 [01:17<01:10,  1.53s/it]
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Epoch 14 Training:  53%|█████▎    | 51/96 [01:18<01:09,  1.53s/it]
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Epoch 14 Training:  54%|█████▍    | 52/96 [01:20<01:07,  1.53s/it]
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Epoch 14 Training:  55%|█████▌    | 53/96 [01:21<01:05,  1.53s/it]
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Epoch 14 Training:  56%|█████▋    | 54/96 [01:23<01:04,  1.53s/it]
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Epoch 14 Training:  57%|█████▋    | 55/96 [01:24<01:02,  1.53s/it]
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Epoch 14 Training:  58%|█████▊    | 56/96 [01:26<01:01,  1.53s/it]
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Epoch 14 Training:  59%|█████▉    | 57/96 [01:28<00:59,  1.54s/it]
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Epoch 14 Training:  60%|██████    | 58/96 [01:29<00:58,  1.54s/it]
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Epoch 14 Training:  61%|██████▏   | 59/96 [01:31<00:56,  1.54s/it]
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Epoch 14 Training:  62%|██████▎   | 60/96 [01:32<00:55,  1.54s/it]
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Epoch 14 Training:  64%|██████▎   | 61/96 [01:34<00:53,  1.54s/it]
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Epoch 14 Training:  65%|██████▍   | 62/96 [01:35<00:52,  1.54s/it]
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Epoch 14 Training:  66%|██████▌   | 63/96 [01:37<00:50,  1.54s/it]
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Epoch 14 Training:  67%|██████▋   | 64/96 [01:38<00:49,  1.54s/it]
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Epoch 14 Training:  68%|██████▊   | 65/96 [01:40<00:47,  1.54s/it]
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Epoch 14 Training:  69%|██████▉   | 66/96 [01:41<00:46,  1.54s/it]
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Epoch 14 Training:  70%|██████▉   | 67/96 [01:43<00:44,  1.54s/it]
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Epoch 14 Training:  71%|███████   | 68/96 [01:44<00:42,  1.53s/it]
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Epoch 14 Training:  72%|███████▏  | 69/96 [01:46<00:41,  1.53s/it]
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Epoch 14 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s][2026-10-02 08:52:33] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0014 | Epoch 014] train/policy_loss=21.5088 train/unweighted_policy_loss=4.3018 train/loss=21.5088 train/grad_norm=0.0506
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Epoch 14 Training: 100%|██████████| 96/96 [02:28<00:00,  1.54s/it]
+[2026-10-02 08:52:33] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0015 | Epoch 014] timing/rollout_seconds=179.2466 timing/feedback_seconds=5.3613 timing/optimization_seconds=148.8552 timing/cycle_seconds=330.8872
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+[2026-10-02 08:52:50] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0015 | Epoch 015] eval/places_domain_rl/reward_domain_mean=0.3137 eval/places_domain_rl/reward_domain_std=0.0551 eval/places_domain_rl/reward_ssim_to_stage1_mean=0.5846 eval/places_domain_rl/reward_ssim_to_stage1_std=0.1395 eval/places_domain_rl/reward_structure_mean=0.6996 eval/places_domain_rl/reward_structure_std=0.1477
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+
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Pointwise Rewards: domain: 100%|██████████| 6/6 [00:01<00:00,  4.03it/s]
+
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Pointwise Rewards: structure:  67%|██████▋   | 4/6 [00:00<00:00,  4.97it/s]
Pointwise Rewards: structure:  83%|████████▎ | 5/6 [00:01<00:00,  4.94it/s]
Pointwise Rewards: structure: 100%|██████████| 6/6 [00:01<00:00,  5.19it/s]
Pointwise Rewards: structure: 100%|██████████| 6/6 [00:01<00:00,  5.03it/s]
+[2026-10-02 08:55:53] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0015 | Epoch 015] train/reward_domain_mean=0.2911 train/reward_domain_std=0.0677 train/reward_structure_mean=0.7241 train/reward_structure_std=0.1364 train/reward_domain_group_std_mean=0.0190 train/reward_domain_group_std_max=0.0336 train/reward_domain_group_std_min=0.0091 train/reward_domain_group_mean_std=0.0647 train/reward_structure_group_std_mean=0.0069 train/reward_structure_group_std_max=0.0178 train/reward_structure_group_std_min=0.0013 train/reward_structure_group_mean_std=0.1362 train/reward_domain_zero_std_ratio=0 train/reward_structure_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.5955 train/adv_max=4.2327 train/adv_abs_mean=0.9690
+
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Epoch 15 Training:   7%|▋         | 7/96 [00:10<02:17,  1.55s/it]
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Epoch 15 Training:  14%|█▎        | 13/96 [00:20<02:08,  1.55s/it]
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Epoch 15 Training:  15%|█▍        | 14/96 [00:21<02:07,  1.55s/it]
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Epoch 15 Training:  43%|████▎     | 41/96 [01:03<01:24,  1.54s/it]
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Epoch 15 Training:  44%|████▍     | 42/96 [01:04<01:23,  1.54s/it]
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Epoch 15 Training:  45%|████▍     | 43/96 [01:06<01:21,  1.54s/it]
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Epoch 15 Training:  46%|████▌     | 44/96 [01:07<01:20,  1.54s/it]
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Epoch 15 Training:  47%|████▋     | 45/96 [01:09<01:18,  1.54s/it]
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Epoch 15 Training:  48%|████▊     | 46/96 [01:11<01:17,  1.54s/it]
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Epoch 15 Training:  49%|████▉     | 47/96 [01:12<01:15,  1.54s/it]
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Epoch 15 Training:  50%|█████     | 48/96 [01:14<01:13,  1.54s/it]
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Epoch 15 Training:  51%|█████     | 49/96 [01:15<01:12,  1.54s/it]
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Epoch 15 Training:  52%|█████▏    | 50/96 [01:17<01:10,  1.54s/it]
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Epoch 15 Training:  53%|█████▎    | 51/96 [01:18<01:09,  1.54s/it]
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Epoch 15 Training:  54%|█████▍    | 52/96 [01:20<01:07,  1.54s/it]
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Epoch 15 Training:  55%|█████▌    | 53/96 [01:21<01:06,  1.54s/it]
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Epoch 15 Training:  56%|█████▋    | 54/96 [01:23<01:04,  1.54s/it]
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Epoch 15 Training:  57%|█████▋    | 55/96 [01:24<01:03,  1.54s/it]
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Epoch 15 Training:  58%|█████▊    | 56/96 [01:26<01:01,  1.54s/it]
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Epoch 15 Training:  59%|█████▉    | 57/96 [01:27<01:00,  1.54s/it]
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Epoch 15 Training:  60%|██████    | 58/96 [01:29<00:58,  1.54s/it]
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Epoch 15 Training:  61%|██████▏   | 59/96 [01:31<00:57,  1.54s/it]
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Epoch 15 Training:  62%|██████▎   | 60/96 [01:32<00:55,  1.54s/it]
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Epoch 15 Training:  64%|██████▎   | 61/96 [01:34<00:53,  1.54s/it]
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Epoch 15 Training:  65%|██████▍   | 62/96 [01:35<00:52,  1.54s/it]
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Epoch 15 Training:  66%|██████▌   | 63/96 [01:37<00:50,  1.54s/it]
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Epoch 15 Training:  67%|██████▋   | 64/96 [01:38<00:49,  1.54s/it]
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Epoch 15 Training:  68%|██████▊   | 65/96 [01:40<00:47,  1.54s/it]
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Epoch 15 Training:  69%|██████▉   | 66/96 [01:41<00:46,  1.54s/it]
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Epoch 15 Training:  70%|██████▉   | 67/96 [01:43<00:44,  1.54s/it]
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Epoch 15 Training:  71%|███████   | 68/96 [01:44<00:43,  1.54s/it]
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Epoch 15 Training:  72%|███████▏  | 69/96 [01:46<00:41,  1.54s/it]
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Epoch 15 Training:  73%|███████▎  | 70/96 [01:48<00:40,  1.54s/it]
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Epoch 15 Training:  74%|███████▍  | 71/96 [01:49<00:38,  1.54s/it]
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Epoch 15 Training:  75%|███████▌  | 72/96 [01:51<00:36,  1.54s/it]
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Epoch 15 Training:  76%|███████▌  | 73/96 [01:52<00:35,  1.54s/it]
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Epoch 15 Training:  77%|███████▋  | 74/96 [01:54<00:33,  1.54s/it]
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Epoch 15 Training:  78%|███████▊  | 75/96 [01:55<00:32,  1.54s/it]
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Epoch 15 Training:  79%|███████▉  | 76/96 [01:57<00:30,  1.54s/it]
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Epoch 15 Training:  80%|████████  | 77/96 [01:58<00:29,  1.54s/it]
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Epoch 15 Training:  81%|████████▏ | 78/96 [02:00<00:27,  1.54s/it]
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Epoch 15 Training:  82%|████████▏ | 79/96 [02:01<00:26,  1.54s/it]
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Epoch 15 Training:  83%|████████▎ | 80/96 [02:03<00:24,  1.54s/it]
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Epoch 15 Training:  84%|████████▍ | 81/96 [02:04<00:23,  1.54s/it]
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Epoch 15 Training:  85%|████████▌ | 82/96 [02:06<00:21,  1.54s/it]
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Epoch 15 Training:  86%|████████▋ | 83/96 [02:08<00:20,  1.54s/it]
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Epoch 15 Training:  88%|████████▊ | 84/96 [02:09<00:18,  1.54s/it]
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Epoch 15 Training:  89%|████████▊ | 85/96 [02:11<00:16,  1.54s/it]
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Epoch 15 Training:  90%|████████▉ | 86/96 [02:12<00:15,  1.54s/it]
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Epoch 15 Training:  91%|█████████ | 87/96 [02:14<00:13,  1.54s/it]
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Epoch 15 Training:  92%|█████████▏| 88/96 [02:15<00:12,  1.54s/it]
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Epoch 15 Training:  93%|█████████▎| 89/96 [02:17<00:10,  1.54s/it]
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Epoch 15 Training:  94%|█████████▍| 90/96 [02:18<00:09,  1.54s/it]
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Epoch 15 Training:  95%|█████████▍| 91/96 [02:20<00:07,  1.54s/it]
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Epoch 15 Training:  99%|█████████▉| 95/96 [02:26<00:01,  1.54s/it]
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Epoch 15 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s][2026-10-02 08:58:21] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0015 | Epoch 015] train/policy_loss=21.3045 train/unweighted_policy_loss=4.2609 train/loss=21.3045 train/grad_norm=0.0545
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Epoch 15 Training: 100%|██████████| 96/96 [02:28<00:00,  1.56s/it]
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+[2026-10-02 08:58:21] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0016 | Epoch 015] timing/rollout_seconds=179.4215 timing/feedback_seconds=5.3386 timing/optimization_seconds=148.8225 timing/cycle_seconds=331.1012
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+
Pointwise Rewards: domain:   0%|          | 0/6 [00:00<?, ?it/s]
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Pointwise Rewards: domain: 100%|██████████| 6/6 [00:01<00:00,  4.08it/s]
+
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Pointwise Rewards: structure:  33%|███▎      | 2/6 [00:00<00:00,  4.89it/s]
Pointwise Rewards: structure:  50%|█████     | 3/6 [00:00<00:00,  5.26it/s]
Pointwise Rewards: structure:  67%|██████▋   | 4/6 [00:00<00:00,  5.11it/s]
Pointwise Rewards: structure:  83%|████████▎ | 5/6 [00:00<00:00,  5.03it/s]
Pointwise Rewards: structure: 100%|██████████| 6/6 [00:01<00:00,  5.28it/s]
Pointwise Rewards: structure: 100%|██████████| 6/6 [00:01<00:00,  5.15it/s]
+[2026-10-02 09:01:24] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0016 | Epoch 016] train/reward_domain_mean=0.2919 train/reward_domain_std=0.0640 train/reward_structure_mean=0.6934 train/reward_structure_std=0.1783 train/reward_domain_group_std_mean=0.0204 train/reward_domain_group_std_max=0.0428 train/reward_domain_group_std_min=0.0095 train/reward_domain_group_mean_std=0.0602 train/reward_structure_group_std_mean=0.0102 train/reward_structure_group_std_max=0.0407 train/reward_structure_group_std_min=0.0029 train/reward_structure_group_mean_std=0.1779 train/reward_domain_zero_std_ratio=0 train/reward_structure_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-6.0525 train/adv_max=3.5329 train/adv_abs_mean=0.8637
+
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.02it/s]
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Epoch 16 Training:  18%|█▊        | 17/96 [00:26<02:01,  1.54s/it]
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Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.15it/s]
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Epoch 16 Training:  19%|█▉        | 18/96 [00:27<02:00,  1.54s/it]
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Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Training:  20%|█▉        | 19/96 [00:29<01:58,  1.54s/it]
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Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Training:  21%|██        | 20/96 [00:30<01:56,  1.54s/it]
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Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Training:  22%|██▏       | 21/96 [00:32<01:55,  1.54s/it]
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Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Training:  23%|██▎       | 22/96 [00:33<01:53,  1.54s/it]
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Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Training:  24%|██▍       | 23/96 [00:35<01:52,  1.54s/it]
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Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Training:  25%|██▌       | 24/96 [00:36<01:50,  1.54s/it]
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Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Training:  26%|██▌       | 25/96 [00:38<01:49,  1.55s/it]
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Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 16 Training:  27%|██▋       | 26/96 [00:40<01:48,  1.55s/it]
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Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Training:  28%|██▊       | 27/96 [00:41<01:46,  1.55s/it]
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Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 16 Training:  29%|██▉       | 28/96 [00:43<01:44,  1.54s/it]
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Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.97it/s]
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Epoch 16 Training:  30%|███       | 29/96 [00:44<01:43,  1.54s/it]
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Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 16 Training:  31%|███▏      | 30/96 [00:46<01:41,  1.54s/it]
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Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 16 Training:  32%|███▏      | 31/96 [00:47<01:40,  1.54s/it]
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Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 16 Training:  33%|███▎      | 32/96 [00:49<01:38,  1.54s/it]
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Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 16 Training:  34%|███▍      | 33/96 [00:50<01:36,  1.54s/it]
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Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.15it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 16 Training:  35%|███▌      | 34/96 [00:52<01:35,  1.54s/it]
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Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 16 Training:  36%|███▋      | 35/96 [00:53<01:33,  1.54s/it]
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Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Training:  38%|███▊      | 36/96 [00:55<01:32,  1.54s/it]
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Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.15it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.98it/s]
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Epoch 16 Training:  39%|███▊      | 37/96 [00:56<01:30,  1.54s/it]
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Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 16 Training:  40%|███▉      | 38/96 [00:58<01:29,  1.54s/it]
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Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 16 Training:  41%|████      | 39/96 [01:00<01:27,  1.54s/it]
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Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 16 Training:  42%|████▏     | 40/96 [01:01<01:26,  1.54s/it]
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Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 16 Training:  43%|████▎     | 41/96 [01:03<01:24,  1.54s/it]
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Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 16 Training:  44%|████▍     | 42/96 [01:04<01:23,  1.54s/it]
+
Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 16 Training:  45%|████▍     | 43/96 [01:06<01:21,  1.54s/it]
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Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 16 Training:  46%|████▌     | 44/96 [01:07<01:19,  1.54s/it]
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Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 16 Training:  47%|████▋     | 45/96 [01:09<01:18,  1.54s/it]
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Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 16 Training:  48%|████▊     | 46/96 [01:10<01:16,  1.54s/it]
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Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 16 Training:  49%|████▉     | 47/96 [01:12<01:15,  1.54s/it]
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Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.02it/s]
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Epoch 16 Training:  50%|█████     | 48/96 [01:13<01:13,  1.54s/it]
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Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 16 Training:  51%|█████     | 49/96 [01:15<01:12,  1.54s/it]
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Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.97it/s]
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Epoch 16 Training:  52%|█████▏    | 50/96 [01:16<01:10,  1.54s/it]
+
Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 16 Training:  53%|█████▎    | 51/96 [01:18<01:09,  1.54s/it]
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Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Training:  54%|█████▍    | 52/96 [01:20<01:07,  1.54s/it]
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Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Training:  55%|█████▌    | 53/96 [01:21<01:06,  1.54s/it]
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Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Training:  56%|█████▋    | 54/96 [01:23<01:04,  1.54s/it]
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Epoch 16 Training:  57%|█████▋    | 55/96 [01:24<01:03,  1.54s/it]
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Epoch 16 Training:  58%|█████▊    | 56/96 [01:26<01:01,  1.54s/it]
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Epoch 16 Training:  59%|█████▉    | 57/96 [01:27<00:59,  1.54s/it]
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Epoch 16 Training:  60%|██████    | 58/96 [01:29<00:58,  1.54s/it]
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Epoch 16 Training:  61%|██████▏   | 59/96 [01:30<00:56,  1.54s/it]
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Epoch 16 Training:  62%|██████▎   | 60/96 [01:32<00:55,  1.54s/it]
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Epoch 16 Training:  64%|██████▎   | 61/96 [01:33<00:53,  1.54s/it]
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Epoch 16 Training:  65%|██████▍   | 62/96 [01:35<00:52,  1.54s/it]
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Epoch 16 Training:  66%|██████▌   | 63/96 [01:36<00:50,  1.54s/it]
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Epoch 16 Training:  67%|██████▋   | 64/96 [01:38<00:49,  1.54s/it]
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Epoch 16 Training:  68%|██████▊   | 65/96 [01:40<00:47,  1.54s/it]
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Epoch 16 Training:  69%|██████▉   | 66/96 [01:41<00:46,  1.54s/it]
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Epoch 16 Training:  70%|██████▉   | 67/96 [01:43<00:44,  1.54s/it]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s][2026-10-02 09:03:52] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0016 | Epoch 016] train/policy_loss=21.4006 train/unweighted_policy_loss=4.2801 train/loss=21.4006 train/grad_norm=0.0701
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+[2026-10-02 09:03:52] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0017 | Epoch 016] timing/rollout_seconds=179.4056 timing/feedback_seconds=5.3686 timing/optimization_seconds=148.4133 timing/cycle_seconds=330.7314
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Pointwise Rewards: structure: 100%|██████████| 6/6 [00:01<00:00,  5.29it/s]
+[2026-10-02 09:06:55] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0017 | Epoch 017] train/reward_domain_mean=0.3245 train/reward_domain_std=0.0605 train/reward_structure_mean=0.7324 train/reward_structure_std=0.1383 train/reward_domain_group_std_mean=0.0179 train/reward_domain_group_std_max=0.0281 train/reward_domain_group_std_min=0.0098 train/reward_domain_group_mean_std=0.0576 train/reward_structure_group_std_mean=0.0065 train/reward_structure_group_std_max=0.0139 train/reward_structure_group_std_min=0.0025 train/reward_structure_group_mean_std=0.1381 train/reward_domain_zero_std_ratio=0 train/reward_structure_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.5715 train/adv_max=3.8089 train/adv_abs_mean=0.9829
+
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Epoch 17 Training:  46%|████▌     | 44/96 [01:07<01:19,  1.54s/it]
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Epoch 17 Training:  47%|████▋     | 45/96 [01:09<01:18,  1.54s/it]
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Epoch 17 Training:  48%|████▊     | 46/96 [01:10<01:16,  1.54s/it]
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Epoch 17 Training:  49%|████▉     | 47/96 [01:12<01:15,  1.54s/it]
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Epoch 17 Training:  50%|█████     | 48/96 [01:13<01:13,  1.54s/it]
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Epoch 17 Training:  51%|█████     | 49/96 [01:15<01:12,  1.54s/it]
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Epoch 17 Training:  52%|█████▏    | 50/96 [01:16<01:10,  1.54s/it]
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Epoch 17 Training:  53%|█████▎    | 51/96 [01:18<01:09,  1.54s/it]
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Epoch 17 Training:  54%|█████▍    | 52/96 [01:19<01:07,  1.54s/it]
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Epoch 17 Training:  55%|█████▌    | 53/96 [01:21<01:06,  1.54s/it]
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Epoch 17 Training:  56%|█████▋    | 54/96 [01:22<01:04,  1.54s/it]
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Epoch 17 Training:  57%|█████▋    | 55/96 [01:24<01:02,  1.54s/it]
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Epoch 17 Training:  58%|█████▊    | 56/96 [01:26<01:01,  1.54s/it]
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Epoch 17 Training:  59%|█████▉    | 57/96 [01:27<00:59,  1.54s/it]
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Epoch 17 Training:  60%|██████    | 58/96 [01:29<00:58,  1.54s/it]
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Epoch 17 Training:  61%|██████▏   | 59/96 [01:30<00:56,  1.54s/it]
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Epoch 17 Training:  62%|██████▎   | 60/96 [01:32<00:55,  1.54s/it]
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Epoch 17 Training:  64%|██████▎   | 61/96 [01:33<00:53,  1.54s/it]
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Epoch 17 Training:  65%|██████▍   | 62/96 [01:35<00:52,  1.54s/it]
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Epoch 17 Training:  66%|██████▌   | 63/96 [01:36<00:50,  1.54s/it]
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Epoch 17 Training:  67%|██████▋   | 64/96 [01:38<00:49,  1.54s/it]
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Epoch 17 Training:  68%|██████▊   | 65/96 [01:39<00:47,  1.54s/it]
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Epoch 17 Training:  69%|██████▉   | 66/96 [01:41<00:46,  1.54s/it]
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Epoch 17 Training:  70%|██████▉   | 67/96 [01:42<00:44,  1.54s/it]
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Epoch 17 Training:  71%|███████   | 68/96 [01:44<00:43,  1.54s/it]
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Epoch 17 Training:  72%|███████▏  | 69/96 [01:46<00:41,  1.54s/it]
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Epoch 17 Training:  73%|███████▎  | 70/96 [01:47<00:40,  1.54s/it]
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Epoch 17 Training:  74%|███████▍  | 71/96 [01:49<00:38,  1.54s/it]
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Epoch 17 Training:  75%|███████▌  | 72/96 [01:50<00:36,  1.54s/it]
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Epoch 17 Training:  76%|███████▌  | 73/96 [01:52<00:35,  1.54s/it]
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Epoch 17 Training:  77%|███████▋  | 74/96 [01:53<00:33,  1.54s/it]
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Epoch 17 Training:  78%|███████▊  | 75/96 [01:55<00:32,  1.54s/it]
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Epoch 17 Training:  79%|███████▉  | 76/96 [01:56<00:30,  1.54s/it]
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Epoch 17 Training:  80%|████████  | 77/96 [01:58<00:29,  1.54s/it]
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Epoch 17 Training:  81%|████████▏ | 78/96 [01:59<00:27,  1.54s/it]
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Epoch 17 Training:  82%|████████▏ | 79/96 [02:01<00:26,  1.54s/it]
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Epoch 17 Training:  83%|████████▎ | 80/96 [02:02<00:24,  1.54s/it]
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Epoch 17 Training:  84%|████████▍ | 81/96 [02:04<00:23,  1.54s/it]
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Epoch 17 Training:  85%|████████▌ | 82/96 [02:06<00:21,  1.54s/it]
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Epoch 17 Training:  86%|████████▋ | 83/96 [02:07<00:19,  1.54s/it]
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Epoch 17 Training:  88%|████████▊ | 84/96 [02:09<00:18,  1.54s/it]
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Epoch 17 Training:  89%|████████▊ | 85/96 [02:10<00:16,  1.54s/it]
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Epoch 17 Training:  90%|████████▉ | 86/96 [02:12<00:15,  1.54s/it]
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Epoch 17 Training:  91%|█████████ | 87/96 [02:13<00:13,  1.54s/it]
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Epoch 17 Training:  92%|█████████▏| 88/96 [02:15<00:12,  1.54s/it]
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Epoch 17 Training:  93%|█████████▎| 89/96 [02:16<00:10,  1.54s/it]
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Epoch 17 Training:  94%|█████████▍| 90/96 [02:18<00:09,  1.54s/it]
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Epoch 17 Training:  95%|█████████▍| 91/96 [02:19<00:07,  1.54s/it]
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Epoch 17 Training:  96%|█████████▌| 92/96 [02:21<00:06,  1.54s/it]
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Epoch 17 Training:  97%|█████████▋| 93/96 [02:22<00:04,  1.54s/it]
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Epoch 17 Training:  98%|█████████▊| 94/96 [02:24<00:03,  1.54s/it]
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Epoch 17 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.15it/s][2026-10-02 09:09:22] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0017 | Epoch 017] train/policy_loss=21.3077 train/unweighted_policy_loss=4.2615 train/loss=21.3077 train/grad_norm=0.0447
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+[2026-10-02 09:09:22] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0018 | Epoch 017] timing/rollout_seconds=179.4283 timing/feedback_seconds=5.4319 timing/optimization_seconds=148.2736 timing/cycle_seconds=330.5574
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Pointwise Rewards: domain:   0%|          | 0/6 [00:00<?, ?it/s]
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Pointwise Rewards: domain: 100%|██████████| 6/6 [00:01<00:00,  4.19it/s]
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+
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Pointwise Rewards: structure:  50%|█████     | 3/6 [00:00<00:00,  5.06it/s]
Pointwise Rewards: structure:  67%|██████▋   | 4/6 [00:00<00:00,  4.93it/s]
Pointwise Rewards: structure:  83%|████████▎ | 5/6 [00:00<00:00,  4.90it/s]
Pointwise Rewards: structure: 100%|██████████| 6/6 [00:01<00:00,  5.21it/s]
Pointwise Rewards: structure: 100%|██████████| 6/6 [00:01<00:00,  5.15it/s]
+[2026-10-02 09:12:25] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0018 | Epoch 018] train/reward_domain_mean=0.3048 train/reward_domain_std=0.0751 train/reward_structure_mean=0.6981 train/reward_structure_std=0.1435 train/reward_domain_group_std_mean=0.0191 train/reward_domain_group_std_max=0.0329 train/reward_domain_group_std_min=0.0077 train/reward_domain_group_mean_std=0.0724 train/reward_structure_group_std_mean=0.0083 train/reward_structure_group_std_max=0.0169 train/reward_structure_group_std_min=0.0016 train/reward_structure_group_mean_std=0.1432 train/reward_domain_zero_std_ratio=0 train/reward_structure_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.1563 train/adv_max=3.8694 train/adv_abs_mean=0.9230
+
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Epoch 18 Training:  21%|██        | 20/96 [00:30<01:57,  1.55s/it]
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Epoch 18 Training:  22%|██▏       | 21/96 [00:32<01:56,  1.55s/it]
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Epoch 18 Training:  23%|██▎       | 22/96 [00:33<01:54,  1.54s/it]
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Epoch 18 Training:  24%|██▍       | 23/96 [00:35<01:52,  1.55s/it]
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Epoch 18 Training:  25%|██▌       | 24/96 [00:37<01:51,  1.54s/it]
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Epoch 18 Training:  26%|██▌       | 25/96 [00:38<01:49,  1.54s/it]
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Epoch 18 Training:  27%|██▋       | 26/96 [00:40<01:48,  1.54s/it]
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Epoch 18 Training:  28%|██▊       | 27/96 [00:41<01:46,  1.54s/it]
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Epoch 18 Training:  29%|██▉       | 28/96 [00:43<01:44,  1.54s/it]
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Epoch 18 Training:  30%|███       | 29/96 [00:44<01:43,  1.54s/it]
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Epoch 18 Training:  31%|███▏      | 30/96 [00:46<01:41,  1.54s/it]
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Epoch 18 Training:  32%|███▏      | 31/96 [00:47<01:40,  1.54s/it]
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Epoch 18 Training:  33%|███▎      | 32/96 [00:49<01:38,  1.54s/it]
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Epoch 18 Training:  34%|███▍      | 33/96 [00:50<01:37,  1.54s/it]
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Epoch 18 Training:  35%|███▌      | 34/96 [00:52<01:35,  1.54s/it]
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Epoch 18 Training:  36%|███▋      | 35/96 [00:53<01:33,  1.54s/it]
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Epoch 18 Training:  38%|███▊      | 36/96 [00:55<01:32,  1.54s/it]
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Epoch 18 Training:  39%|███▊      | 37/96 [00:57<01:30,  1.54s/it]
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Epoch 18 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 18 Training:  40%|███▉      | 38/96 [00:58<01:29,  1.54s/it]
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Epoch 18 Training:  41%|████      | 39/96 [01:00<01:27,  1.54s/it]
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Epoch 18 Training:  42%|████▏     | 40/96 [01:01<01:26,  1.54s/it]
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Epoch 18 Training:  43%|████▎     | 41/96 [01:03<01:24,  1.54s/it]
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Epoch 18 Training:  44%|████▍     | 42/96 [01:04<01:23,  1.54s/it]
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Epoch 18 Training:  45%|████▍     | 43/96 [01:06<01:21,  1.54s/it]
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Epoch 18 Training:  46%|████▌     | 44/96 [01:07<01:20,  1.54s/it]
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Epoch 18 Training:  47%|████▋     | 45/96 [01:09<01:18,  1.54s/it]
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Epoch 18 Training:  48%|████▊     | 46/96 [01:10<01:16,  1.54s/it]
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Epoch 18 Training:  49%|████▉     | 47/96 [01:12<01:15,  1.54s/it]
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Epoch 18 Training:  50%|█████     | 48/96 [01:14<01:13,  1.54s/it]
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Epoch 18 Training:  51%|█████     | 49/96 [01:15<01:12,  1.54s/it]
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Epoch 18 Training:  52%|█████▏    | 50/96 [01:17<01:10,  1.54s/it]
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Epoch 18 Training:  53%|█████▎    | 51/96 [01:18<01:09,  1.54s/it]
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Epoch 18 Training:  54%|█████▍    | 52/96 [01:20<01:07,  1.54s/it]
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Epoch 18 Training:  55%|█████▌    | 53/96 [01:21<01:06,  1.54s/it]
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Epoch 18 Training:  56%|█████▋    | 54/96 [01:23<01:04,  1.54s/it]
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Epoch 18 Training:  57%|█████▋    | 55/96 [01:24<01:03,  1.54s/it]
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Epoch 18 Training:  58%|█████▊    | 56/96 [01:26<01:01,  1.54s/it]
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Epoch 18 Training:  59%|█████▉    | 57/96 [01:27<01:00,  1.54s/it]
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Epoch 18 Training:  60%|██████    | 58/96 [01:29<00:58,  1.54s/it]
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Epoch 18 Training:  61%|██████▏   | 59/96 [01:30<00:56,  1.54s/it]
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Epoch 18 Training:  62%|██████▎   | 60/96 [01:32<00:55,  1.54s/it]
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Epoch 18 Training:  64%|██████▎   | 61/96 [01:34<00:53,  1.54s/it]
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Epoch 18 Training:  65%|██████▍   | 62/96 [01:35<00:52,  1.54s/it]
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Epoch 18 Training:  66%|██████▌   | 63/96 [01:37<00:50,  1.54s/it]
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Epoch 18 Training:  67%|██████▋   | 64/96 [01:38<00:49,  1.54s/it]
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Epoch 18 Training:  68%|██████▊   | 65/96 [01:40<00:47,  1.54s/it]
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Epoch 18 Training:  69%|██████▉   | 66/96 [01:41<00:46,  1.54s/it]
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Epoch 18 Training:  70%|██████▉   | 67/96 [01:43<00:44,  1.54s/it]
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Epoch 18 Training:  71%|███████   | 68/96 [01:44<00:43,  1.54s/it]
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Epoch 18 Training:  72%|███████▏  | 69/96 [01:46<00:41,  1.54s/it]
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Epoch 18 Training:  73%|███████▎  | 70/96 [01:47<00:40,  1.54s/it]
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Epoch 18 Training:  99%|█████████▉| 95/96 [02:26<00:01,  1.54s/it]
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Epoch 18 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s][2026-10-02 09:14:53] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0018 | Epoch 018] train/policy_loss=21.5097 train/unweighted_policy_loss=4.3019 train/loss=21.5097 train/grad_norm=0.0491
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Epoch 18 Training: 100%|██████████| 96/96 [02:28<00:00,  1.54s/it]
+[2026-10-02 09:14:53] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0019 | Epoch 018] timing/rollout_seconds=179.3008 timing/feedback_seconds=5.2864 timing/optimization_seconds=148.7180 timing/cycle_seconds=330.8262
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Pointwise Rewards: domain:  83%|████████▎ | 5/6 [00:01<00:00,  4.19it/s]
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Pointwise Rewards: domain: 100%|██████████| 6/6 [00:01<00:00,  4.16it/s]
+
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Pointwise Rewards: structure:  50%|█████     | 3/6 [00:00<00:00,  5.41it/s]
Pointwise Rewards: structure:  67%|██████▋   | 4/6 [00:00<00:00,  5.25it/s]
Pointwise Rewards: structure:  83%|████████▎ | 5/6 [00:00<00:00,  5.29it/s]
Pointwise Rewards: structure: 100%|██████████| 6/6 [00:01<00:00,  5.51it/s]
Pointwise Rewards: structure: 100%|██████████| 6/6 [00:01<00:00,  5.41it/s]
+[2026-10-02 09:17:56] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0019 | Epoch 019] train/reward_domain_mean=0.3232 train/reward_domain_std=0.0660 train/reward_structure_mean=0.7057 train/reward_structure_std=0.1515 train/reward_domain_group_std_mean=0.0198 train/reward_domain_group_std_max=0.0352 train/reward_domain_group_std_min=0.0079 train/reward_domain_group_mean_std=0.0626 train/reward_structure_group_std_mean=0.0079 train/reward_structure_group_std_max=0.0234 train/reward_structure_group_std_min=0.0029 train/reward_structure_group_mean_std=0.1513 train/reward_domain_zero_std_ratio=0 train/reward_structure_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.2535 train/adv_max=4.1439 train/adv_abs_mean=1.0094
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Epoch 19 Training:  11%|█▏        | 11/96 [00:16<02:10,  1.54s/it]
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Epoch 19 Training:  15%|█▍        | 14/96 [00:21<02:06,  1.54s/it]
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Epoch 19 Training:  16%|█▌        | 15/96 [00:23<02:04,  1.54s/it]
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Epoch 19 Training:  17%|█▋        | 16/96 [00:24<02:04,  1.55s/it]
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Epoch 19 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s][2026-10-02 09:20:24] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0019 | Epoch 019] train/policy_loss=21.6371 train/unweighted_policy_loss=4.3274 train/loss=21.6371 train/grad_norm=0.0645
+
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+[2026-10-02 09:20:24] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0020 | Epoch 019] timing/rollout_seconds=179.3463 timing/feedback_seconds=5.3852 timing/optimization_seconds=148.3223 timing/cycle_seconds=330.4865
+[2026-10-02 09:20:24] [Rank 0] [INFO] [flow_factory.models.abc]: Saving LoRA weights for transformer role=base to /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/runs/klein4b_nft_domain_sirst/saves/klein4b_nft_domain_sirst/checkpoints/checkpoint-20
+[2026-10-02 09:20:24] [Rank 0] [INFO] [flow_factory.models.abc]: Checkpoint saved successfully to /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/runs/klein4b_nft_domain_sirst/saves/klein4b_nft_domain_sirst/checkpoints/checkpoint-20
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+[2026-10-02 09:20:41] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0020 | Epoch 020] eval/places_domain_rl/reward_domain_mean=0.3314 eval/places_domain_rl/reward_domain_std=0.0554 eval/places_domain_rl/reward_ssim_to_stage1_mean=0.5816 eval/places_domain_rl/reward_ssim_to_stage1_std=0.1332 eval/places_domain_rl/reward_structure_mean=0.7110 eval/places_domain_rl/reward_structure_std=0.1390
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+[2026-10-02 09:23:44] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0020 | Epoch 020] train/reward_domain_mean=0.3353 train/reward_domain_std=0.0655 train/reward_structure_mean=0.6979 train/reward_structure_std=0.1577 train/reward_domain_group_std_mean=0.0189 train/reward_domain_group_std_max=0.0326 train/reward_domain_group_std_min=0.0096 train/reward_domain_group_mean_std=0.0625 train/reward_structure_group_std_mean=0.0084 train/reward_structure_group_std_max=0.0268 train/reward_structure_group_std_min=0.0023 train/reward_structure_group_mean_std=0.1574 train/reward_domain_zero_std_ratio=0 train/reward_structure_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.7892 train/adv_max=5.1159 train/adv_abs_mean=0.9849
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Epoch 20 Training:  16%|█▌        | 15/96 [00:23<02:05,  1.55s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 20 Training:  17%|█▋        | 16/96 [00:24<02:03,  1.55s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.02it/s]
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Epoch 20 Training:  18%|█▊        | 17/96 [00:26<02:01,  1.54s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.15it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.98it/s]
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Epoch 20 Training:  19%|█▉        | 18/96 [00:27<02:00,  1.54s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 20 Training:  20%|█▉        | 19/96 [00:29<01:58,  1.54s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Training:  21%|██        | 20/96 [00:30<01:57,  1.54s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.02it/s]
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Epoch 20 Training:  22%|██▏       | 21/96 [00:32<02:01,  1.63s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.02it/s]
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Epoch 20 Training:  23%|██▎       | 22/96 [00:34<01:58,  1.60s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.98it/s]
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Epoch 20 Training:  24%|██▍       | 23/96 [00:35<01:55,  1.58s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.97it/s]
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Epoch 20 Training:  25%|██▌       | 24/96 [00:37<01:53,  1.57s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.15it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.02it/s]
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Epoch 20 Training:  26%|██▌       | 25/96 [00:38<01:50,  1.56s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.02it/s]
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Epoch 20 Training:  27%|██▋       | 26/96 [00:40<01:48,  1.55s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.55it/s]
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Epoch 20 Training:  28%|██▊       | 27/96 [00:42<01:51,  1.62s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.96it/s]
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Epoch 20 Training:  29%|██▉       | 28/96 [00:43<01:49,  1.60s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.02it/s]
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Epoch 20 Training:  30%|███       | 29/96 [00:45<01:46,  1.58s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 20 Training:  31%|███▏      | 30/96 [00:46<01:43,  1.57s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.98it/s]
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Epoch 20 Training:  32%|███▏      | 31/96 [00:48<01:41,  1.56s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.02it/s]
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Epoch 20 Training:  33%|███▎      | 32/96 [00:49<01:39,  1.55s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.02it/s]
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Epoch 20 Training:  34%|███▍      | 33/96 [00:51<01:37,  1.55s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.96it/s]
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Epoch 20 Training:  35%|███▌      | 34/96 [00:52<01:36,  1.55s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.15it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 20 Training:  36%|███▋      | 35/96 [00:54<01:34,  1.55s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.79it/s]
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Epoch 20 Training:  38%|███▊      | 36/96 [00:56<01:34,  1.58s/it]
+
Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.97it/s]
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Epoch 20 Training:  39%|███▊      | 37/96 [00:57<01:32,  1.57s/it]
+
Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.02it/s]
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Epoch 20 Training:  40%|███▉      | 38/96 [00:59<01:30,  1.56s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 20 Training:  41%|████      | 39/96 [01:00<01:28,  1.55s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 20 Training:  42%|████▏     | 40/96 [01:02<01:26,  1.55s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 20 Training:  43%|████▎     | 41/96 [01:03<01:25,  1.55s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.97it/s]
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Epoch 20 Training:  44%|████▍     | 42/96 [01:05<01:23,  1.55s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 20 Training:  45%|████▍     | 43/96 [01:06<01:22,  1.55s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.15it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.02it/s]
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Epoch 20 Training:  46%|████▌     | 44/96 [01:08<01:20,  1.54s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 20 Training:  47%|████▋     | 45/96 [01:09<01:18,  1.54s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 20 Training:  48%|████▊     | 46/96 [01:11<01:17,  1.54s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 20 Training:  49%|████▉     | 47/96 [01:13<01:15,  1.54s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.02it/s]
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Epoch 20 Training:  50%|█████     | 48/96 [01:14<01:13,  1.54s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 20 Training:  51%|█████     | 49/96 [01:16<01:12,  1.54s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Training:  52%|█████▏    | 50/96 [01:17<01:10,  1.53s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Training:  53%|█████▎    | 51/96 [01:19<01:09,  1.54s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Training:  54%|█████▍    | 52/96 [01:20<01:07,  1.54s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Training:  55%|█████▌    | 53/96 [01:22<01:05,  1.53s/it]
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Epoch 20 Training:  56%|█████▋    | 54/96 [01:23<01:04,  1.53s/it]
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Epoch 20 Training:  57%|█████▋    | 55/96 [01:25<01:02,  1.53s/it]
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Epoch 20 Training:  58%|█████▊    | 56/96 [01:26<01:01,  1.53s/it]
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Epoch 20 Training:  59%|█████▉    | 57/96 [01:28<00:59,  1.53s/it]
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Epoch 20 Training:  60%|██████    | 58/96 [01:29<00:58,  1.53s/it]
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Epoch 20 Training:  61%|██████▏   | 59/96 [01:31<00:56,  1.53s/it]
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Epoch 20 Training:  62%|██████▎   | 60/96 [01:32<00:55,  1.53s/it]
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Epoch 20 Training:  64%|██████▎   | 61/96 [01:34<00:53,  1.53s/it]
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Epoch 20 Training:  65%|██████▍   | 62/96 [01:36<00:52,  1.53s/it]
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Epoch 20 Training:  66%|██████▌   | 63/96 [01:37<00:50,  1.53s/it]
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Epoch 20 Training:  67%|██████▋   | 64/96 [01:39<00:48,  1.53s/it]
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Epoch 20 Training:  68%|██████▊   | 65/96 [01:40<00:47,  1.53s/it]
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Epoch 20 Training:  69%|██████▉   | 66/96 [01:42<00:45,  1.53s/it]
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Epoch 20 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s][2026-10-02 09:26:12] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0020 | Epoch 020] train/policy_loss=21.8821 train/unweighted_policy_loss=4.3764 train/loss=21.8821 train/grad_norm=0.0592
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+[2026-10-02 09:26:12] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0021 | Epoch 020] timing/rollout_seconds=179.3500 timing/feedback_seconds=5.0873 timing/optimization_seconds=149.1146 timing/cycle_seconds=331.3083
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Epoch 21 Sampling:  91%|█████████ | 87/96 [02:40<00:16,  1.84s/it]
Epoch 21 Sampling:  92%|█████████▏| 88/96 [02:42<00:14,  1.84s/it]
Epoch 21 Sampling:  93%|█████████▎| 89/96 [02:44<00:12,  1.84s/it]
Epoch 21 Sampling:  94%|█████████▍| 90/96 [02:46<00:11,  1.84s/it]
Epoch 21 Sampling:  95%|█████████▍| 91/96 [02:47<00:09,  1.84s/it]
Epoch 21 Sampling:  96%|█████████▌| 92/96 [02:49<00:07,  1.84s/it]
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Epoch 21 Sampling:  98%|█████████▊| 94/96 [02:53<00:03,  1.84s/it]
Epoch 21 Sampling:  99%|█████████▉| 95/96 [02:55<00:01,  1.84s/it]
Epoch 21 Sampling: 100%|██████████| 96/96 [02:57<00:00,  1.84s/it]
Epoch 21 Sampling: 100%|██████████| 96/96 [02:57<00:00,  1.85s/it]
+
Pointwise Rewards: domain:   0%|          | 0/6 [00:00<?, ?it/s]
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Pointwise Rewards: domain:  33%|███▎      | 2/6 [00:00<00:00,  4.11it/s]
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Pointwise Rewards: domain:  83%|████████▎ | 5/6 [00:01<00:00,  4.15it/s]
Pointwise Rewards: domain: 100%|██████████| 6/6 [00:01<00:00,  4.12it/s]
Pointwise Rewards: domain: 100%|██████████| 6/6 [00:01<00:00,  4.11it/s]
+
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Pointwise Rewards: structure:  33%|███▎      | 2/6 [00:00<00:00,  5.04it/s]
Pointwise Rewards: structure:  50%|█████     | 3/6 [00:00<00:00,  5.12it/s]
Pointwise Rewards: structure:  67%|██████▋   | 4/6 [00:00<00:00,  5.06it/s]
Pointwise Rewards: structure:  83%|████████▎ | 5/6 [00:00<00:00,  5.20it/s]
Pointwise Rewards: structure: 100%|██████████| 6/6 [00:01<00:00,  5.06it/s]
Pointwise Rewards: structure: 100%|██████████| 6/6 [00:01<00:00,  5.08it/s]
+[2026-10-02 09:29:15] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0021 | Epoch 021] train/reward_domain_mean=0.3334 train/reward_domain_std=0.0611 train/reward_structure_mean=0.6872 train/reward_structure_std=0.1617 train/reward_domain_group_std_mean=0.0185 train/reward_domain_group_std_max=0.0325 train/reward_domain_group_std_min=0.0078 train/reward_domain_group_mean_std=0.0579 train/reward_structure_group_std_mean=0.0082 train/reward_structure_group_std_max=0.0222 train/reward_structure_group_std_min=0.0021 train/reward_structure_group_mean_std=0.1615 train/reward_domain_zero_std_ratio=0 train/reward_structure_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-6.2809 train/adv_max=3.5308 train/adv_abs_mean=1.0108
+
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Epoch 21 Training:   1%|          | 1/96 [00:01<02:20,  1.48s/it]
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Epoch 21 Training:   2%|▏         | 2/96 [00:03<02:22,  1.52s/it]
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Epoch 21 Training:   3%|▎         | 3/96 [00:04<02:21,  1.53s/it]
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Epoch 21 Training:   4%|▍         | 4/96 [00:06<02:20,  1.53s/it]
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Epoch 21 Training:   5%|▌         | 5/96 [00:07<02:19,  1.53s/it]
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Epoch 21 Training:   6%|▋         | 6/96 [00:09<02:19,  1.55s/it]
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Epoch 21 Training:   7%|▋         | 7/96 [00:10<02:17,  1.54s/it]
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Epoch 21 Training:   8%|▊         | 8/96 [00:12<02:15,  1.54s/it]
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Epoch 21 Training:   9%|▉         | 9/96 [00:13<02:13,  1.54s/it]
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Epoch 21 Training:  10%|█         | 10/96 [00:15<02:11,  1.53s/it]
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Epoch 21 Training:  11%|█▏        | 11/96 [00:16<02:10,  1.53s/it]
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Epoch 21 Training:  12%|█▎        | 12/96 [00:18<02:08,  1.53s/it]
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Epoch 21 Training:  14%|█▎        | 13/96 [00:19<02:07,  1.53s/it]
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Epoch 21 Training:  15%|█▍        | 14/96 [00:21<02:06,  1.54s/it]
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Epoch 21 Training:  16%|█▌        | 15/96 [00:23<02:04,  1.54s/it]
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Epoch 21 Training:  17%|█▋        | 16/96 [00:24<02:02,  1.53s/it]
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Epoch 21 Training:  18%|█▊        | 17/96 [00:26<02:01,  1.53s/it]
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Epoch 21 Training:  19%|█▉        | 18/96 [00:27<01:59,  1.53s/it]
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Epoch 21 Training:  20%|█▉        | 19/96 [00:29<01:57,  1.53s/it]
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Epoch 21 Training:  21%|██        | 20/96 [00:30<01:56,  1.53s/it]
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Epoch 21 Training:  22%|██▏       | 21/96 [00:32<01:54,  1.53s/it]
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Epoch 21 Training:  23%|██▎       | 22/96 [00:33<01:53,  1.53s/it]
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Epoch 21 Training:  24%|██▍       | 23/96 [00:35<01:51,  1.53s/it]
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Epoch 21 Training:  25%|██▌       | 24/96 [00:36<01:50,  1.53s/it]
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Epoch 21 Training:  26%|██▌       | 25/96 [00:38<01:48,  1.53s/it]
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Epoch 21 Training:  27%|██▋       | 26/96 [00:39<01:47,  1.53s/it]
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Epoch 21 Training:  28%|██▊       | 27/96 [00:41<01:45,  1.53s/it]
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Epoch 21 Training:  29%|██▉       | 28/96 [00:42<01:44,  1.53s/it]
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Epoch 21 Training:  30%|███       | 29/96 [00:44<01:42,  1.53s/it]
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Epoch 21 Training:  31%|███▏      | 30/96 [00:45<01:41,  1.53s/it]
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Epoch 21 Training:  32%|███▏      | 31/96 [00:47<01:39,  1.53s/it]
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Epoch 21 Training:  33%|███▎      | 32/96 [00:49<01:38,  1.53s/it]
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Epoch 21 Training:  34%|███▍      | 33/96 [00:50<01:36,  1.53s/it]
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Epoch 21 Training:  35%|███▌      | 34/96 [00:52<01:35,  1.53s/it]
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Epoch 21 Training:  36%|███▋      | 35/96 [00:53<01:33,  1.53s/it]
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Epoch 21 Training:  38%|███▊      | 36/96 [00:55<01:31,  1.53s/it]
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Epoch 21 Training:  39%|███▊      | 37/96 [00:56<01:30,  1.53s/it]
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Epoch 21 Training:  44%|████▍     | 42/96 [01:04<01:22,  1.54s/it]
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Epoch 21 Training:  45%|████▍     | 43/96 [01:05<01:21,  1.53s/it]
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Epoch 21 Training:  46%|████▌     | 44/96 [01:07<01:19,  1.54s/it]
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Epoch 21 Training:  47%|████▋     | 45/96 [01:08<01:18,  1.53s/it]
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Epoch 21 Training:  48%|████▊     | 46/96 [01:10<01:16,  1.53s/it]
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Epoch 21 Training:  49%|████▉     | 47/96 [01:12<01:15,  1.53s/it]
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Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 21 Training:  50%|█████     | 48/96 [01:13<01:13,  1.53s/it]
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Epoch 21 Training:  51%|█████     | 49/96 [01:15<01:12,  1.53s/it]
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Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 21 Training:  52%|█████▏    | 50/96 [01:16<01:10,  1.53s/it]
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Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.97it/s]
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Epoch 21 Training:  53%|█████▎    | 51/96 [01:18<01:09,  1.54s/it]
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Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 21 Training:  54%|█████▍    | 52/96 [01:19<01:07,  1.54s/it]
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Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 21 Training:  55%|█████▌    | 53/96 [01:21<01:06,  1.54s/it]
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Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 21 Training:  56%|█████▋    | 54/96 [01:22<01:04,  1.53s/it]
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Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 21 Training:  57%|█████▋    | 55/96 [01:24<01:02,  1.53s/it]
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Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 21 Training:  58%|█████▊    | 56/96 [01:25<01:01,  1.53s/it]
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Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 21 Training:  59%|█████▉    | 57/96 [01:27<00:59,  1.53s/it]
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Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.02it/s]
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Epoch 21 Training:  60%|██████    | 58/96 [01:28<00:58,  1.53s/it]
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Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.02it/s]
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Epoch 21 Training:  61%|██████▏   | 59/96 [01:30<00:56,  1.53s/it]
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Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.02it/s]
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Epoch 21 Training:  62%|██████▎   | 60/96 [01:31<00:55,  1.53s/it]
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Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.02it/s]
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Epoch 21 Training:  64%|██████▎   | 61/96 [01:33<00:53,  1.53s/it]
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Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.02it/s]
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Epoch 21 Training:  65%|██████▍   | 62/96 [01:35<00:52,  1.53s/it]
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Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 21 Training:  66%|██████▌   | 63/96 [01:36<00:50,  1.53s/it]
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Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 21 Training:  67%|██████▋   | 64/96 [01:38<00:49,  1.53s/it]
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Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 21 Training:  68%|██████▊   | 65/96 [01:39<00:47,  1.53s/it]
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Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.02it/s]
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Epoch 21 Training:  69%|██████▉   | 66/96 [01:41<00:45,  1.53s/it]
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Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 21 Training:  70%|██████▉   | 67/96 [01:42<00:44,  1.53s/it]
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Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 21 Training:  71%|███████   | 68/96 [01:44<00:42,  1.53s/it]
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Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 21 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 21 Training:  72%|███████▏  | 69/96 [01:45<00:41,  1.53s/it]
+
Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 21 Training:  73%|███████▎  | 70/96 [01:47<00:39,  1.53s/it]
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Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 21 Training:  74%|███████▍  | 71/96 [01:48<00:38,  1.54s/it]
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Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 21 Training:  75%|███████▌  | 72/96 [01:50<00:36,  1.54s/it]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.02it/s]
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Epoch 21 Training:  76%|███████▌  | 73/96 [01:51<00:35,  1.53s/it]
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Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.02it/s]
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Epoch 21 Training:  77%|███████▋  | 74/96 [01:53<00:33,  1.53s/it]
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Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.02it/s]
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Epoch 21 Training:  78%|███████▊  | 75/96 [01:54<00:32,  1.53s/it]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.02it/s]
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Epoch 21 Training:  79%|███████▉  | 76/96 [01:56<00:30,  1.53s/it]
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Epoch 21 Training:  80%|████████  | 77/96 [01:58<00:29,  1.53s/it]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 21 Training:  81%|████████▏ | 78/96 [01:59<00:27,  1.53s/it]
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Epoch 21 Training:  82%|████████▏ | 79/96 [02:01<00:26,  1.53s/it]
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Epoch 21 Training:  83%|████████▎ | 80/96 [02:02<00:24,  1.53s/it]
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Epoch 21 Training:  84%|████████▍ | 81/96 [02:04<00:23,  1.53s/it]
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Epoch 21 Training:  85%|████████▌ | 82/96 [02:05<00:21,  1.53s/it]
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Epoch 21 Training:  86%|████████▋ | 83/96 [02:07<00:19,  1.53s/it]
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Epoch 21 Training:  88%|████████▊ | 84/96 [02:08<00:18,  1.53s/it]
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Epoch 21 Training:  89%|████████▊ | 85/96 [02:10<00:16,  1.53s/it]
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Epoch 21 Training:  90%|████████▉ | 86/96 [02:11<00:15,  1.53s/it]
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Epoch 21 Training:  91%|█████████ | 87/96 [02:13<00:13,  1.53s/it]
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Epoch 21 Training:  92%|█████████▏| 88/96 [02:14<00:12,  1.53s/it]
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Epoch 21 Training:  93%|█████████▎| 89/96 [02:16<00:10,  1.53s/it]
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Epoch 21 Training:  94%|█████████▍| 90/96 [02:17<00:09,  1.53s/it]
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Epoch 21 Training:  95%|█████████▍| 91/96 [02:19<00:07,  1.53s/it]
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Epoch 21 Training:  96%|█████████▌| 92/96 [02:21<00:06,  1.53s/it]
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Epoch 21 Training:  97%|█████████▋| 93/96 [02:22<00:04,  1.53s/it]
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Epoch 21 Training:  99%|█████████▉| 95/96 [02:25<00:01,  1.53s/it]
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Epoch 21 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s][2026-10-02 09:31:42] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0021 | Epoch 021] train/policy_loss=21.7809 train/unweighted_policy_loss=4.3562 train/loss=21.7809 train/grad_norm=0.0705
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Epoch 21 Training: 100%|██████████| 96/96 [02:27<00:00,  1.55s/it]
Epoch 21 Training: 100%|██████████| 96/96 [02:27<00:00,  1.53s/it]
+[2026-10-02 09:31:42] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0022 | Epoch 021] timing/rollout_seconds=179.3693 timing/feedback_seconds=5.4693 timing/optimization_seconds=147.8779 timing/cycle_seconds=330.0113
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+
Pointwise Rewards: domain:   0%|          | 0/6 [00:00<?, ?it/s]
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Pointwise Rewards: domain:  33%|███▎      | 2/6 [00:00<00:00,  4.06it/s]
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Pointwise Rewards: domain: 100%|██████████| 6/6 [00:01<00:00,  4.26it/s]
Pointwise Rewards: domain: 100%|██████████| 6/6 [00:01<00:00,  4.20it/s]
+
Pointwise Rewards: structure:   0%|          | 0/6 [00:00<?, ?it/s]
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Pointwise Rewards: structure:  33%|███▎      | 2/6 [00:00<00:00,  5.03it/s]
Pointwise Rewards: structure:  50%|█████     | 3/6 [00:00<00:00,  5.39it/s]
Pointwise Rewards: structure:  67%|██████▋   | 4/6 [00:00<00:00,  5.13it/s]
Pointwise Rewards: structure:  83%|████████▎ | 5/6 [00:00<00:00,  5.14it/s]
Pointwise Rewards: structure: 100%|██████████| 6/6 [00:01<00:00,  5.10it/s]
Pointwise Rewards: structure: 100%|██████████| 6/6 [00:01<00:00,  5.11it/s]
+[2026-10-02 09:34:45] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0022 | Epoch 022] train/reward_domain_mean=0.3545 train/reward_domain_std=0.0748 train/reward_structure_mean=0.7053 train/reward_structure_std=0.1520 train/reward_domain_group_std_mean=0.0191 train/reward_domain_group_std_max=0.0367 train/reward_domain_group_std_min=0.0088 train/reward_domain_group_mean_std=0.0721 train/reward_structure_group_std_mean=0.0068 train/reward_structure_group_std_max=0.0181 train/reward_structure_group_std_min=0.0019 train/reward_structure_group_mean_std=0.1518 train/reward_domain_zero_std_ratio=0 train/reward_structure_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.6546 train/adv_max=3.6327 train/adv_abs_mean=0.9409
+
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+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 22 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.95it/s]
+
                                                                
Epoch 22 Training:  20%|█▉        | 19/96 [00:29<02:01,  1.58s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 22 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.02it/s]
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Epoch 22 Training:  21%|██        | 20/96 [00:31<01:59,  1.57s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 22 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
+
                                                                
Epoch 22 Training:  22%|██▏       | 21/96 [00:32<01:57,  1.56s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 22 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
+
                                                                
Epoch 22 Training:  23%|██▎       | 22/96 [00:34<01:55,  1.56s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 22 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
+
                                                                
Epoch 22 Training:  24%|██▍       | 23/96 [00:35<01:53,  1.55s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 22 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.95it/s]
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Epoch 22 Training:  25%|██▌       | 24/96 [00:37<01:51,  1.56s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.15it/s]
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Epoch 22 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.94it/s]
+
                                                                
Epoch 22 Training:  26%|██▌       | 25/96 [00:39<01:50,  1.56s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 22 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.95it/s]
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Epoch 22 Training:  27%|██▋       | 26/96 [00:40<01:49,  1.56s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.15it/s]
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Epoch 22 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.95it/s]
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Epoch 22 Training:  28%|██▊       | 27/96 [00:42<01:47,  1.56s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 22 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.94it/s]
+
                                                                
Epoch 22 Training:  29%|██▉       | 28/96 [00:43<01:46,  1.56s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 22 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.95it/s]
+
                                                                
Epoch 22 Training:  30%|███       | 29/96 [00:45<01:44,  1.56s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 22 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.95it/s]
+
                                                                
Epoch 22 Training:  31%|███▏      | 30/96 [00:46<01:43,  1.56s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 22 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.95it/s]
+
                                                                
Epoch 22 Training:  32%|███▏      | 31/96 [00:48<01:41,  1.56s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 22 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.95it/s]
+
                                                                
Epoch 22 Training:  33%|███▎      | 32/96 [00:50<01:39,  1.56s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 22 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.94it/s]
+
                                                                
Epoch 22 Training:  34%|███▍      | 33/96 [00:51<01:38,  1.56s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 22 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
+
                                                                
Epoch 22 Training:  35%|███▌      | 34/96 [00:53<01:36,  1.56s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 22 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
+
                                                                
Epoch 22 Training:  36%|███▋      | 35/96 [00:54<01:34,  1.55s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 22 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
+
                                                                
Epoch 22 Training:  38%|███▊      | 36/96 [00:56<01:32,  1.55s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 22 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
+
                                                                
Epoch 22 Training:  39%|███▊      | 37/96 [00:57<01:31,  1.55s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 22 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.95it/s]
+
                                                                
Epoch 22 Training:  40%|███▉      | 38/96 [00:59<01:29,  1.55s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 22 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.95it/s]
+
                                                                
Epoch 22 Training:  41%|████      | 39/96 [01:00<01:28,  1.55s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 22 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
+
                                                                
Epoch 22 Training:  42%|████▏     | 40/96 [01:02<01:26,  1.55s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 22 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
+
                                                                
Epoch 22 Training:  43%|████▎     | 41/96 [01:03<01:25,  1.55s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 22 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.95it/s]
+
                                                                
Epoch 22 Training:  44%|████▍     | 42/96 [01:05<01:23,  1.55s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.15it/s]
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Epoch 22 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.95it/s]
+
                                                                
Epoch 22 Training:  45%|████▍     | 43/96 [01:07<01:22,  1.56s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 22 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.94it/s]
+
                                                                
Epoch 22 Training:  46%|████▌     | 44/96 [01:08<01:21,  1.56s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 22 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.95it/s]
+
                                                                
Epoch 22 Training:  47%|████▋     | 45/96 [01:10<01:19,  1.56s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 22 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.95it/s]
+
                                                                
Epoch 22 Training:  48%|████▊     | 46/96 [01:11<01:18,  1.56s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 22 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.72it/s]
+
                                                                
Epoch 22 Training:  49%|████▉     | 47/96 [01:13<01:18,  1.59s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 22 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.95it/s]
+
                                                                
Epoch 22 Training:  50%|█████     | 48/96 [01:14<01:16,  1.58s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 22 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.95it/s]
+
                                                                
Epoch 22 Training:  51%|█████     | 49/96 [01:16<01:14,  1.58s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 22 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.94it/s]
+
                                                                
Epoch 22 Training:  52%|█████▏    | 50/96 [01:18<01:12,  1.57s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 22 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.94it/s]
+
                                                                
Epoch 22 Training:  53%|█████▎    | 51/96 [01:19<01:10,  1.57s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 22 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.95it/s]
+
                                                                
Epoch 22 Training:  54%|█████▍    | 52/96 [01:21<01:09,  1.57s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 22 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.96it/s]
+
                                                                
Epoch 22 Training:  55%|█████▌    | 53/96 [01:22<01:07,  1.57s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 22 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.95it/s]
+
                                                                
Epoch 22 Training:  56%|█████▋    | 54/96 [01:24<01:05,  1.56s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 22 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.95it/s]
+
                                                                
Epoch 22 Training:  57%|█████▋    | 55/96 [01:25<01:04,  1.56s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 22 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.95it/s]
+
                                                                
Epoch 22 Training:  58%|█████▊    | 56/96 [01:27<01:02,  1.56s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 22 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.94it/s]
+
                                                                
Epoch 22 Training:  59%|█████▉    | 57/96 [01:29<01:00,  1.56s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 22 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.95it/s]
+
                                                                
Epoch 22 Training:  60%|██████    | 58/96 [01:30<00:59,  1.56s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 22 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.95it/s]
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Epoch 22 Training:  61%|██████▏   | 59/96 [01:32<00:57,  1.56s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 22 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
+
                                                                
Epoch 22 Training:  62%|██████▎   | 60/96 [01:33<00:56,  1.56s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 22 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.95it/s]
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Epoch 22 Training:  64%|██████▎   | 61/96 [01:35<00:54,  1.56s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 22 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.95it/s]
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Epoch 22 Training:  65%|██████▍   | 62/96 [01:36<00:53,  1.56s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 22 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.95it/s]
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Epoch 22 Training:  66%|██████▌   | 63/96 [01:38<00:52,  1.60s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 22 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.95it/s]
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Epoch 22 Training:  67%|██████▋   | 64/96 [01:40<00:50,  1.59s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 22 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.96it/s]
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Epoch 22 Training:  68%|██████▊   | 65/96 [01:41<00:48,  1.58s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 22 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.96it/s]
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Epoch 22 Training:  69%|██████▉   | 66/96 [01:43<00:47,  1.57s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.15it/s]
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Epoch 22 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.97it/s]
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Epoch 22 Training:  70%|██████▉   | 67/96 [01:44<00:45,  1.57s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 22 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.96it/s]
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Epoch 22 Training:  71%|███████   | 68/96 [01:46<00:43,  1.56s/it]
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Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 22 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.97it/s]
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Epoch 22 Training:  72%|███████▏  | 69/96 [01:47<00:42,  1.56s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 22 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.95it/s]
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Epoch 22 Training:  77%|███████▋  | 74/96 [01:55<00:34,  1.56s/it]
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Epoch 22 Training:  81%|████████▏ | 78/96 [02:01<00:28,  1.56s/it]
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Epoch 22 Training:  82%|████████▏ | 79/96 [02:03<00:26,  1.56s/it]
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Epoch 22 Training:  97%|█████████▋| 93/96 [02:25<00:04,  1.56s/it]
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Epoch 22 Training:  98%|█████████▊| 94/96 [02:26<00:03,  1.56s/it]
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Epoch 22 Training:  99%|█████████▉| 95/96 [02:28<00:01,  1.56s/it]
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Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s][2026-10-02 09:37:15] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0022 | Epoch 022] train/policy_loss=21.9713 train/unweighted_policy_loss=4.3943 train/loss=21.9713 train/grad_norm=0.0391
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Epoch 22 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.83it/s]
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Epoch 22 Training: 100%|██████████| 96/96 [02:29<00:00,  1.57s/it]
Epoch 22 Training: 100%|██████████| 96/96 [02:29<00:00,  1.56s/it]
+[2026-10-02 09:37:15] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0023 | Epoch 022] timing/rollout_seconds=179.2284 timing/feedback_seconds=5.6804 timing/optimization_seconds=150.6302 timing/cycle_seconds=332.7157
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+[2026-10-02 09:40:18] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0023 | Epoch 023] train/reward_domain_mean=0.3421 train/reward_domain_std=0.0763 train/reward_structure_mean=0.6944 train/reward_structure_std=0.1408 train/reward_domain_group_std_mean=0.0181 train/reward_domain_group_std_max=0.0415 train/reward_domain_group_std_min=0.0082 train/reward_domain_group_mean_std=0.0738 train/reward_structure_group_std_mean=0.0075 train/reward_structure_group_std_max=0.0290 train/reward_structure_group_std_min=0.0025 train/reward_structure_group_mean_std=0.1405 train/reward_domain_zero_std_ratio=0 train/reward_structure_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-3.9847 train/adv_max=4.7490 train/adv_abs_mean=0.9231
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Epoch 23 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s][2026-10-02 09:42:46] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0023 | Epoch 023] train/policy_loss=21.7713 train/unweighted_policy_loss=4.3543 train/loss=21.7713 train/grad_norm=0.0488
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+[2026-10-02 09:42:46] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0024 | Epoch 023] timing/rollout_seconds=179.3322 timing/feedback_seconds=5.6785 timing/optimization_seconds=148.8848 timing/cycle_seconds=331.0854
+
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+
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+[2026-10-02 09:45:49] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0024 | Epoch 024] train/reward_domain_mean=0.3735 train/reward_domain_std=0.0697 train/reward_structure_mean=0.7249 train/reward_structure_std=0.1289 train/reward_domain_group_std_mean=0.0181 train/reward_domain_group_std_max=0.0312 train/reward_domain_group_std_min=0.0076 train/reward_domain_group_mean_std=0.0671 train/reward_structure_group_std_mean=0.0078 train/reward_structure_group_std_max=0.0194 train/reward_structure_group_std_min=0.0022 train/reward_structure_group_mean_std=0.1286 train/reward_domain_zero_std_ratio=0 train/reward_structure_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.8741 train/adv_max=3.7711 train/adv_abs_mean=0.9362
+
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Epoch 24 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 24 Training:  23%|██▎       | 22/96 [00:33<01:54,  1.55s/it]
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Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 24 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.95it/s]
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Epoch 24 Training:  24%|██▍       | 23/96 [00:35<01:53,  1.55s/it]
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Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 24 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 24 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.97it/s]
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Epoch 24 Training:  25%|██▌       | 24/96 [00:37<01:51,  1.55s/it]
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Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 24 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.98it/s]
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Epoch 24 Training:  26%|██▌       | 25/96 [00:38<01:49,  1.55s/it]
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Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 24 Training:  27%|██▋       | 26/96 [00:40<01:48,  1.55s/it]
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Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 24 Training:  28%|██▊       | 27/96 [00:41<01:47,  1.56s/it]
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Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 24 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.95it/s]
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Epoch 24 Training:  29%|██▉       | 28/96 [00:43<01:45,  1.56s/it]
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Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 24 Training:  30%|███       | 29/96 [00:44<01:43,  1.55s/it]
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Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 24 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 24 Training:  31%|███▏      | 30/96 [00:46<01:42,  1.55s/it]
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Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 24 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 24 Training:  32%|███▏      | 31/96 [00:47<01:40,  1.54s/it]
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Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 24 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 24 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.02it/s]
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Epoch 24 Training:  33%|███▎      | 32/96 [00:49<01:38,  1.54s/it]
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Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 24 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 24 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 24 Training:  34%|███▍      | 33/96 [00:51<01:37,  1.54s/it]
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Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 24 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.95it/s]
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Epoch 24 Training:  35%|███▌      | 34/96 [00:52<01:35,  1.55s/it]
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Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 24 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 24 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.95it/s]
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Epoch 24 Training:  36%|███▋      | 35/96 [00:54<01:34,  1.55s/it]
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Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 24 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 24 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.95it/s]
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Epoch 24 Training:  38%|███▊      | 36/96 [00:55<01:33,  1.55s/it]
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Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 24 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 24 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.95it/s]
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Epoch 24 Training:  39%|███▊      | 37/96 [00:57<01:31,  1.56s/it]
+
Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 24 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 24 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 24 Training:  40%|███▉      | 38/96 [00:58<01:29,  1.55s/it]
+
Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 24 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 24 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.98it/s]
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Epoch 24 Training:  41%|████      | 39/96 [01:00<01:28,  1.55s/it]
+
Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 24 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 24 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 24 Training:  42%|████▏     | 40/96 [01:01<01:26,  1.55s/it]
+
Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 24 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 24 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.02it/s]
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Epoch 24 Training:  43%|████▎     | 41/96 [01:03<01:24,  1.54s/it]
+
Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 24 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 24 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 24 Training:  44%|████▍     | 42/96 [01:04<01:23,  1.54s/it]
+
Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 24 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 24 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.96it/s]
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Epoch 24 Training:  45%|████▍     | 43/96 [01:06<01:21,  1.55s/it]
+
Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 24 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 24 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.95it/s]
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Epoch 24 Training:  46%|████▌     | 44/96 [01:08<01:20,  1.55s/it]
+
Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 24 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 24 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.02it/s]
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Epoch 24 Training:  47%|████▋     | 45/96 [01:09<01:18,  1.55s/it]
+
Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 24 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 24 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.98it/s]
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Epoch 24 Training:  48%|████▊     | 46/96 [01:11<01:17,  1.55s/it]
+
Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 24 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 24 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.02it/s]
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Epoch 24 Training:  49%|████▉     | 47/96 [01:12<01:15,  1.54s/it]
+
Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 24 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 24 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 24 Training:  50%|█████     | 48/96 [01:14<01:14,  1.54s/it]
+
Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 24 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 24 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.94it/s]
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Epoch 24 Training:  51%|█████     | 49/96 [01:15<01:12,  1.55s/it]
+
Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 24 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 24 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.94it/s]
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Epoch 24 Training:  52%|█████▏    | 50/96 [01:17<01:11,  1.55s/it]
+
Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 24 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.95it/s]
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Epoch 24 Training:  53%|█████▎    | 51/96 [01:18<01:10,  1.56s/it]
+
Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 24 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 24 Training:  54%|█████▍    | 52/96 [01:20<01:08,  1.55s/it]
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Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 24 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 24 Training:  55%|█████▌    | 53/96 [01:21<01:06,  1.55s/it]
+
Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 24 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 24 Training:  56%|█████▋    | 54/96 [01:23<01:04,  1.54s/it]
+
Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 24 Training:  57%|█████▋    | 55/96 [01:25<01:03,  1.54s/it]
+
Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 24 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 24 Training:  58%|█████▊    | 56/96 [01:26<01:01,  1.54s/it]
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Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 24 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.15it/s]
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Epoch 24 Training:  59%|█████▉    | 57/96 [01:28<01:00,  1.54s/it]
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Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 24 Training:  60%|██████    | 58/96 [01:29<00:58,  1.54s/it]
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Epoch 24 Training:  61%|██████▏   | 59/96 [01:31<00:56,  1.54s/it]
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Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 24 Training:  62%|██████▎   | 60/96 [01:32<00:55,  1.54s/it]
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Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 24 Training:  64%|██████▎   | 61/96 [01:34<00:53,  1.54s/it]
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Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 24 Training:  65%|██████▍   | 62/96 [01:35<00:52,  1.54s/it]
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Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 24 Training:  66%|██████▌   | 63/96 [01:37<00:50,  1.54s/it]
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Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 24 Training:  67%|██████▋   | 64/96 [01:38<00:49,  1.54s/it]
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Epoch 24 Training:  68%|██████▊   | 65/96 [01:40<00:47,  1.54s/it]
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Epoch 24 Training:  69%|██████▉   | 66/96 [01:42<00:46,  1.55s/it]
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Epoch 24 Training:  70%|██████▉   | 67/96 [01:43<00:44,  1.55s/it]
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Epoch 24 Training:  71%|███████   | 68/96 [01:45<00:43,  1.55s/it]
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Epoch 24 Training:  72%|███████▏  | 69/96 [01:46<00:41,  1.55s/it]
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Epoch 24 Training:  73%|███████▎  | 70/96 [01:48<00:40,  1.55s/it]
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Epoch 24 Training:  74%|███████▍  | 71/96 [01:49<00:38,  1.55s/it]
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Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 24 Training:  75%|███████▌  | 72/96 [01:51<00:37,  1.55s/it]
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Epoch 24 Training:  99%|█████████▉| 95/96 [02:27<00:01,  1.56s/it]
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Epoch 24 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s][2026-10-02 09:48:18] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0024 | Epoch 024] train/policy_loss=21.7224 train/unweighted_policy_loss=4.3445 train/loss=21.7224 train/grad_norm=0.0490
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+[2026-10-02 09:48:18] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0025 | Epoch 024] timing/rollout_seconds=179.3338 timing/feedback_seconds=6.0076 timing/optimization_seconds=149.4659 timing/cycle_seconds=331.8848
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+[2026-10-02 09:48:35] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0025 | Epoch 025] eval/places_domain_rl/reward_domain_mean=0.3845 eval/places_domain_rl/reward_domain_std=0.0530 eval/places_domain_rl/reward_ssim_to_stage1_mean=0.5494 eval/places_domain_rl/reward_ssim_to_stage1_std=0.1313 eval/places_domain_rl/reward_structure_mean=0.7016 eval/places_domain_rl/reward_structure_std=0.1369
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Epoch 25 Sampling:  93%|█████████▎| 89/96 [02:44<00:12,  1.84s/it]
Epoch 25 Sampling:  94%|█████████▍| 90/96 [02:46<00:11,  1.84s/it]
Epoch 25 Sampling:  95%|█████████▍| 91/96 [02:47<00:09,  1.84s/it]
Epoch 25 Sampling:  96%|█████████▌| 92/96 [02:49<00:07,  1.84s/it]
Epoch 25 Sampling:  97%|█████████▋| 93/96 [02:51<00:05,  1.84s/it]
Epoch 25 Sampling:  98%|█████████▊| 94/96 [02:53<00:03,  1.84s/it]
Epoch 25 Sampling:  99%|█████████▉| 95/96 [02:55<00:01,  1.84s/it]
Epoch 25 Sampling: 100%|██████████| 96/96 [02:57<00:00,  1.84s/it]
Epoch 25 Sampling: 100%|██████████| 96/96 [02:57<00:00,  1.85s/it]
+
Pointwise Rewards: domain:   0%|          | 0/6 [00:00<?, ?it/s]
Pointwise Rewards: domain:  17%|█▋        | 1/6 [00:00<00:01,  3.95it/s]
Pointwise Rewards: domain:  33%|███▎      | 2/6 [00:00<00:01,  3.97it/s]
Pointwise Rewards: domain:  50%|█████     | 3/6 [00:00<00:00,  4.04it/s]
Pointwise Rewards: domain:  67%|██████▋   | 4/6 [00:00<00:00,  4.04it/s]
Pointwise Rewards: domain:  83%|████████▎ | 5/6 [00:01<00:00,  4.14it/s]
Pointwise Rewards: domain: 100%|██████████| 6/6 [00:01<00:00,  4.15it/s]
Pointwise Rewards: domain: 100%|██████████| 6/6 [00:01<00:00,  4.10it/s]
+
Pointwise Rewards: structure:   0%|          | 0/6 [00:00<?, ?it/s]
Pointwise Rewards: structure:  17%|█▋        | 1/6 [00:00<00:01,  4.06it/s]
Pointwise Rewards: structure:  33%|███▎      | 2/6 [00:00<00:00,  4.94it/s]
Pointwise Rewards: structure:  50%|█████     | 3/6 [00:00<00:00,  5.25it/s]
Pointwise Rewards: structure:  67%|██████▋   | 4/6 [00:00<00:00,  5.17it/s]
Pointwise Rewards: structure:  83%|████████▎ | 5/6 [00:01<00:00,  4.62it/s]
Pointwise Rewards: structure: 100%|██████████| 6/6 [00:01<00:00,  5.03it/s]
Pointwise Rewards: structure: 100%|██████████| 6/6 [00:01<00:00,  4.94it/s]
+[2026-10-02 09:51:38] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0025 | Epoch 025] train/reward_domain_mean=0.3877 train/reward_domain_std=0.0644 train/reward_structure_mean=0.7213 train/reward_structure_std=0.1275 train/reward_domain_group_std_mean=0.0175 train/reward_domain_group_std_max=0.0385 train/reward_domain_group_std_min=0.0061 train/reward_domain_group_mean_std=0.0616 train/reward_structure_group_std_mean=0.0085 train/reward_structure_group_std_max=0.0235 train/reward_structure_group_std_min=0.0019 train/reward_structure_group_mean_std=0.1271 train/reward_domain_zero_std_ratio=0 train/reward_structure_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.3102 train/adv_max=3.8613 train/adv_abs_mean=1.0234
+
Epoch 25 Training:   0%|          | 0/96 [00:00<?, ?it/s]
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Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 25 Training:   1%|          | 1/96 [00:01<02:20,  1.48s/it]
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Epoch 25 Training:   2%|▏         | 2/96 [00:03<02:22,  1.51s/it]
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Epoch 25 Training:   3%|▎         | 3/96 [00:04<02:21,  1.52s/it]
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Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 25 Training:   4%|▍         | 4/96 [00:06<02:20,  1.53s/it]
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Epoch 25 Training:   5%|▌         | 5/96 [00:07<02:19,  1.53s/it]
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Epoch 25 Training:   6%|▋         | 6/96 [00:09<02:18,  1.53s/it]
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Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 25 Training:   7%|▋         | 7/96 [00:10<02:16,  1.53s/it]
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Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 25 Training:   8%|▊         | 8/96 [00:12<02:14,  1.53s/it]
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Epoch 25 Training:   9%|▉         | 9/96 [00:13<02:13,  1.53s/it]
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Epoch 25 Training:  10%|█         | 10/96 [00:15<02:11,  1.53s/it]
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Epoch 25 Training:  11%|█▏        | 11/96 [00:16<02:10,  1.54s/it]
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Epoch 25 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
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Epoch 25 Training:  12%|█▎        | 12/96 [00:18<02:09,  1.54s/it]
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Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 25 Training:  14%|█▎        | 13/96 [00:19<02:08,  1.54s/it]
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Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 25 Training:  15%|█▍        | 14/96 [00:21<02:06,  1.54s/it]
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Epoch 25 Training:  16%|█▌        | 15/96 [00:23<02:04,  1.54s/it]
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Epoch 25 Training:  17%|█▋        | 16/96 [00:24<02:03,  1.54s/it]
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Epoch 25 Training:  18%|█▊        | 17/96 [00:26<02:01,  1.54s/it]
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Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 25 Training:  19%|█▉        | 18/96 [00:27<01:59,  1.54s/it]
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Epoch 25 Training:  20%|█▉        | 19/96 [00:29<01:58,  1.54s/it]
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Epoch 25 Training:  21%|██        | 20/96 [00:30<01:56,  1.54s/it]
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Epoch 25 Training:  22%|██▏       | 21/96 [00:32<01:55,  1.54s/it]
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Epoch 25 Training:  23%|██▎       | 22/96 [00:33<01:53,  1.54s/it]
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Epoch 25 Training:  24%|██▍       | 23/96 [00:35<01:52,  1.54s/it]
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Epoch 25 Training:  25%|██▌       | 24/96 [00:36<01:50,  1.53s/it]
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Epoch 25 Training:  26%|██▌       | 25/96 [00:38<01:48,  1.54s/it]
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Epoch 25 Training:  27%|██▋       | 26/96 [00:39<01:47,  1.53s/it]
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Epoch 25 Training:  28%|██▊       | 27/96 [00:41<01:45,  1.53s/it]
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Epoch 25 Training:  29%|██▉       | 28/96 [00:42<01:44,  1.53s/it]
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Epoch 25 Training:  30%|███       | 29/96 [00:44<01:42,  1.53s/it]
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Epoch 25 Training:  31%|███▏      | 30/96 [00:46<01:41,  1.53s/it]
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Epoch 25 Training:  32%|███▏      | 31/96 [00:47<01:39,  1.53s/it]
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Epoch 25 Training:  33%|███▎      | 32/96 [00:49<01:38,  1.54s/it]
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Epoch 25 Training:  34%|███▍      | 33/96 [00:50<01:36,  1.54s/it]
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Epoch 25 Training:  35%|███▌      | 34/96 [00:52<01:35,  1.54s/it]
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Epoch 25 Training:  36%|███▋      | 35/96 [00:53<01:33,  1.54s/it]
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Epoch 25 Training:  38%|███▊      | 36/96 [00:55<01:32,  1.54s/it]
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Epoch 25 Training:  39%|███▊      | 37/96 [00:56<01:30,  1.54s/it]
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Epoch 25 Training:  40%|███▉      | 38/96 [00:58<01:29,  1.54s/it]
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Epoch 25 Training:  41%|████      | 39/96 [00:59<01:27,  1.54s/it]
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Epoch 25 Training:  42%|████▏     | 40/96 [01:01<01:26,  1.54s/it]
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Epoch 25 Training:  43%|████▎     | 41/96 [01:02<01:24,  1.54s/it]
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Epoch 25 Training:  44%|████▍     | 42/96 [01:04<01:22,  1.54s/it]
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Epoch 25 Training:  45%|████▍     | 43/96 [01:06<01:21,  1.54s/it]
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Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 25 Training:  46%|████▌     | 44/96 [01:07<01:19,  1.54s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 25 Training:  47%|████▋     | 45/96 [01:09<01:18,  1.54s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 25 Training:  48%|████▊     | 46/96 [01:10<01:16,  1.54s/it]
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Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 25 Training:  49%|████▉     | 47/96 [01:12<01:15,  1.54s/it]
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Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 25 Training:  50%|█████     | 48/96 [01:13<01:13,  1.54s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 25 Training:  51%|█████     | 49/96 [01:15<01:12,  1.53s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 25 Training:  52%|█████▏    | 50/96 [01:16<01:10,  1.53s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.98it/s]
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Epoch 25 Training:  53%|█████▎    | 51/96 [01:18<01:09,  1.54s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 25 Training:  54%|█████▍    | 52/96 [01:19<01:07,  1.54s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
+
                                                                
Epoch 25 Training:  55%|█████▌    | 53/96 [01:21<01:06,  1.54s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
+
                                                                
Epoch 25 Training:  56%|█████▋    | 54/96 [01:22<01:04,  1.54s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
+
                                                                
Epoch 25 Training:  57%|█████▋    | 55/96 [01:24<01:03,  1.54s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
+
                                                                
Epoch 25 Training:  58%|█████▊    | 56/96 [01:26<01:01,  1.54s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
+
                                                                
Epoch 25 Training:  59%|█████▉    | 57/96 [01:27<00:59,  1.54s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
+
                                                                
Epoch 25 Training:  60%|██████    | 58/96 [01:29<00:58,  1.54s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
+
                                                                
Epoch 25 Training:  61%|██████▏   | 59/96 [01:30<00:56,  1.54s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
+
                                                                
Epoch 25 Training:  62%|██████▎   | 60/96 [01:32<00:55,  1.54s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
+
                                                                
Epoch 25 Training:  64%|██████▎   | 61/96 [01:33<00:53,  1.54s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
+
                                                                
Epoch 25 Training:  65%|██████▍   | 62/96 [01:35<00:52,  1.54s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
+
                                                                
Epoch 25 Training:  66%|██████▌   | 63/96 [01:36<00:50,  1.54s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
+
                                                                
Epoch 25 Training:  67%|██████▋   | 64/96 [01:38<00:49,  1.54s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
+
                                                                
Epoch 25 Training:  68%|██████▊   | 65/96 [01:39<00:47,  1.54s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
+
                                                                
Epoch 25 Training:  69%|██████▉   | 66/96 [01:41<00:46,  1.54s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
+
                                                                
Epoch 25 Training:  70%|██████▉   | 67/96 [01:42<00:44,  1.54s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
+
                                                                
Epoch 25 Training:  71%|███████   | 68/96 [01:44<00:43,  1.54s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
+
                                                                
Epoch 25 Training:  72%|███████▏  | 69/96 [01:45<00:41,  1.54s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.15it/s]
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Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.02it/s]
+
                                                                
Epoch 25 Training:  73%|███████▎  | 70/96 [01:47<00:39,  1.54s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
+
                                                                
Epoch 25 Training:  74%|███████▍  | 71/96 [01:49<00:38,  1.54s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
+
                                                                
Epoch 25 Training:  75%|███████▌  | 72/96 [01:50<00:36,  1.54s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
+
                                                                
Epoch 25 Training:  76%|███████▌  | 73/96 [01:52<00:35,  1.54s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
+
                                                                
Epoch 25 Training:  77%|███████▋  | 74/96 [01:53<00:33,  1.54s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
+
                                                                
Epoch 25 Training:  78%|███████▊  | 75/96 [01:55<00:32,  1.54s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
+
                                                                
Epoch 25 Training:  79%|███████▉  | 76/96 [01:56<00:30,  1.54s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
+
                                                                
Epoch 25 Training:  80%|████████  | 77/96 [01:58<00:29,  1.54s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
+
                                                                
Epoch 25 Training:  81%|████████▏ | 78/96 [01:59<00:27,  1.54s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
+
                                                                
Epoch 25 Training:  82%|████████▏ | 79/96 [02:01<00:26,  1.54s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
+
                                                                
Epoch 25 Training:  83%|████████▎ | 80/96 [02:02<00:24,  1.54s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
+
                                                                
Epoch 25 Training:  84%|████████▍ | 81/96 [02:04<00:23,  1.54s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 25 Training:  85%|████████▌ | 82/96 [02:05<00:21,  1.54s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 25 Training:  86%|████████▋ | 83/96 [02:07<00:19,  1.54s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 25 Training:  88%|████████▊ | 84/96 [02:09<00:18,  1.54s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
+
                                                                
Epoch 25 Training:  89%|████████▊ | 85/96 [02:10<00:16,  1.54s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 25 Training:  90%|████████▉ | 86/96 [02:12<00:15,  1.54s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 25 Training:  91%|█████████ | 87/96 [02:13<00:13,  1.54s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 25 Training:  92%|█████████▏| 88/96 [02:15<00:12,  1.54s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
+
                                                                
Epoch 25 Training:  93%|█████████▎| 89/96 [02:16<00:10,  1.54s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 25 Training:  94%|█████████▍| 90/96 [02:18<00:09,  1.54s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 25 Training:  95%|█████████▍| 91/96 [02:19<00:07,  1.54s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 25 Training:  96%|█████████▌| 92/96 [02:21<00:06,  1.54s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 25 Training:  97%|█████████▋| 93/96 [02:22<00:04,  1.54s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 25 Training:  98%|█████████▊| 94/96 [02:24<00:03,  1.54s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s][2026-10-02 09:54:06] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0025 | Epoch 025] train/policy_loss=21.5019 train/unweighted_policy_loss=4.3004 train/loss=21.5019 train/grad_norm=0.0527
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+[2026-10-02 09:54:06] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0026 | Epoch 025] timing/rollout_seconds=179.2743 timing/feedback_seconds=5.2331 timing/optimization_seconds=148.2744 timing/cycle_seconds=330.6788
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Pointwise Rewards: domain:   0%|          | 0/6 [00:00<?, ?it/s]
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Pointwise Rewards: domain: 100%|██████████| 6/6 [00:01<00:00,  4.17it/s]
+
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Pointwise Rewards: structure:  50%|█████     | 3/6 [00:00<00:00,  5.15it/s]
Pointwise Rewards: structure:  67%|██████▋   | 4/6 [00:00<00:00,  5.20it/s]
Pointwise Rewards: structure:  83%|████████▎ | 5/6 [00:00<00:00,  5.13it/s]
Pointwise Rewards: structure: 100%|██████████| 6/6 [00:01<00:00,  5.09it/s]
Pointwise Rewards: structure: 100%|██████████| 6/6 [00:01<00:00,  5.10it/s]
+[2026-10-02 09:57:09] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0026 | Epoch 026] train/reward_domain_mean=0.3960 train/reward_domain_std=0.0631 train/reward_structure_mean=0.6581 train/reward_structure_std=0.1681 train/reward_domain_group_std_mean=0.0166 train/reward_domain_group_std_max=0.0363 train/reward_domain_group_std_min=0.0080 train/reward_domain_group_mean_std=0.0605 train/reward_structure_group_std_mean=0.0077 train/reward_structure_group_std_max=0.0193 train/reward_structure_group_std_min=0.0016 train/reward_structure_group_mean_std=0.1679 train/reward_domain_zero_std_ratio=0 train/reward_structure_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.0477 train/adv_max=4.0855 train/adv_abs_mean=0.9796
+
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Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
+
                                                                
Epoch 26 Training:  21%|██        | 20/96 [00:30<01:57,  1.54s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
+
                                                                
Epoch 26 Training:  22%|██▏       | 21/96 [00:32<01:55,  1.54s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
+
                                                                
Epoch 26 Training:  23%|██▎       | 22/96 [00:33<01:53,  1.54s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
+
                                                                
Epoch 26 Training:  24%|██▍       | 23/96 [00:35<01:52,  1.54s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.15it/s]
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Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
+
                                                                
Epoch 26 Training:  25%|██▌       | 24/96 [00:36<01:50,  1.54s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
+
                                                                
Epoch 26 Training:  26%|██▌       | 25/96 [00:38<01:49,  1.54s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
+
                                                                
Epoch 26 Training:  27%|██▋       | 26/96 [00:39<01:47,  1.54s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
+
                                                                
Epoch 26 Training:  28%|██▊       | 27/96 [00:41<01:46,  1.54s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
+
                                                                
Epoch 26 Training:  29%|██▉       | 28/96 [00:43<01:44,  1.54s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
+
                                                                
Epoch 26 Training:  30%|███       | 29/96 [00:44<01:43,  1.54s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
+
                                                                
Epoch 26 Training:  31%|███▏      | 30/96 [00:46<01:41,  1.54s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
+
                                                                
Epoch 26 Training:  32%|███▏      | 31/96 [00:47<01:39,  1.54s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
+
                                                                
Epoch 26 Training:  33%|███▎      | 32/96 [00:49<01:38,  1.54s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
+
                                                                
Epoch 26 Training:  34%|███▍      | 33/96 [00:50<01:36,  1.54s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
+
                                                                
Epoch 26 Training:  35%|███▌      | 34/96 [00:52<01:35,  1.54s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
+
                                                                
Epoch 26 Training:  36%|███▋      | 35/96 [00:53<01:33,  1.54s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
+
                                                                
Epoch 26 Training:  38%|███▊      | 36/96 [00:55<01:32,  1.54s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
+
                                                                
Epoch 26 Training:  39%|███▊      | 37/96 [00:56<01:30,  1.54s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
+
                                                                
Epoch 26 Training:  40%|███▉      | 38/96 [00:58<01:29,  1.54s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
+
                                                                
Epoch 26 Training:  41%|████      | 39/96 [00:59<01:27,  1.54s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
+
                                                                
Epoch 26 Training:  42%|████▏     | 40/96 [01:01<01:26,  1.54s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
+
                                                                
Epoch 26 Training:  43%|████▎     | 41/96 [01:03<01:24,  1.54s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.15it/s]
+
Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
+
                                                                
Epoch 26 Training:  44%|████▍     | 42/96 [01:04<01:23,  1.54s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
+
                                                                
Epoch 26 Training:  45%|████▍     | 43/96 [01:06<01:21,  1.54s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
+
                                                                
Epoch 26 Training:  46%|████▌     | 44/96 [01:07<01:19,  1.54s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
+
                                                                
Epoch 26 Training:  47%|████▋     | 45/96 [01:09<01:18,  1.54s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
+
                                                                
Epoch 26 Training:  48%|████▊     | 46/96 [01:10<01:16,  1.54s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
+
                                                                
Epoch 26 Training:  49%|████▉     | 47/96 [01:12<01:15,  1.54s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
+
                                                                
Epoch 26 Training:  50%|█████     | 48/96 [01:13<01:13,  1.54s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
+
                                                                
Epoch 26 Training:  51%|█████     | 49/96 [01:15<01:12,  1.54s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
+
                                                                
Epoch 26 Training:  52%|█████▏    | 50/96 [01:16<01:10,  1.54s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.02it/s]
+
                                                                
Epoch 26 Training:  53%|█████▎    | 51/96 [01:18<01:09,  1.54s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
+
                                                                
Epoch 26 Training:  54%|█████▍    | 52/96 [01:19<01:07,  1.54s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
+
                                                                
Epoch 26 Training:  55%|█████▌    | 53/96 [01:21<01:06,  1.54s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
+
                                                                
Epoch 26 Training:  56%|█████▋    | 54/96 [01:23<01:04,  1.54s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
+
                                                                
Epoch 26 Training:  57%|█████▋    | 55/96 [01:24<01:03,  1.54s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
+
                                                                
Epoch 26 Training:  58%|█████▊    | 56/96 [01:26<01:01,  1.54s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
+
                                                                
Epoch 26 Training:  59%|█████▉    | 57/96 [01:27<01:00,  1.54s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
+
                                                                
Epoch 26 Training:  60%|██████    | 58/96 [01:29<00:58,  1.54s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
+
                                                                
Epoch 26 Training:  61%|██████▏   | 59/96 [01:30<00:56,  1.54s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
+
                                                                
Epoch 26 Training:  62%|██████▎   | 60/96 [01:32<00:55,  1.54s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
+
                                                                
Epoch 26 Training:  64%|██████▎   | 61/96 [01:33<00:53,  1.54s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
+
                                                                
Epoch 26 Training:  65%|██████▍   | 62/96 [01:35<00:52,  1.54s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
+
                                                                
Epoch 26 Training:  66%|██████▌   | 63/96 [01:36<00:50,  1.54s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
+
                                                                
Epoch 26 Training:  67%|██████▋   | 64/96 [01:38<00:49,  1.54s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
+
                                                                
Epoch 26 Training:  68%|██████▊   | 65/96 [01:40<00:47,  1.54s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
+
                                                                
Epoch 26 Training:  69%|██████▉   | 66/96 [01:41<00:46,  1.54s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
+
                                                                
Epoch 26 Training:  70%|██████▉   | 67/96 [01:43<00:44,  1.54s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
+
                                                                
Epoch 26 Training:  71%|███████   | 68/96 [01:44<00:43,  1.54s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
+
                                                                
Epoch 26 Training:  72%|███████▏  | 69/96 [01:46<00:41,  1.54s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
+
                                                                
Epoch 26 Training:  73%|███████▎  | 70/96 [01:47<00:39,  1.54s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 26 Training:  74%|███████▍  | 71/96 [01:49<00:38,  1.54s/it]
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Epoch 26 Training:  75%|███████▌  | 72/96 [01:50<00:36,  1.54s/it]
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Epoch 26 Training:  76%|███████▌  | 73/96 [01:52<00:35,  1.54s/it]
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Epoch 26 Training:  77%|███████▋  | 74/96 [01:53<00:33,  1.54s/it]
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Epoch 26 Training:  78%|███████▊  | 75/96 [01:55<00:32,  1.54s/it]
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Epoch 26 Training:  79%|███████▉  | 76/96 [01:56<00:30,  1.54s/it]
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Epoch 26 Training:  80%|████████  | 77/96 [01:58<00:29,  1.54s/it]
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Epoch 26 Training:  81%|████████▏ | 78/96 [02:00<00:27,  1.54s/it]
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Epoch 26 Training:  82%|████████▏ | 79/96 [02:01<00:26,  1.54s/it]
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Epoch 26 Training:  83%|████████▎ | 80/96 [02:03<00:24,  1.54s/it]
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Epoch 26 Training:  90%|████████▉ | 86/96 [02:12<00:15,  1.54s/it]
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Epoch 26 Training:  92%|█████████▏| 88/96 [02:15<00:12,  1.54s/it]
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Epoch 26 Training:  93%|█████████▎| 89/96 [02:16<00:10,  1.54s/it]
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Epoch 26 Training:  97%|█████████▋| 93/96 [02:23<00:04,  1.54s/it]
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Epoch 26 Training:  98%|█████████▊| 94/96 [02:24<00:03,  1.54s/it]
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Epoch 26 Training:  99%|█████████▉| 95/96 [02:26<00:01,  1.55s/it]
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Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s][2026-10-02 09:59:37] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0026 | Epoch 026] train/policy_loss=21.5675 train/unweighted_policy_loss=4.3135 train/loss=21.5675 train/grad_norm=0.0660
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Epoch 26 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.87it/s]
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Epoch 26 Training: 100%|██████████| 96/96 [02:27<00:00,  1.57s/it]
Epoch 26 Training: 100%|██████████| 96/96 [02:27<00:00,  1.54s/it]
+[2026-10-02 09:59:37] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0027 | Epoch 026] timing/rollout_seconds=179.2918 timing/feedback_seconds=5.8534 timing/optimization_seconds=148.7806 timing/cycle_seconds=331.1475
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+
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Pointwise Rewards: structure: 100%|██████████| 6/6 [00:01<00:00,  5.03it/s]
Pointwise Rewards: structure: 100%|██████████| 6/6 [00:01<00:00,  5.00it/s]
+[2026-10-02 10:02:40] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0027 | Epoch 027] train/reward_domain_mean=0.3903 train/reward_domain_std=0.0552 train/reward_structure_mean=0.7018 train/reward_structure_std=0.1355 train/reward_domain_group_std_mean=0.0170 train/reward_domain_group_std_max=0.0324 train/reward_domain_group_std_min=0.0070 train/reward_domain_group_mean_std=0.0523 train/reward_structure_group_std_mean=0.0066 train/reward_structure_group_std_max=0.0148 train/reward_structure_group_std_min=0.0024 train/reward_structure_group_mean_std=0.1353 train/reward_domain_zero_std_ratio=0 train/reward_structure_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.8927 train/adv_max=3.3261 train/adv_abs_mean=0.9582
+
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Epoch 27 Training:   1%|          | 1/96 [00:01<02:20,  1.48s/it]
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Epoch 27 Training:   7%|▋         | 7/96 [00:10<02:16,  1.54s/it]
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Epoch 27 Training:   9%|▉         | 9/96 [00:13<02:13,  1.54s/it]
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Epoch 27 Training:  10%|█         | 10/96 [00:15<02:12,  1.54s/it]
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Epoch 27 Training:  11%|█▏        | 11/96 [00:16<02:10,  1.54s/it]
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Epoch 27 Training:  14%|█▎        | 13/96 [00:19<02:07,  1.54s/it]
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Epoch 27 Training:  15%|█▍        | 14/96 [00:21<02:05,  1.54s/it]
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Epoch 27 Training:  16%|█▌        | 15/96 [00:23<02:04,  1.54s/it]
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Epoch 27 Training:  17%|█▋        | 16/96 [00:24<02:02,  1.54s/it]
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Epoch 27 Training:  18%|█▊        | 17/96 [00:26<02:01,  1.54s/it]
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Epoch 27 Training:  19%|█▉        | 18/96 [00:27<01:59,  1.54s/it]
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Epoch 27 Training:  23%|██▎       | 22/96 [00:33<01:53,  1.54s/it]
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Epoch 27 Training:  24%|██▍       | 23/96 [00:35<01:52,  1.54s/it]
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Epoch 27 Training:  25%|██▌       | 24/96 [00:36<01:50,  1.54s/it]
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Epoch 27 Training:  26%|██▌       | 25/96 [00:38<01:49,  1.54s/it]
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Epoch 27 Training:  27%|██▋       | 26/96 [00:39<01:47,  1.54s/it]
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Epoch 27 Training:  28%|██▊       | 27/96 [00:41<01:46,  1.54s/it]
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Epoch 27 Training:  29%|██▉       | 28/96 [00:43<01:44,  1.54s/it]
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Epoch 27 Training:  33%|███▎      | 32/96 [00:49<01:38,  1.54s/it]
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Epoch 27 Training:  34%|███▍      | 33/96 [00:50<01:36,  1.54s/it]
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Epoch 27 Training:  49%|████▉     | 47/96 [01:12<01:15,  1.54s/it]
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Epoch 27 Training:  50%|█████     | 48/96 [01:13<01:13,  1.54s/it]
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Epoch 27 Training:  51%|█████     | 49/96 [01:15<01:12,  1.54s/it]
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Epoch 27 Training:  52%|█████▏    | 50/96 [01:16<01:10,  1.54s/it]
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Epoch 27 Training:  53%|█████▎    | 51/96 [01:18<01:09,  1.54s/it]
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Epoch 27 Training:  54%|█████▍    | 52/96 [01:19<01:07,  1.54s/it]
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Epoch 27 Training:  55%|█████▌    | 53/96 [01:21<01:06,  1.54s/it]
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Epoch 27 Training:  56%|█████▋    | 54/96 [01:22<01:04,  1.54s/it]
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Epoch 27 Training:  57%|█████▋    | 55/96 [01:24<01:03,  1.54s/it]
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Epoch 27 Training:  58%|█████▊    | 56/96 [01:26<01:01,  1.54s/it]
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Epoch 27 Training:  59%|█████▉    | 57/96 [01:27<00:59,  1.54s/it]
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Epoch 27 Training:  60%|██████    | 58/96 [01:29<00:58,  1.54s/it]
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Epoch 27 Training:  61%|██████▏   | 59/96 [01:30<00:56,  1.54s/it]
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Epoch 27 Training:  62%|██████▎   | 60/96 [01:32<00:55,  1.54s/it]
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Epoch 27 Training:  64%|██████▎   | 61/96 [01:33<00:53,  1.54s/it]
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Epoch 27 Training:  65%|██████▍   | 62/96 [01:35<00:52,  1.54s/it]
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Epoch 27 Training:  66%|██████▌   | 63/96 [01:36<00:50,  1.54s/it]
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Epoch 27 Training:  67%|██████▋   | 64/96 [01:38<00:49,  1.54s/it]
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Epoch 27 Training:  68%|██████▊   | 65/96 [01:39<00:47,  1.54s/it]
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Epoch 27 Training:  69%|██████▉   | 66/96 [01:41<00:46,  1.54s/it]
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Epoch 27 Training:  70%|██████▉   | 67/96 [01:42<00:44,  1.54s/it]
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Epoch 27 Training:  71%|███████   | 68/96 [01:44<00:43,  1.54s/it]
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Epoch 27 Training:  72%|███████▏  | 69/96 [01:46<00:41,  1.54s/it]
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Epoch 27 Training:  73%|███████▎  | 70/96 [01:47<00:39,  1.54s/it]
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Epoch 27 Training:  74%|███████▍  | 71/96 [01:49<00:38,  1.54s/it]
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Epoch 27 Training:  75%|███████▌  | 72/96 [01:50<00:36,  1.54s/it]
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Epoch 27 Training:  76%|███████▌  | 73/96 [01:52<00:35,  1.54s/it]
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Epoch 27 Training:  77%|███████▋  | 74/96 [01:53<00:33,  1.54s/it]
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Epoch 27 Training:  78%|███████▊  | 75/96 [01:55<00:32,  1.54s/it]
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Epoch 27 Training:  79%|███████▉  | 76/96 [01:56<00:30,  1.54s/it]
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Epoch 27 Training:  80%|████████  | 77/96 [01:58<00:29,  1.54s/it]
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Epoch 27 Training:  81%|████████▏ | 78/96 [01:59<00:27,  1.54s/it]
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Epoch 27 Training:  82%|████████▏ | 79/96 [02:01<00:26,  1.54s/it]
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Epoch 27 Training:  83%|████████▎ | 80/96 [02:02<00:24,  1.54s/it]
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Epoch 27 Training:  84%|████████▍ | 81/96 [02:04<00:23,  1.54s/it]
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Epoch 27 Training:  85%|████████▌ | 82/96 [02:06<00:21,  1.54s/it]
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Epoch 27 Training:  86%|████████▋ | 83/96 [02:07<00:19,  1.54s/it]
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Epoch 27 Training:  88%|████████▊ | 84/96 [02:09<00:18,  1.54s/it]
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Epoch 27 Training:  89%|████████▊ | 85/96 [02:10<00:16,  1.54s/it]
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Epoch 27 Training:  90%|████████▉ | 86/96 [02:12<00:15,  1.54s/it]
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Epoch 27 Training:  91%|█████████ | 87/96 [02:13<00:13,  1.54s/it]
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Epoch 27 Training:  92%|█████████▏| 88/96 [02:15<00:12,  1.54s/it]
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Epoch 27 Training:  93%|█████████▎| 89/96 [02:16<00:10,  1.54s/it]
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Epoch 27 Training:  94%|█████████▍| 90/96 [02:18<00:09,  1.54s/it]
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Epoch 27 Training:  95%|█████████▍| 91/96 [02:19<00:07,  1.54s/it]
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Epoch 27 Training:  96%|█████████▌| 92/96 [02:21<00:06,  1.55s/it]
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Epoch 27 Training:  97%|█████████▋| 93/96 [02:23<00:04,  1.55s/it]
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Epoch 27 Training:  98%|█████████▊| 94/96 [02:24<00:03,  1.54s/it]
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Epoch 27 Training:  99%|█████████▉| 95/96 [02:26<00:01,  1.54s/it]
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Epoch 27 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s][2026-10-02 10:05:08] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0027 | Epoch 027] train/policy_loss=21.4123 train/unweighted_policy_loss=4.2825 train/loss=21.4123 train/grad_norm=0.0379
+
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Epoch 27 Training: 100%|██████████| 96/96 [02:27<00:00,  1.56s/it]
Epoch 27 Training: 100%|██████████| 96/96 [02:27<00:00,  1.54s/it]
+[2026-10-02 10:05:08] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0028 | Epoch 027] timing/rollout_seconds=179.6941 timing/feedback_seconds=5.9651 timing/optimization_seconds=148.3143 timing/cycle_seconds=330.8683
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+
Pointwise Rewards: domain:   0%|          | 0/6 [00:00<?, ?it/s]
Pointwise Rewards: domain:  17%|█▋        | 1/6 [00:00<00:01,  3.86it/s]
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Pointwise Rewards: domain:  50%|█████     | 3/6 [00:00<00:00,  4.05it/s]
Pointwise Rewards: domain:  67%|██████▋   | 4/6 [00:00<00:00,  4.12it/s]
Pointwise Rewards: domain:  83%|████████▎ | 5/6 [00:01<00:00,  4.09it/s]
Pointwise Rewards: domain: 100%|██████████| 6/6 [00:01<00:00,  4.13it/s]
Pointwise Rewards: domain: 100%|██████████| 6/6 [00:01<00:00,  4.08it/s]
+
Pointwise Rewards: structure:   0%|          | 0/6 [00:00<?, ?it/s]
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Pointwise Rewards: structure:  33%|███▎      | 2/6 [00:00<00:00,  4.79it/s]
Pointwise Rewards: structure:  50%|█████     | 3/6 [00:00<00:00,  4.48it/s]
Pointwise Rewards: structure:  67%|██████▋   | 4/6 [00:00<00:00,  4.75it/s]
Pointwise Rewards: structure:  83%|████████▎ | 5/6 [00:01<00:00,  5.16it/s]
Pointwise Rewards: structure: 100%|██████████| 6/6 [00:01<00:00,  5.45it/s]
Pointwise Rewards: structure: 100%|██████████| 6/6 [00:01<00:00,  5.09it/s]
+[2026-10-02 10:08:11] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0028 | Epoch 028] train/reward_domain_mean=0.3997 train/reward_domain_std=0.0587 train/reward_structure_mean=0.7174 train/reward_structure_std=0.1289 train/reward_domain_group_std_mean=0.0167 train/reward_domain_group_std_max=0.0440 train/reward_domain_group_std_min=0.0078 train/reward_domain_group_mean_std=0.0559 train/reward_structure_group_std_mean=0.0064 train/reward_structure_group_std_max=0.0279 train/reward_structure_group_std_min=0.0009 train/reward_structure_group_mean_std=0.1286 train/reward_domain_zero_std_ratio=0 train/reward_structure_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.5299 train/adv_max=3.2665 train/adv_abs_mean=0.9460
+
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Epoch 28 Training:   7%|▋         | 7/96 [00:10<02:16,  1.54s/it]
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Epoch 28 Training:   8%|▊         | 8/96 [00:12<02:15,  1.54s/it]
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Epoch 28 Training:   9%|▉         | 9/96 [00:13<02:13,  1.54s/it]
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Epoch 28 Training:  11%|█▏        | 11/96 [00:16<02:10,  1.54s/it]
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Epoch 28 Training:  14%|█▎        | 13/96 [00:19<02:07,  1.54s/it]
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Epoch 28 Training:  15%|█▍        | 14/96 [00:21<02:06,  1.54s/it]
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Epoch 28 Training:  19%|█▉        | 18/96 [00:27<02:00,  1.54s/it]
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Epoch 28 Training:  22%|██▏       | 21/96 [00:32<01:55,  1.54s/it]
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Epoch 28 Training:  23%|██▎       | 22/96 [00:33<01:53,  1.54s/it]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 28 Training:  24%|██▍       | 23/96 [00:35<01:52,  1.54s/it]
+
Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 28 Training:  25%|██▌       | 24/96 [00:36<01:50,  1.54s/it]
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Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 28 Training:  26%|██▌       | 25/96 [00:38<01:49,  1.54s/it]
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Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 28 Training:  27%|██▋       | 26/96 [00:39<01:47,  1.54s/it]
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Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 28 Training:  28%|██▊       | 27/96 [00:41<01:46,  1.54s/it]
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Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 28 Training:  29%|██▉       | 28/96 [00:43<01:44,  1.54s/it]
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Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 28 Training:  30%|███       | 29/96 [00:44<01:43,  1.54s/it]
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Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 28 Training:  31%|███▏      | 30/96 [00:46<01:41,  1.54s/it]
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Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 28 Training:  32%|███▏      | 31/96 [00:47<01:40,  1.54s/it]
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Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 28 Training:  33%|███▎      | 32/96 [00:49<01:38,  1.54s/it]
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Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 28 Training:  34%|███▍      | 33/96 [00:50<01:37,  1.54s/it]
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Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.52it/s]
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Epoch 28 Training:  35%|███▌      | 34/96 [00:52<01:40,  1.62s/it]
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Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 28 Training:  36%|███▋      | 35/96 [00:54<01:37,  1.60s/it]
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Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 28 Training:  38%|███▊      | 36/96 [00:55<01:34,  1.58s/it]
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Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 28 Training:  39%|███▊      | 37/96 [00:57<01:32,  1.57s/it]
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Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 28 Training:  40%|███▉      | 38/96 [00:58<01:30,  1.56s/it]
+
Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 28 Training:  41%|████      | 39/96 [01:00<01:28,  1.55s/it]
+
Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 28 Training:  42%|████▏     | 40/96 [01:01<01:26,  1.55s/it]
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Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 28 Training:  43%|████▎     | 41/96 [01:03<01:25,  1.55s/it]
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Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 28 Training:  44%|████▍     | 42/96 [01:04<01:23,  1.54s/it]
+
Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 28 Training:  45%|████▍     | 43/96 [01:06<01:21,  1.54s/it]
+
Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 28 Training:  46%|████▌     | 44/96 [01:08<01:21,  1.58s/it]
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Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 28 Training:  47%|████▋     | 45/96 [01:09<01:19,  1.57s/it]
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Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 28 Training:  48%|████▊     | 46/96 [01:11<01:17,  1.56s/it]
+
Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 28 Training:  49%|████▉     | 47/96 [01:12<01:15,  1.55s/it]
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Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 28 Training:  50%|█████     | 48/96 [01:14<01:14,  1.55s/it]
+
Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 28 Training:  51%|█████     | 49/96 [01:15<01:12,  1.54s/it]
+
Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 28 Training:  52%|█████▏    | 50/96 [01:17<01:10,  1.54s/it]
+
Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 28 Training:  53%|█████▎    | 51/96 [01:18<01:09,  1.54s/it]
+
Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 28 Training:  54%|█████▍    | 52/96 [01:20<01:07,  1.54s/it]
+
Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 28 Training:  55%|█████▌    | 53/96 [01:21<01:06,  1.54s/it]
+
Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.15it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.02it/s]
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Epoch 28 Training:  56%|█████▋    | 54/96 [01:23<01:04,  1.54s/it]
+
Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 28 Training:  57%|█████▋    | 55/96 [01:24<01:03,  1.54s/it]
+
Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 28 Training:  58%|█████▊    | 56/96 [01:26<01:01,  1.54s/it]
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Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 28 Training:  59%|█████▉    | 57/96 [01:28<01:00,  1.54s/it]
+
Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 28 Training:  60%|██████    | 58/96 [01:29<00:58,  1.54s/it]
+
Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Training:  61%|██████▏   | 59/96 [01:31<00:59,  1.61s/it]
+
Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 28 Training:  62%|██████▎   | 60/96 [01:32<00:57,  1.59s/it]
+
Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 28 Training:  64%|██████▎   | 61/96 [01:34<00:55,  1.57s/it]
+
Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 28 Training:  65%|██████▍   | 62/96 [01:35<00:53,  1.56s/it]
+
Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 28 Training:  66%|██████▌   | 63/96 [01:37<00:51,  1.56s/it]
+
Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.98it/s]
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Epoch 28 Training:  67%|██████▋   | 64/96 [01:39<00:49,  1.56s/it]
+
Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Training:  68%|██████▊   | 65/96 [01:40<00:48,  1.55s/it]
+
Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 28 Training:  69%|██████▉   | 66/96 [01:42<00:46,  1.55s/it]
+
Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.02it/s]
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Epoch 28 Training:  70%|██████▉   | 67/96 [01:43<00:44,  1.54s/it]
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Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 28 Training:  71%|███████   | 68/96 [01:45<00:43,  1.54s/it]
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Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 28 Training:  72%|███████▏  | 69/96 [01:46<00:41,  1.54s/it]
+
Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 28 Training:  73%|███████▎  | 70/96 [01:48<00:40,  1.54s/it]
+
Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 28 Training:  74%|███████▍  | 71/96 [01:49<00:38,  1.54s/it]
+
Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Training:  75%|███████▌  | 72/96 [01:51<00:36,  1.54s/it]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 28 Training:  76%|███████▌  | 73/96 [01:52<00:35,  1.54s/it]
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Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Training:  77%|███████▋  | 74/96 [01:54<00:33,  1.54s/it]
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Epoch 28 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s][2026-10-02 10:10:40] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0028 | Epoch 028] train/policy_loss=21.5381 train/unweighted_policy_loss=4.3076 train/loss=21.5381 train/grad_norm=0.0418
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+[2026-10-02 10:10:40] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0029 | Epoch 028] timing/rollout_seconds=179.6395 timing/feedback_seconds=5.9305 timing/optimization_seconds=149.3554 timing/cycle_seconds=331.8966
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Pointwise Rewards: structure: 100%|██████████| 6/6 [00:01<00:00,  5.16it/s]
+[2026-10-02 10:13:43] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0029 | Epoch 029] train/reward_domain_mean=0.3999 train/reward_domain_std=0.0648 train/reward_structure_mean=0.6438 train/reward_structure_std=0.1411 train/reward_domain_group_std_mean=0.0177 train/reward_domain_group_std_max=0.0333 train/reward_domain_group_std_min=0.0081 train/reward_domain_group_mean_std=0.0621 train/reward_structure_group_std_mean=0.0078 train/reward_structure_group_std_max=0.0265 train/reward_structure_group_std_min=0.0032 train/reward_structure_group_mean_std=0.1408 train/reward_domain_zero_std_ratio=0 train/reward_structure_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.2026 train/adv_max=3.9413 train/adv_abs_mean=0.9582
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Epoch 29 Training:  17%|█▋        | 16/96 [00:24<02:03,  1.54s/it]
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Epoch 29 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s][2026-10-02 10:16:11] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0029 | Epoch 029] train/policy_loss=21.8646 train/unweighted_policy_loss=4.3729 train/loss=21.8646 train/grad_norm=0.0515
+
+
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+
                                                                
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+[2026-10-02 10:16:11] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0030 | Epoch 029] timing/rollout_seconds=179.4723 timing/feedback_seconds=5.6452 timing/optimization_seconds=148.6240 timing/cycle_seconds=331.0114
+[2026-10-02 10:16:11,074] [INFO] [flow_factory.train]: Training completed successfully
+wandb: 
+wandb: 🚀 View run klein4b_nft_domain_sirst at: https://wandb.ai/315229706-xi-an-jiaotong-university-/IR-Engine/runs/gklyn30y
+wandb: Find logs at: ../../../ir_engine/runs/klein4b_nft_domain_sirst/wandb/run-20261002_072850-gklyn30y/logs
diff --git a/runs/klein4b_nft_domain_sirst_tex/saves/klein4b_nft_domain_sirst_tex/checkpoints/checkpoint-10/README.md b/runs/klein4b_nft_domain_sirst_tex/saves/klein4b_nft_domain_sirst_tex/checkpoints/checkpoint-10/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..2b78a7a995873d5db3090fca56885fe89cdf97e1
--- /dev/null
+++ b/runs/klein4b_nft_domain_sirst_tex/saves/klein4b_nft_domain_sirst_tex/checkpoints/checkpoint-10/README.md
@@ -0,0 +1,204 @@
+---
+base_model: /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/flow_factory_gpu_gate_models/FLUX.2-klein-base-4B/transformer
+library_name: peft
+tags:
+- lora
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]
+### Framework versions
+
+- PEFT 0.20.0
\ No newline at end of file
diff --git a/runs/klein4b_nft_domain_sirst_tex/saves/klein4b_nft_domain_sirst_tex/checkpoints/checkpoint-10/adapter_config.json b/runs/klein4b_nft_domain_sirst_tex/saves/klein4b_nft_domain_sirst_tex/checkpoints/checkpoint-10/adapter_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..9b159a904532113773bf2ffb68819c4dffb3fc25
--- /dev/null
+++ b/runs/klein4b_nft_domain_sirst_tex/saves/klein4b_nft_domain_sirst_tex/checkpoints/checkpoint-10/adapter_config.json
@@ -0,0 +1,59 @@
+{
+  "alora_invocation_tokens": null,
+  "alpha_pattern": {},
+  "arrow_config": null,
+  "auto_mapping": {
+    "base_model_class": "Flux2Transformer2DModel",
+    "parent_library": "diffusers.models.transformers.transformer_flux2"
+  },
+  "base_model_name_or_path": null,
+  "bias": "none",
+  "corda_config": null,
+  "ensure_weight_tying": false,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": "gaussian",
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 128,
+  "lora_bias": false,
+  "lora_dropout": 0.0,
+  "lora_ga_config": null,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "monteclora_config": null,
+  "peft_type": "LORA",
+  "peft_version": "0.20.0",
+  "qalora_group_size": 16,
+  "r": 64,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "attn.to_k",
+    "attn.add_q_proj",
+    "ff.linear_out",
+    "attn.to_q",
+    "attn.to_add_out",
+    "attn.to_qkv_mlp_proj",
+    "attn.add_v_proj",
+    "ff_context.linear_out",
+    "attn.to_out.0",
+    "ff.linear_in",
+    "attn.add_k_proj",
+    "attn.to_v",
+    "ff_context.linear_in"
+  ],
+  "target_parameters": null,
+  "task_type": null,
+  "trainable_token_indices": null,
+  "use_bdlora": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false,
+  "velora_config": null
+}
\ No newline at end of file
diff --git a/runs/klein4b_nft_domain_sirst_tex/saves/klein4b_nft_domain_sirst_tex/checkpoints/checkpoint-10/manifest.json b/runs/klein4b_nft_domain_sirst_tex/saves/klein4b_nft_domain_sirst_tex/checkpoints/checkpoint-10/manifest.json
new file mode 100644
index 0000000000000000000000000000000000000000..4443fd995e0f6f3bd9b7e3dacea93df8d345ecfb
--- /dev/null
+++ b/runs/klein4b_nft_domain_sirst_tex/saves/klein4b_nft_domain_sirst_tex/checkpoints/checkpoint-10/manifest.json
@@ -0,0 +1,16 @@
+{
+  "base_role": "base",
+  "entries": [
+    {
+      "component": "transformer",
+      "path": ".",
+      "role": "base"
+    }
+  ],
+  "finetune_type": "lora",
+  "format_version": 1,
+  "primary": {
+    "component": "transformer",
+    "path": "."
+  }
+}
diff --git a/runs/klein4b_nft_domain_sirst_tex/train.log b/runs/klein4b_nft_domain_sirst_tex/train.log
new file mode 100644
index 0000000000000000000000000000000000000000..3add008a361ab8f894eae54cd3099cbdc298b4ec
--- /dev/null
+++ b/runs/klein4b_nft_domain_sirst_tex/train.log
@@ -0,0 +1,12260 @@
+[2026-10-02 07:53:21,488] [INFO] [flow_factory.cli]: ============================================================
+[2026-10-02 07:53:21,488] [INFO] [flow_factory.cli]: Flow-Factory Launch Configuration
+[2026-10-02 07:53:21,488] [INFO] [flow_factory.cli]:   Num machines:       1
+[2026-10-02 07:53:21,488] [INFO] [flow_factory.cli]:   Machine rank:       0
+[2026-10-02 07:53:21,489] [INFO] [flow_factory.cli]:   Num processes:      8
+[2026-10-02 07:53:21,489] [INFO] [flow_factory.cli]:   Master node:        localhost:29515
+[2026-10-02 07:53:21,489] [INFO] [flow_factory.cli]:   Mixed precision:    bf16
+[2026-10-02 07:53:21,489] [INFO] [flow_factory.cli]:   Accelerate config:  config/deepspeed/deepspeed_zero2.yaml
+[2026-10-02 07:53:21,489] [INFO] [flow_factory.cli]: ============================================================
+W1002 07:53:29.783000 3649266 site-packages/torch/distributed/run.py:852] 
+W1002 07:53:29.783000 3649266 site-packages/torch/distributed/run.py:852] *****************************************
+W1002 07:53:29.783000 3649266 site-packages/torch/distributed/run.py:852] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
+W1002 07:53:29.783000 3649266 site-packages/torch/distributed/run.py:852] *****************************************
+[2026-10-02 07:53:36] [Rank 0] [WARNING] [flow_factory.hparams.abc]: NFTTrainingArguments.from_dict captured 3 unknown key(s) into extra_kwargs: ['condition_image_size', 'flat_steps', 'ramp_rate']. Verify these are intentional (e.g., adapter-specific kwargs); typos against declared fields will be silently accepted otherwise.
+[2026-10-02 07:53:36] [Rank 0] [INFO] [flow_factory.hparams.training_args._base]: World Size: 8
+[2026-10-02 07:53:36] [Rank 0] [WARNING] [flow_factory.hparams.abc]: EvaluationArguments.from_dict captured 1 unknown key(s) into extra_kwargs: ['condition_image_size']. Verify these are intentional (e.g., adapter-specific kwargs); typos against declared fields will be silently accepted otherwise.
+[2026-10-02 07:53:36] [Rank 0] [WARNING] [flow_factory.hparams.abc]: RewardArguments.from_dict captured 2 unknown key(s) into extra_kwargs: ['encoder', 'probe']. Verify these are intentional (e.g., adapter-specific kwargs); typos against declared fields will be silently accepted otherwise.
+[2026-10-02 07:53:36] [Rank 0] [WARNING] [flow_factory.hparams.abc]: RewardArguments.from_dict captured 2 unknown key(s) into extra_kwargs: ['encoder', 'probe']. Verify these are intentional (e.g., adapter-specific kwargs); typos against declared fields will be silently accepted otherwise.
+[2026-10-02 07:53:36,444] [INFO] [flow_factory.train]: ====================================================================================================
+[2026-10-02 07:53:36,444] [INFO] [flow_factory.train]: Flow-Factory Training Initialized
+[2026-10-02 07:53:36,444] [INFO] [flow_factory.train]: World Size: 8
+[2026-10-02 07:53:36,444] [INFO] [flow_factory.train]: ====================================================================================================
+[2026-10-02 07:53:36,444] [INFO] [flow_factory.train]: Config: /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/runs/klein4b_nft_domain_sirst_tex/train.yaml
+[2026-10-02 07:53:36,452] [INFO] [flow_factory.train]: 
+launcher: accelerate
+config_file: config/deepspeed/deepspeed_zero2.yaml
+num_processes: 8
+main_process_port: 29515
+mixed_precision: bf16
+data:
+  dataset_dir: data
+  cache_dir: ~/.cache/flow_factory/datasets
+  image_dir: null
+  video_dir: null
+  audio_dir: null
+  preprocessing_batch_size: 8
+  dataloader_num_workers: 8
+  enable_preprocess: true
+  force_reprocess: false
+  max_dataset_size: null
+  preprocess_parallelism: local
+  sampler_type: group_contiguous
+  sampler_subgroup_size: null
+  datasets:
+  - extra_kwargs: {}
+    name: places_domain_rl
+    dataset_dir: /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/places_domain_rl
+    image_dir: null
+    video_dir: null
+    audio_dir: null
+    train:
+      extra_kwargs: {}
+      enabled: true
+      split: train
+      weight: 1
+      max_dataset_size: null
+      unique_sample_num_per_epoch: 48
+      num_batches_per_epoch: null
+    eval:
+      extra_kwargs: {}
+      enabled: true
+      split: test
+      max_dataset_size: null
+      resolution: null
+      num_inference_steps: null
+      guidance_scale: null
+    source_id: 0
+model:
+  model_name_or_path: /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/flow_factory_gpu_gate_models/FLUX.2-klein-base-4B
+  finetune_type: lora
+  trainable_parameters_dtype: bfloat16
+  component_load_dtypes: null
+  frozen_parameters_dtype: null
+  target_components:
+  - transformer
+  target_modules: default
+  model_type: flux2-klein
+  lora_rank: 64
+  lora_alpha: 128
+  resume_path: /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/runs/klein4b_sft_flir_full_r64a128_lr1e-4/saves/klein4b_sft_flir_full_r64a128_lr1e-4/checkpoints/checkpoint-12
+  resume_type: lora
+scheduler:
+  dynamics_type: ODE
+  noise_level: 0.0
+  num_sde_steps: 0
+  sde_steps: []
+  seed: 42
+training:
+  trainer_type: nft
+  resolution:
+  - 512
+  - 640
+  height: 512
+  width: 640
+  num_frames: null
+  frame_rate: null
+  max_epochs: 30
+  per_device_batch_size: 1
+  gradient_step_per_epoch: 1
+  max_grad_norm: 1.0
+  num_batches_per_epoch: 96
+  gradient_accumulation_steps: 192
+  num_inner_epochs: 1
+  group_size: 16
+  unique_sample_num_per_epoch: 48
+  num_inference_steps: 8
+  guidance_scale: 1.0
+  ref_param_device: cuda
+  seed: 42
+  learning_rate: 1.0e-05
+  adam_weight_decay: 0.0001
+  adam_betas:
+  - 0.9
+  - 0.999
+  adam_epsilon: 1.0e-08
+  enable_gradient_checkpointing: false
+  offload_samples_to_cpu: false
+  ema_decay: 0.5
+  ema_update_interval: 1
+  ema_device: cuda
+  ema_decay_schedule: piecewise_linear
+  latent_storage_dtype: fp16
+  shuffle_samples: true
+  reward_optimization_overlap: false
+  reward_optimization_overlap_mode: ready
+  reward_optimization_overlap_poll_interval: 0.05
+  global_std: false
+  advantage_aggregation: gdpo
+  nft_beta: 0.1
+  off_policy: true
+  adv_clip_range:
+  - -5.0
+  - 5.0
+  kl_type: v-based
+  kl_beta: 0.0
+  num_train_timesteps: 2
+  time_sampling_strategy: discrete
+  time_shift: 3.0
+  timestep_range:
+  - 0.0
+  - 0.5
+  flat_steps: 0
+  ramp_rate: 0.001
+  condition_image_size: 1024
+eval:
+  resolution:
+  - 512
+  - 640
+  height: 512
+  width: 640
+  num_frames: null
+  frame_rate: null
+  per_device_batch_size: 1
+  seed: 42
+  guidance_scale: 1.0
+  num_inference_steps: 8
+  eval_freq: 5
+  condition_image_size: 1024
+log:
+  run_name: klein4b_nft_domain_sirst_tex
+  project: IR-Engine
+  logging_backend: wandb
+  save_dir: /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/runs/klein4b_nft_domain_sirst_tex/saves
+  save_freq: 10
+  save_model_only: true
+  verbose: true
+acceleration:
+  shared: []
+  rollout: []
+reward:
+  reward_0:
+    name: domain
+    reward_model: ir_rewards.domain.DomainProbeReward
+    weight:
+      places_domain_rl: 1.0
+    dtype: float32
+    device: cuda
+    batch_size: 16
+    async_reward: false
+    num_workers: 1
+    applicable_datasets:
+    - places_domain_rl
+    probe: /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/domain_probe.npz
+    encoder: /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/cache/dinov2-small
+  reward_1:
+    name: structure
+    reward_model: ir_rewards.fidelity.CoarseStructureReward
+    weight:
+      places_domain_rl: 1.0
+    dtype: float32
+    device: cuda
+    batch_size: 16
+    async_reward: false
+    num_workers: 1
+    applicable_datasets:
+    - places_domain_rl
+  reward_2:
+    name: texture
+    reward_model: ir_rewards.fidelity.DISTSReward
+    weight:
+      places_domain_rl: 1.0
+    dtype: float32
+    device: cuda
+    batch_size: 16
+    async_reward: false
+    num_workers: 1
+    applicable_datasets:
+    - places_domain_rl
+eval_reward:
+  reward_0:
+    name: domain
+    reward_model: ir_rewards.domain.DomainProbeReward
+    weight:
+      places_domain_rl: 1.0
+    dtype: float32
+    device: cuda
+    batch_size: 16
+    async_reward: false
+    num_workers: 1
+    applicable_datasets:
+    - places_domain_rl
+    probe: /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/irstd/domain_probe.npz
+    encoder: /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/cache/dinov2-small
+  reward_1:
+    name: structure
+    reward_model: ir_rewards.fidelity.CoarseStructureReward
+    weight:
+      places_domain_rl: 1.0
+    dtype: float32
+    device: cuda
+    batch_size: 16
+    async_reward: false
+    num_workers: 1
+    applicable_datasets:
+    - places_domain_rl
+  reward_2:
+    name: ssim_to_stage1
+    reward_model: ir_rewards.fidelity.SSIMReward
+    weight:
+      places_domain_rl: 1.0
+    dtype: float32
+    device: cuda
+    batch_size: 16
+    async_reward: false
+    num_workers: 1
+    applicable_datasets:
+    - places_domain_rl
+  reward_3:
+    name: texture
+    reward_model: ir_rewards.fidelity.DISTSReward
+    weight:
+      places_domain_rl: 1.0
+    dtype: float32
+    device: cuda
+    batch_size: 16
+    async_reward: false
+    num_workers: 1
+    applicable_datasets:
+    - places_domain_rl
+optimizer:
+  optimizer_configs:
+  - extra_kwargs: {}
+    name: default
+    optimizer: adamw
+    learning_rate: 5.0e-05
+    weight_decay: 0.0001
+    max_grad_norm: 1.0
+    update_frequency: 1
+    betas:
+    - 0.9
+    - 0.999
+    eps: 1.0e-08
+
+[2026-10-02 07:53:36,452] [INFO] [flow_factory.train]: ====================================================================================================
+[2026-10-02 07:53:45,884] [WARNING] [accelerate.utils.other]: [RANK 0] Detected kernel version 5.4.241, which is below the recommended minimum of 5.5.0; this can cause the process to hang. It is recommended to upgrade the kernel to the minimum version or higher.
+[2026-10-02 07:53:45] [Rank 0] [INFO] [flow_factory.models.loader]: Loading model architecture: flux2-klein...
+
Loading pipeline components...:   0%|          | 0/5 [00:00<?, ?it/s][2026-10-02 07:53:46] [Rank 2] [INFO] [flow_factory.models.loader]: Loading model architecture: flux2-klein...
+[2026-10-02 07:53:46] [Rank 4] [INFO] [flow_factory.models.loader]: Loading model architecture: flux2-klein...
+[2026-10-02 07:53:46] [Rank 1] [INFO] [flow_factory.models.loader]: Loading model architecture: flux2-klein...
+[2026-10-02 07:53:46] [Rank 6] [INFO] [flow_factory.models.loader]: Loading model architecture: flux2-klein...
+[2026-10-02 07:53:46] [Rank 7] [INFO] [flow_factory.models.loader]: Loading model architecture: flux2-klein...
+[2026-10-02 07:53:46] [Rank 3] [INFO] [flow_factory.models.loader]: Loading model architecture: flux2-klein...
+[2026-10-02 07:53:46] [Rank 5] [INFO] [flow_factory.models.loader]: Loading model architecture: flux2-klein...
+
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+
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+
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+
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+[2026-10-02 07:53:53] [Rank 0] [INFO] [flow_factory.scheduler.loader]: Loaded SDE scheduler: FlowMatchEulerDiscreteSDEScheduler
+
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+[2026-10-02 07:53:53] [Rank 2] [INFO] [flow_factory.scheduler.loader]: Loaded SDE scheduler: FlowMatchEulerDiscreteSDEScheduler
+[2026-10-02 07:53:53] [Rank 4] [INFO] [flow_factory.scheduler.loader]: Loaded SDE scheduler: FlowMatchEulerDiscreteSDEScheduler
+
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+
Loading weights: 100%|██████████| 398/398 [00:00<00:00, 9989.67it/s]
+[2026-10-02 07:53:54] [Rank 7] [INFO] [flow_factory.scheduler.loader]: Loaded SDE scheduler: FlowMatchEulerDiscreteSDEScheduler
+[2026-10-02 07:53:54] [Rank 3] [INFO] [flow_factory.scheduler.loader]: Loaded SDE scheduler: FlowMatchEulerDiscreteSDEScheduler
+
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+[2026-10-02 07:53:54] [Rank 5] [INFO] [flow_factory.scheduler.loader]: Loaded SDE scheduler: FlowMatchEulerDiscreteSDEScheduler
+[2026-10-02 07:53:54] [Rank 6] [INFO] [flow_factory.scheduler.loader]: Loaded SDE scheduler: FlowMatchEulerDiscreteSDEScheduler
+
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Loading weights: 100%|██████████| 398/398 [00:00<00:00, 8155.18it/s]
+[2026-10-02 07:53:54] [Rank 1] [INFO] [flow_factory.scheduler.loader]: Loaded SDE scheduler: FlowMatchEulerDiscreteSDEScheduler
+[2026-10-02 07:53:55] [Rank 0] [INFO] [flow_factory.models.abc]: Checkpoint loaded successfully from /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/runs/klein4b_sft_flir_full_r64a128_lr1e-4/saves/klein4b_sft_flir_full_r64a128_lr1e-4/checkpoints/checkpoint-12 (type=lora)
+[2026-10-02 07:53:55] [Rank 6] [INFO] [flow_factory.models.abc]: Froze ALL transformer parameters
+[2026-10-02 07:53:55] [Rank 3] [INFO] [flow_factory.models.abc]: Froze ALL transformer parameters
+[2026-10-02 07:53:55] [Rank 7] [INFO] [flow_factory.models.abc]: Froze ALL transformer parameters
+[2026-10-02 07:53:55] [Rank 6] [INFO] [flow_factory.models.abc]: Component transformer already has 'default' adapter. Skipping initialization but enabling gradients.
+[2026-10-02 07:53:55] [Rank 0] [INFO] [flow_factory.models.abc]: Froze ALL transformer parameters
+[2026-10-02 07:53:55] [Rank 5] [INFO] [flow_factory.models.abc]: Froze ALL transformer parameters
+[2026-10-02 07:53:55] [Rank 3] [INFO] [flow_factory.models.abc]: Component transformer already has 'default' adapter. Skipping initialization but enabling gradients.
+[2026-10-02 07:53:55] [Rank 2] [INFO] [flow_factory.models.abc]: Froze ALL transformer parameters
+[2026-10-02 07:53:55] [Rank 7] [INFO] [flow_factory.models.abc]: Component transformer already has 'default' adapter. Skipping initialization but enabling gradients.
+[2026-10-02 07:53:55] [Rank 0] [INFO] [flow_factory.models.abc]: Component transformer already has 'default' adapter. Skipping initialization but enabling gradients.
+[2026-10-02 07:53:55] [Rank 5] [INFO] [flow_factory.models.abc]: Component transformer already has 'default' adapter. Skipping initialization but enabling gradients.
+[2026-10-02 07:53:55] [Rank 2] [INFO] [flow_factory.models.abc]: Component transformer already has 'default' adapter. Skipping initialization but enabling gradients.
+[2026-10-02 07:53:55] [Rank 4] [INFO] [flow_factory.models.abc]: Froze ALL transformer parameters
+[2026-10-02 07:53:55] [Rank 1] [INFO] [flow_factory.models.abc]: Froze ALL transformer parameters
+[2026-10-02 07:53:55] [Rank 4] [INFO] [flow_factory.models.abc]: Component transformer already has 'default' adapter. Skipping initialization but enabling gradients.
+[2026-10-02 07:53:55] [Rank 1] [INFO] [flow_factory.models.abc]: Component transformer already has 'default' adapter. Skipping initialization but enabling gradients.
+[2026-10-02 07:53:55] [Rank 0] [INFO] [flow_factory.models.abc]: Set 160 trainable parameters to torch.bfloat16; frozen component policy -> {'text_encoder': None, 'transformer': None, 'vae': None}
+[2026-10-02 07:53:55] [Rank 6] [INFO] [flow_factory.models.abc]: Set 160 trainable parameters to torch.bfloat16; frozen component policy -> {'text_encoder': None, 'transformer': None, 'vae': None}
+[2026-10-02 07:53:55] [Rank 7] [INFO] [flow_factory.models.abc]: Set 160 trainable parameters to torch.bfloat16; frozen component policy -> {'text_encoder': None, 'transformer': None, 'vae': None}
+[2026-10-02 07:53:55] [Rank 3] [INFO] [flow_factory.models.abc]: Set 160 trainable parameters to torch.bfloat16; frozen component policy -> {'text_encoder': None, 'transformer': None, 'vae': None}
+[2026-10-02 07:53:55] [Rank 6] [INFO] [flow_factory.models.loader]: Successfully loaded Flux2KleinAdapter
+[2026-10-02 07:53:55] [Rank 7] [INFO] [flow_factory.models.loader]: Successfully loaded Flux2KleinAdapter
+[2026-10-02 07:53:55] [Rank 5] [INFO] [flow_factory.models.abc]: Set 160 trainable parameters to torch.bfloat16; frozen component policy -> {'text_encoder': None, 'transformer': None, 'vae': None}
+[2026-10-02 07:53:55] [Rank 3] [INFO] [flow_factory.models.loader]: Successfully loaded Flux2KleinAdapter
+[2026-10-02 07:53:55] [Rank 5] [INFO] [flow_factory.models.loader]: Successfully loaded Flux2KleinAdapter
+[2026-10-02 07:53:55] [Rank 0] [INFO] [flow_factory.models.abc]: Precision inventory stage=initialize component=text_encoder {'trainable': {}, 'frozen': {'torch.bfloat16': 4022468096}}
+[2026-10-02 07:53:55] [Rank 2] [INFO] [flow_factory.models.abc]: Set 160 trainable parameters to torch.bfloat16; frozen component policy -> {'text_encoder': None, 'transformer': None, 'vae': None}
+[2026-10-02 07:53:55] [Rank 2] [INFO] [flow_factory.models.loader]: Successfully loaded Flux2KleinAdapter
+[2026-10-02 07:53:55] [Rank 1] [INFO] [flow_factory.models.abc]: Set 160 trainable parameters to torch.bfloat16; frozen component policy -> {'text_encoder': None, 'transformer': None, 'vae': None}
+[2026-10-02 07:53:55] [Rank 1] [INFO] [flow_factory.models.loader]: Successfully loaded Flux2KleinAdapter
+[2026-10-02 07:53:55] [Rank 4] [INFO] [flow_factory.models.abc]: Set 160 trainable parameters to torch.bfloat16; frozen component policy -> {'text_encoder': None, 'transformer': None, 'vae': None}
+[2026-10-02 07:53:55] [Rank 4] [INFO] [flow_factory.models.loader]: Successfully loaded Flux2KleinAdapter
+[2026-10-02 07:53:55] [Rank 0] [INFO] [flow_factory.models.abc]: Precision inventory stage=initialize component=transformer {'trainable': {'torch.bfloat16': 76677120}, 'frozen': {'torch.float32': 3875544576}}
+[2026-10-02 07:53:55] [Rank 0] [INFO] [flow_factory.models.abc]: Precision inventory stage=initialize component=vae {'trainable': {}, 'frozen': {'torch.float32': 84046115}}
+[2026-10-02 07:53:55] [Rank 0] [INFO] [flow_factory.models.loader]: Successfully loaded Flux2KleinAdapter
+[2026-10-02 07:53:57] [Rank 0] [INFO] [flow_factory.data_utils.loader]: Preprocessing train dataset shard 0000/0007 -> /root/.cache/flow_factory/datasets/82135bc75fb8475b64b8342a3ef5f714.tmp/_parts/rank_0000_of_0008/cache-82135bc75fb8475b64b8342a3ef5f714_shard0000of0007.arrow
+[2026-10-02 07:53:57] [Rank 1] [INFO] [flow_factory.data_utils.loader]: Preprocessing train dataset shard 0001/0007 -> /root/.cache/flow_factory/datasets/82135bc75fb8475b64b8342a3ef5f714.tmp/_parts/rank_0001_of_0008/cache-82135bc75fb8475b64b8342a3ef5f714_shard0001of0007.arrow
+[2026-10-02 07:53:57] [Rank 2] [INFO] [flow_factory.data_utils.loader]: Preprocessing train dataset shard 0002/0007 -> /root/.cache/flow_factory/datasets/82135bc75fb8475b64b8342a3ef5f714.tmp/_parts/rank_0002_of_0008/cache-82135bc75fb8475b64b8342a3ef5f714_shard0002of0007.arrow
+[2026-10-02 07:53:57] [Rank 4] [INFO] [flow_factory.data_utils.loader]: Preprocessing train dataset shard 0004/0007 -> /root/.cache/flow_factory/datasets/82135bc75fb8475b64b8342a3ef5f714.tmp/_parts/rank_0004_of_0008/cache-82135bc75fb8475b64b8342a3ef5f714_shard0004of0007.arrow
+[2026-10-02 07:53:57] [Rank 7] [INFO] [flow_factory.data_utils.loader]: Preprocessing train dataset shard 0007/0007 -> /root/.cache/flow_factory/datasets/82135bc75fb8475b64b8342a3ef5f714.tmp/_parts/rank_0007_of_0008/cache-82135bc75fb8475b64b8342a3ef5f714_shard0007of0007.arrow
+[2026-10-02 07:53:57] [Rank 5] [INFO] [flow_factory.data_utils.loader]: Preprocessing train dataset shard 0005/0007 -> /root/.cache/flow_factory/datasets/82135bc75fb8475b64b8342a3ef5f714.tmp/_parts/rank_0005_of_0008/cache-82135bc75fb8475b64b8342a3ef5f714_shard0005of0007.arrow
+[2026-10-02 07:53:57] [Rank 3] [INFO] [flow_factory.data_utils.loader]: Preprocessing train dataset shard 0003/0007 -> /root/.cache/flow_factory/datasets/82135bc75fb8475b64b8342a3ef5f714.tmp/_parts/rank_0003_of_0008/cache-82135bc75fb8475b64b8342a3ef5f714_shard0003of0007.arrow
+[2026-10-02 07:53:57] [Rank 6] [INFO] [flow_factory.data_utils.loader]: Preprocessing train dataset shard 0006/0007 -> /root/.cache/flow_factory/datasets/82135bc75fb8475b64b8342a3ef5f714.tmp/_parts/rank_0006_of_0008/cache-82135bc75fb8475b64b8342a3ef5f714_shard0006of0007.arrow
+
Generating train split: 0 examples [00:00, ? examples/s]
Generating train split: 3600 examples [00:00, 487206.20 examples/s]
+/opt/conda/envs/ff-qi21/lib/python3.10/contextlib.py:135: UserWarning: CUDA reports that you have 8 available devices, and you have used fork_rng without explicitly specifying which devices are being used. For safety, we initialize *every* CUDA device by default, which can be quite slow if you have a lot of CUDAs. If you know that you are only making use of a few CUDA devices, set the environment variable CUDA_VISIBLE_DEVICES or the 'devices' keyword argument of fork_rng with the set of devices you are actually using. For example, if you are using CPU only, set device.upper()_VISIBLE_DEVICES= or devices=[]; if you are using device 0 only, set CUDA_VISIBLE_DEVICES=0 or devices=[0].  To initialize all devices and suppress this warning, set the 'devices' keyword argument to `range(torch.cuda.device_count())`.
+  return next(self.gen)
+/opt/conda/envs/ff-qi21/lib/python3.10/contextlib.py:135: UserWarning: CUDA reports that you have 8 available devices, and you have used fork_rng without explicitly specifying which devices are being used. For safety, we initialize *every* CUDA device by default, which can be quite slow if you have a lot of CUDAs. If you know that you are only making use of a few CUDA devices, set the environment variable CUDA_VISIBLE_DEVICES or the 'devices' keyword argument of fork_rng with the set of devices you are actually using. For example, if you are using CPU only, set device.upper()_VISIBLE_DEVICES= or devices=[]; if you are using device 0 only, set CUDA_VISIBLE_DEVICES=0 or devices=[0].  To initialize all devices and suppress this warning, set the 'devices' keyword argument to `range(torch.cuda.device_count())`.
+  return next(self.gen)
+/opt/conda/envs/ff-qi21/lib/python3.10/contextlib.py:135: UserWarning: CUDA reports that you have 8 available devices, and you have used fork_rng without explicitly specifying which devices are being used. For safety, we initialize *every* CUDA device by default, which can be quite slow if you have a lot of CUDAs. If you know that you are only making use of a few CUDA devices, set the environment variable CUDA_VISIBLE_DEVICES or the 'devices' keyword argument of fork_rng with the set of devices you are actually using. For example, if you are using CPU only, set device.upper()_VISIBLE_DEVICES= or devices=[]; if you are using device 0 only, set CUDA_VISIBLE_DEVICES=0 or devices=[0].  To initialize all devices and suppress this warning, set the 'devices' keyword argument to `range(torch.cuda.device_count())`.
+  return next(self.gen)
+/opt/conda/envs/ff-qi21/lib/python3.10/contextlib.py:135: UserWarning: CUDA reports that you have 8 available devices, and you have used fork_rng without explicitly specifying which devices are being used. For safety, we initialize *every* CUDA device by default, which can be quite slow if you have a lot of CUDAs. If you know that you are only making use of a few CUDA devices, set the environment variable CUDA_VISIBLE_DEVICES or the 'devices' keyword argument of fork_rng with the set of devices you are actually using. For example, if you are using CPU only, set device.upper()_VISIBLE_DEVICES= or devices=[]; if you are using device 0 only, set CUDA_VISIBLE_DEVICES=0 or devices=[0].  To initialize all devices and suppress this warning, set the 'devices' keyword argument to `range(torch.cuda.device_count())`.
+  return next(self.gen)
+/opt/conda/envs/ff-qi21/lib/python3.10/contextlib.py:135: UserWarning: CUDA reports that you have 8 available devices, and you have used fork_rng without explicitly specifying which devices are being used. For safety, we initialize *every* CUDA device by default, which can be quite slow if you have a lot of CUDAs. If you know that you are only making use of a few CUDA devices, set the environment variable CUDA_VISIBLE_DEVICES or the 'devices' keyword argument of fork_rng with the set of devices you are actually using. For example, if you are using CPU only, set device.upper()_VISIBLE_DEVICES= or devices=[]; if you are using device 0 only, set CUDA_VISIBLE_DEVICES=0 or devices=[0].  To initialize all devices and suppress this warning, set the 'devices' keyword argument to `range(torch.cuda.device_count())`.
+  return next(self.gen)
+/opt/conda/envs/ff-qi21/lib/python3.10/contextlib.py:135: UserWarning: CUDA reports that you have 8 available devices, and you have used fork_rng without explicitly specifying which devices are being used. For safety, we initialize *every* CUDA device by default, which can be quite slow if you have a lot of CUDAs. If you know that you are only making use of a few CUDA devices, set the environment variable CUDA_VISIBLE_DEVICES or the 'devices' keyword argument of fork_rng with the set of devices you are actually using. For example, if you are using CPU only, set device.upper()_VISIBLE_DEVICES= or devices=[]; if you are using device 0 only, set CUDA_VISIBLE_DEVICES=0 or devices=[0].  To initialize all devices and suppress this warning, set the 'devices' keyword argument to `range(torch.cuda.device_count())`.
+  return next(self.gen)
+/opt/conda/envs/ff-qi21/lib/python3.10/contextlib.py:135: UserWarning: CUDA reports that you have 8 available devices, and you have used fork_rng without explicitly specifying which devices are being used. For safety, we initialize *every* CUDA device by default, which can be quite slow if you have a lot of CUDAs. If you know that you are only making use of a few CUDA devices, set the environment variable CUDA_VISIBLE_DEVICES or the 'devices' keyword argument of fork_rng with the set of devices you are actually using. For example, if you are using CPU only, set device.upper()_VISIBLE_DEVICES= or devices=[]; if you are using device 0 only, set CUDA_VISIBLE_DEVICES=0 or devices=[0].  To initialize all devices and suppress this warning, set the 'devices' keyword argument to `range(torch.cuda.device_count())`.
+  return next(self.gen)
+/opt/conda/envs/ff-qi21/lib/python3.10/contextlib.py:135: UserWarning: CUDA reports that you have 8 available devices, and you have used fork_rng without explicitly specifying which devices are being used. For safety, we initialize *every* CUDA device by default, which can be quite slow if you have a lot of CUDAs. If you know that you are only making use of a few CUDA devices, set the environment variable CUDA_VISIBLE_DEVICES or the 'devices' keyword argument of fork_rng with the set of devices you are actually using. For example, if you are using CPU only, set device.upper()_VISIBLE_DEVICES= or devices=[]; if you are using device 0 only, set CUDA_VISIBLE_DEVICES=0 or devices=[0].  To initialize all devices and suppress this warning, set the 'devices' keyword argument to `range(torch.cuda.device_count())`.
+  return next(self.gen)
+
[Preprocessing train dataset] Shard 0000/0007:   0%|          | 0/450 [00:00<?, ? examples/s]
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[Preprocessing train dataset] Shard 0000/0007: 100%|██████████| 450/450 [02:24<00:00,  3.12 examples/s]
+[2026-10-02 07:56:24] [Rank 0] [INFO] [flow_factory.data_utils.loader]: [local] Consolidated 8 part(s) for train split -> /root/.cache/flow_factory/datasets/82135bc75fb8475b64b8342a3ef5f714
+[2026-10-02 07:56:24] [Rank 3] [INFO] [flow_factory.data_utils.loader]: Multi-source: built DataLoader for 'places_domain_rl' (dataset_size=3600, unique_sample_num_per_epoch=48, num_batches_per_epoch=96)
+[2026-10-02 07:56:24] [Rank 5] [INFO] [flow_factory.data_utils.loader]: Multi-source: built DataLoader for 'places_domain_rl' (dataset_size=3600, unique_sample_num_per_epoch=48, num_batches_per_epoch=96)
+[2026-10-02 07:56:24] [Rank 7] [INFO] [flow_factory.data_utils.loader]: Multi-source: built DataLoader for 'places_domain_rl' (dataset_size=3600, unique_sample_num_per_epoch=48, num_batches_per_epoch=96)
+[2026-10-02 07:56:24] [Rank 2] [INFO] [flow_factory.data_utils.loader]: Multi-source: built DataLoader for 'places_domain_rl' (dataset_size=3600, unique_sample_num_per_epoch=48, num_batches_per_epoch=96)
+[2026-10-02 07:56:24] [Rank 6] [INFO] [flow_factory.data_utils.loader]: Multi-source: built DataLoader for 'places_domain_rl' (dataset_size=3600, unique_sample_num_per_epoch=48, num_batches_per_epoch=96)
+[2026-10-02 07:56:24] [Rank 0] [INFO] [flow_factory.data_utils.loader]: Multi-source: built DataLoader for 'places_domain_rl' (dataset_size=3600, unique_sample_num_per_epoch=48, num_batches_per_epoch=96)
+[2026-10-02 07:56:24] [Rank 4] [INFO] [flow_factory.data_utils.loader]: Multi-source: built DataLoader for 'places_domain_rl' (dataset_size=3600, unique_sample_num_per_epoch=48, num_batches_per_epoch=96)
+[2026-10-02 07:56:24] [Rank 1] [INFO] [flow_factory.data_utils.loader]: Multi-source: built DataLoader for 'places_domain_rl' (dataset_size=3600, unique_sample_num_per_epoch=48, num_batches_per_epoch=96)
+[2026-10-02 07:56:24] [Rank 3] [INFO] [flow_factory.data_utils.loader]: Preprocessing test dataset shard 0003/0007 -> /root/.cache/flow_factory/datasets/5208a8ea74fd47a93c48d046f8731b58.tmp/_parts/rank_0003_of_0008/cache-5208a8ea74fd47a93c48d046f8731b58_shard0003of0007.arrow
+[2026-10-02 07:56:24] [Rank 6] [INFO] [flow_factory.data_utils.loader]: Preprocessing test dataset shard 0006/0007 -> /root/.cache/flow_factory/datasets/5208a8ea74fd47a93c48d046f8731b58.tmp/_parts/rank_0006_of_0008/cache-5208a8ea74fd47a93c48d046f8731b58_shard0006of0007.arrow
+[2026-10-02 07:56:24] [Rank 7] [INFO] [flow_factory.data_utils.loader]: Preprocessing test dataset shard 0007/0007 -> /root/.cache/flow_factory/datasets/5208a8ea74fd47a93c48d046f8731b58.tmp/_parts/rank_0007_of_0008/cache-5208a8ea74fd47a93c48d046f8731b58_shard0007of0007.arrow
+[2026-10-02 07:56:24] [Rank 2] [INFO] [flow_factory.data_utils.loader]: Preprocessing test dataset shard 0002/0007 -> /root/.cache/flow_factory/datasets/5208a8ea74fd47a93c48d046f8731b58.tmp/_parts/rank_0002_of_0008/cache-5208a8ea74fd47a93c48d046f8731b58_shard0002of0007.arrow
+[2026-10-02 07:56:24] [Rank 5] [INFO] [flow_factory.data_utils.loader]: Preprocessing test dataset shard 0005/0007 -> /root/.cache/flow_factory/datasets/5208a8ea74fd47a93c48d046f8731b58.tmp/_parts/rank_0005_of_0008/cache-5208a8ea74fd47a93c48d046f8731b58_shard0005of0007.arrow
+[2026-10-02 07:56:24] [Rank 0] [INFO] [flow_factory.data_utils.loader]: Preprocessing test dataset shard 0000/0007 -> /root/.cache/flow_factory/datasets/5208a8ea74fd47a93c48d046f8731b58.tmp/_parts/rank_0000_of_0008/cache-5208a8ea74fd47a93c48d046f8731b58_shard0000of0007.arrow
+[2026-10-02 07:56:24] [Rank 4] [INFO] [flow_factory.data_utils.loader]: Preprocessing test dataset shard 0004/0007 -> /root/.cache/flow_factory/datasets/5208a8ea74fd47a93c48d046f8731b58.tmp/_parts/rank_0004_of_0008/cache-5208a8ea74fd47a93c48d046f8731b58_shard0004of0007.arrow
+[2026-10-02 07:56:24] [Rank 1] [INFO] [flow_factory.data_utils.loader]: Preprocessing test dataset shard 0001/0007 -> /root/.cache/flow_factory/datasets/5208a8ea74fd47a93c48d046f8731b58.tmp/_parts/rank_0001_of_0008/cache-5208a8ea74fd47a93c48d046f8731b58_shard0001of0007.arrow
+
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+[2026-10-02 07:56:29] [Rank 0] [INFO] [flow_factory.data_utils.loader]: [local] Consolidated 8 part(s) for test split -> /root/.cache/flow_factory/datasets/5208a8ea74fd47a93c48d046f8731b58
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+
+
+/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.
+  warnings.warn(
+/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.
+  warnings.warn(
+/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.
+  warnings.warn(
+/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.
+  warnings.warn(
+/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.
+  warnings.warn(
+/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.
+  warnings.warn(
+/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.
+  warnings.warn(
+/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.
+  warnings.warn(
+/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=VGG16_Weights.IMAGENET1K_V1`. You can also use `weights=VGG16_Weights.DEFAULT` to get the most up-to-date weights.
+  warnings.warn(msg)
+/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=VGG16_Weights.IMAGENET1K_V1`. You can also use `weights=VGG16_Weights.DEFAULT` to get the most up-to-date weights.
+  warnings.warn(msg)
+/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=VGG16_Weights.IMAGENET1K_V1`. You can also use `weights=VGG16_Weights.DEFAULT` to get the most up-to-date weights.
+  warnings.warn(msg)
+/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=VGG16_Weights.IMAGENET1K_V1`. You can also use `weights=VGG16_Weights.DEFAULT` to get the most up-to-date weights.
+  warnings.warn(msg)
+/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=VGG16_Weights.IMAGENET1K_V1`. You can also use `weights=VGG16_Weights.DEFAULT` to get the most up-to-date weights.
+  warnings.warn(msg)
+/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=VGG16_Weights.IMAGENET1K_V1`. You can also use `weights=VGG16_Weights.DEFAULT` to get the most up-to-date weights.
+  warnings.warn(msg)
+/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=VGG16_Weights.IMAGENET1K_V1`. You can also use `weights=VGG16_Weights.DEFAULT` to get the most up-to-date weights.
+  warnings.warn(msg)
+/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=VGG16_Weights.IMAGENET1K_V1`. You can also use `weights=VGG16_Weights.DEFAULT` to get the most up-to-date weights.
+  warnings.warn(msg)
+wandb: [wandb.login()] Loaded credentials for https://api.wandb.ai from /root/.netrc.
+wandb: Currently logged in as: 315229706 (315229706-xi-an-jiaotong-university-) to https://api.wandb.ai. Use `wandb login --relogin` to force relogin
+wandb: setting up run qwz3tol2
+wandb: Tracking run with wandb version 0.28.2
+wandb: Run data is saved locally in /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/runs/klein4b_nft_domain_sirst_tex/wandb/run-20261002_075647-qwz3tol2
+wandb: Run `wandb offline` to turn off syncing.
+wandb: Syncing run klein4b_nft_domain_sirst_tex
+wandb: ⭐️ View project at https://wandb.ai/315229706-xi-an-jiaotong-university-/IR-Engine
+wandb: 🚀 View run at https://wandb.ai/315229706-xi-an-jiaotong-university-/IR-Engine/runs/qwz3tol2
+[2026-10-02 07:56:48] [Rank 0] [INFO] [flow_factory.models.abc]: ======================================================================
+[2026-10-02 07:56:48] [Rank 0] [INFO] [flow_factory.models.abc]: Transformer Trainable Parameters:
+[2026-10-02 07:56:48] [Rank 0] [INFO] [flow_factory.models.abc]:   Total:        3,952,221,696 (  7.36 GB)
+[2026-10-02 07:56:48] [Rank 0] [INFO] [flow_factory.models.abc]:   Trainable:       76,677,120 (  0.14 GB)
+[2026-10-02 07:56:48] [Rank 0] [INFO] [flow_factory.models.abc]:   Percentage:           1.94%
+[2026-10-02 07:56:48] [Rank 0] [INFO] [flow_factory.models.abc]: ======================================================================
+[2026-10-02 07:56:48] [Rank 0] [INFO] [flow_factory.models.abc]: Saving LoRA weights for transformer role=base to /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/runs/klein4b_nft_domain_sirst_tex/saves/klein4b_nft_domain_sirst_tex/checkpoints/checkpoint-0
+[2026-10-02 07:56:49] [Rank 0] [INFO] [flow_factory.models.abc]: Checkpoint saved successfully to /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/runs/klein4b_nft_domain_sirst_tex/saves/klein4b_nft_domain_sirst_tex/checkpoints/checkpoint-0
+
Eval/places_domain_rl:   0%|          | 0/8 [00:00<?, ?it/s][2026-10-02 07:56:50] [Rank 7] [WARNING] [flow_factory.models.flux.flux2_klein]: FLUX.2 does not support batch inference with varying condition images per sample. Falling back to single-sample inference. This warning will only appear once.
+[2026-10-02 07:56:50] [Rank 5] [WARNING] [flow_factory.models.flux.flux2_klein]: FLUX.2 does not support batch inference with varying condition images per sample. Falling back to single-sample inference. This warning will only appear once.
+[2026-10-02 07:56:50] [Rank 2] [WARNING] [flow_factory.models.flux.flux2_klein]: FLUX.2 does not support batch inference with varying condition images per sample. Falling back to single-sample inference. This warning will only appear once.
+[2026-10-02 07:56:50] [Rank 3] [WARNING] [flow_factory.models.flux.flux2_klein]: FLUX.2 does not support batch inference with varying condition images per sample. Falling back to single-sample inference. This warning will only appear once.
+[2026-10-02 07:56:50] [Rank 6] [WARNING] [flow_factory.models.flux.flux2_klein]: FLUX.2 does not support batch inference with varying condition images per sample. Falling back to single-sample inference. This warning will only appear once.
+[2026-10-02 07:56:50] [Rank 0] [WARNING] [flow_factory.models.flux.flux2_klein]: FLUX.2 does not support batch inference with varying condition images per sample. Falling back to single-sample inference. This warning will only appear once.
+[2026-10-02 07:56:50] [Rank 4] [WARNING] [flow_factory.models.flux.flux2_klein]: FLUX.2 does not support batch inference with varying condition images per sample. Falling back to single-sample inference. This warning will only appear once.
+[2026-10-02 07:56:50] [Rank 1] [WARNING] [flow_factory.models.flux.flux2_klein]: FLUX.2 does not support batch inference with varying condition images per sample. Falling back to single-sample inference. This warning will only appear once.
+
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+
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+
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+
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+[2026-10-02 07:57:07] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0000 | Epoch 000] eval/places_domain_rl/reward_domain_mean=-0.0524 eval/places_domain_rl/reward_domain_std=0.1177 eval/places_domain_rl/reward_ssim_to_stage1_mean=0.5735 eval/places_domain_rl/reward_ssim_to_stage1_std=0.1248 eval/places_domain_rl/reward_structure_mean=0.6991 eval/places_domain_rl/reward_structure_std=0.1363 eval/places_domain_rl/reward_texture_mean=0.7946 eval/places_domain_rl/reward_texture_std=0.0484
+
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+
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+[2026-10-02 08:00:11] [Rank 5] [WARNING] [flow_factory.models.flux.flux2_klein]: Flux.2: Ragged I2I detected (varying condition image sizes). Processing samples individually (warning shown once).
+[2026-10-02 08:00:11] [Rank 7] [WARNING] [flow_factory.models.flux.flux2_klein]: Flux.2: Ragged I2I detected (varying condition image sizes). Processing samples individually (warning shown once).
+[2026-10-02 08:00:11] [Rank 4] [WARNING] [flow_factory.models.flux.flux2_klein]: Flux.2: Ragged I2I detected (varying condition image sizes). Processing samples individually (warning shown once).
+[2026-10-02 08:00:11] [Rank 6] [WARNING] [flow_factory.models.flux.flux2_klein]: Flux.2: Ragged I2I detected (varying condition image sizes). Processing samples individually (warning shown once).
+[2026-10-02 08:00:11] [Rank 3] [WARNING] [flow_factory.models.flux.flux2_klein]: Flux.2: Ragged I2I detected (varying condition image sizes). Processing samples individually (warning shown once).
+[2026-10-02 08:00:11] [Rank 1] [WARNING] [flow_factory.models.flux.flux2_klein]: Flux.2: Ragged I2I detected (varying condition image sizes). Processing samples individually (warning shown once).
+[2026-10-02 08:00:11] [Rank 2] [WARNING] [flow_factory.models.flux.flux2_klein]: Flux.2: Ragged I2I detected (varying condition image sizes). Processing samples individually (warning shown once).
+[2026-10-02 08:00:11] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0000 | Epoch 000] train/reward_domain_mean=-0.0656 train/reward_domain_std=0.1100 train/reward_structure_mean=0.7306 train/reward_structure_std=0.1053 train/reward_texture_mean=0.7970 train/reward_texture_std=0.0519 train/reward_domain_group_std_mean=0.0335 train/reward_domain_group_std_max=0.0672 train/reward_domain_group_std_min=0.0087 train/reward_domain_group_mean_std=0.1037 train/reward_structure_group_std_mean=0.0356 train/reward_structure_group_std_max=0.1273 train/reward_structure_group_std_min=0.0107 train/reward_structure_group_mean_std=0.0963 train/reward_texture_group_std_mean=0.0299 train/reward_texture_group_std_max=0.0683 train/reward_texture_group_std_min=0.0141 train/reward_texture_group_mean_std=0.0406 train/reward_domain_zero_std_ratio=0 train/reward_structure_zero_std_ratio=0 train/reward_texture_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.5710 train/adv_max=5.1657 train/adv_abs_mean=1.3273
+
Epoch 0 Training:   0%|          | 0/96 [00:00<?, ?it/s][2026-10-02 08:00:11] [Rank 0] [WARNING] [flow_factory.models.flux.flux2_klein]: Flux.2: Ragged I2I detected (varying condition image sizes). Processing samples individually (warning shown once).
+
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Epoch 0 Training:  18%|█▊        | 17/96 [00:26<02:01,  1.54s/it]
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Epoch 0 Training:  19%|█▉        | 18/96 [00:27<01:59,  1.54s/it]
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Epoch 0 Training:  21%|██        | 20/96 [00:30<01:56,  1.54s/it]
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Epoch 0 Training:  24%|██▍       | 23/96 [00:35<01:52,  1.53s/it]
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Epoch 0 Training:  26%|██▌       | 25/96 [00:38<01:49,  1.54s/it]
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Epoch 0 Training:  27%|██▋       | 26/96 [00:39<01:47,  1.54s/it]
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Epoch 0 Training:  28%|██▊       | 27/96 [00:41<01:46,  1.54s/it]
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Epoch 0 Training:  29%|██▉       | 28/96 [00:43<01:44,  1.54s/it]
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Epoch 0 Training:  30%|███       | 29/96 [00:44<01:43,  1.54s/it]
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Epoch 0 Training:  31%|███▏      | 30/96 [00:46<01:41,  1.54s/it]
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Epoch 0 Training:  32%|███▏      | 31/96 [00:47<01:40,  1.54s/it]
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Epoch 0 Training:  33%|███▎      | 32/96 [00:49<01:38,  1.54s/it]
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Epoch 0 Training:  34%|███▍      | 33/96 [00:50<01:37,  1.54s/it]
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Epoch 0 Training:  35%|███▌      | 34/96 [00:52<01:35,  1.54s/it]
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Epoch 0 Training:  36%|███▋      | 35/96 [00:53<01:33,  1.54s/it]
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Epoch 0 Training:  38%|███▊      | 36/96 [00:55<01:32,  1.55s/it]
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Epoch 0 Training:  39%|███▊      | 37/96 [00:56<01:31,  1.55s/it]
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Epoch 0 Training:  40%|███▉      | 38/96 [00:58<01:29,  1.55s/it]
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Epoch 0 Training:  41%|████      | 39/96 [01:00<01:27,  1.54s/it]
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Epoch 0 Training:  42%|████▏     | 40/96 [01:01<01:26,  1.54s/it]
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Epoch 0 Training:  43%|████▎     | 41/96 [01:03<01:24,  1.54s/it]
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Epoch 0 Training:  44%|████▍     | 42/96 [01:04<01:23,  1.54s/it]
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Epoch 0 Training:  45%|████▍     | 43/96 [01:06<01:21,  1.54s/it]
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Epoch 0 Training:  46%|████▌     | 44/96 [01:07<01:19,  1.54s/it]
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Epoch 0 Training:  47%|████▋     | 45/96 [01:09<01:18,  1.54s/it]
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Epoch 0 Training:  48%|████▊     | 46/96 [01:10<01:16,  1.54s/it]
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Epoch 0 Training:  49%|████▉     | 47/96 [01:12<01:15,  1.54s/it]
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Epoch 0 Training:  50%|█████     | 48/96 [01:13<01:13,  1.54s/it]
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Epoch 0 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 0 Training:  51%|█████     | 49/96 [01:15<01:12,  1.54s/it]
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Epoch 0 Training:  52%|█████▏    | 50/96 [01:16<01:10,  1.54s/it]
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Epoch 0 Training:  53%|█████▎    | 51/96 [01:18<01:09,  1.54s/it]
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Epoch 0 Training:  54%|█████▍    | 52/96 [01:20<01:07,  1.54s/it]
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Epoch 0 Training:  55%|█████▌    | 53/96 [01:21<01:06,  1.54s/it]
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Epoch 0 Training:  56%|█████▋    | 54/96 [01:23<01:04,  1.54s/it]
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Epoch 0 Training:  57%|█████▋    | 55/96 [01:24<01:03,  1.54s/it]
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Epoch 0 Training:  58%|█████▊    | 56/96 [01:26<01:01,  1.54s/it]
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Epoch 0 Training:  59%|█████▉    | 57/96 [01:27<01:00,  1.54s/it]
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Epoch 0 Training:  60%|██████    | 58/96 [01:29<00:58,  1.54s/it]
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Epoch 0 Training:  61%|██████▏   | 59/96 [01:30<00:56,  1.54s/it]
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Epoch 0 Training:  62%|██████▎   | 60/96 [01:32<00:55,  1.54s/it]
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Epoch 0 Training:  64%|██████▎   | 61/96 [01:33<00:53,  1.54s/it]
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Epoch 0 Training:  65%|██████▍   | 62/96 [01:35<00:52,  1.54s/it]
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Epoch 0 Training:  66%|██████▌   | 63/96 [01:36<00:50,  1.54s/it]
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Epoch 0 Training:  67%|██████▋   | 64/96 [01:38<00:49,  1.54s/it]
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Epoch 0 Training:  68%|██████▊   | 65/96 [01:39<00:47,  1.54s/it]
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Epoch 0 Training:  69%|██████▉   | 66/96 [01:41<00:46,  1.54s/it]
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Epoch 0 Training:  70%|██████▉   | 67/96 [01:43<00:44,  1.54s/it]
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Epoch 0 Training:  71%|███████   | 68/96 [01:44<00:43,  1.54s/it]
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Epoch 0 Training:  72%|███████▏  | 69/96 [01:46<00:41,  1.54s/it]
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Epoch 0 Training:  73%|███████▎  | 70/96 [01:47<00:40,  1.54s/it]
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Epoch 0 Training:  74%|███████▍  | 71/96 [01:49<00:38,  1.54s/it]
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Epoch 0 Training:  75%|███████▌  | 72/96 [01:50<00:36,  1.54s/it]
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Epoch 0 Training:  76%|███████▌  | 73/96 [01:52<00:35,  1.54s/it]
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Epoch 0 Training:  77%|███████▋  | 74/96 [01:53<00:33,  1.54s/it]
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Epoch 0 Training:  78%|███████▊  | 75/96 [01:55<00:32,  1.54s/it]
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Epoch 0 Training:  79%|███████▉  | 76/96 [01:56<00:30,  1.54s/it]
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Epoch 0 Training:  80%|████████  | 77/96 [01:58<00:29,  1.54s/it]
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Epoch 0 Training:  81%|████████▏ | 78/96 [01:59<00:27,  1.54s/it]
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Epoch 0 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s][2026-10-02 08:02:39] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0000 | Epoch 000] train/policy_loss=27.4727 train/unweighted_policy_loss=5.4945 train/loss=27.4727 train/grad_norm=0.0786
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+[2026-10-02 08:02:39] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0001 | Epoch 000] timing/rollout_seconds=178.9612 timing/feedback_seconds=6.4545 timing/optimization_seconds=148.3920 timing/cycle_seconds=331.9286
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+[2026-10-02 08:05:43] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0001 | Epoch 001] train/reward_domain_mean=0.0124 train/reward_domain_std=0.1103 train/reward_structure_mean=0.7094 train/reward_structure_std=0.1422 train/reward_texture_mean=0.7461 train/reward_texture_std=0.0681 train/reward_domain_group_std_mean=0.0467 train/reward_domain_group_std_max=0.0910 train/reward_domain_group_std_min=0.0176 train/reward_domain_group_mean_std=0.0983 train/reward_structure_group_std_mean=0.0242 train/reward_structure_group_std_max=0.0613 train/reward_structure_group_std_min=0.0079 train/reward_structure_group_mean_std=0.1396 train/reward_texture_group_std_mean=0.0363 train/reward_texture_group_std_max=0.0697 train/reward_texture_group_std_min=0.0138 train/reward_texture_group_mean_std=0.0565 train/reward_domain_zero_std_ratio=0 train/reward_structure_zero_std_ratio=0 train/reward_texture_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.8486 train/adv_max=5.6666 train/adv_abs_mean=1.2112
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Epoch 1 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.04it/s]
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Epoch 1 Training:   1%|          | 1/96 [00:01<02:20,  1.48s/it]
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Epoch 1 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 1 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 1 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 1 Training:   2%|▏         | 2/96 [00:03<02:22,  1.51s/it]
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Epoch 1 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 1 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 1 Training:   3%|▎         | 3/96 [00:04<02:21,  1.53s/it]
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Epoch 1 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 1 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 1 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 1 Training:   4%|▍         | 4/96 [00:06<02:20,  1.53s/it]
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Epoch 1 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 1 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 1 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 1 Training:   5%|▌         | 5/96 [00:07<02:19,  1.53s/it]
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Epoch 1 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 1 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 1 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 1 Training:   6%|▋         | 6/96 [00:09<02:18,  1.54s/it]
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Epoch 1 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 1 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 1 Training:   7%|▋         | 7/96 [00:10<02:16,  1.53s/it]
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Epoch 1 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 1 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 1 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 1 Training:   8%|▊         | 8/96 [00:12<02:15,  1.53s/it]
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Epoch 1 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 1 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 1 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 1 Training:   9%|▉         | 9/96 [00:13<02:13,  1.54s/it]
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Epoch 1 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 1 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 1 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 1 Training:  10%|█         | 10/96 [00:15<02:11,  1.53s/it]
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Epoch 1 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 1 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 1 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.97it/s]
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Epoch 1 Training:  11%|█▏        | 11/96 [00:16<02:10,  1.54s/it]
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Epoch 1 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 1 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 1 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 1 Training:  12%|█▎        | 12/96 [00:18<02:09,  1.54s/it]
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Epoch 1 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 1 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 1 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 1 Training:  14%|█▎        | 13/96 [00:19<02:07,  1.54s/it]
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Epoch 1 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 1 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 1 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 1 Training:  15%|█▍        | 14/96 [00:21<02:06,  1.54s/it]
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Epoch 1 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 1 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 1 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 1 Training:  16%|█▌        | 15/96 [00:23<02:04,  1.54s/it]
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Epoch 1 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 1 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 1 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 1 Training:  17%|█▋        | 16/96 [00:24<02:02,  1.54s/it]
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Epoch 1 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 1 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 1 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 1 Training:  18%|█▊        | 17/96 [00:26<02:01,  1.53s/it]
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Epoch 1 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 1 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 1 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 1 Training:  19%|█▉        | 18/96 [00:27<01:59,  1.53s/it]
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Epoch 1 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 1 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 1 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 1 Training:  20%|█▉        | 19/96 [00:29<01:58,  1.53s/it]
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Epoch 1 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 1 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 1 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 1 Training:  21%|██        | 20/96 [00:30<01:56,  1.53s/it]
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Epoch 1 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 1 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 1 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 1 Training:  22%|██▏       | 21/96 [00:32<01:55,  1.53s/it]
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Epoch 1 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 1 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 1 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 1 Training:  23%|██▎       | 22/96 [00:33<01:53,  1.54s/it]
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Epoch 1 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 1 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 1 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.91it/s]
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Epoch 1 Training:  24%|██▍       | 23/96 [00:35<01:53,  1.55s/it]
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Epoch 1 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 1 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 1 Training:  25%|██▌       | 24/96 [00:36<01:51,  1.55s/it]
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Epoch 1 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 1 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 1 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 1 Training:  26%|██▌       | 25/96 [00:38<01:49,  1.54s/it]
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Epoch 1 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 1 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 1 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 1 Training:  27%|██▋       | 26/96 [00:39<01:47,  1.54s/it]
+
Epoch 1 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 1 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 1 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 1 Training:  28%|██▊       | 27/96 [00:41<01:46,  1.54s/it]
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Epoch 1 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 1 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 1 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 1 Training:  29%|██▉       | 28/96 [00:42<01:44,  1.54s/it]
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Epoch 1 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 1 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 1 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 1 Training:  30%|███       | 29/96 [00:44<01:42,  1.54s/it]
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Epoch 1 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 1 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 1 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 1 Training:  31%|███▏      | 30/96 [00:46<01:41,  1.54s/it]
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Epoch 1 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 1 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 1 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 1 Training:  32%|███▏      | 31/96 [00:47<01:39,  1.53s/it]
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Epoch 1 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 1 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 1 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 1 Training:  33%|███▎      | 32/96 [00:49<01:38,  1.54s/it]
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Epoch 1 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 1 Training:  34%|███▍      | 33/96 [00:50<01:36,  1.54s/it]
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Epoch 1 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 1 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 1 Training:  35%|███▌      | 34/96 [00:52<01:35,  1.53s/it]
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Epoch 1 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 1 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.98it/s]
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Epoch 1 Training:  36%|███▋      | 35/96 [00:53<01:33,  1.54s/it]
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Epoch 1 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 1 Training:  38%|███▊      | 36/96 [00:55<01:32,  1.54s/it]
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Epoch 1 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 1 Training:  39%|███▊      | 37/96 [00:56<01:30,  1.54s/it]
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Epoch 1 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 1 Training:  40%|███▉      | 38/96 [00:58<01:29,  1.54s/it]
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Epoch 1 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 1 Training:  41%|████      | 39/96 [00:59<01:27,  1.54s/it]
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Epoch 1 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 1 Training:  42%|████▏     | 40/96 [01:01<01:26,  1.54s/it]
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Epoch 1 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 1 Training:  43%|████▎     | 41/96 [01:02<01:24,  1.54s/it]
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Epoch 1 Training:  44%|████▍     | 42/96 [01:04<01:22,  1.54s/it]
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Epoch 1 Training:  45%|████▍     | 43/96 [01:06<01:21,  1.54s/it]
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Epoch 1 Training:  46%|████▌     | 44/96 [01:07<01:19,  1.54s/it]
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Epoch 1 Training:  47%|████▋     | 45/96 [01:09<01:18,  1.54s/it]
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Epoch 1 Training:  48%|████▊     | 46/96 [01:10<01:16,  1.54s/it]
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Epoch 1 Training:  49%|████▉     | 47/96 [01:12<01:15,  1.54s/it]
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Epoch 1 Training:  50%|█████     | 48/96 [01:13<01:13,  1.54s/it]
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Epoch 1 Training:  51%|█████     | 49/96 [01:15<01:12,  1.54s/it]
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Epoch 1 Training:  52%|█████▏    | 50/96 [01:16<01:10,  1.54s/it]
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Epoch 1 Training:  53%|█████▎    | 51/96 [01:18<01:09,  1.54s/it]
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Epoch 1 Training:  54%|█████▍    | 52/96 [01:19<01:07,  1.54s/it]
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Epoch 1 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s][2026-10-02 08:08:10] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0001 | Epoch 001] train/policy_loss=26.5889 train/unweighted_policy_loss=5.3178 train/loss=26.5889 train/grad_norm=0.0836
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+[2026-10-02 08:08:10] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0002 | Epoch 001] timing/rollout_seconds=178.7687 timing/feedback_seconds=6.2223 timing/optimization_seconds=148.1934 timing/cycle_seconds=331.4695
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+[2026-10-02 08:11:15] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0002 | Epoch 002] train/reward_domain_mean=0.1022 train/reward_domain_std=0.0937 train/reward_structure_mean=0.6963 train/reward_structure_std=0.1450 train/reward_texture_mean=0.7343 train/reward_texture_std=0.0597 train/reward_domain_group_std_mean=0.0441 train/reward_domain_group_std_max=0.0853 train/reward_domain_group_std_min=0.0166 train/reward_domain_group_mean_std=0.0812 train/reward_structure_group_std_mean=0.0217 train/reward_structure_group_std_max=0.0526 train/reward_structure_group_std_min=0.0094 train/reward_structure_group_mean_std=0.1430 train/reward_texture_group_std_mean=0.0251 train/reward_texture_group_std_max=0.0646 train/reward_texture_group_std_min=0.0129 train/reward_texture_group_mean_std=0.0532 train/reward_domain_zero_std_ratio=0 train/reward_structure_zero_std_ratio=0 train/reward_texture_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.3588 train/adv_max=4.8716 train/adv_abs_mean=1.1194
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Epoch 2 Training:  27%|██▋       | 26/96 [00:40<01:48,  1.55s/it]
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Epoch 2 Training:  28%|██▊       | 27/96 [00:41<01:46,  1.55s/it]
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Epoch 2 Training:  29%|██▉       | 28/96 [00:43<01:45,  1.55s/it]
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Epoch 2 Training:  30%|███       | 29/96 [00:44<01:43,  1.55s/it]
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Epoch 2 Training:  31%|███▏      | 30/96 [00:46<01:41,  1.55s/it]
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Epoch 2 Training:  32%|███▏      | 31/96 [00:47<01:40,  1.55s/it]
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Epoch 2 Training:  33%|███▎      | 32/96 [00:49<01:39,  1.55s/it]
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Epoch 2 Training:  34%|███▍      | 33/96 [00:50<01:37,  1.55s/it]
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Epoch 2 Training:  35%|███▌      | 34/96 [00:52<01:35,  1.55s/it]
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Epoch 2 Training:  36%|███▋      | 35/96 [00:54<01:34,  1.55s/it]
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Epoch 2 Training:  38%|███▊      | 36/96 [00:55<01:32,  1.55s/it]
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Epoch 2 Training:  39%|███▊      | 37/96 [00:57<01:31,  1.54s/it]
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Epoch 2 Training:  40%|███▉      | 38/96 [00:58<01:29,  1.55s/it]
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Epoch 2 Training:  41%|████      | 39/96 [01:00<01:28,  1.54s/it]
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Epoch 2 Training:  42%|████▏     | 40/96 [01:01<01:26,  1.55s/it]
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Epoch 2 Training:  43%|████▎     | 41/96 [01:03<01:24,  1.54s/it]
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Epoch 2 Training:  44%|████▍     | 42/96 [01:04<01:23,  1.55s/it]
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Epoch 2 Training:  45%|████▍     | 43/96 [01:06<01:21,  1.54s/it]
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Epoch 2 Training:  46%|████▌     | 44/96 [01:07<01:20,  1.55s/it]
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Epoch 2 Training:  47%|████▋     | 45/96 [01:09<01:18,  1.55s/it]
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Epoch 2 Training:  48%|████▊     | 46/96 [01:11<01:17,  1.55s/it]
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Epoch 2 Training:  49%|████▉     | 47/96 [01:12<01:15,  1.55s/it]
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Epoch 2 Training:  50%|█████     | 48/96 [01:14<01:14,  1.55s/it]
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Epoch 2 Training:  51%|█████     | 49/96 [01:15<01:12,  1.55s/it]
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Epoch 2 Training:  52%|█████▏    | 50/96 [01:17<01:11,  1.55s/it]
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Epoch 2 Training:  53%|█████▎    | 51/96 [01:18<01:09,  1.55s/it]
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Epoch 2 Training:  54%|█████▍    | 52/96 [01:20<01:08,  1.55s/it]
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Epoch 2 Training:  55%|█████▌    | 53/96 [01:21<01:06,  1.55s/it]
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Epoch 2 Training:  56%|█████▋    | 54/96 [01:23<01:04,  1.54s/it]
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Epoch 2 Training:  57%|█████▋    | 55/96 [01:25<01:03,  1.54s/it]
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Epoch 2 Training:  58%|█████▊    | 56/96 [01:26<01:01,  1.54s/it]
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Epoch 2 Training:  59%|█████▉    | 57/96 [01:28<01:00,  1.54s/it]
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Epoch 2 Training:  60%|██████    | 58/96 [01:29<00:58,  1.54s/it]
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Epoch 2 Training:  61%|██████▏   | 59/96 [01:31<00:57,  1.54s/it]
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Epoch 2 Training:  62%|██████▎   | 60/96 [01:32<00:55,  1.54s/it]
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Epoch 2 Training:  64%|██████▎   | 61/96 [01:34<00:54,  1.54s/it]
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Epoch 2 Training:  65%|██████▍   | 62/96 [01:35<00:52,  1.55s/it]
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Epoch 2 Training:  66%|██████▌   | 63/96 [01:37<00:50,  1.54s/it]
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Epoch 2 Training:  67%|██████▋   | 64/96 [01:38<00:49,  1.54s/it]
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Epoch 2 Training:  68%|██████▊   | 65/96 [01:40<00:47,  1.54s/it]
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Epoch 2 Training:  69%|██████▉   | 66/96 [01:41<00:46,  1.54s/it]
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Epoch 2 Training:  70%|██████▉   | 67/96 [01:43<00:44,  1.55s/it]
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Epoch 2 Training:  71%|███████   | 68/96 [01:45<00:43,  1.55s/it]
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Epoch 2 Training:  72%|███████▏  | 69/96 [01:46<00:41,  1.55s/it]
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Epoch 2 Training:  73%|███████▎  | 70/96 [01:48<00:40,  1.55s/it]
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Epoch 2 Training:  74%|███████▍  | 71/96 [01:49<00:38,  1.55s/it]
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Epoch 2 Training:  75%|███████▌  | 72/96 [01:51<00:37,  1.55s/it]
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Epoch 2 Training:  76%|███████▌  | 73/96 [01:52<00:35,  1.55s/it]
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Epoch 2 Training:  77%|███████▋  | 74/96 [01:54<00:34,  1.55s/it]
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Epoch 2 Training:  78%|███████▊  | 75/96 [01:55<00:32,  1.55s/it]
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Epoch 2 Training:  79%|███████▉  | 76/96 [01:57<00:31,  1.56s/it]
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Epoch 2 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s][2026-10-02 08:13:43] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0002 | Epoch 002] train/policy_loss=25.5283 train/unweighted_policy_loss=5.1057 train/loss=25.5283 train/grad_norm=0.1043
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+[2026-10-02 08:13:43] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0003 | Epoch 002] timing/rollout_seconds=179.0890 timing/feedback_seconds=5.9007 timing/optimization_seconds=149.1470 timing/cycle_seconds=332.7866
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+[2026-10-02 08:16:48] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0003 | Epoch 003] train/reward_domain_mean=0.0366 train/reward_domain_std=0.1288 train/reward_structure_mean=0.7094 train/reward_structure_std=0.1381 train/reward_texture_mean=0.7656 train/reward_texture_std=0.0513 train/reward_domain_group_std_mean=0.0388 train/reward_domain_group_std_max=0.0756 train/reward_domain_group_std_min=0.0109 train/reward_domain_group_mean_std=0.1217 train/reward_structure_group_std_mean=0.0218 train/reward_structure_group_std_max=0.0506 train/reward_structure_group_std_min=0.0070 train/reward_structure_group_mean_std=0.1359 train/reward_texture_group_std_mean=0.0184 train/reward_texture_group_std_max=0.0434 train/reward_texture_group_std_min=0.0054 train/reward_texture_group_mean_std=0.0474 train/reward_domain_zero_std_ratio=0 train/reward_structure_zero_std_ratio=0 train/reward_texture_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.8723 train/adv_max=4.9343 train/adv_abs_mean=1.2446
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Epoch 3 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s][2026-10-02 08:19:16] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0003 | Epoch 003] train/policy_loss=25.4463 train/unweighted_policy_loss=5.0893 train/loss=25.4463 train/grad_norm=0.1017
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+[2026-10-02 08:19:16] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0004 | Epoch 003] timing/rollout_seconds=179.3667 timing/feedback_seconds=6.4057 timing/optimization_seconds=148.6161 timing/cycle_seconds=332.6992
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+[2026-10-02 08:22:20] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0004 | Epoch 004] train/reward_domain_mean=0.0634 train/reward_domain_std=0.1268 train/reward_structure_mean=0.7325 train/reward_structure_std=0.1530 train/reward_texture_mean=0.7720 train/reward_texture_std=0.0528 train/reward_domain_group_std_mean=0.0337 train/reward_domain_group_std_max=0.0744 train/reward_domain_group_std_min=0.0123 train/reward_domain_group_mean_std=0.1215 train/reward_structure_group_std_mean=0.0208 train/reward_structure_group_std_max=0.0616 train/reward_structure_group_std_min=0.0057 train/reward_structure_group_mean_std=0.1511 train/reward_texture_group_std_mean=0.0171 train/reward_texture_group_std_max=0.0418 train/reward_texture_group_std_min=0.0060 train/reward_texture_group_mean_std=0.0492 train/reward_domain_zero_std_ratio=0 train/reward_structure_zero_std_ratio=0 train/reward_texture_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-6.1923 train/adv_max=5.0683 train/adv_abs_mean=1.2914
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Epoch 4 Training:  23%|██▎       | 22/96 [00:33<01:53,  1.54s/it]
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Epoch 4 Training:  24%|██▍       | 23/96 [00:35<01:52,  1.54s/it]
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Epoch 4 Training:  25%|██▌       | 24/96 [00:36<01:50,  1.54s/it]
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Epoch 4 Training:  26%|██▌       | 25/96 [00:38<01:49,  1.54s/it]
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Epoch 4 Training:  27%|██▋       | 26/96 [00:40<01:47,  1.54s/it]
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Epoch 4 Training:  28%|██▊       | 27/96 [00:41<01:46,  1.54s/it]
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Epoch 4 Training:  29%|██▉       | 28/96 [00:43<01:44,  1.54s/it]
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Epoch 4 Training:  30%|███       | 29/96 [00:44<01:43,  1.54s/it]
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Epoch 4 Training:  31%|███▏      | 30/96 [00:46<01:41,  1.54s/it]
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Epoch 4 Training:  32%|███▏      | 31/96 [00:47<01:39,  1.54s/it]
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Epoch 4 Training:  33%|███▎      | 32/96 [00:49<01:38,  1.54s/it]
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Epoch 4 Training:  34%|███▍      | 33/96 [00:50<01:36,  1.54s/it]
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Epoch 4 Training:  35%|███▌      | 34/96 [00:52<01:35,  1.54s/it]
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Epoch 4 Training:  36%|███▋      | 35/96 [00:53<01:33,  1.54s/it]
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Epoch 4 Training:  38%|███▊      | 36/96 [00:55<01:32,  1.54s/it]
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Epoch 4 Training:  39%|███▊      | 37/96 [00:57<01:32,  1.58s/it]
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Epoch 4 Training:  40%|███▉      | 38/96 [00:58<01:30,  1.56s/it]
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Epoch 4 Training:  41%|████      | 39/96 [01:00<01:28,  1.56s/it]
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Epoch 4 Training:  42%|████▏     | 40/96 [01:01<01:26,  1.55s/it]
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Epoch 4 Training:  43%|████▎     | 41/96 [01:03<01:25,  1.55s/it]
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Epoch 4 Training:  44%|████▍     | 42/96 [01:04<01:23,  1.54s/it]
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Epoch 4 Training:  45%|████▍     | 43/96 [01:06<01:21,  1.54s/it]
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Epoch 4 Training:  46%|████▌     | 44/96 [01:07<01:21,  1.58s/it]
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Epoch 4 Training:  47%|████▋     | 45/96 [01:09<01:19,  1.56s/it]
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Epoch 4 Training:  48%|████▊     | 46/96 [01:10<01:17,  1.55s/it]
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Epoch 4 Training:  49%|████▉     | 47/96 [01:12<01:15,  1.55s/it]
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Epoch 4 Training:  50%|█████     | 48/96 [01:14<01:14,  1.55s/it]
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Epoch 4 Training:  51%|█████     | 49/96 [01:15<01:16,  1.64s/it]
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Epoch 4 Training:  52%|█████▏    | 50/96 [01:17<01:13,  1.61s/it]
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Epoch 4 Training:  53%|█████▎    | 51/96 [01:18<01:11,  1.59s/it]
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Epoch 4 Training:  54%|█████▍    | 52/96 [01:20<01:09,  1.57s/it]
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Epoch 4 Training:  55%|█████▌    | 53/96 [01:22<01:07,  1.56s/it]
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Epoch 4 Training:  56%|█████▋    | 54/96 [01:23<01:05,  1.55s/it]
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Epoch 4 Training:  57%|█████▋    | 55/96 [01:25<01:03,  1.55s/it]
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Epoch 4 Training:  58%|█████▊    | 56/96 [01:26<01:01,  1.54s/it]
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Epoch 4 Training:  59%|█████▉    | 57/96 [01:28<01:00,  1.54s/it]
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Epoch 4 Training:  60%|██████    | 58/96 [01:29<00:58,  1.54s/it]
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Epoch 4 Training:  61%|██████▏   | 59/96 [01:31<00:56,  1.54s/it]
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Epoch 4 Training:  62%|██████▎   | 60/96 [01:32<00:55,  1.54s/it]
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Epoch 4 Training:  64%|██████▎   | 61/96 [01:34<00:53,  1.54s/it]
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Epoch 4 Training:  65%|██████▍   | 62/96 [01:35<00:52,  1.53s/it]
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Epoch 4 Training:  66%|██████▌   | 63/96 [01:37<00:50,  1.54s/it]
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Epoch 4 Training:  67%|██████▋   | 64/96 [01:38<00:49,  1.53s/it]
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Epoch 4 Training:  68%|██████▊   | 65/96 [01:40<00:47,  1.53s/it]
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Epoch 4 Training:  69%|██████▉   | 66/96 [01:42<00:46,  1.54s/it]
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Epoch 4 Training:  70%|██████▉   | 67/96 [01:43<00:44,  1.54s/it]
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Epoch 4 Training:  71%|███████   | 68/96 [01:45<00:43,  1.55s/it]
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Epoch 4 Training:  72%|███████▏  | 69/96 [01:46<00:41,  1.54s/it]
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Epoch 4 Training:  73%|███████▎  | 70/96 [01:48<00:40,  1.54s/it]
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Epoch 4 Training:  74%|███████▍  | 71/96 [01:49<00:38,  1.54s/it]
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Epoch 4 Training:  75%|███████▌  | 72/96 [01:51<00:36,  1.54s/it]
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Epoch 4 Training:  76%|███████▌  | 73/96 [01:52<00:35,  1.54s/it]
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Epoch 4 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s][2026-10-02 08:24:48] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0004 | Epoch 004] train/policy_loss=24.7213 train/unweighted_policy_loss=4.9443 train/loss=24.7213 train/grad_norm=0.0865
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+[2026-10-02 08:24:48] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0005 | Epoch 004] timing/rollout_seconds=178.6687 timing/feedback_seconds=6.3406 timing/optimization_seconds=148.9682 timing/cycle_seconds=332.2742
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+[2026-10-02 08:25:06] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0005 | Epoch 005] eval/places_domain_rl/reward_domain_mean=0.0944 eval/places_domain_rl/reward_domain_std=0.1166 eval/places_domain_rl/reward_ssim_to_stage1_mean=0.5938 eval/places_domain_rl/reward_ssim_to_stage1_std=0.1316 eval/places_domain_rl/reward_structure_mean=0.7235 eval/places_domain_rl/reward_structure_std=0.1463 eval/places_domain_rl/reward_texture_mean=0.7642 eval/places_domain_rl/reward_texture_std=0.0502
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+[2026-10-02 08:28:10] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0005 | Epoch 005] train/reward_domain_mean=0.1104 train/reward_domain_std=0.1059 train/reward_structure_mean=0.6647 train/reward_structure_std=0.1863 train/reward_texture_mean=0.7426 train/reward_texture_std=0.0599 train/reward_domain_group_std_mean=0.0344 train/reward_domain_group_std_max=0.0747 train/reward_domain_group_std_min=0.0116 train/reward_domain_group_mean_std=0.0992 train/reward_structure_group_std_mean=0.0187 train/reward_structure_group_std_max=0.0683 train/reward_structure_group_std_min=0.0058 train/reward_structure_group_mean_std=0.1850 train/reward_texture_group_std_mean=0.0144 train/reward_texture_group_std_max=0.0274 train/reward_texture_group_std_min=0.0060 train/reward_texture_group_mean_std=0.0579 train/reward_domain_zero_std_ratio=0 train/reward_structure_zero_std_ratio=0 train/reward_texture_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.0975 train/adv_max=4.8426 train/adv_abs_mean=1.2633
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Epoch 5 Training:  43%|████▎     | 41/96 [01:03<01:24,  1.54s/it]
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Epoch 5 Training:  44%|████▍     | 42/96 [01:04<01:23,  1.54s/it]
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Epoch 5 Training:  45%|████▍     | 43/96 [01:06<01:21,  1.54s/it]
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Epoch 5 Training:  46%|████▌     | 44/96 [01:07<01:20,  1.54s/it]
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Epoch 5 Training:  47%|████▋     | 45/96 [01:09<01:18,  1.54s/it]
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Epoch 5 Training:  48%|████▊     | 46/96 [01:10<01:16,  1.54s/it]
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Epoch 5 Training:  49%|████▉     | 47/96 [01:12<01:15,  1.54s/it]
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Epoch 5 Training:  50%|█████     | 48/96 [01:13<01:14,  1.54s/it]
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Epoch 5 Training:  51%|█████     | 49/96 [01:15<01:12,  1.54s/it]
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Epoch 5 Training:  52%|█████▏    | 50/96 [01:16<01:10,  1.54s/it]
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Epoch 5 Training:  53%|█████▎    | 51/96 [01:18<01:09,  1.54s/it]
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Epoch 5 Training:  54%|█████▍    | 52/96 [01:19<01:07,  1.54s/it]
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Epoch 5 Training:  55%|█████▌    | 53/96 [01:21<01:06,  1.54s/it]
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Epoch 5 Training:  56%|█████▋    | 54/96 [01:23<01:04,  1.54s/it]
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Epoch 5 Training:  57%|█████▋    | 55/96 [01:24<01:03,  1.54s/it]
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Epoch 5 Training:  58%|█████▊    | 56/96 [01:26<01:01,  1.54s/it]
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Epoch 5 Training:  59%|█████▉    | 57/96 [01:27<01:00,  1.54s/it]
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Epoch 5 Training:  60%|██████    | 58/96 [01:29<00:58,  1.54s/it]
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Epoch 5 Training:  61%|██████▏   | 59/96 [01:30<00:56,  1.54s/it]
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Epoch 5 Training:  62%|██████▎   | 60/96 [01:32<00:55,  1.54s/it]
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Epoch 5 Training:  64%|██████▎   | 61/96 [01:33<00:54,  1.55s/it]
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Epoch 5 Training:  65%|██████▍   | 62/96 [01:35<00:52,  1.54s/it]
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Epoch 5 Training:  66%|██████▌   | 63/96 [01:36<00:50,  1.54s/it]
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Epoch 5 Training:  67%|██████▋   | 64/96 [01:38<00:49,  1.54s/it]
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Epoch 5 Training:  68%|██████▊   | 65/96 [01:40<00:47,  1.54s/it]
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Epoch 5 Training:  69%|██████▉   | 66/96 [01:41<00:46,  1.54s/it]
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Epoch 5 Training:  70%|██████▉   | 67/96 [01:43<00:44,  1.54s/it]
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Epoch 5 Training:  71%|███████   | 68/96 [01:44<00:43,  1.54s/it]
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Epoch 5 Training:  72%|███████▏  | 69/96 [01:46<00:41,  1.54s/it]
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Epoch 5 Training:  73%|███████▎  | 70/96 [01:47<00:40,  1.54s/it]
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Epoch 5 Training:  74%|███████▍  | 71/96 [01:49<00:38,  1.54s/it]
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Epoch 5 Training:  75%|███████▌  | 72/96 [01:50<00:36,  1.54s/it]
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Epoch 5 Training:  76%|███████▌  | 73/96 [01:52<00:35,  1.54s/it]
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Epoch 5 Training:  77%|███████▋  | 74/96 [01:53<00:33,  1.54s/it]
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Epoch 5 Training:  78%|███████▊  | 75/96 [01:55<00:32,  1.54s/it]
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Epoch 5 Training:  79%|███████▉  | 76/96 [01:56<00:30,  1.54s/it]
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Epoch 5 Training:  80%|████████  | 77/96 [01:58<00:29,  1.54s/it]
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Epoch 5 Training:  81%|████████▏ | 78/96 [02:00<00:27,  1.54s/it]
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Epoch 5 Training:  82%|████████▏ | 79/96 [02:01<00:26,  1.54s/it]
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Epoch 5 Training:  83%|████████▎ | 80/96 [02:03<00:24,  1.54s/it]
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Epoch 5 Training:  84%|████████▍ | 81/96 [02:04<00:23,  1.54s/it]
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Epoch 5 Training:  85%|████████▌ | 82/96 [02:06<00:21,  1.54s/it]
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Epoch 5 Training:  86%|████████▋ | 83/96 [02:07<00:19,  1.54s/it]
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Epoch 5 Training:  88%|████████▊ | 84/96 [02:09<00:18,  1.54s/it]
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Epoch 5 Training:  89%|████████▊ | 85/96 [02:10<00:16,  1.54s/it]
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Epoch 5 Training:  90%|████████▉ | 86/96 [02:12<00:15,  1.54s/it]
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Epoch 5 Training:  91%|█████████ | 87/96 [02:13<00:13,  1.54s/it]
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Epoch 5 Training:  92%|█████████▏| 88/96 [02:15<00:12,  1.54s/it]
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Epoch 5 Training:  93%|█████████▎| 89/96 [02:16<00:10,  1.54s/it]
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Epoch 5 Training:  94%|█████████▍| 90/96 [02:18<00:09,  1.54s/it]
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Epoch 5 Training:  95%|█████████▍| 91/96 [02:20<00:07,  1.54s/it]
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Epoch 5 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s][2026-10-02 08:30:38] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0005 | Epoch 005] train/policy_loss=24.4374 train/unweighted_policy_loss=4.8875 train/loss=24.4374 train/grad_norm=0.1032
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+[2026-10-02 08:30:38] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0006 | Epoch 005] timing/rollout_seconds=178.8204 timing/feedback_seconds=6.7251 timing/optimization_seconds=148.4574 timing/cycle_seconds=332.1955
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+[2026-10-02 08:33:42] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0006 | Epoch 006] train/reward_domain_mean=0.0859 train/reward_domain_std=0.1161 train/reward_structure_mean=0.7101 train/reward_structure_std=0.1271 train/reward_texture_mean=0.7569 train/reward_texture_std=0.0505 train/reward_domain_group_std_mean=0.0327 train/reward_domain_group_std_max=0.0799 train/reward_domain_group_std_min=0.0121 train/reward_domain_group_mean_std=0.1104 train/reward_structure_group_std_mean=0.0147 train/reward_structure_group_std_max=0.0532 train/reward_structure_group_std_min=0.0043 train/reward_structure_group_mean_std=0.1259 train/reward_texture_group_std_mean=0.0142 train/reward_texture_group_std_max=0.0240 train/reward_texture_group_std_min=0.0069 train/reward_texture_group_mean_std=0.0483 train/reward_domain_zero_std_ratio=0 train/reward_structure_zero_std_ratio=0 train/reward_texture_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.6539 train/adv_max=4.2216 train/adv_abs_mean=1.2508
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Epoch 6 Training:  15%|█▍        | 14/96 [00:21<02:07,  1.56s/it]
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Epoch 6 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 6 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.78it/s]
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Epoch 6 Training:  16%|█▌        | 15/96 [00:23<02:08,  1.59s/it]
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Epoch 6 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 6 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 6 Training:  17%|█▋        | 16/96 [00:24<02:06,  1.58s/it]
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Epoch 6 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 6 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 6 Training:  18%|█▊        | 17/96 [00:26<02:03,  1.57s/it]
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Epoch 6 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 6 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.02it/s]
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Epoch 6 Training:  19%|█▉        | 18/96 [00:28<02:01,  1.56s/it]
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Epoch 6 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 6 Training:  20%|█▉        | 19/96 [00:29<01:59,  1.55s/it]
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Epoch 6 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 6 Training:  21%|██        | 20/96 [00:31<01:57,  1.55s/it]
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Epoch 6 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 6 Training:  22%|██▏       | 21/96 [00:32<01:55,  1.54s/it]
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Epoch 6 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 6 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.02it/s]
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Epoch 6 Training:  23%|██▎       | 22/96 [00:34<01:53,  1.54s/it]
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Epoch 6 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 6 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.02it/s]
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Epoch 6 Training:  24%|██▍       | 23/96 [00:35<01:52,  1.54s/it]
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Epoch 6 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 6 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 6 Training:  25%|██▌       | 24/96 [00:37<01:50,  1.54s/it]
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Epoch 6 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 6 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 6 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 6 Training:  26%|██▌       | 25/96 [00:38<01:49,  1.54s/it]
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Epoch 6 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 6 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 6 Training:  27%|██▋       | 26/96 [00:40<01:47,  1.54s/it]
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Epoch 6 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 6 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 6 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 6 Training:  28%|██▊       | 27/96 [00:41<01:46,  1.54s/it]
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Epoch 6 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 6 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
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Epoch 6 Training:  29%|██▉       | 28/96 [00:43<01:45,  1.55s/it]
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Epoch 6 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 6 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.96it/s]
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Epoch 6 Training:  30%|███       | 29/96 [00:45<01:43,  1.55s/it]
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Epoch 6 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 6 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 6 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 6 Training:  31%|███▏      | 30/96 [00:46<01:42,  1.55s/it]
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Epoch 6 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 6 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 6 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.02it/s]
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Epoch 6 Training:  32%|███▏      | 31/96 [00:48<01:40,  1.54s/it]
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Epoch 6 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 6 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 6 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 6 Training:  33%|███▎      | 32/96 [00:49<01:38,  1.54s/it]
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Epoch 6 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 6 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 6 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 6 Training:  34%|███▍      | 33/96 [00:51<01:37,  1.54s/it]
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Epoch 6 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 6 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 6 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 6 Training:  35%|███▌      | 34/96 [00:52<01:35,  1.54s/it]
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Epoch 6 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 6 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 6 Training:  36%|███▋      | 35/96 [00:54<01:33,  1.54s/it]
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Epoch 6 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 6 Training:  38%|███▊      | 36/96 [00:55<01:32,  1.54s/it]
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Epoch 6 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 6 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 6 Training:  39%|███▊      | 37/96 [00:57<01:30,  1.54s/it]
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Epoch 6 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 6 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 6 Training:  40%|███▉      | 38/96 [00:58<01:29,  1.54s/it]
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Epoch 6 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 6 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 6 Training:  41%|████      | 39/96 [01:00<01:27,  1.54s/it]
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Epoch 6 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 6 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.15it/s]
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Epoch 6 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.02it/s]
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Epoch 6 Training:  42%|████▏     | 40/96 [01:01<01:26,  1.54s/it]
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Epoch 6 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 6 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 6 Training:  43%|████▎     | 41/96 [01:03<01:24,  1.54s/it]
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Epoch 6 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 6 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 6 Training:  44%|████▍     | 42/96 [01:05<01:23,  1.54s/it]
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Epoch 6 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 6 Training:  45%|████▍     | 43/96 [01:06<01:21,  1.54s/it]
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Epoch 6 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 6 Training:  46%|████▌     | 44/96 [01:08<01:20,  1.54s/it]
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Epoch 6 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 6 Training:  47%|████▋     | 45/96 [01:09<01:18,  1.54s/it]
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Epoch 6 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 6 Training:  48%|████▊     | 46/96 [01:11<01:16,  1.54s/it]
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Epoch 6 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 6 Training:  49%|████▉     | 47/96 [01:12<01:15,  1.54s/it]
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Epoch 6 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 6 Training:  50%|█████     | 48/96 [01:14<01:13,  1.54s/it]
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Epoch 6 Training:  51%|█████     | 49/96 [01:15<01:12,  1.54s/it]
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Epoch 6 Training:  52%|█████▏    | 50/96 [01:17<01:10,  1.54s/it]
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Epoch 6 Training:  53%|█████▎    | 51/96 [01:18<01:09,  1.54s/it]
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Epoch 6 Training:  54%|█████▍    | 52/96 [01:20<01:07,  1.54s/it]
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Epoch 6 Training:  55%|█████▌    | 53/96 [01:21<01:06,  1.54s/it]
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Epoch 6 Training:  56%|█████▋    | 54/96 [01:23<01:04,  1.54s/it]
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Epoch 6 Training:  57%|█████▋    | 55/96 [01:24<01:02,  1.54s/it]
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Epoch 6 Training:  58%|█████▊    | 56/96 [01:26<01:01,  1.54s/it]
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Epoch 6 Training:  59%|█████▉    | 57/96 [01:28<01:00,  1.54s/it]
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Epoch 6 Training:  60%|██████    | 58/96 [01:29<00:58,  1.54s/it]
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Epoch 6 Training:  61%|██████▏   | 59/96 [01:31<00:56,  1.54s/it]
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Epoch 6 Training:  62%|██████▎   | 60/96 [01:32<00:55,  1.54s/it]
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Epoch 6 Training:  64%|██████▎   | 61/96 [01:34<00:53,  1.54s/it]
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Epoch 6 Training:  65%|██████▍   | 62/96 [01:35<00:52,  1.54s/it]
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Epoch 6 Training:  66%|██████▌   | 63/96 [01:37<00:50,  1.54s/it]
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Epoch 6 Training:  67%|██████▋   | 64/96 [01:38<00:49,  1.54s/it]
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Epoch 6 Training:  68%|██████▊   | 65/96 [01:40<00:47,  1.54s/it]
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Epoch 6 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s][2026-10-02 08:36:10] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0006 | Epoch 006] train/policy_loss=23.9441 train/unweighted_policy_loss=4.7888 train/loss=23.9441 train/grad_norm=0.1409
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+[2026-10-02 08:36:10] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0007 | Epoch 006] timing/rollout_seconds=179.1094 timing/feedback_seconds=7.3048 timing/optimization_seconds=148.7431 timing/cycle_seconds=332.4721
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+[2026-10-02 08:39:14] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0007 | Epoch 007] train/reward_domain_mean=0.0904 train/reward_domain_std=0.1034 train/reward_structure_mean=0.7282 train/reward_structure_std=0.1262 train/reward_texture_mean=0.7642 train/reward_texture_std=0.0449 train/reward_domain_group_std_mean=0.0306 train/reward_domain_group_std_max=0.0609 train/reward_domain_group_std_min=0.0054 train/reward_domain_group_mean_std=0.0980 train/reward_structure_group_std_mean=0.0135 train/reward_structure_group_std_max=0.0339 train/reward_structure_group_std_min=0.0044 train/reward_structure_group_mean_std=0.1254 train/reward_texture_group_std_mean=0.0135 train/reward_texture_group_std_max=0.0267 train/reward_texture_group_std_min=0.0042 train/reward_texture_group_mean_std=0.0425 train/reward_domain_zero_std_ratio=0 train/reward_structure_zero_std_ratio=0 train/reward_texture_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.9771 train/adv_max=4.9145 train/adv_abs_mean=1.2896
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Epoch 7 Training:  41%|████      | 39/96 [00:59<01:27,  1.54s/it]
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Epoch 7 Training:  42%|████▏     | 40/96 [01:01<01:26,  1.54s/it]
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Epoch 7 Training:  43%|████▎     | 41/96 [01:02<01:24,  1.54s/it]
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Epoch 7 Training:  44%|████▍     | 42/96 [01:04<01:22,  1.54s/it]
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Epoch 7 Training:  45%|████▍     | 43/96 [01:06<01:21,  1.54s/it]
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Epoch 7 Training:  46%|████▌     | 44/96 [01:07<01:19,  1.54s/it]
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Epoch 7 Training:  47%|████▋     | 45/96 [01:09<01:18,  1.54s/it]
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Epoch 7 Training:  48%|████▊     | 46/96 [01:10<01:16,  1.54s/it]
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Epoch 7 Training:  49%|████▉     | 47/96 [01:12<01:15,  1.54s/it]
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Epoch 7 Training:  50%|█████     | 48/96 [01:13<01:13,  1.54s/it]
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Epoch 7 Training:  51%|█████     | 49/96 [01:15<01:12,  1.54s/it]
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Epoch 7 Training:  52%|█████▏    | 50/96 [01:16<01:10,  1.54s/it]
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Epoch 7 Training:  53%|█████▎    | 51/96 [01:18<01:09,  1.53s/it]
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Epoch 7 Training:  54%|█████▍    | 52/96 [01:19<01:07,  1.53s/it]
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Epoch 7 Training:  55%|█████▌    | 53/96 [01:21<01:06,  1.55s/it]
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Epoch 7 Training:  56%|█████▋    | 54/96 [01:22<01:04,  1.54s/it]
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Epoch 7 Training:  57%|█████▋    | 55/96 [01:24<01:03,  1.54s/it]
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Epoch 7 Training:  58%|█████▊    | 56/96 [01:26<01:01,  1.54s/it]
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Epoch 7 Training:  59%|█████▉    | 57/96 [01:27<00:59,  1.54s/it]
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Epoch 7 Training:  60%|██████    | 58/96 [01:29<00:58,  1.54s/it]
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Epoch 7 Training:  61%|██████▏   | 59/96 [01:30<00:56,  1.54s/it]
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Epoch 7 Training:  62%|██████▎   | 60/96 [01:32<00:55,  1.54s/it]
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Epoch 7 Training:  64%|██████▎   | 61/96 [01:33<00:53,  1.54s/it]
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Epoch 7 Training:  65%|██████▍   | 62/96 [01:35<00:52,  1.54s/it]
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Epoch 7 Training:  66%|██████▌   | 63/96 [01:36<00:50,  1.54s/it]
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Epoch 7 Training:  67%|██████▋   | 64/96 [01:38<00:49,  1.54s/it]
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Epoch 7 Training:  68%|██████▊   | 65/96 [01:39<00:47,  1.54s/it]
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Epoch 7 Training:  69%|██████▉   | 66/96 [01:41<00:46,  1.54s/it]
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Epoch 7 Training:  70%|██████▉   | 67/96 [01:42<00:44,  1.54s/it]
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Epoch 7 Training:  71%|███████   | 68/96 [01:44<00:42,  1.53s/it]
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Epoch 7 Training:  72%|███████▏  | 69/96 [01:46<00:41,  1.54s/it]
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Epoch 7 Training:  73%|███████▎  | 70/96 [01:47<00:39,  1.54s/it]
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Epoch 7 Training:  74%|███████▍  | 71/96 [01:49<00:38,  1.54s/it]
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Epoch 7 Training:  75%|███████▌  | 72/96 [01:50<00:36,  1.54s/it]
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Epoch 7 Training:  76%|███████▌  | 73/96 [01:52<00:35,  1.54s/it]
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Epoch 7 Training:  77%|███████▋  | 74/96 [01:53<00:33,  1.53s/it]
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Epoch 7 Training:  78%|███████▊  | 75/96 [01:55<00:32,  1.54s/it]
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Epoch 7 Training:  79%|███████▉  | 76/96 [01:56<00:30,  1.54s/it]
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Epoch 7 Training:  80%|████████  | 77/96 [01:58<00:29,  1.53s/it]
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Epoch 7 Training:  81%|████████▏ | 78/96 [01:59<00:27,  1.54s/it]
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Epoch 7 Training:  82%|████████▏ | 79/96 [02:01<00:26,  1.53s/it]
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Epoch 7 Training:  83%|████████▎ | 80/96 [02:02<00:24,  1.53s/it]
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Epoch 7 Training:  84%|████████▍ | 81/96 [02:04<00:23,  1.53s/it]
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Epoch 7 Training:  85%|████████▌ | 82/96 [02:05<00:21,  1.53s/it]
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Epoch 7 Training:  86%|████████▋ | 83/96 [02:07<00:19,  1.53s/it]
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Epoch 7 Training:  88%|████████▊ | 84/96 [02:09<00:18,  1.53s/it]
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Epoch 7 Training:  89%|████████▊ | 85/96 [02:10<00:16,  1.53s/it]
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Epoch 7 Training:  90%|████████▉ | 86/96 [02:12<00:15,  1.53s/it]
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Epoch 7 Training:  91%|█████████ | 87/96 [02:13<00:13,  1.53s/it]
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Epoch 7 Training:  92%|█████████▏| 88/96 [02:15<00:12,  1.54s/it]
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Epoch 7 Training:  93%|█████████▎| 89/96 [02:16<00:10,  1.54s/it]
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Epoch 7 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s][2026-10-02 08:41:42] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0007 | Epoch 007] train/policy_loss=23.8280 train/unweighted_policy_loss=4.7656 train/loss=23.8280 train/grad_norm=0.1057
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+[2026-10-02 08:41:42] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0008 | Epoch 007] timing/rollout_seconds=178.7589 timing/feedback_seconds=6.9562 timing/optimization_seconds=148.1925 timing/cycle_seconds=331.4845
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+[2026-10-02 08:44:46] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0008 | Epoch 008] train/reward_domain_mean=0.0685 train/reward_domain_std=0.1085 train/reward_structure_mean=0.7398 train/reward_structure_std=0.1460 train/reward_texture_mean=0.7711 train/reward_texture_std=0.0512 train/reward_domain_group_std_mean=0.0262 train/reward_domain_group_std_max=0.0540 train/reward_domain_group_std_min=0.0096 train/reward_domain_group_mean_std=0.1048 train/reward_structure_group_std_mean=0.0137 train/reward_structure_group_std_max=0.0466 train/reward_structure_group_std_min=0.0019 train/reward_structure_group_mean_std=0.1452 train/reward_texture_group_std_mean=0.0116 train/reward_texture_group_std_max=0.0242 train/reward_texture_group_std_min=0.0054 train/reward_texture_group_mean_std=0.0497 train/reward_domain_zero_std_ratio=0 train/reward_structure_zero_std_ratio=0 train/reward_texture_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-6.3536 train/adv_max=4.7565 train/adv_abs_mean=1.3200
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Epoch 8 Training:  12%|█▎        | 12/96 [00:18<02:08,  1.54s/it]
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Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Training:  14%|█▎        | 13/96 [00:19<02:07,  1.54s/it]
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Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Training:  15%|█▍        | 14/96 [00:21<02:05,  1.54s/it]
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Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 8 Training:  16%|█▌        | 15/96 [00:23<02:04,  1.54s/it]
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Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 8 Training:  17%|█▋        | 16/96 [00:24<02:02,  1.54s/it]
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Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.98it/s]
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Epoch 8 Training:  18%|█▊        | 17/96 [00:26<02:01,  1.54s/it]
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Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 8 Training:  19%|█▉        | 18/96 [00:27<01:59,  1.54s/it]
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Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Training:  20%|█▉        | 19/96 [00:29<01:58,  1.54s/it]
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Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Training:  21%|██        | 20/96 [00:30<01:56,  1.54s/it]
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Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Training:  22%|██▏       | 21/96 [00:32<01:55,  1.54s/it]
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Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.98it/s]
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Epoch 8 Training:  23%|██▎       | 22/96 [00:33<01:53,  1.54s/it]
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Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 8 Training:  24%|██▍       | 23/96 [00:35<01:52,  1.54s/it]
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Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Training:  25%|██▌       | 24/96 [00:36<01:50,  1.54s/it]
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Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 8 Training:  26%|██▌       | 25/96 [00:38<01:49,  1.54s/it]
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Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 8 Training:  27%|██▋       | 26/96 [00:39<01:47,  1.54s/it]
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Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 8 Training:  28%|██▊       | 27/96 [00:41<01:46,  1.54s/it]
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Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 8 Training:  29%|██▉       | 28/96 [00:42<01:44,  1.54s/it]
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Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 8 Training:  30%|███       | 29/96 [00:44<01:42,  1.54s/it]
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Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 8 Training:  31%|███▏      | 30/96 [00:46<01:41,  1.54s/it]
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Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 8 Training:  32%|███▏      | 31/96 [00:47<01:39,  1.54s/it]
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Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 8 Training:  33%|███▎      | 32/96 [00:49<01:38,  1.54s/it]
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Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.97it/s]
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Epoch 8 Training:  34%|███▍      | 33/96 [00:50<01:36,  1.54s/it]
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Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.98it/s]
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Epoch 8 Training:  35%|███▌      | 34/96 [00:52<01:35,  1.54s/it]
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Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 8 Training:  36%|███▋      | 35/96 [00:53<01:33,  1.54s/it]
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Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 8 Training:  38%|███▊      | 36/96 [00:55<01:32,  1.54s/it]
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Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 8 Training:  39%|███▊      | 37/96 [00:56<01:30,  1.54s/it]
+
Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.97it/s]
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Epoch 8 Training:  40%|███▉      | 38/96 [00:58<01:29,  1.54s/it]
+
Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 8 Training:  41%|████      | 39/96 [00:59<01:27,  1.54s/it]
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Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 8 Training:  42%|████▏     | 40/96 [01:01<01:26,  1.54s/it]
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Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 8 Training:  43%|████▎     | 41/96 [01:03<01:24,  1.54s/it]
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Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.98it/s]
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Epoch 8 Training:  44%|████▍     | 42/96 [01:04<01:23,  1.54s/it]
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Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 8 Training:  45%|████▍     | 43/96 [01:06<01:21,  1.54s/it]
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Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Training:  46%|████▌     | 44/96 [01:07<01:20,  1.54s/it]
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Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Training:  47%|████▋     | 45/96 [01:09<01:18,  1.54s/it]
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Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Training:  48%|████▊     | 46/96 [01:10<01:17,  1.54s/it]
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Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Training:  49%|████▉     | 47/96 [01:12<01:15,  1.54s/it]
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Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Training:  50%|█████     | 48/96 [01:13<01:14,  1.54s/it]
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Epoch 8 Training:  51%|█████     | 49/96 [01:15<01:12,  1.55s/it]
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Epoch 8 Training:  52%|█████▏    | 50/96 [01:16<01:11,  1.56s/it]
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Epoch 8 Training:  53%|█████▎    | 51/96 [01:18<01:10,  1.57s/it]
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Epoch 8 Training:  54%|█████▍    | 52/96 [01:20<01:08,  1.56s/it]
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Epoch 8 Training:  55%|█████▌    | 53/96 [01:21<01:06,  1.55s/it]
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Epoch 8 Training:  56%|█████▋    | 54/96 [01:23<01:05,  1.55s/it]
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Epoch 8 Training:  57%|█████▋    | 55/96 [01:24<01:03,  1.55s/it]
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Epoch 8 Training:  58%|█████▊    | 56/96 [01:26<01:01,  1.54s/it]
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Epoch 8 Training:  59%|█████▉    | 57/96 [01:27<01:00,  1.54s/it]
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Epoch 8 Training:  60%|██████    | 58/96 [01:29<00:58,  1.54s/it]
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Epoch 8 Training:  61%|██████▏   | 59/96 [01:30<00:57,  1.54s/it]
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Epoch 8 Training:  62%|██████▎   | 60/96 [01:32<00:55,  1.54s/it]
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Epoch 8 Training:  64%|██████▎   | 61/96 [01:33<00:53,  1.54s/it]
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Epoch 8 Training:  65%|██████▍   | 62/96 [01:35<00:52,  1.54s/it]
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Epoch 8 Training:  66%|██████▌   | 63/96 [01:37<00:50,  1.54s/it]
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Epoch 8 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s][2026-10-02 08:47:14] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0008 | Epoch 008] train/policy_loss=23.3975 train/unweighted_policy_loss=4.6795 train/loss=23.3975 train/grad_norm=0.0935
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+[2026-10-02 08:47:14] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0009 | Epoch 008] timing/rollout_seconds=178.6686 timing/feedback_seconds=6.9942 timing/optimization_seconds=148.6556 timing/cycle_seconds=331.9187
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+[2026-10-02 08:50:18] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0009 | Epoch 009] train/reward_domain_mean=0.0450 train/reward_domain_std=0.1249 train/reward_structure_mean=0.7167 train/reward_structure_std=0.1303 train/reward_texture_mean=0.7789 train/reward_texture_std=0.0396 train/reward_domain_group_std_mean=0.0258 train/reward_domain_group_std_max=0.0584 train/reward_domain_group_std_min=0.0085 train/reward_domain_group_mean_std=0.1217 train/reward_structure_group_std_mean=0.0148 train/reward_structure_group_std_max=0.0309 train/reward_structure_group_std_min=0.0044 train/reward_structure_group_mean_std=0.1293 train/reward_texture_group_std_mean=0.0114 train/reward_texture_group_std_max=0.0248 train/reward_texture_group_std_min=0.0035 train/reward_texture_group_mean_std=0.0376 train/reward_domain_zero_std_ratio=0 train/reward_structure_zero_std_ratio=0 train/reward_texture_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-8.6870 train/adv_max=5.9124 train/adv_abs_mean=1.3631
+
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Epoch 9 Training:   8%|▊         | 8/96 [00:12<02:14,  1.52s/it]
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Epoch 9 Training:  23%|██▎       | 22/96 [00:33<01:53,  1.53s/it]
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Epoch 9 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 9 Training:  39%|███▊      | 37/96 [00:56<01:29,  1.52s/it]
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Epoch 9 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 9 Training:  40%|███▉      | 38/96 [00:57<01:28,  1.52s/it]
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Epoch 9 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 9 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.04it/s]
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Epoch 9 Training:  41%|████      | 39/96 [00:59<01:26,  1.53s/it]
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Epoch 9 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 9 Training:  42%|████▏     | 40/96 [01:01<01:25,  1.53s/it]
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Epoch 9 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 9 Training:  43%|████▎     | 41/96 [01:02<01:23,  1.53s/it]
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Epoch 9 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 9 Training:  44%|████▍     | 42/96 [01:04<01:22,  1.52s/it]
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Epoch 9 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 9 Training:  45%|████▍     | 43/96 [01:05<01:20,  1.52s/it]
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Epoch 9 Training:  46%|████▌     | 44/96 [01:07<01:19,  1.52s/it]
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Epoch 9 Training:  47%|████▋     | 45/96 [01:08<01:17,  1.52s/it]
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Epoch 9 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 9 Training:  48%|████▊     | 46/96 [01:10<01:17,  1.54s/it]
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Epoch 9 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 9 Training:  49%|████▉     | 47/96 [01:11<01:15,  1.54s/it]
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Epoch 9 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 9 Training:  50%|█████     | 48/96 [01:13<01:13,  1.53s/it]
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Epoch 9 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 9 Training:  51%|█████     | 49/96 [01:14<01:11,  1.53s/it]
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Epoch 9 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 9 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.02it/s]
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Epoch 9 Training:  52%|█████▏    | 50/96 [01:16<01:10,  1.53s/it]
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Epoch 9 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 9 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.04it/s]
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Epoch 9 Training:  53%|█████▎    | 51/96 [01:17<01:08,  1.53s/it]
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Epoch 9 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 9 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.02it/s]
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Epoch 9 Training:  54%|█████▍    | 52/96 [01:19<01:07,  1.53s/it]
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Epoch 9 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 9 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.02it/s]
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Epoch 9 Training:  55%|█████▌    | 53/96 [01:20<01:05,  1.53s/it]
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Epoch 9 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 9 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 9 Training:  56%|█████▋    | 54/96 [01:22<01:04,  1.53s/it]
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Epoch 9 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 9 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.02it/s]
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Epoch 9 Training:  57%|█████▋    | 55/96 [01:23<01:02,  1.53s/it]
+
Epoch 9 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 9 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.02it/s]
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Epoch 9 Training:  58%|█████▊    | 56/96 [01:25<01:01,  1.53s/it]
+
Epoch 9 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 9 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 9 Training:  59%|█████▉    | 57/96 [01:27<00:59,  1.53s/it]
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Epoch 9 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 9 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 9 Training:  60%|██████    | 58/96 [01:28<00:58,  1.53s/it]
+
Epoch 9 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 9 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 9 Training:  61%|██████▏   | 59/96 [01:30<00:56,  1.53s/it]
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Epoch 9 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 9 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.02it/s]
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Epoch 9 Training:  62%|██████▎   | 60/96 [01:31<00:54,  1.53s/it]
+
Epoch 9 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 9 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 9 Training:  64%|██████▎   | 61/96 [01:33<00:53,  1.52s/it]
+
Epoch 9 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 9 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 9 Training:  65%|██████▍   | 62/96 [01:34<00:51,  1.52s/it]
+
Epoch 9 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 9 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 9 Training:  66%|██████▌   | 63/96 [01:36<00:50,  1.52s/it]
+
Epoch 9 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 9 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 9 Training:  67%|██████▋   | 64/96 [01:37<00:48,  1.52s/it]
+
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Epoch 9 Training:  68%|██████▊   | 65/96 [01:39<00:47,  1.52s/it]
+
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Epoch 9 Training:  69%|██████▉   | 66/96 [01:40<00:45,  1.52s/it]
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Epoch 9 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 9 Training:  70%|██████▉   | 67/96 [01:42<00:44,  1.52s/it]
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Epoch 9 Training:  71%|███████   | 68/96 [01:43<00:42,  1.52s/it]
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Epoch 9 Training:  72%|███████▏  | 69/96 [01:45<00:41,  1.52s/it]
+
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Epoch 9 Training:  73%|███████▎  | 70/96 [01:46<00:39,  1.53s/it]
+
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Epoch 9 Training:  74%|███████▍  | 71/96 [01:48<00:38,  1.53s/it]
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Epoch 9 Training:  75%|███████▌  | 72/96 [01:49<00:36,  1.52s/it]
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Epoch 9 Training:  76%|███████▌  | 73/96 [01:51<00:35,  1.52s/it]
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Epoch 9 Training:  77%|███████▋  | 74/96 [01:52<00:33,  1.52s/it]
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Epoch 9 Training:  78%|███████▊  | 75/96 [01:54<00:32,  1.52s/it]
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Epoch 9 Training:  79%|███████▉  | 76/96 [01:55<00:30,  1.52s/it]
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Epoch 9 Training:  80%|████████  | 77/96 [01:57<00:28,  1.52s/it]
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Epoch 9 Training:  81%|████████▏ | 78/96 [01:59<00:27,  1.52s/it]
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Epoch 9 Training:  82%|████████▏ | 79/96 [02:00<00:25,  1.52s/it]
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Epoch 9 Training:  83%|████████▎ | 80/96 [02:02<00:24,  1.52s/it]
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Epoch 9 Training:  84%|████████▍ | 81/96 [02:03<00:22,  1.52s/it]
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Epoch 9 Training:  85%|████████▌ | 82/96 [02:05<00:21,  1.52s/it]
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Epoch 9 Training:  86%|████████▋ | 83/96 [02:06<00:19,  1.53s/it]
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Epoch 9 Training:  88%|████████▊ | 84/96 [02:08<00:18,  1.53s/it]
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Epoch 9 Training:  89%|████████▊ | 85/96 [02:09<00:16,  1.53s/it]
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Epoch 9 Training:  90%|████████▉ | 86/96 [02:11<00:15,  1.53s/it]
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Epoch 9 Training:  91%|█████████ | 87/96 [02:12<00:13,  1.52s/it]
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+[2026-10-02 08:52:44] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0010 | Epoch 009] timing/rollout_seconds=178.6353 timing/feedback_seconds=6.8505 timing/optimization_seconds=147.2299 timing/cycle_seconds=330.4876
+[2026-10-02 08:52:44] [Rank 0] [INFO] [flow_factory.models.abc]: Saving LoRA weights for transformer role=base to /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/runs/klein4b_nft_domain_sirst_tex/saves/klein4b_nft_domain_sirst_tex/checkpoints/checkpoint-10
+[2026-10-02 08:52:45] [Rank 0] [INFO] [flow_factory.models.abc]: Checkpoint saved successfully to /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/runs/klein4b_nft_domain_sirst_tex/saves/klein4b_nft_domain_sirst_tex/checkpoints/checkpoint-10
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+[2026-10-02 08:53:03] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0010 | Epoch 010] eval/places_domain_rl/reward_domain_mean=0.0714 eval/places_domain_rl/reward_domain_std=0.1189 eval/places_domain_rl/reward_ssim_to_stage1_mean=0.6067 eval/places_domain_rl/reward_ssim_to_stage1_std=0.1246 eval/places_domain_rl/reward_structure_mean=0.7352 eval/places_domain_rl/reward_structure_std=0.1299 eval/places_domain_rl/reward_texture_mean=0.7782 eval/places_domain_rl/reward_texture_std=0.0500
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+[2026-10-02 08:56:07] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0010 | Epoch 010] train/reward_domain_mean=0.0431 train/reward_domain_std=0.1111 train/reward_structure_mean=0.7093 train/reward_structure_std=0.1408 train/reward_texture_mean=0.7738 train/reward_texture_std=0.0427 train/reward_domain_group_std_mean=0.0274 train/reward_domain_group_std_max=0.0606 train/reward_domain_group_std_min=0.0061 train/reward_domain_group_mean_std=0.1071 train/reward_structure_group_std_mean=0.0133 train/reward_structure_group_std_max=0.0618 train/reward_structure_group_std_min=0.0038 train/reward_structure_group_mean_std=0.1398 train/reward_texture_group_std_mean=0.0120 train/reward_texture_group_std_max=0.0275 train/reward_texture_group_std_min=0.0056 train/reward_texture_group_mean_std=0.0407 train/reward_domain_zero_std_ratio=0 train/reward_structure_zero_std_ratio=0 train/reward_texture_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-6.2573 train/adv_max=4.5409 train/adv_abs_mean=1.3694
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Epoch 10 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 10 Training:   3%|▎         | 3/96 [00:04<02:20,  1.51s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Training:   4%|▍         | 4/96 [00:06<02:19,  1.52s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Training:   5%|▌         | 5/96 [00:07<02:18,  1.52s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Training:   6%|▋         | 6/96 [00:09<02:16,  1.52s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.04it/s]
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Epoch 10 Training:   7%|▋         | 7/96 [00:10<02:15,  1.52s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Training:   8%|▊         | 8/96 [00:12<02:14,  1.53s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Training:   9%|▉         | 9/96 [00:13<02:12,  1.52s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Training:  10%|█         | 10/96 [00:15<02:11,  1.52s/it]
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Epoch 10 Training:  11%|█▏        | 11/96 [00:16<02:09,  1.52s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.04it/s]
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Epoch 10 Training:  12%|█▎        | 12/96 [00:18<02:07,  1.52s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 10 Training:  14%|█▎        | 13/96 [00:19<02:06,  1.52s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Training:  15%|█▍        | 14/96 [00:21<02:04,  1.52s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.02it/s]
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Epoch 10 Training:  16%|█▌        | 15/96 [00:22<02:03,  1.52s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 10 Training:  17%|█▋        | 16/96 [00:24<02:01,  1.52s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.04it/s]
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Epoch 10 Training:  18%|█▊        | 17/96 [00:25<02:00,  1.52s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Training:  19%|█▉        | 18/96 [00:27<01:58,  1.52s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Training:  20%|█▉        | 19/96 [00:28<01:57,  1.52s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Training:  21%|██        | 20/96 [00:30<01:55,  1.52s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 10 Training:  22%|██▏       | 21/96 [00:31<01:54,  1.52s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 10 Training:  23%|██▎       | 22/96 [00:33<01:53,  1.53s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Training:  24%|██▍       | 23/96 [00:35<01:51,  1.53s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 10 Training:  25%|██▌       | 24/96 [00:36<01:49,  1.53s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Training:  26%|██▌       | 25/96 [00:38<01:48,  1.53s/it]
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Epoch 10 Training:  27%|██▋       | 26/96 [00:39<01:46,  1.53s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 10 Training:  28%|██▊       | 27/96 [00:41<01:45,  1.53s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 10 Training:  29%|██▉       | 28/96 [00:42<01:43,  1.52s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 10 Training:  30%|███       | 29/96 [00:44<01:42,  1.52s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 10 Training:  31%|███▏      | 30/96 [00:45<01:40,  1.52s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 10 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 10 Training:  32%|███▏      | 31/96 [00:47<01:39,  1.52s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 10 Training:  33%|███▎      | 32/96 [00:48<01:37,  1.52s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Training:  34%|███▍      | 33/96 [00:50<01:35,  1.52s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Training:  35%|███▌      | 34/96 [00:51<01:34,  1.52s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Training:  36%|███▋      | 35/96 [00:53<01:32,  1.52s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Training:  38%|███▊      | 36/96 [00:54<01:31,  1.52s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Training:  39%|███▊      | 37/96 [00:56<01:30,  1.53s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Training:  40%|███▉      | 38/96 [00:57<01:29,  1.54s/it]
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Epoch 10 Training:  41%|████      | 39/96 [00:59<01:27,  1.53s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Training:  42%|████▏     | 40/96 [01:00<01:25,  1.53s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Training:  43%|████▎     | 41/96 [01:02<01:24,  1.53s/it]
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Epoch 10 Training:  44%|████▍     | 42/96 [01:04<01:22,  1.53s/it]
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Epoch 10 Training:  45%|████▍     | 43/96 [01:05<01:20,  1.53s/it]
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Epoch 10 Training:  46%|████▌     | 44/96 [01:07<01:19,  1.52s/it]
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Epoch 10 Training:  47%|████▋     | 45/96 [01:08<01:17,  1.52s/it]
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Epoch 10 Training:  48%|████▊     | 46/96 [01:10<01:16,  1.53s/it]
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Epoch 10 Training:  49%|████▉     | 47/96 [01:11<01:15,  1.54s/it]
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Epoch 10 Training:  50%|█████     | 48/96 [01:13<01:13,  1.53s/it]
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Epoch 10 Training:  51%|█████     | 49/96 [01:14<01:11,  1.53s/it]
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Epoch 10 Training:  52%|█████▏    | 50/96 [01:16<01:10,  1.53s/it]
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Epoch 10 Training:  53%|█████▎    | 51/96 [01:17<01:08,  1.53s/it]
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Epoch 10 Training:  54%|█████▍    | 52/96 [01:19<01:07,  1.53s/it]
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Epoch 10 Training:  55%|█████▌    | 53/96 [01:20<01:05,  1.52s/it]
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Epoch 10 Training:  56%|█████▋    | 54/96 [01:22<01:04,  1.53s/it]
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Epoch 10 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s][2026-10-02 08:58:33] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0010 | Epoch 010] train/policy_loss=23.3031 train/unweighted_policy_loss=4.6606 train/loss=23.3031 train/grad_norm=0.1339
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+[2026-10-02 08:58:33] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0011 | Epoch 010] timing/rollout_seconds=178.4414 timing/feedback_seconds=6.2885 timing/optimization_seconds=147.1804 timing/cycle_seconds=330.1635
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+
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Pointwise Rewards: structure: 100%|██████████| 6/6 [00:01<00:00,  4.96it/s]
+
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Pointwise Rewards: texture: 100%|██████████| 6/6 [00:02<00:00,  2.52it/s]
+[2026-10-02 09:01:38] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0011 | Epoch 011] train/reward_domain_mean=0.0730 train/reward_domain_std=0.1155 train/reward_structure_mean=0.7435 train/reward_structure_std=0.1521 train/reward_texture_mean=0.7871 train/reward_texture_std=0.0429 train/reward_domain_group_std_mean=0.0244 train/reward_domain_group_std_max=0.0455 train/reward_domain_group_std_min=0.0096 train/reward_domain_group_mean_std=0.1126 train/reward_structure_group_std_mean=0.0132 train/reward_structure_group_std_max=0.0945 train/reward_structure_group_std_min=0.0025 train/reward_structure_group_mean_std=0.1509 train/reward_texture_group_std_mean=0.0101 train/reward_texture_group_std_max=0.0262 train/reward_texture_group_std_min=0.0059 train/reward_texture_group_mean_std=0.0415 train/reward_domain_zero_std_ratio=0 train/reward_structure_zero_std_ratio=0 train/reward_texture_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.5689 train/adv_max=4.2821 train/adv_abs_mean=1.3571
+
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Epoch 11 Training:   2%|▏         | 2/96 [00:03<02:22,  1.51s/it]
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Epoch 11 Training:   3%|▎         | 3/96 [00:04<02:21,  1.52s/it]
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Epoch 11 Training:   4%|▍         | 4/96 [00:06<02:20,  1.52s/it]
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Epoch 11 Training:   5%|▌         | 5/96 [00:07<02:18,  1.53s/it]
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Epoch 11 Training:   6%|▋         | 6/96 [00:09<02:17,  1.52s/it]
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Epoch 11 Training:   7%|▋         | 7/96 [00:10<02:15,  1.53s/it]
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Epoch 11 Training:   8%|▊         | 8/96 [00:12<02:14,  1.53s/it]
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Epoch 11 Training:   9%|▉         | 9/96 [00:13<02:12,  1.52s/it]
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Epoch 11 Training:  10%|█         | 10/96 [00:15<02:10,  1.52s/it]
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Epoch 11 Training:  11%|█▏        | 11/96 [00:16<02:09,  1.53s/it]
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Epoch 11 Training:  12%|█▎        | 12/96 [00:18<02:08,  1.53s/it]
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Epoch 11 Training:  14%|█▎        | 13/96 [00:19<02:06,  1.53s/it]
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Epoch 11 Training:  15%|█▍        | 14/96 [00:21<02:05,  1.53s/it]
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Epoch 11 Training:  16%|█▌        | 15/96 [00:22<02:03,  1.53s/it]
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Epoch 11 Training:  17%|█▋        | 16/96 [00:24<02:01,  1.52s/it]
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Epoch 11 Training:  18%|█▊        | 17/96 [00:25<02:00,  1.52s/it]
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Epoch 11 Training:  19%|█▉        | 18/96 [00:27<01:58,  1.52s/it]
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Epoch 11 Training:  20%|█▉        | 19/96 [00:28<01:57,  1.52s/it]
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Epoch 11 Training:  21%|██        | 20/96 [00:30<01:55,  1.52s/it]
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Epoch 11 Training:  22%|██▏       | 21/96 [00:32<01:54,  1.52s/it]
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Epoch 11 Training:  23%|██▎       | 22/96 [00:33<01:52,  1.52s/it]
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Epoch 11 Training:  24%|██▍       | 23/96 [00:35<01:57,  1.62s/it]
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Epoch 11 Training:  25%|██▌       | 24/96 [00:36<01:54,  1.59s/it]
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Epoch 11 Training:  26%|██▌       | 25/96 [00:38<01:51,  1.57s/it]
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Epoch 11 Training:  27%|██▋       | 26/96 [00:39<01:48,  1.55s/it]
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Epoch 11 Training:  28%|██▊       | 27/96 [00:41<01:46,  1.55s/it]
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Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 11 Training:  29%|██▉       | 28/96 [00:42<01:44,  1.54s/it]
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Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 11 Training:  30%|███       | 29/96 [00:44<01:42,  1.53s/it]
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Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 11 Training:  31%|███▏      | 30/96 [00:46<01:40,  1.53s/it]
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Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 11 Training:  32%|███▏      | 31/96 [00:47<01:39,  1.53s/it]
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Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 11 Training:  33%|███▎      | 32/96 [00:49<01:38,  1.54s/it]
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Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 11 Training:  34%|███▍      | 33/96 [00:50<01:36,  1.54s/it]
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Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 11 Training:  35%|███▌      | 34/96 [00:52<01:35,  1.54s/it]
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Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 11 Training:  36%|███▋      | 35/96 [00:53<01:33,  1.54s/it]
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Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 11 Training:  38%|███▊      | 36/96 [00:55<01:31,  1.53s/it]
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Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 11 Training:  39%|███▊      | 37/96 [00:56<01:30,  1.53s/it]
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Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 11 Training:  40%|███▉      | 38/96 [00:58<01:28,  1.53s/it]
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Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.02it/s]
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Epoch 11 Training:  41%|████      | 39/96 [00:59<01:27,  1.53s/it]
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Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 11 Training:  42%|████▏     | 40/96 [01:01<01:25,  1.53s/it]
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Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 11 Training:  43%|████▎     | 41/96 [01:02<01:24,  1.53s/it]
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Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 11 Training:  44%|████▍     | 42/96 [01:04<01:22,  1.53s/it]
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Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.02it/s]
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Epoch 11 Training:  45%|████▍     | 43/96 [01:05<01:20,  1.53s/it]
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Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.98it/s]
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Epoch 11 Training:  46%|████▌     | 44/96 [01:07<01:19,  1.53s/it]
+
Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 11 Training:  47%|████▋     | 45/96 [01:09<01:18,  1.53s/it]
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Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 11 Training:  48%|████▊     | 46/96 [01:10<01:16,  1.53s/it]
+
Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.04it/s]
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Epoch 11 Training:  49%|████▉     | 47/96 [01:12<01:14,  1.53s/it]
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Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 11 Training:  50%|█████     | 48/96 [01:13<01:13,  1.53s/it]
+
Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 11 Training:  51%|█████     | 49/96 [01:15<01:11,  1.52s/it]
+
Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 11 Training:  52%|█████▏    | 50/96 [01:16<01:10,  1.52s/it]
+
Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.15it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 11 Training:  53%|█████▎    | 51/96 [01:18<01:08,  1.53s/it]
+
Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 11 Training:  54%|█████▍    | 52/96 [01:19<01:07,  1.53s/it]
+
Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.04it/s]
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Epoch 11 Training:  55%|█████▌    | 53/96 [01:21<01:05,  1.53s/it]
+
Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.04it/s]
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Epoch 11 Training:  56%|█████▋    | 54/96 [01:22<01:04,  1.52s/it]
+
Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 11 Training:  57%|█████▋    | 55/96 [01:24<01:02,  1.52s/it]
+
Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 11 Training:  58%|█████▊    | 56/96 [01:25<01:00,  1.52s/it]
+
Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 11 Training:  59%|█████▉    | 57/96 [01:27<00:59,  1.52s/it]
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Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.04it/s]
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Epoch 11 Training:  60%|██████    | 58/96 [01:28<00:57,  1.52s/it]
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Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.04it/s]
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Epoch 11 Training:  61%|██████▏   | 59/96 [01:30<00:56,  1.52s/it]
+
Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 11 Training:  62%|██████▎   | 60/96 [01:31<00:54,  1.52s/it]
+
Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 11 Training:  64%|██████▎   | 61/96 [01:33<00:53,  1.52s/it]
+
Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.04it/s]
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Epoch 11 Training:  65%|██████▍   | 62/96 [01:34<00:51,  1.52s/it]
+
Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Training:  66%|██████▌   | 63/96 [01:36<00:50,  1.52s/it]
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Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 11 Training:  67%|██████▋   | 64/96 [01:37<00:48,  1.52s/it]
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Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 11 Training:  68%|██████▊   | 65/96 [01:39<00:47,  1.52s/it]
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Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 11 Training:  69%|██████▉   | 66/96 [01:40<00:45,  1.52s/it]
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Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Training:  70%|██████▉   | 67/96 [01:42<00:44,  1.52s/it]
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Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Training:  71%|███████   | 68/96 [01:44<00:42,  1.52s/it]
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Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 11 Training:  72%|███████▏  | 69/96 [01:45<00:41,  1.52s/it]
+
Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Training:  73%|███████▎  | 70/96 [01:47<00:39,  1.52s/it]
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Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.04it/s]
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Epoch 11 Training:  74%|███████▍  | 71/96 [01:48<00:38,  1.52s/it]
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Epoch 11 Training:  75%|███████▌  | 72/96 [01:50<00:36,  1.52s/it]
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Epoch 11 Training:  76%|███████▌  | 73/96 [01:51<00:34,  1.52s/it]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 11 Training:  77%|███████▋  | 74/96 [01:53<00:33,  1.52s/it]
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Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 11 Training:  78%|███████▊  | 75/96 [01:54<00:31,  1.52s/it]
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Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 11 Training:  79%|███████▉  | 76/96 [01:56<00:30,  1.52s/it]
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Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Training:  80%|████████  | 77/96 [01:57<00:28,  1.52s/it]
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Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Training:  81%|████████▏ | 78/96 [01:59<00:27,  1.52s/it]
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Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s][2026-10-02 09:04:04] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0011 | Epoch 011] train/policy_loss=22.7151 train/unweighted_policy_loss=4.5430 train/loss=22.7151 train/grad_norm=0.2128
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+[2026-10-02 09:04:04] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0012 | Epoch 011] timing/rollout_seconds=178.3892 timing/feedback_seconds=7.0136 timing/optimization_seconds=147.6631 timing/cycle_seconds=330.9576
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+[2026-10-02 09:07:08] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0012 | Epoch 012] train/reward_domain_mean=0.0983 train/reward_domain_std=0.0998 train/reward_structure_mean=0.7241 train/reward_structure_std=0.1508 train/reward_texture_mean=0.7664 train/reward_texture_std=0.0523 train/reward_domain_group_std_mean=0.0257 train/reward_domain_group_std_max=0.0557 train/reward_domain_group_std_min=0.0094 train/reward_domain_group_mean_std=0.0960 train/reward_structure_group_std_mean=0.0131 train/reward_structure_group_std_max=0.0726 train/reward_structure_group_std_min=0.0040 train/reward_structure_group_mean_std=0.1498 train/reward_texture_group_std_mean=0.0105 train/reward_texture_group_std_max=0.0222 train/reward_texture_group_std_min=0.0052 train/reward_texture_group_mean_std=0.0512 train/reward_domain_zero_std_ratio=0 train/reward_structure_zero_std_ratio=0 train/reward_texture_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-6.0812 train/adv_max=3.8747 train/adv_abs_mean=1.2240
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.19it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 12 Training:   1%|          | 1/96 [00:01<02:20,  1.48s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 12 Training:   2%|▏         | 2/96 [00:03<02:21,  1.51s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
+
Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 12 Training:   3%|▎         | 3/96 [00:04<02:25,  1.57s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
+
Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 12 Training:   4%|▍         | 4/96 [00:06<02:22,  1.55s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 12 Training:   5%|▌         | 5/96 [00:07<02:20,  1.54s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 12 Training:   6%|▋         | 6/96 [00:09<02:18,  1.54s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
+
Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.02it/s]
+
                                                                
Epoch 12 Training:   7%|▋         | 7/96 [00:10<02:16,  1.53s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
+
Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 12 Training:   8%|▊         | 8/96 [00:12<02:14,  1.53s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 12 Training:   9%|▉         | 9/96 [00:13<02:12,  1.53s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
+
Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 12 Training:  10%|█         | 10/96 [00:15<02:11,  1.53s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 12 Training:  11%|█▏        | 11/96 [00:16<02:09,  1.53s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 12 Training:  12%|█▎        | 12/96 [00:18<02:08,  1.53s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 12 Training:  14%|█▎        | 13/96 [00:19<02:06,  1.53s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 12 Training:  15%|█▍        | 14/96 [00:21<02:05,  1.53s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
+
Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 12 Training:  16%|█▌        | 15/96 [00:22<02:03,  1.52s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
+
Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 12 Training:  17%|█▋        | 16/96 [00:24<02:01,  1.52s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 12 Training:  18%|█▊        | 17/96 [00:25<02:00,  1.53s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
+
Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 12 Training:  19%|█▉        | 18/96 [00:27<01:58,  1.52s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 12 Training:  20%|█▉        | 19/96 [00:29<01:57,  1.52s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
+
Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 12 Training:  21%|██        | 20/96 [00:30<01:55,  1.52s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 12 Training:  22%|██▏       | 21/96 [00:32<01:54,  1.53s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 12 Training:  23%|██▎       | 22/96 [00:33<01:52,  1.53s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.02it/s]
+
                                                                
Epoch 12 Training:  24%|██▍       | 23/96 [00:35<01:51,  1.53s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.02it/s]
+
                                                                
Epoch 12 Training:  25%|██▌       | 24/96 [00:36<01:50,  1.53s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 12 Training:  26%|██▌       | 25/96 [00:38<01:48,  1.53s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
+
Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 12 Training:  27%|██▋       | 26/96 [00:39<01:46,  1.53s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 12 Training:  28%|██▊       | 27/96 [00:41<01:45,  1.53s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 12 Training:  29%|██▉       | 28/96 [00:42<01:43,  1.53s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 12 Training:  30%|███       | 29/96 [00:44<01:42,  1.53s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 12 Training:  31%|███▏      | 30/96 [00:45<01:41,  1.54s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 12 Training:  32%|███▏      | 31/96 [00:47<01:39,  1.53s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 12 Training:  33%|███▎      | 32/96 [00:48<01:38,  1.53s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 12 Training:  34%|███▍      | 33/96 [00:50<01:36,  1.53s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 12 Training:  35%|███▌      | 34/96 [00:51<01:34,  1.53s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 12 Training:  36%|███▋      | 35/96 [00:53<01:33,  1.53s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
+
                                                                
Epoch 12 Training:  38%|███▊      | 36/96 [00:55<01:31,  1.53s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 12 Training:  39%|███▊      | 37/96 [00:56<01:30,  1.53s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 12 Training:  40%|███▉      | 38/96 [00:58<01:28,  1.53s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 12 Training:  41%|████      | 39/96 [00:59<01:27,  1.53s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 12 Training:  42%|████▏     | 40/96 [01:01<01:25,  1.53s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 12 Training:  43%|████▎     | 41/96 [01:02<01:23,  1.52s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 12 Training:  44%|████▍     | 42/96 [01:04<01:22,  1.53s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 12 Training:  45%|████▍     | 43/96 [01:05<01:20,  1.53s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 12 Training:  46%|████▌     | 44/96 [01:07<01:19,  1.52s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 12 Training:  47%|████▋     | 45/96 [01:08<01:17,  1.52s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 12 Training:  48%|████▊     | 46/96 [01:10<01:16,  1.52s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 12 Training:  49%|████▉     | 47/96 [01:11<01:14,  1.52s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.50it/s]
+
                                                                
Epoch 12 Training:  50%|█████     | 48/96 [01:13<01:17,  1.61s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 12 Training:  51%|█████     | 49/96 [01:15<01:14,  1.58s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.02it/s]
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Epoch 12 Training:  52%|█████▏    | 50/96 [01:16<01:12,  1.57s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 12 Training:  53%|█████▎    | 51/96 [01:18<01:09,  1.55s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 12 Training:  54%|█████▍    | 52/96 [01:19<01:07,  1.54s/it]
+
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Epoch 12 Training:  56%|█████▋    | 54/96 [01:22<01:04,  1.54s/it]
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Epoch 12 Training:  57%|█████▋    | 55/96 [01:24<01:02,  1.54s/it]
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Epoch 12 Training:  58%|█████▊    | 56/96 [01:25<01:01,  1.53s/it]
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Epoch 12 Training:  59%|█████▉    | 57/96 [01:27<00:59,  1.53s/it]
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Epoch 12 Training:  81%|████████▏ | 78/96 [01:59<00:27,  1.52s/it]
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Epoch 12 Training:  82%|████████▏ | 79/96 [02:01<00:25,  1.52s/it]
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Epoch 12 Training:  99%|█████████▉| 95/96 [02:25<00:01,  1.52s/it]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s][2026-10-02 09:09:35] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0012 | Epoch 012] train/policy_loss=22.8015 train/unweighted_policy_loss=4.5603 train/loss=22.8015 train/grad_norm=0.1253
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Epoch 12 Training: 100%|██████████| 96/96 [02:27<00:00,  1.53s/it]
+[2026-10-02 09:09:35] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0013 | Epoch 012] timing/rollout_seconds=178.4803 timing/feedback_seconds=6.6925 timing/optimization_seconds=147.7536 timing/cycle_seconds=330.8260
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+[2026-10-02 09:12:39] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0013 | Epoch 013] train/reward_domain_mean=0.1124 train/reward_domain_std=0.1084 train/reward_structure_mean=0.7565 train/reward_structure_std=0.0949 train/reward_texture_mean=0.7743 train/reward_texture_std=0.0367 train/reward_domain_group_std_mean=0.0263 train/reward_domain_group_std_max=0.0503 train/reward_domain_group_std_min=0.0120 train/reward_domain_group_mean_std=0.1049 train/reward_structure_group_std_mean=0.0114 train/reward_structure_group_std_max=0.0330 train/reward_structure_group_std_min=0.0046 train/reward_structure_group_mean_std=0.0940 train/reward_texture_group_std_mean=0.0112 train/reward_texture_group_std_max=0.0212 train/reward_texture_group_std_min=0.0052 train/reward_texture_group_mean_std=0.0348 train/reward_domain_zero_std_ratio=0 train/reward_structure_zero_std_ratio=0 train/reward_texture_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.4525 train/adv_max=5.7264 train/adv_abs_mean=1.3176
+
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Epoch 13 Training:  27%|██▋       | 26/96 [00:40<01:48,  1.55s/it]
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Epoch 13 Training:  28%|██▊       | 27/96 [00:41<01:47,  1.55s/it]
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Epoch 13 Training:  29%|██▉       | 28/96 [00:43<01:45,  1.55s/it]
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Epoch 13 Training:  30%|███       | 29/96 [00:44<01:43,  1.55s/it]
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Epoch 13 Training:  31%|███▏      | 30/96 [00:46<01:42,  1.55s/it]
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Epoch 13 Training:  32%|███▏      | 31/96 [00:47<01:40,  1.55s/it]
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Epoch 13 Training:  33%|███▎      | 32/96 [00:49<01:38,  1.55s/it]
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Epoch 13 Training:  34%|███▍      | 33/96 [00:50<01:37,  1.55s/it]
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Epoch 13 Training:  35%|███▌      | 34/96 [00:52<01:35,  1.54s/it]
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Epoch 13 Training:  36%|███▋      | 35/96 [00:53<01:34,  1.54s/it]
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Epoch 13 Training:  38%|███▊      | 36/96 [00:55<01:32,  1.54s/it]
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Epoch 13 Training:  39%|███▊      | 37/96 [00:57<01:30,  1.54s/it]
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Epoch 13 Training:  40%|███▉      | 38/96 [00:58<01:29,  1.54s/it]
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Epoch 13 Training:  41%|████      | 39/96 [01:00<01:27,  1.54s/it]
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Epoch 13 Training:  42%|████▏     | 40/96 [01:01<01:26,  1.54s/it]
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Epoch 13 Training:  43%|████▎     | 41/96 [01:03<01:24,  1.54s/it]
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Epoch 13 Training:  44%|████▍     | 42/96 [01:04<01:23,  1.54s/it]
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Epoch 13 Training:  45%|████▍     | 43/96 [01:06<01:21,  1.54s/it]
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Epoch 13 Training:  46%|████▌     | 44/96 [01:07<01:20,  1.54s/it]
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Epoch 13 Training:  47%|████▋     | 45/96 [01:09<01:18,  1.54s/it]
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Epoch 13 Training:  48%|████▊     | 46/96 [01:10<01:17,  1.54s/it]
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Epoch 13 Training:  49%|████▉     | 47/96 [01:12<01:15,  1.54s/it]
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Epoch 13 Training:  50%|█████     | 48/96 [01:14<01:13,  1.54s/it]
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Epoch 13 Training:  51%|█████     | 49/96 [01:15<01:12,  1.54s/it]
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Epoch 13 Training:  52%|█████▏    | 50/96 [01:17<01:10,  1.54s/it]
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Epoch 13 Training:  53%|█████▎    | 51/96 [01:18<01:09,  1.54s/it]
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Epoch 13 Training:  54%|█████▍    | 52/96 [01:20<01:07,  1.54s/it]
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Epoch 13 Training:  55%|█████▌    | 53/96 [01:21<01:06,  1.54s/it]
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Epoch 13 Training:  56%|█████▋    | 54/96 [01:23<01:04,  1.54s/it]
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Epoch 13 Training:  57%|█████▋    | 55/96 [01:24<01:03,  1.54s/it]
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Epoch 13 Training:  58%|█████▊    | 56/96 [01:26<01:01,  1.54s/it]
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Epoch 13 Training:  59%|█████▉    | 57/96 [01:27<01:00,  1.54s/it]
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Epoch 13 Training:  60%|██████    | 58/96 [01:29<00:58,  1.54s/it]
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Epoch 13 Training:  61%|██████▏   | 59/96 [01:30<00:57,  1.54s/it]
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Epoch 13 Training:  62%|██████▎   | 60/96 [01:32<00:55,  1.54s/it]
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Epoch 13 Training:  64%|██████▎   | 61/96 [01:34<00:53,  1.54s/it]
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Epoch 13 Training:  65%|██████▍   | 62/96 [01:35<00:52,  1.54s/it]
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Epoch 13 Training:  66%|██████▌   | 63/96 [01:37<00:50,  1.54s/it]
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Epoch 13 Training:  67%|██████▋   | 64/96 [01:38<00:49,  1.54s/it]
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Epoch 13 Training:  68%|██████▊   | 65/96 [01:40<00:47,  1.54s/it]
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Epoch 13 Training:  69%|██████▉   | 66/96 [01:41<00:46,  1.54s/it]
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Epoch 13 Training:  70%|██████▉   | 67/96 [01:43<00:44,  1.54s/it]
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Epoch 13 Training:  71%|███████   | 68/96 [01:44<00:43,  1.54s/it]
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Epoch 13 Training:  72%|███████▏  | 69/96 [01:46<00:41,  1.54s/it]
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Epoch 13 Training:  73%|███████▎  | 70/96 [01:47<00:40,  1.54s/it]
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Epoch 13 Training:  74%|███████▍  | 71/96 [01:49<00:38,  1.54s/it]
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Epoch 13 Training:  75%|███████▌  | 72/96 [01:51<00:36,  1.54s/it]
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Epoch 13 Training:  76%|███████▌  | 73/96 [01:52<00:35,  1.54s/it]
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Epoch 13 Training:  77%|███████▋  | 74/96 [01:54<00:33,  1.54s/it]
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Epoch 13 Training:  78%|███████▊  | 75/96 [01:55<00:32,  1.54s/it]
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Epoch 13 Training:  79%|███████▉  | 76/96 [01:57<00:30,  1.54s/it]
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Epoch 13 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s][2026-10-02 09:15:07] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0013 | Epoch 013] train/policy_loss=22.5532 train/unweighted_policy_loss=4.5106 train/loss=22.5532 train/grad_norm=0.1320
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+[2026-10-02 09:15:07] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0014 | Epoch 013] timing/rollout_seconds=178.4767 timing/feedback_seconds=6.7720 timing/optimization_seconds=148.8270 timing/cycle_seconds=331.9486
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+[2026-10-02 09:18:12] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0014 | Epoch 014] train/reward_domain_mean=0.1291 train/reward_domain_std=0.1143 train/reward_structure_mean=0.7380 train/reward_structure_std=0.1189 train/reward_texture_mean=0.7694 train/reward_texture_std=0.0490 train/reward_domain_group_std_mean=0.0249 train/reward_domain_group_std_max=0.0565 train/reward_domain_group_std_min=0.0095 train/reward_domain_group_mean_std=0.1112 train/reward_structure_group_std_mean=0.0108 train/reward_structure_group_std_max=0.0240 train/reward_structure_group_std_min=0.0044 train/reward_structure_group_mean_std=0.1183 train/reward_texture_group_std_mean=0.0101 train/reward_texture_group_std_max=0.0202 train/reward_texture_group_std_min=0.0045 train/reward_texture_group_mean_std=0.0479 train/reward_domain_zero_std_ratio=0 train/reward_structure_zero_std_ratio=0 train/reward_texture_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-6.0717 train/adv_max=5.0113 train/adv_abs_mean=1.3022
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Epoch 14 Training:  99%|█████████▉| 95/96 [02:26<00:01,  1.54s/it]
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Epoch 14 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s][2026-10-02 09:20:40] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0014 | Epoch 014] train/policy_loss=22.2746 train/unweighted_policy_loss=4.4549 train/loss=22.2746 train/grad_norm=0.1115
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Epoch 14 Training: 100%|██████████| 96/96 [02:28<00:00,  1.55s/it]
+[2026-10-02 09:20:40] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0015 | Epoch 014] timing/rollout_seconds=179.0225 timing/feedback_seconds=7.4105 timing/optimization_seconds=149.0122 timing/cycle_seconds=332.8881
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+[2026-10-02 09:20:58] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0015 | Epoch 015] eval/places_domain_rl/reward_domain_mean=0.1262 eval/places_domain_rl/reward_domain_std=0.1032 eval/places_domain_rl/reward_ssim_to_stage1_mean=0.5905 eval/places_domain_rl/reward_ssim_to_stage1_std=0.1361 eval/places_domain_rl/reward_structure_mean=0.7084 eval/places_domain_rl/reward_structure_std=0.1572 eval/places_domain_rl/reward_texture_mean=0.7736 eval/places_domain_rl/reward_texture_std=0.0437
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+[2026-10-02 09:24:03] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0015 | Epoch 015] train/reward_domain_mean=0.1137 train/reward_domain_std=0.1104 train/reward_structure_mean=0.7547 train/reward_structure_std=0.1138 train/reward_texture_mean=0.7799 train/reward_texture_std=0.0420 train/reward_domain_group_std_mean=0.0236 train/reward_domain_group_std_max=0.0559 train/reward_domain_group_std_min=0.0096 train/reward_domain_group_mean_std=0.1075 train/reward_structure_group_std_mean=0.0089 train/reward_structure_group_std_max=0.0211 train/reward_structure_group_std_min=0.0022 train/reward_structure_group_mean_std=0.1133 train/reward_texture_group_std_mean=0.0091 train/reward_texture_group_std_max=0.0174 train/reward_texture_group_std_min=0.0049 train/reward_texture_group_mean_std=0.0409 train/reward_domain_zero_std_ratio=0 train/reward_structure_zero_std_ratio=0 train/reward_texture_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.5076 train/adv_max=7.1273 train/adv_abs_mean=1.3021
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Epoch 15 Training:  17%|█▋        | 16/96 [00:24<02:03,  1.55s/it]
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Epoch 15 Training:  18%|█▊        | 17/96 [00:26<02:01,  1.54s/it]
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Epoch 15 Training:  19%|█▉        | 18/96 [00:27<02:00,  1.54s/it]
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Epoch 15 Training:  20%|█▉        | 19/96 [00:29<01:58,  1.54s/it]
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Epoch 15 Training:  21%|██        | 20/96 [00:30<01:56,  1.54s/it]
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Epoch 15 Training:  22%|██▏       | 21/96 [00:32<01:55,  1.54s/it]
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Epoch 15 Training:  23%|██▎       | 22/96 [00:33<01:54,  1.54s/it]
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Epoch 15 Training:  24%|██▍       | 23/96 [00:35<01:52,  1.54s/it]
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Epoch 15 Training:  25%|██▌       | 24/96 [00:36<01:50,  1.54s/it]
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Epoch 15 Training:  26%|██▌       | 25/96 [00:38<01:49,  1.54s/it]
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Epoch 15 Training:  27%|██▋       | 26/96 [00:39<01:47,  1.54s/it]
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Epoch 15 Training:  28%|██▊       | 27/96 [00:41<01:46,  1.54s/it]
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Epoch 15 Training:  29%|██▉       | 28/96 [00:42<01:44,  1.54s/it]
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Epoch 15 Training:  30%|███       | 29/96 [00:44<01:42,  1.54s/it]
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Epoch 15 Training:  31%|███▏      | 30/96 [00:46<01:41,  1.54s/it]
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Epoch 15 Training:  32%|███▏      | 31/96 [00:47<01:39,  1.54s/it]
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Epoch 15 Training:  33%|███▎      | 32/96 [00:49<01:38,  1.54s/it]
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Epoch 15 Training:  34%|███▍      | 33/96 [00:50<01:36,  1.54s/it]
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Epoch 15 Training:  35%|███▌      | 34/96 [00:52<01:35,  1.54s/it]
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Epoch 15 Training:  36%|███▋      | 35/96 [00:53<01:33,  1.54s/it]
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Epoch 15 Training:  38%|███▊      | 36/96 [00:55<01:32,  1.54s/it]
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Epoch 15 Training:  39%|███▊      | 37/96 [00:56<01:30,  1.54s/it]
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Epoch 15 Training:  40%|███▉      | 38/96 [00:58<01:29,  1.54s/it]
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Epoch 15 Training:  41%|████      | 39/96 [00:59<01:27,  1.54s/it]
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Epoch 15 Training:  42%|████▏     | 40/96 [01:01<01:26,  1.54s/it]
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Epoch 15 Training:  43%|████▎     | 41/96 [01:02<01:24,  1.54s/it]
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Epoch 15 Training:  44%|████▍     | 42/96 [01:04<01:22,  1.54s/it]
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Epoch 15 Training:  45%|████▍     | 43/96 [01:06<01:21,  1.54s/it]
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Epoch 15 Training:  46%|████▌     | 44/96 [01:07<01:19,  1.53s/it]
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Epoch 15 Training:  47%|████▋     | 45/96 [01:09<01:18,  1.54s/it]
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Epoch 15 Training:  48%|████▊     | 46/96 [01:10<01:16,  1.53s/it]
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Epoch 15 Training:  49%|████▉     | 47/96 [01:12<01:15,  1.54s/it]
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Epoch 15 Training:  50%|█████     | 48/96 [01:13<01:13,  1.54s/it]
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Epoch 15 Training:  51%|█████     | 49/96 [01:15<01:12,  1.54s/it]
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Epoch 15 Training:  52%|█████▏    | 50/96 [01:16<01:10,  1.54s/it]
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Epoch 15 Training:  53%|█████▎    | 51/96 [01:18<01:09,  1.54s/it]
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Epoch 15 Training:  54%|█████▍    | 52/96 [01:19<01:07,  1.54s/it]
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Epoch 15 Training:  55%|█████▌    | 53/96 [01:21<01:06,  1.54s/it]
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Epoch 15 Training:  56%|█████▋    | 54/96 [01:22<01:04,  1.54s/it]
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Epoch 15 Training:  57%|█████▋    | 55/96 [01:24<01:02,  1.54s/it]
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Epoch 15 Training:  58%|█████▊    | 56/96 [01:26<01:01,  1.54s/it]
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Epoch 15 Training:  59%|█████▉    | 57/96 [01:27<00:59,  1.54s/it]
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Epoch 15 Training:  60%|██████    | 58/96 [01:29<00:58,  1.53s/it]
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Epoch 15 Training:  61%|██████▏   | 59/96 [01:30<00:56,  1.54s/it]
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Epoch 15 Training:  62%|██████▎   | 60/96 [01:32<00:55,  1.54s/it]
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Epoch 15 Training:  64%|██████▎   | 61/96 [01:33<00:53,  1.54s/it]
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Epoch 15 Training:  65%|██████▍   | 62/96 [01:35<00:52,  1.53s/it]
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Epoch 15 Training:  66%|██████▌   | 63/96 [01:36<00:50,  1.53s/it]
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Epoch 15 Training:  67%|██████▋   | 64/96 [01:38<00:49,  1.53s/it]
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Epoch 15 Training:  68%|██████▊   | 65/96 [01:39<00:47,  1.54s/it]
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Epoch 15 Training:  69%|██████▉   | 66/96 [01:41<00:46,  1.53s/it]
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Epoch 15 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s][2026-10-02 09:26:30] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0015 | Epoch 015] train/policy_loss=22.3078 train/unweighted_policy_loss=4.4616 train/loss=22.3078 train/grad_norm=0.0837
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+[2026-10-02 09:26:30] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0016 | Epoch 015] timing/rollout_seconds=178.8833 timing/feedback_seconds=7.2101 timing/optimization_seconds=148.4961 timing/cycle_seconds=332.5934
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+
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Pointwise Rewards: structure: 100%|██████████| 6/6 [00:01<00:00,  5.19it/s]
+
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Pointwise Rewards: texture: 100%|██████████| 6/6 [00:01<00:00,  3.05it/s]
+[2026-10-02 09:29:35] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0016 | Epoch 016] train/reward_domain_mean=0.0898 train/reward_domain_std=0.0887 train/reward_structure_mean=0.7233 train/reward_structure_std=0.1634 train/reward_texture_mean=0.7859 train/reward_texture_std=0.0408 train/reward_domain_group_std_mean=0.0224 train/reward_domain_group_std_max=0.0405 train/reward_domain_group_std_min=0.0094 train/reward_domain_group_mean_std=0.0855 train/reward_structure_group_std_mean=0.0147 train/reward_structure_group_std_max=0.0724 train/reward_structure_group_std_min=0.0040 train/reward_structure_group_mean_std=0.1622 train/reward_texture_group_std_mean=0.0092 train/reward_texture_group_std_max=0.0202 train/reward_texture_group_std_min=0.0045 train/reward_texture_group_mean_std=0.0396 train/reward_domain_zero_std_ratio=0 train/reward_structure_zero_std_ratio=0 train/reward_texture_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.4234 train/adv_max=7.3426 train/adv_abs_mean=1.3370
+
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Epoch 16 Training:   2%|▏         | 2/96 [00:03<02:22,  1.51s/it]
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Epoch 16 Training:   3%|▎         | 3/96 [00:04<02:21,  1.52s/it]
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Epoch 16 Training:   4%|▍         | 4/96 [00:06<02:20,  1.53s/it]
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Epoch 16 Training:   5%|▌         | 5/96 [00:07<02:19,  1.53s/it]
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Epoch 16 Training:   6%|▋         | 6/96 [00:09<02:18,  1.53s/it]
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Epoch 16 Training:   7%|▋         | 7/96 [00:10<02:16,  1.54s/it]
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Epoch 16 Training:   8%|▊         | 8/96 [00:12<02:15,  1.53s/it]
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Epoch 16 Training:   9%|▉         | 9/96 [00:13<02:13,  1.54s/it]
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Epoch 16 Training:  10%|█         | 10/96 [00:15<02:12,  1.54s/it]
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Epoch 16 Training:  11%|█▏        | 11/96 [00:16<02:12,  1.55s/it]
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Epoch 16 Training:  12%|█▎        | 12/96 [00:18<02:10,  1.55s/it]
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Epoch 16 Training:  14%|█▎        | 13/96 [00:19<02:08,  1.55s/it]
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Epoch 16 Training:  15%|█▍        | 14/96 [00:21<02:06,  1.54s/it]
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Epoch 16 Training:  16%|█▌        | 15/96 [00:23<02:04,  1.54s/it]
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Epoch 16 Training:  17%|█▋        | 16/96 [00:24<02:03,  1.54s/it]
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Epoch 16 Training:  18%|█▊        | 17/96 [00:26<02:01,  1.54s/it]
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Epoch 16 Training:  19%|█▉        | 18/96 [00:27<02:00,  1.54s/it]
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Epoch 16 Training:  20%|█▉        | 19/96 [00:29<01:58,  1.54s/it]
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Epoch 16 Training:  21%|██        | 20/96 [00:30<01:56,  1.54s/it]
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Epoch 16 Training:  22%|██▏       | 21/96 [00:32<01:55,  1.54s/it]
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Epoch 16 Training:  23%|██▎       | 22/96 [00:33<01:53,  1.54s/it]
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Epoch 16 Training:  24%|██▍       | 23/96 [00:35<01:52,  1.54s/it]
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Epoch 16 Training:  25%|██▌       | 24/96 [00:36<01:50,  1.54s/it]
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Epoch 16 Training:  26%|██▌       | 25/96 [00:38<01:49,  1.54s/it]
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Epoch 16 Training:  27%|██▋       | 26/96 [00:39<01:47,  1.54s/it]
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Epoch 16 Training:  28%|██▊       | 27/96 [00:41<01:46,  1.54s/it]
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Epoch 16 Training:  29%|██▉       | 28/96 [00:43<01:44,  1.54s/it]
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Epoch 16 Training:  30%|███       | 29/96 [00:44<01:43,  1.54s/it]
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Epoch 16 Training:  32%|███▏      | 31/96 [00:47<01:40,  1.54s/it]
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Epoch 16 Training:  33%|███▎      | 32/96 [00:49<01:38,  1.54s/it]
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Epoch 16 Training:  34%|███▍      | 33/96 [00:50<01:36,  1.54s/it]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 16 Training:  43%|████▎     | 41/96 [01:03<01:24,  1.54s/it]
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Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 16 Training:  44%|████▍     | 42/96 [01:04<01:23,  1.54s/it]
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Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Training:  45%|████▍     | 43/96 [01:06<01:21,  1.54s/it]
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Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Training:  46%|████▌     | 44/96 [01:07<01:19,  1.54s/it]
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Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 16 Training:  47%|████▋     | 45/96 [01:09<01:18,  1.54s/it]
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Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Training:  48%|████▊     | 46/96 [01:10<01:16,  1.54s/it]
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Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Training:  49%|████▉     | 47/96 [01:12<01:15,  1.54s/it]
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Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 16 Training:  50%|█████     | 48/96 [01:13<01:13,  1.54s/it]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 16 Training:  51%|█████     | 49/96 [01:15<01:12,  1.54s/it]
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Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 16 Training:  52%|█████▏    | 50/96 [01:16<01:10,  1.54s/it]
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Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Training:  53%|█████▎    | 51/96 [01:18<01:09,  1.54s/it]
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Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 16 Training:  54%|█████▍    | 52/96 [01:20<01:07,  1.54s/it]
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Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 16 Training:  55%|█████▌    | 53/96 [01:21<01:06,  1.54s/it]
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Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 16 Training:  56%|█████▋    | 54/96 [01:23<01:04,  1.54s/it]
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Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 16 Training:  57%|█████▋    | 55/96 [01:24<01:03,  1.54s/it]
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Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.98it/s]
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Epoch 16 Training:  58%|█████▊    | 56/96 [01:26<01:01,  1.54s/it]
+
Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 16 Training:  59%|█████▉    | 57/96 [01:27<00:59,  1.54s/it]
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Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 16 Training:  60%|██████    | 58/96 [01:29<00:58,  1.54s/it]
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Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 16 Training:  61%|██████▏   | 59/96 [01:30<00:56,  1.54s/it]
+
Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 16 Training:  62%|██████▎   | 60/96 [01:32<00:55,  1.54s/it]
+
Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 16 Training:  64%|██████▎   | 61/96 [01:33<00:53,  1.54s/it]
+
Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 16 Training:  65%|██████▍   | 62/96 [01:35<00:52,  1.54s/it]
+
Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 16 Training:  66%|██████▌   | 63/96 [01:36<00:50,  1.54s/it]
+
Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 16 Training:  67%|██████▋   | 64/96 [01:38<00:49,  1.54s/it]
+
Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 16 Training:  68%|██████▊   | 65/96 [01:40<00:47,  1.54s/it]
+
Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 16 Training:  69%|██████▉   | 66/96 [01:41<00:46,  1.54s/it]
+
Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 16 Training:  70%|██████▉   | 67/96 [01:43<00:44,  1.54s/it]
+
Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 16 Training:  71%|███████   | 68/96 [01:44<00:43,  1.54s/it]
+
Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 16 Training:  72%|███████▏  | 69/96 [01:46<00:41,  1.54s/it]
+
Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 16 Training:  73%|███████▎  | 70/96 [01:47<00:39,  1.54s/it]
+
Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 16 Training:  74%|███████▍  | 71/96 [01:49<00:38,  1.54s/it]
+
Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 16 Training:  75%|███████▌  | 72/96 [01:50<00:36,  1.54s/it]
+
Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 16 Training:  76%|███████▌  | 73/96 [01:52<00:35,  1.54s/it]
+
Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.14it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.02it/s]
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Epoch 16 Training:  77%|███████▋  | 74/96 [01:53<00:33,  1.54s/it]
+
Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.98it/s]
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Epoch 16 Training:  78%|███████▊  | 75/96 [01:55<00:32,  1.54s/it]
+
Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 16 Training:  79%|███████▉  | 76/96 [01:56<00:30,  1.54s/it]
+
Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 16 Training:  80%|████████  | 77/96 [01:58<00:29,  1.54s/it]
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Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 16 Training:  81%|████████▏ | 78/96 [02:00<00:27,  1.54s/it]
+
Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 16 Training:  82%|████████▏ | 79/96 [02:01<00:26,  1.54s/it]
+
Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 16 Training:  83%|████████▎ | 80/96 [02:03<00:24,  1.54s/it]
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Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 16 Training:  84%|████████▍ | 81/96 [02:04<00:23,  1.54s/it]
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Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Training:  85%|████████▌ | 82/96 [02:06<00:21,  1.54s/it]
+
Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Training:  86%|████████▋ | 83/96 [02:07<00:19,  1.54s/it]
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Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 16 Training:  88%|████████▊ | 84/96 [02:09<00:18,  1.54s/it]
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Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Training:  89%|████████▊ | 85/96 [02:10<00:16,  1.54s/it]
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Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Training:  90%|████████▉ | 86/96 [02:12<00:15,  1.54s/it]
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Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 16 Training:  91%|█████████ | 87/96 [02:13<00:13,  1.54s/it]
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Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 16 Training:  92%|█████████▏| 88/96 [02:15<00:12,  1.54s/it]
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Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 16 Training:  93%|█████████▎| 89/96 [02:16<00:10,  1.54s/it]
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Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Training:  94%|█████████▍| 90/96 [02:18<00:09,  1.54s/it]
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Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Training:  95%|█████████▍| 91/96 [02:20<00:07,  1.54s/it]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s][2026-10-02 09:32:02] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0016 | Epoch 016] train/policy_loss=22.5656 train/unweighted_policy_loss=4.5131 train/loss=22.5656 train/grad_norm=0.2524
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+[2026-10-02 09:32:02] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0017 | Epoch 016] timing/rollout_seconds=178.8187 timing/feedback_seconds=7.3291 timing/optimization_seconds=148.4363 timing/cycle_seconds=332.0196
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+[2026-10-02 09:35:06] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0017 | Epoch 017] train/reward_domain_mean=0.1234 train/reward_domain_std=0.1071 train/reward_structure_mean=0.7627 train/reward_structure_std=0.1242 train/reward_texture_mean=0.7907 train/reward_texture_std=0.0377 train/reward_domain_group_std_mean=0.0222 train/reward_domain_group_std_max=0.0435 train/reward_domain_group_std_min=0.0064 train/reward_domain_group_mean_std=0.1045 train/reward_structure_group_std_mean=0.0088 train/reward_structure_group_std_max=0.0256 train/reward_structure_group_std_min=0.0029 train/reward_structure_group_mean_std=0.1239 train/reward_texture_group_std_mean=0.0084 train/reward_texture_group_std_max=0.0178 train/reward_texture_group_std_min=0.0038 train/reward_texture_group_mean_std=0.0366 train/reward_domain_zero_std_ratio=0 train/reward_structure_zero_std_ratio=0 train/reward_texture_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.6090 train/adv_max=5.0808 train/adv_abs_mean=1.3690
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Epoch 17 Training:  15%|█▍        | 14/96 [00:21<02:06,  1.54s/it]
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Epoch 17 Training:  16%|█▌        | 15/96 [00:23<02:04,  1.54s/it]
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Epoch 17 Training:  17%|█▋        | 16/96 [00:24<02:03,  1.54s/it]
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Epoch 17 Training:  18%|█▊        | 17/96 [00:26<02:01,  1.54s/it]
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Epoch 17 Training:  19%|█▉        | 18/96 [00:27<02:00,  1.54s/it]
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Epoch 17 Training:  20%|█▉        | 19/96 [00:29<01:58,  1.54s/it]
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Epoch 17 Training:  21%|██        | 20/96 [00:30<01:57,  1.54s/it]
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Epoch 17 Training:  22%|██▏       | 21/96 [00:32<01:55,  1.54s/it]
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Epoch 17 Training:  23%|██▎       | 22/96 [00:33<01:54,  1.54s/it]
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Epoch 17 Training:  24%|██▍       | 23/96 [00:35<01:52,  1.54s/it]
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Epoch 17 Training:  25%|██▌       | 24/96 [00:36<01:50,  1.54s/it]
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Epoch 17 Training:  26%|██▌       | 25/96 [00:38<01:49,  1.54s/it]
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Epoch 17 Training:  27%|██▋       | 26/96 [00:40<01:47,  1.54s/it]
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Epoch 17 Training:  28%|██▊       | 27/96 [00:41<01:46,  1.54s/it]
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Epoch 17 Training:  29%|██▉       | 28/96 [00:43<01:44,  1.54s/it]
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Epoch 17 Training:  30%|███       | 29/96 [00:44<01:43,  1.54s/it]
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Epoch 17 Training:  31%|███▏      | 30/96 [00:46<01:41,  1.54s/it]
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Epoch 17 Training:  32%|███▏      | 31/96 [00:47<01:40,  1.54s/it]
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Epoch 17 Training:  33%|███▎      | 32/96 [00:49<01:38,  1.54s/it]
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Epoch 17 Training:  34%|███▍      | 33/96 [00:50<01:36,  1.54s/it]
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Epoch 17 Training:  35%|███▌      | 34/96 [00:52<01:35,  1.54s/it]
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Epoch 17 Training:  36%|███▋      | 35/96 [00:53<01:33,  1.54s/it]
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Epoch 17 Training:  38%|███▊      | 36/96 [00:55<01:32,  1.54s/it]
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Epoch 17 Training:  39%|███▊      | 37/96 [00:56<01:30,  1.54s/it]
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Epoch 17 Training:  40%|███▉      | 38/96 [00:58<01:29,  1.54s/it]
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Epoch 17 Training:  41%|████      | 39/96 [01:00<01:28,  1.54s/it]
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Epoch 17 Training:  42%|████▏     | 40/96 [01:01<01:26,  1.55s/it]
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Epoch 17 Training:  43%|████▎     | 41/96 [01:03<01:25,  1.55s/it]
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Epoch 17 Training:  44%|████▍     | 42/96 [01:04<01:24,  1.56s/it]
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Epoch 17 Training:  45%|████▍     | 43/96 [01:06<01:22,  1.56s/it]
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Epoch 17 Training:  46%|████▌     | 44/96 [01:07<01:20,  1.55s/it]
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Epoch 17 Training:  47%|████▋     | 45/96 [01:09<01:19,  1.55s/it]
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Epoch 17 Training:  48%|████▊     | 46/96 [01:10<01:17,  1.55s/it]
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Epoch 17 Training:  49%|████▉     | 47/96 [01:12<01:16,  1.55s/it]
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Epoch 17 Training:  50%|█████     | 48/96 [01:14<01:14,  1.55s/it]
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Epoch 17 Training:  51%|█████     | 49/96 [01:15<01:12,  1.55s/it]
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Epoch 17 Training:  52%|█████▏    | 50/96 [01:17<01:11,  1.55s/it]
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Epoch 17 Training:  53%|█████▎    | 51/96 [01:18<01:09,  1.55s/it]
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Epoch 17 Training:  54%|█████▍    | 52/96 [01:20<01:07,  1.55s/it]
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Epoch 17 Training:  55%|█████▌    | 53/96 [01:21<01:06,  1.54s/it]
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Epoch 17 Training:  56%|█████▋    | 54/96 [01:23<01:04,  1.54s/it]
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Epoch 17 Training:  57%|█████▋    | 55/96 [01:24<01:03,  1.54s/it]
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Epoch 17 Training:  58%|█████▊    | 56/96 [01:26<01:01,  1.54s/it]
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Epoch 17 Training:  59%|█████▉    | 57/96 [01:27<01:00,  1.54s/it]
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Epoch 17 Training:  60%|██████    | 58/96 [01:29<00:58,  1.54s/it]
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Epoch 17 Training:  61%|██████▏   | 59/96 [01:31<00:56,  1.54s/it]
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Epoch 17 Training:  62%|██████▎   | 60/96 [01:32<00:55,  1.54s/it]
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Epoch 17 Training:  64%|██████▎   | 61/96 [01:34<00:53,  1.54s/it]
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Epoch 17 Training:  65%|██████▍   | 62/96 [01:35<00:52,  1.54s/it]
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Epoch 17 Training:  66%|██████▌   | 63/96 [01:37<00:50,  1.54s/it]
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Epoch 17 Training:  67%|██████▋   | 64/96 [01:38<00:49,  1.54s/it]
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Epoch 17 Training:  68%|██████▊   | 65/96 [01:40<00:47,  1.54s/it]
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Epoch 17 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s][2026-10-02 09:37:35] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0017 | Epoch 017] train/policy_loss=22.6013 train/unweighted_policy_loss=4.5203 train/loss=22.6013 train/grad_norm=0.1001
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+[2026-10-02 09:37:35] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0018 | Epoch 017] timing/rollout_seconds=178.6864 timing/feedback_seconds=7.1309 timing/optimization_seconds=148.8068 timing/cycle_seconds=332.2585
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+
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+[2026-10-02 09:40:39] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0018 | Epoch 018] train/reward_domain_mean=0.0783 train/reward_domain_std=0.0955 train/reward_structure_mean=0.7274 train/reward_structure_std=0.1218 train/reward_texture_mean=0.7867 train/reward_texture_std=0.0409 train/reward_domain_group_std_mean=0.0222 train/reward_domain_group_std_max=0.0442 train/reward_domain_group_std_min=0.0102 train/reward_domain_group_mean_std=0.0926 train/reward_structure_group_std_mean=0.0104 train/reward_structure_group_std_max=0.0347 train/reward_structure_group_std_min=0.0032 train/reward_structure_group_mean_std=0.1212 train/reward_texture_group_std_mean=0.0083 train/reward_texture_group_std_max=0.0145 train/reward_texture_group_std_min=0.0041 train/reward_texture_group_mean_std=0.0400 train/reward_domain_zero_std_ratio=0 train/reward_structure_zero_std_ratio=0 train/reward_texture_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-6.7365 train/adv_max=4.8849 train/adv_abs_mean=1.2749
+
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Epoch 18 Training:   2%|▏         | 2/96 [00:03<02:25,  1.55s/it]
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Epoch 18 Training:   3%|▎         | 3/96 [00:04<02:23,  1.54s/it]
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Epoch 18 Training:   4%|▍         | 4/96 [00:06<02:21,  1.54s/it]
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Epoch 18 Training:   5%|▌         | 5/96 [00:07<02:20,  1.54s/it]
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Epoch 18 Training:   6%|▋         | 6/96 [00:09<02:18,  1.54s/it]
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Epoch 18 Training:   7%|▋         | 7/96 [00:10<02:17,  1.54s/it]
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Epoch 18 Training:   8%|▊         | 8/96 [00:12<02:15,  1.54s/it]
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Epoch 18 Training:   9%|▉         | 9/96 [00:13<02:14,  1.54s/it]
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Epoch 18 Training:  10%|█         | 10/96 [00:15<02:12,  1.54s/it]
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Epoch 18 Training:  11%|█▏        | 11/96 [00:16<02:10,  1.54s/it]
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Epoch 18 Training:  12%|█▎        | 12/96 [00:18<02:09,  1.54s/it]
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Epoch 18 Training:  14%|█▎        | 13/96 [00:20<02:07,  1.54s/it]
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Epoch 18 Training:  15%|█▍        | 14/96 [00:21<02:06,  1.54s/it]
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Epoch 18 Training:  16%|█▌        | 15/96 [00:23<02:04,  1.54s/it]
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Epoch 18 Training:  18%|█▊        | 17/96 [00:26<02:01,  1.54s/it]
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Epoch 18 Training:  19%|█▉        | 18/96 [00:27<02:00,  1.54s/it]
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Epoch 18 Training:  21%|██        | 20/96 [00:30<01:57,  1.54s/it]
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Epoch 18 Training:  22%|██▏       | 21/96 [00:32<01:55,  1.54s/it]
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Epoch 18 Training:  23%|██▎       | 22/96 [00:33<01:53,  1.54s/it]
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Epoch 18 Training:  24%|██▍       | 23/96 [00:35<01:52,  1.54s/it]
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Epoch 18 Training:  25%|██▌       | 24/96 [00:36<01:50,  1.54s/it]
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Epoch 18 Training:  26%|██▌       | 25/96 [00:38<01:49,  1.54s/it]
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Epoch 18 Training:  27%|██▋       | 26/96 [00:40<01:47,  1.54s/it]
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Epoch 18 Training:  28%|██▊       | 27/96 [00:41<01:46,  1.54s/it]
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Epoch 18 Training:  29%|██▉       | 28/96 [00:43<01:44,  1.54s/it]
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Epoch 18 Training:  33%|███▎      | 32/96 [00:49<01:38,  1.54s/it]
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Epoch 18 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 18 Training:  41%|████      | 39/96 [01:00<01:27,  1.54s/it]
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Epoch 18 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 18 Training:  42%|████▏     | 40/96 [01:01<01:26,  1.54s/it]
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Epoch 18 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 18 Training:  43%|████▎     | 41/96 [01:03<01:24,  1.54s/it]
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Epoch 18 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 18 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 18 Training:  44%|████▍     | 42/96 [01:04<01:23,  1.54s/it]
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Epoch 18 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 18 Training:  45%|████▍     | 43/96 [01:06<01:21,  1.54s/it]
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Epoch 18 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 18 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 18 Training:  46%|████▌     | 44/96 [01:07<01:20,  1.54s/it]
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Epoch 18 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 18 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 18 Training:  47%|████▋     | 45/96 [01:09<01:18,  1.54s/it]
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Epoch 18 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 18 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 18 Training:  48%|████▊     | 46/96 [01:10<01:16,  1.54s/it]
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Epoch 18 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 18 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 18 Training:  49%|████▉     | 47/96 [01:12<01:15,  1.54s/it]
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Epoch 18 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 18 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 18 Training:  50%|█████     | 48/96 [01:13<01:13,  1.54s/it]
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Epoch 18 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 18 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 18 Training:  51%|█████     | 49/96 [01:15<01:12,  1.54s/it]
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Epoch 18 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 18 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.98it/s]
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Epoch 18 Training:  52%|█████▏    | 50/96 [01:17<01:10,  1.54s/it]
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Epoch 18 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 18 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 18 Training:  53%|█████▎    | 51/96 [01:18<01:09,  1.54s/it]
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Epoch 18 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 18 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 18 Training:  54%|█████▍    | 52/96 [01:20<01:07,  1.54s/it]
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Epoch 18 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 18 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 18 Training:  55%|█████▌    | 53/96 [01:21<01:06,  1.54s/it]
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Epoch 18 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 18 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 18 Training:  56%|█████▋    | 54/96 [01:23<01:04,  1.54s/it]
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Epoch 18 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 18 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 18 Training:  57%|█████▋    | 55/96 [01:24<01:03,  1.54s/it]
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Epoch 18 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 18 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.98it/s]
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Epoch 18 Training:  58%|█████▊    | 56/96 [01:26<01:01,  1.54s/it]
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Epoch 18 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 18 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 18 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 18 Training:  59%|█████▉    | 57/96 [01:27<01:00,  1.54s/it]
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Epoch 18 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 18 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 18 Training:  60%|██████    | 58/96 [01:29<00:58,  1.54s/it]
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Epoch 18 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 18 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 18 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 18 Training:  61%|██████▏   | 59/96 [01:30<00:57,  1.54s/it]
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Epoch 18 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 18 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 18 Training:  62%|██████▎   | 60/96 [01:32<00:55,  1.54s/it]
+
Epoch 18 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 18 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 18 Training:  64%|██████▎   | 61/96 [01:33<00:53,  1.54s/it]
+
Epoch 18 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 18 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 18 Training:  65%|██████▍   | 62/96 [01:35<00:52,  1.54s/it]
+
Epoch 18 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 18 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 18 Training:  66%|██████▌   | 63/96 [01:37<00:50,  1.54s/it]
+
Epoch 18 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 18 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 18 Training:  67%|██████▋   | 64/96 [01:38<00:49,  1.54s/it]
+
Epoch 18 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 18 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 18 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 18 Training:  68%|██████▊   | 65/96 [01:40<00:47,  1.54s/it]
+
Epoch 18 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 18 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 18 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.97it/s]
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Epoch 18 Training:  69%|██████▉   | 66/96 [01:41<00:46,  1.54s/it]
+
Epoch 18 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 18 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 18 Training:  70%|██████▉   | 67/96 [01:43<00:44,  1.54s/it]
+
Epoch 18 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 18 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.98it/s]
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Epoch 18 Training:  71%|███████   | 68/96 [01:44<00:43,  1.54s/it]
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Epoch 18 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 18 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 18 Training:  72%|███████▏  | 69/96 [01:46<00:41,  1.54s/it]
+
Epoch 18 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 18 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 18 Training:  73%|███████▎  | 70/96 [01:47<00:40,  1.54s/it]
+
Epoch 18 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 18 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 18 Training:  74%|███████▍  | 71/96 [01:49<00:38,  1.54s/it]
+
Epoch 18 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 18 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 18 Training:  75%|███████▌  | 72/96 [01:50<00:36,  1.54s/it]
+
Epoch 18 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 18 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 18 Training:  76%|███████▌  | 73/96 [01:52<00:35,  1.54s/it]
+
Epoch 18 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 18 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.98it/s]
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Epoch 18 Training:  77%|███████▋  | 74/96 [01:54<00:33,  1.54s/it]
+
Epoch 18 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 18 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 18 Training:  78%|███████▊  | 75/96 [01:55<00:32,  1.54s/it]
+
Epoch 18 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 18 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 18 Training:  79%|███████▉  | 76/96 [01:57<00:30,  1.54s/it]
+
Epoch 18 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 18 Training:  80%|████████  | 77/96 [01:58<00:29,  1.54s/it]
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Epoch 18 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 18 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 18 Training:  81%|████████▏ | 78/96 [02:00<00:27,  1.54s/it]
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Epoch 18 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 18 Training:  82%|████████▏ | 79/96 [02:01<00:26,  1.54s/it]
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Epoch 18 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 18 Training:  83%|████████▎ | 80/96 [02:03<00:24,  1.54s/it]
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Epoch 18 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 18 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 18 Training:  84%|████████▍ | 81/96 [02:04<00:23,  1.54s/it]
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Epoch 18 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 18 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.98it/s]
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Epoch 18 Training:  85%|████████▌ | 82/96 [02:06<00:21,  1.54s/it]
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Epoch 18 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 18 Training:  86%|████████▋ | 83/96 [02:07<00:20,  1.54s/it]
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Epoch 18 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 18 Training:  88%|████████▊ | 84/96 [02:09<00:18,  1.54s/it]
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Epoch 18 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 18 Training:  89%|████████▊ | 85/96 [02:10<00:16,  1.54s/it]
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Epoch 18 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 18 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 18 Training:  90%|████████▉ | 86/96 [02:12<00:15,  1.54s/it]
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Epoch 18 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 18 Training:  91%|█████████ | 87/96 [02:14<00:13,  1.54s/it]
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Epoch 18 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 18 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.95it/s]
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Epoch 18 Training:  92%|█████████▏| 88/96 [02:15<00:12,  1.55s/it]
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Epoch 18 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 18 Training:  93%|█████████▎| 89/96 [02:17<00:10,  1.54s/it]
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Epoch 18 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 18 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s][2026-10-02 09:43:07] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0018 | Epoch 018] train/policy_loss=22.6529 train/unweighted_policy_loss=4.5306 train/loss=22.6529 train/grad_norm=0.1127
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+[2026-10-02 09:43:07] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0019 | Epoch 018] timing/rollout_seconds=179.0116 timing/feedback_seconds=7.2839 timing/optimization_seconds=148.6106 timing/cycle_seconds=332.3151
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+[2026-10-02 09:46:11] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0019 | Epoch 019] train/reward_domain_mean=0.1219 train/reward_domain_std=0.1064 train/reward_structure_mean=0.7156 train/reward_structure_std=0.1335 train/reward_texture_mean=0.7855 train/reward_texture_std=0.0426 train/reward_domain_group_std_mean=0.0212 train/reward_domain_group_std_max=0.0538 train/reward_domain_group_std_min=0.0068 train/reward_domain_group_mean_std=0.1039 train/reward_structure_group_std_mean=0.0115 train/reward_structure_group_std_max=0.0339 train/reward_structure_group_std_min=0.0038 train/reward_structure_group_mean_std=0.1329 train/reward_texture_group_std_mean=0.0078 train/reward_texture_group_std_max=0.0146 train/reward_texture_group_std_min=0.0037 train/reward_texture_group_mean_std=0.0418 train/reward_domain_zero_std_ratio=0 train/reward_structure_zero_std_ratio=0 train/reward_texture_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.6626 train/adv_max=4.9739 train/adv_abs_mean=1.4198
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Epoch 19 Training:  12%|█▎        | 12/96 [00:18<02:11,  1.57s/it]
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Epoch 19 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 19 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 19 Training:  14%|█▎        | 13/96 [00:20<02:09,  1.56s/it]
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Epoch 19 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 19 Training:  15%|█▍        | 14/96 [00:21<02:07,  1.55s/it]
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Epoch 19 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 19 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 19 Training:  16%|█▌        | 15/96 [00:23<02:05,  1.55s/it]
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Epoch 19 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 19 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.98it/s]
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Epoch 19 Training:  17%|█▋        | 16/96 [00:24<02:03,  1.55s/it]
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Epoch 19 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 19 Training:  18%|█▊        | 17/96 [00:26<02:02,  1.55s/it]
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Epoch 19 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 19 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 19 Training:  19%|█▉        | 18/96 [00:28<02:00,  1.55s/it]
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Epoch 19 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 19 Training:  20%|█▉        | 19/96 [00:29<01:59,  1.55s/it]
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Epoch 19 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 19 Training:  21%|██        | 20/96 [00:31<01:57,  1.54s/it]
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Epoch 19 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 19 Training:  22%|██▏       | 21/96 [00:32<01:55,  1.54s/it]
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Epoch 19 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 19 Training:  23%|██▎       | 22/96 [00:34<01:54,  1.54s/it]
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Epoch 19 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 19 Training:  24%|██▍       | 23/96 [00:35<01:52,  1.54s/it]
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Epoch 19 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 19 Training:  25%|██▌       | 24/96 [00:37<01:51,  1.54s/it]
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Epoch 19 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 19 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 19 Training:  26%|██▌       | 25/96 [00:38<01:49,  1.54s/it]
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Epoch 19 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 19 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 19 Training:  27%|██▋       | 26/96 [00:40<01:47,  1.54s/it]
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Epoch 19 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 19 Training:  28%|██▊       | 27/96 [00:41<01:46,  1.54s/it]
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Epoch 19 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 19 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.98it/s]
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Epoch 19 Training:  29%|██▉       | 28/96 [00:43<01:44,  1.54s/it]
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Epoch 19 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 19 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 19 Training:  30%|███       | 29/96 [00:45<01:43,  1.54s/it]
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Epoch 19 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 19 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 19 Training:  31%|███▏      | 30/96 [00:46<01:41,  1.54s/it]
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Epoch 19 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 19 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 19 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 19 Training:  32%|███▏      | 31/96 [00:48<01:40,  1.54s/it]
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Epoch 19 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 19 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 19 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 19 Training:  33%|███▎      | 32/96 [00:49<01:38,  1.54s/it]
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Epoch 19 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 19 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 19 Training:  34%|███▍      | 33/96 [00:51<01:37,  1.54s/it]
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Epoch 19 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 19 Training:  35%|███▌      | 34/96 [00:52<01:35,  1.54s/it]
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Epoch 19 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 19 Training:  36%|███▋      | 35/96 [00:54<01:34,  1.54s/it]
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Epoch 19 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 19 Training:  38%|███▊      | 36/96 [00:55<01:32,  1.54s/it]
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Epoch 19 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 19 Training:  39%|███▊      | 37/96 [00:57<01:30,  1.54s/it]
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Epoch 19 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 19 Training:  40%|███▉      | 38/96 [00:58<01:29,  1.54s/it]
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Epoch 19 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 19 Training:  41%|████      | 39/96 [01:00<01:27,  1.54s/it]
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Epoch 19 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 19 Training:  42%|████▏     | 40/96 [01:01<01:26,  1.54s/it]
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Epoch 19 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 19 Training:  43%|████▎     | 41/96 [01:03<01:24,  1.54s/it]
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Epoch 19 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 19 Training:  44%|████▍     | 42/96 [01:05<01:23,  1.54s/it]
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Epoch 19 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 19 Training:  45%|████▍     | 43/96 [01:06<01:21,  1.54s/it]
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Epoch 19 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 19 Training:  46%|████▌     | 44/96 [01:08<01:20,  1.54s/it]
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Epoch 19 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.97it/s]
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Epoch 19 Training:  47%|████▋     | 45/96 [01:09<01:18,  1.54s/it]
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Epoch 19 Training:  48%|████▊     | 46/96 [01:11<01:17,  1.54s/it]
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Epoch 19 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 19 Training:  49%|████▉     | 47/96 [01:12<01:15,  1.54s/it]
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Epoch 19 Training:  50%|█████     | 48/96 [01:14<01:13,  1.54s/it]
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Epoch 19 Training:  51%|█████     | 49/96 [01:15<01:12,  1.54s/it]
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Epoch 19 Training:  52%|█████▏    | 50/96 [01:17<01:10,  1.54s/it]
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Epoch 19 Training:  53%|█████▎    | 51/96 [01:18<01:09,  1.54s/it]
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Epoch 19 Training:  54%|█████▍    | 52/96 [01:20<01:07,  1.54s/it]
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Epoch 19 Training:  55%|█████▌    | 53/96 [01:22<01:06,  1.54s/it]
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Epoch 19 Training:  56%|█████▋    | 54/96 [01:23<01:04,  1.54s/it]
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Epoch 19 Training:  57%|█████▋    | 55/96 [01:25<01:03,  1.54s/it]
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Epoch 19 Training:  58%|█████▊    | 56/96 [01:26<01:02,  1.55s/it]
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Epoch 19 Training:  59%|█████▉    | 57/96 [01:28<01:00,  1.55s/it]
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Epoch 19 Training:  60%|██████    | 58/96 [01:29<00:58,  1.55s/it]
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Epoch 19 Training:  61%|██████▏   | 59/96 [01:31<00:57,  1.55s/it]
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Epoch 19 Training:  62%|██████▎   | 60/96 [01:32<00:55,  1.54s/it]
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Epoch 19 Training:  64%|██████▎   | 61/96 [01:34<00:54,  1.54s/it]
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Epoch 19 Training:  65%|██████▍   | 62/96 [01:35<00:52,  1.54s/it]
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Epoch 19 Training:  66%|██████▌   | 63/96 [01:37<00:50,  1.54s/it]
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+[2026-10-02 09:48:39] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0020 | Epoch 019] timing/rollout_seconds=178.7925 timing/feedback_seconds=6.1131 timing/optimization_seconds=149.1092 timing/cycle_seconds=332.5432
+[2026-10-02 09:48:40] [Rank 0] [INFO] [flow_factory.models.abc]: Saving LoRA weights for transformer role=base to /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/runs/klein4b_nft_domain_sirst_tex/saves/klein4b_nft_domain_sirst_tex/checkpoints/checkpoint-20
+[2026-10-02 09:48:40] [Rank 0] [INFO] [flow_factory.models.abc]: Checkpoint saved successfully to /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/runs/klein4b_nft_domain_sirst_tex/saves/klein4b_nft_domain_sirst_tex/checkpoints/checkpoint-20
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+[2026-10-02 09:48:58] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0020 | Epoch 020] eval/places_domain_rl/reward_domain_mean=0.1476 eval/places_domain_rl/reward_domain_std=0.0987 eval/places_domain_rl/reward_ssim_to_stage1_mean=0.6097 eval/places_domain_rl/reward_ssim_to_stage1_std=0.1238 eval/places_domain_rl/reward_structure_mean=0.7318 eval/places_domain_rl/reward_structure_std=0.1272 eval/places_domain_rl/reward_texture_mean=0.7770 eval/places_domain_rl/reward_texture_std=0.0451
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+[2026-10-02 09:52:06] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0020 | Epoch 020] train/reward_domain_mean=0.1580 train/reward_domain_std=0.1182 train/reward_structure_mean=0.7224 train/reward_structure_std=0.1467 train/reward_texture_mean=0.7718 train/reward_texture_std=0.0373 train/reward_domain_group_std_mean=0.0234 train/reward_domain_group_std_max=0.0414 train/reward_domain_group_std_min=0.0074 train/reward_domain_group_mean_std=0.1157 train/reward_structure_group_std_mean=0.0093 train/reward_structure_group_std_max=0.0270 train/reward_structure_group_std_min=0.0024 train/reward_structure_group_mean_std=0.1463 train/reward_texture_group_std_mean=0.0100 train/reward_texture_group_std_max=0.0213 train/reward_texture_group_std_min=0.0040 train/reward_texture_group_mean_std=0.0357 train/reward_domain_zero_std_ratio=0 train/reward_structure_zero_std_ratio=0 train/reward_texture_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.8112 train/adv_max=4.8358 train/adv_abs_mean=1.2362
+
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Epoch 20 Training:   8%|▊         | 8/96 [00:12<02:18,  1.57s/it]
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Epoch 20 Training:  11%|█▏        | 11/96 [00:17<02:12,  1.56s/it]
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Epoch 20 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 20 Training:  30%|███       | 29/96 [00:45<01:43,  1.54s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 20 Training:  31%|███▏      | 30/96 [00:46<01:41,  1.54s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 20 Training:  32%|███▏      | 31/96 [00:48<01:40,  1.54s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 20 Training:  33%|███▎      | 32/96 [00:49<01:38,  1.54s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 20 Training:  34%|███▍      | 33/96 [00:51<01:37,  1.54s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.98it/s]
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Epoch 20 Training:  35%|███▌      | 34/96 [00:52<01:35,  1.54s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.15it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 20 Training:  36%|███▋      | 35/96 [00:54<01:34,  1.54s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Training:  38%|███▊      | 36/96 [00:55<01:32,  1.54s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Training:  39%|███▊      | 37/96 [00:57<01:30,  1.54s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.98it/s]
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Epoch 20 Training:  40%|███▉      | 38/96 [00:59<01:29,  1.54s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 20 Training:  41%|████      | 39/96 [01:00<01:27,  1.54s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 20 Training:  42%|████▏     | 40/96 [01:02<01:26,  1.54s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 20 Training:  43%|████▎     | 41/96 [01:03<01:24,  1.54s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 20 Training:  44%|████▍     | 42/96 [01:05<01:23,  1.54s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 20 Training:  45%|████▍     | 43/96 [01:06<01:21,  1.54s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 20 Training:  46%|████▌     | 44/96 [01:08<01:20,  1.54s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 20 Training:  47%|████▋     | 45/96 [01:09<01:18,  1.54s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 20 Training:  48%|████▊     | 46/96 [01:11<01:16,  1.54s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.73it/s]
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Epoch 20 Training:  49%|████▉     | 47/96 [01:13<01:17,  1.58s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.98it/s]
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Epoch 20 Training:  50%|█████     | 48/96 [01:14<01:15,  1.57s/it]
+
Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.98it/s]
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Epoch 20 Training:  51%|█████     | 49/96 [01:16<01:13,  1.56s/it]
+
Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 20 Training:  52%|█████▏    | 50/96 [01:17<01:11,  1.56s/it]
+
Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 20 Training:  53%|█████▎    | 51/96 [01:19<01:09,  1.55s/it]
+
Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 20 Training:  54%|█████▍    | 52/96 [01:20<01:08,  1.55s/it]
+
Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 20 Training:  55%|█████▌    | 53/96 [01:22<01:06,  1.55s/it]
+
Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 20 Training:  56%|█████▋    | 54/96 [01:23<01:04,  1.54s/it]
+
Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 20 Training:  57%|█████▋    | 55/96 [01:25<01:03,  1.54s/it]
+
Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 20 Training:  58%|█████▊    | 56/96 [01:26<01:01,  1.54s/it]
+
Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 20 Training:  59%|█████▉    | 57/96 [01:28<01:00,  1.54s/it]
+
Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.51it/s]
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Epoch 20 Training:  60%|██████    | 58/96 [01:30<01:01,  1.62s/it]
+
Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 20 Training:  61%|██████▏   | 59/96 [01:31<00:59,  1.60s/it]
+
Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 20 Training:  62%|██████▎   | 60/96 [01:33<00:56,  1.58s/it]
+
Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.97it/s]
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Epoch 20 Training:  64%|██████▎   | 61/96 [01:34<00:55,  1.57s/it]
+
Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 20 Training:  65%|██████▍   | 62/96 [01:36<00:53,  1.56s/it]
+
Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 20 Training:  66%|██████▌   | 63/96 [01:37<00:51,  1.56s/it]
+
Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Training:  67%|██████▋   | 64/96 [01:39<00:49,  1.55s/it]
+
Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Training:  68%|██████▊   | 65/96 [01:41<00:47,  1.55s/it]
+
Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.97it/s]
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Epoch 20 Training:  69%|██████▉   | 66/96 [01:42<00:46,  1.55s/it]
+
Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 20 Training:  70%|██████▉   | 67/96 [01:44<00:44,  1.55s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.15it/s]
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Epoch 20 Training:  71%|███████   | 68/96 [01:45<00:43,  1.55s/it]
+
Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Training:  72%|███████▏  | 69/96 [01:47<00:41,  1.55s/it]
+
Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Training:  73%|███████▎  | 70/96 [01:48<00:40,  1.54s/it]
+
Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Training:  74%|███████▍  | 71/96 [01:50<00:38,  1.54s/it]
+
Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Training:  75%|███████▌  | 72/96 [01:51<00:37,  1.58s/it]
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Epoch 20 Training:  76%|███████▌  | 73/96 [01:53<00:36,  1.57s/it]
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Epoch 20 Training:  77%|███████▋  | 74/96 [01:55<00:34,  1.56s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Training:  78%|███████▊  | 75/96 [01:56<00:32,  1.55s/it]
+
Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Training:  79%|███████▉  | 76/96 [01:58<00:30,  1.55s/it]
+
Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Training:  80%|████████  | 77/96 [01:59<00:29,  1.55s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Training:  81%|████████▏ | 78/96 [02:01<00:27,  1.55s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Training:  82%|████████▏ | 79/96 [02:02<00:26,  1.55s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s][2026-10-02 09:54:35] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0020 | Epoch 020] train/policy_loss=22.6756 train/unweighted_policy_loss=4.5351 train/loss=22.6756 train/grad_norm=0.0974
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+[2026-10-02 09:54:35] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0021 | Epoch 020] timing/rollout_seconds=179.1367 timing/feedback_seconds=9.5400 timing/optimization_seconds=153.4853 timing/cycle_seconds=337.3411
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+[2026-10-02 09:57:40] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0021 | Epoch 021] train/reward_domain_mean=0.1538 train/reward_domain_std=0.1002 train/reward_structure_mean=0.7086 train/reward_structure_std=0.1634 train/reward_texture_mean=0.7694 train/reward_texture_std=0.0493 train/reward_domain_group_std_mean=0.0230 train/reward_domain_group_std_max=0.0481 train/reward_domain_group_std_min=0.0084 train/reward_domain_group_mean_std=0.0971 train/reward_structure_group_std_mean=0.0096 train/reward_structure_group_std_max=0.0361 train/reward_structure_group_std_min=0.0028 train/reward_structure_group_mean_std=0.1630 train/reward_texture_group_std_mean=0.0095 train/reward_texture_group_std_max=0.0195 train/reward_texture_group_std_min=0.0045 train/reward_texture_group_mean_std=0.0483 train/reward_domain_zero_std_ratio=0 train/reward_structure_zero_std_ratio=0 train/reward_texture_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.4498 train/adv_max=4.4384 train/adv_abs_mean=1.2096
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Epoch 21 Training:   2%|▏         | 2/96 [00:03<02:22,  1.51s/it]
+
Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 21 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 21 Training:   3%|▎         | 3/96 [00:04<02:21,  1.52s/it]
+
Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 21 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 21 Training:   4%|▍         | 4/96 [00:06<02:20,  1.53s/it]
+
Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 21 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.98it/s]
+
                                                                
Epoch 21 Training:   5%|▌         | 5/96 [00:07<02:19,  1.54s/it]
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Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 21 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.97it/s]
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Epoch 21 Training:   6%|▋         | 6/96 [00:09<02:18,  1.54s/it]
+
Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 21 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.98it/s]
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Epoch 21 Training:   7%|▋         | 7/96 [00:10<02:17,  1.54s/it]
+
Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 21 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.98it/s]
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Epoch 21 Training:   8%|▊         | 8/96 [00:12<02:15,  1.54s/it]
+
Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 21 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.96it/s]
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Epoch 21 Training:   9%|▉         | 9/96 [00:13<02:14,  1.55s/it]
+
Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 21 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 21 Training:  10%|█         | 10/96 [00:15<02:12,  1.54s/it]
+
Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 21 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
+
                                                                
Epoch 21 Training:  11%|█▏        | 11/96 [00:16<02:11,  1.54s/it]
+
Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 21 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.98it/s]
+
                                                                
Epoch 21 Training:  12%|█▎        | 12/96 [00:18<02:09,  1.54s/it]
+
Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 21 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
+
                                                                
Epoch 21 Training:  14%|█▎        | 13/96 [00:19<02:07,  1.54s/it]
+
Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 21 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
+
                                                                
Epoch 21 Training:  15%|█▍        | 14/96 [00:21<02:06,  1.54s/it]
+
Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 21 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
+
                                                                
Epoch 21 Training:  16%|█▌        | 15/96 [00:23<02:04,  1.54s/it]
+
Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 21 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
+
                                                                
Epoch 21 Training:  17%|█▋        | 16/96 [00:24<02:03,  1.54s/it]
+
Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 21 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 21 Training:  18%|█▊        | 17/96 [00:26<02:01,  1.54s/it]
+
Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 21 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
+
                                                                
Epoch 21 Training:  19%|█▉        | 18/96 [00:27<02:00,  1.54s/it]
+
Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 21 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
+
                                                                
Epoch 21 Training:  20%|█▉        | 19/96 [00:29<01:58,  1.54s/it]
+
Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 21 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
+
                                                                
Epoch 21 Training:  21%|██        | 20/96 [00:30<01:56,  1.54s/it]
+
Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 21 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
+
                                                                
Epoch 21 Training:  22%|██▏       | 21/96 [00:32<01:55,  1.54s/it]
+
Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 21 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
+
                                                                
Epoch 21 Training:  23%|██▎       | 22/96 [00:33<01:53,  1.54s/it]
+
Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 21 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 21 Training:  24%|██▍       | 23/96 [00:35<01:52,  1.54s/it]
+
Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 21 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 21 Training:  25%|██▌       | 24/96 [00:36<01:50,  1.54s/it]
+
Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 21 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
+
                                                                
Epoch 21 Training:  26%|██▌       | 25/96 [00:38<01:49,  1.54s/it]
+
Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 21 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.98it/s]
+
                                                                
Epoch 21 Training:  27%|██▋       | 26/96 [00:39<01:47,  1.54s/it]
+
Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 21 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 21 Training:  28%|██▊       | 27/96 [00:41<01:46,  1.54s/it]
+
Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 21 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
+
                                                                
Epoch 21 Training:  29%|██▉       | 28/96 [00:43<01:44,  1.54s/it]
+
Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 21 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.96it/s]
+
                                                                
Epoch 21 Training:  30%|███       | 29/96 [00:44<01:43,  1.55s/it]
+
Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 21 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
+
                                                                
Epoch 21 Training:  31%|███▏      | 30/96 [00:46<01:41,  1.54s/it]
+
Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 21 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 21 Training:  32%|███▏      | 31/96 [00:47<01:40,  1.54s/it]
+
Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 21 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.02it/s]
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Epoch 21 Training:  33%|███▎      | 32/96 [00:49<01:38,  1.54s/it]
+
Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 21 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.02it/s]
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Epoch 21 Training:  34%|███▍      | 33/96 [00:50<01:37,  1.54s/it]
+
Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 21 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 21 Training:  35%|███▌      | 34/96 [00:52<01:35,  1.54s/it]
+
Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 21 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 21 Training:  36%|███▋      | 35/96 [00:53<01:33,  1.54s/it]
+
Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 21 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 21 Training:  38%|███▊      | 36/96 [00:55<01:32,  1.54s/it]
+
Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 21 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.98it/s]
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Epoch 21 Training:  39%|███▊      | 37/96 [00:56<01:30,  1.54s/it]
+
Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 21 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 21 Training:  40%|███▉      | 38/96 [00:58<01:29,  1.54s/it]
+
Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 21 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 21 Training:  41%|████      | 39/96 [01:00<01:27,  1.54s/it]
+
Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 21 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 21 Training:  42%|████▏     | 40/96 [01:01<01:26,  1.54s/it]
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Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 21 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 21 Training:  43%|████▎     | 41/96 [01:03<01:24,  1.54s/it]
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Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 21 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 21 Training:  44%|████▍     | 42/96 [01:04<01:23,  1.54s/it]
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Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 21 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 21 Training:  45%|████▍     | 43/96 [01:06<01:21,  1.54s/it]
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Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 21 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 21 Training:  46%|████▌     | 44/96 [01:07<01:20,  1.54s/it]
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Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 21 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 21 Training:  47%|████▋     | 45/96 [01:09<01:18,  1.54s/it]
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Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 21 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 21 Training:  48%|████▊     | 46/96 [01:10<01:16,  1.54s/it]
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Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 21 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 21 Training:  49%|████▉     | 47/96 [01:12<01:15,  1.54s/it]
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Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 21 Training:  50%|█████     | 48/96 [01:13<01:13,  1.54s/it]
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Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 21 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 21 Training:  51%|█████     | 49/96 [01:15<01:12,  1.54s/it]
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Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 21 Training:  52%|█████▏    | 50/96 [01:16<01:10,  1.54s/it]
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Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 21 Training:  53%|█████▎    | 51/96 [01:18<01:09,  1.54s/it]
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Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 21 Training:  54%|█████▍    | 52/96 [01:20<01:07,  1.54s/it]
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Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.94it/s]
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Epoch 21 Training:  55%|█████▌    | 53/96 [01:21<01:06,  1.55s/it]
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Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 21 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s][2026-10-02 10:00:08] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0021 | Epoch 021] train/policy_loss=22.5214 train/unweighted_policy_loss=4.5043 train/loss=22.5214 train/grad_norm=0.0891
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+[2026-10-02 10:00:08] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0022 | Epoch 021] timing/rollout_seconds=179.3143 timing/feedback_seconds=7.2846 timing/optimization_seconds=148.6783 timing/cycle_seconds=332.5895
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+
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Pointwise Rewards: domain: 100%|██████████| 6/6 [00:01<00:00,  4.06it/s]
+
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Pointwise Rewards: structure: 100%|██████████| 6/6 [00:01<00:00,  5.15it/s]
Pointwise Rewards: structure: 100%|██████████| 6/6 [00:01<00:00,  5.07it/s]
+
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Pointwise Rewards: texture:  50%|█████     | 3/6 [00:00<00:00,  3.13it/s]
Pointwise Rewards: texture:  67%|██████▋   | 4/6 [00:01<00:00,  3.07it/s]
Pointwise Rewards: texture:  83%|████████▎ | 5/6 [00:01<00:00,  3.06it/s]
Pointwise Rewards: texture: 100%|██████████| 6/6 [00:01<00:00,  3.03it/s]
Pointwise Rewards: texture: 100%|██████████| 6/6 [00:01<00:00,  3.04it/s]
+[2026-10-02 10:03:12] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0022 | Epoch 022] train/reward_domain_mean=0.1850 train/reward_domain_std=0.1020 train/reward_structure_mean=0.7175 train/reward_structure_std=0.1478 train/reward_texture_mean=0.7549 train/reward_texture_std=0.0547 train/reward_domain_group_std_mean=0.0235 train/reward_domain_group_std_max=0.0521 train/reward_domain_group_std_min=0.0134 train/reward_domain_group_mean_std=0.0990 train/reward_structure_group_std_mean=0.0083 train/reward_structure_group_std_max=0.0185 train/reward_structure_group_std_min=0.0034 train/reward_structure_group_mean_std=0.1475 train/reward_texture_group_std_mean=0.0116 train/reward_texture_group_std_max=0.0227 train/reward_texture_group_std_min=0.0043 train/reward_texture_group_mean_std=0.0533 train/reward_domain_zero_std_ratio=0 train/reward_structure_zero_std_ratio=0 train/reward_texture_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.5587 train/adv_max=6.4471 train/adv_abs_mean=1.1781
+
Epoch 22 Training:   0%|          | 0/96 [00:00<?, ?it/s]
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Epoch 22 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 22 Training:   1%|          | 1/96 [00:01<02:20,  1.48s/it]
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Epoch 22 Training:   2%|▏         | 2/96 [00:03<02:22,  1.52s/it]
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Epoch 22 Training:   3%|▎         | 3/96 [00:04<02:22,  1.53s/it]
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Epoch 22 Training:   4%|▍         | 4/96 [00:06<02:20,  1.53s/it]
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Epoch 22 Training:   5%|▌         | 5/96 [00:07<02:19,  1.53s/it]
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Epoch 22 Training:   6%|▋         | 6/96 [00:09<02:18,  1.54s/it]
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Epoch 22 Training:   7%|▋         | 7/96 [00:10<02:16,  1.54s/it]
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Epoch 22 Training:   8%|▊         | 8/96 [00:12<02:15,  1.54s/it]
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Epoch 22 Training:   9%|▉         | 9/96 [00:13<02:14,  1.55s/it]
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Epoch 22 Training:  10%|█         | 10/96 [00:15<02:15,  1.58s/it]
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Epoch 22 Training:  11%|█▏        | 11/96 [00:17<02:13,  1.57s/it]
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Epoch 22 Training:  12%|█▎        | 12/96 [00:18<02:11,  1.56s/it]
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Epoch 22 Training:  14%|█▎        | 13/96 [00:20<02:09,  1.56s/it]
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Epoch 22 Training:  15%|█▍        | 14/96 [00:21<02:07,  1.55s/it]
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Epoch 22 Training:  16%|█▌        | 15/96 [00:23<02:05,  1.55s/it]
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Epoch 22 Training:  17%|█▋        | 16/96 [00:24<02:03,  1.55s/it]
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Epoch 22 Training:  18%|█▊        | 17/96 [00:26<02:02,  1.55s/it]
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Epoch 22 Training:  19%|█▉        | 18/96 [00:27<02:00,  1.55s/it]
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Epoch 22 Training:  20%|█▉        | 19/96 [00:29<01:58,  1.54s/it]
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Epoch 22 Training:  21%|██        | 20/96 [00:30<01:57,  1.54s/it]
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Epoch 22 Training:  22%|██▏       | 21/96 [00:32<01:55,  1.54s/it]
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Epoch 22 Training:  23%|██▎       | 22/96 [00:33<01:54,  1.54s/it]
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Epoch 22 Training:  24%|██▍       | 23/96 [00:35<01:52,  1.54s/it]
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Epoch 22 Training:  25%|██▌       | 24/96 [00:37<01:50,  1.54s/it]
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Epoch 22 Training:  26%|██▌       | 25/96 [00:38<01:49,  1.54s/it]
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Epoch 22 Training:  27%|██▋       | 26/96 [00:40<01:47,  1.54s/it]
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Epoch 22 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
+
                                                                
Epoch 22 Training:  28%|██▊       | 27/96 [00:41<01:46,  1.54s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 22 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
+
                                                                
Epoch 22 Training:  29%|██▉       | 28/96 [00:43<01:44,  1.54s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 22 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
+
                                                                
Epoch 22 Training:  30%|███       | 29/96 [00:44<01:43,  1.54s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 22 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
+
                                                                
Epoch 22 Training:  31%|███▏      | 30/96 [00:46<01:41,  1.54s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
+
Epoch 22 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
+
                                                                
Epoch 22 Training:  32%|███▏      | 31/96 [00:47<01:40,  1.54s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 22 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
+
                                                                
Epoch 22 Training:  33%|███▎      | 32/96 [00:49<01:38,  1.54s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 22 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
+
                                                                
Epoch 22 Training:  34%|███▍      | 33/96 [00:50<01:36,  1.54s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 22 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.70it/s]
+
                                                                
Epoch 22 Training:  35%|███▌      | 34/96 [00:52<01:38,  1.58s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 22 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
+
                                                                
Epoch 22 Training:  36%|███▋      | 35/96 [00:54<01:41,  1.66s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 22 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.98it/s]
+
                                                                
Epoch 22 Training:  38%|███▊      | 36/96 [00:55<01:37,  1.63s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 22 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
+
                                                                
Epoch 22 Training:  39%|███▊      | 37/96 [00:57<01:34,  1.60s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.15it/s]
+
Epoch 22 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
+
                                                                
Epoch 22 Training:  40%|███▉      | 38/96 [00:59<01:31,  1.58s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 22 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
+
                                                                
Epoch 22 Training:  41%|████      | 39/96 [01:00<01:29,  1.57s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
+
Epoch 22 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
+
                                                                
Epoch 22 Training:  42%|████▏     | 40/96 [01:02<01:27,  1.56s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 22 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
+
                                                                
Epoch 22 Training:  43%|████▎     | 41/96 [01:03<01:25,  1.55s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 22 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
+
                                                                
Epoch 22 Training:  44%|████▍     | 42/96 [01:05<01:23,  1.55s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
+
Epoch 22 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
+
                                                                
Epoch 22 Training:  45%|████▍     | 43/96 [01:06<01:21,  1.55s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 22 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
+
                                                                
Epoch 22 Training:  46%|████▌     | 44/96 [01:08<01:20,  1.54s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 22 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
+
                                                                
Epoch 22 Training:  47%|████▋     | 45/96 [01:09<01:18,  1.54s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 22 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.95it/s]
+
                                                                
Epoch 22 Training:  48%|████▊     | 46/96 [01:11<01:17,  1.55s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
+
Epoch 22 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.88it/s]
+
                                                                
Epoch 22 Training:  49%|████▉     | 47/96 [01:12<01:16,  1.56s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
+
Epoch 22 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.89it/s]
+
                                                                
Epoch 22 Training:  50%|█████     | 48/96 [01:14<01:15,  1.57s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 22 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
+
                                                                
Epoch 22 Training:  51%|█████     | 49/96 [01:16<01:13,  1.56s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
+
Epoch 22 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.98it/s]
+
                                                                
Epoch 22 Training:  52%|█████▏    | 50/96 [01:17<01:11,  1.56s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 22 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
+
                                                                
Epoch 22 Training:  53%|█████▎    | 51/96 [01:19<01:09,  1.55s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 22 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
+
                                                                
Epoch 22 Training:  54%|█████▍    | 52/96 [01:20<01:08,  1.55s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 22 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
+
                                                                
Epoch 22 Training:  55%|█████▌    | 53/96 [01:22<01:06,  1.55s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 22 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
+
                                                                
Epoch 22 Training:  56%|█████▋    | 54/96 [01:23<01:04,  1.54s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 22 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
+
                                                                
Epoch 22 Training:  57%|█████▋    | 55/96 [01:25<01:03,  1.54s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 22 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
+
                                                                
Epoch 22 Training:  58%|█████▊    | 56/96 [01:26<01:01,  1.54s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 22 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
+
                                                                
Epoch 22 Training:  59%|█████▉    | 57/96 [01:28<01:00,  1.54s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 22 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
+
                                                                
Epoch 22 Training:  60%|██████    | 58/96 [01:29<00:58,  1.54s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 22 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
+
                                                                
Epoch 22 Training:  61%|██████▏   | 59/96 [01:31<00:56,  1.54s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 22 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
+
                                                                
Epoch 22 Training:  62%|██████▎   | 60/96 [01:33<00:55,  1.54s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 22 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
+
                                                                
Epoch 22 Training:  64%|██████▎   | 61/96 [01:34<00:53,  1.54s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 22 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
+
                                                                
Epoch 22 Training:  65%|██████▍   | 62/96 [01:36<00:52,  1.54s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 22 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
+
                                                                
Epoch 22 Training:  66%|██████▌   | 63/96 [01:37<00:50,  1.54s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 22 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.98it/s]
+
                                                                
Epoch 22 Training:  67%|██████▋   | 64/96 [01:39<00:49,  1.54s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 22 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
+
                                                                
Epoch 22 Training:  68%|██████▊   | 65/96 [01:40<00:47,  1.54s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 22 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
+
                                                                
Epoch 22 Training:  69%|██████▉   | 66/96 [01:42<00:46,  1.54s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 22 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.97it/s]
+
                                                                
Epoch 22 Training:  70%|██████▉   | 67/96 [01:43<00:44,  1.54s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.15it/s]
+
Epoch 22 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.02it/s]
+
                                                                
Epoch 22 Training:  71%|███████   | 68/96 [01:45<00:43,  1.55s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.15it/s]
+
Epoch 22 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 22 Training:  72%|███████▏  | 69/96 [01:46<00:41,  1.54s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 22 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.97it/s]
+
                                                                
Epoch 22 Training:  73%|███████▎  | 70/96 [01:48<00:40,  1.55s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 22 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
+
                                                                
Epoch 22 Training:  74%|███████▍  | 71/96 [01:50<00:38,  1.54s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
+
Epoch 22 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
+
                                                                
Epoch 22 Training:  75%|███████▌  | 72/96 [01:51<00:36,  1.54s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 22 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
+
                                                                
Epoch 22 Training:  76%|███████▌  | 73/96 [01:53<00:35,  1.54s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 22 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
+
                                                                
Epoch 22 Training:  77%|███████▋  | 74/96 [01:54<00:33,  1.54s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 22 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.96it/s]
+
                                                                
Epoch 22 Training:  78%|███████▊  | 75/96 [01:56<00:32,  1.54s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 22 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
+
                                                                
Epoch 22 Training:  79%|███████▉  | 76/96 [01:57<00:30,  1.54s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 22 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
+
                                                                
Epoch 22 Training:  80%|████████  | 77/96 [01:59<00:29,  1.54s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 22 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.97it/s]
+
                                                                
Epoch 22 Training:  81%|████████▏ | 78/96 [02:00<00:27,  1.54s/it]
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Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s][2026-10-02 10:05:41] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0022 | Epoch 022] train/policy_loss=22.6796 train/unweighted_policy_loss=4.5359 train/loss=22.6796 train/grad_norm=0.1152
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+[2026-10-02 10:05:41] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0023 | Epoch 022] timing/rollout_seconds=179.0151 timing/feedback_seconds=7.4670 timing/optimization_seconds=149.3222 timing/cycle_seconds=333.1144
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+[2026-10-02 10:08:45] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0023 | Epoch 023] train/reward_domain_mean=0.1669 train/reward_domain_std=0.1026 train/reward_structure_mean=0.7161 train/reward_structure_std=0.1399 train/reward_texture_mean=0.7476 train/reward_texture_std=0.0566 train/reward_domain_group_std_mean=0.0212 train/reward_domain_group_std_max=0.0508 train/reward_domain_group_std_min=0.0085 train/reward_domain_group_mean_std=0.1000 train/reward_structure_group_std_mean=0.0086 train/reward_structure_group_std_max=0.0226 train/reward_structure_group_std_min=0.0021 train/reward_structure_group_mean_std=0.1396 train/reward_texture_group_std_mean=0.0111 train/reward_texture_group_std_max=0.0318 train/reward_texture_group_std_min=0.0041 train/reward_texture_group_mean_std=0.0552 train/reward_domain_zero_std_ratio=0 train/reward_structure_zero_std_ratio=0 train/reward_texture_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-6.4502 train/adv_max=4.8716 train/adv_abs_mean=1.2805
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Epoch 23 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.19it/s]
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Epoch 23 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 23 Training:   1%|          | 1/96 [00:01<02:21,  1.49s/it]
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Epoch 23 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 23 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 23 Training:   2%|▏         | 2/96 [00:03<02:22,  1.52s/it]
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Epoch 23 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 23 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 23 Training:   3%|▎         | 3/96 [00:04<02:22,  1.53s/it]
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Epoch 23 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 23 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 23 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 23 Training:   4%|▍         | 4/96 [00:06<02:20,  1.53s/it]
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Epoch 23 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 23 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 23 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 23 Training:   5%|▌         | 5/96 [00:07<02:19,  1.53s/it]
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Epoch 23 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 23 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 23 Training:   6%|▋         | 6/96 [00:09<02:18,  1.53s/it]
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Epoch 23 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 23 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.02it/s]
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Epoch 23 Training:   7%|▋         | 7/96 [00:10<02:17,  1.54s/it]
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Epoch 23 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 23 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 23 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 23 Training:   8%|▊         | 8/96 [00:12<02:15,  1.54s/it]
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Epoch 23 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 23 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 23 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 23 Training:   9%|▉         | 9/96 [00:13<02:13,  1.54s/it]
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Epoch 23 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 23 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 23 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 23 Training:  10%|█         | 10/96 [00:15<02:12,  1.54s/it]
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Epoch 23 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 23 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 23 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 23 Training:  11%|█▏        | 11/96 [00:16<02:10,  1.54s/it]
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Epoch 23 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 23 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 23 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 23 Training:  12%|█▎        | 12/96 [00:18<02:09,  1.54s/it]
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Epoch 23 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 23 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 23 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.94it/s]
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Epoch 23 Training:  14%|█▎        | 13/96 [00:19<02:08,  1.55s/it]
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Epoch 23 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 23 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 23 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 23 Training:  15%|█▍        | 14/96 [00:21<02:06,  1.54s/it]
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Epoch 23 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 23 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.15it/s]
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Epoch 23 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.98it/s]
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Epoch 23 Training:  16%|█▌        | 15/96 [00:23<02:05,  1.54s/it]
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Epoch 23 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 23 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 23 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.94it/s]
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Epoch 23 Training:  17%|█▋        | 16/96 [00:24<02:04,  1.55s/it]
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Epoch 23 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 23 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 23 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.95it/s]
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Epoch 23 Training:  18%|█▊        | 17/96 [00:26<02:02,  1.55s/it]
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Epoch 23 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 23 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 23 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
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Epoch 23 Training:  19%|█▉        | 18/96 [00:27<02:01,  1.56s/it]
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Epoch 23 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 23 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 23 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.96it/s]
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Epoch 23 Training:  20%|█▉        | 19/96 [00:29<01:59,  1.56s/it]
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Epoch 23 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 23 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 23 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.94it/s]
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Epoch 23 Training:  21%|██        | 20/96 [00:30<01:58,  1.56s/it]
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Epoch 23 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 23 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 23 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 23 Training:  22%|██▏       | 21/96 [00:32<01:56,  1.55s/it]
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Epoch 23 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 23 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 23 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.97it/s]
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Epoch 23 Training:  23%|██▎       | 22/96 [00:33<01:54,  1.55s/it]
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Epoch 23 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 23 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 23 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 23 Training:  24%|██▍       | 23/96 [00:35<01:52,  1.55s/it]
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Epoch 23 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 23 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 23 Training:  25%|██▌       | 24/96 [00:37<01:51,  1.54s/it]
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Epoch 23 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 23 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 23 Training:  26%|██▌       | 25/96 [00:38<01:49,  1.54s/it]
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Epoch 23 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 23 Training:  27%|██▋       | 26/96 [00:40<01:47,  1.54s/it]
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Epoch 23 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 23 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.97it/s]
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Epoch 23 Training:  28%|██▊       | 27/96 [00:41<01:46,  1.54s/it]
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Epoch 23 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 23 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 23 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.97it/s]
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Epoch 23 Training:  29%|██▉       | 28/96 [00:43<01:45,  1.55s/it]
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Epoch 23 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 23 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 23 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 23 Training:  30%|███       | 29/96 [00:44<01:43,  1.54s/it]
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Epoch 23 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 23 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 23 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 23 Training:  31%|███▏      | 30/96 [00:46<01:41,  1.54s/it]
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Epoch 23 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 23 Training:  32%|███▏      | 31/96 [00:47<01:40,  1.54s/it]
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Epoch 23 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 23 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 23 Training:  33%|███▎      | 32/96 [00:49<01:38,  1.54s/it]
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Epoch 23 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 23 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 23 Training:  34%|███▍      | 33/96 [00:50<01:36,  1.54s/it]
+
Epoch 23 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 23 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.98it/s]
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Epoch 23 Training:  35%|███▌      | 34/96 [00:52<01:35,  1.54s/it]
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Epoch 23 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 23 Training:  36%|███▋      | 35/96 [00:53<01:33,  1.54s/it]
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Epoch 23 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 23 Training:  38%|███▊      | 36/96 [00:55<01:32,  1.54s/it]
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Epoch 23 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 23 Training:  39%|███▊      | 37/96 [00:57<01:30,  1.54s/it]
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Epoch 23 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 23 Training:  40%|███▉      | 38/96 [00:58<01:29,  1.54s/it]
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Epoch 23 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 23 Training:  41%|████      | 39/96 [01:00<01:27,  1.54s/it]
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Epoch 23 Training:  42%|████▏     | 40/96 [01:01<01:26,  1.54s/it]
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Epoch 23 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 23 Training:  43%|████▎     | 41/96 [01:03<01:24,  1.54s/it]
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Epoch 23 Training:  44%|████▍     | 42/96 [01:04<01:22,  1.54s/it]
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Epoch 23 Training:  45%|████▍     | 43/96 [01:06<01:21,  1.54s/it]
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Epoch 23 Training:  46%|████▌     | 44/96 [01:07<01:19,  1.54s/it]
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Epoch 23 Training:  47%|████▋     | 45/96 [01:09<01:18,  1.54s/it]
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Epoch 23 Training:  48%|████▊     | 46/96 [01:10<01:17,  1.54s/it]
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Epoch 23 Training:  49%|████▉     | 47/96 [01:12<01:15,  1.54s/it]
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Epoch 23 Training:  50%|█████     | 48/96 [01:13<01:13,  1.54s/it]
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Epoch 23 Training:  51%|█████     | 49/96 [01:15<01:12,  1.54s/it]
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Epoch 23 Training:  52%|█████▏    | 50/96 [01:17<01:10,  1.54s/it]
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Epoch 23 Training:  53%|█████▎    | 51/96 [01:18<01:09,  1.54s/it]
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Epoch 23 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s][2026-10-02 10:11:13] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0023 | Epoch 023] train/policy_loss=22.4005 train/unweighted_policy_loss=4.4801 train/loss=22.4005 train/grad_norm=0.1272
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+[2026-10-02 10:11:13] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0024 | Epoch 023] timing/rollout_seconds=178.8669 timing/feedback_seconds=7.4135 timing/optimization_seconds=148.7327 timing/cycle_seconds=332.4807
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+
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+
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+[2026-10-02 10:14:17] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0024 | Epoch 024] train/reward_domain_mean=0.1645 train/reward_domain_std=0.1087 train/reward_structure_mean=0.7456 train/reward_structure_std=0.1262 train/reward_texture_mean=0.7750 train/reward_texture_std=0.0529 train/reward_domain_group_std_mean=0.0205 train/reward_domain_group_std_max=0.0421 train/reward_domain_group_std_min=0.0072 train/reward_domain_group_mean_std=0.1066 train/reward_structure_group_std_mean=0.0093 train/reward_structure_group_std_max=0.0535 train/reward_structure_group_std_min=0.0031 train/reward_structure_group_mean_std=0.1256 train/reward_texture_group_std_mean=0.0096 train/reward_texture_group_std_max=0.0238 train/reward_texture_group_std_min=0.0041 train/reward_texture_group_mean_std=0.0518 train/reward_domain_zero_std_ratio=0 train/reward_structure_zero_std_ratio=0 train/reward_texture_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.5359 train/adv_max=4.4736 train/adv_abs_mean=1.2398
+
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Epoch 24 Training:   4%|▍         | 4/96 [00:06<02:20,  1.53s/it]
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Epoch 24 Training:   8%|▊         | 8/96 [00:12<02:15,  1.53s/it]
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Epoch 24 Training:  10%|█         | 10/96 [00:15<02:12,  1.54s/it]
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Epoch 24 Training:  23%|██▎       | 22/96 [00:33<01:53,  1.54s/it]
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Epoch 24 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 24 Training:  26%|██▌       | 25/96 [00:38<01:49,  1.54s/it]
+
Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 24 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 24 Training:  27%|██▋       | 26/96 [00:39<01:47,  1.54s/it]
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Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 24 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 24 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 24 Training:  28%|██▊       | 27/96 [00:41<01:46,  1.54s/it]
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Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 24 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 24 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 24 Training:  29%|██▉       | 28/96 [00:43<01:44,  1.54s/it]
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Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 24 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 24 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.02it/s]
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Epoch 24 Training:  30%|███       | 29/96 [00:44<01:43,  1.54s/it]
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Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 24 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 24 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.02it/s]
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Epoch 24 Training:  31%|███▏      | 30/96 [00:46<01:41,  1.54s/it]
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Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 24 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 24 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 24 Training:  32%|███▏      | 31/96 [00:47<01:40,  1.54s/it]
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Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 24 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 24 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 24 Training:  33%|███▎      | 32/96 [00:49<01:38,  1.54s/it]
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Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 24 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 24 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 24 Training:  34%|███▍      | 33/96 [00:50<01:37,  1.54s/it]
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Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 24 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 24 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 24 Training:  35%|███▌      | 34/96 [00:52<01:35,  1.54s/it]
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Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 24 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 24 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.02it/s]
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Epoch 24 Training:  36%|███▋      | 35/96 [00:53<01:33,  1.54s/it]
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Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 24 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 24 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.02it/s]
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Epoch 24 Training:  38%|███▊      | 36/96 [00:55<01:32,  1.54s/it]
+
Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 24 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 24 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 24 Training:  39%|███▊      | 37/96 [00:56<01:30,  1.54s/it]
+
Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 24 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 24 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 24 Training:  40%|███▉      | 38/96 [00:58<01:29,  1.54s/it]
+
Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 24 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 24 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 24 Training:  41%|████      | 39/96 [00:59<01:27,  1.54s/it]
+
Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 24 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 24 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 24 Training:  42%|████▏     | 40/96 [01:01<01:26,  1.54s/it]
+
Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 24 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 24 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.02it/s]
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Epoch 24 Training:  43%|████▎     | 41/96 [01:03<01:24,  1.54s/it]
+
Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 24 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 24 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 24 Training:  44%|████▍     | 42/96 [01:04<01:23,  1.54s/it]
+
Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 24 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 24 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.98it/s]
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Epoch 24 Training:  45%|████▍     | 43/96 [01:06<01:21,  1.54s/it]
+
Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 24 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 24 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.96it/s]
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Epoch 24 Training:  46%|████▌     | 44/96 [01:07<01:20,  1.55s/it]
+
Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 24 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 24 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 24 Training:  47%|████▋     | 45/96 [01:09<01:19,  1.55s/it]
+
Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 24 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 24 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 24 Training:  48%|████▊     | 46/96 [01:10<01:17,  1.55s/it]
+
Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 24 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 24 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 24 Training:  49%|████▉     | 47/96 [01:12<01:15,  1.54s/it]
+
Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 24 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 24 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.02it/s]
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Epoch 24 Training:  50%|█████     | 48/96 [01:13<01:13,  1.54s/it]
+
Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 24 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 24 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 24 Training:  51%|█████     | 49/96 [01:15<01:12,  1.54s/it]
+
Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 24 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 24 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 24 Training:  52%|█████▏    | 50/96 [01:16<01:10,  1.54s/it]
+
Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 24 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 24 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.02it/s]
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Epoch 24 Training:  53%|█████▎    | 51/96 [01:18<01:09,  1.54s/it]
+
Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 24 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 24 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 24 Training:  54%|█████▍    | 52/96 [01:19<01:07,  1.54s/it]
+
Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 24 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 24 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 24 Training:  55%|█████▌    | 53/96 [01:21<01:06,  1.54s/it]
+
Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 24 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 24 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 24 Training:  56%|█████▋    | 54/96 [01:23<01:04,  1.54s/it]
+
Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 24 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.02it/s]
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Epoch 24 Training:  57%|█████▋    | 55/96 [01:24<01:03,  1.54s/it]
+
Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 24 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 24 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 24 Training:  58%|█████▊    | 56/96 [01:26<01:01,  1.54s/it]
+
Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 24 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 24 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 24 Training:  59%|█████▉    | 57/96 [01:27<01:00,  1.54s/it]
+
Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 24 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 24 Training:  60%|██████    | 58/96 [01:29<00:58,  1.54s/it]
+
Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 24 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 24 Training:  61%|██████▏   | 59/96 [01:30<00:56,  1.54s/it]
+
Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 24 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 24 Training:  62%|██████▎   | 60/96 [01:32<00:55,  1.54s/it]
+
Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 24 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 24 Training:  64%|██████▎   | 61/96 [01:33<00:53,  1.54s/it]
+
Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 24 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 24 Training:  65%|██████▍   | 62/96 [01:35<00:52,  1.54s/it]
+
Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 24 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.02it/s]
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Epoch 24 Training:  66%|██████▌   | 63/96 [01:36<00:50,  1.54s/it]
+
Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 24 Training:  67%|██████▋   | 64/96 [01:38<00:49,  1.54s/it]
+
Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 24 Training:  68%|██████▊   | 65/96 [01:39<00:47,  1.54s/it]
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Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 24 Training:  69%|██████▉   | 66/96 [01:41<00:46,  1.54s/it]
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Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 24 Training:  70%|██████▉   | 67/96 [01:43<00:44,  1.54s/it]
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Epoch 24 Training:  71%|███████   | 68/96 [01:44<00:43,  1.54s/it]
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Epoch 24 Training:  72%|███████▏  | 69/96 [01:46<00:41,  1.54s/it]
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Epoch 24 Training:  73%|███████▎  | 70/96 [01:47<00:40,  1.54s/it]
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Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 24 Training:  74%|███████▍  | 71/96 [01:49<00:38,  1.54s/it]
+
Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 24 Training:  75%|███████▌  | 72/96 [01:50<00:36,  1.54s/it]
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Epoch 24 Training:  76%|███████▌  | 73/96 [01:52<00:35,  1.54s/it]
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Epoch 24 Training:  77%|███████▋  | 74/96 [01:53<00:33,  1.54s/it]
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Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 24 Training:  78%|███████▊  | 75/96 [01:55<00:32,  1.54s/it]
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Epoch 24 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s][2026-10-02 10:16:45] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0024 | Epoch 024] train/policy_loss=22.7744 train/unweighted_policy_loss=4.5549 train/loss=22.7744 train/grad_norm=0.1075
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+[2026-10-02 10:16:45] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0025 | Epoch 024] timing/rollout_seconds=178.8488 timing/feedback_seconds=7.1671 timing/optimization_seconds=148.5051 timing/cycle_seconds=331.9045
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+[2026-10-02 10:17:03] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0025 | Epoch 025] eval/places_domain_rl/reward_domain_mean=0.1559 eval/places_domain_rl/reward_domain_std=0.0996 eval/places_domain_rl/reward_ssim_to_stage1_mean=0.6065 eval/places_domain_rl/reward_ssim_to_stage1_std=0.1233 eval/places_domain_rl/reward_structure_mean=0.7311 eval/places_domain_rl/reward_structure_std=0.1266 eval/places_domain_rl/reward_texture_mean=0.7785 eval/places_domain_rl/reward_texture_std=0.0438
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+[2026-10-02 10:20:07] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0025 | Epoch 025] train/reward_domain_mean=0.1748 train/reward_domain_std=0.1101 train/reward_structure_mean=0.7480 train/reward_structure_std=0.1056 train/reward_texture_mean=0.7627 train/reward_texture_std=0.0430 train/reward_domain_group_std_mean=0.0223 train/reward_domain_group_std_max=0.0468 train/reward_domain_group_std_min=0.0098 train/reward_domain_group_mean_std=0.1075 train/reward_structure_group_std_mean=0.0093 train/reward_structure_group_std_max=0.0257 train/reward_structure_group_std_min=0.0024 train/reward_structure_group_mean_std=0.1050 train/reward_texture_group_std_mean=0.0098 train/reward_texture_group_std_max=0.0252 train/reward_texture_group_std_min=0.0045 train/reward_texture_group_mean_std=0.0417 train/reward_domain_zero_std_ratio=0 train/reward_structure_zero_std_ratio=0 train/reward_texture_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.3309 train/adv_max=6.5262 train/adv_abs_mean=1.3148
+
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Epoch 25 Training:   8%|▊         | 8/96 [00:12<02:15,  1.54s/it]
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Epoch 25 Training:   9%|▉         | 9/96 [00:13<02:14,  1.54s/it]
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Epoch 25 Training:  14%|█▎        | 13/96 [00:20<02:07,  1.54s/it]
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Epoch 25 Training:  15%|█▍        | 14/96 [00:21<02:06,  1.54s/it]
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Epoch 25 Training:  19%|█▉        | 18/96 [00:27<02:00,  1.54s/it]
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Epoch 25 Training:  23%|██▎       | 22/96 [00:33<01:54,  1.55s/it]
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Epoch 25 Training:  24%|██▍       | 23/96 [00:35<01:52,  1.54s/it]
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Epoch 25 Training:  25%|██▌       | 24/96 [00:36<01:51,  1.54s/it]
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Epoch 25 Training:  26%|██▌       | 25/96 [00:38<01:49,  1.54s/it]
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Epoch 25 Training:  27%|██▋       | 26/96 [00:40<01:47,  1.54s/it]
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Epoch 25 Training:  28%|██▊       | 27/96 [00:41<01:46,  1.54s/it]
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Epoch 25 Training:  29%|██▉       | 28/96 [00:43<01:44,  1.54s/it]
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Epoch 25 Training:  33%|███▎      | 32/96 [00:49<01:38,  1.54s/it]
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Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.98it/s]
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Epoch 25 Training:  45%|████▍     | 43/96 [01:06<01:21,  1.54s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
+
                                                                
Epoch 25 Training:  46%|████▌     | 44/96 [01:07<01:20,  1.54s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
+
                                                                
Epoch 25 Training:  47%|████▋     | 45/96 [01:09<01:18,  1.54s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
+
                                                                
Epoch 25 Training:  48%|████▊     | 46/96 [01:10<01:17,  1.54s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
+
                                                                
Epoch 25 Training:  49%|████▉     | 47/96 [01:12<01:15,  1.54s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
+
                                                                
Epoch 25 Training:  50%|█████     | 48/96 [01:13<01:13,  1.54s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
+
                                                                
Epoch 25 Training:  51%|█████     | 49/96 [01:15<01:12,  1.54s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
+
                                                                
Epoch 25 Training:  52%|█████▏    | 50/96 [01:17<01:10,  1.54s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
+
                                                                
Epoch 25 Training:  53%|█████▎    | 51/96 [01:18<01:09,  1.54s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
+
                                                                
Epoch 25 Training:  54%|█████▍    | 52/96 [01:20<01:07,  1.54s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
+
                                                                
Epoch 25 Training:  55%|█████▌    | 53/96 [01:21<01:06,  1.54s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
+
                                                                
Epoch 25 Training:  56%|█████▋    | 54/96 [01:23<01:04,  1.54s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
+
Epoch 25 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.94it/s]
+
                                                                
Epoch 25 Training:  57%|█████▋    | 55/96 [01:24<01:03,  1.55s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
+
                                                                
Epoch 25 Training:  58%|█████▊    | 56/96 [01:26<01:01,  1.54s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.98it/s]
+
                                                                
Epoch 25 Training:  59%|█████▉    | 57/96 [01:27<01:00,  1.54s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
+
                                                                
Epoch 25 Training:  60%|██████    | 58/96 [01:29<00:58,  1.54s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.02it/s]
+
                                                                
Epoch 25 Training:  61%|██████▏   | 59/96 [01:30<00:57,  1.54s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.98it/s]
+
                                                                
Epoch 25 Training:  62%|██████▎   | 60/96 [01:32<00:55,  1.55s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.98it/s]
+
                                                                
Epoch 25 Training:  64%|██████▎   | 61/96 [01:34<00:54,  1.55s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 25 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.97it/s]
+
                                                                
Epoch 25 Training:  65%|██████▍   | 62/96 [01:35<00:52,  1.55s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
+
                                                                
Epoch 25 Training:  66%|██████▌   | 63/96 [01:37<00:50,  1.54s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
+
                                                                
Epoch 25 Training:  67%|██████▋   | 64/96 [01:38<00:49,  1.54s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
+
                                                                
Epoch 25 Training:  68%|██████▊   | 65/96 [01:40<00:47,  1.54s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
+
                                                                
Epoch 25 Training:  69%|██████▉   | 66/96 [01:41<00:46,  1.54s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
+
                                                                
Epoch 25 Training:  70%|██████▉   | 67/96 [01:43<00:44,  1.54s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.98it/s]
+
                                                                
Epoch 25 Training:  71%|███████   | 68/96 [01:44<00:43,  1.54s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
+
                                                                
Epoch 25 Training:  72%|███████▏  | 69/96 [01:46<00:41,  1.54s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
+
                                                                
Epoch 25 Training:  73%|███████▎  | 70/96 [01:47<00:40,  1.54s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
+
                                                                
Epoch 25 Training:  74%|███████▍  | 71/96 [01:49<00:38,  1.54s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.98it/s]
+
                                                                
Epoch 25 Training:  75%|███████▌  | 72/96 [01:51<00:37,  1.54s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
+
                                                                
Epoch 25 Training:  76%|███████▌  | 73/96 [01:52<00:35,  1.54s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 25 Training:  77%|███████▋  | 74/96 [01:54<00:33,  1.54s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 25 Training:  78%|███████▊  | 75/96 [01:55<00:32,  1.55s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
+
                                                                
Epoch 25 Training:  79%|███████▉  | 76/96 [01:57<00:30,  1.55s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
+
                                                                
Epoch 25 Training:  80%|████████  | 77/96 [01:58<00:29,  1.55s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
+
                                                                
Epoch 25 Training:  81%|████████▏ | 78/96 [02:00<00:27,  1.54s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
+
                                                                
Epoch 25 Training:  82%|████████▏ | 79/96 [02:01<00:26,  1.54s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
+
                                                                
Epoch 25 Training:  83%|████████▎ | 80/96 [02:03<00:24,  1.54s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.98it/s]
+
                                                                
Epoch 25 Training:  84%|████████▍ | 81/96 [02:04<00:23,  1.54s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
+
Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
+
                                                                
Epoch 25 Training:  85%|████████▌ | 82/96 [02:06<00:21,  1.54s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
+
                                                                
Epoch 25 Training:  86%|████████▋ | 83/96 [02:07<00:20,  1.54s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
+
                                                                
Epoch 25 Training:  88%|████████▊ | 84/96 [02:09<00:18,  1.54s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
+
                                                                
Epoch 25 Training:  89%|████████▊ | 85/96 [02:11<00:16,  1.54s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
+
                                                                
Epoch 25 Training:  90%|████████▉ | 86/96 [02:12<00:15,  1.54s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
+
                                                                
Epoch 25 Training:  91%|█████████ | 87/96 [02:14<00:13,  1.54s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
+
                                                                
Epoch 25 Training:  92%|█████████▏| 88/96 [02:15<00:12,  1.54s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
+
                                                                
Epoch 25 Training:  93%|█████████▎| 89/96 [02:17<00:10,  1.54s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
+
                                                                
Epoch 25 Training:  94%|█████████▍| 90/96 [02:18<00:09,  1.54s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
+
                                                                
Epoch 25 Training:  95%|█████████▍| 91/96 [02:20<00:07,  1.54s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
+
                                                                
Epoch 25 Training:  96%|█████████▌| 92/96 [02:21<00:06,  1.54s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
+
                                                                
Epoch 25 Training:  97%|█████████▋| 93/96 [02:23<00:04,  1.54s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s][2026-10-02 10:22:35] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0025 | Epoch 025] train/policy_loss=22.9709 train/unweighted_policy_loss=4.5942 train/loss=22.9709 train/grad_norm=0.0897
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+[2026-10-02 10:22:35] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0026 | Epoch 025] timing/rollout_seconds=178.7804 timing/feedback_seconds=6.5047 timing/optimization_seconds=148.7822 timing/cycle_seconds=332.2084
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+[2026-10-02 10:25:39] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0026 | Epoch 026] train/reward_domain_mean=0.1290 train/reward_domain_std=0.1055 train/reward_structure_mean=0.7014 train/reward_structure_std=0.1619 train/reward_texture_mean=0.7790 train/reward_texture_std=0.0431 train/reward_domain_group_std_mean=0.0222 train/reward_domain_group_std_max=0.0463 train/reward_domain_group_std_min=0.0112 train/reward_domain_group_mean_std=0.1029 train/reward_structure_group_std_mean=0.0095 train/reward_structure_group_std_max=0.0664 train/reward_structure_group_std_min=0.0018 train/reward_structure_group_mean_std=0.1614 train/reward_texture_group_std_mean=0.0085 train/reward_texture_group_std_max=0.0217 train/reward_texture_group_std_min=0.0045 train/reward_texture_group_mean_std=0.0421 train/reward_domain_zero_std_ratio=0 train/reward_structure_zero_std_ratio=0 train/reward_texture_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.9895 train/adv_max=4.9758 train/adv_abs_mean=1.3055
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Epoch 26 Training:  17%|█▋        | 16/96 [00:24<02:03,  1.54s/it]
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Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
+
                                                                
Epoch 26 Training:  18%|█▊        | 17/96 [00:26<02:01,  1.54s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
+
                                                                
Epoch 26 Training:  19%|█▉        | 18/96 [00:27<02:00,  1.54s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
+
                                                                
Epoch 26 Training:  20%|█▉        | 19/96 [00:29<01:58,  1.54s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.02it/s]
+
                                                                
Epoch 26 Training:  21%|██        | 20/96 [00:30<01:56,  1.54s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
+
                                                                
Epoch 26 Training:  22%|██▏       | 21/96 [00:32<01:55,  1.54s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
+
                                                                
Epoch 26 Training:  23%|██▎       | 22/96 [00:33<01:54,  1.54s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
+
                                                                
Epoch 26 Training:  24%|██▍       | 23/96 [00:35<01:52,  1.54s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
+
                                                                
Epoch 26 Training:  25%|██▌       | 24/96 [00:36<01:50,  1.54s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
+
                                                                
Epoch 26 Training:  26%|██▌       | 25/96 [00:38<01:49,  1.54s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
+
                                                                
Epoch 26 Training:  27%|██▋       | 26/96 [00:39<01:47,  1.54s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.02it/s]
+
                                                                
Epoch 26 Training:  28%|██▊       | 27/96 [00:41<01:46,  1.54s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.02it/s]
+
                                                                
Epoch 26 Training:  29%|██▉       | 28/96 [00:43<01:44,  1.54s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 26 Training:  30%|███       | 29/96 [00:44<01:43,  1.54s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
+
Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
+
                                                                
Epoch 26 Training:  31%|███▏      | 30/96 [00:46<01:41,  1.54s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
+
                                                                
Epoch 26 Training:  32%|███▏      | 31/96 [00:47<01:39,  1.54s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
+
                                                                
Epoch 26 Training:  33%|███▎      | 32/96 [00:49<01:38,  1.54s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
+
Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
+
                                                                
Epoch 26 Training:  34%|███▍      | 33/96 [00:50<01:36,  1.54s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
+
                                                                
Epoch 26 Training:  35%|███▌      | 34/96 [00:52<01:35,  1.54s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
+
                                                                
Epoch 26 Training:  36%|███▋      | 35/96 [00:53<01:33,  1.54s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
+
                                                                
Epoch 26 Training:  38%|███▊      | 36/96 [00:55<01:32,  1.54s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
+
                                                                
Epoch 26 Training:  39%|███▊      | 37/96 [00:56<01:30,  1.54s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
+
                                                                
Epoch 26 Training:  40%|███▉      | 38/96 [00:58<01:29,  1.54s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.02it/s]
+
                                                                
Epoch 26 Training:  41%|████      | 39/96 [00:59<01:27,  1.54s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
+
Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.98it/s]
+
                                                                
Epoch 26 Training:  42%|████▏     | 40/96 [01:01<01:26,  1.54s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
+
                                                                
Epoch 26 Training:  43%|████▎     | 41/96 [01:03<01:24,  1.54s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
+
                                                                
Epoch 26 Training:  44%|████▍     | 42/96 [01:04<01:23,  1.54s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
+
                                                                
Epoch 26 Training:  45%|████▍     | 43/96 [01:06<01:21,  1.54s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
+
                                                                
Epoch 26 Training:  46%|████▌     | 44/96 [01:07<01:20,  1.54s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
+
                                                                
Epoch 26 Training:  47%|████▋     | 45/96 [01:09<01:18,  1.54s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
+
                                                                
Epoch 26 Training:  48%|████▊     | 46/96 [01:10<01:16,  1.54s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
+
                                                                
Epoch 26 Training:  49%|████▉     | 47/96 [01:12<01:15,  1.54s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
+
Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
+
                                                                
Epoch 26 Training:  50%|█████     | 48/96 [01:13<01:13,  1.54s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
+
                                                                
Epoch 26 Training:  51%|█████     | 49/96 [01:15<01:12,  1.54s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
+
                                                                
Epoch 26 Training:  52%|█████▏    | 50/96 [01:16<01:10,  1.54s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
+
                                                                
Epoch 26 Training:  53%|█████▎    | 51/96 [01:18<01:09,  1.54s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 26 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.94it/s]
+
                                                                
Epoch 26 Training:  54%|█████▍    | 52/96 [01:20<01:08,  1.55s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
+
                                                                
Epoch 26 Training:  55%|█████▌    | 53/96 [01:21<01:06,  1.54s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
+
                                                                
Epoch 26 Training:  56%|█████▋    | 54/96 [01:23<01:04,  1.54s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
+
                                                                
Epoch 26 Training:  57%|█████▋    | 55/96 [01:24<01:03,  1.54s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
+
                                                                
Epoch 26 Training:  58%|█████▊    | 56/96 [01:26<01:01,  1.54s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.02it/s]
+
                                                                
Epoch 26 Training:  59%|█████▉    | 57/96 [01:27<01:00,  1.54s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
+
Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
+
                                                                
Epoch 26 Training:  60%|██████    | 58/96 [01:29<00:58,  1.54s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
+
                                                                
Epoch 26 Training:  61%|██████▏   | 59/96 [01:30<00:56,  1.54s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
+
                                                                
Epoch 26 Training:  62%|██████▎   | 60/96 [01:32<00:55,  1.54s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
+
                                                                
Epoch 26 Training:  64%|██████▎   | 61/96 [01:33<00:53,  1.54s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
+
                                                                
Epoch 26 Training:  65%|██████▍   | 62/96 [01:35<00:52,  1.54s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.02it/s]
+
                                                                
Epoch 26 Training:  66%|██████▌   | 63/96 [01:36<00:50,  1.54s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
+
                                                                
Epoch 26 Training:  67%|██████▋   | 64/96 [01:38<00:49,  1.54s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
+
                                                                
Epoch 26 Training:  68%|██████▊   | 65/96 [01:40<00:47,  1.54s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 26 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
+
                                                                
Epoch 26 Training:  69%|██████▉   | 66/96 [01:41<00:46,  1.55s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 26 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.97it/s]
+
                                                                
Epoch 26 Training:  70%|██████▉   | 67/96 [01:43<00:45,  1.55s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 26 Training:  82%|████████▏ | 79/96 [02:01<00:26,  1.54s/it]
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Epoch 26 Training:  99%|█████████▉| 95/96 [02:26<00:01,  1.54s/it]
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Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s][2026-10-02 10:28:07] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0026 | Epoch 026] train/policy_loss=23.4713 train/unweighted_policy_loss=4.6943 train/loss=23.4713 train/grad_norm=0.0983
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Epoch 26 Training: 100%|██████████| 96/96 [02:27<00:00,  1.54s/it]
+[2026-10-02 10:28:07] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0027 | Epoch 026] timing/rollout_seconds=178.7330 timing/feedback_seconds=7.1292 timing/optimization_seconds=148.5187 timing/cycle_seconds=331.9969
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Epoch 27 Sampling: 100%|██████████| 96/96 [02:56<00:00,  1.84s/it]
+
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Pointwise Rewards: structure: 100%|██████████| 6/6 [00:01<00:00,  4.72it/s]
+
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Pointwise Rewards: texture: 100%|██████████| 6/6 [00:01<00:00,  3.06it/s]
+[2026-10-02 10:31:11] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0027 | Epoch 027] train/reward_domain_mean=0.1190 train/reward_domain_std=0.0958 train/reward_structure_mean=0.7311 train/reward_structure_std=0.1302 train/reward_texture_mean=0.7781 train/reward_texture_std=0.0460 train/reward_domain_group_std_mean=0.0214 train/reward_domain_group_std_max=0.0375 train/reward_domain_group_std_min=0.0113 train/reward_domain_group_mean_std=0.0932 train/reward_structure_group_std_mean=0.0083 train/reward_structure_group_std_max=0.0228 train/reward_structure_group_std_min=0.0033 train/reward_structure_group_mean_std=0.1299 train/reward_texture_group_std_mean=0.0081 train/reward_texture_group_std_max=0.0274 train/reward_texture_group_std_min=0.0028 train/reward_texture_group_mean_std=0.0451 train/reward_domain_zero_std_ratio=0 train/reward_structure_zero_std_ratio=0 train/reward_texture_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.7267 train/adv_max=5.2406 train/adv_abs_mean=1.2966
+
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Epoch 27 Training:   1%|          | 1/96 [00:01<02:21,  1.49s/it]
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Epoch 27 Training:   2%|▏         | 2/96 [00:03<02:22,  1.52s/it]
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Epoch 27 Training:   3%|▎         | 3/96 [00:04<02:22,  1.53s/it]
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Epoch 27 Training:   4%|▍         | 4/96 [00:06<02:21,  1.53s/it]
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Epoch 27 Training:   5%|▌         | 5/96 [00:07<02:19,  1.54s/it]
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Epoch 27 Training:   6%|▋         | 6/96 [00:09<02:27,  1.64s/it]
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Epoch 27 Training:   7%|▋         | 7/96 [00:11<02:22,  1.61s/it]
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Epoch 27 Training:   8%|▊         | 8/96 [00:12<02:19,  1.58s/it]
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Epoch 27 Training:   9%|▉         | 9/96 [00:14<02:16,  1.57s/it]
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Epoch 27 Training:  10%|█         | 10/96 [00:15<02:14,  1.56s/it]
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Epoch 27 Training:  11%|█▏        | 11/96 [00:17<02:12,  1.55s/it]
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Epoch 27 Training:  12%|█▎        | 12/96 [00:18<02:10,  1.55s/it]
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Epoch 27 Training:  14%|█▎        | 13/96 [00:20<02:08,  1.55s/it]
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Epoch 27 Training:  15%|█▍        | 14/96 [00:21<02:06,  1.54s/it]
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Epoch 27 Training:  16%|█▌        | 15/96 [00:23<02:04,  1.54s/it]
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Epoch 27 Training:  17%|█▋        | 16/96 [00:24<02:03,  1.54s/it]
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Epoch 27 Training:  18%|█▊        | 17/96 [00:26<02:01,  1.54s/it]
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Epoch 27 Training:  19%|█▉        | 18/96 [00:27<02:00,  1.54s/it]
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Epoch 27 Training:  20%|█▉        | 19/96 [00:29<01:58,  1.54s/it]
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Epoch 27 Training:  21%|██        | 20/96 [00:31<01:57,  1.55s/it]
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Epoch 27 Training:  22%|██▏       | 21/96 [00:32<01:56,  1.55s/it]
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Epoch 27 Training:  23%|██▎       | 22/96 [00:34<01:54,  1.55s/it]
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Epoch 27 Training:  24%|██▍       | 23/96 [00:35<01:52,  1.55s/it]
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Epoch 27 Training:  25%|██▌       | 24/96 [00:37<01:51,  1.55s/it]
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Epoch 27 Training:  26%|██▌       | 25/96 [00:38<01:49,  1.54s/it]
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Epoch 27 Training:  27%|██▋       | 26/96 [00:40<01:47,  1.54s/it]
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Epoch 27 Training:  28%|██▊       | 27/96 [00:41<01:46,  1.54s/it]
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Epoch 27 Training:  29%|██▉       | 28/96 [00:43<01:44,  1.54s/it]
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Epoch 27 Training:  30%|███       | 29/96 [00:44<01:43,  1.54s/it]
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Epoch 27 Training:  31%|███▏      | 30/96 [00:46<01:41,  1.54s/it]
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Epoch 27 Training:  32%|███▏      | 31/96 [00:48<01:40,  1.54s/it]
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Epoch 27 Training:  33%|███▎      | 32/96 [00:49<01:38,  1.54s/it]
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Epoch 27 Training:  34%|███▍      | 33/96 [00:51<01:36,  1.54s/it]
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Epoch 27 Training:  35%|███▌      | 34/96 [00:52<01:35,  1.54s/it]
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Epoch 27 Training:  36%|███▋      | 35/96 [00:54<01:33,  1.54s/it]
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Epoch 27 Training:  38%|███▊      | 36/96 [00:55<01:32,  1.54s/it]
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Epoch 27 Training:  39%|███▊      | 37/96 [00:57<01:30,  1.54s/it]
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Epoch 27 Training:  41%|████      | 39/96 [01:00<01:27,  1.54s/it]
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Epoch 27 Training:  42%|████▏     | 40/96 [01:01<01:26,  1.54s/it]
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Epoch 27 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 27 Training:  43%|████▎     | 41/96 [01:03<01:24,  1.54s/it]
+
Epoch 27 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 27 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 27 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 27 Training:  44%|████▍     | 42/96 [01:04<01:23,  1.54s/it]
+
Epoch 27 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 27 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 27 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
+
                                                                
Epoch 27 Training:  45%|████▍     | 43/96 [01:06<01:21,  1.54s/it]
+
Epoch 27 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 27 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 27 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
+
                                                                
Epoch 27 Training:  46%|████▌     | 44/96 [01:08<01:20,  1.54s/it]
+
Epoch 27 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 27 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 27 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
+
                                                                
Epoch 27 Training:  47%|████▋     | 45/96 [01:09<01:18,  1.54s/it]
+
Epoch 27 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 27 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 27 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
+
                                                                
Epoch 27 Training:  48%|████▊     | 46/96 [01:11<01:16,  1.54s/it]
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Epoch 27 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 27 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 27 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
+
                                                                
Epoch 27 Training:  49%|████▉     | 47/96 [01:12<01:15,  1.54s/it]
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Epoch 27 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 27 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 27 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 27 Training:  50%|█████     | 48/96 [01:14<01:13,  1.54s/it]
+
Epoch 27 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 27 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 27 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
+
                                                                
Epoch 27 Training:  51%|█████     | 49/96 [01:15<01:12,  1.54s/it]
+
Epoch 27 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 27 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 27 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 27 Training:  52%|█████▏    | 50/96 [01:17<01:10,  1.54s/it]
+
Epoch 27 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 27 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 27 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.94it/s]
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Epoch 27 Training:  53%|█████▎    | 51/96 [01:18<01:09,  1.54s/it]
+
Epoch 27 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 27 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 27 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 27 Training:  54%|█████▍    | 52/96 [01:20<01:07,  1.54s/it]
+
Epoch 27 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 27 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 27 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
+
                                                                
Epoch 27 Training:  55%|█████▌    | 53/96 [01:21<01:06,  1.54s/it]
+
Epoch 27 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 27 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 27 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
+
                                                                
Epoch 27 Training:  56%|█████▋    | 54/96 [01:23<01:04,  1.54s/it]
+
Epoch 27 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 27 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 27 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
+
                                                                
Epoch 27 Training:  57%|█████▋    | 55/96 [01:24<01:03,  1.54s/it]
+
Epoch 27 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 27 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 27 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
+
                                                                
Epoch 27 Training:  58%|█████▊    | 56/96 [01:26<01:01,  1.54s/it]
+
Epoch 27 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 27 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 27 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
+
                                                                
Epoch 27 Training:  59%|█████▉    | 57/96 [01:28<01:00,  1.54s/it]
+
Epoch 27 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 27 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 27 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
+
                                                                
Epoch 27 Training:  60%|██████    | 58/96 [01:29<00:58,  1.54s/it]
+
Epoch 27 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 27 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 27 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.98it/s]
+
                                                                
Epoch 27 Training:  61%|██████▏   | 59/96 [01:31<00:56,  1.54s/it]
+
Epoch 27 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 27 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 27 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.98it/s]
+
                                                                
Epoch 27 Training:  62%|██████▎   | 60/96 [01:32<00:55,  1.54s/it]
+
Epoch 27 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 27 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 27 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
+
                                                                
Epoch 27 Training:  64%|██████▎   | 61/96 [01:34<00:53,  1.54s/it]
+
Epoch 27 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 27 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 27 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
+
                                                                
Epoch 27 Training:  65%|██████▍   | 62/96 [01:35<00:52,  1.54s/it]
+
Epoch 27 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 27 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 27 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
+
                                                                
Epoch 27 Training:  66%|██████▌   | 63/96 [01:37<00:50,  1.54s/it]
+
Epoch 27 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 27 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 27 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.96it/s]
+
                                                                
Epoch 27 Training:  67%|██████▋   | 64/96 [01:38<00:49,  1.54s/it]
+
Epoch 27 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 27 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 27 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
+
                                                                
Epoch 27 Training:  68%|██████▊   | 65/96 [01:40<00:47,  1.54s/it]
+
Epoch 27 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 27 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 27 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 27 Training:  69%|██████▉   | 66/96 [01:41<00:46,  1.55s/it]
+
Epoch 27 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 27 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 27 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
+
                                                                
Epoch 27 Training:  70%|██████▉   | 67/96 [01:43<00:44,  1.55s/it]
+
Epoch 27 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 27 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 27 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
+
                                                                
Epoch 27 Training:  71%|███████   | 68/96 [01:44<00:43,  1.54s/it]
+
Epoch 27 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 27 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 27 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
+
                                                                
Epoch 27 Training:  72%|███████▏  | 69/96 [01:46<00:41,  1.54s/it]
+
Epoch 27 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 27 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 27 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
+
                                                                
Epoch 27 Training:  73%|███████▎  | 70/96 [01:48<00:40,  1.54s/it]
+
Epoch 27 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 27 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 27 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
+
                                                                
Epoch 27 Training:  74%|███████▍  | 71/96 [01:49<00:38,  1.54s/it]
+
Epoch 27 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 27 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 27 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 27 Training:  75%|███████▌  | 72/96 [01:51<00:36,  1.54s/it]
+
Epoch 27 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 27 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 27 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
+
                                                                
Epoch 27 Training:  76%|███████▌  | 73/96 [01:52<00:35,  1.54s/it]
+
Epoch 27 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 27 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 27 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
+
                                                                
Epoch 27 Training:  77%|███████▋  | 74/96 [01:54<00:33,  1.54s/it]
+
Epoch 27 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 27 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 27 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 27 Training:  78%|███████▊  | 75/96 [01:55<00:32,  1.54s/it]
+
Epoch 27 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 27 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 27 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 27 Training:  79%|███████▉  | 76/96 [01:57<00:30,  1.54s/it]
+
Epoch 27 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 27 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 27 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 27 Training:  80%|████████  | 77/96 [01:58<00:29,  1.54s/it]
+
Epoch 27 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 27 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 27 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 27 Training:  81%|████████▏ | 78/96 [02:00<00:27,  1.54s/it]
+
Epoch 27 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 27 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 27 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 27 Training:  82%|████████▏ | 79/96 [02:01<00:26,  1.54s/it]
+
Epoch 27 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 27 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 27 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 27 Training:  83%|████████▎ | 80/96 [02:03<00:24,  1.54s/it]
+
Epoch 27 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 27 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 27 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 27 Training:  84%|████████▍ | 81/96 [02:04<00:23,  1.54s/it]
+
Epoch 27 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 27 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 27 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 27 Training:  85%|████████▌ | 82/96 [02:06<00:21,  1.54s/it]
+
Epoch 27 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 27 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 27 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 27 Training:  86%|████████▋ | 83/96 [02:08<00:19,  1.54s/it]
+
Epoch 27 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 27 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 27 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 27 Training:  88%|████████▊ | 84/96 [02:09<00:18,  1.54s/it]
+
Epoch 27 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 27 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 27 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 27 Training:  89%|████████▊ | 85/96 [02:11<00:16,  1.54s/it]
+
Epoch 27 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 27 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 27 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 27 Training:  90%|████████▉ | 86/96 [02:12<00:15,  1.54s/it]
+
Epoch 27 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 27 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 27 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 27 Training:  91%|█████████ | 87/96 [02:14<00:13,  1.54s/it]
+
Epoch 27 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 27 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 27 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 27 Training:  92%|█████████▏| 88/96 [02:15<00:12,  1.54s/it]
+
Epoch 27 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 27 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 27 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 27 Training:  93%|█████████▎| 89/96 [02:17<00:10,  1.54s/it]
+
Epoch 27 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 27 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 27 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 27 Training:  94%|█████████▍| 90/96 [02:18<00:09,  1.54s/it]
+
Epoch 27 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 27 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 27 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 27 Training:  95%|█████████▍| 91/96 [02:20<00:07,  1.54s/it]
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Epoch 27 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 27 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 27 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 27 Training:  96%|█████████▌| 92/96 [02:21<00:06,  1.54s/it]
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Epoch 27 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s][2026-10-02 10:33:39] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0027 | Epoch 027] train/policy_loss=23.6525 train/unweighted_policy_loss=4.7305 train/loss=23.6525 train/grad_norm=0.1059
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+[2026-10-02 10:33:39] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0028 | Epoch 027] timing/rollout_seconds=178.7818 timing/feedback_seconds=7.0570 timing/optimization_seconds=148.7454 timing/cycle_seconds=332.1060
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+[2026-10-02 10:36:43] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0028 | Epoch 028] train/reward_domain_mean=0.1409 train/reward_domain_std=0.1003 train/reward_structure_mean=0.7414 train/reward_structure_std=0.1247 train/reward_texture_mean=0.7866 train/reward_texture_std=0.0349 train/reward_domain_group_std_mean=0.0206 train/reward_domain_group_std_max=0.0453 train/reward_domain_group_std_min=0.0075 train/reward_domain_group_mean_std=0.0978 train/reward_structure_group_std_mean=0.0078 train/reward_structure_group_std_max=0.0291 train/reward_structure_group_std_min=0.0027 train/reward_structure_group_mean_std=0.1244 train/reward_texture_group_std_mean=0.0078 train/reward_texture_group_std_max=0.0120 train/reward_texture_group_std_min=0.0040 train/reward_texture_group_mean_std=0.0340 train/reward_domain_zero_std_ratio=0 train/reward_structure_zero_std_ratio=0 train/reward_texture_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.7001 train/adv_max=5.9910 train/adv_abs_mean=1.3294
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Epoch 28 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 28 Training:  16%|█▌        | 15/96 [00:23<02:06,  1.56s/it]
+
Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 28 Training:  17%|█▋        | 16/96 [00:24<02:04,  1.56s/it]
+
Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 28 Training:  18%|█▊        | 17/96 [00:26<02:02,  1.55s/it]
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Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 28 Training:  19%|█▉        | 18/96 [00:28<02:00,  1.55s/it]
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Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 28 Training:  20%|█▉        | 19/96 [00:29<01:59,  1.55s/it]
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Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 28 Training:  21%|██        | 20/96 [00:31<01:57,  1.55s/it]
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Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 28 Training:  22%|██▏       | 21/96 [00:32<01:55,  1.55s/it]
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Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 28 Training:  23%|██▎       | 22/96 [00:34<01:54,  1.55s/it]
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Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 28 Training:  24%|██▍       | 23/96 [00:35<01:52,  1.55s/it]
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Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.97it/s]
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Epoch 28 Training:  25%|██▌       | 24/96 [00:37<01:51,  1.55s/it]
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Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 28 Training:  26%|██▌       | 25/96 [00:38<01:49,  1.55s/it]
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Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 28 Training:  27%|██▋       | 26/96 [00:40<01:48,  1.54s/it]
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Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 28 Training:  28%|██▊       | 27/96 [00:41<01:46,  1.54s/it]
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Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 28 Training:  29%|██▉       | 28/96 [00:43<01:44,  1.54s/it]
+
Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 28 Training:  30%|███       | 29/96 [00:44<01:43,  1.54s/it]
+
Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.15it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 28 Training:  31%|███▏      | 30/96 [00:46<01:41,  1.54s/it]
+
Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 28 Training:  32%|███▏      | 31/96 [00:48<01:40,  1.54s/it]
+
Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 28 Training:  33%|███▎      | 32/96 [00:49<01:38,  1.54s/it]
+
Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.90it/s]
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Epoch 28 Training:  34%|███▍      | 33/96 [00:51<01:38,  1.56s/it]
+
Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 28 Training:  35%|███▌      | 34/96 [00:52<01:36,  1.55s/it]
+
Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 28 Training:  36%|███▋      | 35/96 [00:54<01:34,  1.55s/it]
+
Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 28 Training:  38%|███▊      | 36/96 [00:55<01:32,  1.55s/it]
+
Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 28 Training:  39%|███▊      | 37/96 [00:57<01:31,  1.55s/it]
+
Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.15it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.98it/s]
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Epoch 28 Training:  40%|███▉      | 38/96 [00:58<01:29,  1.55s/it]
+
Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 28 Training:  41%|████      | 39/96 [01:00<01:28,  1.55s/it]
+
Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.96it/s]
+
                                                                
Epoch 28 Training:  42%|████▏     | 40/96 [01:02<01:26,  1.55s/it]
+
Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.98it/s]
+
                                                                
Epoch 28 Training:  43%|████▎     | 41/96 [01:03<01:25,  1.55s/it]
+
Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 28 Training:  44%|████▍     | 42/96 [01:05<01:23,  1.55s/it]
+
Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 28 Training:  45%|████▍     | 43/96 [01:06<01:22,  1.55s/it]
+
Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 28 Training:  46%|████▌     | 44/96 [01:08<01:20,  1.55s/it]
+
Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.91it/s]
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Epoch 28 Training:  47%|████▋     | 45/96 [01:09<01:19,  1.56s/it]
+
Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.96it/s]
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Epoch 28 Training:  48%|████▊     | 46/96 [01:11<01:17,  1.56s/it]
+
Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 28 Training:  49%|████▉     | 47/96 [01:12<01:16,  1.55s/it]
+
Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 28 Training:  50%|█████     | 48/96 [01:14<01:14,  1.55s/it]
+
Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 28 Training:  51%|█████     | 49/96 [01:16<01:12,  1.55s/it]
+
Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 28 Training:  52%|█████▏    | 50/96 [01:17<01:11,  1.55s/it]
+
Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 28 Training:  53%|█████▎    | 51/96 [01:19<01:09,  1.55s/it]
+
Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.15it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 28 Training:  54%|█████▍    | 52/96 [01:20<01:08,  1.55s/it]
+
Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 28 Training:  55%|█████▌    | 53/96 [01:22<01:06,  1.55s/it]
+
Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 28 Training:  56%|█████▋    | 54/96 [01:23<01:04,  1.55s/it]
+
Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 28 Training:  57%|█████▋    | 55/96 [01:25<01:03,  1.55s/it]
+
Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 28 Training:  58%|█████▊    | 56/96 [01:26<01:01,  1.55s/it]
+
Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Training:  59%|█████▉    | 57/96 [01:28<01:00,  1.55s/it]
+
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Epoch 28 Training:  60%|██████    | 58/96 [01:29<00:58,  1.55s/it]
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Epoch 28 Training:  61%|██████▏   | 59/96 [01:31<00:57,  1.55s/it]
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Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Training:  62%|██████▎   | 60/96 [01:33<00:55,  1.54s/it]
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Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Training:  64%|██████▎   | 61/96 [01:34<00:54,  1.54s/it]
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Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 28 Training:  65%|██████▍   | 62/96 [01:36<00:52,  1.55s/it]
+
Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 28 Training:  66%|██████▌   | 63/96 [01:37<00:51,  1.56s/it]
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Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Training:  67%|██████▋   | 64/96 [01:39<00:49,  1.56s/it]
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Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.98it/s]
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Epoch 28 Training:  68%|██████▊   | 65/96 [01:41<00:51,  1.65s/it]
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Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s][2026-10-02 10:39:13] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0028 | Epoch 028] train/policy_loss=23.7289 train/unweighted_policy_loss=4.7458 train/loss=23.7289 train/grad_norm=0.0999
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+[2026-10-02 10:39:13] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0029 | Epoch 028] timing/rollout_seconds=178.7247 timing/feedback_seconds=7.1564 timing/optimization_seconds=149.8779 timing/cycle_seconds=333.3123
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+
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+[2026-10-02 10:42:17] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0029 | Epoch 029] train/reward_domain_mean=0.1264 train/reward_domain_std=0.1000 train/reward_structure_mean=0.6757 train/reward_structure_std=0.1424 train/reward_texture_mean=0.7747 train/reward_texture_std=0.0426 train/reward_domain_group_std_mean=0.0234 train/reward_domain_group_std_max=0.0443 train/reward_domain_group_std_min=0.0094 train/reward_domain_group_mean_std=0.0969 train/reward_structure_group_std_mean=0.0098 train/reward_structure_group_std_max=0.0316 train/reward_structure_group_std_min=0.0040 train/reward_structure_group_mean_std=0.1420 train/reward_texture_group_std_mean=0.0084 train/reward_texture_group_std_max=0.0225 train/reward_texture_group_std_min=0.0039 train/reward_texture_group_mean_std=0.0416 train/reward_domain_zero_std_ratio=0 train/reward_structure_zero_std_ratio=0 train/reward_texture_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-8.3795 train/adv_max=5.3617 train/adv_abs_mean=1.3617
+
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Epoch 29 Training:   3%|▎         | 3/96 [00:04<02:21,  1.52s/it]
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Epoch 29 Training:   4%|▍         | 4/96 [00:06<02:20,  1.53s/it]
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Epoch 29 Training:   5%|▌         | 5/96 [00:07<02:19,  1.53s/it]
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Epoch 29 Training:   6%|▋         | 6/96 [00:09<02:18,  1.53s/it]
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Epoch 29 Training:   7%|▋         | 7/96 [00:10<02:16,  1.54s/it]
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Epoch 29 Training:   8%|▊         | 8/96 [00:12<02:15,  1.54s/it]
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Epoch 29 Training:   9%|▉         | 9/96 [00:13<02:13,  1.54s/it]
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Epoch 29 Training:  10%|█         | 10/96 [00:15<02:12,  1.54s/it]
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Epoch 29 Training:  11%|█▏        | 11/96 [00:16<02:10,  1.54s/it]
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Epoch 29 Training:  12%|█▎        | 12/96 [00:18<02:09,  1.54s/it]
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Epoch 29 Training:  14%|█▎        | 13/96 [00:19<02:07,  1.54s/it]
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Epoch 29 Training:  15%|█▍        | 14/96 [00:21<02:06,  1.54s/it]
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Epoch 29 Training:  16%|█▌        | 15/96 [00:23<02:04,  1.54s/it]
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Epoch 29 Training:  17%|█▋        | 16/96 [00:24<02:03,  1.54s/it]
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Epoch 29 Training:  18%|█▊        | 17/96 [00:26<02:01,  1.54s/it]
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Epoch 29 Training:  19%|█▉        | 18/96 [00:27<02:00,  1.54s/it]
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Epoch 29 Training:  21%|██        | 20/96 [00:30<01:57,  1.54s/it]
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Epoch 29 Training:  22%|██▏       | 21/96 [00:32<01:55,  1.54s/it]
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Epoch 29 Training:  23%|██▎       | 22/96 [00:33<01:54,  1.54s/it]
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Epoch 29 Training:  24%|██▍       | 23/96 [00:35<01:52,  1.54s/it]
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Epoch 29 Training:  25%|██▌       | 24/96 [00:36<01:50,  1.54s/it]
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Epoch 29 Training:  26%|██▌       | 25/96 [00:38<01:49,  1.54s/it]
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Epoch 29 Training:  27%|██▋       | 26/96 [00:39<01:47,  1.54s/it]
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Epoch 29 Training:  28%|██▊       | 27/96 [00:41<01:46,  1.54s/it]
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Epoch 29 Training:  29%|██▉       | 28/96 [00:43<01:44,  1.54s/it]
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Epoch 29 Training:  33%|███▎      | 32/96 [00:49<01:38,  1.54s/it]
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Epoch 29 Training:  41%|████      | 39/96 [00:59<01:27,  1.54s/it]
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Epoch 29 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 29 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 29 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 29 Training:  42%|████▏     | 40/96 [01:01<01:26,  1.54s/it]
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Epoch 29 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 29 Training:  43%|████▎     | 41/96 [01:03<01:24,  1.54s/it]
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Epoch 29 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 29 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 29 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 29 Training:  44%|████▍     | 42/96 [01:04<01:23,  1.54s/it]
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Epoch 29 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 29 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 29 Training:  45%|████▍     | 43/96 [01:06<01:21,  1.54s/it]
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Epoch 29 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 29 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.98it/s]
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Epoch 29 Training:  46%|████▌     | 44/96 [01:07<01:20,  1.54s/it]
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Epoch 29 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 29 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 29 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 29 Training:  47%|████▋     | 45/96 [01:09<01:18,  1.54s/it]
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Epoch 29 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 29 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.98it/s]
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Epoch 29 Training:  48%|████▊     | 46/96 [01:10<01:16,  1.54s/it]
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Epoch 29 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 29 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 29 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 29 Training:  49%|████▉     | 47/96 [01:12<01:15,  1.54s/it]
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Epoch 29 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 29 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 29 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 29 Training:  50%|█████     | 48/96 [01:13<01:13,  1.54s/it]
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Epoch 29 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 29 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 29 Training:  51%|█████     | 49/96 [01:15<01:12,  1.54s/it]
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Epoch 29 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 29 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 29 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.97it/s]
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Epoch 29 Training:  52%|█████▏    | 50/96 [01:16<01:10,  1.54s/it]
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Epoch 29 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 29 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 29 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 29 Training:  53%|█████▎    | 51/96 [01:18<01:09,  1.54s/it]
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Epoch 29 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 29 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 29 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 29 Training:  54%|█████▍    | 52/96 [01:19<01:07,  1.54s/it]
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Epoch 29 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 29 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 29 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 29 Training:  55%|█████▌    | 53/96 [01:21<01:06,  1.54s/it]
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Epoch 29 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 29 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 29 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.97it/s]
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Epoch 29 Training:  56%|█████▋    | 54/96 [01:23<01:04,  1.54s/it]
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Epoch 29 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 29 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 29 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 29 Training:  57%|█████▋    | 55/96 [01:24<01:03,  1.54s/it]
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Epoch 29 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 29 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 29 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.98it/s]
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Epoch 29 Training:  58%|█████▊    | 56/96 [01:26<01:01,  1.54s/it]
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Epoch 29 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 29 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 29 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 29 Training:  59%|█████▉    | 57/96 [01:27<01:00,  1.54s/it]
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Epoch 29 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 29 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 29 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 29 Training:  60%|██████    | 58/96 [01:29<00:58,  1.55s/it]
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Epoch 29 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 29 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 29 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.97it/s]
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Epoch 29 Training:  61%|██████▏   | 59/96 [01:30<00:57,  1.55s/it]
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Epoch 29 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 29 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 29 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.97it/s]
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Epoch 29 Training:  62%|██████▎   | 60/96 [01:32<00:55,  1.55s/it]
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Epoch 29 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 29 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.97it/s]
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Epoch 29 Training:  64%|██████▎   | 61/96 [01:33<00:54,  1.55s/it]
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Epoch 29 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 29 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 29 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.96it/s]
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Epoch 29 Training:  65%|██████▍   | 62/96 [01:35<00:52,  1.55s/it]
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Epoch 29 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 29 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 29 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 29 Training:  66%|██████▌   | 63/96 [01:36<00:51,  1.55s/it]
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Epoch 29 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 29 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 29 Training:  67%|██████▋   | 64/96 [01:38<00:49,  1.54s/it]
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Epoch 29 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 29 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 29 Training:  68%|██████▊   | 65/96 [01:40<00:47,  1.54s/it]
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Epoch 29 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 29 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 29 Training:  69%|██████▉   | 66/96 [01:41<00:46,  1.54s/it]
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Epoch 29 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 29 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.98it/s]
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Epoch 29 Training:  70%|██████▉   | 67/96 [01:43<00:44,  1.54s/it]
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Epoch 29 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 29 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.98it/s]
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Epoch 29 Training:  71%|███████   | 68/96 [01:44<00:43,  1.54s/it]
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Epoch 29 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 29 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.02it/s]
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Epoch 29 Training:  72%|███████▏  | 69/96 [01:46<00:41,  1.54s/it]
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Epoch 29 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.98it/s]
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Epoch 29 Training:  73%|███████▎  | 70/96 [01:47<00:40,  1.54s/it]
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Epoch 29 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 29 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 29 Training:  74%|███████▍  | 71/96 [01:49<00:38,  1.54s/it]
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Epoch 29 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 29 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.98it/s]
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Epoch 29 Training:  75%|███████▌  | 72/96 [01:50<00:36,  1.54s/it]
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Epoch 29 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 29 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 29 Training:  76%|███████▌  | 73/96 [01:52<00:35,  1.54s/it]
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Epoch 29 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 29 Training:  77%|███████▋  | 74/96 [01:53<00:33,  1.54s/it]
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Epoch 29 Training:  78%|███████▊  | 75/96 [01:55<00:32,  1.54s/it]
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Epoch 29 Training:  79%|███████▉  | 76/96 [01:56<00:30,  1.54s/it]
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Epoch 29 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 29 Training:  80%|████████  | 77/96 [01:58<00:29,  1.54s/it]
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Epoch 29 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 29 Training:  81%|████████▏ | 78/96 [02:00<00:27,  1.54s/it]
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Epoch 29 Training:  82%|████████▏ | 79/96 [02:01<00:26,  1.54s/it]
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Epoch 29 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 29 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 29 Training:  83%|████████▎ | 80/96 [02:03<00:24,  1.54s/it]
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Epoch 29 Training:  84%|████████▍ | 81/96 [02:04<00:23,  1.54s/it]
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Epoch 29 Training:  85%|████████▌ | 82/96 [02:06<00:21,  1.54s/it]
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Epoch 29 Training:  86%|████████▋ | 83/96 [02:07<00:19,  1.54s/it]
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Epoch 29 Training:  88%|████████▊ | 84/96 [02:09<00:18,  1.54s/it]
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Epoch 29 Training:  89%|████████▊ | 85/96 [02:10<00:16,  1.54s/it]
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Epoch 29 Training:  90%|████████▉ | 86/96 [02:12<00:15,  1.54s/it]
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Epoch 29 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 29 Training:  91%|█████████ | 87/96 [02:13<00:13,  1.54s/it]
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Epoch 29 Training:  92%|█████████▏| 88/96 [02:15<00:12,  1.54s/it]
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Epoch 29 Training:  93%|█████████▎| 89/96 [02:17<00:10,  1.54s/it]
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Epoch 29 Training:  94%|█████████▍| 90/96 [02:18<00:09,  1.54s/it]
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+
                                                                
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+
                                                                
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Epoch 29 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s][2026-10-02 10:44:45] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0029 | Epoch 029] train/policy_loss=23.8886 train/unweighted_policy_loss=4.7777 train/loss=23.8886 train/grad_norm=0.1074
+
+
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+
                                                                
Epoch 29 Training: 100%|██████████| 96/96 [02:27<00:00,  1.56s/it]
Epoch 29 Training: 100%|██████████| 96/96 [02:27<00:00,  1.54s/it]
+[2026-10-02 10:44:45] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0030 | Epoch 029] timing/rollout_seconds=178.8411 timing/feedback_seconds=7.1945 timing/optimization_seconds=148.4784 timing/cycle_seconds=331.9884
+[2026-10-02 10:44:45,169] [INFO] [flow_factory.train]: Training completed successfully
+wandb: 
+wandb: 🚀 View run klein4b_nft_domain_sirst_tex at: https://wandb.ai/315229706-xi-an-jiaotong-university-/IR-Engine/runs/qwz3tol2
+wandb: Find logs at: ../../../ir_engine/runs/klein4b_nft_domain_sirst_tex/wandb/run-20261002_075647-qwz3tol2/logs
diff --git a/runs/klein4b_nft_domain_tex_w025_from_ep72/saves/klein4b_nft_domain_tex_w025_from_ep72/checkpoints/checkpoint-10/README.md b/runs/klein4b_nft_domain_tex_w025_from_ep72/saves/klein4b_nft_domain_tex_w025_from_ep72/checkpoints/checkpoint-10/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..2b78a7a995873d5db3090fca56885fe89cdf97e1
--- /dev/null
+++ b/runs/klein4b_nft_domain_tex_w025_from_ep72/saves/klein4b_nft_domain_tex_w025_from_ep72/checkpoints/checkpoint-10/README.md
@@ -0,0 +1,204 @@
+---
+base_model: /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/flow_factory_gpu_gate_models/FLUX.2-klein-base-4B/transformer
+library_name: peft
+tags:
+- lora
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]
+### Framework versions
+
+- PEFT 0.20.0
\ No newline at end of file
diff --git a/runs/klein4b_nft_domain_tex_w025_from_ep72/saves/klein4b_nft_domain_tex_w025_from_ep72/checkpoints/checkpoint-10/adapter_config.json b/runs/klein4b_nft_domain_tex_w025_from_ep72/saves/klein4b_nft_domain_tex_w025_from_ep72/checkpoints/checkpoint-10/adapter_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..db211e87ba81d72f895e526e9cd1ac3aa23f9169
--- /dev/null
+++ b/runs/klein4b_nft_domain_tex_w025_from_ep72/saves/klein4b_nft_domain_tex_w025_from_ep72/checkpoints/checkpoint-10/adapter_config.json
@@ -0,0 +1,59 @@
+{
+  "alora_invocation_tokens": null,
+  "alpha_pattern": {},
+  "arrow_config": null,
+  "auto_mapping": {
+    "base_model_class": "Flux2Transformer2DModel",
+    "parent_library": "diffusers.models.transformers.transformer_flux2"
+  },
+  "base_model_name_or_path": null,
+  "bias": "none",
+  "corda_config": null,
+  "ensure_weight_tying": false,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": "gaussian",
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 128,
+  "lora_bias": false,
+  "lora_dropout": 0.0,
+  "lora_ga_config": null,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "monteclora_config": null,
+  "peft_type": "LORA",
+  "peft_version": "0.20.0",
+  "qalora_group_size": 16,
+  "r": 64,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "attn.to_v",
+    "attn.add_k_proj",
+    "attn.to_add_out",
+    "ff.linear_out",
+    "ff.linear_in",
+    "ff_context.linear_out",
+    "attn.add_v_proj",
+    "attn.to_qkv_mlp_proj",
+    "attn.to_out.0",
+    "attn.to_q",
+    "ff_context.linear_in",
+    "attn.to_k",
+    "attn.add_q_proj"
+  ],
+  "target_parameters": null,
+  "task_type": null,
+  "trainable_token_indices": null,
+  "use_bdlora": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false,
+  "velora_config": null
+}
\ No newline at end of file
diff --git a/runs/klein4b_nft_domain_tex_w025_from_ep72/saves/klein4b_nft_domain_tex_w025_from_ep72/checkpoints/checkpoint-10/manifest.json b/runs/klein4b_nft_domain_tex_w025_from_ep72/saves/klein4b_nft_domain_tex_w025_from_ep72/checkpoints/checkpoint-10/manifest.json
new file mode 100644
index 0000000000000000000000000000000000000000..4443fd995e0f6f3bd9b7e3dacea93df8d345ecfb
--- /dev/null
+++ b/runs/klein4b_nft_domain_tex_w025_from_ep72/saves/klein4b_nft_domain_tex_w025_from_ep72/checkpoints/checkpoint-10/manifest.json
@@ -0,0 +1,16 @@
+{
+  "base_role": "base",
+  "entries": [
+    {
+      "component": "transformer",
+      "path": ".",
+      "role": "base"
+    }
+  ],
+  "finetune_type": "lora",
+  "format_version": 1,
+  "primary": {
+    "component": "transformer",
+    "path": "."
+  }
+}
diff --git a/runs/klein4b_nft_domain_tex_w050_from_ep72/saves/klein4b_nft_domain_tex_w050_from_ep72/checkpoints/checkpoint-10/README.md b/runs/klein4b_nft_domain_tex_w050_from_ep72/saves/klein4b_nft_domain_tex_w050_from_ep72/checkpoints/checkpoint-10/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..2b78a7a995873d5db3090fca56885fe89cdf97e1
--- /dev/null
+++ b/runs/klein4b_nft_domain_tex_w050_from_ep72/saves/klein4b_nft_domain_tex_w050_from_ep72/checkpoints/checkpoint-10/README.md
@@ -0,0 +1,204 @@
+---
+base_model: /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/flow_factory_gpu_gate_models/FLUX.2-klein-base-4B/transformer
+library_name: peft
+tags:
+- lora
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]
+### Framework versions
+
+- PEFT 0.20.0
\ No newline at end of file
diff --git a/runs/klein4b_nft_domain_tex_w050_from_ep72/saves/klein4b_nft_domain_tex_w050_from_ep72/checkpoints/checkpoint-10/adapter_config.json b/runs/klein4b_nft_domain_tex_w050_from_ep72/saves/klein4b_nft_domain_tex_w050_from_ep72/checkpoints/checkpoint-10/adapter_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..57511ea75e65850c2ba39657c7cdc5be8b82bb84
--- /dev/null
+++ b/runs/klein4b_nft_domain_tex_w050_from_ep72/saves/klein4b_nft_domain_tex_w050_from_ep72/checkpoints/checkpoint-10/adapter_config.json
@@ -0,0 +1,59 @@
+{
+  "alora_invocation_tokens": null,
+  "alpha_pattern": {},
+  "arrow_config": null,
+  "auto_mapping": {
+    "base_model_class": "Flux2Transformer2DModel",
+    "parent_library": "diffusers.models.transformers.transformer_flux2"
+  },
+  "base_model_name_or_path": null,
+  "bias": "none",
+  "corda_config": null,
+  "ensure_weight_tying": false,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": "gaussian",
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 128,
+  "lora_bias": false,
+  "lora_dropout": 0.0,
+  "lora_ga_config": null,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "monteclora_config": null,
+  "peft_type": "LORA",
+  "peft_version": "0.20.0",
+  "qalora_group_size": 16,
+  "r": 64,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "attn.to_v",
+    "ff_context.linear_in",
+    "attn.add_q_proj",
+    "ff_context.linear_out",
+    "attn.to_out.0",
+    "ff.linear_out",
+    "attn.to_k",
+    "attn.to_q",
+    "attn.add_k_proj",
+    "attn.to_qkv_mlp_proj",
+    "attn.add_v_proj",
+    "attn.to_add_out",
+    "ff.linear_in"
+  ],
+  "target_parameters": null,
+  "task_type": null,
+  "trainable_token_indices": null,
+  "use_bdlora": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false,
+  "velora_config": null
+}
\ No newline at end of file
diff --git a/runs/klein4b_nft_domain_tex_w050_from_ep72/saves/klein4b_nft_domain_tex_w050_from_ep72/checkpoints/checkpoint-10/manifest.json b/runs/klein4b_nft_domain_tex_w050_from_ep72/saves/klein4b_nft_domain_tex_w050_from_ep72/checkpoints/checkpoint-10/manifest.json
new file mode 100644
index 0000000000000000000000000000000000000000..4443fd995e0f6f3bd9b7e3dacea93df8d345ecfb
--- /dev/null
+++ b/runs/klein4b_nft_domain_tex_w050_from_ep72/saves/klein4b_nft_domain_tex_w050_from_ep72/checkpoints/checkpoint-10/manifest.json
@@ -0,0 +1,16 @@
+{
+  "base_role": "base",
+  "entries": [
+    {
+      "component": "transformer",
+      "path": ".",
+      "role": "base"
+    }
+  ],
+  "finetune_type": "lora",
+  "format_version": 1,
+  "primary": {
+    "component": "transformer",
+    "path": "."
+  }
+}
diff --git a/runs/klein4b_nft_flir_full_ssim_dists/saves/klein4b_nft_flir_full_ssim_dists/checkpoints/checkpoint-0/README.md b/runs/klein4b_nft_flir_full_ssim_dists/saves/klein4b_nft_flir_full_ssim_dists/checkpoints/checkpoint-0/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..2b78a7a995873d5db3090fca56885fe89cdf97e1
--- /dev/null
+++ b/runs/klein4b_nft_flir_full_ssim_dists/saves/klein4b_nft_flir_full_ssim_dists/checkpoints/checkpoint-0/README.md
@@ -0,0 +1,204 @@
+---
+base_model: /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/flow_factory_gpu_gate_models/FLUX.2-klein-base-4B/transformer
+library_name: peft
+tags:
+- lora
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]
+### Framework versions
+
+- PEFT 0.20.0
\ No newline at end of file
diff --git a/runs/klein4b_nft_flir_full_ssim_dists/saves/klein4b_nft_flir_full_ssim_dists/checkpoints/checkpoint-0/adapter_config.json b/runs/klein4b_nft_flir_full_ssim_dists/saves/klein4b_nft_flir_full_ssim_dists/checkpoints/checkpoint-0/adapter_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..62d53518029788b868cd05dfd4ed9460b764611d
--- /dev/null
+++ b/runs/klein4b_nft_flir_full_ssim_dists/saves/klein4b_nft_flir_full_ssim_dists/checkpoints/checkpoint-0/adapter_config.json
@@ -0,0 +1,59 @@
+{
+  "alora_invocation_tokens": null,
+  "alpha_pattern": {},
+  "arrow_config": null,
+  "auto_mapping": {
+    "base_model_class": "Flux2Transformer2DModel",
+    "parent_library": "diffusers.models.transformers.transformer_flux2"
+  },
+  "base_model_name_or_path": null,
+  "bias": "none",
+  "corda_config": null,
+  "ensure_weight_tying": false,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": "gaussian",
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 128,
+  "lora_bias": false,
+  "lora_dropout": 0.0,
+  "lora_ga_config": null,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "monteclora_config": null,
+  "peft_type": "LORA",
+  "peft_version": "0.20.0",
+  "qalora_group_size": 16,
+  "r": 64,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "attn.add_k_proj",
+    "attn.add_v_proj",
+    "attn.add_q_proj",
+    "attn.to_q",
+    "attn.to_out.0",
+    "attn.to_v",
+    "attn.to_qkv_mlp_proj",
+    "attn.to_k",
+    "ff_context.linear_out",
+    "ff_context.linear_in",
+    "attn.to_add_out",
+    "ff.linear_in",
+    "ff.linear_out"
+  ],
+  "target_parameters": null,
+  "task_type": null,
+  "trainable_token_indices": null,
+  "use_bdlora": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false,
+  "velora_config": null
+}
\ No newline at end of file
diff --git a/runs/klein4b_nft_flir_full_ssim_dists/saves/klein4b_nft_flir_full_ssim_dists/checkpoints/checkpoint-0/manifest.json b/runs/klein4b_nft_flir_full_ssim_dists/saves/klein4b_nft_flir_full_ssim_dists/checkpoints/checkpoint-0/manifest.json
new file mode 100644
index 0000000000000000000000000000000000000000..4443fd995e0f6f3bd9b7e3dacea93df8d345ecfb
--- /dev/null
+++ b/runs/klein4b_nft_flir_full_ssim_dists/saves/klein4b_nft_flir_full_ssim_dists/checkpoints/checkpoint-0/manifest.json
@@ -0,0 +1,16 @@
+{
+  "base_role": "base",
+  "entries": [
+    {
+      "component": "transformer",
+      "path": ".",
+      "role": "base"
+    }
+  ],
+  "finetune_type": "lora",
+  "format_version": 1,
+  "primary": {
+    "component": "transformer",
+    "path": "."
+  }
+}
diff --git a/runs/klein4b_nft_flir_full_ssim_dists/saves/klein4b_nft_flir_full_ssim_dists/checkpoints/checkpoint-100/README.md b/runs/klein4b_nft_flir_full_ssim_dists/saves/klein4b_nft_flir_full_ssim_dists/checkpoints/checkpoint-100/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..2b78a7a995873d5db3090fca56885fe89cdf97e1
--- /dev/null
+++ b/runs/klein4b_nft_flir_full_ssim_dists/saves/klein4b_nft_flir_full_ssim_dists/checkpoints/checkpoint-100/README.md
@@ -0,0 +1,204 @@
+---
+base_model: /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/flow_factory_gpu_gate_models/FLUX.2-klein-base-4B/transformer
+library_name: peft
+tags:
+- lora
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]
+### Framework versions
+
+- PEFT 0.20.0
\ No newline at end of file
diff --git a/runs/klein4b_nft_flir_full_ssim_dists/saves/klein4b_nft_flir_full_ssim_dists/checkpoints/checkpoint-100/adapter_config.json b/runs/klein4b_nft_flir_full_ssim_dists/saves/klein4b_nft_flir_full_ssim_dists/checkpoints/checkpoint-100/adapter_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..62d53518029788b868cd05dfd4ed9460b764611d
--- /dev/null
+++ b/runs/klein4b_nft_flir_full_ssim_dists/saves/klein4b_nft_flir_full_ssim_dists/checkpoints/checkpoint-100/adapter_config.json
@@ -0,0 +1,59 @@
+{
+  "alora_invocation_tokens": null,
+  "alpha_pattern": {},
+  "arrow_config": null,
+  "auto_mapping": {
+    "base_model_class": "Flux2Transformer2DModel",
+    "parent_library": "diffusers.models.transformers.transformer_flux2"
+  },
+  "base_model_name_or_path": null,
+  "bias": "none",
+  "corda_config": null,
+  "ensure_weight_tying": false,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": "gaussian",
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 128,
+  "lora_bias": false,
+  "lora_dropout": 0.0,
+  "lora_ga_config": null,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "monteclora_config": null,
+  "peft_type": "LORA",
+  "peft_version": "0.20.0",
+  "qalora_group_size": 16,
+  "r": 64,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "attn.add_k_proj",
+    "attn.add_v_proj",
+    "attn.add_q_proj",
+    "attn.to_q",
+    "attn.to_out.0",
+    "attn.to_v",
+    "attn.to_qkv_mlp_proj",
+    "attn.to_k",
+    "ff_context.linear_out",
+    "ff_context.linear_in",
+    "attn.to_add_out",
+    "ff.linear_in",
+    "ff.linear_out"
+  ],
+  "target_parameters": null,
+  "task_type": null,
+  "trainable_token_indices": null,
+  "use_bdlora": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false,
+  "velora_config": null
+}
\ No newline at end of file
diff --git a/runs/klein4b_nft_flir_full_ssim_dists/saves/klein4b_nft_flir_full_ssim_dists/checkpoints/checkpoint-100/manifest.json b/runs/klein4b_nft_flir_full_ssim_dists/saves/klein4b_nft_flir_full_ssim_dists/checkpoints/checkpoint-100/manifest.json
new file mode 100644
index 0000000000000000000000000000000000000000..4443fd995e0f6f3bd9b7e3dacea93df8d345ecfb
--- /dev/null
+++ b/runs/klein4b_nft_flir_full_ssim_dists/saves/klein4b_nft_flir_full_ssim_dists/checkpoints/checkpoint-100/manifest.json
@@ -0,0 +1,16 @@
+{
+  "base_role": "base",
+  "entries": [
+    {
+      "component": "transformer",
+      "path": ".",
+      "role": "base"
+    }
+  ],
+  "finetune_type": "lora",
+  "format_version": 1,
+  "primary": {
+    "component": "transformer",
+    "path": "."
+  }
+}
diff --git a/runs/klein4b_nft_flir_full_ssim_dists/saves/klein4b_nft_flir_full_ssim_dists/checkpoints/checkpoint-20/README.md b/runs/klein4b_nft_flir_full_ssim_dists/saves/klein4b_nft_flir_full_ssim_dists/checkpoints/checkpoint-20/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..2b78a7a995873d5db3090fca56885fe89cdf97e1
--- /dev/null
+++ b/runs/klein4b_nft_flir_full_ssim_dists/saves/klein4b_nft_flir_full_ssim_dists/checkpoints/checkpoint-20/README.md
@@ -0,0 +1,204 @@
+---
+base_model: /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/flow_factory_gpu_gate_models/FLUX.2-klein-base-4B/transformer
+library_name: peft
+tags:
+- lora
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]
+### Framework versions
+
+- PEFT 0.20.0
\ No newline at end of file
diff --git a/runs/klein4b_nft_flir_full_ssim_dists/saves/klein4b_nft_flir_full_ssim_dists/checkpoints/checkpoint-20/adapter_config.json b/runs/klein4b_nft_flir_full_ssim_dists/saves/klein4b_nft_flir_full_ssim_dists/checkpoints/checkpoint-20/adapter_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..62d53518029788b868cd05dfd4ed9460b764611d
--- /dev/null
+++ b/runs/klein4b_nft_flir_full_ssim_dists/saves/klein4b_nft_flir_full_ssim_dists/checkpoints/checkpoint-20/adapter_config.json
@@ -0,0 +1,59 @@
+{
+  "alora_invocation_tokens": null,
+  "alpha_pattern": {},
+  "arrow_config": null,
+  "auto_mapping": {
+    "base_model_class": "Flux2Transformer2DModel",
+    "parent_library": "diffusers.models.transformers.transformer_flux2"
+  },
+  "base_model_name_or_path": null,
+  "bias": "none",
+  "corda_config": null,
+  "ensure_weight_tying": false,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": "gaussian",
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 128,
+  "lora_bias": false,
+  "lora_dropout": 0.0,
+  "lora_ga_config": null,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "monteclora_config": null,
+  "peft_type": "LORA",
+  "peft_version": "0.20.0",
+  "qalora_group_size": 16,
+  "r": 64,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "attn.add_k_proj",
+    "attn.add_v_proj",
+    "attn.add_q_proj",
+    "attn.to_q",
+    "attn.to_out.0",
+    "attn.to_v",
+    "attn.to_qkv_mlp_proj",
+    "attn.to_k",
+    "ff_context.linear_out",
+    "ff_context.linear_in",
+    "attn.to_add_out",
+    "ff.linear_in",
+    "ff.linear_out"
+  ],
+  "target_parameters": null,
+  "task_type": null,
+  "trainable_token_indices": null,
+  "use_bdlora": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false,
+  "velora_config": null
+}
\ No newline at end of file
diff --git a/runs/klein4b_nft_flir_full_ssim_dists/saves/klein4b_nft_flir_full_ssim_dists/checkpoints/checkpoint-20/manifest.json b/runs/klein4b_nft_flir_full_ssim_dists/saves/klein4b_nft_flir_full_ssim_dists/checkpoints/checkpoint-20/manifest.json
new file mode 100644
index 0000000000000000000000000000000000000000..4443fd995e0f6f3bd9b7e3dacea93df8d345ecfb
--- /dev/null
+++ b/runs/klein4b_nft_flir_full_ssim_dists/saves/klein4b_nft_flir_full_ssim_dists/checkpoints/checkpoint-20/manifest.json
@@ -0,0 +1,16 @@
+{
+  "base_role": "base",
+  "entries": [
+    {
+      "component": "transformer",
+      "path": ".",
+      "role": "base"
+    }
+  ],
+  "finetune_type": "lora",
+  "format_version": 1,
+  "primary": {
+    "component": "transformer",
+    "path": "."
+  }
+}
diff --git a/runs/klein4b_nft_flir_full_ssim_dists/train.log b/runs/klein4b_nft_flir_full_ssim_dists/train.log
new file mode 100644
index 0000000000000000000000000000000000000000..71496fa583f133b6c3e5409a0e572e4b62cf3f60
--- /dev/null
+++ b/runs/klein4b_nft_flir_full_ssim_dists/train.log
@@ -0,0 +1,39615 @@
+[2026-10-01 13:03:34,030] [INFO] [flow_factory.cli]: ============================================================
+[2026-10-01 13:03:34,031] [INFO] [flow_factory.cli]: Flow-Factory Launch Configuration
+[2026-10-01 13:03:34,031] [INFO] [flow_factory.cli]:   Num machines:       1
+[2026-10-01 13:03:34,031] [INFO] [flow_factory.cli]:   Machine rank:       0
+[2026-10-01 13:03:34,031] [INFO] [flow_factory.cli]:   Num processes:      8
+[2026-10-01 13:03:34,031] [INFO] [flow_factory.cli]:   Master node:        localhost:29512
+[2026-10-01 13:03:34,031] [INFO] [flow_factory.cli]:   Mixed precision:    bf16
+[2026-10-01 13:03:34,031] [INFO] [flow_factory.cli]:   Accelerate config:  config/deepspeed/deepspeed_zero2.yaml
+[2026-10-01 13:03:34,031] [INFO] [flow_factory.cli]: ============================================================
+W1001 13:03:45.477000 4088694 site-packages/torch/distributed/run.py:852] 
+W1001 13:03:45.477000 4088694 site-packages/torch/distributed/run.py:852] *****************************************
+W1001 13:03:45.477000 4088694 site-packages/torch/distributed/run.py:852] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
+W1001 13:03:45.477000 4088694 site-packages/torch/distributed/run.py:852] *****************************************
+[2026-10-01 13:03:51] [Rank 0] [WARNING] [flow_factory.hparams.abc]: NFTTrainingArguments.from_dict captured 3 unknown key(s) into extra_kwargs: ['condition_image_size', 'flat_steps', 'ramp_rate']. Verify these are intentional (e.g., adapter-specific kwargs); typos against declared fields will be silently accepted otherwise.
+[2026-10-01 13:03:51] [Rank 0] [INFO] [flow_factory.hparams.training_args._base]: World Size: 8
+[2026-10-01 13:03:51] [Rank 0] [WARNING] [flow_factory.hparams.abc]: EvaluationArguments.from_dict captured 1 unknown key(s) into extra_kwargs: ['condition_image_size']. Verify these are intentional (e.g., adapter-specific kwargs); typos against declared fields will be silently accepted otherwise.
+[2026-10-01 13:03:51,037] [INFO] [flow_factory.train]: ====================================================================================================
+[2026-10-01 13:03:51,037] [INFO] [flow_factory.train]: Flow-Factory Training Initialized
+[2026-10-01 13:03:51,037] [INFO] [flow_factory.train]: World Size: 8
+[2026-10-01 13:03:51,037] [INFO] [flow_factory.train]: ====================================================================================================
+[2026-10-01 13:03:51,037] [INFO] [flow_factory.train]: Config: /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/runs/klein4b_nft_flir_full_ssim_dists/train.yaml
+[2026-10-01 13:03:51,044] [INFO] [flow_factory.train]: 
+launcher: accelerate
+config_file: config/deepspeed/deepspeed_zero2.yaml
+num_processes: 8
+main_process_port: 29512
+mixed_precision: bf16
+data:
+  dataset_dir: data
+  cache_dir: ~/.cache/flow_factory/datasets
+  image_dir: null
+  video_dir: null
+  audio_dir: null
+  preprocessing_batch_size: 8
+  dataloader_num_workers: 8
+  enable_preprocess: true
+  force_reprocess: false
+  max_dataset_size: null
+  preprocess_parallelism: local
+  sampler_type: group_contiguous
+  sampler_subgroup_size: null
+  datasets:
+  - extra_kwargs: {}
+    name: flir_rl_full
+    dataset_dir: /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/flir_rl_full
+    image_dir: null
+    video_dir: null
+    audio_dir: null
+    train:
+      extra_kwargs: {}
+      enabled: true
+      split: train
+      weight: 1
+      max_dataset_size: null
+      unique_sample_num_per_epoch: 48
+      num_batches_per_epoch: null
+    eval:
+      extra_kwargs: {}
+      enabled: true
+      split: test
+      max_dataset_size: null
+      resolution: null
+      num_inference_steps: null
+      guidance_scale: null
+    source_id: 0
+model:
+  model_name_or_path: /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/flow_factory_gpu_gate_models/FLUX.2-klein-base-4B
+  finetune_type: lora
+  trainable_parameters_dtype: bfloat16
+  component_load_dtypes: null
+  frozen_parameters_dtype: null
+  target_components:
+  - transformer
+  target_modules: default
+  model_type: flux2-klein
+  lora_rank: 64
+  lora_alpha: 128
+  resume_path: /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/runs/klein4b_sft_flir_full_r64a128_lr1e-4/saves/klein4b_sft_flir_full_r64a128_lr1e-4/checkpoints/checkpoint-12
+  resume_type: lora
+scheduler:
+  dynamics_type: ODE
+  noise_level: 0.0
+  num_sde_steps: 0
+  sde_steps: []
+  seed: 42
+training:
+  trainer_type: nft
+  resolution:
+  - 512
+  - 640
+  height: 512
+  width: 640
+  num_frames: null
+  frame_rate: null
+  max_epochs: 200
+  per_device_batch_size: 1
+  gradient_step_per_epoch: 1
+  max_grad_norm: 1.0
+  num_batches_per_epoch: 96
+  gradient_accumulation_steps: 192
+  num_inner_epochs: 1
+  group_size: 16
+  unique_sample_num_per_epoch: 48
+  num_inference_steps: 8
+  guidance_scale: 1.0
+  ref_param_device: cuda
+  seed: 42
+  learning_rate: 1.0e-05
+  adam_weight_decay: 0.0001
+  adam_betas:
+  - 0.9
+  - 0.999
+  adam_epsilon: 1.0e-08
+  enable_gradient_checkpointing: false
+  offload_samples_to_cpu: false
+  ema_decay: 0.5
+  ema_update_interval: 1
+  ema_device: cuda
+  ema_decay_schedule: piecewise_linear
+  latent_storage_dtype: fp16
+  shuffle_samples: true
+  reward_optimization_overlap: false
+  reward_optimization_overlap_mode: ready
+  reward_optimization_overlap_poll_interval: 0.05
+  global_std: false
+  advantage_aggregation: gdpo
+  nft_beta: 0.1
+  off_policy: true
+  adv_clip_range:
+  - -5.0
+  - 5.0
+  kl_type: v-based
+  kl_beta: 0.0
+  num_train_timesteps: 2
+  time_sampling_strategy: discrete
+  time_shift: 3.0
+  timestep_range:
+  - 0.0
+  - 0.5
+  flat_steps: 0
+  ramp_rate: 0.001
+  condition_image_size: 1024
+eval:
+  resolution:
+  - 512
+  - 640
+  height: 512
+  width: 640
+  num_frames: null
+  frame_rate: null
+  per_device_batch_size: 1
+  seed: 42
+  guidance_scale: 1.0
+  num_inference_steps: 28
+  eval_freq: 10
+  condition_image_size: 1024
+log:
+  run_name: klein4b_nft_flir_full_ssim_dists
+  project: IR-Engine
+  logging_backend: wandb
+  save_dir: /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/runs/klein4b_nft_flir_full_ssim_dists/saves
+  save_freq: 20
+  save_model_only: true
+  verbose: true
+acceleration:
+  shared: []
+  rollout: []
+reward:
+  reward_0:
+    name: ssim
+    reward_model: ir_rewards.fidelity.SSIMReward
+    weight:
+      flir_rl_full: 1.0
+    dtype: float32
+    device: cuda
+    batch_size: 16
+    async_reward: false
+    num_workers: 1
+    applicable_datasets:
+    - flir_rl_full
+  reward_1:
+    name: dists
+    reward_model: ir_rewards.fidelity.DISTSReward
+    weight:
+      flir_rl_full: 1.0
+    dtype: float32
+    device: cuda
+    batch_size: 16
+    async_reward: false
+    num_workers: 1
+    applicable_datasets:
+    - flir_rl_full
+eval_reward:
+  reward_0:
+    name: ssim
+    reward_model: ir_rewards.fidelity.SSIMReward
+    weight:
+      flir_rl_full: 1.0
+    dtype: float32
+    device: cuda
+    batch_size: 16
+    async_reward: false
+    num_workers: 1
+    applicable_datasets:
+    - flir_rl_full
+  reward_1:
+    name: psnr
+    reward_model: ir_rewards.fidelity.PSNRReward
+    weight:
+      flir_rl_full: 1.0
+    dtype: float32
+    device: cuda
+    batch_size: 16
+    async_reward: false
+    num_workers: 1
+    applicable_datasets:
+    - flir_rl_full
+  reward_2:
+    name: dists
+    reward_model: ir_rewards.fidelity.DISTSReward
+    weight:
+      flir_rl_full: 1.0
+    dtype: float32
+    device: cuda
+    batch_size: 16
+    async_reward: false
+    num_workers: 1
+    applicable_datasets:
+    - flir_rl_full
+optimizer:
+  optimizer_configs:
+  - extra_kwargs: {}
+    name: default
+    optimizer: adamw
+    learning_rate: 5.0e-05
+    weight_decay: 0.0001
+    max_grad_norm: 1.0
+    update_frequency: 1
+    betas:
+    - 0.9
+    - 0.999
+    eps: 1.0e-08
+
+[2026-10-01 13:03:51,044] [INFO] [flow_factory.train]: ====================================================================================================
+[2026-10-01 13:04:02] [Rank 3] [INFO] [flow_factory.models.loader]: Loading model architecture: flux2-klein...
+[2026-10-01 13:04:02] [Rank 6] [INFO] [flow_factory.models.loader]: Loading model architecture: flux2-klein...
+[2026-10-01 13:04:02] [Rank 5] [INFO] [flow_factory.models.loader]: Loading model architecture: flux2-klein...
+[2026-10-01 13:04:02] [Rank 4] [INFO] [flow_factory.models.loader]: Loading model architecture: flux2-klein...
+[2026-10-01 13:04:02] [Rank 7] [INFO] [flow_factory.models.loader]: Loading model architecture: flux2-klein...
+[2026-10-01 13:04:02,752] [WARNING] [accelerate.utils.other]: [RANK 0] Detected kernel version 5.4.241, which is below the recommended minimum of 5.5.0; this can cause the process to hang. It is recommended to upgrade the kernel to the minimum version or higher.
+[2026-10-01 13:04:02] [Rank 2] [INFO] [flow_factory.models.loader]: Loading model architecture: flux2-klein...
+[2026-10-01 13:04:02] [Rank 0] [INFO] [flow_factory.models.loader]: Loading model architecture: flux2-klein...
+[2026-10-01 13:04:02] [Rank 1] [INFO] [flow_factory.models.loader]: Loading model architecture: flux2-klein...
+
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+
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+
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+[2026-10-01 13:04:11] [Rank 6] [INFO] [flow_factory.scheduler.loader]: Loaded SDE scheduler: FlowMatchEulerDiscreteSDEScheduler
+[2026-10-01 13:04:11] [Rank 5] [INFO] [flow_factory.scheduler.loader]: Loaded SDE scheduler: FlowMatchEulerDiscreteSDEScheduler
+[2026-10-01 13:04:12] [Rank 3] [INFO] [flow_factory.scheduler.loader]: Loaded SDE scheduler: FlowMatchEulerDiscreteSDEScheduler
+
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+[2026-10-01 13:04:12] [Rank 7] [INFO] [flow_factory.scheduler.loader]: Loaded SDE scheduler: FlowMatchEulerDiscreteSDEScheduler
+[2026-10-01 13:04:12] [Rank 1] [INFO] [flow_factory.scheduler.loader]: Loaded SDE scheduler: FlowMatchEulerDiscreteSDEScheduler
+[2026-10-01 13:04:12] [Rank 2] [INFO] [flow_factory.scheduler.loader]: Loaded SDE scheduler: FlowMatchEulerDiscreteSDEScheduler
+[2026-10-01 13:04:12] [Rank 4] [INFO] [flow_factory.scheduler.loader]: Loaded SDE scheduler: FlowMatchEulerDiscreteSDEScheduler
+
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+[2026-10-01 13:04:12] [Rank 0] [INFO] [flow_factory.scheduler.loader]: Loaded SDE scheduler: FlowMatchEulerDiscreteSDEScheduler
+[2026-10-01 13:04:13] [Rank 0] [INFO] [flow_factory.models.abc]: LoRA adapter loaded for transformer role=base from /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/runs/klein4b_sft_flir_full_r64a128_lr1e-4/saves/klein4b_sft_flir_full_r64a128_lr1e-4/checkpoints/checkpoint-12
+[2026-10-01 13:04:14] [Rank 0] [INFO] [flow_factory.models.abc]: Checkpoint loaded successfully from /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/runs/klein4b_sft_flir_full_r64a128_lr1e-4/saves/klein4b_sft_flir_full_r64a128_lr1e-4/checkpoints/checkpoint-12 (type=lora)
+[2026-10-01 13:04:14] [Rank 4] [INFO] [flow_factory.models.abc]: Froze ALL transformer parameters
+[2026-10-01 13:04:14] [Rank 4] [INFO] [flow_factory.models.abc]: Component transformer already has 'default' adapter. Skipping initialization but enabling gradients.
+[2026-10-01 13:04:14] [Rank 1] [INFO] [flow_factory.models.abc]: Froze ALL transformer parameters
+[2026-10-01 13:04:14] [Rank 2] [INFO] [flow_factory.models.abc]: Froze ALL transformer parameters
+[2026-10-01 13:04:14] [Rank 6] [INFO] [flow_factory.models.abc]: Froze ALL transformer parameters
+[2026-10-01 13:04:14] [Rank 5] [INFO] [flow_factory.models.abc]: Froze ALL transformer parameters
+[2026-10-01 13:04:14] [Rank 1] [INFO] [flow_factory.models.abc]: Component transformer already has 'default' adapter. Skipping initialization but enabling gradients.
+[2026-10-01 13:04:14] [Rank 7] [INFO] [flow_factory.models.abc]: Froze ALL transformer parameters
+[2026-10-01 13:04:14] [Rank 3] [INFO] [flow_factory.models.abc]: Froze ALL transformer parameters
+[2026-10-01 13:04:14] [Rank 2] [INFO] [flow_factory.models.abc]: Component transformer already has 'default' adapter. Skipping initialization but enabling gradients.
+[2026-10-01 13:04:14] [Rank 6] [INFO] [flow_factory.models.abc]: Component transformer already has 'default' adapter. Skipping initialization but enabling gradients.
+[2026-10-01 13:04:14] [Rank 0] [INFO] [flow_factory.models.abc]: Froze ALL transformer parameters
+[2026-10-01 13:04:14] [Rank 5] [INFO] [flow_factory.models.abc]: Component transformer already has 'default' adapter. Skipping initialization but enabling gradients.
+[2026-10-01 13:04:14] [Rank 7] [INFO] [flow_factory.models.abc]: Component transformer already has 'default' adapter. Skipping initialization but enabling gradients.
+[2026-10-01 13:04:14] [Rank 3] [INFO] [flow_factory.models.abc]: Component transformer already has 'default' adapter. Skipping initialization but enabling gradients.
+[2026-10-01 13:04:14] [Rank 0] [INFO] [flow_factory.models.abc]: Component transformer already has 'default' adapter. Skipping initialization but enabling gradients.
+[2026-10-01 13:04:14] [Rank 4] [INFO] [flow_factory.models.abc]: Set 160 trainable parameters to torch.bfloat16; frozen component policy -> {'text_encoder': None, 'transformer': None, 'vae': None}
+[2026-10-01 13:04:14] [Rank 4] [INFO] [flow_factory.models.loader]: Successfully loaded Flux2KleinAdapter
+[2026-10-01 13:04:14] [Rank 1] [INFO] [flow_factory.models.abc]: Set 160 trainable parameters to torch.bfloat16; frozen component policy -> {'text_encoder': None, 'transformer': None, 'vae': None}
+[2026-10-01 13:04:14] [Rank 1] [INFO] [flow_factory.models.loader]: Successfully loaded Flux2KleinAdapter
+[2026-10-01 13:04:14] [Rank 5] [INFO] [flow_factory.models.abc]: Set 160 trainable parameters to torch.bfloat16; frozen component policy -> {'text_encoder': None, 'transformer': None, 'vae': None}
+[2026-10-01 13:04:14] [Rank 5] [INFO] [flow_factory.models.loader]: Successfully loaded Flux2KleinAdapter
+[2026-10-01 13:04:14] [Rank 6] [INFO] [flow_factory.models.abc]: Set 160 trainable parameters to torch.bfloat16; frozen component policy -> {'text_encoder': None, 'transformer': None, 'vae': None}
+[2026-10-01 13:04:14] [Rank 0] [INFO] [flow_factory.models.abc]: Set 160 trainable parameters to torch.bfloat16; frozen component policy -> {'text_encoder': None, 'transformer': None, 'vae': None}
+[2026-10-01 13:04:14] [Rank 6] [INFO] [flow_factory.models.loader]: Successfully loaded Flux2KleinAdapter
+[2026-10-01 13:04:14] [Rank 7] [INFO] [flow_factory.models.abc]: Set 160 trainable parameters to torch.bfloat16; frozen component policy -> {'text_encoder': None, 'transformer': None, 'vae': None}
+[2026-10-01 13:04:14] [Rank 2] [INFO] [flow_factory.models.abc]: Set 160 trainable parameters to torch.bfloat16; frozen component policy -> {'text_encoder': None, 'transformer': None, 'vae': None}
+[2026-10-01 13:04:14] [Rank 7] [INFO] [flow_factory.models.loader]: Successfully loaded Flux2KleinAdapter
+[2026-10-01 13:04:14] [Rank 2] [INFO] [flow_factory.models.loader]: Successfully loaded Flux2KleinAdapter
+[2026-10-01 13:04:14] [Rank 3] [INFO] [flow_factory.models.abc]: Set 160 trainable parameters to torch.bfloat16; frozen component policy -> {'text_encoder': None, 'transformer': None, 'vae': None}
+[2026-10-01 13:04:14] [Rank 0] [INFO] [flow_factory.models.abc]: Precision inventory stage=initialize component=text_encoder {'trainable': {}, 'frozen': {'torch.bfloat16': 4022468096}}
+[2026-10-01 13:04:14] [Rank 3] [INFO] [flow_factory.models.loader]: Successfully loaded Flux2KleinAdapter
+[2026-10-01 13:04:14] [Rank 0] [INFO] [flow_factory.models.abc]: Precision inventory stage=initialize component=transformer {'trainable': {'torch.bfloat16': 76677120}, 'frozen': {'torch.float32': 3875544576}}
+[2026-10-01 13:04:14] [Rank 0] [INFO] [flow_factory.models.abc]: Precision inventory stage=initialize component=vae {'trainable': {}, 'frozen': {'torch.float32': 84046115}}
+[2026-10-01 13:04:14] [Rank 0] [INFO] [flow_factory.models.loader]: Successfully loaded Flux2KleinAdapter
+[2026-10-01 13:04:16] [Rank 3] [INFO] [flow_factory.data_utils.loader]: Preprocessing train dataset shard 0003/0007 -> /root/.cache/flow_factory/datasets/baae786eff625d7a251da3508a68a964.tmp/_parts/rank_0003_of_0008/cache-baae786eff625d7a251da3508a68a964_shard0003of0007.arrow
+[2026-10-01 13:04:16] [Rank 1] [INFO] [flow_factory.data_utils.loader]: Preprocessing train dataset shard 0001/0007 -> /root/.cache/flow_factory/datasets/baae786eff625d7a251da3508a68a964.tmp/_parts/rank_0001_of_0008/cache-baae786eff625d7a251da3508a68a964_shard0001of0007.arrow
+[2026-10-01 13:04:16] [Rank 2] [INFO] [flow_factory.data_utils.loader]: Preprocessing train dataset shard 0002/0007 -> /root/.cache/flow_factory/datasets/baae786eff625d7a251da3508a68a964.tmp/_parts/rank_0002_of_0008/cache-baae786eff625d7a251da3508a68a964_shard0002of0007.arrow
+[2026-10-01 13:04:16] [Rank 5] [INFO] [flow_factory.data_utils.loader]: Preprocessing train dataset shard 0005/0007 -> /root/.cache/flow_factory/datasets/baae786eff625d7a251da3508a68a964.tmp/_parts/rank_0005_of_0008/cache-baae786eff625d7a251da3508a68a964_shard0005of0007.arrow
+[2026-10-01 13:04:16] [Rank 7] [INFO] [flow_factory.data_utils.loader]: Preprocessing train dataset shard 0007/0007 -> /root/.cache/flow_factory/datasets/baae786eff625d7a251da3508a68a964.tmp/_parts/rank_0007_of_0008/cache-baae786eff625d7a251da3508a68a964_shard0007of0007.arrow
+[2026-10-01 13:04:16] [Rank 6] [INFO] [flow_factory.data_utils.loader]: Preprocessing train dataset shard 0006/0007 -> /root/.cache/flow_factory/datasets/baae786eff625d7a251da3508a68a964.tmp/_parts/rank_0006_of_0008/cache-baae786eff625d7a251da3508a68a964_shard0006of0007.arrow
+[2026-10-01 13:04:16] [Rank 4] [INFO] [flow_factory.data_utils.loader]: Preprocessing train dataset shard 0004/0007 -> /root/.cache/flow_factory/datasets/baae786eff625d7a251da3508a68a964.tmp/_parts/rank_0004_of_0008/cache-baae786eff625d7a251da3508a68a964_shard0004of0007.arrow
+[2026-10-01 13:04:16] [Rank 0] [INFO] [flow_factory.data_utils.loader]: Preprocessing train dataset shard 0000/0007 -> /root/.cache/flow_factory/datasets/baae786eff625d7a251da3508a68a964.tmp/_parts/rank_0000_of_0008/cache-baae786eff625d7a251da3508a68a964_shard0000of0007.arrow
+/opt/conda/envs/ff-qi21/lib/python3.10/contextlib.py:135: UserWarning: CUDA reports that you have 8 available devices, and you have used fork_rng without explicitly specifying which devices are being used. For safety, we initialize *every* CUDA device by default, which can be quite slow if you have a lot of CUDAs. If you know that you are only making use of a few CUDA devices, set the environment variable CUDA_VISIBLE_DEVICES or the 'devices' keyword argument of fork_rng with the set of devices you are actually using. For example, if you are using CPU only, set device.upper()_VISIBLE_DEVICES= or devices=[]; if you are using device 0 only, set CUDA_VISIBLE_DEVICES=0 or devices=[0].  To initialize all devices and suppress this warning, set the 'devices' keyword argument to `range(torch.cuda.device_count())`.
+  return next(self.gen)
+/opt/conda/envs/ff-qi21/lib/python3.10/contextlib.py:135: UserWarning: CUDA reports that you have 8 available devices, and you have used fork_rng without explicitly specifying which devices are being used. For safety, we initialize *every* CUDA device by default, which can be quite slow if you have a lot of CUDAs. If you know that you are only making use of a few CUDA devices, set the environment variable CUDA_VISIBLE_DEVICES or the 'devices' keyword argument of fork_rng with the set of devices you are actually using. For example, if you are using CPU only, set device.upper()_VISIBLE_DEVICES= or devices=[]; if you are using device 0 only, set CUDA_VISIBLE_DEVICES=0 or devices=[0].  To initialize all devices and suppress this warning, set the 'devices' keyword argument to `range(torch.cuda.device_count())`.
+  return next(self.gen)
+/opt/conda/envs/ff-qi21/lib/python3.10/contextlib.py:135: UserWarning: CUDA reports that you have 8 available devices, and you have used fork_rng without explicitly specifying which devices are being used. For safety, we initialize *every* CUDA device by default, which can be quite slow if you have a lot of CUDAs. If you know that you are only making use of a few CUDA devices, set the environment variable CUDA_VISIBLE_DEVICES or the 'devices' keyword argument of fork_rng with the set of devices you are actually using. For example, if you are using CPU only, set device.upper()_VISIBLE_DEVICES= or devices=[]; if you are using device 0 only, set CUDA_VISIBLE_DEVICES=0 or devices=[0].  To initialize all devices and suppress this warning, set the 'devices' keyword argument to `range(torch.cuda.device_count())`.
+  return next(self.gen)
+/opt/conda/envs/ff-qi21/lib/python3.10/contextlib.py:135: UserWarning: CUDA reports that you have 8 available devices, and you have used fork_rng without explicitly specifying which devices are being used. For safety, we initialize *every* CUDA device by default, which can be quite slow if you have a lot of CUDAs. If you know that you are only making use of a few CUDA devices, set the environment variable CUDA_VISIBLE_DEVICES or the 'devices' keyword argument of fork_rng with the set of devices you are actually using. For example, if you are using CPU only, set device.upper()_VISIBLE_DEVICES= or devices=[]; if you are using device 0 only, set CUDA_VISIBLE_DEVICES=0 or devices=[0].  To initialize all devices and suppress this warning, set the 'devices' keyword argument to `range(torch.cuda.device_count())`.
+  return next(self.gen)
+/opt/conda/envs/ff-qi21/lib/python3.10/contextlib.py:135: UserWarning: CUDA reports that you have 8 available devices, and you have used fork_rng without explicitly specifying which devices are being used. For safety, we initialize *every* CUDA device by default, which can be quite slow if you have a lot of CUDAs. If you know that you are only making use of a few CUDA devices, set the environment variable CUDA_VISIBLE_DEVICES or the 'devices' keyword argument of fork_rng with the set of devices you are actually using. For example, if you are using CPU only, set device.upper()_VISIBLE_DEVICES= or devices=[]; if you are using device 0 only, set CUDA_VISIBLE_DEVICES=0 or devices=[0].  To initialize all devices and suppress this warning, set the 'devices' keyword argument to `range(torch.cuda.device_count())`.
+  return next(self.gen)
+/opt/conda/envs/ff-qi21/lib/python3.10/contextlib.py:135: UserWarning: CUDA reports that you have 8 available devices, and you have used fork_rng without explicitly specifying which devices are being used. For safety, we initialize *every* CUDA device by default, which can be quite slow if you have a lot of CUDAs. If you know that you are only making use of a few CUDA devices, set the environment variable CUDA_VISIBLE_DEVICES or the 'devices' keyword argument of fork_rng with the set of devices you are actually using. For example, if you are using CPU only, set device.upper()_VISIBLE_DEVICES= or devices=[]; if you are using device 0 only, set CUDA_VISIBLE_DEVICES=0 or devices=[0].  To initialize all devices and suppress this warning, set the 'devices' keyword argument to `range(torch.cuda.device_count())`.
+  return next(self.gen)
+/opt/conda/envs/ff-qi21/lib/python3.10/contextlib.py:135: UserWarning: CUDA reports that you have 8 available devices, and you have used fork_rng without explicitly specifying which devices are being used. For safety, we initialize *every* CUDA device by default, which can be quite slow if you have a lot of CUDAs. If you know that you are only making use of a few CUDA devices, set the environment variable CUDA_VISIBLE_DEVICES or the 'devices' keyword argument of fork_rng with the set of devices you are actually using. For example, if you are using CPU only, set device.upper()_VISIBLE_DEVICES= or devices=[]; if you are using device 0 only, set CUDA_VISIBLE_DEVICES=0 or devices=[0].  To initialize all devices and suppress this warning, set the 'devices' keyword argument to `range(torch.cuda.device_count())`.
+  return next(self.gen)
+/opt/conda/envs/ff-qi21/lib/python3.10/contextlib.py:135: UserWarning: CUDA reports that you have 8 available devices, and you have used fork_rng without explicitly specifying which devices are being used. For safety, we initialize *every* CUDA device by default, which can be quite slow if you have a lot of CUDAs. If you know that you are only making use of a few CUDA devices, set the environment variable CUDA_VISIBLE_DEVICES or the 'devices' keyword argument of fork_rng with the set of devices you are actually using. For example, if you are using CPU only, set device.upper()_VISIBLE_DEVICES= or devices=[]; if you are using device 0 only, set CUDA_VISIBLE_DEVICES=0 or devices=[0].  To initialize all devices and suppress this warning, set the 'devices' keyword argument to `range(torch.cuda.device_count())`.
+  return next(self.gen)
+
[Preprocessing train dataset] Shard 0000/0007:   0%|          | 0/449 [00:00<?, ? examples/s]
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[Preprocessing train dataset] Shard 0000/0007:  21%|██▏       | 96/449 [00:31<01:57,  2.99 examples/s]
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[Preprocessing train dataset] Shard 0000/0007:  25%|██▍       | 112/449 [00:37<01:52,  3.01 examples/s]
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[Preprocessing train dataset] Shard 0000/0007:  37%|███▋      | 168/449 [00:55<01:33,  3.00 examples/s]
[Preprocessing train dataset] Shard 0000/0007:  39%|███▉      | 176/449 [00:58<01:30,  3.02 examples/s]
[Preprocessing train dataset] Shard 0000/0007:  41%|████      | 184/449 [01:01<01:27,  3.04 examples/s]
[Preprocessing train dataset] Shard 0000/0007:  43%|████▎     | 192/449 [01:03<01:24,  3.05 examples/s]
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[Preprocessing train dataset] Shard 0000/0007:  46%|████▋     | 208/449 [01:08<01:17,  3.12 examples/s]
[Preprocessing train dataset] Shard 0000/0007:  48%|████▊     | 216/449 [01:11<01:14,  3.12 examples/s]
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[Preprocessing train dataset] Shard 0000/0007:  64%|██████▍   | 288/449 [01:34<00:52,  3.09 examples/s]
[Preprocessing train dataset] Shard 0000/0007:  66%|██████▌   | 296/449 [01:37<00:50,  3.06 examples/s]
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[Preprocessing train dataset] Shard 0000/0007:  80%|████████  | 360/449 [02:00<00:30,  2.95 examples/s]
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[Preprocessing train dataset] Shard 0000/0007: 100%|█████████▉| 448/449 [02:29<00:00,  3.06 examples/s]
[Preprocessing train dataset] Shard 0000/0007: 100%|██████████| 449/449 [02:29<00:00,  2.98 examples/s]
[Preprocessing train dataset] Shard 0000/0007: 100%|██████████| 449/449 [02:29<00:00,  2.99 examples/s]
+[2026-10-01 13:07:03] [Rank 0] [INFO] [flow_factory.data_utils.loader]: [local] Consolidated 8 part(s) for train split -> /root/.cache/flow_factory/datasets/baae786eff625d7a251da3508a68a964
+[2026-10-01 13:07:03] [Rank 6] [INFO] [flow_factory.data_utils.loader]: Multi-source: built DataLoader for 'flir_rl_full' (dataset_size=3599, unique_sample_num_per_epoch=48, num_batches_per_epoch=96)
+[2026-10-01 13:07:03] [Rank 1] [INFO] [flow_factory.data_utils.loader]: Multi-source: built DataLoader for 'flir_rl_full' (dataset_size=3599, unique_sample_num_per_epoch=48, num_batches_per_epoch=96)
+[2026-10-01 13:07:03] [Rank 7] [INFO] [flow_factory.data_utils.loader]: Multi-source: built DataLoader for 'flir_rl_full' (dataset_size=3599, unique_sample_num_per_epoch=48, num_batches_per_epoch=96)
+[2026-10-01 13:07:03] [Rank 5] [INFO] [flow_factory.data_utils.loader]: Multi-source: built DataLoader for 'flir_rl_full' (dataset_size=3599, unique_sample_num_per_epoch=48, num_batches_per_epoch=96)
+[2026-10-01 13:07:03] [Rank 2] [INFO] [flow_factory.data_utils.loader]: Multi-source: built DataLoader for 'flir_rl_full' (dataset_size=3599, unique_sample_num_per_epoch=48, num_batches_per_epoch=96)
+[2026-10-01 13:07:03] [Rank 4] [INFO] [flow_factory.data_utils.loader]: Multi-source: built DataLoader for 'flir_rl_full' (dataset_size=3599, unique_sample_num_per_epoch=48, num_batches_per_epoch=96)
+[2026-10-01 13:07:03] [Rank 0] [INFO] [flow_factory.data_utils.loader]: Multi-source: built DataLoader for 'flir_rl_full' (dataset_size=3599, unique_sample_num_per_epoch=48, num_batches_per_epoch=96)
+[2026-10-01 13:07:03] [Rank 3] [INFO] [flow_factory.data_utils.loader]: Multi-source: built DataLoader for 'flir_rl_full' (dataset_size=3599, unique_sample_num_per_epoch=48, num_batches_per_epoch=96)
+[2026-10-01 13:07:04] [Rank 2] [INFO] [flow_factory.data_utils.loader]: Preprocessing test dataset shard 0002/0007 -> /root/.cache/flow_factory/datasets/b5da67312a677dd412c582089e5bc14b.tmp/_parts/rank_0002_of_0008/cache-b5da67312a677dd412c582089e5bc14b_shard0002of0007.arrow
+[2026-10-01 13:07:04] [Rank 0] [INFO] [flow_factory.data_utils.loader]: Preprocessing test dataset shard 0000/0007 -> /root/.cache/flow_factory/datasets/b5da67312a677dd412c582089e5bc14b.tmp/_parts/rank_0000_of_0008/cache-b5da67312a677dd412c582089e5bc14b_shard0000of0007.arrow
+[2026-10-01 13:07:04] [Rank 5] [INFO] [flow_factory.data_utils.loader]: Preprocessing test dataset shard 0005/0007 -> /root/.cache/flow_factory/datasets/b5da67312a677dd412c582089e5bc14b.tmp/_parts/rank_0005_of_0008/cache-b5da67312a677dd412c582089e5bc14b_shard0005of0007.arrow
+[2026-10-01 13:07:04] [Rank 6] [INFO] [flow_factory.data_utils.loader]: Preprocessing test dataset shard 0006/0007 -> /root/.cache/flow_factory/datasets/b5da67312a677dd412c582089e5bc14b.tmp/_parts/rank_0006_of_0008/cache-b5da67312a677dd412c582089e5bc14b_shard0006of0007.arrow
+[2026-10-01 13:07:04] [Rank 1] [INFO] [flow_factory.data_utils.loader]: Preprocessing test dataset shard 0001/0007 -> /root/.cache/flow_factory/datasets/b5da67312a677dd412c582089e5bc14b.tmp/_parts/rank_0001_of_0008/cache-b5da67312a677dd412c582089e5bc14b_shard0001of0007.arrow
+[2026-10-01 13:07:04] [Rank 4] [INFO] [flow_factory.data_utils.loader]: Preprocessing test dataset shard 0004/0007 -> /root/.cache/flow_factory/datasets/b5da67312a677dd412c582089e5bc14b.tmp/_parts/rank_0004_of_0008/cache-b5da67312a677dd412c582089e5bc14b_shard0004of0007.arrow
+[2026-10-01 13:07:04] [Rank 3] [INFO] [flow_factory.data_utils.loader]: Preprocessing test dataset shard 0003/0007 -> /root/.cache/flow_factory/datasets/b5da67312a677dd412c582089e5bc14b.tmp/_parts/rank_0003_of_0008/cache-b5da67312a677dd412c582089e5bc14b_shard0003of0007.arrow
+[2026-10-01 13:07:04] [Rank 7] [INFO] [flow_factory.data_utils.loader]: Preprocessing test dataset shard 0007/0007 -> /root/.cache/flow_factory/datasets/b5da67312a677dd412c582089e5bc14b.tmp/_parts/rank_0007_of_0008/cache-b5da67312a677dd412c582089e5bc14b_shard0007of0007.arrow
+
[Preprocessing test dataset] Shard 0000/0007:   0%|          | 0/8 [00:00<?, ? examples/s]
[Preprocessing test dataset] Shard 0000/0007: 100%|██████████| 8/8 [00:02<00:00,  3.07 examples/s]
[Preprocessing test dataset] Shard 0000/0007: 100%|██████████| 8/8 [00:02<00:00,  3.06 examples/s]
+[2026-10-01 13:07:09] [Rank 0] [INFO] [flow_factory.data_utils.loader]: [local] Consolidated 8 part(s) for test split -> /root/.cache/flow_factory/datasets/b5da67312a677dd412c582089e5bc14b
+/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.
+  warnings.warn(
+/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.
+  warnings.warn(
+/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=VGG16_Weights.IMAGENET1K_V1`. You can also use `weights=VGG16_Weights.DEFAULT` to get the most up-to-date weights.
+  warnings.warn(msg)
+/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=VGG16_Weights.IMAGENET1K_V1`. You can also use `weights=VGG16_Weights.DEFAULT` to get the most up-to-date weights.
+  warnings.warn(msg)
+/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.
+  warnings.warn(
+/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.
+  warnings.warn(
+/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.
+  warnings.warn(
+/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=VGG16_Weights.IMAGENET1K_V1`. You can also use `weights=VGG16_Weights.DEFAULT` to get the most up-to-date weights.
+  warnings.warn(msg)
+/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=VGG16_Weights.IMAGENET1K_V1`. You can also use `weights=VGG16_Weights.DEFAULT` to get the most up-to-date weights.
+  warnings.warn(msg)
+/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=VGG16_Weights.IMAGENET1K_V1`. You can also use `weights=VGG16_Weights.DEFAULT` to get the most up-to-date weights.
+  warnings.warn(msg)
+/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.
+  warnings.warn(
+/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=VGG16_Weights.IMAGENET1K_V1`. You can also use `weights=VGG16_Weights.DEFAULT` to get the most up-to-date weights.
+  warnings.warn(msg)
+/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.
+  warnings.warn(
+/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=VGG16_Weights.IMAGENET1K_V1`. You can also use `weights=VGG16_Weights.DEFAULT` to get the most up-to-date weights.
+  warnings.warn(msg)
+/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.
+  warnings.warn(
+/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=VGG16_Weights.IMAGENET1K_V1`. You can also use `weights=VGG16_Weights.DEFAULT` to get the most up-to-date weights.
+  warnings.warn(msg)
+wandb: [wandb.login()] Loaded credentials for https://api.wandb.ai from /root/.netrc.
+wandb: Currently logged in as: 315229706 (315229706-xi-an-jiaotong-university-) to https://api.wandb.ai. Use `wandb login --relogin` to force relogin
+wandb: setting up run 17vgpvjl
+wandb: Tracking run with wandb version 0.28.2
+wandb: Run data is saved locally in /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/runs/klein4b_nft_flir_full_ssim_dists/wandb/run-20261001_130726-17vgpvjl
+wandb: Run `wandb offline` to turn off syncing.
+wandb: Syncing run klein4b_nft_flir_full_ssim_dists
+wandb: ⭐️ View project at https://wandb.ai/315229706-xi-an-jiaotong-university-/IR-Engine
+wandb: 🚀 View run at https://wandb.ai/315229706-xi-an-jiaotong-university-/IR-Engine/runs/17vgpvjl
+[2026-10-01 13:07:28] [Rank 0] [INFO] [flow_factory.models.abc]: ======================================================================
+[2026-10-01 13:07:28] [Rank 0] [INFO] [flow_factory.models.abc]: Transformer Trainable Parameters:
+[2026-10-01 13:07:28] [Rank 0] [INFO] [flow_factory.models.abc]:   Total:        3,952,221,696 (  7.36 GB)
+[2026-10-01 13:07:28] [Rank 0] [INFO] [flow_factory.models.abc]:   Trainable:       76,677,120 (  0.14 GB)
+[2026-10-01 13:07:28] [Rank 0] [INFO] [flow_factory.models.abc]:   Percentage:           1.94%
+[2026-10-01 13:07:28] [Rank 0] [INFO] [flow_factory.models.abc]: ======================================================================
+[2026-10-01 13:07:28] [Rank 0] [INFO] [flow_factory.models.abc]: Saving LoRA weights for transformer role=base to /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/runs/klein4b_nft_flir_full_ssim_dists/saves/klein4b_nft_flir_full_ssim_dists/checkpoints/checkpoint-0
+[2026-10-01 13:07:28] [Rank 0] [INFO] [flow_factory.models.abc]: Checkpoint saved successfully to /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/runs/klein4b_nft_flir_full_ssim_dists/saves/klein4b_nft_flir_full_ssim_dists/checkpoints/checkpoint-0
+
Eval/flir_rl_full:   0%|          | 0/8 [00:00<?, ?it/s][2026-10-01 13:07:29] [Rank 5] [WARNING] [flow_factory.models.flux.flux2_klein]: FLUX.2 does not support batch inference with varying condition images per sample. Falling back to single-sample inference. This warning will only appear once.
+[2026-10-01 13:07:29] [Rank 0] [WARNING] [flow_factory.models.flux.flux2_klein]: FLUX.2 does not support batch inference with varying condition images per sample. Falling back to single-sample inference. This warning will only appear once.
+[2026-10-01 13:07:29] [Rank 6] [WARNING] [flow_factory.models.flux.flux2_klein]: FLUX.2 does not support batch inference with varying condition images per sample. Falling back to single-sample inference. This warning will only appear once.
+[2026-10-01 13:07:29] [Rank 1] [WARNING] [flow_factory.models.flux.flux2_klein]: FLUX.2 does not support batch inference with varying condition images per sample. Falling back to single-sample inference. This warning will only appear once.
+[2026-10-01 13:07:29] [Rank 2] [WARNING] [flow_factory.models.flux.flux2_klein]: FLUX.2 does not support batch inference with varying condition images per sample. Falling back to single-sample inference. This warning will only appear once.
+[2026-10-01 13:07:29] [Rank 7] [WARNING] [flow_factory.models.flux.flux2_klein]: FLUX.2 does not support batch inference with varying condition images per sample. Falling back to single-sample inference. This warning will only appear once.
+[2026-10-01 13:07:29] [Rank 3] [WARNING] [flow_factory.models.flux.flux2_klein]: FLUX.2 does not support batch inference with varying condition images per sample. Falling back to single-sample inference. This warning will only appear once.
+[2026-10-01 13:07:30] [Rank 4] [WARNING] [flow_factory.models.flux.flux2_klein]: FLUX.2 does not support batch inference with varying condition images per sample. Falling back to single-sample inference. This warning will only appear once.
+
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+
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Pointwise Rewards: ssim: 100%|██████████| 1/1 [00:00<00:00,  4.37it/s]
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+
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Pointwise Rewards: psnr: 100%|██████████| 1/1 [00:00<00:00, 10.87it/s]
+
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Pointwise Rewards: dists: 100%|██████████| 1/1 [00:00<00:00,  6.78it/s]
+[2026-10-01 13:08:22] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0000 | Epoch 000] eval/flir_rl_full/reward_dists_mean=0.8262 eval/flir_rl_full/reward_dists_std=0.0368 eval/flir_rl_full/reward_psnr_mean=19.3257 eval/flir_rl_full/reward_psnr_std=3.0044 eval/flir_rl_full/reward_ssim_mean=0.5843 eval/flir_rl_full/reward_ssim_std=0.0870
+
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+
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Pointwise Rewards: ssim:  83%|████████▎ | 5/6 [00:01<00:00,  4.73it/s]
Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:01<00:00,  5.14it/s]
Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:01<00:00,  4.83it/s]
+
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Pointwise Rewards: dists:  83%|████████▎ | 5/6 [00:01<00:00,  3.09it/s]
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Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.15it/s]
+[2026-10-01 13:11:31] [Rank 6] [WARNING] [flow_factory.models.flux.flux2_klein]: Flux.2: Ragged I2I detected (varying condition image sizes). Processing samples individually (warning shown once).
+[2026-10-01 13:11:31] [Rank 5] [WARNING] [flow_factory.models.flux.flux2_klein]: Flux.2: Ragged I2I detected (varying condition image sizes). Processing samples individually (warning shown once).
+[2026-10-01 13:11:31] [Rank 3] [WARNING] [flow_factory.models.flux.flux2_klein]: Flux.2: Ragged I2I detected (varying condition image sizes). Processing samples individually (warning shown once).
+[2026-10-01 13:11:31] [Rank 7] [WARNING] [flow_factory.models.flux.flux2_klein]: Flux.2: Ragged I2I detected (varying condition image sizes). Processing samples individually (warning shown once).
+[2026-10-01 13:11:31] [Rank 4] [WARNING] [flow_factory.models.flux.flux2_klein]: Flux.2: Ragged I2I detected (varying condition image sizes). Processing samples individually (warning shown once).
+[2026-10-01 13:11:31] [Rank 1] [WARNING] [flow_factory.models.flux.flux2_klein]: Flux.2: Ragged I2I detected (varying condition image sizes). Processing samples individually (warning shown once).
+[2026-10-01 13:11:31] [Rank 2] [WARNING] [flow_factory.models.flux.flux2_klein]: Flux.2: Ragged I2I detected (varying condition image sizes). Processing samples individually (warning shown once).
+[2026-10-01 13:11:32] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0000 | Epoch 000] train/reward_dists_mean=0.8144 train/reward_dists_std=0.0371 train/reward_ssim_mean=0.6404 train/reward_ssim_std=0.1034 train/reward_dists_group_std_mean=0.0261 train/reward_dists_group_std_max=0.0432 train/reward_dists_group_std_min=0.0080 train/reward_dists_group_mean_std=0.0250 train/reward_ssim_group_std_mean=0.0255 train/reward_ssim_group_std_max=0.0699 train/reward_ssim_group_std_min=0.0075 train/reward_ssim_group_mean_std=0.0993 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.9939 train/adv_max=5.1004 train/adv_abs_mean=1.2727
+
Epoch 0 Training:   0%|          | 0/96 [00:00<?, ?it/s][2026-10-01 13:11:32] [Rank 0] [WARNING] [flow_factory.models.flux.flux2_klein]: Flux.2: Ragged I2I detected (varying condition image sizes). Processing samples individually (warning shown once).
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Epoch 0 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.96it/s]
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Epoch 0 Training:  47%|████▋     | 45/96 [01:10<01:19,  1.55s/it]
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Epoch 0 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 0 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.94it/s]
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Epoch 0 Training:  48%|████▊     | 46/96 [01:11<01:17,  1.56s/it]
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Epoch 0 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.96it/s]
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Epoch 0 Training:  49%|████▉     | 47/96 [01:13<01:16,  1.56s/it]
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Epoch 0 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.97it/s]
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Epoch 0 Training:  50%|█████     | 48/96 [01:14<01:14,  1.55s/it]
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Epoch 0 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.95it/s]
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Epoch 0 Training:  51%|█████     | 49/96 [01:16<01:13,  1.55s/it]
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Epoch 0 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.96it/s]
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Epoch 0 Training:  52%|█████▏    | 50/96 [01:17<01:11,  1.55s/it]
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Epoch 0 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.95it/s]
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Epoch 0 Training:  53%|█████▎    | 51/96 [01:19<01:09,  1.55s/it]
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Epoch 0 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.97it/s]
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Epoch 0 Training:  54%|█████▍    | 52/96 [01:20<01:08,  1.55s/it]
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Epoch 0 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.96it/s]
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Epoch 0 Training:  55%|█████▌    | 53/96 [01:22<01:06,  1.55s/it]
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Epoch 0 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.96it/s]
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Epoch 0 Training:  56%|█████▋    | 54/96 [01:24<01:05,  1.55s/it]
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Epoch 0 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.97it/s]
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Epoch 0 Training:  57%|█████▋    | 55/96 [01:25<01:03,  1.55s/it]
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Epoch 0 Training:  58%|█████▊    | 56/96 [01:27<01:02,  1.55s/it]
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Epoch 0 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.95it/s]
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Epoch 0 Training:  59%|█████▉    | 57/96 [01:28<01:00,  1.55s/it]
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Epoch 0 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 0 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
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Epoch 0 Training:  60%|██████    | 58/96 [01:30<00:59,  1.56s/it]
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Epoch 0 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.96it/s]
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Epoch 0 Training:  61%|██████▏   | 59/96 [01:31<00:57,  1.55s/it]
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Epoch 0 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.96it/s]
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Epoch 0 Training:  62%|██████▎   | 60/96 [01:33<00:55,  1.55s/it]
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Epoch 0 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 0 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.96it/s]
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Epoch 0 Training:  64%|██████▎   | 61/96 [01:34<00:54,  1.55s/it]
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Epoch 0 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.95it/s]
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Epoch 0 Training:  65%|██████▍   | 62/96 [01:36<00:52,  1.55s/it]
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Epoch 0 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 0 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.98it/s]
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Epoch 0 Training:  66%|██████▌   | 63/96 [01:37<00:51,  1.55s/it]
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Epoch 0 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 0 Training:  67%|██████▋   | 64/96 [01:39<00:49,  1.55s/it]
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Epoch 0 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.96it/s]
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Epoch 0 Training:  68%|██████▊   | 65/96 [01:41<00:48,  1.55s/it]
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Epoch 0 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 0 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 0 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.97it/s]
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Epoch 0 Training:  69%|██████▉   | 66/96 [01:42<00:46,  1.55s/it]
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Epoch 0 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 0 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.98it/s]
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Epoch 0 Training:  70%|██████▉   | 67/96 [01:44<00:45,  1.55s/it]
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Epoch 0 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.96it/s]
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Epoch 0 Training:  71%|███████   | 68/96 [01:45<00:43,  1.55s/it]
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Epoch 0 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 0 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.97it/s]
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Epoch 0 Training:  72%|███████▏  | 69/96 [01:47<00:41,  1.55s/it]
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Epoch 0 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 0 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.96it/s]
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Epoch 0 Training:  73%|███████▎  | 70/96 [01:48<00:40,  1.55s/it]
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Epoch 0 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 0 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.96it/s]
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Epoch 0 Training:  74%|███████▍  | 71/96 [01:50<00:38,  1.55s/it]
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Epoch 0 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.95it/s]
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Epoch 0 Training:  75%|███████▌  | 72/96 [01:51<00:37,  1.55s/it]
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Epoch 0 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.95it/s]
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Epoch 0 Training:  76%|███████▌  | 73/96 [01:53<00:35,  1.55s/it]
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Epoch 0 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.96it/s]
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Epoch 0 Training:  77%|███████▋  | 74/96 [01:55<00:34,  1.55s/it]
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Epoch 0 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.96it/s]
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Epoch 0 Training:  78%|███████▊  | 75/96 [01:56<00:32,  1.55s/it]
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Epoch 0 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.96it/s]
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Epoch 0 Training:  79%|███████▉  | 76/96 [01:58<00:31,  1.55s/it]
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Epoch 0 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.96it/s]
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Epoch 0 Training:  80%|████████  | 77/96 [01:59<00:29,  1.55s/it]
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Epoch 0 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 0 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.96it/s]
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Epoch 0 Training:  81%|████████▏ | 78/96 [02:01<00:27,  1.55s/it]
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Epoch 0 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 0 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.95it/s]
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Epoch 0 Training:  82%|████████▏ | 79/96 [02:02<00:26,  1.55s/it]
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Epoch 0 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.96it/s]
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Epoch 0 Training:  83%|████████▎ | 80/96 [02:04<00:24,  1.55s/it]
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Epoch 0 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.97it/s]
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Epoch 0 Training:  84%|████████▍ | 81/96 [02:05<00:23,  1.55s/it]
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Epoch 0 Training:  85%|████████▌ | 82/96 [02:07<00:21,  1.55s/it]
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Epoch 0 Training:  86%|████████▋ | 83/96 [02:09<00:20,  1.55s/it]
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Epoch 0 Training:  88%|████████▊ | 84/96 [02:10<00:18,  1.56s/it]
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Epoch 0 Training:  89%|████████▊ | 85/96 [02:12<00:17,  1.56s/it]
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Epoch 0 Training:  90%|████████▉ | 86/96 [02:13<00:15,  1.56s/it]
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Epoch 0 Training:  91%|█████████ | 87/96 [02:15<00:13,  1.55s/it]
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Epoch 0 Training:  92%|█████████▏| 88/96 [02:16<00:12,  1.56s/it]
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Epoch 0 Training:  93%|█████████▎| 89/96 [02:18<00:10,  1.56s/it]
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Epoch 0 Training:  94%|█████████▍| 90/96 [02:19<00:09,  1.55s/it]
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Epoch 0 Training:  95%|█████████▍| 91/96 [02:21<00:07,  1.55s/it]
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Epoch 0 Training:  96%|█████████▌| 92/96 [02:23<00:06,  1.55s/it]
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Epoch 0 Training:  97%|█████████▋| 93/96 [02:24<00:04,  1.55s/it]
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Epoch 0 Training:  98%|█████████▊| 94/96 [02:26<00:03,  1.55s/it]
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Epoch 0 Training:  99%|█████████▉| 95/96 [02:27<00:01,  1.56s/it]
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Epoch 0 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s][2026-10-01 13:14:01] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0000 | Epoch 000] train/policy_loss=26.6981 train/unweighted_policy_loss=5.3396 train/loss=26.6981 train/grad_norm=0.0779
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+[2026-10-01 13:14:01] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0001 | Epoch 000] timing/rollout_seconds=185.9322 timing/feedback_seconds=8.2517 timing/optimization_seconds=150.0248 timing/cycle_seconds=338.8612
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+
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+[2026-10-01 13:17:08] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0001 | Epoch 001] train/reward_dists_mean=0.8333 train/reward_dists_std=0.0365 train/reward_ssim_mean=0.5833 train/reward_ssim_std=0.0912 train/reward_dists_group_std_mean=0.0097 train/reward_dists_group_std_max=0.0173 train/reward_dists_group_std_min=0.0047 train/reward_dists_group_mean_std=0.0351 train/reward_ssim_group_std_mean=0.0227 train/reward_ssim_group_std_max=0.0423 train/reward_ssim_group_std_min=0.0089 train/reward_ssim_group_mean_std=0.0880 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.3982 train/adv_max=5.2347 train/adv_abs_mean=1.3220
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Epoch 1 Training:  22%|██▏       | 21/96 [00:32<01:56,  1.55s/it]
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Epoch 1 Training:  23%|██▎       | 22/96 [00:34<01:54,  1.55s/it]
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Epoch 1 Training:  24%|██▍       | 23/96 [00:35<01:53,  1.55s/it]
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Epoch 1 Training:  25%|██▌       | 24/96 [00:37<01:51,  1.55s/it]
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Epoch 1 Training:  26%|██▌       | 25/96 [00:38<01:50,  1.55s/it]
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Epoch 1 Training:  27%|██▋       | 26/96 [00:40<01:48,  1.55s/it]
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Epoch 1 Training:  28%|██▊       | 27/96 [00:41<01:47,  1.55s/it]
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Epoch 1 Training:  29%|██▉       | 28/96 [00:43<01:45,  1.55s/it]
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Epoch 1 Training:  30%|███       | 29/96 [00:45<01:43,  1.55s/it]
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Epoch 1 Training:  31%|███▏      | 30/96 [00:46<01:42,  1.55s/it]
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Epoch 1 Training:  32%|███▏      | 31/96 [00:48<01:40,  1.55s/it]
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Epoch 1 Training:  33%|███▎      | 32/96 [00:49<01:39,  1.55s/it]
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Epoch 1 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 1 Training:  34%|███▍      | 33/96 [00:51<01:37,  1.55s/it]
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Epoch 1 Training:  35%|███▌      | 34/96 [00:52<01:36,  1.55s/it]
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Epoch 1 Training:  36%|███▋      | 35/96 [00:54<01:34,  1.55s/it]
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Epoch 1 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.97it/s]
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Epoch 1 Training:  38%|███▊      | 36/96 [00:55<01:33,  1.56s/it]
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Epoch 1 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 1 Training:  39%|███▊      | 37/96 [00:57<01:31,  1.56s/it]
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Epoch 1 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 1 Training:  40%|███▉      | 38/96 [00:59<01:30,  1.55s/it]
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Epoch 1 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 1 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.98it/s]
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Epoch 1 Training:  41%|████      | 39/96 [01:00<01:28,  1.56s/it]
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Epoch 1 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 1 Training:  42%|████▏     | 40/96 [01:02<01:26,  1.55s/it]
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Epoch 1 Training:  43%|████▎     | 41/96 [01:03<01:25,  1.55s/it]
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Epoch 1 Training:  44%|████▍     | 42/96 [01:05<01:23,  1.55s/it]
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Epoch 1 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 1 Training:  45%|████▍     | 43/96 [01:06<01:22,  1.55s/it]
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Epoch 1 Training:  46%|████▌     | 44/96 [01:08<01:20,  1.55s/it]
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Epoch 1 Training:  47%|████▋     | 45/96 [01:09<01:19,  1.55s/it]
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Epoch 1 Training:  48%|████▊     | 46/96 [01:11<01:17,  1.55s/it]
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Epoch 1 Training:  49%|████▉     | 47/96 [01:12<01:15,  1.55s/it]
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Epoch 1 Training:  50%|█████     | 48/96 [01:14<01:14,  1.55s/it]
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Epoch 1 Training:  51%|█████     | 49/96 [01:16<01:12,  1.55s/it]
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Epoch 1 Training:  52%|█████▏    | 50/96 [01:17<01:11,  1.55s/it]
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Epoch 1 Training:  53%|█████▎    | 51/96 [01:19<01:09,  1.55s/it]
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Epoch 1 Training:  54%|█████▍    | 52/96 [01:20<01:08,  1.55s/it]
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Epoch 1 Training:  55%|█████▌    | 53/96 [01:22<01:06,  1.55s/it]
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Epoch 1 Training:  56%|█████▋    | 54/96 [01:23<01:05,  1.55s/it]
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Epoch 1 Training:  57%|█████▋    | 55/96 [01:25<01:03,  1.55s/it]
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Epoch 1 Training:  58%|█████▊    | 56/96 [01:26<01:02,  1.55s/it]
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Epoch 1 Training:  59%|█████▉    | 57/96 [01:28<01:00,  1.55s/it]
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Epoch 1 Training:  60%|██████    | 58/96 [01:30<00:58,  1.55s/it]
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Epoch 1 Training:  61%|██████▏   | 59/96 [01:31<00:57,  1.55s/it]
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Epoch 1 Training:  62%|██████▎   | 60/96 [01:33<00:55,  1.55s/it]
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Epoch 1 Training:  64%|██████▎   | 61/96 [01:34<00:54,  1.55s/it]
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Epoch 1 Training:  65%|██████▍   | 62/96 [01:36<00:52,  1.55s/it]
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Epoch 1 Training:  66%|██████▌   | 63/96 [01:37<00:51,  1.55s/it]
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Epoch 1 Training:  67%|██████▋   | 64/96 [01:39<00:49,  1.55s/it]
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Epoch 1 Training:  68%|██████▊   | 65/96 [01:40<00:48,  1.55s/it]
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Epoch 1 Training:  69%|██████▉   | 66/96 [01:42<00:46,  1.55s/it]
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Epoch 1 Training:  70%|██████▉   | 67/96 [01:43<00:44,  1.55s/it]
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Epoch 1 Training:  71%|███████   | 68/96 [01:45<00:43,  1.55s/it]
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Epoch 1 Training:  72%|███████▏  | 69/96 [01:47<00:41,  1.55s/it]
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Epoch 1 Training:  73%|███████▎  | 70/96 [01:48<00:40,  1.55s/it]
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Epoch 1 Training:  74%|███████▍  | 71/96 [01:50<00:38,  1.55s/it]
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Epoch 1 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s][2026-10-01 13:19:37] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0001 | Epoch 001] train/policy_loss=26.5376 train/unweighted_policy_loss=5.3075 train/loss=26.5376 train/grad_norm=0.0801
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Epoch 1 Training: 100%|██████████| 96/96 [02:29<00:00,  1.55s/it]
+[2026-10-01 13:19:37] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0002 | Epoch 001] timing/rollout_seconds=180.0346 timing/feedback_seconds=8.8779 timing/optimization_seconds=149.8571 timing/cycle_seconds=335.5572
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Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:01<00:00,  5.13it/s]
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+[2026-10-01 13:22:40] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0002 | Epoch 002] train/reward_dists_mean=0.8291 train/reward_dists_std=0.0431 train/reward_ssim_mean=0.6016 train/reward_ssim_std=0.0872 train/reward_dists_group_std_mean=0.0098 train/reward_dists_group_std_max=0.0232 train/reward_dists_group_std_min=0.0052 train/reward_dists_group_mean_std=0.0418 train/reward_ssim_group_std_mean=0.0191 train/reward_ssim_group_std_max=0.0426 train/reward_ssim_group_std_min=0.0059 train/reward_ssim_group_mean_std=0.0848 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.0898 train/adv_max=4.2373 train/adv_abs_mean=1.2457
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Epoch 2 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s][2026-10-01 13:25:09] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0002 | Epoch 002] train/policy_loss=26.2390 train/unweighted_policy_loss=5.2478 train/loss=26.2390 train/grad_norm=0.0613
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+[2026-10-01 13:25:09] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0003 | Epoch 002] timing/rollout_seconds=179.9168 timing/feedback_seconds=5.2043 timing/optimization_seconds=149.6590 timing/cycle_seconds=332.5005
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+
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+[2026-10-01 13:28:12] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0003 | Epoch 003] train/reward_dists_mean=0.8242 train/reward_dists_std=0.0355 train/reward_ssim_mean=0.6274 train/reward_ssim_std=0.1055 train/reward_dists_group_std_mean=0.0111 train/reward_dists_group_std_max=0.0215 train/reward_dists_group_std_min=0.0040 train/reward_dists_group_mean_std=0.0335 train/reward_ssim_group_std_mean=0.0179 train/reward_ssim_group_std_max=0.0366 train/reward_ssim_group_std_min=0.0081 train/reward_ssim_group_mean_std=0.1038 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.0993 train/adv_max=4.8529 train/adv_abs_mean=1.1966
+
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Epoch 3 Training:  39%|███▊      | 37/96 [00:57<01:31,  1.55s/it]
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Epoch 3 Training:  41%|████      | 39/96 [01:00<01:28,  1.55s/it]
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Epoch 3 Training:  42%|████▏     | 40/96 [01:02<01:26,  1.55s/it]
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Epoch 3 Training:  43%|████▎     | 41/96 [01:03<01:25,  1.55s/it]
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Epoch 3 Training:  44%|████▍     | 42/96 [01:05<01:23,  1.56s/it]
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Epoch 3 Training:  45%|████▍     | 43/96 [01:06<01:22,  1.56s/it]
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Epoch 3 Training:  46%|████▌     | 44/96 [01:08<01:21,  1.56s/it]
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Epoch 3 Training:  47%|████▋     | 45/96 [01:09<01:19,  1.56s/it]
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Epoch 3 Training:  48%|████▊     | 46/96 [01:11<01:17,  1.55s/it]
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Epoch 3 Training:  49%|████▉     | 47/96 [01:12<01:16,  1.55s/it]
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Epoch 3 Training:  51%|█████     | 49/96 [01:16<01:12,  1.55s/it]
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Epoch 3 Training:  52%|█████▏    | 50/96 [01:17<01:11,  1.55s/it]
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Epoch 3 Training:  53%|█████▎    | 51/96 [01:19<01:09,  1.55s/it]
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Epoch 3 Training:  54%|█████▍    | 52/96 [01:20<01:08,  1.55s/it]
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Epoch 3 Training:  55%|█████▌    | 53/96 [01:22<01:06,  1.55s/it]
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Epoch 3 Training:  56%|█████▋    | 54/96 [01:23<01:05,  1.55s/it]
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Epoch 3 Training:  57%|█████▋    | 55/96 [01:25<01:03,  1.55s/it]
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Epoch 3 Training:  58%|█████▊    | 56/96 [01:26<01:02,  1.55s/it]
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Epoch 3 Training:  59%|█████▉    | 57/96 [01:28<01:00,  1.55s/it]
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Epoch 3 Training:  60%|██████    | 58/96 [01:30<00:59,  1.55s/it]
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Epoch 3 Training:  61%|██████▏   | 59/96 [01:31<00:57,  1.55s/it]
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Epoch 3 Training:  62%|██████▎   | 60/96 [01:33<00:55,  1.55s/it]
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Epoch 3 Training:  64%|██████▎   | 61/96 [01:34<00:54,  1.55s/it]
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Epoch 3 Training:  65%|██████▍   | 62/96 [01:36<00:52,  1.55s/it]
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Epoch 3 Training:  66%|██████▌   | 63/96 [01:37<00:51,  1.55s/it]
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Epoch 3 Training:  67%|██████▋   | 64/96 [01:39<00:49,  1.56s/it]
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Epoch 3 Training:  68%|██████▊   | 65/96 [01:40<00:48,  1.56s/it]
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Epoch 3 Training:  71%|███████   | 68/96 [01:45<00:43,  1.56s/it]
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Epoch 3 Training:  74%|███████▍  | 71/96 [01:50<00:40,  1.62s/it]
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Epoch 3 Training:  75%|███████▌  | 72/96 [01:52<00:38,  1.60s/it]
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Epoch 3 Training:  77%|███████▋  | 74/96 [01:55<00:34,  1.58s/it]
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Epoch 3 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s][2026-10-01 13:30:41] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0003 | Epoch 003] train/policy_loss=26.7171 train/unweighted_policy_loss=5.3434 train/loss=26.7171 train/grad_norm=0.0827
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+[2026-10-01 13:30:41] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0004 | Epoch 003] timing/rollout_seconds=179.3096 timing/feedback_seconds=4.1807 timing/optimization_seconds=150.1005 timing/cycle_seconds=332.3330
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+[2026-10-01 13:33:45] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0004 | Epoch 004] train/reward_dists_mean=0.8227 train/reward_dists_std=0.0335 train/reward_ssim_mean=0.6298 train/reward_ssim_std=0.0924 train/reward_dists_group_std_mean=0.0113 train/reward_dists_group_std_max=0.0227 train/reward_dists_group_std_min=0.0059 train/reward_dists_group_mean_std=0.0314 train/reward_ssim_group_std_mean=0.0148 train/reward_ssim_group_std_max=0.0263 train/reward_ssim_group_std_min=0.0071 train/reward_ssim_group_mean_std=0.0910 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.4911 train/adv_max=4.0254 train/adv_abs_mean=1.1844
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Epoch 4 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s][2026-10-01 13:36:16] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0004 | Epoch 004] train/policy_loss=26.1306 train/unweighted_policy_loss=5.2261 train/loss=26.1306 train/grad_norm=0.0709
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+[2026-10-01 13:36:16] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0005 | Epoch 004] timing/rollout_seconds=179.9908 timing/feedback_seconds=5.3011 timing/optimization_seconds=151.9019 timing/cycle_seconds=334.7844
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+
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+[2026-10-01 13:39:20] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0005 | Epoch 005] train/reward_dists_mean=0.8187 train/reward_dists_std=0.0291 train/reward_ssim_mean=0.6541 train/reward_ssim_std=0.0895 train/reward_dists_group_std_mean=0.0114 train/reward_dists_group_std_max=0.0220 train/reward_dists_group_std_min=0.0059 train/reward_dists_group_mean_std=0.0265 train/reward_ssim_group_std_mean=0.0135 train/reward_ssim_group_std_max=0.0310 train/reward_ssim_group_std_min=0.0043 train/reward_ssim_group_mean_std=0.0883 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.3392 train/adv_max=4.1320 train/adv_abs_mean=1.2357
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Epoch 5 Training:  27%|██▋       | 26/96 [00:40<01:49,  1.57s/it]
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Epoch 5 Training:  28%|██▊       | 27/96 [00:42<01:48,  1.57s/it]
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Epoch 5 Training:  29%|██▉       | 28/96 [00:43<01:46,  1.57s/it]
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Epoch 5 Training:  30%|███       | 29/96 [00:45<01:45,  1.57s/it]
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Epoch 5 Training:  31%|███▏      | 30/96 [00:47<01:44,  1.58s/it]
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Epoch 5 Training:  32%|███▏      | 31/96 [00:48<01:42,  1.58s/it]
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Epoch 5 Training:  33%|███▎      | 32/96 [00:50<01:40,  1.57s/it]
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Epoch 5 Training:  34%|███▍      | 33/96 [00:51<01:38,  1.57s/it]
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Epoch 5 Training:  35%|███▌      | 34/96 [00:53<01:37,  1.57s/it]
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Epoch 5 Training:  36%|███▋      | 35/96 [00:54<01:35,  1.57s/it]
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Epoch 5 Training:  38%|███▊      | 36/96 [00:56<01:34,  1.57s/it]
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Epoch 5 Training:  39%|███▊      | 37/96 [00:58<01:32,  1.57s/it]
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Epoch 5 Training:  40%|███▉      | 38/96 [00:59<01:31,  1.57s/it]
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Epoch 5 Training:  41%|████      | 39/96 [01:01<01:29,  1.57s/it]
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Epoch 5 Training:  42%|████▏     | 40/96 [01:02<01:27,  1.57s/it]
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Epoch 5 Training:  43%|████▎     | 41/96 [01:04<01:26,  1.57s/it]
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Epoch 5 Training:  44%|████▍     | 42/96 [01:05<01:24,  1.57s/it]
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Epoch 5 Training:  45%|████▍     | 43/96 [01:07<01:23,  1.57s/it]
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Epoch 5 Training:  46%|████▌     | 44/96 [01:09<01:21,  1.57s/it]
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Epoch 5 Training:  47%|████▋     | 45/96 [01:10<01:20,  1.57s/it]
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Epoch 5 Training:  48%|████▊     | 46/96 [01:12<01:18,  1.57s/it]
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Epoch 5 Training:  49%|████▉     | 47/96 [01:13<01:17,  1.57s/it]
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Epoch 5 Training:  50%|█████     | 48/96 [01:15<01:15,  1.57s/it]
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Epoch 5 Training:  51%|█████     | 49/96 [01:16<01:13,  1.57s/it]
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Epoch 5 Training:  52%|█████▏    | 50/96 [01:18<01:12,  1.57s/it]
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Epoch 5 Training:  53%|█████▎    | 51/96 [01:20<01:10,  1.57s/it]
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Epoch 5 Training:  54%|█████▍    | 52/96 [01:21<01:09,  1.57s/it]
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Epoch 5 Training:  55%|█████▌    | 53/96 [01:23<01:07,  1.57s/it]
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Epoch 5 Training:  56%|█████▋    | 54/96 [01:24<01:05,  1.57s/it]
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Epoch 5 Training:  57%|█████▋    | 55/96 [01:26<01:04,  1.57s/it]
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Epoch 5 Training:  58%|█████▊    | 56/96 [01:27<01:02,  1.57s/it]
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Epoch 5 Training:  59%|█████▉    | 57/96 [01:29<01:01,  1.57s/it]
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Epoch 5 Training:  60%|██████    | 58/96 [01:31<00:59,  1.57s/it]
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Epoch 5 Training:  61%|██████▏   | 59/96 [01:32<00:58,  1.57s/it]
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Epoch 5 Training:  62%|██████▎   | 60/96 [01:34<00:56,  1.57s/it]
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Epoch 5 Training:  64%|██████▎   | 61/96 [01:35<00:54,  1.57s/it]
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Epoch 5 Training:  65%|██████▍   | 62/96 [01:37<00:53,  1.57s/it]
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Epoch 5 Training:  66%|██████▌   | 63/96 [01:38<00:51,  1.57s/it]
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Epoch 5 Training:  67%|██████▋   | 64/96 [01:40<00:50,  1.57s/it]
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Epoch 5 Training:  68%|██████▊   | 65/96 [01:42<00:48,  1.57s/it]
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Epoch 5 Training:  69%|██████▉   | 66/96 [01:43<00:47,  1.58s/it]
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Epoch 5 Training:  71%|███████   | 68/96 [01:46<00:44,  1.58s/it]
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Epoch 5 Training:  72%|███████▏  | 69/96 [01:48<00:42,  1.58s/it]
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Epoch 5 Training:  73%|███████▎  | 70/96 [01:49<00:41,  1.58s/it]
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Epoch 5 Training:  74%|███████▍  | 71/96 [01:51<00:39,  1.58s/it]
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Epoch 5 Training:  75%|███████▌  | 72/96 [01:53<00:37,  1.58s/it]
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Epoch 5 Training:  76%|███████▌  | 73/96 [01:54<00:36,  1.57s/it]
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Epoch 5 Training:  77%|███████▋  | 74/96 [01:56<00:34,  1.57s/it]
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Epoch 5 Training:  78%|███████▊  | 75/96 [01:57<00:32,  1.57s/it]
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Epoch 5 Training:  79%|███████▉  | 76/96 [01:59<00:31,  1.57s/it]
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Epoch 5 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s][2026-10-01 13:41:51] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0005 | Epoch 005] train/policy_loss=25.7124 train/unweighted_policy_loss=5.1425 train/loss=25.7124 train/grad_norm=0.0753
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+[2026-10-01 13:41:51] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0006 | Epoch 005] timing/rollout_seconds=180.3199 timing/feedback_seconds=5.6417 timing/optimization_seconds=151.5047 timing/cycle_seconds=334.6476
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+[2026-10-01 13:44:54] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0006 | Epoch 006] train/reward_dists_mean=0.8171 train/reward_dists_std=0.0363 train/reward_ssim_mean=0.6371 train/reward_ssim_std=0.1254 train/reward_dists_group_std_mean=0.0117 train/reward_dists_group_std_max=0.0213 train/reward_dists_group_std_min=0.0047 train/reward_dists_group_mean_std=0.0342 train/reward_ssim_group_std_mean=0.0106 train/reward_ssim_group_std_max=0.0275 train/reward_ssim_group_std_min=0.0042 train/reward_ssim_group_mean_std=0.1249 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.7108 train/adv_max=5.3845 train/adv_abs_mean=1.2430
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Epoch 6 Training:   1%|          | 1/96 [00:01<02:21,  1.49s/it]
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Epoch 6 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 6 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 6 Training:   2%|▏         | 2/96 [00:03<02:24,  1.54s/it]
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Epoch 6 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 6 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.94it/s]
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Epoch 6 Training:   3%|▎         | 3/96 [00:04<02:24,  1.55s/it]
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Epoch 6 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 6 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.95it/s]
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Epoch 6 Training:   4%|▍         | 4/96 [00:06<02:23,  1.56s/it]
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Epoch 6 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 6 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.15it/s]
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Epoch 6 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 6 Training:   5%|▌         | 5/96 [00:07<02:22,  1.56s/it]
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Epoch 6 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 6 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 6 Training:   6%|▋         | 6/96 [00:09<02:21,  1.57s/it]
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Epoch 6 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 6 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 6 Training:   7%|▋         | 7/96 [00:10<02:19,  1.57s/it]
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Epoch 6 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 6 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.96it/s]
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Epoch 6 Training:   8%|▊         | 8/96 [00:12<02:17,  1.57s/it]
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Epoch 6 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 6 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 6 Training:   9%|▉         | 9/96 [00:14<02:16,  1.57s/it]
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Epoch 6 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 6 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 6 Training:  10%|█         | 10/96 [00:15<02:15,  1.57s/it]
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Epoch 6 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 6 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 6 Training:  11%|█▏        | 11/96 [00:17<02:13,  1.57s/it]
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Epoch 6 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 6 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 6 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 6 Training:  12%|█▎        | 12/96 [00:19<02:19,  1.66s/it]
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Epoch 6 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 6 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.15it/s]
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Epoch 6 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 6 Training:  14%|█▎        | 13/96 [00:20<02:15,  1.64s/it]
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Epoch 6 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 6 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 6 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.95it/s]
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Epoch 6 Training:  15%|█▍        | 14/96 [00:22<02:12,  1.61s/it]
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Epoch 6 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 6 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 6 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.96it/s]
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Epoch 6 Training:  16%|█▌        | 15/96 [00:23<02:09,  1.60s/it]
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Epoch 6 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 6 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 6 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.97it/s]
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Epoch 6 Training:  17%|█▋        | 16/96 [00:25<02:06,  1.58s/it]
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Epoch 6 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 6 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 6 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.96it/s]
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Epoch 6 Training:  18%|█▊        | 17/96 [00:26<02:04,  1.58s/it]
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Epoch 6 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 6 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.15it/s]
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Epoch 6 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.94it/s]
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Epoch 6 Training:  19%|█▉        | 18/96 [00:28<02:02,  1.57s/it]
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Epoch 6 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 6 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 6 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.95it/s]
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Epoch 6 Training:  20%|█▉        | 19/96 [00:29<02:00,  1.57s/it]
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Epoch 6 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 6 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 6 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.91it/s]
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Epoch 6 Training:  21%|██        | 20/96 [00:31<01:59,  1.57s/it]
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Epoch 6 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 6 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 6 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.96it/s]
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Epoch 6 Training:  22%|██▏       | 21/96 [00:33<01:57,  1.57s/it]
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Epoch 6 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 6 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 6 Training:  23%|██▎       | 22/96 [00:34<01:56,  1.57s/it]
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Epoch 6 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 6 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.96it/s]
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Epoch 6 Training:  24%|██▍       | 23/96 [00:36<01:54,  1.56s/it]
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Epoch 6 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 6 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.96it/s]
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Epoch 6 Training:  25%|██▌       | 24/96 [00:37<01:52,  1.56s/it]
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Epoch 6 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 6 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 6 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 6 Training:  26%|██▌       | 25/96 [00:39<01:51,  1.57s/it]
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Epoch 6 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 6 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 6 Training:  27%|██▋       | 26/96 [00:40<01:49,  1.57s/it]
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Epoch 6 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 6 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 6 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.95it/s]
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Epoch 6 Training:  28%|██▊       | 27/96 [00:42<01:48,  1.57s/it]
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Epoch 6 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 6 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 6 Training:  29%|██▉       | 28/96 [00:44<01:46,  1.57s/it]
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Epoch 6 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 6 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 6 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
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Epoch 6 Training:  30%|███       | 29/96 [00:45<01:45,  1.57s/it]
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Epoch 6 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 6 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.95it/s]
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Epoch 6 Training:  31%|███▏      | 30/96 [00:47<01:43,  1.57s/it]
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Epoch 6 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 6 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.96it/s]
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Epoch 6 Training:  32%|███▏      | 31/96 [00:48<01:41,  1.56s/it]
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Epoch 6 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 6 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.94it/s]
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Epoch 6 Training:  33%|███▎      | 32/96 [00:50<01:40,  1.57s/it]
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Epoch 6 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 6 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.95it/s]
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Epoch 6 Training:  34%|███▍      | 33/96 [00:51<01:38,  1.57s/it]
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Epoch 6 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 6 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.95it/s]
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Epoch 6 Training:  35%|███▌      | 34/96 [00:53<01:37,  1.56s/it]
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Epoch 6 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 6 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.91it/s]
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Epoch 6 Training:  36%|███▋      | 35/96 [00:55<01:35,  1.57s/it]
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Epoch 6 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 6 Training:  38%|███▊      | 36/96 [00:56<01:34,  1.57s/it]
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Epoch 6 Training:  39%|███▊      | 37/96 [00:58<01:32,  1.57s/it]
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Epoch 6 Training:  40%|███▉      | 38/96 [00:59<01:31,  1.57s/it]
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Epoch 6 Training:  41%|████      | 39/96 [01:01<01:30,  1.59s/it]
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Epoch 6 Training:  42%|████▏     | 40/96 [01:02<01:28,  1.58s/it]
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Epoch 6 Training:  43%|████▎     | 41/96 [01:04<01:26,  1.58s/it]
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Epoch 6 Training:  44%|████▍     | 42/96 [01:06<01:25,  1.58s/it]
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Epoch 6 Training:  45%|████▍     | 43/96 [01:07<01:23,  1.58s/it]
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Epoch 6 Training:  46%|████▌     | 44/96 [01:09<01:21,  1.58s/it]
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Epoch 6 Training:  47%|████▋     | 45/96 [01:10<01:20,  1.57s/it]
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Epoch 6 Training:  48%|████▊     | 46/96 [01:12<01:18,  1.57s/it]
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Epoch 6 Training:  49%|████▉     | 47/96 [01:13<01:17,  1.57s/it]
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Epoch 6 Training:  50%|█████     | 48/96 [01:15<01:15,  1.57s/it]
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Epoch 6 Training:  51%|█████     | 49/96 [01:17<01:13,  1.57s/it]
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Epoch 6 Training:  52%|█████▏    | 50/96 [01:18<01:12,  1.57s/it]
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Epoch 6 Training:  53%|█████▎    | 51/96 [01:20<01:10,  1.57s/it]
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Epoch 6 Training:  54%|█████▍    | 52/96 [01:21<01:09,  1.57s/it]
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Epoch 6 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s][2026-10-01 13:47:25] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0006 | Epoch 006] train/policy_loss=25.4418 train/unweighted_policy_loss=5.0884 train/loss=25.4418 train/grad_norm=0.0591
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+[2026-10-01 13:47:25] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0007 | Epoch 006] timing/rollout_seconds=179.8501 timing/feedback_seconds=4.9555 timing/optimization_seconds=151.6716 timing/cycle_seconds=334.3990
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+
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+[2026-10-01 13:50:29] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0007 | Epoch 007] train/reward_dists_mean=0.8195 train/reward_dists_std=0.0343 train/reward_ssim_mean=0.6506 train/reward_ssim_std=0.0964 train/reward_dists_group_std_mean=0.0116 train/reward_dists_group_std_max=0.0350 train/reward_dists_group_std_min=0.0060 train/reward_dists_group_mean_std=0.0319 train/reward_ssim_group_std_mean=0.0105 train/reward_ssim_group_std_max=0.0208 train/reward_ssim_group_std_min=0.0044 train/reward_ssim_group_mean_std=0.0957 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.2123 train/adv_max=4.7945 train/adv_abs_mean=1.2753
+
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Epoch 7 Training:  43%|████▎     | 41/96 [01:04<01:26,  1.57s/it]
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Epoch 7 Training:  48%|████▊     | 46/96 [01:12<01:18,  1.58s/it]
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Epoch 7 Training:  53%|█████▎    | 51/96 [01:20<01:10,  1.57s/it]
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Epoch 7 Training:  56%|█████▋    | 54/96 [01:24<01:06,  1.57s/it]
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Epoch 7 Training:  57%|█████▋    | 55/96 [01:26<01:04,  1.57s/it]
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Epoch 7 Training:  58%|█████▊    | 56/96 [01:27<01:02,  1.57s/it]
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Epoch 7 Training:  59%|█████▉    | 57/96 [01:29<01:01,  1.57s/it]
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Epoch 7 Training:  61%|██████▏   | 59/96 [01:32<00:58,  1.57s/it]
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Epoch 7 Training:  62%|██████▎   | 60/96 [01:34<00:56,  1.57s/it]
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Epoch 7 Training:  64%|██████▎   | 61/96 [01:35<00:55,  1.58s/it]
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Epoch 7 Training:  65%|██████▍   | 62/96 [01:37<00:53,  1.58s/it]
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Epoch 7 Training:  66%|██████▌   | 63/96 [01:39<00:52,  1.58s/it]
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Epoch 7 Training:  67%|██████▋   | 64/96 [01:40<00:50,  1.58s/it]
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Epoch 7 Training:  69%|██████▉   | 66/96 [01:43<00:47,  1.58s/it]
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Epoch 7 Training:  79%|███████▉  | 76/96 [01:59<00:31,  1.58s/it]
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Epoch 7 Training:  80%|████████  | 77/96 [02:01<00:29,  1.57s/it]
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Epoch 7 Training:  81%|████████▏ | 78/96 [02:02<00:28,  1.57s/it]
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Epoch 7 Training:  82%|████████▏ | 79/96 [02:04<00:26,  1.57s/it]
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Epoch 7 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s][2026-10-01 13:53:00] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0007 | Epoch 007] train/policy_loss=25.4977 train/unweighted_policy_loss=5.0995 train/loss=25.4977 train/grad_norm=0.0605
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+[2026-10-01 13:53:00] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0008 | Epoch 007] timing/rollout_seconds=180.1665 timing/feedback_seconds=5.6189 timing/optimization_seconds=151.7133 timing/cycle_seconds=334.8710
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+
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+[2026-10-01 13:56:04] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0008 | Epoch 008] train/reward_dists_mean=0.8155 train/reward_dists_std=0.0309 train/reward_ssim_mean=0.6549 train/reward_ssim_std=0.0912 train/reward_dists_group_std_mean=0.0099 train/reward_dists_group_std_max=0.0204 train/reward_dists_group_std_min=0.0053 train/reward_dists_group_mean_std=0.0291 train/reward_ssim_group_std_mean=0.0116 train/reward_ssim_group_std_max=0.0336 train/reward_ssim_group_std_min=0.0055 train/reward_ssim_group_mean_std=0.0902 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.5170 train/adv_max=5.1409 train/adv_abs_mean=1.2792
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Epoch 8 Training:   4%|▍         | 4/96 [00:06<02:24,  1.57s/it]
+
Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.15it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.98it/s]
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Epoch 8 Training:   5%|▌         | 5/96 [00:07<02:22,  1.57s/it]
+
Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
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Epoch 8 Training:   6%|▋         | 6/96 [00:09<02:21,  1.57s/it]
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Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.94it/s]
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Epoch 8 Training:   7%|▋         | 7/96 [00:10<02:19,  1.57s/it]
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Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.94it/s]
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Epoch 8 Training:   8%|▊         | 8/96 [00:12<02:18,  1.57s/it]
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Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
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Epoch 8 Training:   9%|▉         | 9/96 [00:14<02:16,  1.57s/it]
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Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 8 Training:  10%|█         | 10/96 [00:15<02:15,  1.57s/it]
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Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.91it/s]
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Epoch 8 Training:  11%|█▏        | 11/96 [00:17<02:13,  1.57s/it]
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Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.90it/s]
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Epoch 8 Training:  12%|█▎        | 12/96 [00:18<02:12,  1.58s/it]
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Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.89it/s]
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Epoch 8 Training:  14%|█▎        | 13/96 [00:20<02:11,  1.58s/it]
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Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.91it/s]
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Epoch 8 Training:  15%|█▍        | 14/96 [00:22<02:09,  1.58s/it]
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Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.90it/s]
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Epoch 8 Training:  16%|█▌        | 15/96 [00:23<02:08,  1.58s/it]
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Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.88it/s]
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Epoch 8 Training:  17%|█▋        | 16/96 [00:25<02:06,  1.59s/it]
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Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.91it/s]
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Epoch 8 Training:  18%|█▊        | 17/96 [00:26<02:05,  1.58s/it]
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Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.90it/s]
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Epoch 8 Training:  19%|█▉        | 18/96 [00:28<02:03,  1.58s/it]
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Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.89it/s]
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Epoch 8 Training:  20%|█▉        | 19/96 [00:29<02:02,  1.59s/it]
+
Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.90it/s]
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Epoch 8 Training:  21%|██        | 20/96 [00:31<02:00,  1.59s/it]
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Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.90it/s]
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Epoch 8 Training:  22%|██▏       | 21/96 [00:33<01:58,  1.59s/it]
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Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.89it/s]
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Epoch 8 Training:  23%|██▎       | 22/96 [00:34<01:57,  1.59s/it]
+
Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.89it/s]
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Epoch 8 Training:  24%|██▍       | 23/96 [00:36<01:55,  1.59s/it]
+
Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.89it/s]
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Epoch 8 Training:  25%|██▌       | 24/96 [00:37<01:54,  1.59s/it]
+
Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.89it/s]
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Epoch 8 Training:  26%|██▌       | 25/96 [00:39<01:52,  1.59s/it]
+
Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.89it/s]
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Epoch 8 Training:  27%|██▋       | 26/96 [00:41<01:51,  1.59s/it]
+
Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.90it/s]
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Epoch 8 Training:  28%|██▊       | 27/96 [00:42<01:49,  1.59s/it]
+
Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.89it/s]
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Epoch 8 Training:  29%|██▉       | 28/96 [00:44<01:47,  1.59s/it]
+
Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.90it/s]
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Epoch 8 Training:  30%|███       | 29/96 [00:45<01:46,  1.59s/it]
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Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.89it/s]
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Epoch 8 Training:  31%|███▏      | 30/96 [00:47<01:44,  1.59s/it]
+
Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
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Epoch 8 Training:  32%|███▏      | 31/96 [00:48<01:42,  1.58s/it]
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Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.89it/s]
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Epoch 8 Training:  33%|███▎      | 32/96 [00:50<01:41,  1.58s/it]
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Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.90it/s]
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Epoch 8 Training:  34%|███▍      | 33/96 [00:52<01:39,  1.58s/it]
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Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.90it/s]
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Epoch 8 Training:  35%|███▌      | 34/96 [00:53<01:38,  1.58s/it]
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Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.91it/s]
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Epoch 8 Training:  36%|███▋      | 35/96 [00:55<01:36,  1.58s/it]
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Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.91it/s]
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Epoch 8 Training:  38%|███▊      | 36/96 [00:56<01:34,  1.58s/it]
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Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.89it/s]
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Epoch 8 Training:  39%|███▊      | 37/96 [00:58<01:33,  1.58s/it]
+
Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.91it/s]
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Epoch 8 Training:  40%|███▉      | 38/96 [01:00<01:31,  1.58s/it]
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Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.90it/s]
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Epoch 8 Training:  41%|████      | 39/96 [01:01<01:30,  1.58s/it]
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Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.90it/s]
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Epoch 8 Training:  42%|████▏     | 40/96 [01:03<01:28,  1.59s/it]
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Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.89it/s]
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Epoch 8 Training:  43%|████▎     | 41/96 [01:04<01:27,  1.59s/it]
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Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.91it/s]
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Epoch 8 Training:  44%|████▍     | 42/96 [01:06<01:25,  1.58s/it]
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Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Training:  45%|████▍     | 43/96 [01:07<01:23,  1.58s/it]
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Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Training:  46%|████▌     | 44/96 [01:09<01:22,  1.58s/it]
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Epoch 8 Training:  47%|████▋     | 45/96 [01:11<01:20,  1.58s/it]
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Epoch 8 Training:  48%|████▊     | 46/96 [01:12<01:19,  1.58s/it]
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Epoch 8 Training:  49%|████▉     | 47/96 [01:14<01:17,  1.58s/it]
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Epoch 8 Training:  50%|█████     | 48/96 [01:15<01:16,  1.58s/it]
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Epoch 8 Training:  51%|█████     | 49/96 [01:17<01:14,  1.58s/it]
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Epoch 8 Training:  52%|█████▏    | 50/96 [01:19<01:12,  1.59s/it]
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Epoch 8 Training:  53%|█████▎    | 51/96 [01:20<01:11,  1.59s/it]
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Epoch 8 Training:  54%|█████▍    | 52/96 [01:22<01:09,  1.59s/it]
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Epoch 8 Training:  55%|█████▌    | 53/96 [01:23<01:08,  1.59s/it]
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Epoch 8 Training:  56%|█████▋    | 54/96 [01:25<01:06,  1.59s/it]
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Epoch 8 Training:  57%|█████▋    | 55/96 [01:27<01:05,  1.59s/it]
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Epoch 8 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s][2026-10-01 13:58:36] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0008 | Epoch 008] train/policy_loss=25.4252 train/unweighted_policy_loss=5.0850 train/loss=25.4252 train/grad_norm=0.0758
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+[2026-10-01 13:58:36] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0009 | Epoch 008] timing/rollout_seconds=180.2432 timing/feedback_seconds=5.3212 timing/optimization_seconds=152.6371 timing/cycle_seconds=335.8553
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+
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+
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+[2026-10-01 14:01:40] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0009 | Epoch 009] train/reward_dists_mean=0.8308 train/reward_dists_std=0.0285 train/reward_ssim_mean=0.6688 train/reward_ssim_std=0.0940 train/reward_dists_group_std_mean=0.0092 train/reward_dists_group_std_max=0.0158 train/reward_dists_group_std_min=0.0047 train/reward_dists_group_mean_std=0.0269 train/reward_ssim_group_std_mean=0.0093 train/reward_ssim_group_std_max=0.0184 train/reward_ssim_group_std_min=0.0030 train/reward_ssim_group_mean_std=0.0934 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.0786 train/adv_max=4.7864 train/adv_abs_mean=1.2664
+
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Epoch 9 Training:  33%|███▎      | 32/96 [00:50<01:40,  1.57s/it]
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Epoch 9 Training:  34%|███▍      | 33/96 [00:51<01:39,  1.57s/it]
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Epoch 9 Training:  35%|███▌      | 34/96 [00:53<01:37,  1.58s/it]
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Epoch 9 Training:  36%|███▋      | 35/96 [00:54<01:36,  1.58s/it]
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Epoch 9 Training:  38%|███▊      | 36/96 [00:56<01:34,  1.58s/it]
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Epoch 9 Training:  39%|███▊      | 37/96 [00:58<01:32,  1.58s/it]
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Epoch 9 Training:  40%|███▉      | 38/96 [00:59<01:31,  1.57s/it]
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Epoch 9 Training:  41%|████      | 39/96 [01:01<01:29,  1.57s/it]
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Epoch 9 Training:  42%|████▏     | 40/96 [01:02<01:28,  1.58s/it]
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Epoch 9 Training:  43%|████▎     | 41/96 [01:04<01:26,  1.58s/it]
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Epoch 9 Training:  44%|████▍     | 42/96 [01:05<01:25,  1.58s/it]
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Epoch 9 Training:  45%|████▍     | 43/96 [01:07<01:23,  1.58s/it]
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Epoch 9 Training:  46%|████▌     | 44/96 [01:09<01:22,  1.58s/it]
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Epoch 9 Training:  47%|████▋     | 45/96 [01:10<01:20,  1.58s/it]
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Epoch 9 Training:  48%|████▊     | 46/96 [01:12<01:18,  1.58s/it]
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Epoch 9 Training:  49%|████▉     | 47/96 [01:13<01:17,  1.58s/it]
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Epoch 9 Training:  50%|█████     | 48/96 [01:15<01:15,  1.58s/it]
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Epoch 9 Training:  51%|█████     | 49/96 [01:17<01:14,  1.58s/it]
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Epoch 9 Training:  52%|█████▏    | 50/96 [01:18<01:12,  1.57s/it]
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Epoch 9 Training:  53%|█████▎    | 51/96 [01:20<01:10,  1.57s/it]
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Epoch 9 Training:  54%|█████▍    | 52/96 [01:21<01:09,  1.57s/it]
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Epoch 9 Training:  55%|█████▌    | 53/96 [01:23<01:07,  1.58s/it]
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Epoch 9 Training:  56%|█████▋    | 54/96 [01:24<01:06,  1.57s/it]
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Epoch 9 Training:  57%|█████▋    | 55/96 [01:26<01:04,  1.57s/it]
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Epoch 9 Training:  58%|█████▊    | 56/96 [01:28<01:03,  1.58s/it]
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Epoch 9 Training:  59%|█████▉    | 57/96 [01:29<01:01,  1.58s/it]
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Epoch 9 Training:  60%|██████    | 58/96 [01:31<00:59,  1.57s/it]
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Epoch 9 Training:  61%|██████▏   | 59/96 [01:32<00:58,  1.57s/it]
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Epoch 9 Training:  62%|██████▎   | 60/96 [01:34<00:56,  1.57s/it]
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Epoch 9 Training:  64%|██████▎   | 61/96 [01:35<00:55,  1.57s/it]
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Epoch 9 Training:  65%|██████▍   | 62/96 [01:37<00:53,  1.57s/it]
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Epoch 9 Training:  66%|██████▌   | 63/96 [01:39<00:51,  1.57s/it]
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Epoch 9 Training:  67%|██████▋   | 64/96 [01:40<00:50,  1.57s/it]
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Epoch 9 Training:  68%|██████▊   | 65/96 [01:42<00:48,  1.57s/it]
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Epoch 9 Training:  69%|██████▉   | 66/96 [01:43<00:47,  1.57s/it]
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Epoch 9 Training:  70%|██████▉   | 67/96 [01:45<00:45,  1.57s/it]
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Epoch 9 Training:  71%|███████   | 68/96 [01:46<00:44,  1.57s/it]
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Epoch 9 Training:  72%|███████▏  | 69/96 [01:48<00:42,  1.57s/it]
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Epoch 9 Training:  73%|███████▎  | 70/96 [01:50<00:40,  1.57s/it]
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Epoch 9 Training:  74%|███████▍  | 71/96 [01:51<00:39,  1.58s/it]
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Epoch 9 Training:  75%|███████▌  | 72/96 [01:53<00:37,  1.58s/it]
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Epoch 9 Training:  76%|███████▌  | 73/96 [01:54<00:36,  1.58s/it]
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Epoch 9 Training:  77%|███████▋  | 74/96 [01:56<00:34,  1.58s/it]
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Epoch 9 Training:  78%|███████▊  | 75/96 [01:57<00:33,  1.58s/it]
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Epoch 9 Training:  79%|███████▉  | 76/96 [01:59<00:31,  1.58s/it]
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Epoch 9 Training:  80%|████████  | 77/96 [02:01<00:29,  1.58s/it]
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Epoch 9 Training:  81%|████████▏ | 78/96 [02:02<00:28,  1.57s/it]
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Epoch 9 Training:  82%|████████▏ | 79/96 [02:04<00:26,  1.57s/it]
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Epoch 9 Training:  83%|████████▎ | 80/96 [02:05<00:25,  1.57s/it]
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Epoch 9 Training:  84%|████████▍ | 81/96 [02:07<00:23,  1.57s/it]
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Epoch 9 Training:  85%|████████▌ | 82/96 [02:08<00:22,  1.57s/it]
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Epoch 9 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s][2026-10-01 14:04:11] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0009 | Epoch 009] train/policy_loss=24.7021 train/unweighted_policy_loss=4.9404 train/loss=24.7021 train/grad_norm=0.0769
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+[2026-10-01 14:04:11] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0010 | Epoch 009] timing/rollout_seconds=180.1265 timing/feedback_seconds=5.4416 timing/optimization_seconds=151.7787 timing/cycle_seconds=334.8191
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+[2026-10-01 14:05:04] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0010 | Epoch 010] eval/flir_rl_full/reward_dists_mean=0.8326 eval/flir_rl_full/reward_dists_std=0.0354 eval/flir_rl_full/reward_psnr_mean=19.9257 eval/flir_rl_full/reward_psnr_std=3.0136 eval/flir_rl_full/reward_ssim_mean=0.6291 eval/flir_rl_full/reward_ssim_std=0.0848
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+[2026-10-01 14:08:08] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0010 | Epoch 010] train/reward_dists_mean=0.8275 train/reward_dists_std=0.0278 train/reward_ssim_mean=0.6327 train/reward_ssim_std=0.0935 train/reward_dists_group_std_mean=0.0087 train/reward_dists_group_std_max=0.0167 train/reward_dists_group_std_min=0.0036 train/reward_dists_group_mean_std=0.0263 train/reward_ssim_group_std_mean=0.0115 train/reward_ssim_group_std_max=0.0316 train/reward_ssim_group_std_min=0.0036 train/reward_ssim_group_mean_std=0.0926 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.0779 train/adv_max=6.1359 train/adv_abs_mean=1.2354
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Epoch 10 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.95it/s]
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Epoch 10 Training:   2%|▏         | 2/96 [00:03<02:23,  1.53s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.98it/s]
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Epoch 10 Training:   3%|▎         | 3/96 [00:04<02:22,  1.54s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.15it/s]
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Epoch 10 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
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Epoch 10 Training:   4%|▍         | 4/96 [00:06<02:22,  1.55s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
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Epoch 10 Training:   5%|▌         | 5/96 [00:07<02:21,  1.56s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.96it/s]
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Epoch 10 Training:   6%|▋         | 6/96 [00:09<02:20,  1.56s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
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Epoch 10 Training:   7%|▋         | 7/96 [00:10<02:19,  1.56s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 10 Training:   8%|▊         | 8/96 [00:12<02:17,  1.57s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.97it/s]
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Epoch 10 Training:   9%|▉         | 9/96 [00:13<02:15,  1.56s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Training:  10%|█         | 10/96 [00:15<02:14,  1.57s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.96it/s]
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Epoch 10 Training:  11%|█▏        | 11/96 [00:17<02:12,  1.56s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.96it/s]
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Epoch 10 Training:  12%|█▎        | 12/96 [00:18<02:11,  1.56s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.96it/s]
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Epoch 10 Training:  14%|█▎        | 13/96 [00:20<02:09,  1.56s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.97it/s]
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Epoch 10 Training:  15%|█▍        | 14/96 [00:21<02:07,  1.56s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
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Epoch 10 Training:  16%|█▌        | 15/96 [00:23<02:06,  1.56s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 10 Training:  17%|█▋        | 16/96 [00:24<02:05,  1.57s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 10 Training:  18%|█▊        | 17/96 [00:26<02:03,  1.57s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
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Epoch 10 Training:  19%|█▉        | 18/96 [00:28<02:02,  1.57s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
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Epoch 10 Training:  20%|█▉        | 19/96 [00:29<02:01,  1.57s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 10 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
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Epoch 10 Training:  21%|██        | 20/96 [00:31<01:59,  1.57s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.91it/s]
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Epoch 10 Training:  22%|██▏       | 21/96 [00:32<01:58,  1.57s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
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Epoch 10 Training:  23%|██▎       | 22/96 [00:34<01:56,  1.57s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.91it/s]
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Epoch 10 Training:  24%|██▍       | 23/96 [00:35<01:55,  1.58s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Training:  25%|██▌       | 24/96 [00:37<01:53,  1.58s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
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Epoch 10 Training:  26%|██▌       | 25/96 [00:39<01:51,  1.57s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Training:  27%|██▋       | 26/96 [00:40<01:50,  1.57s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.91it/s]
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Epoch 10 Training:  28%|██▊       | 27/96 [00:42<01:48,  1.57s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 10 Training:  29%|██▉       | 28/96 [00:43<01:47,  1.57s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 10 Training:  30%|███       | 29/96 [00:45<01:45,  1.57s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
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Epoch 10 Training:  31%|███▏      | 30/96 [00:46<01:43,  1.57s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Training:  32%|███▏      | 31/96 [00:48<01:41,  1.57s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Training:  33%|███▎      | 32/96 [00:50<01:40,  1.57s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Training:  34%|███▍      | 33/96 [00:51<01:38,  1.57s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Training:  35%|███▌      | 34/96 [00:53<01:37,  1.57s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Training:  36%|███▋      | 35/96 [00:54<01:35,  1.57s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Training:  38%|███▊      | 36/96 [00:56<01:34,  1.57s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Training:  39%|███▊      | 37/96 [00:57<01:32,  1.57s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Training:  40%|███▉      | 38/96 [00:59<01:31,  1.57s/it]
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Epoch 10 Training:  41%|████      | 39/96 [01:01<01:29,  1.57s/it]
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Epoch 10 Training:  42%|████▏     | 40/96 [01:02<01:28,  1.57s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Training:  43%|████▎     | 41/96 [01:04<01:26,  1.57s/it]
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Epoch 10 Training:  44%|████▍     | 42/96 [01:05<01:24,  1.57s/it]
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Epoch 10 Training:  45%|████▍     | 43/96 [01:07<01:23,  1.57s/it]
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Epoch 10 Training:  46%|████▌     | 44/96 [01:08<01:21,  1.57s/it]
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Epoch 10 Training:  47%|████▋     | 45/96 [01:10<01:20,  1.57s/it]
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Epoch 10 Training:  48%|████▊     | 46/96 [01:12<01:18,  1.57s/it]
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Epoch 10 Training:  49%|████▉     | 47/96 [01:13<01:16,  1.57s/it]
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Epoch 10 Training:  50%|█████     | 48/96 [01:15<01:15,  1.57s/it]
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Epoch 10 Training:  51%|█████     | 49/96 [01:16<01:13,  1.57s/it]
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Epoch 10 Training:  52%|█████▏    | 50/96 [01:18<01:12,  1.57s/it]
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Epoch 10 Training:  53%|█████▎    | 51/96 [01:19<01:10,  1.57s/it]
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Epoch 10 Training:  54%|█████▍    | 52/96 [01:21<01:09,  1.57s/it]
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Epoch 10 Training:  55%|█████▌    | 53/96 [01:23<01:07,  1.57s/it]
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Epoch 10 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s][2026-10-01 14:10:38] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0010 | Epoch 010] train/policy_loss=25.2502 train/unweighted_policy_loss=5.0500 train/loss=25.2502 train/grad_norm=0.0574
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+[2026-10-01 14:10:38] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0011 | Epoch 010] timing/rollout_seconds=180.1054 timing/feedback_seconds=5.3525 timing/optimization_seconds=151.4792 timing/cycle_seconds=334.5367
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+
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+
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+[2026-10-01 14:13:42] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0011 | Epoch 011] train/reward_dists_mean=0.8263 train/reward_dists_std=0.0406 train/reward_ssim_mean=0.6330 train/reward_ssim_std=0.1048 train/reward_dists_group_std_mean=0.0081 train/reward_dists_group_std_max=0.0212 train/reward_dists_group_std_min=0.0036 train/reward_dists_group_mean_std=0.0396 train/reward_ssim_group_std_mean=0.0109 train/reward_ssim_group_std_max=0.0320 train/reward_ssim_group_std_min=0.0047 train/reward_ssim_group_mean_std=0.1041 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.3679 train/adv_max=3.9761 train/adv_abs_mean=1.2007
+
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Epoch 11 Training:  30%|███       | 29/96 [00:45<01:45,  1.57s/it]
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Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
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Epoch 11 Training:  31%|███▏      | 30/96 [00:47<01:43,  1.57s/it]
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Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.94it/s]
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Epoch 11 Training:  32%|███▏      | 31/96 [00:48<01:42,  1.57s/it]
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Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 11 Training:  33%|███▎      | 32/96 [00:50<01:40,  1.58s/it]
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Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 11 Training:  34%|███▍      | 33/96 [00:51<01:39,  1.58s/it]
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Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
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Epoch 11 Training:  35%|███▌      | 34/96 [00:53<01:37,  1.58s/it]
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Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.95it/s]
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Epoch 11 Training:  36%|███▋      | 35/96 [00:54<01:36,  1.58s/it]
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Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
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Epoch 11 Training:  38%|███▊      | 36/96 [00:56<01:34,  1.57s/it]
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Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
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Epoch 11 Training:  39%|███▊      | 37/96 [00:58<01:32,  1.57s/it]
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Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
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Epoch 11 Training:  40%|███▉      | 38/96 [00:59<01:31,  1.57s/it]
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Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
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Epoch 11 Training:  41%|████      | 39/96 [01:01<01:29,  1.57s/it]
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Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.94it/s]
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Epoch 11 Training:  42%|████▏     | 40/96 [01:02<01:28,  1.57s/it]
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Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
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Epoch 11 Training:  43%|████▎     | 41/96 [01:04<01:26,  1.57s/it]
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Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
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Epoch 11 Training:  44%|████▍     | 42/96 [01:05<01:24,  1.57s/it]
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Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.96it/s]
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Epoch 11 Training:  45%|████▍     | 43/96 [01:07<01:23,  1.57s/it]
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Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.95it/s]
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Epoch 11 Training:  46%|████▌     | 44/96 [01:09<01:21,  1.57s/it]
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Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.94it/s]
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Epoch 11 Training:  47%|████▋     | 45/96 [01:10<01:19,  1.57s/it]
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Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 11 Training:  48%|████▊     | 46/96 [01:12<01:18,  1.57s/it]
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Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 11 Training:  49%|████▉     | 47/96 [01:13<01:17,  1.57s/it]
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Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
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Epoch 11 Training:  50%|█████     | 48/96 [01:15<01:15,  1.57s/it]
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Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.91it/s]
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Epoch 11 Training:  51%|█████     | 49/96 [01:16<01:14,  1.57s/it]
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Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.96it/s]
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Epoch 11 Training:  52%|█████▏    | 50/96 [01:18<01:12,  1.57s/it]
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Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 11 Training:  53%|█████▎    | 51/96 [01:20<01:10,  1.57s/it]
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Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 11 Training:  54%|█████▍    | 52/96 [01:21<01:09,  1.57s/it]
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Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
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Epoch 11 Training:  55%|█████▌    | 53/96 [01:23<01:07,  1.57s/it]
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Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
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Epoch 11 Training:  56%|█████▋    | 54/96 [01:24<01:06,  1.57s/it]
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Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.95it/s]
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Epoch 11 Training:  57%|█████▋    | 55/96 [01:26<01:04,  1.57s/it]
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Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.94it/s]
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Epoch 11 Training:  58%|█████▊    | 56/96 [01:27<01:02,  1.57s/it]
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Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Training:  59%|█████▉    | 57/96 [01:29<01:01,  1.57s/it]
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Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Training:  60%|██████    | 58/96 [01:31<00:59,  1.57s/it]
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Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.94it/s]
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Epoch 11 Training:  61%|██████▏   | 59/96 [01:32<00:57,  1.57s/it]
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Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
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Epoch 11 Training:  62%|██████▎   | 60/96 [01:34<00:56,  1.57s/it]
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Epoch 11 Training:  64%|██████▎   | 61/96 [01:35<00:54,  1.57s/it]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
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Epoch 11 Training:  65%|██████▍   | 62/96 [01:37<00:53,  1.57s/it]
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Epoch 11 Training:  66%|██████▌   | 63/96 [01:38<00:51,  1.57s/it]
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Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 11 Training:  67%|██████▋   | 64/96 [01:40<00:50,  1.57s/it]
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Epoch 11 Training:  68%|██████▊   | 65/96 [01:42<00:48,  1.57s/it]
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Epoch 11 Training:  69%|██████▉   | 66/96 [01:43<00:47,  1.57s/it]
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Epoch 11 Training:  70%|██████▉   | 67/96 [01:45<00:45,  1.57s/it]
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Epoch 11 Training:  71%|███████   | 68/96 [01:46<00:43,  1.57s/it]
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Epoch 11 Training:  72%|███████▏  | 69/96 [01:48<00:42,  1.57s/it]
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Epoch 11 Training:  73%|███████▎  | 70/96 [01:49<00:40,  1.57s/it]
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Epoch 11 Training:  74%|███████▍  | 71/96 [01:51<00:39,  1.57s/it]
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Epoch 11 Training:  75%|███████▌  | 72/96 [01:53<00:37,  1.57s/it]
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Epoch 11 Training:  76%|███████▌  | 73/96 [01:54<00:36,  1.57s/it]
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Epoch 11 Training:  77%|███████▋  | 74/96 [01:56<00:34,  1.57s/it]
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Epoch 11 Training:  78%|███████▊  | 75/96 [01:57<00:32,  1.57s/it]
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Epoch 11 Training:  79%|███████▉  | 76/96 [01:59<00:31,  1.57s/it]
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Epoch 11 Training:  80%|████████  | 77/96 [02:00<00:29,  1.57s/it]
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Epoch 11 Training:  81%|████████▏ | 78/96 [02:02<00:29,  1.64s/it]
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Epoch 11 Training:  82%|████████▏ | 79/96 [02:04<00:27,  1.62s/it]
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Epoch 11 Training:  83%|████████▎ | 80/96 [02:05<00:25,  1.60s/it]
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Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s][2026-10-01 14:16:13] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0011 | Epoch 011] train/policy_loss=25.1886 train/unweighted_policy_loss=5.0377 train/loss=25.1886 train/grad_norm=0.0761
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+[2026-10-01 14:16:13] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0012 | Epoch 011] timing/rollout_seconds=180.1715 timing/feedback_seconds=5.6156 timing/optimization_seconds=151.6188 timing/cycle_seconds=334.7704
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+[2026-10-01 14:19:17] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0012 | Epoch 012] train/reward_dists_mean=0.8297 train/reward_dists_std=0.0274 train/reward_ssim_mean=0.6490 train/reward_ssim_std=0.1116 train/reward_dists_group_std_mean=0.0082 train/reward_dists_group_std_max=0.0199 train/reward_dists_group_std_min=0.0045 train/reward_dists_group_mean_std=0.0260 train/reward_ssim_group_std_mean=0.0111 train/reward_ssim_group_std_max=0.0238 train/reward_ssim_group_std_min=0.0044 train/reward_ssim_group_mean_std=0.1110 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.7729 train/adv_max=4.7289 train/adv_abs_mean=1.2300
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.94it/s]
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Epoch 12 Training:   5%|▌         | 5/96 [00:07<02:22,  1.56s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
+
                                                                
Epoch 12 Training:   6%|▋         | 6/96 [00:09<02:20,  1.56s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
+
                                                                
Epoch 12 Training:   7%|▋         | 7/96 [00:10<02:18,  1.56s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
+
                                                                
Epoch 12 Training:   8%|▊         | 8/96 [00:12<02:17,  1.56s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.15it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
+
                                                                
Epoch 12 Training:   9%|▉         | 9/96 [00:13<02:15,  1.56s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.95it/s]
+
                                                                
Epoch 12 Training:  10%|█         | 10/96 [00:15<02:14,  1.56s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
+
                                                                
Epoch 12 Training:  11%|█▏        | 11/96 [00:17<02:12,  1.56s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.95it/s]
+
                                                                
Epoch 12 Training:  12%|█▎        | 12/96 [00:18<02:11,  1.56s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
+
                                                                
Epoch 12 Training:  14%|█▎        | 13/96 [00:20<02:09,  1.56s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
+
                                                                
Epoch 12 Training:  15%|█▍        | 14/96 [00:21<02:07,  1.56s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.95it/s]
+
                                                                
Epoch 12 Training:  16%|█▌        | 15/96 [00:23<02:06,  1.56s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.95it/s]
+
                                                                
Epoch 12 Training:  17%|█▋        | 16/96 [00:24<02:05,  1.56s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
+
                                                                
Epoch 12 Training:  18%|█▊        | 17/96 [00:26<02:03,  1.56s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
+
                                                                
Epoch 12 Training:  19%|█▉        | 18/96 [00:28<02:01,  1.56s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
+
                                                                
Epoch 12 Training:  20%|█▉        | 19/96 [00:29<01:59,  1.56s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
+
                                                                
Epoch 12 Training:  21%|██        | 20/96 [00:31<01:58,  1.55s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.15it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.94it/s]
+
                                                                
Epoch 12 Training:  22%|██▏       | 21/96 [00:32<01:57,  1.56s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
+
                                                                
Epoch 12 Training:  23%|██▎       | 22/96 [00:34<01:55,  1.56s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.94it/s]
+
                                                                
Epoch 12 Training:  24%|██▍       | 23/96 [00:35<01:54,  1.56s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
+
                                                                
Epoch 12 Training:  25%|██▌       | 24/96 [00:37<01:52,  1.56s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
+
                                                                
Epoch 12 Training:  26%|██▌       | 25/96 [00:38<01:50,  1.55s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.94it/s]
+
                                                                
Epoch 12 Training:  27%|██▋       | 26/96 [00:40<01:49,  1.56s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
+
                                                                
Epoch 12 Training:  28%|██▊       | 27/96 [00:42<01:47,  1.56s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.97it/s]
+
                                                                
Epoch 12 Training:  29%|██▉       | 28/96 [00:43<01:45,  1.56s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
+
                                                                
Epoch 12 Training:  30%|███       | 29/96 [00:45<01:44,  1.55s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
+
                                                                
Epoch 12 Training:  31%|███▏      | 30/96 [00:46<01:42,  1.55s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.94it/s]
+
                                                                
Epoch 12 Training:  32%|███▏      | 31/96 [00:48<01:41,  1.56s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.95it/s]
+
                                                                
Epoch 12 Training:  33%|███▎      | 32/96 [00:49<01:40,  1.56s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.15it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
+
                                                                
Epoch 12 Training:  34%|███▍      | 33/96 [00:51<01:38,  1.57s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.15it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.94it/s]
+
                                                                
Epoch 12 Training:  35%|███▌      | 34/96 [00:53<01:37,  1.57s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.88it/s]
+
                                                                
Epoch 12 Training:  36%|███▋      | 35/96 [00:54<01:36,  1.58s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.89it/s]
+
                                                                
Epoch 12 Training:  38%|███▊      | 36/96 [00:56<01:34,  1.58s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.90it/s]
+
                                                                
Epoch 12 Training:  39%|███▊      | 37/96 [00:57<01:33,  1.59s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.94it/s]
+
                                                                
Epoch 12 Training:  40%|███▉      | 38/96 [00:59<01:31,  1.58s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.95it/s]
+
                                                                
Epoch 12 Training:  41%|████      | 39/96 [01:00<01:29,  1.58s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.94it/s]
+
                                                                
Epoch 12 Training:  42%|████▏     | 40/96 [01:02<01:28,  1.58s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.94it/s]
+
                                                                
Epoch 12 Training:  43%|████▎     | 41/96 [01:04<01:26,  1.58s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.94it/s]
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Epoch 12 Training:  44%|████▍     | 42/96 [01:05<01:25,  1.58s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
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Epoch 12 Training:  45%|████▍     | 43/96 [01:07<01:24,  1.59s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 12 Training:  46%|████▌     | 44/96 [01:08<01:21,  1.58s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.95it/s]
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Epoch 12 Training:  47%|████▋     | 45/96 [01:10<01:20,  1.57s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 12 Training:  48%|████▊     | 46/96 [01:11<01:18,  1.57s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 12 Training:  49%|████▉     | 47/96 [01:13<01:17,  1.57s/it]
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Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 12 Training:  50%|█████     | 48/96 [01:15<01:15,  1.57s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.91it/s]
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Epoch 12 Training:  51%|█████     | 49/96 [01:16<01:13,  1.57s/it]
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Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 12 Training:  52%|█████▏    | 50/96 [01:18<01:12,  1.57s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.97it/s]
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Epoch 12 Training:  53%|█████▎    | 51/96 [01:19<01:10,  1.56s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 12 Training:  54%|█████▍    | 52/96 [01:21<01:08,  1.57s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.14it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.98it/s]
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Epoch 12 Training:  55%|█████▌    | 53/96 [01:22<01:07,  1.57s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.98it/s]
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Epoch 12 Training:  56%|█████▋    | 54/96 [01:24<01:05,  1.57s/it]
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Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.91it/s]
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Epoch 12 Training:  57%|█████▋    | 55/96 [01:26<01:04,  1.57s/it]
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Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 12 Training:  58%|█████▊    | 56/96 [01:27<01:02,  1.57s/it]
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Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s][2026-10-01 14:21:47] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0012 | Epoch 012] train/policy_loss=24.7460 train/unweighted_policy_loss=4.9492 train/loss=24.7460 train/grad_norm=0.0734
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+[2026-10-01 14:21:47] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0013 | Epoch 012] timing/rollout_seconds=180.0523 timing/feedback_seconds=5.4812 timing/optimization_seconds=151.3507 timing/cycle_seconds=334.3323
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+[2026-10-01 14:24:51] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0013 | Epoch 013] train/reward_dists_mean=0.8283 train/reward_dists_std=0.0332 train/reward_ssim_mean=0.6433 train/reward_ssim_std=0.1142 train/reward_dists_group_std_mean=0.0080 train/reward_dists_group_std_max=0.0154 train/reward_dists_group_std_min=0.0036 train/reward_dists_group_mean_std=0.0321 train/reward_ssim_group_std_mean=0.0116 train/reward_ssim_group_std_max=0.0247 train/reward_ssim_group_std_min=0.0044 train/reward_ssim_group_mean_std=0.1135 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.9415 train/adv_max=5.0657 train/adv_abs_mean=1.2095
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Epoch 13 Training:  34%|███▍      | 33/96 [00:52<01:38,  1.57s/it]
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Epoch 13 Training:  35%|███▌      | 34/96 [00:53<01:37,  1.57s/it]
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Epoch 13 Training:  36%|███▋      | 35/96 [00:55<01:35,  1.57s/it]
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Epoch 13 Training:  38%|███▊      | 36/96 [00:56<01:34,  1.57s/it]
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Epoch 13 Training:  39%|███▊      | 37/96 [00:58<01:32,  1.57s/it]
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Epoch 13 Training:  40%|███▉      | 38/96 [00:59<01:31,  1.57s/it]
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Epoch 13 Training:  41%|████      | 39/96 [01:01<01:29,  1.57s/it]
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Epoch 13 Training:  42%|████▏     | 40/96 [01:03<01:28,  1.58s/it]
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Epoch 13 Training:  43%|████▎     | 41/96 [01:04<01:26,  1.58s/it]
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Epoch 13 Training:  44%|████▍     | 42/96 [01:06<01:25,  1.58s/it]
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Epoch 13 Training:  45%|████▍     | 43/96 [01:07<01:23,  1.58s/it]
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Epoch 13 Training:  46%|████▌     | 44/96 [01:09<01:21,  1.58s/it]
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Epoch 13 Training:  47%|████▋     | 45/96 [01:10<01:20,  1.58s/it]
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Epoch 13 Training:  48%|████▊     | 46/96 [01:12<01:18,  1.58s/it]
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Epoch 13 Training:  49%|████▉     | 47/96 [01:14<01:17,  1.58s/it]
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Epoch 13 Training:  50%|█████     | 48/96 [01:15<01:15,  1.58s/it]
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Epoch 13 Training:  51%|█████     | 49/96 [01:17<01:14,  1.57s/it]
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Epoch 13 Training:  52%|█████▏    | 50/96 [01:18<01:12,  1.57s/it]
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Epoch 13 Training:  53%|█████▎    | 51/96 [01:20<01:10,  1.58s/it]
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Epoch 13 Training:  54%|█████▍    | 52/96 [01:21<01:09,  1.57s/it]
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Epoch 13 Training:  55%|█████▌    | 53/96 [01:23<01:07,  1.57s/it]
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Epoch 13 Training:  56%|█████▋    | 54/96 [01:25<01:06,  1.57s/it]
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Epoch 13 Training:  57%|█████▋    | 55/96 [01:26<01:04,  1.57s/it]
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Epoch 13 Training:  58%|█████▊    | 56/96 [01:28<01:03,  1.58s/it]
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Epoch 13 Training:  59%|█████▉    | 57/96 [01:29<01:01,  1.58s/it]
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Epoch 13 Training:  60%|██████    | 58/96 [01:31<01:00,  1.58s/it]
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Epoch 13 Training:  61%|██████▏   | 59/96 [01:32<00:58,  1.58s/it]
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Epoch 13 Training:  62%|██████▎   | 60/96 [01:34<00:56,  1.58s/it]
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Epoch 13 Training:  64%|██████▎   | 61/96 [01:36<00:55,  1.58s/it]
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Epoch 13 Training:  65%|██████▍   | 62/96 [01:37<00:53,  1.58s/it]
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Epoch 13 Training:  66%|██████▌   | 63/96 [01:39<00:52,  1.58s/it]
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Epoch 13 Training:  67%|██████▋   | 64/96 [01:40<00:50,  1.58s/it]
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Epoch 13 Training:  68%|██████▊   | 65/96 [01:42<00:48,  1.58s/it]
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Epoch 13 Training:  69%|██████▉   | 66/96 [01:44<00:47,  1.58s/it]
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Epoch 13 Training:  70%|██████▉   | 67/96 [01:45<00:45,  1.58s/it]
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Epoch 13 Training:  71%|███████   | 68/96 [01:47<00:44,  1.58s/it]
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Epoch 13 Training:  72%|███████▏  | 69/96 [01:48<00:42,  1.58s/it]
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Epoch 13 Training:  73%|███████▎  | 70/96 [01:50<00:40,  1.58s/it]
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Epoch 13 Training:  74%|███████▍  | 71/96 [01:51<00:39,  1.58s/it]
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Epoch 13 Training:  75%|███████▌  | 72/96 [01:53<00:37,  1.58s/it]
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Epoch 13 Training:  76%|███████▌  | 73/96 [01:55<00:36,  1.58s/it]
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Epoch 13 Training:  77%|███████▋  | 74/96 [01:56<00:34,  1.58s/it]
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Epoch 13 Training:  78%|███████▊  | 75/96 [01:58<00:33,  1.58s/it]
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Epoch 13 Training:  79%|███████▉  | 76/96 [01:59<00:31,  1.58s/it]
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Epoch 13 Training:  80%|████████  | 77/96 [02:01<00:29,  1.58s/it]
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Epoch 13 Training:  81%|████████▏ | 78/96 [02:02<00:28,  1.57s/it]
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Epoch 13 Training:  82%|████████▏ | 79/96 [02:04<00:26,  1.57s/it]
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Epoch 13 Training:  83%|████████▎ | 80/96 [02:06<00:25,  1.57s/it]
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Epoch 13 Training:  84%|████████▍ | 81/96 [02:07<00:23,  1.57s/it]
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Epoch 13 Training:  85%|████████▌ | 82/96 [02:09<00:21,  1.57s/it]
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Epoch 13 Training:  86%|████████▋ | 83/96 [02:10<00:20,  1.57s/it]
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Epoch 13 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s][2026-10-01 14:27:22] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0013 | Epoch 013] train/policy_loss=24.8439 train/unweighted_policy_loss=4.9688 train/loss=24.8439 train/grad_norm=0.0758
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+[2026-10-01 14:27:22] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0014 | Epoch 013] timing/rollout_seconds=180.1963 timing/feedback_seconds=5.7792 timing/optimization_seconds=151.8899 timing/cycle_seconds=335.0193
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+[2026-10-01 14:30:26] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0014 | Epoch 014] train/reward_dists_mean=0.8275 train/reward_dists_std=0.0314 train/reward_ssim_mean=0.6568 train/reward_ssim_std=0.1094 train/reward_dists_group_std_mean=0.0089 train/reward_dists_group_std_max=0.0159 train/reward_dists_group_std_min=0.0043 train/reward_dists_group_mean_std=0.0300 train/reward_ssim_group_std_mean=0.0094 train/reward_ssim_group_std_max=0.0171 train/reward_ssim_group_std_min=0.0031 train/reward_ssim_group_mean_std=0.1090 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.7225 train/adv_max=4.0080 train/adv_abs_mean=1.2208
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Epoch 14 Training:   8%|▊         | 8/96 [00:12<02:17,  1.57s/it]
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Epoch 14 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 14 Training:   9%|▉         | 9/96 [00:14<02:16,  1.57s/it]
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Epoch 14 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 14 Training:  10%|█         | 10/96 [00:15<02:14,  1.57s/it]
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Epoch 14 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 14 Training:  11%|█▏        | 11/96 [00:17<02:13,  1.57s/it]
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Epoch 14 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 14 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 14 Training:  12%|█▎        | 12/96 [00:18<02:12,  1.57s/it]
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Epoch 14 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 14 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
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Epoch 14 Training:  14%|█▎        | 13/96 [00:20<02:10,  1.57s/it]
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Epoch 14 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 14 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.96it/s]
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Epoch 14 Training:  15%|█▍        | 14/96 [00:21<02:08,  1.57s/it]
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Epoch 14 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 14 Training:  16%|█▌        | 15/96 [00:23<02:06,  1.57s/it]
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Epoch 14 Training:  17%|█▋        | 16/96 [00:25<02:11,  1.64s/it]
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Epoch 14 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 14 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.90it/s]
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Epoch 14 Training:  18%|█▊        | 17/96 [00:26<02:08,  1.62s/it]
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Epoch 14 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 14 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
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Epoch 14 Training:  19%|█▉        | 18/96 [00:28<02:05,  1.61s/it]
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Epoch 14 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 14 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.91it/s]
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Epoch 14 Training:  20%|█▉        | 19/96 [00:29<02:02,  1.60s/it]
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Epoch 14 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 14 Training:  21%|██        | 20/96 [00:31<02:00,  1.59s/it]
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Epoch 14 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 14 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.96it/s]
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Epoch 14 Training:  22%|██▏       | 21/96 [00:33<01:58,  1.58s/it]
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Epoch 14 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 14 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 14 Training:  23%|██▎       | 22/96 [00:34<01:56,  1.58s/it]
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Epoch 14 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 14 Training:  24%|██▍       | 23/96 [00:36<01:55,  1.58s/it]
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Epoch 14 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 14 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.94it/s]
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Epoch 14 Training:  25%|██▌       | 24/96 [00:37<01:53,  1.57s/it]
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Epoch 14 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 14 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.91it/s]
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Epoch 14 Training:  26%|██▌       | 25/96 [00:39<01:51,  1.57s/it]
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Epoch 14 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 14 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.91it/s]
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Epoch 14 Training:  27%|██▋       | 26/96 [00:40<01:50,  1.58s/it]
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Epoch 14 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 14 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.95it/s]
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Epoch 14 Training:  28%|██▊       | 27/96 [00:42<01:48,  1.57s/it]
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Epoch 14 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 14 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.95it/s]
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Epoch 14 Training:  29%|██▉       | 28/96 [00:44<01:46,  1.57s/it]
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Epoch 14 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 14 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 14 Training:  30%|███       | 29/96 [00:45<01:45,  1.57s/it]
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Epoch 14 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 14 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.94it/s]
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Epoch 14 Training:  31%|███▏      | 30/96 [00:47<01:43,  1.57s/it]
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Epoch 14 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 14 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.89it/s]
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Epoch 14 Training:  32%|███▏      | 31/96 [00:48<01:42,  1.57s/it]
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Epoch 14 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 14 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.94it/s]
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Epoch 14 Training:  33%|███▎      | 32/96 [00:50<01:40,  1.57s/it]
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Epoch 14 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 14 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
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Epoch 14 Training:  34%|███▍      | 33/96 [00:51<01:38,  1.57s/it]
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Epoch 14 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 14 Training:  35%|███▌      | 34/96 [00:53<01:37,  1.57s/it]
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Epoch 14 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 14 Training:  36%|███▋      | 35/96 [00:55<01:35,  1.56s/it]
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Epoch 14 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 14 Training:  38%|███▊      | 36/96 [00:56<01:33,  1.56s/it]
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Epoch 14 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 14 Training:  39%|███▊      | 37/96 [00:58<01:31,  1.56s/it]
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Epoch 14 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 14 Training:  40%|███▉      | 38/96 [00:59<01:30,  1.56s/it]
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Epoch 14 Training:  41%|████      | 39/96 [01:01<01:28,  1.56s/it]
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Epoch 14 Training:  42%|████▏     | 40/96 [01:02<01:27,  1.56s/it]
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Epoch 14 Training:  43%|████▎     | 41/96 [01:04<01:25,  1.56s/it]
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Epoch 14 Training:  44%|████▍     | 42/96 [01:05<01:23,  1.55s/it]
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Epoch 14 Training:  45%|████▍     | 43/96 [01:07<01:22,  1.56s/it]
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Epoch 14 Training:  46%|████▌     | 44/96 [01:09<01:21,  1.56s/it]
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Epoch 14 Training:  47%|████▋     | 45/96 [01:10<01:19,  1.56s/it]
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Epoch 14 Training:  48%|████▊     | 46/96 [01:12<01:17,  1.56s/it]
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Epoch 14 Training:  49%|████▉     | 47/96 [01:13<01:16,  1.56s/it]
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Epoch 14 Training:  50%|█████     | 48/96 [01:15<01:14,  1.56s/it]
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Epoch 14 Training:  51%|█████     | 49/96 [01:16<01:13,  1.56s/it]
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Epoch 14 Training:  52%|█████▏    | 50/96 [01:18<01:11,  1.56s/it]
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Epoch 14 Training:  53%|█████▎    | 51/96 [01:20<01:10,  1.56s/it]
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Epoch 14 Training:  54%|█████▍    | 52/96 [01:21<01:09,  1.57s/it]
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Epoch 14 Training:  55%|█████▌    | 53/96 [01:23<01:07,  1.57s/it]
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Epoch 14 Training:  56%|█████▋    | 54/96 [01:24<01:07,  1.60s/it]
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Epoch 14 Training:  57%|█████▋    | 55/96 [01:26<01:05,  1.59s/it]
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Epoch 14 Training:  58%|█████▊    | 56/96 [01:28<01:03,  1.59s/it]
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Epoch 14 Training:  59%|█████▉    | 57/96 [01:29<01:01,  1.59s/it]
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Epoch 14 Training:  60%|██████    | 58/96 [01:31<01:00,  1.59s/it]
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Epoch 14 Training:  61%|██████▏   | 59/96 [01:32<00:58,  1.58s/it]
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Epoch 14 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s][2026-10-01 14:32:56] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0014 | Epoch 014] train/policy_loss=24.4898 train/unweighted_policy_loss=4.8980 train/loss=24.4898 train/grad_norm=0.0632
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+[2026-10-01 14:32:56] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0015 | Epoch 014] timing/rollout_seconds=179.5351 timing/feedback_seconds=5.2643 timing/optimization_seconds=151.3484 timing/cycle_seconds=333.8875
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+
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+[2026-10-01 14:36:00] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0015 | Epoch 015] train/reward_dists_mean=0.8295 train/reward_dists_std=0.0317 train/reward_ssim_mean=0.6718 train/reward_ssim_std=0.0889 train/reward_dists_group_std_mean=0.0089 train/reward_dists_group_std_max=0.0166 train/reward_dists_group_std_min=0.0043 train/reward_dists_group_mean_std=0.0303 train/reward_ssim_group_std_mean=0.0094 train/reward_ssim_group_std_max=0.0200 train/reward_ssim_group_std_min=0.0034 train/reward_ssim_group_mean_std=0.0883 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.5914 train/adv_max=4.0861 train/adv_abs_mean=1.2187
+
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Epoch 15 Training:  38%|███▊      | 36/96 [00:56<01:34,  1.58s/it]
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Epoch 15 Training:  39%|███▊      | 37/96 [00:57<01:33,  1.58s/it]
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Epoch 15 Training:  40%|███▉      | 38/96 [00:59<01:31,  1.58s/it]
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Epoch 15 Training:  41%|████      | 39/96 [01:01<01:29,  1.58s/it]
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Epoch 15 Training:  42%|████▏     | 40/96 [01:02<01:28,  1.58s/it]
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Epoch 15 Training:  43%|████▎     | 41/96 [01:04<01:26,  1.58s/it]
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Epoch 15 Training:  44%|████▍     | 42/96 [01:05<01:25,  1.57s/it]
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Epoch 15 Training:  45%|████▍     | 43/96 [01:07<01:23,  1.57s/it]
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Epoch 15 Training:  46%|████▌     | 44/96 [01:08<01:21,  1.57s/it]
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Epoch 15 Training:  47%|████▋     | 45/96 [01:10<01:19,  1.56s/it]
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Epoch 15 Training:  48%|████▊     | 46/96 [01:12<01:18,  1.57s/it]
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Epoch 15 Training:  49%|████▉     | 47/96 [01:13<01:16,  1.57s/it]
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Epoch 15 Training:  50%|█████     | 48/96 [01:15<01:15,  1.57s/it]
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Epoch 15 Training:  51%|█████     | 49/96 [01:16<01:13,  1.57s/it]
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Epoch 15 Training:  52%|█████▏    | 50/96 [01:18<01:12,  1.57s/it]
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Epoch 15 Training:  53%|█████▎    | 51/96 [01:19<01:10,  1.57s/it]
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Epoch 15 Training:  54%|█████▍    | 52/96 [01:21<01:09,  1.57s/it]
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Epoch 15 Training:  55%|█████▌    | 53/96 [01:23<01:07,  1.57s/it]
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Epoch 15 Training:  56%|█████▋    | 54/96 [01:24<01:05,  1.57s/it]
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Epoch 15 Training:  57%|█████▋    | 55/96 [01:26<01:04,  1.56s/it]
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Epoch 15 Training:  58%|█████▊    | 56/96 [01:27<01:02,  1.57s/it]
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Epoch 15 Training:  59%|█████▉    | 57/96 [01:29<01:01,  1.57s/it]
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Epoch 15 Training:  60%|██████    | 58/96 [01:30<00:59,  1.57s/it]
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Epoch 15 Training:  61%|██████▏   | 59/96 [01:32<00:58,  1.57s/it]
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Epoch 15 Training:  62%|██████▎   | 60/96 [01:34<00:56,  1.57s/it]
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Epoch 15 Training:  64%|██████▎   | 61/96 [01:35<00:55,  1.57s/it]
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Epoch 15 Training:  65%|██████▍   | 62/96 [01:37<00:53,  1.57s/it]
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Epoch 15 Training:  66%|██████▌   | 63/96 [01:38<00:51,  1.57s/it]
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Epoch 15 Training:  67%|██████▋   | 64/96 [01:40<00:50,  1.57s/it]
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Epoch 15 Training:  68%|██████▊   | 65/96 [01:41<00:48,  1.57s/it]
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Epoch 15 Training:  69%|██████▉   | 66/96 [01:43<00:47,  1.57s/it]
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Epoch 15 Training:  70%|██████▉   | 67/96 [01:44<00:45,  1.57s/it]
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Epoch 15 Training:  71%|███████   | 68/96 [01:46<00:43,  1.57s/it]
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Epoch 15 Training:  72%|███████▏  | 69/96 [01:48<00:42,  1.57s/it]
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Epoch 15 Training:  73%|███████▎  | 70/96 [01:49<00:40,  1.57s/it]
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Epoch 15 Training:  74%|███████▍  | 71/96 [01:51<00:39,  1.57s/it]
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Epoch 15 Training:  75%|███████▌  | 72/96 [01:52<00:37,  1.57s/it]
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Epoch 15 Training:  76%|███████▌  | 73/96 [01:54<00:36,  1.57s/it]
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Epoch 15 Training:  77%|███████▋  | 74/96 [01:55<00:34,  1.56s/it]
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Epoch 15 Training:  78%|███████▊  | 75/96 [01:57<00:32,  1.56s/it]
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Epoch 15 Training:  79%|███████▉  | 76/96 [01:59<00:31,  1.56s/it]
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Epoch 15 Training:  80%|████████  | 77/96 [02:00<00:29,  1.56s/it]
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Epoch 15 Training:  81%|████████▏ | 78/96 [02:02<00:28,  1.57s/it]
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Epoch 15 Training:  82%|████████▏ | 79/96 [02:03<00:26,  1.57s/it]
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Epoch 15 Training:  83%|████████▎ | 80/96 [02:05<00:25,  1.57s/it]
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Epoch 15 Training:  84%|████████▍ | 81/96 [02:06<00:23,  1.57s/it]
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Epoch 15 Training:  85%|████████▌ | 82/96 [02:08<00:22,  1.57s/it]
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Epoch 15 Training:  86%|████████▋ | 83/96 [02:10<00:20,  1.57s/it]
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Epoch 15 Training:  88%|████████▊ | 84/96 [02:11<00:18,  1.57s/it]
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Epoch 15 Training:  89%|████████▊ | 85/96 [02:13<00:17,  1.57s/it]
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Epoch 15 Training:  90%|████████▉ | 86/96 [02:14<00:15,  1.57s/it]
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Epoch 15 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s][2026-10-01 14:38:30] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0015 | Epoch 015] train/policy_loss=24.3958 train/unweighted_policy_loss=4.8792 train/loss=24.3958 train/grad_norm=0.0697
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+[2026-10-01 14:38:30] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0016 | Epoch 015] timing/rollout_seconds=179.9744 timing/feedback_seconds=5.6266 timing/optimization_seconds=151.1445 timing/cycle_seconds=333.9946
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+
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+
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+[2026-10-01 14:41:34] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0016 | Epoch 016] train/reward_dists_mean=0.8406 train/reward_dists_std=0.0290 train/reward_ssim_mean=0.6591 train/reward_ssim_std=0.0921 train/reward_dists_group_std_mean=0.0077 train/reward_dists_group_std_max=0.0191 train/reward_dists_group_std_min=0.0039 train/reward_dists_group_mean_std=0.0278 train/reward_ssim_group_std_mean=0.0101 train/reward_ssim_group_std_max=0.0190 train/reward_ssim_group_std_min=0.0037 train/reward_ssim_group_mean_std=0.0915 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.2836 train/adv_max=4.2394 train/adv_abs_mean=1.2523
+
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Epoch 16 Training:  11%|█▏        | 11/96 [00:17<02:13,  1.58s/it]
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Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.94it/s]
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Epoch 16 Training:  12%|█▎        | 12/96 [00:18<02:12,  1.57s/it]
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Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.91it/s]
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Epoch 16 Training:  14%|█▎        | 13/96 [00:20<02:10,  1.57s/it]
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Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 16 Training:  15%|█▍        | 14/96 [00:21<02:09,  1.57s/it]
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Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 16 Training:  16%|█▌        | 15/96 [00:23<02:07,  1.57s/it]
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Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.91it/s]
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Epoch 16 Training:  17%|█▋        | 16/96 [00:25<02:05,  1.57s/it]
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Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.91it/s]
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Epoch 16 Training:  18%|█▊        | 17/96 [00:26<02:04,  1.57s/it]
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Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 16 Training:  19%|█▉        | 18/96 [00:28<02:02,  1.57s/it]
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Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 16 Training:  20%|█▉        | 19/96 [00:29<02:01,  1.57s/it]
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Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.90it/s]
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Epoch 16 Training:  21%|██        | 20/96 [00:31<01:59,  1.58s/it]
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Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.90it/s]
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Epoch 16 Training:  22%|██▏       | 21/96 [00:33<01:58,  1.58s/it]
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Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.94it/s]
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Epoch 16 Training:  23%|██▎       | 22/96 [00:34<01:56,  1.57s/it]
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Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
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Epoch 16 Training:  24%|██▍       | 23/96 [00:36<01:54,  1.57s/it]
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Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.91it/s]
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Epoch 16 Training:  25%|██▌       | 24/96 [00:37<01:53,  1.57s/it]
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Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 16 Training:  26%|██▌       | 25/96 [00:39<01:51,  1.57s/it]
+
Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 16 Training:  27%|██▋       | 26/96 [00:40<01:50,  1.57s/it]
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Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.90it/s]
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Epoch 16 Training:  28%|██▊       | 27/96 [00:42<01:48,  1.57s/it]
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Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.91it/s]
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Epoch 16 Training:  29%|██▉       | 28/96 [00:44<01:47,  1.57s/it]
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Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.91it/s]
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Epoch 16 Training:  30%|███       | 29/96 [00:45<01:45,  1.58s/it]
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Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 16 Training:  31%|███▏      | 30/96 [00:47<01:44,  1.58s/it]
+
Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 16 Training:  32%|███▏      | 31/96 [00:48<01:42,  1.58s/it]
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Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.91it/s]
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Epoch 16 Training:  33%|███▎      | 32/96 [00:50<01:40,  1.58s/it]
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Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.91it/s]
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Epoch 16 Training:  34%|███▍      | 33/96 [00:51<01:39,  1.58s/it]
+
Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.91it/s]
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Epoch 16 Training:  35%|███▌      | 34/96 [00:53<01:37,  1.58s/it]
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Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.91it/s]
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Epoch 16 Training:  36%|███▋      | 35/96 [00:55<01:36,  1.58s/it]
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Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.15it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.91it/s]
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Epoch 16 Training:  38%|███▊      | 36/96 [00:56<01:34,  1.58s/it]
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Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 16 Training:  39%|███▊      | 37/96 [00:58<01:32,  1.57s/it]
+
Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.90it/s]
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Epoch 16 Training:  40%|███▉      | 38/96 [00:59<01:31,  1.58s/it]
+
Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.91it/s]
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Epoch 16 Training:  41%|████      | 39/96 [01:01<01:29,  1.58s/it]
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Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.15it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 16 Training:  42%|████▏     | 40/96 [01:02<01:28,  1.58s/it]
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Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 16 Training:  43%|████▎     | 41/96 [01:04<01:26,  1.58s/it]
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Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.91it/s]
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Epoch 16 Training:  44%|████▍     | 42/96 [01:06<01:25,  1.57s/it]
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Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 16 Training:  45%|████▍     | 43/96 [01:07<01:23,  1.57s/it]
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Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 16 Training:  46%|████▌     | 44/96 [01:09<01:21,  1.57s/it]
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Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 16 Training:  47%|████▋     | 45/96 [01:10<01:20,  1.57s/it]
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Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.91it/s]
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Epoch 16 Training:  48%|████▊     | 46/96 [01:12<01:18,  1.57s/it]
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Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 16 Training:  49%|████▉     | 47/96 [01:13<01:17,  1.57s/it]
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Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.94it/s]
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Epoch 16 Training:  50%|█████     | 48/96 [01:15<01:15,  1.57s/it]
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Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.91it/s]
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Epoch 16 Training:  51%|█████     | 49/96 [01:17<01:13,  1.57s/it]
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Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.90it/s]
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Epoch 16 Training:  52%|█████▏    | 50/96 [01:18<01:12,  1.57s/it]
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Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 16 Training:  53%|█████▎    | 51/96 [01:20<01:10,  1.57s/it]
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Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.91it/s]
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Epoch 16 Training:  54%|█████▍    | 52/96 [01:21<01:09,  1.57s/it]
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Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 16 Training:  55%|█████▌    | 53/96 [01:23<01:07,  1.57s/it]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
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Epoch 16 Training:  56%|█████▋    | 54/96 [01:24<01:05,  1.57s/it]
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Epoch 16 Training:  57%|█████▋    | 55/96 [01:26<01:04,  1.57s/it]
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Epoch 16 Training:  58%|█████▊    | 56/96 [01:28<01:02,  1.57s/it]
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Epoch 16 Training:  59%|█████▉    | 57/96 [01:29<01:01,  1.57s/it]
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Epoch 16 Training:  60%|██████    | 58/96 [01:31<00:59,  1.57s/it]
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Epoch 16 Training:  61%|██████▏   | 59/96 [01:32<00:57,  1.57s/it]
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Epoch 16 Training:  62%|██████▎   | 60/96 [01:34<00:56,  1.57s/it]
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Epoch 16 Training:  64%|██████▎   | 61/96 [01:35<00:54,  1.57s/it]
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Epoch 16 Training:  65%|██████▍   | 62/96 [01:37<00:53,  1.57s/it]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s][2026-10-01 14:44:05] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0016 | Epoch 016] train/policy_loss=24.3420 train/unweighted_policy_loss=4.8684 train/loss=24.3420 train/grad_norm=0.0900
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+[2026-10-01 14:44:05] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0017 | Epoch 016] timing/rollout_seconds=180.0370 timing/feedback_seconds=5.8190 timing/optimization_seconds=151.3332 timing/cycle_seconds=334.3136
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+
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+[2026-10-01 14:47:08] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0017 | Epoch 017] train/reward_dists_mean=0.8301 train/reward_dists_std=0.0352 train/reward_ssim_mean=0.6527 train/reward_ssim_std=0.1034 train/reward_dists_group_std_mean=0.0087 train/reward_dists_group_std_max=0.0166 train/reward_dists_group_std_min=0.0038 train/reward_dists_group_mean_std=0.0339 train/reward_ssim_group_std_mean=0.0096 train/reward_ssim_group_std_max=0.0216 train/reward_ssim_group_std_min=0.0031 train/reward_ssim_group_mean_std=0.1029 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.4665 train/adv_max=4.6386 train/adv_abs_mean=1.2386
+
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Epoch 17 Training:  41%|████      | 39/96 [01:01<01:29,  1.58s/it]
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Epoch 17 Training:  42%|████▏     | 40/96 [01:02<01:28,  1.57s/it]
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Epoch 17 Training:  43%|████▎     | 41/96 [01:04<01:26,  1.57s/it]
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Epoch 17 Training:  44%|████▍     | 42/96 [01:06<01:24,  1.57s/it]
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Epoch 17 Training:  45%|████▍     | 43/96 [01:07<01:23,  1.57s/it]
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Epoch 17 Training:  46%|████▌     | 44/96 [01:09<01:21,  1.58s/it]
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Epoch 17 Training:  47%|████▋     | 45/96 [01:10<01:20,  1.57s/it]
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Epoch 17 Training:  48%|████▊     | 46/96 [01:12<01:18,  1.57s/it]
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Epoch 17 Training:  49%|████▉     | 47/96 [01:13<01:17,  1.58s/it]
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Epoch 17 Training:  50%|█████     | 48/96 [01:15<01:15,  1.57s/it]
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Epoch 17 Training:  51%|█████     | 49/96 [01:17<01:14,  1.57s/it]
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Epoch 17 Training:  52%|█████▏    | 50/96 [01:18<01:12,  1.57s/it]
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Epoch 17 Training:  53%|█████▎    | 51/96 [01:20<01:10,  1.57s/it]
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Epoch 17 Training:  54%|█████▍    | 52/96 [01:21<01:09,  1.57s/it]
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Epoch 17 Training:  55%|█████▌    | 53/96 [01:23<01:07,  1.57s/it]
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Epoch 17 Training:  56%|█████▋    | 54/96 [01:24<01:06,  1.57s/it]
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Epoch 17 Training:  57%|█████▋    | 55/96 [01:26<01:04,  1.57s/it]
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Epoch 17 Training:  58%|█████▊    | 56/96 [01:28<01:03,  1.58s/it]
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Epoch 17 Training:  59%|█████▉    | 57/96 [01:29<01:01,  1.58s/it]
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Epoch 17 Training:  60%|██████    | 58/96 [01:31<00:59,  1.58s/it]
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Epoch 17 Training:  61%|██████▏   | 59/96 [01:32<00:58,  1.57s/it]
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Epoch 17 Training:  62%|██████▎   | 60/96 [01:34<00:56,  1.58s/it]
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Epoch 17 Training:  64%|██████▎   | 61/96 [01:35<00:55,  1.58s/it]
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Epoch 17 Training:  65%|██████▍   | 62/96 [01:37<00:53,  1.58s/it]
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Epoch 17 Training:  66%|██████▌   | 63/96 [01:39<00:52,  1.58s/it]
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Epoch 17 Training:  67%|██████▋   | 64/96 [01:40<00:50,  1.58s/it]
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Epoch 17 Training:  68%|██████▊   | 65/96 [01:42<00:48,  1.58s/it]
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Epoch 17 Training:  69%|██████▉   | 66/96 [01:43<00:47,  1.58s/it]
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Epoch 17 Training:  70%|██████▉   | 67/96 [01:45<00:45,  1.58s/it]
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Epoch 17 Training:  71%|███████   | 68/96 [01:46<00:44,  1.57s/it]
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Epoch 17 Training:  72%|███████▏  | 69/96 [01:48<00:42,  1.58s/it]
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Epoch 17 Training:  73%|███████▎  | 70/96 [01:50<00:40,  1.58s/it]
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Epoch 17 Training:  74%|███████▍  | 71/96 [01:51<00:39,  1.58s/it]
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Epoch 17 Training:  75%|███████▌  | 72/96 [01:53<00:37,  1.58s/it]
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Epoch 17 Training:  76%|███████▌  | 73/96 [01:54<00:36,  1.58s/it]
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Epoch 17 Training:  77%|███████▋  | 74/96 [01:56<00:34,  1.58s/it]
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Epoch 17 Training:  78%|███████▊  | 75/96 [01:58<00:33,  1.58s/it]
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Epoch 17 Training:  79%|███████▉  | 76/96 [01:59<00:31,  1.58s/it]
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Epoch 17 Training:  80%|████████  | 77/96 [02:01<00:29,  1.58s/it]
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Epoch 17 Training:  81%|████████▏ | 78/96 [02:02<00:28,  1.58s/it]
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Epoch 17 Training:  82%|████████▏ | 79/96 [02:04<00:26,  1.58s/it]
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Epoch 17 Training:  83%|████████▎ | 80/96 [02:05<00:25,  1.57s/it]
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Epoch 17 Training:  84%|████████▍ | 81/96 [02:07<00:23,  1.57s/it]
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Epoch 17 Training:  85%|████████▌ | 82/96 [02:09<00:22,  1.58s/it]
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Epoch 17 Training:  86%|████████▋ | 83/96 [02:10<00:20,  1.58s/it]
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Epoch 17 Training:  88%|████████▊ | 84/96 [02:12<00:18,  1.57s/it]
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Epoch 17 Training:  89%|████████▊ | 85/96 [02:13<00:17,  1.58s/it]
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Epoch 17 Training:  90%|████████▉ | 86/96 [02:15<00:15,  1.58s/it]
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Epoch 17 Training:  91%|█████████ | 87/96 [02:16<00:14,  1.58s/it]
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Epoch 17 Training:  92%|█████████▏| 88/96 [02:18<00:12,  1.58s/it]
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Epoch 17 Training:  93%|█████████▎| 89/96 [02:20<00:11,  1.58s/it]
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Epoch 17 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s][2026-10-01 14:49:39] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0017 | Epoch 017] train/policy_loss=25.0231 train/unweighted_policy_loss=5.0046 train/loss=25.0231 train/grad_norm=0.0679
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+[2026-10-01 14:49:39] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0018 | Epoch 017] timing/rollout_seconds=179.4340 timing/feedback_seconds=5.0915 timing/optimization_seconds=151.7957 timing/cycle_seconds=334.1960
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+
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+
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+[2026-10-01 14:52:42] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0018 | Epoch 018] train/reward_dists_mean=0.8369 train/reward_dists_std=0.0336 train/reward_ssim_mean=0.6367 train/reward_ssim_std=0.1015 train/reward_dists_group_std_mean=0.0077 train/reward_dists_group_std_max=0.0183 train/reward_dists_group_std_min=0.0036 train/reward_dists_group_mean_std=0.0326 train/reward_ssim_group_std_mean=0.0099 train/reward_ssim_group_std_max=0.0221 train/reward_ssim_group_std_min=0.0027 train/reward_ssim_group_mean_std=0.1009 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.6391 train/adv_max=4.4817 train/adv_abs_mean=1.2345
+
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Epoch 18 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 18 Training:  16%|█▌        | 15/96 [00:23<02:07,  1.57s/it]
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Epoch 18 Training:  17%|█▋        | 16/96 [00:25<02:05,  1.57s/it]
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Epoch 18 Training:  18%|█▊        | 17/96 [00:26<02:04,  1.57s/it]
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Epoch 18 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.91it/s]
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Epoch 18 Training:  19%|█▉        | 18/96 [00:28<02:02,  1.57s/it]
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Epoch 18 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 18 Training:  20%|█▉        | 19/96 [00:29<02:01,  1.57s/it]
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Epoch 18 Training:  21%|██        | 20/96 [00:31<01:59,  1.57s/it]
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Epoch 18 Training:  22%|██▏       | 21/96 [00:32<01:57,  1.57s/it]
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Epoch 18 Training:  23%|██▎       | 22/96 [00:34<01:56,  1.57s/it]
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Epoch 18 Training:  24%|██▍       | 23/96 [00:36<01:54,  1.57s/it]
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Epoch 18 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 18 Training:  25%|██▌       | 24/96 [00:37<01:53,  1.57s/it]
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Epoch 18 Training:  26%|██▌       | 25/96 [00:39<01:51,  1.57s/it]
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Epoch 18 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.91it/s]
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Epoch 18 Training:  27%|██▋       | 26/96 [00:40<01:50,  1.57s/it]
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Epoch 18 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 18 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 18 Training:  28%|██▊       | 27/96 [00:42<01:48,  1.57s/it]
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Epoch 18 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 18 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.91it/s]
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Epoch 18 Training:  29%|██▉       | 28/96 [00:43<01:46,  1.57s/it]
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Epoch 18 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 18 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 18 Training:  30%|███       | 29/96 [00:45<01:45,  1.57s/it]
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Epoch 18 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 18 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.91it/s]
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Epoch 18 Training:  31%|███▏      | 30/96 [00:47<01:43,  1.57s/it]
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Epoch 18 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 18 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.91it/s]
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Epoch 18 Training:  32%|███▏      | 31/96 [00:48<01:42,  1.57s/it]
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Epoch 18 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 18 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.94it/s]
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Epoch 18 Training:  33%|███▎      | 32/96 [00:50<01:40,  1.57s/it]
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Epoch 18 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 18 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
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Epoch 18 Training:  34%|███▍      | 33/96 [00:51<01:39,  1.57s/it]
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Epoch 18 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 18 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 18 Training:  35%|███▌      | 34/96 [00:53<01:37,  1.57s/it]
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Epoch 18 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 18 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 18 Training:  36%|███▋      | 35/96 [00:54<01:35,  1.57s/it]
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Epoch 18 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 18 Training:  38%|███▊      | 36/96 [00:56<01:34,  1.57s/it]
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Epoch 18 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 18 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.91it/s]
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Epoch 18 Training:  39%|███▊      | 37/96 [00:58<01:32,  1.57s/it]
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Epoch 18 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 18 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 18 Training:  40%|███▉      | 38/96 [00:59<01:31,  1.57s/it]
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Epoch 18 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 18 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.89it/s]
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Epoch 18 Training:  41%|████      | 39/96 [01:01<01:29,  1.58s/it]
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Epoch 18 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 18 Training:  42%|████▏     | 40/96 [01:02<01:28,  1.57s/it]
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Epoch 18 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 18 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.90it/s]
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Epoch 18 Training:  43%|████▎     | 41/96 [01:04<01:26,  1.58s/it]
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Epoch 18 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 18 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 18 Training:  44%|████▍     | 42/96 [01:05<01:25,  1.57s/it]
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Epoch 18 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.97it/s]
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Epoch 18 Training:  45%|████▍     | 43/96 [01:07<01:23,  1.57s/it]
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Epoch 18 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.91it/s]
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Epoch 18 Training:  46%|████▌     | 44/96 [01:09<01:21,  1.57s/it]
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Epoch 18 Training:  47%|████▋     | 45/96 [01:10<01:19,  1.56s/it]
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Epoch 18 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.91it/s]
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Epoch 18 Training:  48%|████▊     | 46/96 [01:12<01:18,  1.57s/it]
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Epoch 18 Training:  49%|████▉     | 47/96 [01:13<01:16,  1.57s/it]
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Epoch 18 Training:  50%|█████     | 48/96 [01:15<01:15,  1.57s/it]
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Epoch 18 Training:  51%|█████     | 49/96 [01:16<01:13,  1.57s/it]
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Epoch 18 Training:  52%|█████▏    | 50/96 [01:18<01:12,  1.57s/it]
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Epoch 18 Training:  53%|█████▎    | 51/96 [01:20<01:10,  1.57s/it]
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Epoch 18 Training:  54%|█████▍    | 52/96 [01:21<01:09,  1.57s/it]
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Epoch 18 Training:  55%|█████▌    | 53/96 [01:23<01:07,  1.57s/it]
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Epoch 18 Training:  56%|█████▋    | 54/96 [01:24<01:06,  1.57s/it]
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Epoch 18 Training:  57%|█████▋    | 55/96 [01:26<01:04,  1.57s/it]
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Epoch 18 Training:  58%|█████▊    | 56/96 [01:27<01:02,  1.57s/it]
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Epoch 18 Training:  59%|█████▉    | 57/96 [01:29<01:00,  1.56s/it]
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Epoch 18 Training:  60%|██████    | 58/96 [01:31<00:59,  1.56s/it]
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Epoch 18 Training:  61%|██████▏   | 59/96 [01:32<00:57,  1.56s/it]
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Epoch 18 Training:  62%|██████▎   | 60/96 [01:34<00:56,  1.56s/it]
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Epoch 18 Training:  64%|██████▎   | 61/96 [01:35<00:54,  1.56s/it]
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Epoch 18 Training:  65%|██████▍   | 62/96 [01:37<00:52,  1.56s/it]
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Epoch 18 Training:  66%|██████▌   | 63/96 [01:38<00:51,  1.56s/it]
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Epoch 18 Training:  67%|██████▋   | 64/96 [01:40<00:49,  1.56s/it]
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Epoch 18 Training:  68%|██████▊   | 65/96 [01:41<00:48,  1.56s/it]
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Epoch 18 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s][2026-10-01 14:55:13] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0018 | Epoch 018] train/policy_loss=24.7541 train/unweighted_policy_loss=4.9508 train/loss=24.7541 train/grad_norm=0.0714
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+[2026-10-01 14:55:13] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0019 | Epoch 018] timing/rollout_seconds=179.8216 timing/feedback_seconds=5.5089 timing/optimization_seconds=151.0213 timing/cycle_seconds=333.7963
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+
Pointwise Rewards: ssim:   0%|          | 0/6 [00:00<?, ?it/s]
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Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:01<00:00,  5.19it/s]
+
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Pointwise Rewards: dists:  50%|█████     | 3/6 [00:00<00:00,  3.17it/s]
Pointwise Rewards: dists:  67%|██████▋   | 4/6 [00:01<00:00,  3.19it/s]
Pointwise Rewards: dists:  83%|████████▎ | 5/6 [00:01<00:00,  3.15it/s]
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Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.17it/s]
+[2026-10-01 14:58:16] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0019 | Epoch 019] train/reward_dists_mean=0.8430 train/reward_dists_std=0.0323 train/reward_ssim_mean=0.6563 train/reward_ssim_std=0.0910 train/reward_dists_group_std_mean=0.0070 train/reward_dists_group_std_max=0.0218 train/reward_dists_group_std_min=0.0039 train/reward_dists_group_mean_std=0.0313 train/reward_ssim_group_std_mean=0.0090 train/reward_ssim_group_std_max=0.0189 train/reward_ssim_group_std_min=0.0040 train/reward_ssim_group_mean_std=0.0905 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.6419 train/adv_max=4.2212 train/adv_abs_mean=1.2386
+
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Epoch 19 Training:   2%|▏         | 2/96 [00:03<02:24,  1.54s/it]
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Epoch 19 Training:   5%|▌         | 5/96 [00:07<02:22,  1.57s/it]
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Epoch 19 Training:   6%|▋         | 6/96 [00:09<02:21,  1.57s/it]
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Epoch 19 Training:   7%|▋         | 7/96 [00:10<02:19,  1.57s/it]
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Epoch 19 Training:   8%|▊         | 8/96 [00:12<02:18,  1.57s/it]
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Epoch 19 Training:   9%|▉         | 9/96 [00:14<02:16,  1.57s/it]
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Epoch 19 Training:  11%|█▏        | 11/96 [00:17<02:13,  1.57s/it]
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Epoch 19 Training:  12%|█▎        | 12/96 [00:18<02:12,  1.57s/it]
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Epoch 19 Training:  14%|█▎        | 13/96 [00:20<02:10,  1.58s/it]
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Epoch 19 Training:  15%|█▍        | 14/96 [00:21<02:09,  1.57s/it]
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Epoch 19 Training:  16%|█▌        | 15/96 [00:23<02:07,  1.57s/it]
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Epoch 19 Training:  17%|█▋        | 16/96 [00:25<02:05,  1.57s/it]
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Epoch 19 Training:  18%|█▊        | 17/96 [00:26<02:04,  1.57s/it]
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Epoch 19 Training:  19%|█▉        | 18/96 [00:28<02:02,  1.57s/it]
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Epoch 19 Training:  21%|██        | 20/96 [00:31<01:59,  1.57s/it]
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Epoch 19 Training:  22%|██▏       | 21/96 [00:32<01:57,  1.57s/it]
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Epoch 19 Training:  23%|██▎       | 22/96 [00:34<01:58,  1.60s/it]
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Epoch 19 Training:  24%|██▍       | 23/96 [00:36<01:56,  1.59s/it]
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Epoch 19 Training:  25%|██▌       | 24/96 [00:37<01:53,  1.58s/it]
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Epoch 19 Training:  26%|██▌       | 25/96 [00:39<01:52,  1.58s/it]
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Epoch 19 Training:  44%|████▍     | 42/96 [01:06<01:25,  1.58s/it]
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Epoch 19 Training:  45%|████▍     | 43/96 [01:07<01:23,  1.58s/it]
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Epoch 19 Training:  46%|████▌     | 44/96 [01:09<01:21,  1.57s/it]
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Epoch 19 Training:  47%|████▋     | 45/96 [01:10<01:20,  1.57s/it]
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Epoch 19 Training:  48%|████▊     | 46/96 [01:12<01:18,  1.58s/it]
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Epoch 19 Training:  49%|████▉     | 47/96 [01:13<01:17,  1.58s/it]
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Epoch 19 Training:  50%|█████     | 48/96 [01:15<01:15,  1.58s/it]
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Epoch 19 Training:  51%|█████     | 49/96 [01:17<01:14,  1.58s/it]
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Epoch 19 Training:  52%|█████▏    | 50/96 [01:18<01:12,  1.58s/it]
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Epoch 19 Training:  53%|█████▎    | 51/96 [01:20<01:10,  1.58s/it]
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Epoch 19 Training:  54%|█████▍    | 52/96 [01:21<01:09,  1.58s/it]
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Epoch 19 Training:  55%|█████▌    | 53/96 [01:23<01:07,  1.57s/it]
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Epoch 19 Training:  56%|█████▋    | 54/96 [01:25<01:06,  1.57s/it]
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Epoch 19 Training:  57%|█████▋    | 55/96 [01:26<01:04,  1.57s/it]
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Epoch 19 Training:  58%|█████▊    | 56/96 [01:28<01:02,  1.57s/it]
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Epoch 19 Training:  59%|█████▉    | 57/96 [01:29<01:01,  1.57s/it]
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Epoch 19 Training:  60%|██████    | 58/96 [01:31<00:59,  1.57s/it]
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Epoch 19 Training:  61%|██████▏   | 59/96 [01:32<00:58,  1.57s/it]
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Epoch 19 Training:  62%|██████▎   | 60/96 [01:34<00:56,  1.57s/it]
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Epoch 19 Training:  64%|██████▎   | 61/96 [01:36<00:55,  1.57s/it]
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Epoch 19 Training:  65%|██████▍   | 62/96 [01:37<00:53,  1.57s/it]
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Epoch 19 Training:  66%|██████▌   | 63/96 [01:39<00:51,  1.57s/it]
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Epoch 19 Training:  67%|██████▋   | 64/96 [01:40<00:50,  1.57s/it]
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Epoch 19 Training:  68%|██████▊   | 65/96 [01:42<00:48,  1.58s/it]
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Epoch 19 Training:  69%|██████▉   | 66/96 [01:43<00:47,  1.58s/it]
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Epoch 19 Training:  70%|██████▉   | 67/96 [01:45<00:45,  1.58s/it]
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Epoch 19 Training:  71%|███████   | 68/96 [01:47<00:44,  1.58s/it]
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Epoch 19 Training:  72%|███████▏  | 69/96 [01:48<00:42,  1.58s/it]
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Epoch 19 Training:  73%|███████▎  | 70/96 [01:50<00:40,  1.58s/it]
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Epoch 19 Training:  74%|███████▍  | 71/96 [01:51<00:39,  1.58s/it]
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Epoch 19 Training:  75%|███████▌  | 72/96 [01:53<00:37,  1.58s/it]
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Epoch 19 Training:  76%|███████▌  | 73/96 [01:54<00:36,  1.58s/it]
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Epoch 19 Training:  77%|███████▋  | 74/96 [01:56<00:34,  1.58s/it]
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Epoch 19 Training:  78%|███████▊  | 75/96 [01:58<00:33,  1.58s/it]
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Epoch 19 Training:  79%|███████▉  | 76/96 [01:59<00:31,  1.58s/it]
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Epoch 19 Training:  80%|████████  | 77/96 [02:01<00:29,  1.58s/it]
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Epoch 19 Training:  81%|████████▏ | 78/96 [02:02<00:28,  1.57s/it]
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Epoch 19 Training:  82%|████████▏ | 79/96 [02:04<00:26,  1.57s/it]
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Epoch 19 Training:  83%|████████▎ | 80/96 [02:05<00:25,  1.57s/it]
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Epoch 19 Training:  84%|████████▍ | 81/96 [02:07<00:23,  1.57s/it]
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Epoch 19 Training:  85%|████████▌ | 82/96 [02:09<00:21,  1.57s/it]
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Epoch 19 Training:  86%|████████▋ | 83/96 [02:10<00:20,  1.57s/it]
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Epoch 19 Training:  88%|████████▊ | 84/96 [02:12<00:18,  1.57s/it]
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Epoch 19 Training:  89%|████████▊ | 85/96 [02:13<00:17,  1.57s/it]
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Epoch 19 Training:  90%|████████▉ | 86/96 [02:15<00:15,  1.57s/it]
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Epoch 19 Training:  91%|█████████ | 87/96 [02:16<00:14,  1.57s/it]
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Epoch 19 Training:  92%|█████████▏| 88/96 [02:18<00:12,  1.58s/it]
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Epoch 19 Training:  93%|█████████▎| 89/96 [02:20<00:11,  1.58s/it]
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Epoch 19 Training:  94%|█████████▍| 90/96 [02:21<00:09,  1.58s/it]
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Epoch 19 Training:  95%|█████████▍| 91/96 [02:23<00:08,  1.63s/it]
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Epoch 19 Training:  96%|█████████▌| 92/96 [02:25<00:06,  1.61s/it]
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+[2026-10-01 15:00:47] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0020 | Epoch 019] timing/rollout_seconds=179.6604 timing/feedback_seconds=5.6034 timing/optimization_seconds=152.0501 timing/cycle_seconds=334.7690
+[2026-10-01 15:00:48] [Rank 0] [INFO] [flow_factory.models.abc]: Saving LoRA weights for transformer role=base to /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/runs/klein4b_nft_flir_full_ssim_dists/saves/klein4b_nft_flir_full_ssim_dists/checkpoints/checkpoint-20
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+[2026-10-01 15:01:40] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0020 | Epoch 020] eval/flir_rl_full/reward_dists_mean=0.8458 eval/flir_rl_full/reward_dists_std=0.0366 eval/flir_rl_full/reward_psnr_mean=20.0350 eval/flir_rl_full/reward_psnr_std=3.0098 eval/flir_rl_full/reward_ssim_mean=0.6260 eval/flir_rl_full/reward_ssim_std=0.0780
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+[2026-10-01 15:04:44] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0020 | Epoch 020] train/reward_dists_mean=0.8371 train/reward_dists_std=0.0345 train/reward_ssim_mean=0.6453 train/reward_ssim_std=0.0979 train/reward_dists_group_std_mean=0.0069 train/reward_dists_group_std_max=0.0240 train/reward_dists_group_std_min=0.0032 train/reward_dists_group_mean_std=0.0337 train/reward_ssim_group_std_mean=0.0094 train/reward_ssim_group_std_max=0.0224 train/reward_ssim_group_std_min=0.0035 train/reward_ssim_group_mean_std=0.0973 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.0916 train/adv_max=5.0274 train/adv_abs_mean=1.2152
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.98it/s]
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Epoch 20 Training:  11%|█▏        | 11/96 [00:16<02:11,  1.55s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.15it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.98it/s]
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Epoch 20 Training:  12%|█▎        | 12/96 [00:18<02:09,  1.55s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.97it/s]
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Epoch 20 Training:  14%|█▎        | 13/96 [00:20<02:08,  1.55s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.97it/s]
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Epoch 20 Training:  15%|█▍        | 14/96 [00:21<02:06,  1.55s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 20 Training:  16%|█▌        | 15/96 [00:23<02:05,  1.55s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.98it/s]
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Epoch 20 Training:  17%|█▋        | 16/96 [00:24<02:03,  1.55s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.97it/s]
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Epoch 20 Training:  18%|█▊        | 17/96 [00:26<02:02,  1.55s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.98it/s]
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Epoch 20 Training:  19%|█▉        | 18/96 [00:27<02:00,  1.55s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.97it/s]
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Epoch 20 Training:  20%|█▉        | 19/96 [00:29<01:59,  1.55s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.97it/s]
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Epoch 20 Training:  21%|██        | 20/96 [00:30<01:57,  1.55s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 20 Training:  22%|██▏       | 21/96 [00:32<01:56,  1.55s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.98it/s]
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Epoch 20 Training:  23%|██▎       | 22/96 [00:33<01:54,  1.55s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.96it/s]
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Epoch 20 Training:  24%|██▍       | 23/96 [00:35<01:52,  1.55s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.96it/s]
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Epoch 20 Training:  25%|██▌       | 24/96 [00:37<01:51,  1.55s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.96it/s]
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Epoch 20 Training:  26%|██▌       | 25/96 [00:38<01:49,  1.55s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.96it/s]
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Epoch 20 Training:  27%|██▋       | 26/96 [00:40<01:48,  1.55s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.97it/s]
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Epoch 20 Training:  28%|██▊       | 27/96 [00:41<01:46,  1.55s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.98it/s]
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Epoch 20 Training:  29%|██▉       | 28/96 [00:43<01:45,  1.55s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.98it/s]
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Epoch 20 Training:  30%|███       | 29/96 [00:44<01:43,  1.55s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.98it/s]
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Epoch 20 Training:  31%|███▏      | 30/96 [00:46<01:42,  1.55s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.97it/s]
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Epoch 20 Training:  32%|███▏      | 31/96 [00:47<01:40,  1.55s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.98it/s]
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Epoch 20 Training:  33%|███▎      | 32/96 [00:49<01:38,  1.54s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.97it/s]
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Epoch 20 Training:  34%|███▍      | 33/96 [00:50<01:37,  1.54s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.98it/s]
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Epoch 20 Training:  35%|███▌      | 34/96 [00:52<01:35,  1.54s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.98it/s]
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Epoch 20 Training:  36%|███▋      | 35/96 [00:54<01:34,  1.54s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.98it/s]
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Epoch 20 Training:  38%|███▊      | 36/96 [00:55<01:32,  1.54s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.98it/s]
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Epoch 20 Training:  39%|███▊      | 37/96 [00:57<01:30,  1.54s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.97it/s]
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Epoch 20 Training:  40%|███▉      | 38/96 [00:58<01:29,  1.54s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.97it/s]
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Epoch 20 Training:  41%|████      | 39/96 [01:00<01:28,  1.54s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.97it/s]
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Epoch 20 Training:  42%|████▏     | 40/96 [01:01<01:26,  1.54s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.96it/s]
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Epoch 20 Training:  43%|████▎     | 41/96 [01:03<01:25,  1.55s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.97it/s]
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Epoch 20 Training:  44%|████▍     | 42/96 [01:04<01:23,  1.55s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Training:  45%|████▍     | 43/96 [01:06<01:21,  1.55s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Training:  46%|████▌     | 44/96 [01:07<01:20,  1.55s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Training:  47%|████▋     | 45/96 [01:09<01:18,  1.55s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Training:  48%|████▊     | 46/96 [01:11<01:17,  1.55s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Training:  49%|████▉     | 47/96 [01:12<01:15,  1.55s/it]
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Epoch 20 Training:  50%|█████     | 48/96 [01:14<01:14,  1.55s/it]
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Epoch 20 Training:  51%|█████     | 49/96 [01:15<01:12,  1.55s/it]
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Epoch 20 Training:  52%|█████▏    | 50/96 [01:17<01:11,  1.55s/it]
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Epoch 20 Training:  53%|█████▎    | 51/96 [01:18<01:09,  1.55s/it]
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Epoch 20 Training:  54%|█████▍    | 52/96 [01:20<01:08,  1.55s/it]
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Epoch 20 Training:  55%|█████▌    | 53/96 [01:21<01:06,  1.55s/it]
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Epoch 20 Training:  56%|█████▋    | 54/96 [01:23<01:05,  1.55s/it]
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Epoch 20 Training:  57%|█████▋    | 55/96 [01:25<01:03,  1.55s/it]
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Epoch 20 Training:  58%|█████▊    | 56/96 [01:26<01:01,  1.55s/it]
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Epoch 20 Training:  59%|█████▉    | 57/96 [01:28<01:00,  1.55s/it]
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Epoch 20 Training:  60%|██████    | 58/96 [01:29<00:58,  1.55s/it]
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Epoch 20 Training:  61%|██████▏   | 59/96 [01:31<00:57,  1.55s/it]
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Epoch 20 Training:  62%|██████▎   | 60/96 [01:32<00:55,  1.55s/it]
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Epoch 20 Training:  64%|██████▎   | 61/96 [01:34<00:54,  1.55s/it]
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Epoch 20 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s][2026-10-01 15:07:13] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0020 | Epoch 020] train/policy_loss=24.1021 train/unweighted_policy_loss=4.8204 train/loss=24.1021 train/grad_norm=0.0701
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+[2026-10-01 15:07:13] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0021 | Epoch 020] timing/rollout_seconds=179.6464 timing/feedback_seconds=5.6108 timing/optimization_seconds=149.6810 timing/cycle_seconds=332.2824
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+
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+
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Pointwise Rewards: dists:  50%|█████     | 3/6 [00:00<00:00,  3.18it/s]
Pointwise Rewards: dists:  67%|██████▋   | 4/6 [00:01<00:00,  3.16it/s]
Pointwise Rewards: dists:  83%|████████▎ | 5/6 [00:01<00:00,  3.17it/s]
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Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.20it/s]
+[2026-10-01 15:10:16] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0021 | Epoch 021] train/reward_dists_mean=0.8331 train/reward_dists_std=0.0298 train/reward_ssim_mean=0.6375 train/reward_ssim_std=0.1076 train/reward_dists_group_std_mean=0.0073 train/reward_dists_group_std_max=0.0155 train/reward_dists_group_std_min=0.0036 train/reward_dists_group_mean_std=0.0288 train/reward_ssim_group_std_mean=0.0089 train/reward_ssim_group_std_max=0.0166 train/reward_ssim_group_std_min=0.0030 train/reward_ssim_group_mean_std=0.1072 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.3678 train/adv_max=4.0204 train/adv_abs_mean=1.2643
+
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Epoch 21 Training:  11%|█▏        | 11/96 [00:17<02:12,  1.56s/it]
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Epoch 21 Training:  12%|█▎        | 12/96 [00:18<02:10,  1.55s/it]
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Epoch 21 Training:  14%|█▎        | 13/96 [00:20<02:08,  1.55s/it]
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Epoch 21 Training:  15%|█▍        | 14/96 [00:21<02:06,  1.55s/it]
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Epoch 21 Training:  18%|█▊        | 17/96 [00:26<02:02,  1.55s/it]
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Epoch 21 Training:  21%|██        | 20/96 [00:31<01:57,  1.54s/it]
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Epoch 21 Training:  22%|██▏       | 21/96 [00:32<01:55,  1.54s/it]
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Epoch 21 Training:  23%|██▎       | 22/96 [00:34<01:54,  1.54s/it]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.97it/s]
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Epoch 21 Training:  40%|███▉      | 38/96 [00:58<01:29,  1.55s/it]
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Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 21 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.96it/s]
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Epoch 21 Training:  41%|████      | 39/96 [01:00<01:28,  1.55s/it]
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Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 21 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.96it/s]
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Epoch 21 Training:  42%|████▏     | 40/96 [01:02<01:26,  1.55s/it]
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Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 21 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.97it/s]
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Epoch 21 Training:  43%|████▎     | 41/96 [01:03<01:25,  1.55s/it]
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Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 21 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.96it/s]
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Epoch 21 Training:  44%|████▍     | 42/96 [01:05<01:23,  1.55s/it]
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Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.97it/s]
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Epoch 21 Training:  45%|████▍     | 43/96 [01:06<01:22,  1.55s/it]
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Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.96it/s]
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Epoch 21 Training:  46%|████▌     | 44/96 [01:08<01:20,  1.55s/it]
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Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.96it/s]
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Epoch 21 Training:  47%|████▋     | 45/96 [01:09<01:19,  1.55s/it]
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Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.96it/s]
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Epoch 21 Training:  48%|████▊     | 46/96 [01:11<01:17,  1.55s/it]
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Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 21 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.96it/s]
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Epoch 21 Training:  49%|████▉     | 47/96 [01:12<01:16,  1.55s/it]
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Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 21 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.96it/s]
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Epoch 21 Training:  50%|█████     | 48/96 [01:14<01:14,  1.55s/it]
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Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 21 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.97it/s]
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Epoch 21 Training:  51%|█████     | 49/96 [01:15<01:12,  1.55s/it]
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Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 21 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.97it/s]
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Epoch 21 Training:  52%|█████▏    | 50/96 [01:17<01:11,  1.55s/it]
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Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 21 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.97it/s]
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Epoch 21 Training:  53%|█████▎    | 51/96 [01:19<01:09,  1.55s/it]
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Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 21 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.96it/s]
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Epoch 21 Training:  54%|█████▍    | 52/96 [01:20<01:08,  1.55s/it]
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Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 21 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.96it/s]
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Epoch 21 Training:  55%|█████▌    | 53/96 [01:22<01:06,  1.55s/it]
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Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 21 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.94it/s]
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Epoch 21 Training:  56%|█████▋    | 54/96 [01:23<01:05,  1.55s/it]
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Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 21 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.97it/s]
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Epoch 21 Training:  57%|█████▋    | 55/96 [01:25<01:03,  1.55s/it]
+
Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 21 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.96it/s]
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Epoch 21 Training:  58%|█████▊    | 56/96 [01:26<01:02,  1.55s/it]
+
Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 21 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.96it/s]
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Epoch 21 Training:  59%|█████▉    | 57/96 [01:28<01:00,  1.55s/it]
+
Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 21 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.97it/s]
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Epoch 21 Training:  60%|██████    | 58/96 [01:29<00:58,  1.55s/it]
+
Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 21 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.97it/s]
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Epoch 21 Training:  61%|██████▏   | 59/96 [01:31<00:57,  1.55s/it]
+
Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 21 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.97it/s]
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Epoch 21 Training:  62%|██████▎   | 60/96 [01:33<00:55,  1.55s/it]
+
Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 21 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.97it/s]
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Epoch 21 Training:  64%|██████▎   | 61/96 [01:34<00:54,  1.55s/it]
+
Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 21 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.96it/s]
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Epoch 21 Training:  65%|██████▍   | 62/96 [01:36<00:52,  1.55s/it]
+
Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 21 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.97it/s]
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Epoch 21 Training:  66%|██████▌   | 63/96 [01:37<00:51,  1.55s/it]
+
Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 21 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.96it/s]
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Epoch 21 Training:  67%|██████▋   | 64/96 [01:39<00:49,  1.55s/it]
+
Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 21 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.97it/s]
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Epoch 21 Training:  68%|██████▊   | 65/96 [01:40<00:48,  1.55s/it]
+
Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 21 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.96it/s]
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Epoch 21 Training:  69%|██████▉   | 66/96 [01:42<00:46,  1.55s/it]
+
Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 21 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.97it/s]
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Epoch 21 Training:  70%|██████▉   | 67/96 [01:43<00:44,  1.55s/it]
+
Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 21 Training:  71%|███████   | 68/96 [01:45<00:43,  1.55s/it]
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Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 21 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.96it/s]
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Epoch 21 Training:  72%|███████▏  | 69/96 [01:46<00:41,  1.55s/it]
+
Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.96it/s]
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Epoch 21 Training:  73%|███████▎  | 70/96 [01:48<00:40,  1.55s/it]
+
Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.96it/s]
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Epoch 21 Training:  74%|███████▍  | 71/96 [01:50<00:38,  1.55s/it]
+
Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
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Epoch 21 Training:  75%|███████▌  | 72/96 [01:51<00:37,  1.55s/it]
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Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.96it/s]
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Epoch 21 Training:  76%|███████▌  | 73/96 [01:53<00:35,  1.55s/it]
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Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.97it/s]
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Epoch 21 Training:  77%|███████▋  | 74/96 [01:54<00:34,  1.55s/it]
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Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.96it/s]
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Epoch 21 Training:  78%|███████▊  | 75/96 [01:56<00:32,  1.55s/it]
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Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.97it/s]
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Epoch 21 Training:  79%|███████▉  | 76/96 [01:57<00:31,  1.55s/it]
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Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.96it/s]
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Epoch 21 Training:  80%|████████  | 77/96 [01:59<00:29,  1.55s/it]
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Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.97it/s]
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Epoch 21 Training:  81%|████████▏ | 78/96 [02:00<00:27,  1.55s/it]
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Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.96it/s]
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Epoch 21 Training:  82%|████████▏ | 79/96 [02:02<00:26,  1.55s/it]
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Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.97it/s]
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Epoch 21 Training:  83%|████████▎ | 80/96 [02:04<00:24,  1.55s/it]
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Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.96it/s]
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Epoch 21 Training:  84%|████████▍ | 81/96 [02:05<00:23,  1.55s/it]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.96it/s]
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Epoch 21 Training:  85%|████████▌ | 82/96 [02:07<00:21,  1.55s/it]
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Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.97it/s]
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Epoch 21 Training:  86%|████████▋ | 83/96 [02:08<00:20,  1.55s/it]
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Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.96it/s]
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Epoch 21 Training:  88%|████████▊ | 84/96 [02:10<00:18,  1.55s/it]
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Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.96it/s]
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Epoch 21 Training:  89%|████████▊ | 85/96 [02:11<00:17,  1.55s/it]
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Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 21 Training:  90%|████████▉ | 86/96 [02:13<00:15,  1.55s/it]
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Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 21 Training:  91%|█████████ | 87/96 [02:14<00:13,  1.55s/it]
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Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 21 Training:  92%|█████████▏| 88/96 [02:16<00:12,  1.55s/it]
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Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 21 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s][2026-10-01 15:12:45] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0021 | Epoch 021] train/policy_loss=24.8797 train/unweighted_policy_loss=4.9759 train/loss=24.8797 train/grad_norm=0.0601
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+[2026-10-01 15:12:45] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0022 | Epoch 021] timing/rollout_seconds=179.4231 timing/feedback_seconds=5.1664 timing/optimization_seconds=149.6910 timing/cycle_seconds=331.8412
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+
Pointwise Rewards: ssim:   0%|          | 0/6 [00:00<?, ?it/s]
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+
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+[2026-10-01 15:15:48] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0022 | Epoch 022] train/reward_dists_mean=0.8373 train/reward_dists_std=0.0322 train/reward_ssim_mean=0.6388 train/reward_ssim_std=0.0948 train/reward_dists_group_std_mean=0.0071 train/reward_dists_group_std_max=0.0207 train/reward_dists_group_std_min=0.0031 train/reward_dists_group_mean_std=0.0312 train/reward_ssim_group_std_mean=0.0075 train/reward_ssim_group_std_max=0.0135 train/reward_ssim_group_std_min=0.0030 train/reward_ssim_group_mean_std=0.0944 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.1394 train/adv_max=4.1845 train/adv_abs_mean=1.2317
+
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Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 22 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
+
                                                                
Epoch 22 Training:  15%|█▍        | 14/96 [00:21<02:08,  1.57s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 22 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.90it/s]
+
                                                                
Epoch 22 Training:  16%|█▌        | 15/96 [00:23<02:07,  1.57s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 22 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
+
                                                                
Epoch 22 Training:  17%|█▋        | 16/96 [00:25<02:05,  1.57s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 22 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.89it/s]
+
                                                                
Epoch 22 Training:  18%|█▊        | 17/96 [00:26<02:04,  1.58s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 22 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.91it/s]
+
                                                                
Epoch 22 Training:  19%|█▉        | 18/96 [00:28<02:02,  1.58s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 22 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.90it/s]
+
                                                                
Epoch 22 Training:  20%|█▉        | 19/96 [00:29<02:01,  1.58s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 22 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
+
                                                                
Epoch 22 Training:  21%|██        | 20/96 [00:31<01:59,  1.57s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 22 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
+
                                                                
Epoch 22 Training:  22%|██▏       | 21/96 [00:32<01:58,  1.57s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.15it/s]
+
Epoch 22 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
+
                                                                
Epoch 22 Training:  23%|██▎       | 22/96 [00:34<01:56,  1.57s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 22 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.94it/s]
+
                                                                
Epoch 22 Training:  24%|██▍       | 23/96 [00:36<01:54,  1.57s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 22 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.91it/s]
+
                                                                
Epoch 22 Training:  25%|██▌       | 24/96 [00:37<01:53,  1.57s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.15it/s]
+
Epoch 22 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.91it/s]
+
                                                                
Epoch 22 Training:  26%|██▌       | 25/96 [00:39<01:51,  1.57s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 22 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.90it/s]
+
                                                                
Epoch 22 Training:  27%|██▋       | 26/96 [00:40<01:50,  1.57s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 22 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.90it/s]
+
                                                                
Epoch 22 Training:  28%|██▊       | 27/96 [00:42<01:48,  1.57s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 22 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.95it/s]
+
                                                                
Epoch 22 Training:  29%|██▉       | 28/96 [00:43<01:46,  1.57s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 22 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.88it/s]
+
                                                                
Epoch 22 Training:  30%|███       | 29/96 [00:45<01:45,  1.58s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 22 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
+
                                                                
Epoch 22 Training:  31%|███▏      | 30/96 [00:47<01:43,  1.58s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 22 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.90it/s]
+
                                                                
Epoch 22 Training:  32%|███▏      | 31/96 [00:48<01:42,  1.58s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 22 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.90it/s]
+
                                                                
Epoch 22 Training:  33%|███▎      | 32/96 [00:50<01:43,  1.61s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 22 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
+
                                                                
Epoch 22 Training:  34%|███▍      | 33/96 [00:51<01:40,  1.60s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 22 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
+
                                                                
Epoch 22 Training:  35%|███▌      | 34/96 [00:53<01:38,  1.59s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 22 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.94it/s]
+
                                                                
Epoch 22 Training:  36%|███▋      | 35/96 [00:55<01:36,  1.58s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 22 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
+
                                                                
Epoch 22 Training:  38%|███▊      | 36/96 [00:56<01:34,  1.58s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 22 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.91it/s]
+
                                                                
Epoch 22 Training:  39%|███▊      | 37/96 [00:58<01:33,  1.58s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 22 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
+
                                                                
Epoch 22 Training:  40%|███▉      | 38/96 [00:59<01:31,  1.57s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 22 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.95it/s]
+
                                                                
Epoch 22 Training:  41%|████      | 39/96 [01:01<01:29,  1.57s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.15it/s]
+
Epoch 22 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
+
                                                                
Epoch 22 Training:  42%|████▏     | 40/96 [01:02<01:27,  1.57s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 22 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
+
                                                                
Epoch 22 Training:  43%|████▎     | 41/96 [01:04<01:26,  1.57s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 22 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
+
                                                                
Epoch 22 Training:  44%|████▍     | 42/96 [01:06<01:24,  1.57s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 22 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.91it/s]
+
                                                                
Epoch 22 Training:  45%|████▍     | 43/96 [01:07<01:23,  1.57s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 22 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.90it/s]
+
                                                                
Epoch 22 Training:  46%|████▌     | 44/96 [01:09<01:21,  1.57s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 22 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.91it/s]
+
                                                                
Epoch 22 Training:  47%|████▋     | 45/96 [01:10<01:20,  1.57s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 22 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
+
                                                                
Epoch 22 Training:  48%|████▊     | 46/96 [01:12<01:18,  1.57s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 22 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
+
                                                                
Epoch 22 Training:  49%|████▉     | 47/96 [01:13<01:16,  1.57s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
+
Epoch 22 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.91it/s]
+
                                                                
Epoch 22 Training:  50%|█████     | 48/96 [01:15<01:15,  1.57s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 22 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
+
                                                                
Epoch 22 Training:  51%|█████     | 49/96 [01:17<01:13,  1.57s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 22 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
+
                                                                
Epoch 22 Training:  52%|█████▏    | 50/96 [01:18<01:12,  1.57s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 22 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
+
                                                                
Epoch 22 Training:  53%|█████▎    | 51/96 [01:20<01:10,  1.57s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
+
Epoch 22 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
+
                                                                
Epoch 22 Training:  54%|█████▍    | 52/96 [01:21<01:09,  1.57s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 22 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
+
                                                                
Epoch 22 Training:  55%|█████▌    | 53/96 [01:23<01:07,  1.57s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 22 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.91it/s]
+
                                                                
Epoch 22 Training:  56%|█████▋    | 54/96 [01:24<01:05,  1.57s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 22 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.91it/s]
+
                                                                
Epoch 22 Training:  57%|█████▋    | 55/96 [01:26<01:04,  1.57s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 22 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.90it/s]
+
                                                                
Epoch 22 Training:  58%|█████▊    | 56/96 [01:28<01:02,  1.57s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 22 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.94it/s]
+
                                                                
Epoch 22 Training:  59%|█████▉    | 57/96 [01:29<01:01,  1.57s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 22 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 22 Training:  60%|██████    | 58/96 [01:31<01:01,  1.63s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 22 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
+
                                                                
Epoch 22 Training:  61%|██████▏   | 59/96 [01:32<00:59,  1.61s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 22 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.91it/s]
+
                                                                
Epoch 22 Training:  62%|██████▎   | 60/96 [01:34<00:57,  1.60s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 22 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
+
                                                                
Epoch 22 Training:  64%|██████▎   | 61/96 [01:36<00:55,  1.59s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 22 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
+
                                                                
Epoch 22 Training:  65%|██████▍   | 62/96 [01:37<00:53,  1.58s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 22 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 22 Training:  66%|██████▌   | 63/96 [01:39<00:54,  1.67s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 22 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
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Epoch 22 Training:  67%|██████▋   | 64/96 [01:41<00:52,  1.64s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 22 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
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Epoch 22 Training:  72%|███████▏  | 69/96 [01:48<00:42,  1.58s/it]
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Epoch 22 Training:  82%|████████▏ | 79/96 [02:04<00:26,  1.57s/it]
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Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s][2026-10-01 15:18:19] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0022 | Epoch 022] train/policy_loss=24.8048 train/unweighted_policy_loss=4.9610 train/loss=24.8048 train/grad_norm=0.0715
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+[2026-10-01 15:18:19] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0023 | Epoch 022] timing/rollout_seconds=179.3924 timing/feedback_seconds=5.5759 timing/optimization_seconds=151.9211 timing/cycle_seconds=334.4302
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+
Pointwise Rewards: ssim:   0%|          | 0/6 [00:00<?, ?it/s]
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Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:01<00:00,  5.47it/s]
+
Pointwise Rewards: dists:   0%|          | 0/6 [00:00<?, ?it/s]
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Pointwise Rewards: dists:  33%|███▎      | 2/6 [00:00<00:01,  3.27it/s]
Pointwise Rewards: dists:  50%|█████     | 3/6 [00:00<00:00,  3.21it/s]
Pointwise Rewards: dists:  67%|██████▋   | 4/6 [00:01<00:00,  3.25it/s]
Pointwise Rewards: dists:  83%|████████▎ | 5/6 [00:01<00:00,  3.23it/s]
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Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.23it/s]
+[2026-10-01 15:21:22] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0023 | Epoch 023] train/reward_dists_mean=0.8334 train/reward_dists_std=0.0309 train/reward_ssim_mean=0.6579 train/reward_ssim_std=0.0969 train/reward_dists_group_std_mean=0.0072 train/reward_dists_group_std_max=0.0147 train/reward_dists_group_std_min=0.0037 train/reward_dists_group_mean_std=0.0300 train/reward_ssim_group_std_mean=0.0083 train/reward_ssim_group_std_max=0.0184 train/reward_ssim_group_std_min=0.0024 train/reward_ssim_group_mean_std=0.0965 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.5592 train/adv_max=4.3654 train/adv_abs_mean=1.2325
+
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Epoch 23 Training:   2%|▏         | 2/96 [00:03<02:24,  1.53s/it]
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Epoch 23 Training:   3%|▎         | 3/96 [00:04<02:24,  1.55s/it]
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Epoch 23 Training:   4%|▍         | 4/96 [00:06<02:24,  1.57s/it]
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Epoch 23 Training:   5%|▌         | 5/96 [00:07<02:22,  1.56s/it]
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Epoch 23 Training:   6%|▋         | 6/96 [00:09<02:20,  1.56s/it]
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Epoch 23 Training:   7%|▋         | 7/96 [00:10<02:19,  1.57s/it]
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Epoch 23 Training:   8%|▊         | 8/96 [00:12<02:18,  1.57s/it]
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Epoch 23 Training:   9%|▉         | 9/96 [00:14<02:16,  1.57s/it]
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Epoch 23 Training:  10%|█         | 10/96 [00:15<02:15,  1.57s/it]
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Epoch 23 Training:  11%|█▏        | 11/96 [00:17<02:13,  1.57s/it]
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Epoch 23 Training:  12%|█▎        | 12/96 [00:18<02:12,  1.57s/it]
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Epoch 23 Training:  14%|█▎        | 13/96 [00:20<02:10,  1.57s/it]
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Epoch 23 Training:  15%|█▍        | 14/96 [00:21<02:09,  1.57s/it]
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Epoch 23 Training:  16%|█▌        | 15/96 [00:23<02:07,  1.57s/it]
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Epoch 23 Training:  17%|█▋        | 16/96 [00:25<02:05,  1.57s/it]
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Epoch 23 Training:  18%|█▊        | 17/96 [00:26<02:04,  1.57s/it]
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Epoch 23 Training:  19%|█▉        | 18/96 [00:28<02:02,  1.57s/it]
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Epoch 23 Training:  20%|█▉        | 19/96 [00:29<02:01,  1.57s/it]
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Epoch 23 Training:  21%|██        | 20/96 [00:31<01:59,  1.57s/it]
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Epoch 23 Training:  22%|██▏       | 21/96 [00:32<01:58,  1.57s/it]
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Epoch 23 Training:  23%|██▎       | 22/96 [00:34<01:56,  1.57s/it]
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Epoch 23 Training:  24%|██▍       | 23/96 [00:36<01:54,  1.57s/it]
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Epoch 23 Training:  25%|██▌       | 24/96 [00:37<01:53,  1.57s/it]
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Epoch 23 Training:  26%|██▌       | 25/96 [00:39<01:51,  1.57s/it]
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Epoch 23 Training:  27%|██▋       | 26/96 [00:40<01:50,  1.58s/it]
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Epoch 23 Training:  28%|██▊       | 27/96 [00:42<01:48,  1.58s/it]
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Epoch 23 Training:  29%|██▉       | 28/96 [00:43<01:47,  1.58s/it]
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Epoch 23 Training:  30%|███       | 29/96 [00:45<01:45,  1.57s/it]
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Epoch 23 Training:  32%|███▏      | 31/96 [00:48<01:42,  1.57s/it]
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Epoch 23 Training:  33%|███▎      | 32/96 [00:50<01:40,  1.57s/it]
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Epoch 23 Training:  34%|███▍      | 33/96 [00:51<01:39,  1.58s/it]
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Epoch 23 Training:  35%|███▌      | 34/96 [00:53<01:37,  1.58s/it]
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Epoch 23 Training:  36%|███▋      | 35/96 [00:54<01:35,  1.57s/it]
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Epoch 23 Training:  38%|███▊      | 36/96 [00:56<01:34,  1.57s/it]
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Epoch 23 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.91it/s]
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Epoch 23 Training:  43%|████▎     | 41/96 [01:04<01:26,  1.57s/it]
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Epoch 23 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 23 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 23 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 23 Training:  44%|████▍     | 42/96 [01:05<01:24,  1.57s/it]
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Epoch 23 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 23 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
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Epoch 23 Training:  45%|████▍     | 43/96 [01:07<01:23,  1.57s/it]
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Epoch 23 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 23 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
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Epoch 23 Training:  46%|████▌     | 44/96 [01:09<01:21,  1.57s/it]
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Epoch 23 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 23 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
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Epoch 23 Training:  47%|████▋     | 45/96 [01:10<01:19,  1.57s/it]
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Epoch 23 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 23 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 23 Training:  48%|████▊     | 46/96 [01:12<01:18,  1.57s/it]
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Epoch 23 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 23 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 23 Training:  49%|████▉     | 47/96 [01:13<01:16,  1.57s/it]
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Epoch 23 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 23 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 23 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 23 Training:  50%|█████     | 48/96 [01:15<01:15,  1.57s/it]
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Epoch 23 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 23 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 23 Training:  51%|█████     | 49/96 [01:16<01:13,  1.57s/it]
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Epoch 23 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 23 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 23 Training:  52%|█████▏    | 50/96 [01:18<01:12,  1.57s/it]
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Epoch 23 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 23 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 23 Training:  53%|█████▎    | 51/96 [01:20<01:10,  1.57s/it]
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Epoch 23 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 23 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 23 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
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Epoch 23 Training:  54%|█████▍    | 52/96 [01:21<01:08,  1.57s/it]
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Epoch 23 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 23 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 23 Training:  55%|█████▌    | 53/96 [01:23<01:07,  1.57s/it]
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Epoch 23 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 23 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 23 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
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Epoch 23 Training:  56%|█████▋    | 54/96 [01:24<01:05,  1.57s/it]
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Epoch 23 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 23 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
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Epoch 23 Training:  57%|█████▋    | 55/96 [01:26<01:04,  1.57s/it]
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Epoch 23 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 23 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
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Epoch 23 Training:  58%|█████▊    | 56/96 [01:27<01:02,  1.57s/it]
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Epoch 23 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 23 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
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Epoch 23 Training:  59%|█████▉    | 57/96 [01:29<01:01,  1.57s/it]
+
Epoch 23 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 23 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 23 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 23 Training:  60%|██████    | 58/96 [01:31<00:59,  1.57s/it]
+
Epoch 23 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 23 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 23 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 23 Training:  61%|██████▏   | 59/96 [01:32<00:57,  1.57s/it]
+
Epoch 23 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 23 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 23 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
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Epoch 23 Training:  62%|██████▎   | 60/96 [01:34<00:56,  1.57s/it]
+
Epoch 23 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 23 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 23 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.94it/s]
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Epoch 23 Training:  64%|██████▎   | 61/96 [01:35<00:54,  1.57s/it]
+
Epoch 23 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 23 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 23 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.91it/s]
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Epoch 23 Training:  65%|██████▍   | 62/96 [01:37<00:53,  1.57s/it]
+
Epoch 23 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 23 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 23 Training:  66%|██████▌   | 63/96 [01:38<00:51,  1.57s/it]
+
Epoch 23 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 23 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 23 Training:  67%|██████▋   | 64/96 [01:40<00:50,  1.57s/it]
+
Epoch 23 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 23 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 23 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 23 Training:  68%|██████▊   | 65/96 [01:42<00:48,  1.57s/it]
+
Epoch 23 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 23 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 23 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.90it/s]
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Epoch 23 Training:  69%|██████▉   | 66/96 [01:43<00:47,  1.57s/it]
+
Epoch 23 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 23 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 23 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
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Epoch 23 Training:  70%|██████▉   | 67/96 [01:45<00:45,  1.57s/it]
+
Epoch 23 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 23 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 23 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
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Epoch 23 Training:  71%|███████   | 68/96 [01:46<00:43,  1.57s/it]
+
Epoch 23 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 23 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 23 Training:  72%|███████▏  | 69/96 [01:48<00:42,  1.57s/it]
+
Epoch 23 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 23 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 23 Training:  73%|███████▎  | 70/96 [01:49<00:40,  1.57s/it]
+
Epoch 23 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 23 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
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Epoch 23 Training:  74%|███████▍  | 71/96 [01:51<00:39,  1.57s/it]
+
Epoch 23 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 23 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
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Epoch 23 Training:  75%|███████▌  | 72/96 [01:53<00:37,  1.57s/it]
+
Epoch 23 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 23 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
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Epoch 23 Training:  76%|███████▌  | 73/96 [01:54<00:36,  1.57s/it]
+
Epoch 23 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 23 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.94it/s]
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Epoch 23 Training:  77%|███████▋  | 74/96 [01:56<00:34,  1.57s/it]
+
Epoch 23 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 23 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 23 Training:  78%|███████▊  | 75/96 [01:57<00:32,  1.57s/it]
+
Epoch 23 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 23 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 23 Training:  79%|███████▉  | 76/96 [01:59<00:31,  1.57s/it]
+
Epoch 23 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 23 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.15it/s]
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Epoch 23 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 23 Training:  80%|████████  | 77/96 [02:00<00:29,  1.57s/it]
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Epoch 23 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 23 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
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Epoch 23 Training:  81%|████████▏ | 78/96 [02:02<00:28,  1.57s/it]
+
Epoch 23 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 23 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.94it/s]
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Epoch 23 Training:  82%|████████▏ | 79/96 [02:04<00:26,  1.57s/it]
+
Epoch 23 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 23 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
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Epoch 23 Training:  83%|████████▎ | 80/96 [02:05<00:25,  1.57s/it]
+
Epoch 23 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 23 Training:  84%|████████▍ | 81/96 [02:07<00:23,  1.57s/it]
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Epoch 23 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 23 Training:  85%|████████▌ | 82/96 [02:08<00:21,  1.57s/it]
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Epoch 23 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 23 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.91it/s]
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Epoch 23 Training:  86%|████████▋ | 83/96 [02:10<00:20,  1.57s/it]
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Epoch 23 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 23 Training:  88%|████████▊ | 84/96 [02:11<00:18,  1.57s/it]
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Epoch 23 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 23 Training:  89%|████████▊ | 85/96 [02:13<00:17,  1.57s/it]
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Epoch 23 Training:  90%|████████▉ | 86/96 [02:14<00:15,  1.57s/it]
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Epoch 23 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 23 Training:  91%|█████████ | 87/96 [02:16<00:14,  1.57s/it]
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Epoch 23 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 23 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 23 Training:  92%|█████████▏| 88/96 [02:18<00:12,  1.57s/it]
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Epoch 23 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 23 Training:  93%|█████████▎| 89/96 [02:19<00:10,  1.57s/it]
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Epoch 23 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 23 Training:  94%|█████████▍| 90/96 [02:21<00:09,  1.57s/it]
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Epoch 23 Training:  95%|█████████▍| 91/96 [02:22<00:07,  1.57s/it]
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Epoch 23 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s][2026-10-01 15:23:53] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0023 | Epoch 023] train/policy_loss=23.9933 train/unweighted_policy_loss=4.7987 train/loss=23.9933 train/grad_norm=0.0648
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+[2026-10-01 15:23:53] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0024 | Epoch 023] timing/rollout_seconds=179.6058 timing/feedback_seconds=5.1355 timing/optimization_seconds=151.3729 timing/cycle_seconds=333.8512
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+
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+[2026-10-01 15:26:56] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0024 | Epoch 024] train/reward_dists_mean=0.8402 train/reward_dists_std=0.0331 train/reward_ssim_mean=0.6646 train/reward_ssim_std=0.0789 train/reward_dists_group_std_mean=0.0066 train/reward_dists_group_std_max=0.0127 train/reward_dists_group_std_min=0.0036 train/reward_dists_group_mean_std=0.0323 train/reward_ssim_group_std_mean=0.0080 train/reward_ssim_group_std_max=0.0269 train/reward_ssim_group_std_min=0.0028 train/reward_ssim_group_mean_std=0.0784 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.9949 train/adv_max=4.5257 train/adv_abs_mean=1.1982
+
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Epoch 24 Training:  18%|█▊        | 17/96 [00:26<02:02,  1.56s/it]
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Epoch 24 Training:  19%|█▉        | 18/96 [00:27<02:01,  1.55s/it]
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Epoch 24 Training:  20%|█▉        | 19/96 [00:29<01:59,  1.55s/it]
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Epoch 24 Training:  21%|██        | 20/96 [00:31<01:58,  1.56s/it]
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Epoch 24 Training:  22%|██▏       | 21/96 [00:32<01:56,  1.56s/it]
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Epoch 24 Training:  23%|██▎       | 22/96 [00:34<01:55,  1.56s/it]
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Epoch 24 Training:  24%|██▍       | 23/96 [00:35<01:53,  1.56s/it]
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Epoch 24 Training:  25%|██▌       | 24/96 [00:37<01:52,  1.56s/it]
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Epoch 24 Training:  26%|██▌       | 25/96 [00:38<01:50,  1.56s/it]
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Epoch 24 Training:  27%|██▋       | 26/96 [00:40<01:49,  1.56s/it]
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Epoch 24 Training:  28%|██▊       | 27/96 [00:41<01:47,  1.56s/it]
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Epoch 24 Training:  29%|██▉       | 28/96 [00:43<01:45,  1.56s/it]
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Epoch 24 Training:  30%|███       | 29/96 [00:45<01:44,  1.56s/it]
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Epoch 24 Training:  31%|███▏      | 30/96 [00:46<01:42,  1.56s/it]
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Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 24 Training:  32%|███▏      | 31/96 [00:48<01:41,  1.56s/it]
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Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 24 Training:  33%|███▎      | 32/96 [00:49<01:39,  1.56s/it]
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Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 24 Training:  34%|███▍      | 33/96 [00:51<01:38,  1.56s/it]
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Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 24 Training:  35%|███▌      | 34/96 [00:52<01:36,  1.56s/it]
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Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 24 Training:  36%|███▋      | 35/96 [00:54<01:35,  1.56s/it]
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Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 24 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
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Epoch 24 Training:  38%|███▊      | 36/96 [00:55<01:33,  1.56s/it]
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Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 24 Training:  39%|███▊      | 37/96 [00:57<01:32,  1.56s/it]
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Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 24 Training:  40%|███▉      | 38/96 [00:59<01:30,  1.56s/it]
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Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 24 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 24 Training:  41%|████      | 39/96 [01:00<01:29,  1.57s/it]
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Epoch 24 Training:  42%|████▏     | 40/96 [01:02<01:27,  1.57s/it]
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Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 24 Training:  43%|████▎     | 41/96 [01:03<01:26,  1.56s/it]
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Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 24 Training:  44%|████▍     | 42/96 [01:05<01:24,  1.56s/it]
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Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 24 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 24 Training:  45%|████▍     | 43/96 [01:06<01:22,  1.56s/it]
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Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 24 Training:  46%|████▌     | 44/96 [01:08<01:21,  1.57s/it]
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Epoch 24 Training:  47%|████▋     | 45/96 [01:10<01:19,  1.57s/it]
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Epoch 24 Training:  48%|████▊     | 46/96 [01:11<01:18,  1.57s/it]
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Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 24 Training:  49%|████▉     | 47/96 [01:13<01:16,  1.57s/it]
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Epoch 24 Training:  50%|█████     | 48/96 [01:14<01:15,  1.56s/it]
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Epoch 24 Training:  51%|█████     | 49/96 [01:16<01:13,  1.57s/it]
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Epoch 24 Training:  52%|█████▏    | 50/96 [01:17<01:12,  1.57s/it]
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Epoch 24 Training:  53%|█████▎    | 51/96 [01:19<01:10,  1.57s/it]
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Epoch 24 Training:  54%|█████▍    | 52/96 [01:21<01:08,  1.57s/it]
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Epoch 24 Training:  55%|█████▌    | 53/96 [01:22<01:07,  1.57s/it]
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Epoch 24 Training:  56%|█████▋    | 54/96 [01:24<01:05,  1.57s/it]
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Epoch 24 Training:  57%|█████▋    | 55/96 [01:25<01:04,  1.57s/it]
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Epoch 24 Training:  58%|█████▊    | 56/96 [01:27<01:02,  1.56s/it]
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Epoch 24 Training:  59%|█████▉    | 57/96 [01:28<01:01,  1.57s/it]
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Epoch 24 Training:  60%|██████    | 58/96 [01:30<00:59,  1.57s/it]
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Epoch 24 Training:  61%|██████▏   | 59/96 [01:31<00:57,  1.57s/it]
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Epoch 24 Training:  62%|██████▎   | 60/96 [01:33<00:56,  1.56s/it]
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Epoch 24 Training:  64%|██████▎   | 61/96 [01:35<00:54,  1.56s/it]
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Epoch 24 Training:  65%|██████▍   | 62/96 [01:36<00:53,  1.56s/it]
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Epoch 24 Training:  66%|██████▌   | 63/96 [01:38<00:51,  1.56s/it]
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Epoch 24 Training:  67%|██████▋   | 64/96 [01:39<00:50,  1.56s/it]
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Epoch 24 Training:  68%|██████▊   | 65/96 [01:41<00:48,  1.56s/it]
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Epoch 24 Training:  69%|██████▉   | 66/96 [01:42<00:46,  1.56s/it]
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Epoch 24 Training:  70%|██████▉   | 67/96 [01:44<00:45,  1.57s/it]
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Epoch 24 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s][2026-10-01 15:29:26] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0024 | Epoch 024] train/policy_loss=23.9145 train/unweighted_policy_loss=4.7829 train/loss=23.9145 train/grad_norm=0.0733
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+[2026-10-01 15:29:26] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0025 | Epoch 024] timing/rollout_seconds=179.7712 timing/feedback_seconds=5.5017 timing/optimization_seconds=150.5768 timing/cycle_seconds=333.2586
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Epoch 25 Sampling:  96%|█████████▌| 92/96 [02:49<00:07,  1.84s/it]
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Epoch 25 Sampling: 100%|██████████| 96/96 [02:56<00:00,  1.84s/it]
+
Pointwise Rewards: ssim:   0%|          | 0/6 [00:00<?, ?it/s]
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Pointwise Rewards: ssim:  50%|█████     | 3/6 [00:00<00:00,  5.52it/s]
Pointwise Rewards: ssim:  67%|██████▋   | 4/6 [00:00<00:00,  5.55it/s]
Pointwise Rewards: ssim:  83%|████████▎ | 5/6 [00:00<00:00,  5.70it/s]
Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:01<00:00,  5.69it/s]
Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:01<00:00,  5.63it/s]
+
Pointwise Rewards: dists:   0%|          | 0/6 [00:00<?, ?it/s]
Pointwise Rewards: dists:  17%|█▋        | 1/6 [00:00<00:01,  3.30it/s]
Pointwise Rewards: dists:  33%|███▎      | 2/6 [00:00<00:01,  3.31it/s]
Pointwise Rewards: dists:  50%|█████     | 3/6 [00:00<00:00,  3.27it/s]
Pointwise Rewards: dists:  67%|██████▋   | 4/6 [00:01<00:00,  3.26it/s]
Pointwise Rewards: dists:  83%|████████▎ | 5/6 [00:01<00:00,  3.23it/s]
Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.21it/s]
Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.24it/s]
+[2026-10-01 15:32:29] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0025 | Epoch 025] train/reward_dists_mean=0.8373 train/reward_dists_std=0.0244 train/reward_ssim_mean=0.6563 train/reward_ssim_std=0.0951 train/reward_dists_group_std_mean=0.0072 train/reward_dists_group_std_max=0.0142 train/reward_dists_group_std_min=0.0041 train/reward_dists_group_mean_std=0.0232 train/reward_ssim_group_std_mean=0.0081 train/reward_ssim_group_std_max=0.0133 train/reward_ssim_group_std_min=0.0035 train/reward_ssim_group_mean_std=0.0947 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.0832 train/adv_max=4.4061 train/adv_abs_mean=1.2536
+
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Epoch 25 Training:   1%|          | 1/96 [00:01<02:21,  1.49s/it]
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Epoch 25 Training:   2%|▏         | 2/96 [00:03<02:23,  1.53s/it]
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Epoch 25 Training:   3%|▎         | 3/96 [00:04<02:24,  1.55s/it]
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Epoch 25 Training:   4%|▍         | 4/96 [00:06<02:23,  1.56s/it]
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Epoch 25 Training:   5%|▌         | 5/96 [00:07<02:22,  1.56s/it]
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Epoch 25 Training:   6%|▋         | 6/96 [00:09<02:21,  1.57s/it]
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Epoch 25 Training:   7%|▋         | 7/96 [00:10<02:20,  1.57s/it]
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Epoch 25 Training:   8%|▊         | 8/96 [00:12<02:18,  1.58s/it]
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Epoch 25 Training:   9%|▉         | 9/96 [00:14<02:17,  1.58s/it]
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Epoch 25 Training:  10%|█         | 10/96 [00:15<02:15,  1.58s/it]
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Epoch 25 Training:  11%|█▏        | 11/96 [00:17<02:14,  1.58s/it]
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Epoch 25 Training:  12%|█▎        | 12/96 [00:18<02:12,  1.58s/it]
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Epoch 25 Training:  14%|█▎        | 13/96 [00:20<02:10,  1.58s/it]
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Epoch 25 Training:  15%|█▍        | 14/96 [00:21<02:09,  1.58s/it]
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Epoch 25 Training:  16%|█▌        | 15/96 [00:23<02:07,  1.58s/it]
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Epoch 25 Training:  17%|█▋        | 16/96 [00:25<02:06,  1.58s/it]
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Epoch 25 Training:  18%|█▊        | 17/96 [00:26<02:04,  1.58s/it]
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Epoch 25 Training:  19%|█▉        | 18/96 [00:28<02:02,  1.57s/it]
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Epoch 25 Training:  20%|█▉        | 19/96 [00:29<02:01,  1.57s/it]
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Epoch 25 Training:  21%|██        | 20/96 [00:31<01:59,  1.57s/it]
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Epoch 25 Training:  22%|██▏       | 21/96 [00:32<01:58,  1.58s/it]
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Epoch 25 Training:  23%|██▎       | 22/96 [00:34<01:56,  1.57s/it]
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Epoch 25 Training:  24%|██▍       | 23/96 [00:36<01:54,  1.57s/it]
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Epoch 25 Training:  25%|██▌       | 24/96 [00:37<01:53,  1.57s/it]
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Epoch 25 Training:  26%|██▌       | 25/96 [00:39<01:51,  1.58s/it]
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Epoch 25 Training:  27%|██▋       | 26/96 [00:40<01:50,  1.58s/it]
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Epoch 25 Training:  28%|██▊       | 27/96 [00:42<01:48,  1.57s/it]
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Epoch 25 Training:  29%|██▉       | 28/96 [00:43<01:46,  1.57s/it]
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Epoch 25 Training:  30%|███       | 29/96 [00:45<01:45,  1.57s/it]
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Epoch 25 Training:  31%|███▏      | 30/96 [00:47<01:43,  1.57s/it]
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Epoch 25 Training:  32%|███▏      | 31/96 [00:48<01:42,  1.57s/it]
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Epoch 25 Training:  33%|███▎      | 32/96 [00:50<01:40,  1.57s/it]
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Epoch 25 Training:  34%|███▍      | 33/96 [00:51<01:39,  1.58s/it]
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Epoch 25 Training:  35%|███▌      | 34/96 [00:53<01:37,  1.57s/it]
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Epoch 25 Training:  36%|███▋      | 35/96 [00:55<01:35,  1.57s/it]
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Epoch 25 Training:  38%|███▊      | 36/96 [00:56<01:34,  1.57s/it]
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Epoch 25 Training:  39%|███▊      | 37/96 [00:58<01:32,  1.57s/it]
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Epoch 25 Training:  40%|███▉      | 38/96 [00:59<01:31,  1.57s/it]
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Epoch 25 Training:  41%|████      | 39/96 [01:01<01:29,  1.57s/it]
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Epoch 25 Training:  42%|████▏     | 40/96 [01:02<01:27,  1.57s/it]
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Epoch 25 Training:  44%|████▍     | 42/96 [01:06<01:24,  1.57s/it]
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Epoch 25 Training:  45%|████▍     | 43/96 [01:07<01:23,  1.57s/it]
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Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 25 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.91it/s]
+
                                                                
Epoch 25 Training:  46%|████▌     | 44/96 [01:09<01:21,  1.57s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 25 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
+
                                                                
Epoch 25 Training:  47%|████▋     | 45/96 [01:10<01:20,  1.57s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 25 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.91it/s]
+
                                                                
Epoch 25 Training:  48%|████▊     | 46/96 [01:12<01:18,  1.57s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 25 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.91it/s]
+
                                                                
Epoch 25 Training:  49%|████▉     | 47/96 [01:13<01:17,  1.58s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 25 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.91it/s]
+
                                                                
Epoch 25 Training:  50%|█████     | 48/96 [01:15<01:15,  1.57s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 25 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.91it/s]
+
                                                                
Epoch 25 Training:  51%|█████     | 49/96 [01:17<01:14,  1.58s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 25 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.91it/s]
+
                                                                
Epoch 25 Training:  52%|█████▏    | 50/96 [01:18<01:12,  1.58s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 25 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.90it/s]
+
                                                                
Epoch 25 Training:  53%|█████▎    | 51/96 [01:20<01:10,  1.58s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 25 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.89it/s]
+
                                                                
Epoch 25 Training:  54%|█████▍    | 52/96 [01:21<01:09,  1.58s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 25 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.90it/s]
+
                                                                
Epoch 25 Training:  55%|█████▌    | 53/96 [01:23<01:07,  1.58s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 25 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
+
                                                                
Epoch 25 Training:  56%|█████▋    | 54/96 [01:24<01:06,  1.58s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 25 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.90it/s]
+
                                                                
Epoch 25 Training:  57%|█████▋    | 55/96 [01:26<01:04,  1.58s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 25 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.90it/s]
+
                                                                
Epoch 25 Training:  58%|█████▊    | 56/96 [01:28<01:03,  1.58s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 25 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.90it/s]
+
                                                                
Epoch 25 Training:  59%|█████▉    | 57/96 [01:29<01:01,  1.58s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 25 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
+
                                                                
Epoch 25 Training:  60%|██████    | 58/96 [01:31<00:59,  1.57s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 25 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.90it/s]
+
                                                                
Epoch 25 Training:  61%|██████▏   | 59/96 [01:32<00:58,  1.58s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 25 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.91it/s]
+
                                                                
Epoch 25 Training:  62%|██████▎   | 60/96 [01:34<00:56,  1.58s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 25 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.91it/s]
+
                                                                
Epoch 25 Training:  64%|██████▎   | 61/96 [01:35<00:55,  1.58s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 25 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.94it/s]
+
                                                                
Epoch 25 Training:  65%|██████▍   | 62/96 [01:37<00:53,  1.57s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 25 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.94it/s]
+
                                                                
Epoch 25 Training:  66%|██████▌   | 63/96 [01:39<00:51,  1.57s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 25 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
+
                                                                
Epoch 25 Training:  67%|██████▋   | 64/96 [01:40<00:50,  1.57s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 25 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.94it/s]
+
                                                                
Epoch 25 Training:  68%|██████▊   | 65/96 [01:42<00:48,  1.57s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 25 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.91it/s]
+
                                                                
Epoch 25 Training:  69%|██████▉   | 66/96 [01:43<00:47,  1.57s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 25 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.91it/s]
+
                                                                
Epoch 25 Training:  70%|██████▉   | 67/96 [01:45<00:45,  1.57s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 25 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.91it/s]
+
                                                                
Epoch 25 Training:  71%|███████   | 68/96 [01:46<00:44,  1.57s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 25 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.91it/s]
+
                                                                
Epoch 25 Training:  72%|███████▏  | 69/96 [01:48<00:42,  1.57s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 25 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
+
                                                                
Epoch 25 Training:  73%|███████▎  | 70/96 [01:50<00:40,  1.57s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
+
Epoch 25 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.91it/s]
+
                                                                
Epoch 25 Training:  74%|███████▍  | 71/96 [01:51<00:39,  1.57s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
+
Epoch 25 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.91it/s]
+
                                                                
Epoch 25 Training:  75%|███████▌  | 72/96 [01:53<00:37,  1.58s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 25 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
+
                                                                
Epoch 25 Training:  76%|███████▌  | 73/96 [01:54<00:36,  1.57s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 25 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
+
                                                                
Epoch 25 Training:  77%|███████▋  | 74/96 [01:56<00:34,  1.57s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 25 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
+
                                                                
Epoch 25 Training:  78%|███████▊  | 75/96 [01:57<00:33,  1.57s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 25 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.94it/s]
+
                                                                
Epoch 25 Training:  79%|███████▉  | 76/96 [01:59<00:31,  1.57s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 25 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.91it/s]
+
                                                                
Epoch 25 Training:  80%|████████  | 77/96 [02:01<00:29,  1.57s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 25 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.91it/s]
+
                                                                
Epoch 25 Training:  81%|████████▏ | 78/96 [02:02<00:28,  1.57s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 25 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.96it/s]
+
                                                                
Epoch 25 Training:  82%|████████▏ | 79/96 [02:04<00:26,  1.57s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 25 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.94it/s]
+
                                                                
Epoch 25 Training:  83%|████████▎ | 80/96 [02:05<00:25,  1.57s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.15it/s]
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Epoch 25 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.97it/s]
+
                                                                
Epoch 25 Training:  84%|████████▍ | 81/96 [02:07<00:23,  1.57s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 25 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.94it/s]
+
                                                                
Epoch 25 Training:  85%|████████▌ | 82/96 [02:08<00:22,  1.57s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 25 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.96it/s]
+
                                                                
Epoch 25 Training:  86%|████████▋ | 83/96 [02:10<00:20,  1.57s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 25 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.95it/s]
+
                                                                
Epoch 25 Training:  88%|████████▊ | 84/96 [02:12<00:18,  1.57s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.15it/s]
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Epoch 25 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.94it/s]
+
                                                                
Epoch 25 Training:  89%|████████▊ | 85/96 [02:13<00:17,  1.57s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 25 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.94it/s]
+
                                                                
Epoch 25 Training:  90%|████████▉ | 86/96 [02:15<00:15,  1.57s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 25 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.94it/s]
+
                                                                
Epoch 25 Training:  91%|█████████ | 87/96 [02:16<00:14,  1.57s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 25 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.94it/s]
+
                                                                
Epoch 25 Training:  92%|█████████▏| 88/96 [02:18<00:12,  1.57s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.15it/s]
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Epoch 25 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.94it/s]
+
                                                                
Epoch 25 Training:  93%|█████████▎| 89/96 [02:20<00:11,  1.58s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.15it/s]
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Epoch 25 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.94it/s]
+
                                                                
Epoch 25 Training:  94%|█████████▍| 90/96 [02:21<00:09,  1.58s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 25 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.94it/s]
+
                                                                
Epoch 25 Training:  95%|█████████▍| 91/96 [02:23<00:07,  1.58s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.15it/s]
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Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
+
                                                                
Epoch 25 Training:  96%|█████████▌| 92/96 [02:24<00:06,  1.57s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.15it/s]
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Epoch 25 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.94it/s]
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Epoch 25 Training:  97%|█████████▋| 93/96 [02:26<00:04,  1.57s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 25 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.94it/s]
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Epoch 25 Training:  98%|█████████▊| 94/96 [02:27<00:03,  1.57s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s][2026-10-01 15:35:00] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0025 | Epoch 025] train/policy_loss=24.5026 train/unweighted_policy_loss=4.9005 train/loss=24.5026 train/grad_norm=0.0799
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+[2026-10-01 15:35:00] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0026 | Epoch 025] timing/rollout_seconds=179.4840 timing/feedback_seconds=5.3614 timing/optimization_seconds=151.7848 timing/cycle_seconds=334.2650
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Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:01<00:00,  5.63it/s]
+
Pointwise Rewards: dists:   0%|          | 0/6 [00:00<?, ?it/s]
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Pointwise Rewards: dists:  50%|█████     | 3/6 [00:00<00:00,  3.21it/s]
Pointwise Rewards: dists:  67%|██████▋   | 4/6 [00:01<00:00,  3.28it/s]
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Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.27it/s]
+[2026-10-01 15:38:05] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0026 | Epoch 026] train/reward_dists_mean=0.8425 train/reward_dists_std=0.0356 train/reward_ssim_mean=0.6776 train/reward_ssim_std=0.1007 train/reward_dists_group_std_mean=0.0071 train/reward_dists_group_std_max=0.0141 train/reward_dists_group_std_min=0.0033 train/reward_dists_group_mean_std=0.0348 train/reward_ssim_group_std_mean=0.0071 train/reward_ssim_group_std_max=0.0181 train/reward_ssim_group_std_min=0.0020 train/reward_ssim_group_mean_std=0.1004 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.2178 train/adv_max=4.9172 train/adv_abs_mean=1.2038
+
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Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.15it/s]
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Epoch 26 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.94it/s]
+
                                                                
Epoch 26 Training:  21%|██        | 20/96 [00:31<01:59,  1.57s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.15it/s]
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Epoch 26 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.94it/s]
+
                                                                
Epoch 26 Training:  22%|██▏       | 21/96 [00:32<01:57,  1.57s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.15it/s]
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Epoch 26 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.95it/s]
+
                                                                
Epoch 26 Training:  23%|██▎       | 22/96 [00:34<01:56,  1.57s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 26 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
+
                                                                
Epoch 26 Training:  24%|██▍       | 23/96 [00:35<01:54,  1.57s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 26 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.94it/s]
+
                                                                
Epoch 26 Training:  25%|██▌       | 24/96 [00:37<01:53,  1.57s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 26 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
+
                                                                
Epoch 26 Training:  26%|██▌       | 25/96 [00:39<01:51,  1.57s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 26 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.91it/s]
+
                                                                
Epoch 26 Training:  27%|██▋       | 26/96 [00:40<01:50,  1.57s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.15it/s]
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Epoch 26 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
+
                                                                
Epoch 26 Training:  28%|██▊       | 27/96 [00:42<01:48,  1.57s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.15it/s]
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Epoch 26 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.91it/s]
+
                                                                
Epoch 26 Training:  29%|██▉       | 28/96 [00:43<01:46,  1.57s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 26 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
+
                                                                
Epoch 26 Training:  30%|███       | 29/96 [00:45<01:45,  1.57s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 26 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
+
                                                                
Epoch 26 Training:  31%|███▏      | 30/96 [00:46<01:43,  1.57s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 26 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
+
                                                                
Epoch 26 Training:  32%|███▏      | 31/96 [00:48<01:42,  1.57s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 26 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.91it/s]
+
                                                                
Epoch 26 Training:  33%|███▎      | 32/96 [00:50<01:40,  1.57s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 26 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
+
                                                                
Epoch 26 Training:  34%|███▍      | 33/96 [00:51<01:39,  1.57s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 26 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.94it/s]
+
                                                                
Epoch 26 Training:  35%|███▌      | 34/96 [00:53<01:37,  1.57s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.15it/s]
+
Epoch 26 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
+
                                                                
Epoch 26 Training:  36%|███▋      | 35/96 [00:54<01:35,  1.57s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 26 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
+
                                                                
Epoch 26 Training:  38%|███▊      | 36/96 [00:56<01:34,  1.57s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 26 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
+
                                                                
Epoch 26 Training:  39%|███▊      | 37/96 [00:57<01:32,  1.57s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 26 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
+
                                                                
Epoch 26 Training:  40%|███▉      | 38/96 [00:59<01:31,  1.57s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 26 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
+
                                                                
Epoch 26 Training:  41%|████      | 39/96 [01:01<01:29,  1.57s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.15it/s]
+
Epoch 26 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
+
                                                                
Epoch 26 Training:  42%|████▏     | 40/96 [01:02<01:27,  1.57s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.15it/s]
+
Epoch 26 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
+
                                                                
Epoch 26 Training:  43%|████▎     | 41/96 [01:04<01:26,  1.57s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 26 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
+
                                                                
Epoch 26 Training:  44%|████▍     | 42/96 [01:05<01:24,  1.57s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.14it/s]
+
Epoch 26 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
+
                                                                
Epoch 26 Training:  45%|████▍     | 43/96 [01:07<01:23,  1.57s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 26 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.95it/s]
+
                                                                
Epoch 26 Training:  46%|████▌     | 44/96 [01:08<01:21,  1.57s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 26 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
+
                                                                
Epoch 26 Training:  47%|████▋     | 45/96 [01:10<01:19,  1.57s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 26 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
+
                                                                
Epoch 26 Training:  48%|████▊     | 46/96 [01:12<01:18,  1.57s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.15it/s]
+
Epoch 26 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
+
                                                                
Epoch 26 Training:  49%|████▉     | 47/96 [01:13<01:16,  1.57s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 26 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
+
                                                                
Epoch 26 Training:  50%|█████     | 48/96 [01:15<01:15,  1.57s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 26 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
+
                                                                
Epoch 26 Training:  51%|█████     | 49/96 [01:16<01:13,  1.57s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 26 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.94it/s]
+
                                                                
Epoch 26 Training:  52%|█████▏    | 50/96 [01:18<01:12,  1.57s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 26 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
+
                                                                
Epoch 26 Training:  53%|█████▎    | 51/96 [01:19<01:10,  1.57s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 26 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
+
                                                                
Epoch 26 Training:  54%|█████▍    | 52/96 [01:21<01:08,  1.57s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 26 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
+
                                                                
Epoch 26 Training:  55%|█████▌    | 53/96 [01:23<01:07,  1.57s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 26 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
+
                                                                
Epoch 26 Training:  56%|█████▋    | 54/96 [01:24<01:05,  1.57s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 26 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
+
                                                                
Epoch 26 Training:  57%|█████▋    | 55/96 [01:26<01:04,  1.57s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 26 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.94it/s]
+
                                                                
Epoch 26 Training:  58%|█████▊    | 56/96 [01:27<01:02,  1.57s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 26 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
+
                                                                
Epoch 26 Training:  59%|█████▉    | 57/96 [01:29<01:01,  1.57s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 26 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
+
                                                                
Epoch 26 Training:  60%|██████    | 58/96 [01:30<00:59,  1.57s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.15it/s]
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Epoch 26 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
+
                                                                
Epoch 26 Training:  61%|██████▏   | 59/96 [01:32<00:58,  1.57s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.15it/s]
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Epoch 26 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.96it/s]
+
                                                                
Epoch 26 Training:  62%|██████▎   | 60/96 [01:34<00:56,  1.57s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 26 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
+
                                                                
Epoch 26 Training:  64%|██████▎   | 61/96 [01:35<00:54,  1.57s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 26 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
+
                                                                
Epoch 26 Training:  65%|██████▍   | 62/96 [01:37<00:53,  1.57s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 26 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.91it/s]
+
                                                                
Epoch 26 Training:  66%|██████▌   | 63/96 [01:38<00:51,  1.57s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 26 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
+
                                                                
Epoch 26 Training:  67%|██████▋   | 64/96 [01:40<00:50,  1.57s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 26 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
+
                                                                
Epoch 26 Training:  68%|██████▊   | 65/96 [01:41<00:48,  1.57s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 26 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.94it/s]
+
                                                                
Epoch 26 Training:  69%|██████▉   | 66/96 [01:43<00:47,  1.57s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 26 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
+
                                                                
Epoch 26 Training:  70%|██████▉   | 67/96 [01:45<00:45,  1.57s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 26 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
+
                                                                
Epoch 26 Training:  71%|███████   | 68/96 [01:46<00:43,  1.57s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 26 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.94it/s]
+
                                                                
Epoch 26 Training:  72%|███████▏  | 69/96 [01:48<00:42,  1.57s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 26 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.94it/s]
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Epoch 26 Training:  73%|███████▎  | 70/96 [01:49<00:40,  1.57s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 26 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
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Epoch 26 Training:  74%|███████▍  | 71/96 [01:51<00:39,  1.57s/it]
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Epoch 26 Training:  75%|███████▌  | 72/96 [01:52<00:37,  1.57s/it]
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Epoch 26 Training:  76%|███████▌  | 73/96 [01:54<00:36,  1.57s/it]
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Epoch 26 Training:  77%|███████▋  | 74/96 [01:56<00:34,  1.57s/it]
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Epoch 26 Training:  78%|███████▊  | 75/96 [01:57<00:33,  1.57s/it]
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Epoch 26 Training:  79%|███████▉  | 76/96 [01:59<00:31,  1.57s/it]
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Epoch 26 Training:  80%|████████  | 77/96 [02:00<00:29,  1.57s/it]
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Epoch 26 Training:  81%|████████▏ | 78/96 [02:02<00:28,  1.57s/it]
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Epoch 26 Training:  82%|████████▏ | 79/96 [02:03<00:26,  1.57s/it]
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Epoch 26 Training:  83%|████████▎ | 80/96 [02:05<00:25,  1.57s/it]
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Epoch 26 Training:  84%|████████▍ | 81/96 [02:07<00:23,  1.57s/it]
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Epoch 26 Training:  85%|████████▌ | 82/96 [02:08<00:21,  1.57s/it]
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Epoch 26 Training:  86%|████████▋ | 83/96 [02:10<00:20,  1.57s/it]
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Epoch 26 Training:  88%|████████▊ | 84/96 [02:11<00:18,  1.57s/it]
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Epoch 26 Training:  89%|████████▊ | 85/96 [02:13<00:17,  1.57s/it]
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Epoch 26 Training:  90%|████████▉ | 86/96 [02:14<00:15,  1.57s/it]
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Epoch 26 Training:  91%|█████████ | 87/96 [02:16<00:14,  1.57s/it]
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Epoch 26 Training:  92%|█████████▏| 88/96 [02:17<00:12,  1.57s/it]
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Epoch 26 Training:  93%|█████████▎| 89/96 [02:19<00:10,  1.57s/it]
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Epoch 26 Training:  94%|█████████▍| 90/96 [02:21<00:09,  1.57s/it]
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Epoch 26 Training:  95%|█████████▍| 91/96 [02:22<00:07,  1.57s/it]
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Epoch 26 Training:  97%|█████████▋| 93/96 [02:25<00:04,  1.57s/it]
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Epoch 26 Training:  98%|█████████▊| 94/96 [02:27<00:03,  1.57s/it]
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Epoch 26 Training:  99%|█████████▉| 95/96 [02:28<00:01,  1.57s/it]
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Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s][2026-10-01 15:40:36] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0026 | Epoch 026] train/policy_loss=24.2677 train/unweighted_policy_loss=4.8535 train/loss=24.2677 train/grad_norm=0.0559
+
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Epoch 26 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.80it/s]
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Epoch 26 Training: 100%|██████████| 96/96 [02:30<00:00,  1.59s/it]
Epoch 26 Training: 100%|██████████| 96/96 [02:30<00:00,  1.57s/it]
+[2026-10-01 15:40:36] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0027 | Epoch 026] timing/rollout_seconds=179.9350 timing/feedback_seconds=7.2162 timing/optimization_seconds=151.2296 timing/cycle_seconds=335.5624
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Pointwise Rewards: ssim:   0%|          | 0/6 [00:00<?, ?it/s]
Pointwise Rewards: ssim:  17%|█▋        | 1/6 [00:00<00:01,  4.00it/s]
Pointwise Rewards: ssim:  33%|███▎      | 2/6 [00:00<00:00,  4.57it/s]
Pointwise Rewards: ssim:  50%|█████     | 3/6 [00:00<00:00,  5.05it/s]
Pointwise Rewards: ssim:  67%|██████▋   | 4/6 [00:00<00:00,  5.17it/s]
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Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:01<00:00,  5.08it/s]
+
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Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.13it/s]
+[2026-10-01 15:43:40] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0027 | Epoch 027] train/reward_dists_mean=0.8275 train/reward_dists_std=0.0348 train/reward_ssim_mean=0.6582 train/reward_ssim_std=0.1164 train/reward_dists_group_std_mean=0.0082 train/reward_dists_group_std_max=0.0157 train/reward_dists_group_std_min=0.0032 train/reward_dists_group_mean_std=0.0337 train/reward_ssim_group_std_mean=0.0077 train/reward_ssim_group_std_max=0.0187 train/reward_ssim_group_std_min=0.0027 train/reward_ssim_group_mean_std=0.1161 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.8805 train/adv_max=4.6017 train/adv_abs_mean=1.2514
+
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Epoch 27 Training:  49%|████▉     | 47/96 [01:13<01:16,  1.55s/it]
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Epoch 27 Training:  51%|█████     | 49/96 [01:16<01:13,  1.56s/it]
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Epoch 27 Training:  52%|█████▏    | 50/96 [01:17<01:11,  1.56s/it]
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Epoch 27 Training:  53%|█████▎    | 51/96 [01:19<01:09,  1.56s/it]
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Epoch 27 Training:  54%|█████▍    | 52/96 [01:20<01:08,  1.56s/it]
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Epoch 27 Training:  55%|█████▌    | 53/96 [01:22<01:06,  1.56s/it]
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Epoch 27 Training:  56%|█████▋    | 54/96 [01:23<01:05,  1.56s/it]
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Epoch 27 Training:  57%|█████▋    | 55/96 [01:25<01:03,  1.56s/it]
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Epoch 27 Training:  58%|█████▊    | 56/96 [01:27<01:02,  1.56s/it]
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Epoch 27 Training:  59%|█████▉    | 57/96 [01:28<01:00,  1.56s/it]
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Epoch 27 Training:  60%|██████    | 58/96 [01:30<00:59,  1.56s/it]
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Epoch 27 Training:  61%|██████▏   | 59/96 [01:31<00:57,  1.56s/it]
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Epoch 27 Training:  62%|██████▎   | 60/96 [01:33<00:56,  1.56s/it]
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Epoch 27 Training:  64%|██████▎   | 61/96 [01:34<00:55,  1.58s/it]
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Epoch 27 Training:  65%|██████▍   | 62/96 [01:36<00:53,  1.58s/it]
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Epoch 27 Training:  66%|██████▌   | 63/96 [01:38<00:51,  1.57s/it]
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Epoch 27 Training:  67%|██████▋   | 64/96 [01:39<00:50,  1.57s/it]
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Epoch 27 Training:  68%|██████▊   | 65/96 [01:41<00:48,  1.56s/it]
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Epoch 27 Training:  69%|██████▉   | 66/96 [01:42<00:46,  1.56s/it]
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Epoch 27 Training:  70%|██████▉   | 67/96 [01:44<00:45,  1.56s/it]
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Epoch 27 Training:  71%|███████   | 68/96 [01:45<00:43,  1.56s/it]
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Epoch 27 Training:  72%|███████▏  | 69/96 [01:47<00:42,  1.56s/it]
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Epoch 27 Training:  73%|███████▎  | 70/96 [01:48<00:40,  1.56s/it]
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Epoch 27 Training:  74%|███████▍  | 71/96 [01:50<00:38,  1.56s/it]
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Epoch 27 Training:  75%|███████▌  | 72/96 [01:52<00:37,  1.56s/it]
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Epoch 27 Training:  76%|███████▌  | 73/96 [01:53<00:35,  1.56s/it]
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Epoch 27 Training:  77%|███████▋  | 74/96 [01:55<00:34,  1.56s/it]
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Epoch 27 Training:  78%|███████▊  | 75/96 [01:56<00:32,  1.55s/it]
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Epoch 27 Training:  79%|███████▉  | 76/96 [01:58<00:31,  1.56s/it]
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Epoch 27 Training:  80%|████████  | 77/96 [01:59<00:29,  1.56s/it]
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Epoch 27 Training:  81%|████████▏ | 78/96 [02:01<00:28,  1.56s/it]
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Epoch 27 Training:  82%|████████▏ | 79/96 [02:02<00:26,  1.56s/it]
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Epoch 27 Training:  83%|████████▎ | 80/96 [02:04<00:24,  1.56s/it]
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Epoch 27 Training:  84%|████████▍ | 81/96 [02:06<00:23,  1.56s/it]
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Epoch 27 Training:  85%|████████▌ | 82/96 [02:07<00:21,  1.56s/it]
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Epoch 27 Training:  86%|████████▋ | 83/96 [02:09<00:20,  1.56s/it]
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Epoch 27 Training:  88%|████████▊ | 84/96 [02:10<00:18,  1.56s/it]
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Epoch 27 Training:  89%|████████▊ | 85/96 [02:12<00:17,  1.56s/it]
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Epoch 27 Training:  90%|████████▉ | 86/96 [02:13<00:15,  1.56s/it]
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Epoch 27 Training:  91%|█████████ | 87/96 [02:15<00:14,  1.56s/it]
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Epoch 27 Training:  92%|█████████▏| 88/96 [02:16<00:12,  1.56s/it]
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Epoch 27 Training:  93%|█████████▎| 89/96 [02:18<00:11,  1.60s/it]
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Epoch 27 Training:  94%|█████████▍| 90/96 [02:20<00:09,  1.59s/it]
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Epoch 27 Training:  95%|█████████▍| 91/96 [02:21<00:07,  1.58s/it]
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Epoch 27 Training:  96%|█████████▌| 92/96 [02:23<00:06,  1.57s/it]
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Epoch 27 Training:  97%|█████████▋| 93/96 [02:24<00:04,  1.57s/it]
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Epoch 27 Training:  98%|█████████▊| 94/96 [02:26<00:03,  1.56s/it]
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Epoch 27 Training:  99%|█████████▉| 95/96 [02:27<00:01,  1.56s/it]
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Epoch 27 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s][2026-10-01 15:46:10] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0027 | Epoch 027] train/policy_loss=24.5062 train/unweighted_policy_loss=4.9012 train/loss=24.5062 train/grad_norm=0.0698
+
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Epoch 27 Training: 100%|██████████| 96/96 [02:29<00:00,  1.58s/it]
Epoch 27 Training: 100%|██████████| 96/96 [02:29<00:00,  1.56s/it]
+[2026-10-01 15:46:10] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0028 | Epoch 027] timing/rollout_seconds=180.2659 timing/feedback_seconds=6.5633 timing/optimization_seconds=150.5435 timing/cycle_seconds=334.0737
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+
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+
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Pointwise Rewards: dists:  83%|████████▎ | 5/6 [00:01<00:00,  3.19it/s]
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+[2026-10-01 15:49:14] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0028 | Epoch 028] train/reward_dists_mean=0.8383 train/reward_dists_std=0.0286 train/reward_ssim_mean=0.6553 train/reward_ssim_std=0.0703 train/reward_dists_group_std_mean=0.0076 train/reward_dists_group_std_max=0.0142 train/reward_dists_group_std_min=0.0030 train/reward_dists_group_mean_std=0.0274 train/reward_ssim_group_std_mean=0.0083 train/reward_ssim_group_std_max=0.0196 train/reward_ssim_group_std_min=0.0029 train/reward_ssim_group_mean_std=0.0697 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.4445 train/adv_max=4.4806 train/adv_abs_mean=1.2681
+
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Epoch 28 Training:  24%|██▍       | 23/96 [00:35<01:53,  1.56s/it]
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Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.94it/s]
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Epoch 28 Training:  25%|██▌       | 24/96 [00:37<01:52,  1.56s/it]
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Epoch 28 Training:  26%|██▌       | 25/96 [00:38<01:50,  1.56s/it]
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Epoch 28 Training:  27%|██▋       | 26/96 [00:40<01:49,  1.56s/it]
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Epoch 28 Training:  28%|██▊       | 27/96 [00:42<01:48,  1.57s/it]
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Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 28 Training:  29%|██▉       | 28/96 [00:43<01:46,  1.57s/it]
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Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.91it/s]
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Epoch 28 Training:  30%|███       | 29/96 [00:45<01:45,  1.57s/it]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.91it/s]
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Epoch 28 Training:  31%|███▏      | 30/96 [00:46<01:43,  1.57s/it]
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Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.91it/s]
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Epoch 28 Training:  32%|███▏      | 31/96 [00:48<01:42,  1.57s/it]
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Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.95it/s]
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Epoch 28 Training:  33%|███▎      | 32/96 [00:49<01:40,  1.57s/it]
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Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Training:  34%|███▍      | 33/96 [00:51<01:39,  1.57s/it]
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Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 28 Training:  35%|███▌      | 34/96 [00:53<01:37,  1.57s/it]
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Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.91it/s]
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Epoch 28 Training:  36%|███▋      | 35/96 [00:54<01:35,  1.57s/it]
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Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.91it/s]
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Epoch 28 Training:  38%|███▊      | 36/96 [00:56<01:34,  1.57s/it]
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Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 28 Training:  39%|███▊      | 37/96 [00:57<01:32,  1.57s/it]
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Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.97it/s]
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Epoch 28 Training:  40%|███▉      | 38/96 [00:59<01:30,  1.57s/it]
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Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 28 Training:  41%|████      | 39/96 [01:00<01:29,  1.57s/it]
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Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 28 Training:  42%|████▏     | 40/96 [01:02<01:27,  1.57s/it]
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Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.94it/s]
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Epoch 28 Training:  43%|████▎     | 41/96 [01:04<01:26,  1.57s/it]
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Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 28 Training:  44%|████▍     | 42/96 [01:05<01:24,  1.57s/it]
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Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.96it/s]
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Epoch 28 Training:  45%|████▍     | 43/96 [01:07<01:22,  1.57s/it]
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Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 28 Training:  46%|████▌     | 44/96 [01:08<01:21,  1.57s/it]
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Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.94it/s]
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Epoch 28 Training:  47%|████▋     | 45/96 [01:10<01:20,  1.57s/it]
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Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 28 Training:  48%|████▊     | 46/96 [01:11<01:18,  1.57s/it]
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Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.89it/s]
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Epoch 28 Training:  49%|████▉     | 47/96 [01:13<01:17,  1.58s/it]
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Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.96it/s]
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Epoch 28 Training:  50%|█████     | 48/96 [01:15<01:15,  1.57s/it]
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Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 28 Training:  51%|█████     | 49/96 [01:16<01:13,  1.57s/it]
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Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.91it/s]
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Epoch 28 Training:  52%|█████▏    | 50/96 [01:18<01:12,  1.57s/it]
+
Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
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Epoch 28 Training:  53%|█████▎    | 51/96 [01:19<01:10,  1.57s/it]
+
Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 28 Training:  54%|█████▍    | 52/96 [01:21<01:09,  1.57s/it]
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Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 28 Training:  55%|█████▌    | 53/96 [01:22<01:07,  1.57s/it]
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Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.91it/s]
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Epoch 28 Training:  56%|█████▋    | 54/96 [01:24<01:06,  1.57s/it]
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Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 28 Training:  57%|█████▋    | 55/96 [01:26<01:04,  1.57s/it]
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Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.91it/s]
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Epoch 28 Training:  58%|█████▊    | 56/96 [01:27<01:02,  1.57s/it]
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Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.91it/s]
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Epoch 28 Training:  59%|█████▉    | 57/96 [01:29<01:01,  1.57s/it]
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Epoch 28 Training:  60%|██████    | 58/96 [01:30<00:59,  1.57s/it]
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Epoch 28 Training:  61%|██████▏   | 59/96 [01:32<00:58,  1.57s/it]
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Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
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Epoch 28 Training:  62%|██████▎   | 60/96 [01:33<00:56,  1.57s/it]
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Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 28 Training:  64%|██████▎   | 61/96 [01:35<00:54,  1.57s/it]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 28 Training:  65%|██████▍   | 62/96 [01:37<00:53,  1.57s/it]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.91it/s]
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Epoch 28 Training:  66%|██████▌   | 63/96 [01:38<00:51,  1.57s/it]
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Epoch 28 Training:  67%|██████▋   | 64/96 [01:40<00:50,  1.57s/it]
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Epoch 28 Training:  68%|██████▊   | 65/96 [01:41<00:48,  1.57s/it]
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Epoch 28 Training:  69%|██████▉   | 66/96 [01:43<00:47,  1.57s/it]
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Epoch 28 Training:  70%|██████▉   | 67/96 [01:44<00:45,  1.57s/it]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.91it/s]
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Epoch 28 Training:  71%|███████   | 68/96 [01:46<00:44,  1.57s/it]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 28 Training:  72%|███████▏  | 69/96 [01:48<00:42,  1.57s/it]
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Epoch 28 Training:  73%|███████▎  | 70/96 [01:49<00:40,  1.57s/it]
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Epoch 28 Training:  74%|███████▍  | 71/96 [01:51<00:40,  1.60s/it]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 28 Training:  75%|███████▌  | 72/96 [01:53<00:39,  1.63s/it]
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Epoch 28 Training:  76%|███████▌  | 73/96 [01:54<00:37,  1.61s/it]
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Epoch 28 Training:  99%|█████████▉| 95/96 [02:29<00:01,  1.59s/it]
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Epoch 28 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s][2026-10-01 15:51:45] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0028 | Epoch 028] train/policy_loss=23.9412 train/unweighted_policy_loss=4.7882 train/loss=23.9412 train/grad_norm=0.0707
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+[2026-10-01 15:51:45] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0029 | Epoch 028] timing/rollout_seconds=180.1641 timing/feedback_seconds=5.8473 timing/optimization_seconds=151.6162 timing/cycle_seconds=334.7389
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+[2026-10-01 15:54:48] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0029 | Epoch 029] train/reward_dists_mean=0.8433 train/reward_dists_std=0.0322 train/reward_ssim_mean=0.6587 train/reward_ssim_std=0.1099 train/reward_dists_group_std_mean=0.0077 train/reward_dists_group_std_max=0.0178 train/reward_dists_group_std_min=0.0031 train/reward_dists_group_mean_std=0.0311 train/reward_ssim_group_std_mean=0.0081 train/reward_ssim_group_std_max=0.0180 train/reward_ssim_group_std_min=0.0031 train/reward_ssim_group_mean_std=0.1096 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.5823 train/adv_max=4.2174 train/adv_abs_mean=1.2783
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Epoch 29 Training:  54%|█████▍    | 52/96 [01:22<01:09,  1.57s/it]
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Epoch 29 Training:  55%|█████▌    | 53/96 [01:23<01:07,  1.57s/it]
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Epoch 29 Training:  56%|█████▋    | 54/96 [01:25<01:06,  1.57s/it]
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Epoch 29 Training:  57%|█████▋    | 55/96 [01:26<01:04,  1.57s/it]
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Epoch 29 Training:  77%|███████▋  | 74/96 [01:56<00:34,  1.57s/it]
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Epoch 29 Training:  78%|███████▊  | 75/96 [01:58<00:33,  1.57s/it]
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Epoch 29 Training:  79%|███████▉  | 76/96 [01:59<00:31,  1.57s/it]
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Epoch 29 Training:  80%|████████  | 77/96 [02:01<00:29,  1.57s/it]
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Epoch 29 Training:  81%|████████▏ | 78/96 [02:03<00:28,  1.57s/it]
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Epoch 29 Training:  82%|████████▏ | 79/96 [02:04<00:26,  1.57s/it]
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Epoch 29 Training:  83%|████████▎ | 80/96 [02:06<00:25,  1.57s/it]
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Epoch 29 Training:  84%|████████▍ | 81/96 [02:07<00:23,  1.57s/it]
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Epoch 29 Training:  85%|████████▌ | 82/96 [02:09<00:22,  1.57s/it]
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Epoch 29 Training:  86%|████████▋ | 83/96 [02:10<00:20,  1.57s/it]
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Epoch 29 Training:  88%|████████▊ | 84/96 [02:12<00:18,  1.57s/it]
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Epoch 29 Training:  89%|████████▊ | 85/96 [02:14<00:17,  1.58s/it]
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Epoch 29 Training:  90%|████████▉ | 86/96 [02:15<00:15,  1.58s/it]
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Epoch 29 Training:  91%|█████████ | 87/96 [02:17<00:14,  1.58s/it]
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Epoch 29 Training:  92%|█████████▏| 88/96 [02:18<00:12,  1.58s/it]
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Epoch 29 Training:  93%|█████████▎| 89/96 [02:20<00:11,  1.58s/it]
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Epoch 29 Training:  94%|█████████▍| 90/96 [02:21<00:09,  1.58s/it]
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Epoch 29 Training:  95%|█████████▍| 91/96 [02:23<00:07,  1.57s/it]
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Epoch 29 Training:  96%|█████████▌| 92/96 [02:25<00:06,  1.58s/it]
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Epoch 29 Training:  97%|█████████▋| 93/96 [02:26<00:04,  1.57s/it]
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Epoch 29 Training:  98%|█████████▊| 94/96 [02:28<00:03,  1.57s/it]
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Epoch 29 Training:  99%|█████████▉| 95/96 [02:29<00:01,  1.57s/it]
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Epoch 29 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s][2026-10-01 15:57:20] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0029 | Epoch 029] train/policy_loss=23.9745 train/unweighted_policy_loss=4.7949 train/loss=23.9745 train/grad_norm=0.0673
+
+
Epoch 29 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.78it/s]
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Epoch 29 Training: 100%|██████████| 96/96 [02:31<00:00,  1.59s/it]
Epoch 29 Training: 100%|██████████| 96/96 [02:31<00:00,  1.58s/it]
+[2026-10-01 15:57:20] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0030 | Epoch 029] timing/rollout_seconds=180.0881 timing/feedback_seconds=5.8144 timing/optimization_seconds=152.1331 timing/cycle_seconds=335.1192
+
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Eval/flir_rl_full:  25%|██▌       | 2/8 [00:13<00:40,  6.76s/it]
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Eval/flir_rl_full:  62%|██████▎   | 5/8 [00:32<00:19,  6.39s/it]
Eval/flir_rl_full:  75%|███████▌  | 6/8 [00:38<00:12,  6.35s/it]
Eval/flir_rl_full:  88%|████████▊ | 7/8 [00:45<00:06,  6.33s/it]
Eval/flir_rl_full: 100%|██████████| 8/8 [00:51<00:00,  6.42s/it]
Eval/flir_rl_full: 100%|██████████| 8/8 [00:51<00:00,  6.47s/it]
+
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+[2026-10-01 15:58:13] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0030 | Epoch 030] eval/flir_rl_full/reward_dists_mean=0.8456 eval/flir_rl_full/reward_dists_std=0.0331 eval/flir_rl_full/reward_psnr_mean=20.2540 eval/flir_rl_full/reward_psnr_std=2.9288 eval/flir_rl_full/reward_ssim_mean=0.6279 eval/flir_rl_full/reward_ssim_std=0.0808
+
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+
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+[2026-10-01 16:01:17] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0030 | Epoch 030] train/reward_dists_mean=0.8438 train/reward_dists_std=0.0280 train/reward_ssim_mean=0.6396 train/reward_ssim_std=0.0960 train/reward_dists_group_std_mean=0.0072 train/reward_dists_group_std_max=0.0124 train/reward_dists_group_std_min=0.0036 train/reward_dists_group_mean_std=0.0270 train/reward_ssim_group_std_mean=0.0077 train/reward_ssim_group_std_max=0.0208 train/reward_ssim_group_std_min=0.0027 train/reward_ssim_group_mean_std=0.0956 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.8803 train/adv_max=4.0039 train/adv_abs_mean=1.2305
+
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Epoch 30 Training:  21%|██        | 20/96 [00:31<01:59,  1.57s/it]
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Epoch 30 Training:  22%|██▏       | 21/96 [00:32<01:57,  1.57s/it]
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Epoch 30 Training:  23%|██▎       | 22/96 [00:34<01:56,  1.57s/it]
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Epoch 30 Training:  24%|██▍       | 23/96 [00:36<01:54,  1.57s/it]
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Epoch 30 Training:  25%|██▌       | 24/96 [00:37<01:53,  1.57s/it]
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Epoch 30 Training:  26%|██▌       | 25/96 [00:39<01:51,  1.57s/it]
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Epoch 30 Training:  27%|██▋       | 26/96 [00:40<01:50,  1.58s/it]
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Epoch 30 Training:  28%|██▊       | 27/96 [00:42<01:48,  1.57s/it]
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Epoch 30 Training:  29%|██▉       | 28/96 [00:43<01:46,  1.57s/it]
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Epoch 30 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 30 Training:  30%|███       | 29/96 [00:45<01:44,  1.57s/it]
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Epoch 30 Training:  31%|███▏      | 30/96 [00:47<01:43,  1.57s/it]
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Epoch 30 Training:  32%|███▏      | 31/96 [00:48<01:42,  1.57s/it]
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Epoch 30 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.94it/s]
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Epoch 30 Training:  33%|███▎      | 32/96 [00:50<01:40,  1.57s/it]
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Epoch 30 Training:  34%|███▍      | 33/96 [00:51<01:39,  1.57s/it]
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Epoch 30 Training:  35%|███▌      | 34/96 [00:53<01:37,  1.57s/it]
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Epoch 30 Training:  36%|███▋      | 35/96 [00:54<01:35,  1.57s/it]
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Epoch 30 Training:  38%|███▊      | 36/96 [00:56<01:34,  1.57s/it]
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Epoch 30 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 30 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.96it/s]
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Epoch 30 Training:  39%|███▊      | 37/96 [00:58<01:32,  1.57s/it]
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Epoch 30 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 30 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.95it/s]
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Epoch 30 Training:  40%|███▉      | 38/96 [00:59<01:31,  1.57s/it]
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Epoch 30 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.95it/s]
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Epoch 30 Training:  41%|████      | 39/96 [01:01<01:29,  1.57s/it]
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Epoch 30 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 30 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.96it/s]
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Epoch 30 Training:  42%|████▏     | 40/96 [01:02<01:28,  1.57s/it]
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Epoch 30 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.97it/s]
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Epoch 30 Training:  43%|████▎     | 41/96 [01:04<01:26,  1.57s/it]
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Epoch 30 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.96it/s]
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Epoch 30 Training:  44%|████▍     | 42/96 [01:05<01:24,  1.57s/it]
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Epoch 30 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.96it/s]
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Epoch 30 Training:  45%|████▍     | 43/96 [01:07<01:23,  1.57s/it]
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Epoch 30 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.96it/s]
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Epoch 30 Training:  46%|████▌     | 44/96 [01:09<01:21,  1.57s/it]
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Epoch 30 Training:  47%|████▋     | 45/96 [01:10<01:20,  1.57s/it]
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Epoch 30 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.97it/s]
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Epoch 30 Training:  48%|████▊     | 46/96 [01:12<01:18,  1.57s/it]
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Epoch 30 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 30 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
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Epoch 30 Training:  49%|████▉     | 47/96 [01:13<01:17,  1.58s/it]
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Epoch 30 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.96it/s]
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Epoch 30 Training:  50%|█████     | 48/96 [01:15<01:15,  1.58s/it]
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Epoch 30 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.96it/s]
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Epoch 30 Training:  51%|█████     | 49/96 [01:16<01:14,  1.58s/it]
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Epoch 30 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.97it/s]
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Epoch 30 Training:  52%|█████▏    | 50/96 [01:18<01:12,  1.57s/it]
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Epoch 30 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.95it/s]
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Epoch 30 Training:  53%|█████▎    | 51/96 [01:20<01:10,  1.57s/it]
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Epoch 30 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.94it/s]
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Epoch 30 Training:  54%|█████▍    | 52/96 [01:21<01:09,  1.57s/it]
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Epoch 30 Training:  55%|█████▌    | 53/96 [01:23<01:07,  1.58s/it]
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Epoch 30 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.96it/s]
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Epoch 30 Training:  56%|█████▋    | 54/96 [01:24<01:06,  1.57s/it]
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Epoch 30 Training:  57%|█████▋    | 55/96 [01:26<01:04,  1.58s/it]
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Epoch 30 Training:  58%|█████▊    | 56/96 [01:28<01:02,  1.57s/it]
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Epoch 30 Training:  59%|█████▉    | 57/96 [01:29<01:01,  1.57s/it]
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Epoch 30 Training:  60%|██████    | 58/96 [01:31<01:02,  1.64s/it]
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Epoch 30 Training:  61%|██████▏   | 59/96 [01:32<00:59,  1.62s/it]
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Epoch 30 Training:  62%|██████▎   | 60/96 [01:34<00:58,  1.64s/it]
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Epoch 30 Training:  64%|██████▎   | 61/96 [01:36<00:56,  1.62s/it]
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Epoch 30 Training:  65%|██████▍   | 62/96 [01:37<00:54,  1.60s/it]
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Epoch 30 Training:  66%|██████▌   | 63/96 [01:39<00:52,  1.59s/it]
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Epoch 30 Training:  67%|██████▋   | 64/96 [01:40<00:50,  1.59s/it]
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Epoch 30 Training:  68%|██████▊   | 65/96 [01:42<00:49,  1.58s/it]
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Epoch 30 Training:  69%|██████▉   | 66/96 [01:44<00:47,  1.58s/it]
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Epoch 30 Training:  70%|██████▉   | 67/96 [01:45<00:45,  1.58s/it]
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Epoch 30 Training:  71%|███████   | 68/96 [01:47<00:44,  1.58s/it]
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Epoch 30 Training:  72%|███████▏  | 69/96 [01:48<00:42,  1.58s/it]
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Epoch 30 Training:  73%|███████▎  | 70/96 [01:50<00:41,  1.58s/it]
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Epoch 30 Training:  77%|███████▋  | 74/96 [01:56<00:34,  1.57s/it]
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Epoch 30 Training:  82%|████████▏ | 79/96 [02:04<00:26,  1.57s/it]
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Epoch 30 Training:  92%|█████████▏| 88/96 [02:18<00:12,  1.58s/it]
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Epoch 30 Training:  97%|█████████▋| 93/96 [02:26<00:04,  1.58s/it]
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Epoch 30 Training:  98%|█████████▊| 94/96 [02:28<00:03,  1.58s/it]
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Epoch 30 Training:  99%|█████████▉| 95/96 [02:29<00:01,  1.58s/it]
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Epoch 30 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s][2026-10-01 16:03:48] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0030 | Epoch 030] train/policy_loss=24.4107 train/unweighted_policy_loss=4.8821 train/loss=24.4107 train/grad_norm=0.0644
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Epoch 30 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.79it/s]
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Epoch 30 Training: 100%|██████████| 96/96 [02:31<00:00,  1.60s/it]
Epoch 30 Training: 100%|██████████| 96/96 [02:31<00:00,  1.58s/it]
+[2026-10-01 16:03:48] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0031 | Epoch 030] timing/rollout_seconds=179.9306 timing/feedback_seconds=5.8435 timing/optimization_seconds=152.0951 timing/cycle_seconds=334.9985
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Pointwise Rewards: ssim:   0%|          | 0/6 [00:00<?, ?it/s]
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Pointwise Rewards: ssim:  50%|█████     | 3/6 [00:00<00:00,  5.27it/s]
Pointwise Rewards: ssim:  67%|██████▋   | 4/6 [00:00<00:00,  5.59it/s]
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Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:01<00:00,  5.56it/s]
Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:01<00:00,  5.53it/s]
+
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Pointwise Rewards: dists:  67%|██████▋   | 4/6 [00:01<00:00,  3.28it/s]
Pointwise Rewards: dists:  83%|████████▎ | 5/6 [00:01<00:00,  3.32it/s]
Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.27it/s]
Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.28it/s]
+[2026-10-01 16:06:52] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0031 | Epoch 031] train/reward_dists_mean=0.8442 train/reward_dists_std=0.0333 train/reward_ssim_mean=0.6512 train/reward_ssim_std=0.1112 train/reward_dists_group_std_mean=0.0078 train/reward_dists_group_std_max=0.0166 train/reward_dists_group_std_min=0.0032 train/reward_dists_group_mean_std=0.0323 train/reward_ssim_group_std_mean=0.0084 train/reward_ssim_group_std_max=0.0224 train/reward_ssim_group_std_min=0.0026 train/reward_ssim_group_mean_std=0.1108 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.2685 train/adv_max=4.3040 train/adv_abs_mean=1.2646
+
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Epoch 31 Training:  11%|█▏        | 11/96 [00:17<02:14,  1.58s/it]
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Epoch 31 Training:  15%|█▍        | 14/96 [00:22<02:09,  1.58s/it]
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Epoch 31 Training:  22%|██▏       | 21/96 [00:33<01:58,  1.58s/it]
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Epoch 31 Training:  49%|████▉     | 47/96 [01:14<01:17,  1.59s/it]
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Epoch 31 Training:  51%|█████     | 49/96 [01:17<01:14,  1.58s/it]
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Epoch 31 Training:  52%|█████▏    | 50/96 [01:19<01:12,  1.58s/it]
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Epoch 31 Training:  53%|█████▎    | 51/96 [01:20<01:10,  1.58s/it]
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Epoch 31 Training:  54%|█████▍    | 52/96 [01:22<01:09,  1.57s/it]
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Epoch 31 Training:  55%|█████▌    | 53/96 [01:23<01:07,  1.57s/it]
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Epoch 31 Training:  56%|█████▋    | 54/96 [01:25<01:05,  1.57s/it]
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Epoch 31 Training:  57%|█████▋    | 55/96 [01:26<01:04,  1.57s/it]
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Epoch 31 Training:  58%|█████▊    | 56/96 [01:28<01:02,  1.57s/it]
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Epoch 31 Training:  59%|█████▉    | 57/96 [01:30<01:01,  1.58s/it]
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Epoch 31 Training:  60%|██████    | 58/96 [01:31<00:59,  1.58s/it]
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Epoch 31 Training:  61%|██████▏   | 59/96 [01:33<00:58,  1.58s/it]
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Epoch 31 Training:  62%|██████▎   | 60/96 [01:34<00:56,  1.58s/it]
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Epoch 31 Training:  64%|██████▎   | 61/96 [01:36<00:55,  1.57s/it]
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Epoch 31 Training:  65%|██████▍   | 62/96 [01:37<00:53,  1.57s/it]
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Epoch 31 Training:  66%|██████▌   | 63/96 [01:39<00:52,  1.58s/it]
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Epoch 31 Training:  67%|██████▋   | 64/96 [01:41<00:50,  1.58s/it]
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Epoch 31 Training:  68%|██████▊   | 65/96 [01:42<00:48,  1.57s/it]
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Epoch 31 Training:  69%|██████▉   | 66/96 [01:44<00:47,  1.57s/it]
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Epoch 31 Training:  70%|██████▉   | 67/96 [01:45<00:45,  1.57s/it]
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Epoch 31 Training:  71%|███████   | 68/96 [01:47<00:44,  1.58s/it]
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Epoch 31 Training:  72%|███████▏  | 69/96 [01:49<00:42,  1.58s/it]
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Epoch 31 Training:  73%|███████▎  | 70/96 [01:50<00:41,  1.58s/it]
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Epoch 31 Training:  74%|███████▍  | 71/96 [01:52<00:39,  1.59s/it]
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Epoch 31 Training:  75%|███████▌  | 72/96 [01:53<00:38,  1.59s/it]
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Epoch 31 Training:  76%|███████▌  | 73/96 [01:55<00:36,  1.59s/it]
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Epoch 31 Training:  77%|███████▋  | 74/96 [01:56<00:34,  1.58s/it]
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Epoch 31 Training:  78%|███████▊  | 75/96 [01:58<00:33,  1.58s/it]
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Epoch 31 Training:  79%|███████▉  | 76/96 [02:00<00:31,  1.58s/it]
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Epoch 31 Training:  80%|████████  | 77/96 [02:01<00:30,  1.59s/it]
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Epoch 31 Training:  81%|████████▏ | 78/96 [02:03<00:28,  1.59s/it]
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Epoch 31 Training:  82%|████████▏ | 79/96 [02:04<00:27,  1.59s/it]
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Epoch 31 Training:  83%|████████▎ | 80/96 [02:06<00:25,  1.59s/it]
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Epoch 31 Training:  84%|████████▍ | 81/96 [02:08<00:23,  1.59s/it]
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Epoch 31 Training:  85%|████████▌ | 82/96 [02:09<00:22,  1.60s/it]
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Epoch 31 Training:  86%|████████▋ | 83/96 [02:11<00:20,  1.59s/it]
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Epoch 31 Training:  88%|████████▊ | 84/96 [02:12<00:19,  1.58s/it]
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Epoch 31 Training:  89%|████████▊ | 85/96 [02:14<00:17,  1.59s/it]
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Epoch 31 Training:  90%|████████▉ | 86/96 [02:16<00:15,  1.58s/it]
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Epoch 31 Training:  91%|█████████ | 87/96 [02:17<00:14,  1.58s/it]
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Epoch 31 Training:  92%|█████████▏| 88/96 [02:19<00:12,  1.58s/it]
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Epoch 31 Training:  93%|█████████▎| 89/96 [02:20<00:11,  1.58s/it]
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Epoch 31 Training:  94%|█████████▍| 90/96 [02:22<00:09,  1.58s/it]
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Epoch 31 Training:  95%|█████████▍| 91/96 [02:23<00:07,  1.58s/it]
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Epoch 31 Training:  96%|█████████▌| 92/96 [02:25<00:06,  1.58s/it]
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Epoch 31 Training:  97%|█████████▋| 93/96 [02:27<00:04,  1.58s/it]
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Epoch 31 Training:  98%|█████████▊| 94/96 [02:28<00:03,  1.58s/it]
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Epoch 31 Training:  99%|█████████▉| 95/96 [02:30<00:01,  1.58s/it]
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Epoch 31 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s][2026-10-01 16:09:24] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0031 | Epoch 031] train/policy_loss=24.6471 train/unweighted_policy_loss=4.9294 train/loss=24.6471 train/grad_norm=0.0888
+
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Epoch 31 Training: 100%|██████████| 96/96 [02:31<00:00,  1.61s/it]
Epoch 31 Training: 100%|██████████| 96/96 [02:31<00:00,  1.58s/it]
+[2026-10-01 16:09:24] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0032 | Epoch 031] timing/rollout_seconds=180.0255 timing/feedback_seconds=5.8507 timing/optimization_seconds=152.6201 timing/cycle_seconds=335.6172
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+
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+[2026-10-01 16:12:27] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0032 | Epoch 032] train/reward_dists_mean=0.8402 train/reward_dists_std=0.0322 train/reward_ssim_mean=0.6512 train/reward_ssim_std=0.1016 train/reward_dists_group_std_mean=0.0079 train/reward_dists_group_std_max=0.0165 train/reward_dists_group_std_min=0.0038 train/reward_dists_group_mean_std=0.0311 train/reward_ssim_group_std_mean=0.0076 train/reward_ssim_group_std_max=0.0170 train/reward_ssim_group_std_min=0.0021 train/reward_ssim_group_mean_std=0.1012 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.7954 train/adv_max=4.2754 train/adv_abs_mean=1.1740
+
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Epoch 32 Training:  24%|██▍       | 23/96 [00:35<01:54,  1.57s/it]
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Epoch 32 Training:  25%|██▌       | 24/96 [00:37<01:52,  1.57s/it]
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Epoch 32 Training:  26%|██▌       | 25/96 [00:39<01:51,  1.57s/it]
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Epoch 32 Training:  27%|██▋       | 26/96 [00:40<01:49,  1.57s/it]
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Epoch 32 Training:  28%|██▊       | 27/96 [00:42<01:48,  1.57s/it]
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Epoch 32 Training:  29%|██▉       | 28/96 [00:43<01:46,  1.57s/it]
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Epoch 32 Training:  30%|███       | 29/96 [00:45<01:45,  1.57s/it]
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Epoch 32 Training:  31%|███▏      | 30/96 [00:46<01:43,  1.57s/it]
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Epoch 32 Training:  32%|███▏      | 31/96 [00:48<01:41,  1.57s/it]
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Epoch 32 Training:  33%|███▎      | 32/96 [00:50<01:40,  1.57s/it]
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Epoch 32 Training:  34%|███▍      | 33/96 [00:51<01:38,  1.57s/it]
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Epoch 32 Training:  35%|███▌      | 34/96 [00:53<01:37,  1.57s/it]
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Epoch 32 Training:  36%|███▋      | 35/96 [00:54<01:35,  1.57s/it]
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Epoch 32 Training:  38%|███▊      | 36/96 [00:56<01:33,  1.57s/it]
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Epoch 32 Training:  39%|███▊      | 37/96 [00:57<01:32,  1.57s/it]
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Epoch 32 Training:  40%|███▉      | 38/96 [00:59<01:30,  1.56s/it]
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Epoch 32 Training:  41%|████      | 39/96 [01:00<01:29,  1.56s/it]
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Epoch 32 Training:  42%|████▏     | 40/96 [01:02<01:27,  1.56s/it]
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Epoch 32 Training:  43%|████▎     | 41/96 [01:04<01:25,  1.56s/it]
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Epoch 32 Training:  44%|████▍     | 42/96 [01:05<01:24,  1.56s/it]
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Epoch 32 Training:  45%|████▍     | 43/96 [01:07<01:22,  1.56s/it]
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Epoch 32 Training:  46%|████▌     | 44/96 [01:08<01:21,  1.56s/it]
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Epoch 32 Training:  47%|████▋     | 45/96 [01:10<01:19,  1.56s/it]
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Epoch 32 Training:  48%|████▊     | 46/96 [01:11<01:18,  1.56s/it]
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Epoch 32 Training:  49%|████▉     | 47/96 [01:13<01:16,  1.56s/it]
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Epoch 32 Training:  50%|█████     | 48/96 [01:15<01:15,  1.56s/it]
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Epoch 32 Training:  51%|█████     | 49/96 [01:16<01:13,  1.56s/it]
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Epoch 32 Training:  52%|█████▏    | 50/96 [01:18<01:11,  1.56s/it]
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Epoch 32 Training:  53%|█████▎    | 51/96 [01:19<01:10,  1.56s/it]
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Epoch 32 Training:  54%|█████▍    | 52/96 [01:21<01:08,  1.56s/it]
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Epoch 32 Training:  55%|█████▌    | 53/96 [01:22<01:07,  1.56s/it]
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Epoch 32 Training:  56%|█████▋    | 54/96 [01:24<01:05,  1.56s/it]
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Epoch 32 Training:  57%|█████▋    | 55/96 [01:25<01:04,  1.56s/it]
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Epoch 32 Training:  58%|█████▊    | 56/96 [01:27<01:02,  1.56s/it]
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Epoch 32 Training:  59%|█████▉    | 57/96 [01:29<01:00,  1.56s/it]
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Epoch 32 Training:  60%|██████    | 58/96 [01:30<00:59,  1.56s/it]
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Epoch 32 Training:  61%|██████▏   | 59/96 [01:32<00:58,  1.57s/it]
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Epoch 32 Training:  62%|██████▎   | 60/96 [01:33<00:56,  1.57s/it]
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Epoch 32 Training:  64%|██████▎   | 61/96 [01:35<00:54,  1.57s/it]
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Epoch 32 Training:  65%|██████▍   | 62/96 [01:36<00:53,  1.57s/it]
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Epoch 32 Training:  66%|██████▌   | 63/96 [01:38<00:51,  1.57s/it]
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Epoch 32 Training:  67%|██████▋   | 64/96 [01:40<00:50,  1.56s/it]
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Epoch 32 Training:  68%|██████▊   | 65/96 [01:41<00:48,  1.56s/it]
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Epoch 32 Training:  69%|██████▉   | 66/96 [01:43<00:46,  1.56s/it]
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Epoch 32 Training:  70%|██████▉   | 67/96 [01:44<00:45,  1.56s/it]
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Epoch 32 Training:  71%|███████   | 68/96 [01:46<00:43,  1.56s/it]
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Epoch 32 Training:  72%|███████▏  | 69/96 [01:47<00:42,  1.57s/it]
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Epoch 32 Training:  73%|███████▎  | 70/96 [01:49<00:40,  1.57s/it]
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Epoch 32 Training:  74%|███████▍  | 71/96 [01:51<00:39,  1.56s/it]
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Epoch 32 Training:  75%|███████▌  | 72/96 [01:52<00:37,  1.57s/it]
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Epoch 32 Training:  76%|███████▌  | 73/96 [01:54<00:36,  1.57s/it]
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Epoch 32 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s][2026-10-01 16:14:57] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0032 | Epoch 032] train/policy_loss=24.7859 train/unweighted_policy_loss=4.9572 train/loss=24.7859 train/grad_norm=0.0713
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+[2026-10-01 16:14:57] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0033 | Epoch 032] timing/rollout_seconds=179.6150 timing/feedback_seconds=5.5897 timing/optimization_seconds=150.9664 timing/cycle_seconds=333.7261
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+[2026-10-01 16:18:01] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0033 | Epoch 033] train/reward_dists_mean=0.8427 train/reward_dists_std=0.0333 train/reward_ssim_mean=0.6506 train/reward_ssim_std=0.1068 train/reward_dists_group_std_mean=0.0075 train/reward_dists_group_std_max=0.0171 train/reward_dists_group_std_min=0.0031 train/reward_dists_group_mean_std=0.0323 train/reward_ssim_group_std_mean=0.0079 train/reward_ssim_group_std_max=0.0151 train/reward_ssim_group_std_min=0.0033 train/reward_ssim_group_mean_std=0.1065 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.8179 train/adv_max=4.5610 train/adv_abs_mean=1.2299
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Epoch 33 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s][2026-10-01 16:20:31] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0033 | Epoch 033] train/policy_loss=24.5508 train/unweighted_policy_loss=4.9102 train/loss=24.5508 train/grad_norm=0.0582
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+[2026-10-01 16:20:31] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0034 | Epoch 033] timing/rollout_seconds=179.5838 timing/feedback_seconds=5.3352 timing/optimization_seconds=150.7471 timing/cycle_seconds=333.2674
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+
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+[2026-10-01 16:23:34] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0034 | Epoch 034] train/reward_dists_mean=0.8472 train/reward_dists_std=0.0346 train/reward_ssim_mean=0.6499 train/reward_ssim_std=0.1195 train/reward_dists_group_std_mean=0.0074 train/reward_dists_group_std_max=0.0207 train/reward_dists_group_std_min=0.0041 train/reward_dists_group_mean_std=0.0337 train/reward_ssim_group_std_mean=0.0080 train/reward_ssim_group_std_max=0.0171 train/reward_ssim_group_std_min=0.0032 train/reward_ssim_group_mean_std=0.1192 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.1343 train/adv_max=4.7695 train/adv_abs_mean=1.3017
+
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Epoch 34 Training:  27%|██▋       | 26/96 [00:40<01:49,  1.57s/it]
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Epoch 34 Training:  28%|██▊       | 27/96 [00:42<01:48,  1.57s/it]
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Epoch 34 Training:  29%|██▉       | 28/96 [00:43<01:46,  1.57s/it]
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Epoch 34 Training:  30%|███       | 29/96 [00:45<01:45,  1.57s/it]
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Epoch 34 Training:  31%|███▏      | 30/96 [00:47<01:43,  1.57s/it]
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Epoch 34 Training:  32%|███▏      | 31/96 [00:48<01:42,  1.57s/it]
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Epoch 34 Training:  33%|███▎      | 32/96 [00:50<01:40,  1.57s/it]
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Epoch 34 Training:  34%|███▍      | 33/96 [00:51<01:39,  1.58s/it]
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Epoch 34 Training:  35%|███▌      | 34/96 [00:53<01:37,  1.58s/it]
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Epoch 34 Training:  36%|███▋      | 35/96 [00:54<01:35,  1.57s/it]
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Epoch 34 Training:  38%|███▊      | 36/96 [00:56<01:34,  1.57s/it]
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Epoch 34 Training:  39%|███▊      | 37/96 [00:58<01:32,  1.58s/it]
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Epoch 34 Training:  40%|███▉      | 38/96 [00:59<01:31,  1.58s/it]
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Epoch 34 Training:  41%|████      | 39/96 [01:01<01:29,  1.58s/it]
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Epoch 34 Training:  42%|████▏     | 40/96 [01:02<01:28,  1.58s/it]
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Epoch 34 Training:  43%|████▎     | 41/96 [01:04<01:26,  1.57s/it]
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Epoch 34 Training:  44%|████▍     | 42/96 [01:05<01:25,  1.58s/it]
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Epoch 34 Training:  45%|████▍     | 43/96 [01:07<01:23,  1.58s/it]
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Epoch 34 Training:  46%|████▌     | 44/96 [01:09<01:22,  1.58s/it]
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Epoch 34 Training:  47%|████▋     | 45/96 [01:10<01:20,  1.58s/it]
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Epoch 34 Training:  48%|████▊     | 46/96 [01:12<01:18,  1.57s/it]
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Epoch 34 Training:  49%|████▉     | 47/96 [01:13<01:17,  1.57s/it]
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Epoch 34 Training:  50%|█████     | 48/96 [01:15<01:15,  1.57s/it]
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Epoch 34 Training:  51%|█████     | 49/96 [01:17<01:13,  1.57s/it]
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Epoch 34 Training:  52%|█████▏    | 50/96 [01:18<01:12,  1.58s/it]
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Epoch 34 Training:  53%|█████▎    | 51/96 [01:20<01:10,  1.58s/it]
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Epoch 34 Training:  54%|█████▍    | 52/96 [01:21<01:09,  1.57s/it]
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Epoch 34 Training:  55%|█████▌    | 53/96 [01:23<01:07,  1.57s/it]
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Epoch 34 Training:  56%|█████▋    | 54/96 [01:24<01:06,  1.58s/it]
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Epoch 34 Training:  57%|█████▋    | 55/96 [01:26<01:04,  1.58s/it]
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Epoch 34 Training:  58%|█████▊    | 56/96 [01:28<01:03,  1.58s/it]
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Epoch 34 Training:  59%|█████▉    | 57/96 [01:29<01:01,  1.58s/it]
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Epoch 34 Training:  60%|██████    | 58/96 [01:31<00:59,  1.58s/it]
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Epoch 34 Training:  61%|██████▏   | 59/96 [01:32<00:58,  1.58s/it]
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Epoch 34 Training:  62%|██████▎   | 60/96 [01:34<00:56,  1.58s/it]
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Epoch 34 Training:  64%|██████▎   | 61/96 [01:35<00:55,  1.57s/it]
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Epoch 34 Training:  65%|██████▍   | 62/96 [01:37<00:53,  1.57s/it]
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Epoch 34 Training:  66%|██████▌   | 63/96 [01:39<00:51,  1.57s/it]
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Epoch 34 Training:  67%|██████▋   | 64/96 [01:40<00:50,  1.57s/it]
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Epoch 34 Training:  68%|██████▊   | 65/96 [01:42<00:48,  1.57s/it]
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Epoch 34 Training:  69%|██████▉   | 66/96 [01:43<00:47,  1.57s/it]
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Epoch 34 Training:  70%|██████▉   | 67/96 [01:45<00:45,  1.57s/it]
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Epoch 34 Training:  71%|███████   | 68/96 [01:46<00:44,  1.57s/it]
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Epoch 34 Training:  72%|███████▏  | 69/96 [01:48<00:42,  1.57s/it]
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Epoch 34 Training:  73%|███████▎  | 70/96 [01:50<00:40,  1.57s/it]
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Epoch 34 Training:  74%|███████▍  | 71/96 [01:51<00:39,  1.58s/it]
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Epoch 34 Training:  75%|███████▌  | 72/96 [01:53<00:37,  1.58s/it]
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Epoch 34 Training:  76%|███████▌  | 73/96 [01:54<00:36,  1.58s/it]
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Epoch 34 Training:  77%|███████▋  | 74/96 [01:56<00:34,  1.58s/it]
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Epoch 34 Training:  78%|███████▊  | 75/96 [01:57<00:33,  1.58s/it]
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Epoch 34 Training:  79%|███████▉  | 76/96 [01:59<00:31,  1.58s/it]
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Epoch 34 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s][2026-10-01 16:26:05] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0034 | Epoch 034] train/policy_loss=24.8675 train/unweighted_policy_loss=4.9735 train/loss=24.8675 train/grad_norm=0.0651
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+[2026-10-01 16:26:05] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0035 | Epoch 034] timing/rollout_seconds=179.6954 timing/feedback_seconds=5.5307 timing/optimization_seconds=151.7950 timing/cycle_seconds=334.3906
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+[2026-10-01 16:29:09] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0035 | Epoch 035] train/reward_dists_mean=0.8490 train/reward_dists_std=0.0322 train/reward_ssim_mean=0.6522 train/reward_ssim_std=0.1045 train/reward_dists_group_std_mean=0.0077 train/reward_dists_group_std_max=0.0308 train/reward_dists_group_std_min=0.0040 train/reward_dists_group_mean_std=0.0310 train/reward_ssim_group_std_mean=0.0084 train/reward_ssim_group_std_max=0.0176 train/reward_ssim_group_std_min=0.0040 train/reward_ssim_group_mean_std=0.1041 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.8949 train/adv_max=5.0288 train/adv_abs_mean=1.2561
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Epoch 35 Training:   1%|          | 1/96 [00:01<02:20,  1.48s/it]
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Epoch 35 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 35 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 35 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.89it/s]
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Epoch 35 Training:   2%|▏         | 2/96 [00:03<02:25,  1.55s/it]
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Epoch 35 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 35 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 35 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.89it/s]
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Epoch 35 Training:   3%|▎         | 3/96 [00:04<02:25,  1.57s/it]
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Epoch 35 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 35 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.15it/s]
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Epoch 35 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.90it/s]
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Epoch 35 Training:   4%|▍         | 4/96 [00:06<02:24,  1.58s/it]
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Epoch 35 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 35 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.15it/s]
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Epoch 35 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.89it/s]
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Epoch 35 Training:   5%|▌         | 5/96 [00:07<02:23,  1.58s/it]
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Epoch 35 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 35 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 35 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.90it/s]
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Epoch 35 Training:   6%|▋         | 6/96 [00:09<02:22,  1.59s/it]
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Epoch 35 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 35 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 35 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.90it/s]
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Epoch 35 Training:   7%|▋         | 7/96 [00:11<02:21,  1.59s/it]
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Epoch 35 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 35 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 35 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.89it/s]
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Epoch 35 Training:   8%|▊         | 8/96 [00:12<02:19,  1.59s/it]
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Epoch 35 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 35 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 35 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.90it/s]
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Epoch 35 Training:   9%|▉         | 9/96 [00:14<02:18,  1.59s/it]
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Epoch 35 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 35 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 35 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.90it/s]
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Epoch 35 Training:  10%|█         | 10/96 [00:15<02:16,  1.59s/it]
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Epoch 35 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 35 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 35 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.90it/s]
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Epoch 35 Training:  11%|█▏        | 11/96 [00:17<02:15,  1.59s/it]
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Epoch 35 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 35 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 35 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.90it/s]
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Epoch 35 Training:  12%|█▎        | 12/96 [00:18<02:13,  1.59s/it]
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Epoch 35 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 35 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 35 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.89it/s]
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Epoch 35 Training:  14%|█▎        | 13/96 [00:20<02:12,  1.59s/it]
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Epoch 35 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 35 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 35 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.91it/s]
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Epoch 35 Training:  15%|█▍        | 14/96 [00:22<02:10,  1.59s/it]
+
Epoch 35 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 35 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 35 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.90it/s]
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Epoch 35 Training:  16%|█▌        | 15/96 [00:23<02:08,  1.59s/it]
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Epoch 35 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 35 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 35 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.90it/s]
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Epoch 35 Training:  17%|█▋        | 16/96 [00:25<02:07,  1.59s/it]
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Epoch 35 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 35 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 35 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.88it/s]
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Epoch 35 Training:  18%|█▊        | 17/96 [00:26<02:05,  1.59s/it]
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Epoch 35 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 35 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 35 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.90it/s]
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Epoch 35 Training:  19%|█▉        | 18/96 [00:28<02:04,  1.59s/it]
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Epoch 35 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 35 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 35 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.89it/s]
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Epoch 35 Training:  20%|█▉        | 19/96 [00:30<02:02,  1.59s/it]
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Epoch 35 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 35 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 35 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.90it/s]
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Epoch 35 Training:  21%|██        | 20/96 [00:31<02:01,  1.59s/it]
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Epoch 35 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 35 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 35 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.90it/s]
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Epoch 35 Training:  22%|██▏       | 21/96 [00:33<01:59,  1.59s/it]
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Epoch 35 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 35 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.90it/s]
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Epoch 35 Training:  23%|██▎       | 22/96 [00:34<01:57,  1.59s/it]
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Epoch 35 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 35 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 35 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.90it/s]
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Epoch 35 Training:  24%|██▍       | 23/96 [00:36<01:56,  1.59s/it]
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Epoch 35 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 35 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.90it/s]
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Epoch 35 Training:  25%|██▌       | 24/96 [00:38<01:54,  1.59s/it]
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Epoch 35 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 35 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 35 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.90it/s]
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Epoch 35 Training:  26%|██▌       | 25/96 [00:39<01:53,  1.59s/it]
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Epoch 35 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 35 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 35 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.89it/s]
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Epoch 35 Training:  27%|██▋       | 26/96 [00:41<01:51,  1.59s/it]
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Epoch 35 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 35 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 35 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.89it/s]
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Epoch 35 Training:  28%|██▊       | 27/96 [00:42<01:49,  1.59s/it]
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Epoch 35 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 35 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 35 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.89it/s]
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Epoch 35 Training:  29%|██▉       | 28/96 [00:44<01:48,  1.59s/it]
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Epoch 35 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 35 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 35 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.89it/s]
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Epoch 35 Training:  30%|███       | 29/96 [00:46<01:46,  1.59s/it]
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Epoch 35 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 35 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 35 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.90it/s]
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Epoch 35 Training:  31%|███▏      | 30/96 [00:47<01:45,  1.59s/it]
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Epoch 35 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 35 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.89it/s]
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Epoch 35 Training:  32%|███▏      | 31/96 [00:49<01:43,  1.59s/it]
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Epoch 35 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 35 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 35 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.91it/s]
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Epoch 35 Training:  33%|███▎      | 32/96 [00:50<01:41,  1.59s/it]
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Epoch 35 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 35 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.91it/s]
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Epoch 35 Training:  34%|███▍      | 33/96 [00:52<01:40,  1.59s/it]
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Epoch 35 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 35 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.89it/s]
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Epoch 35 Training:  35%|███▌      | 34/96 [00:54<01:38,  1.59s/it]
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Epoch 35 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 35 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
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Epoch 35 Training:  36%|███▋      | 35/96 [00:55<01:37,  1.59s/it]
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Epoch 35 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 35 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.90it/s]
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Epoch 35 Training:  38%|███▊      | 36/96 [00:57<01:35,  1.59s/it]
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Epoch 35 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 35 Training:  39%|███▊      | 37/96 [00:58<01:34,  1.59s/it]
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Epoch 35 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 35 Training:  40%|███▉      | 38/96 [01:00<01:32,  1.59s/it]
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Epoch 35 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 35 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.90it/s]
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Epoch 35 Training:  41%|████      | 39/96 [01:01<01:30,  1.59s/it]
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Epoch 35 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 35 Training:  42%|████▏     | 40/96 [01:03<01:29,  1.59s/it]
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Epoch 35 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 35 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.91it/s]
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Epoch 35 Training:  43%|████▎     | 41/96 [01:05<01:27,  1.59s/it]
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Epoch 35 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 35 Training:  44%|████▍     | 42/96 [01:06<01:25,  1.59s/it]
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Epoch 35 Training:  45%|████▍     | 43/96 [01:08<01:24,  1.59s/it]
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Epoch 35 Training:  46%|████▌     | 44/96 [01:09<01:22,  1.59s/it]
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Epoch 35 Training:  47%|████▋     | 45/96 [01:11<01:21,  1.59s/it]
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Epoch 35 Training:  48%|████▊     | 46/96 [01:13<01:19,  1.59s/it]
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Epoch 35 Training:  49%|████▉     | 47/96 [01:14<01:18,  1.60s/it]
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Epoch 35 Training:  50%|█████     | 48/96 [01:16<01:16,  1.60s/it]
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Epoch 35 Training:  51%|█████     | 49/96 [01:17<01:15,  1.60s/it]
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Epoch 35 Training:  52%|█████▏    | 50/96 [01:19<01:13,  1.60s/it]
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Epoch 35 Training:  53%|█████▎    | 51/96 [01:21<01:12,  1.60s/it]
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Epoch 35 Training:  54%|█████▍    | 52/96 [01:22<01:10,  1.60s/it]
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Epoch 35 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s][2026-10-01 16:31:42] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0035 | Epoch 035] train/policy_loss=24.5292 train/unweighted_policy_loss=4.9058 train/loss=24.5292 train/grad_norm=0.0643
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+[2026-10-01 16:31:42] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0036 | Epoch 035] timing/rollout_seconds=179.7430 timing/feedback_seconds=5.6173 timing/optimization_seconds=153.7323 timing/cycle_seconds=336.6485
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+
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+
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+[2026-10-01 16:34:45] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0036 | Epoch 036] train/reward_dists_mean=0.8507 train/reward_dists_std=0.0256 train/reward_ssim_mean=0.6379 train/reward_ssim_std=0.0999 train/reward_dists_group_std_mean=0.0068 train/reward_dists_group_std_max=0.0124 train/reward_dists_group_std_min=0.0035 train/reward_dists_group_mean_std=0.0246 train/reward_ssim_group_std_mean=0.0078 train/reward_ssim_group_std_max=0.0196 train/reward_ssim_group_std_min=0.0040 train/reward_ssim_group_mean_std=0.0996 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.7595 train/adv_max=4.1733 train/adv_abs_mean=1.2058
+
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Epoch 36 Training:  30%|███       | 29/96 [00:45<01:46,  1.59s/it]
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Epoch 36 Training:  31%|███▏      | 30/96 [00:47<01:44,  1.59s/it]
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Epoch 36 Training:  32%|███▏      | 31/96 [00:49<01:43,  1.59s/it]
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Epoch 36 Training:  33%|███▎      | 32/96 [00:50<01:41,  1.59s/it]
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Epoch 36 Training:  34%|███▍      | 33/96 [00:52<01:40,  1.59s/it]
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Epoch 36 Training:  35%|███▌      | 34/96 [00:53<01:38,  1.59s/it]
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Epoch 36 Training:  36%|███▋      | 35/96 [00:55<01:37,  1.59s/it]
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Epoch 36 Training:  38%|███▊      | 36/96 [00:57<01:35,  1.60s/it]
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Epoch 36 Training:  39%|███▊      | 37/96 [00:58<01:33,  1.59s/it]
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Epoch 36 Training:  40%|███▉      | 38/96 [01:00<01:32,  1.59s/it]
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Epoch 36 Training:  41%|████      | 39/96 [01:01<01:30,  1.59s/it]
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Epoch 36 Training:  42%|████▏     | 40/96 [01:03<01:29,  1.59s/it]
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Epoch 36 Training:  43%|████▎     | 41/96 [01:05<01:27,  1.59s/it]
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Epoch 36 Training:  44%|████▍     | 42/96 [01:06<01:25,  1.59s/it]
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Epoch 36 Training:  45%|████▍     | 43/96 [01:08<01:24,  1.59s/it]
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Epoch 36 Training:  46%|████▌     | 44/96 [01:09<01:22,  1.59s/it]
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Epoch 36 Training:  47%|████▋     | 45/96 [01:11<01:21,  1.59s/it]
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Epoch 36 Training:  48%|████▊     | 46/96 [01:12<01:19,  1.59s/it]
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Epoch 36 Training:  49%|████▉     | 47/96 [01:14<01:17,  1.59s/it]
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Epoch 36 Training:  50%|█████     | 48/96 [01:16<01:16,  1.59s/it]
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Epoch 36 Training:  51%|█████     | 49/96 [01:17<01:14,  1.59s/it]
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Epoch 36 Training:  52%|█████▏    | 50/96 [01:19<01:13,  1.59s/it]
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Epoch 36 Training:  53%|█████▎    | 51/96 [01:20<01:11,  1.59s/it]
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Epoch 36 Training:  54%|█████▍    | 52/96 [01:22<01:09,  1.59s/it]
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Epoch 36 Training:  55%|█████▌    | 53/96 [01:24<01:08,  1.59s/it]
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Epoch 36 Training:  56%|█████▋    | 54/96 [01:25<01:06,  1.59s/it]
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Epoch 36 Training:  57%|█████▋    | 55/96 [01:27<01:05,  1.59s/it]
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Epoch 36 Training:  58%|█████▊    | 56/96 [01:28<01:03,  1.59s/it]
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Epoch 36 Training:  59%|█████▉    | 57/96 [01:30<01:01,  1.59s/it]
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Epoch 36 Training:  60%|██████    | 58/96 [01:32<01:00,  1.59s/it]
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Epoch 36 Training:  61%|██████▏   | 59/96 [01:33<00:58,  1.59s/it]
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Epoch 36 Training:  62%|██████▎   | 60/96 [01:35<00:57,  1.59s/it]
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Epoch 36 Training:  64%|██████▎   | 61/96 [01:36<00:55,  1.59s/it]
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Epoch 36 Training:  65%|██████▍   | 62/96 [01:38<00:53,  1.59s/it]
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Epoch 36 Training:  66%|██████▌   | 63/96 [01:39<00:52,  1.59s/it]
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Epoch 36 Training:  67%|██████▋   | 64/96 [01:41<00:50,  1.59s/it]
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Epoch 36 Training:  68%|██████▊   | 65/96 [01:43<00:49,  1.59s/it]
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Epoch 36 Training:  69%|██████▉   | 66/96 [01:44<00:47,  1.60s/it]
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Epoch 36 Training:  70%|██████▉   | 67/96 [01:46<00:46,  1.59s/it]
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Epoch 36 Training:  71%|███████   | 68/96 [01:47<00:44,  1.59s/it]
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Epoch 36 Training:  72%|███████▏  | 69/96 [01:49<00:42,  1.59s/it]
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Epoch 36 Training:  73%|███████▎  | 70/96 [01:51<00:41,  1.59s/it]
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Epoch 36 Training:  74%|███████▍  | 71/96 [01:52<00:39,  1.59s/it]
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Epoch 36 Training:  75%|███████▌  | 72/96 [01:54<00:38,  1.59s/it]
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Epoch 36 Training:  76%|███████▌  | 73/96 [01:55<00:36,  1.59s/it]
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Epoch 36 Training:  77%|███████▋  | 74/96 [01:57<00:34,  1.59s/it]
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Epoch 36 Training:  78%|███████▊  | 75/96 [01:59<00:33,  1.59s/it]
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Epoch 36 Training:  79%|███████▉  | 76/96 [02:00<00:31,  1.59s/it]
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Epoch 36 Training:  80%|████████  | 77/96 [02:02<00:30,  1.59s/it]
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Epoch 36 Training:  81%|████████▏ | 78/96 [02:03<00:28,  1.59s/it]
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Epoch 36 Training:  82%|████████▏ | 79/96 [02:05<00:27,  1.62s/it]
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Epoch 36 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s][2026-10-01 16:37:18] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0036 | Epoch 036] train/policy_loss=24.7234 train/unweighted_policy_loss=4.9447 train/loss=24.7234 train/grad_norm=0.0660
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+[2026-10-01 16:37:18] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0037 | Epoch 036] timing/rollout_seconds=179.5189 timing/feedback_seconds=5.5179 timing/optimization_seconds=153.7128 timing/cycle_seconds=336.3648
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+[2026-10-01 16:40:21] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0037 | Epoch 037] train/reward_dists_mean=0.8463 train/reward_dists_std=0.0310 train/reward_ssim_mean=0.6510 train/reward_ssim_std=0.1045 train/reward_dists_group_std_mean=0.0074 train/reward_dists_group_std_max=0.0147 train/reward_dists_group_std_min=0.0039 train/reward_dists_group_mean_std=0.0299 train/reward_ssim_group_std_mean=0.0073 train/reward_ssim_group_std_max=0.0208 train/reward_ssim_group_std_min=0.0031 train/reward_ssim_group_mean_std=0.1042 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.4659 train/adv_max=4.8338 train/adv_abs_mean=1.2149
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Epoch 37 Training:   4%|▍         | 4/96 [00:06<02:23,  1.56s/it]
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Epoch 37 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 37 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.95it/s]
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Epoch 37 Training:   5%|▌         | 5/96 [00:07<02:21,  1.56s/it]
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Epoch 37 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 37 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.96it/s]
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Epoch 37 Training:   6%|▋         | 6/96 [00:09<02:20,  1.56s/it]
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Epoch 37 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 37 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 37 Training:   7%|▋         | 7/96 [00:10<02:19,  1.56s/it]
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Epoch 37 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 37 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.97it/s]
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Epoch 37 Training:   8%|▊         | 8/96 [00:12<02:17,  1.56s/it]
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Epoch 37 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 37 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.91it/s]
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Epoch 37 Training:   9%|▉         | 9/96 [00:14<02:16,  1.57s/it]
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Epoch 37 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 37 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.91it/s]
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Epoch 37 Training:  10%|█         | 10/96 [00:15<02:15,  1.57s/it]
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Epoch 37 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 37 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.90it/s]
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Epoch 37 Training:  11%|█▏        | 11/96 [00:17<02:13,  1.57s/it]
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Epoch 37 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 37 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.91it/s]
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Epoch 37 Training:  12%|█▎        | 12/96 [00:18<02:12,  1.57s/it]
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Epoch 37 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 37 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.94it/s]
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Epoch 37 Training:  14%|█▎        | 13/96 [00:20<02:10,  1.57s/it]
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Epoch 37 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 37 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.95it/s]
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Epoch 37 Training:  15%|█▍        | 14/96 [00:21<02:08,  1.57s/it]
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Epoch 37 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 37 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.90it/s]
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Epoch 37 Training:  16%|█▌        | 15/96 [00:23<02:07,  1.57s/it]
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Epoch 37 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 37 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.91it/s]
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Epoch 37 Training:  17%|█▋        | 16/96 [00:25<02:05,  1.57s/it]
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Epoch 37 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 37 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.91it/s]
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Epoch 37 Training:  18%|█▊        | 17/96 [00:26<02:04,  1.57s/it]
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Epoch 37 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 37 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.95it/s]
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Epoch 37 Training:  19%|█▉        | 18/96 [00:28<02:02,  1.57s/it]
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Epoch 37 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 37 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.95it/s]
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Epoch 37 Training:  20%|█▉        | 19/96 [00:29<02:00,  1.56s/it]
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Epoch 37 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 37 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.95it/s]
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Epoch 37 Training:  21%|██        | 20/96 [00:31<01:58,  1.56s/it]
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Epoch 37 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 37 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.90it/s]
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Epoch 37 Training:  22%|██▏       | 21/96 [00:32<01:57,  1.57s/it]
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Epoch 37 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 37 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.95it/s]
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Epoch 37 Training:  23%|██▎       | 22/96 [00:34<01:55,  1.56s/it]
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Epoch 37 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 37 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.90it/s]
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Epoch 37 Training:  24%|██▍       | 23/96 [00:35<01:54,  1.57s/it]
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Epoch 37 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 37 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.94it/s]
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Epoch 37 Training:  25%|██▌       | 24/96 [00:37<01:52,  1.57s/it]
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Epoch 37 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 37 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.95it/s]
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Epoch 37 Training:  26%|██▌       | 25/96 [00:39<01:50,  1.56s/it]
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Epoch 37 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 37 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.89it/s]
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Epoch 37 Training:  27%|██▋       | 26/96 [00:40<01:49,  1.57s/it]
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Epoch 37 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 37 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.95it/s]
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Epoch 37 Training:  28%|██▊       | 27/96 [00:42<01:47,  1.56s/it]
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Epoch 37 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 37 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.77it/s]
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Epoch 37 Training:  29%|██▉       | 28/96 [00:43<01:48,  1.59s/it]
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Epoch 37 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 37 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.90it/s]
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Epoch 37 Training:  30%|███       | 29/96 [00:45<01:46,  1.59s/it]
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Epoch 37 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 37 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.91it/s]
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Epoch 37 Training:  31%|███▏      | 30/96 [00:47<01:44,  1.58s/it]
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Epoch 37 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 37 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.95it/s]
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Epoch 37 Training:  32%|███▏      | 31/96 [00:48<01:42,  1.58s/it]
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Epoch 37 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 37 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.89it/s]
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Epoch 37 Training:  33%|███▎      | 32/96 [00:50<01:40,  1.58s/it]
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Epoch 37 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 37 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.90it/s]
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Epoch 37 Training:  34%|███▍      | 33/96 [00:51<01:39,  1.58s/it]
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Epoch 37 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 37 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.90it/s]
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Epoch 37 Training:  35%|███▌      | 34/96 [00:53<01:40,  1.62s/it]
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Epoch 37 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 37 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.95it/s]
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Epoch 37 Training:  36%|███▋      | 35/96 [00:55<01:37,  1.60s/it]
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Epoch 37 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 37 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.90it/s]
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Epoch 37 Training:  38%|███▊      | 36/96 [00:56<01:35,  1.59s/it]
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Epoch 37 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 37 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.96it/s]
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Epoch 37 Training:  39%|███▊      | 37/96 [00:58<01:33,  1.58s/it]
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Epoch 37 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 37 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.94it/s]
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Epoch 37 Training:  40%|███▉      | 38/96 [00:59<01:31,  1.58s/it]
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Epoch 37 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 37 Training:  41%|████      | 39/96 [01:01<01:29,  1.57s/it]
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Epoch 37 Training:  42%|████▏     | 40/96 [01:02<01:28,  1.58s/it]
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Epoch 37 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 37 Training:  43%|████▎     | 41/96 [01:04<01:26,  1.58s/it]
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Epoch 37 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 37 Training:  44%|████▍     | 42/96 [01:06<01:25,  1.58s/it]
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Epoch 37 Training:  45%|████▍     | 43/96 [01:07<01:23,  1.57s/it]
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Epoch 37 Training:  46%|████▌     | 44/96 [01:09<01:21,  1.57s/it]
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Epoch 37 Training:  47%|████▋     | 45/96 [01:10<01:20,  1.57s/it]
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Epoch 37 Training:  48%|████▊     | 46/96 [01:12<01:18,  1.58s/it]
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Epoch 37 Training:  49%|████▉     | 47/96 [01:13<01:17,  1.58s/it]
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Epoch 37 Training:  50%|█████     | 48/96 [01:15<01:15,  1.58s/it]
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Epoch 37 Training:  51%|█████     | 49/96 [01:17<01:14,  1.58s/it]
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Epoch 37 Training:  52%|█████▏    | 50/96 [01:18<01:12,  1.58s/it]
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Epoch 37 Training:  53%|█████▎    | 51/96 [01:20<01:11,  1.58s/it]
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Epoch 37 Training:  54%|█████▍    | 52/96 [01:21<01:09,  1.58s/it]
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Epoch 37 Training:  55%|█████▌    | 53/96 [01:23<01:08,  1.59s/it]
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Epoch 37 Training:  56%|█████▋    | 54/96 [01:25<01:06,  1.59s/it]
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Epoch 37 Training:  57%|█████▋    | 55/96 [01:26<01:05,  1.59s/it]
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Epoch 37 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s][2026-10-01 16:42:53] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0037 | Epoch 037] train/policy_loss=24.5342 train/unweighted_policy_loss=4.9068 train/loss=24.5342 train/grad_norm=0.0601
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+[2026-10-01 16:42:53] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0038 | Epoch 037] timing/rollout_seconds=179.7808 timing/feedback_seconds=5.5493 timing/optimization_seconds=152.0643 timing/cycle_seconds=334.7952
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+
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+[2026-10-01 16:45:56] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0038 | Epoch 038] train/reward_dists_mean=0.8453 train/reward_dists_std=0.0359 train/reward_ssim_mean=0.6655 train/reward_ssim_std=0.0868 train/reward_dists_group_std_mean=0.0087 train/reward_dists_group_std_max=0.0562 train/reward_dists_group_std_min=0.0031 train/reward_dists_group_mean_std=0.0338 train/reward_ssim_group_std_mean=0.0080 train/reward_ssim_group_std_max=0.0190 train/reward_ssim_group_std_min=0.0027 train/reward_ssim_group_mean_std=0.0863 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.7150 train/adv_max=4.0721 train/adv_abs_mean=1.2117
+
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Epoch 38 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.91it/s]
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Epoch 38 Training:  33%|███▎      | 32/96 [00:50<01:41,  1.58s/it]
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Epoch 38 Training:  34%|███▍      | 33/96 [00:51<01:39,  1.58s/it]
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Epoch 38 Training:  35%|███▌      | 34/96 [00:53<01:37,  1.57s/it]
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Epoch 38 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.94it/s]
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Epoch 38 Training:  36%|███▋      | 35/96 [00:55<01:35,  1.57s/it]
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Epoch 38 Training:  38%|███▊      | 36/96 [00:56<01:34,  1.58s/it]
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Epoch 38 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 38 Training:  39%|███▊      | 37/96 [00:58<01:33,  1.58s/it]
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Epoch 38 Training:  40%|███▉      | 38/96 [00:59<01:31,  1.58s/it]
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Epoch 38 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.91it/s]
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Epoch 38 Training:  41%|████      | 39/96 [01:01<01:30,  1.58s/it]
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Epoch 38 Training:  42%|████▏     | 40/96 [01:02<01:28,  1.58s/it]
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Epoch 38 Training:  43%|████▎     | 41/96 [01:04<01:27,  1.59s/it]
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Epoch 38 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 38 Training:  44%|████▍     | 42/96 [01:06<01:25,  1.58s/it]
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Epoch 38 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 38 Training:  45%|████▍     | 43/96 [01:07<01:23,  1.58s/it]
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Epoch 38 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 38 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
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Epoch 38 Training:  46%|████▌     | 44/96 [01:09<01:21,  1.58s/it]
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Epoch 38 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 38 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 38 Training:  47%|████▋     | 45/96 [01:10<01:20,  1.58s/it]
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Epoch 38 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 38 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 38 Training:  48%|████▊     | 46/96 [01:12<01:18,  1.58s/it]
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Epoch 38 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 38 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 38 Training:  49%|████▉     | 47/96 [01:13<01:17,  1.58s/it]
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Epoch 38 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 38 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.94it/s]
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Epoch 38 Training:  50%|█████     | 48/96 [01:15<01:15,  1.57s/it]
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Epoch 38 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 38 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.91it/s]
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Epoch 38 Training:  51%|█████     | 49/96 [01:17<01:13,  1.57s/it]
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Epoch 38 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 38 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 38 Training:  52%|█████▏    | 50/96 [01:18<01:12,  1.57s/it]
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Epoch 38 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 38 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.91it/s]
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Epoch 38 Training:  53%|█████▎    | 51/96 [01:20<01:10,  1.58s/it]
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Epoch 38 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 38 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.94it/s]
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Epoch 38 Training:  54%|█████▍    | 52/96 [01:21<01:09,  1.57s/it]
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Epoch 38 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 38 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 38 Training:  55%|█████▌    | 53/96 [01:23<01:07,  1.57s/it]
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Epoch 38 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 38 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.94it/s]
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Epoch 38 Training:  56%|█████▋    | 54/96 [01:24<01:06,  1.57s/it]
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Epoch 38 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 38 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 38 Training:  57%|█████▋    | 55/96 [01:26<01:04,  1.57s/it]
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Epoch 38 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 38 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.95it/s]
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Epoch 38 Training:  58%|█████▊    | 56/96 [01:28<01:02,  1.57s/it]
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Epoch 38 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 38 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 38 Training:  59%|█████▉    | 57/96 [01:29<01:01,  1.57s/it]
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Epoch 38 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 38 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.90it/s]
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Epoch 38 Training:  60%|██████    | 58/96 [01:31<00:59,  1.57s/it]
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Epoch 38 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 38 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.94it/s]
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Epoch 38 Training:  61%|██████▏   | 59/96 [01:32<00:58,  1.57s/it]
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Epoch 38 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.95it/s]
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Epoch 38 Training:  62%|██████▎   | 60/96 [01:34<00:56,  1.57s/it]
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Epoch 38 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 38 Training:  64%|██████▎   | 61/96 [01:35<00:55,  1.57s/it]
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Epoch 38 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.94it/s]
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Epoch 38 Training:  65%|██████▍   | 62/96 [01:37<00:53,  1.57s/it]
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Epoch 38 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 38 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 38 Training:  66%|██████▌   | 63/96 [01:39<00:51,  1.57s/it]
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Epoch 38 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 38 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.91it/s]
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Epoch 38 Training:  67%|██████▋   | 64/96 [01:40<00:50,  1.58s/it]
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Epoch 38 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 38 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.91it/s]
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Epoch 38 Training:  68%|██████▊   | 65/96 [01:42<00:48,  1.58s/it]
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Epoch 38 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.94it/s]
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Epoch 38 Training:  69%|██████▉   | 66/96 [01:43<00:47,  1.57s/it]
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Epoch 38 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
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Epoch 38 Training:  70%|██████▉   | 67/96 [01:45<00:45,  1.57s/it]
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Epoch 38 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 38 Training:  71%|███████   | 68/96 [01:46<00:44,  1.58s/it]
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Epoch 38 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 38 Training:  72%|███████▏  | 69/96 [01:48<00:42,  1.58s/it]
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Epoch 38 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
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Epoch 38 Training:  73%|███████▎  | 70/96 [01:50<00:41,  1.58s/it]
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Epoch 38 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 38 Training:  74%|███████▍  | 71/96 [01:51<00:39,  1.58s/it]
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Epoch 38 Training:  75%|███████▌  | 72/96 [01:53<00:37,  1.58s/it]
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Epoch 38 Training:  76%|███████▌  | 73/96 [01:55<00:37,  1.61s/it]
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Epoch 38 Training:  77%|███████▋  | 74/96 [01:56<00:35,  1.60s/it]
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Epoch 38 Training:  78%|███████▊  | 75/96 [01:58<00:33,  1.59s/it]
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Epoch 38 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.91it/s]
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Epoch 38 Training:  79%|███████▉  | 76/96 [01:59<00:31,  1.59s/it]
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Epoch 38 Training:  80%|████████  | 77/96 [02:01<00:30,  1.58s/it]
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Epoch 38 Training:  81%|████████▏ | 78/96 [02:02<00:28,  1.58s/it]
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Epoch 38 Training:  82%|████████▏ | 79/96 [02:04<00:28,  1.66s/it]
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Epoch 38 Training:  83%|████████▎ | 80/96 [02:06<00:26,  1.63s/it]
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Epoch 38 Training:  84%|████████▍ | 81/96 [02:07<00:24,  1.62s/it]
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Epoch 38 Training:  85%|████████▌ | 82/96 [02:09<00:22,  1.60s/it]
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Epoch 38 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s][2026-10-01 16:48:27] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0038 | Epoch 038] train/policy_loss=24.7532 train/unweighted_policy_loss=4.9506 train/loss=24.7532 train/grad_norm=0.0697
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+[2026-10-01 16:48:27] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0039 | Epoch 038] timing/rollout_seconds=179.3992 timing/feedback_seconds=4.9787 timing/optimization_seconds=152.1942 timing/cycle_seconds=334.5399
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+[2026-10-01 16:51:31] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0039 | Epoch 039] train/reward_dists_mean=0.8508 train/reward_dists_std=0.0305 train/reward_ssim_mean=0.6508 train/reward_ssim_std=0.0921 train/reward_dists_group_std_mean=0.0077 train/reward_dists_group_std_max=0.0363 train/reward_dists_group_std_min=0.0029 train/reward_dists_group_mean_std=0.0291 train/reward_ssim_group_std_mean=0.0072 train/reward_ssim_group_std_max=0.0204 train/reward_ssim_group_std_min=0.0036 train/reward_ssim_group_mean_std=0.0917 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.4965 train/adv_max=4.2249 train/adv_abs_mean=1.1699
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Epoch 39 Training:   8%|▊         | 8/96 [00:12<02:18,  1.57s/it]
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Epoch 39 Training:   9%|▉         | 9/96 [00:14<02:16,  1.57s/it]
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Epoch 39 Training:  10%|█         | 10/96 [00:15<02:15,  1.57s/it]
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Epoch 39 Training:  11%|█▏        | 11/96 [00:17<02:13,  1.57s/it]
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Epoch 39 Training:  12%|█▎        | 12/96 [00:18<02:12,  1.57s/it]
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Epoch 39 Training:  14%|█▎        | 13/96 [00:20<02:10,  1.57s/it]
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Epoch 39 Training:  15%|█▍        | 14/96 [00:21<02:09,  1.57s/it]
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Epoch 39 Training:  16%|█▌        | 15/96 [00:23<02:07,  1.57s/it]
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Epoch 39 Training:  17%|█▋        | 16/96 [00:25<02:05,  1.57s/it]
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Epoch 39 Training:  18%|█▊        | 17/96 [00:26<02:04,  1.57s/it]
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Epoch 39 Training:  19%|█▉        | 18/96 [00:28<02:02,  1.58s/it]
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Epoch 39 Training:  20%|█▉        | 19/96 [00:29<02:01,  1.58s/it]
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Epoch 39 Training:  21%|██        | 20/96 [00:31<01:59,  1.58s/it]
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Epoch 39 Training:  22%|██▏       | 21/96 [00:32<01:58,  1.58s/it]
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Epoch 39 Training:  23%|██▎       | 22/96 [00:34<01:56,  1.58s/it]
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Epoch 39 Training:  24%|██▍       | 23/96 [00:36<01:55,  1.59s/it]
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Epoch 39 Training:  25%|██▌       | 24/96 [00:37<01:53,  1.58s/it]
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Epoch 39 Training:  26%|██▌       | 25/96 [00:39<01:52,  1.58s/it]
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Epoch 39 Training:  27%|██▋       | 26/96 [00:40<01:50,  1.58s/it]
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Epoch 39 Training:  28%|██▊       | 27/96 [00:42<01:48,  1.57s/it]
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Epoch 39 Training:  29%|██▉       | 28/96 [00:44<01:47,  1.57s/it]
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Epoch 39 Training:  30%|███       | 29/96 [00:45<01:45,  1.57s/it]
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Epoch 39 Training:  31%|███▏      | 30/96 [00:47<01:43,  1.57s/it]
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Epoch 39 Training:  32%|███▏      | 31/96 [00:48<01:42,  1.57s/it]
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Epoch 39 Training:  33%|███▎      | 32/96 [00:50<01:40,  1.57s/it]
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Epoch 39 Training:  34%|███▍      | 33/96 [00:51<01:38,  1.57s/it]
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Epoch 39 Training:  35%|███▌      | 34/96 [00:53<01:37,  1.57s/it]
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Epoch 39 Training:  36%|███▋      | 35/96 [00:55<01:35,  1.57s/it]
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Epoch 39 Training:  38%|███▊      | 36/96 [00:56<01:34,  1.57s/it]
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Epoch 39 Training:  39%|███▊      | 37/96 [00:58<01:32,  1.57s/it]
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Epoch 39 Training:  40%|███▉      | 38/96 [00:59<01:31,  1.57s/it]
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Epoch 39 Training:  41%|████      | 39/96 [01:01<01:29,  1.58s/it]
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Epoch 39 Training:  42%|████▏     | 40/96 [01:02<01:28,  1.58s/it]
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Epoch 39 Training:  43%|████▎     | 41/96 [01:04<01:27,  1.59s/it]
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Epoch 39 Training:  44%|████▍     | 42/96 [01:06<01:25,  1.59s/it]
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Epoch 39 Training:  45%|████▍     | 43/96 [01:07<01:24,  1.59s/it]
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Epoch 39 Training:  46%|████▌     | 44/96 [01:09<01:22,  1.58s/it]
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Epoch 39 Training:  47%|████▋     | 45/96 [01:10<01:20,  1.58s/it]
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Epoch 39 Training:  48%|████▊     | 46/96 [01:12<01:18,  1.58s/it]
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Epoch 39 Training:  49%|████▉     | 47/96 [01:13<01:17,  1.58s/it]
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Epoch 39 Training:  50%|█████     | 48/96 [01:15<01:15,  1.58s/it]
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Epoch 39 Training:  51%|█████     | 49/96 [01:17<01:14,  1.58s/it]
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Epoch 39 Training:  52%|█████▏    | 50/96 [01:18<01:12,  1.57s/it]
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Epoch 39 Training:  53%|█████▎    | 51/96 [01:20<01:10,  1.57s/it]
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Epoch 39 Training:  54%|█████▍    | 52/96 [01:21<01:09,  1.58s/it]
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Epoch 39 Training:  55%|█████▌    | 53/96 [01:23<01:07,  1.57s/it]
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Epoch 39 Training:  56%|█████▋    | 54/96 [01:24<01:06,  1.57s/it]
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Epoch 39 Training:  57%|█████▋    | 55/96 [01:26<01:04,  1.57s/it]
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Epoch 39 Training:  58%|█████▊    | 56/96 [01:28<01:02,  1.57s/it]
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Epoch 39 Training:  59%|█████▉    | 57/96 [01:29<01:01,  1.57s/it]
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Epoch 39 Training:  60%|██████    | 58/96 [01:31<00:59,  1.57s/it]
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Epoch 39 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s][2026-10-01 16:54:02] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0039 | Epoch 039] train/policy_loss=25.0226 train/unweighted_policy_loss=5.0045 train/loss=25.0226 train/grad_norm=0.0578
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+[2026-10-01 16:54:02] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0040 | Epoch 039] timing/rollout_seconds=180.1353 timing/feedback_seconds=5.9571 timing/optimization_seconds=151.7579 timing/cycle_seconds=334.9214
+[2026-10-01 16:54:04] [Rank 0] [INFO] [flow_factory.models.abc]: Saving LoRA weights for transformer role=base to /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/runs/klein4b_nft_flir_full_ssim_dists/saves/klein4b_nft_flir_full_ssim_dists/checkpoints/checkpoint-40
+[2026-10-01 16:54:04] [Rank 0] [INFO] [flow_factory.models.abc]: Checkpoint saved successfully to /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/runs/klein4b_nft_flir_full_ssim_dists/saves/klein4b_nft_flir_full_ssim_dists/checkpoints/checkpoint-40
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+[2026-10-01 16:54:57] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0040 | Epoch 040] eval/flir_rl_full/reward_dists_mean=0.8472 eval/flir_rl_full/reward_dists_std=0.0317 eval/flir_rl_full/reward_psnr_mean=19.8359 eval/flir_rl_full/reward_psnr_std=2.6780 eval/flir_rl_full/reward_ssim_mean=0.6219 eval/flir_rl_full/reward_ssim_std=0.0836
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+
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+
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+[2026-10-01 16:58:01] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0040 | Epoch 040] train/reward_dists_mean=0.8494 train/reward_dists_std=0.0289 train/reward_ssim_mean=0.6635 train/reward_ssim_std=0.0985 train/reward_dists_group_std_mean=0.0077 train/reward_dists_group_std_max=0.0312 train/reward_dists_group_std_min=0.0038 train/reward_dists_group_mean_std=0.0275 train/reward_ssim_group_std_mean=0.0076 train/reward_ssim_group_std_max=0.0170 train/reward_ssim_group_std_min=0.0025 train/reward_ssim_group_mean_std=0.0982 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.6370 train/adv_max=4.2272 train/adv_abs_mean=1.2478
+
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Epoch 40 Training:  29%|██▉       | 28/96 [00:44<01:47,  1.57s/it]
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Epoch 40 Training:  30%|███       | 29/96 [00:45<01:45,  1.57s/it]
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Epoch 40 Training:  31%|███▏      | 30/96 [00:47<01:43,  1.57s/it]
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Epoch 40 Training:  32%|███▏      | 31/96 [00:48<01:42,  1.57s/it]
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Epoch 40 Training:  33%|███▎      | 32/96 [00:50<01:40,  1.57s/it]
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Epoch 40 Training:  34%|███▍      | 33/96 [00:52<01:39,  1.57s/it]
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Epoch 40 Training:  35%|███▌      | 34/96 [00:53<01:37,  1.57s/it]
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Epoch 40 Training:  38%|███▊      | 36/96 [00:56<01:34,  1.57s/it]
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Epoch 40 Training:  39%|███▊      | 37/96 [00:58<01:32,  1.57s/it]
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Epoch 40 Training:  40%|███▉      | 38/96 [00:59<01:31,  1.57s/it]
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Epoch 40 Training:  41%|████      | 39/96 [01:01<01:29,  1.57s/it]
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Epoch 40 Training:  42%|████▏     | 40/96 [01:03<01:28,  1.57s/it]
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Epoch 40 Training:  43%|████▎     | 41/96 [01:04<01:26,  1.58s/it]
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Epoch 40 Training:  44%|████▍     | 42/96 [01:06<01:24,  1.57s/it]
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Epoch 40 Training:  45%|████▍     | 43/96 [01:07<01:23,  1.58s/it]
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Epoch 40 Training:  46%|████▌     | 44/96 [01:09<01:21,  1.58s/it]
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Epoch 40 Training:  47%|████▋     | 45/96 [01:10<01:20,  1.57s/it]
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Epoch 40 Training:  48%|████▊     | 46/96 [01:12<01:18,  1.58s/it]
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Epoch 40 Training:  49%|████▉     | 47/96 [01:14<01:17,  1.58s/it]
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Epoch 40 Training:  50%|█████     | 48/96 [01:15<01:15,  1.57s/it]
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Epoch 40 Training:  51%|█████     | 49/96 [01:17<01:13,  1.57s/it]
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Epoch 40 Training:  52%|█████▏    | 50/96 [01:18<01:12,  1.57s/it]
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Epoch 40 Training:  53%|█████▎    | 51/96 [01:20<01:10,  1.57s/it]
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Epoch 40 Training:  54%|█████▍    | 52/96 [01:21<01:09,  1.57s/it]
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Epoch 40 Training:  55%|█████▌    | 53/96 [01:23<01:07,  1.57s/it]
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Epoch 40 Training:  56%|█████▋    | 54/96 [01:25<01:06,  1.58s/it]
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Epoch 40 Training:  57%|█████▋    | 55/96 [01:26<01:04,  1.58s/it]
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Epoch 40 Training:  58%|█████▊    | 56/96 [01:28<01:02,  1.57s/it]
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Epoch 40 Training:  59%|█████▉    | 57/96 [01:29<01:01,  1.57s/it]
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Epoch 40 Training:  60%|██████    | 58/96 [01:31<00:59,  1.57s/it]
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Epoch 40 Training:  61%|██████▏   | 59/96 [01:32<00:58,  1.57s/it]
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Epoch 40 Training:  62%|██████▎   | 60/96 [01:34<00:56,  1.58s/it]
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Epoch 40 Training:  64%|██████▎   | 61/96 [01:36<00:55,  1.58s/it]
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Epoch 40 Training:  65%|██████▍   | 62/96 [01:37<00:53,  1.58s/it]
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Epoch 40 Training:  66%|██████▌   | 63/96 [01:39<00:52,  1.58s/it]
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Epoch 40 Training:  67%|██████▋   | 64/96 [01:40<00:50,  1.58s/it]
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Epoch 40 Training:  68%|██████▊   | 65/96 [01:42<00:48,  1.58s/it]
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Epoch 40 Training:  69%|██████▉   | 66/96 [01:44<00:47,  1.58s/it]
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Epoch 40 Training:  70%|██████▉   | 67/96 [01:45<00:45,  1.58s/it]
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Epoch 40 Training:  71%|███████   | 68/96 [01:47<00:44,  1.58s/it]
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Epoch 40 Training:  72%|███████▏  | 69/96 [01:48<00:42,  1.57s/it]
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Epoch 40 Training:  73%|███████▎  | 70/96 [01:50<00:41,  1.58s/it]
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Epoch 40 Training:  74%|███████▍  | 71/96 [01:51<00:39,  1.58s/it]
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Epoch 40 Training:  75%|███████▌  | 72/96 [01:53<00:37,  1.57s/it]
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Epoch 40 Training:  76%|███████▌  | 73/96 [01:55<00:36,  1.57s/it]
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Epoch 40 Training:  77%|███████▋  | 74/96 [01:56<00:34,  1.57s/it]
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Epoch 40 Training:  78%|███████▊  | 75/96 [01:58<00:33,  1.57s/it]
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Epoch 40 Training:  79%|███████▉  | 76/96 [01:59<00:31,  1.58s/it]
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Epoch 40 Training:  80%|████████  | 77/96 [02:01<00:29,  1.58s/it]
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Epoch 40 Training:  81%|████████▏ | 78/96 [02:02<00:28,  1.58s/it]
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Epoch 40 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s][2026-10-01 17:00:32] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0040 | Epoch 040] train/policy_loss=23.8621 train/unweighted_policy_loss=4.7724 train/loss=23.8621 train/grad_norm=0.0670
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+[2026-10-01 17:00:32] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0041 | Epoch 040] timing/rollout_seconds=180.0329 timing/feedback_seconds=5.6375 timing/optimization_seconds=151.9911 timing/cycle_seconds=334.9168
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+[2026-10-01 17:03:36] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0041 | Epoch 041] train/reward_dists_mean=0.8405 train/reward_dists_std=0.0343 train/reward_ssim_mean=0.6578 train/reward_ssim_std=0.1119 train/reward_dists_group_std_mean=0.0084 train/reward_dists_group_std_max=0.0223 train/reward_dists_group_std_min=0.0038 train/reward_dists_group_mean_std=0.0331 train/reward_ssim_group_std_mean=0.0089 train/reward_ssim_group_std_max=0.0182 train/reward_ssim_group_std_min=0.0036 train/reward_ssim_group_mean_std=0.1115 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.1126 train/adv_max=5.5523 train/adv_abs_mean=1.2361
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Epoch 41 Training:   3%|▎         | 3/96 [00:04<02:25,  1.56s/it]
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Epoch 41 Training:   4%|▍         | 4/96 [00:06<02:24,  1.57s/it]
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Epoch 41 Training:   5%|▌         | 5/96 [00:07<02:23,  1.57s/it]
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Epoch 41 Training:   6%|▋         | 6/96 [00:09<02:21,  1.57s/it]
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Epoch 41 Training:   7%|▋         | 7/96 [00:10<02:20,  1.57s/it]
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Epoch 41 Training:   8%|▊         | 8/96 [00:12<02:18,  1.57s/it]
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Epoch 41 Training:   9%|▉         | 9/96 [00:14<02:17,  1.58s/it]
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Epoch 41 Training:  10%|█         | 10/96 [00:15<02:15,  1.58s/it]
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Epoch 41 Training:  11%|█▏        | 11/96 [00:17<02:14,  1.58s/it]
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Epoch 41 Training:  12%|█▎        | 12/96 [00:18<02:12,  1.58s/it]
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Epoch 41 Training:  14%|█▎        | 13/96 [00:20<02:11,  1.58s/it]
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Epoch 41 Training:  15%|█▍        | 14/96 [00:22<02:09,  1.58s/it]
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Epoch 41 Training:  16%|█▌        | 15/96 [00:23<02:07,  1.58s/it]
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Epoch 41 Training:  17%|█▋        | 16/96 [00:25<02:06,  1.58s/it]
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Epoch 41 Training:  18%|█▊        | 17/96 [00:26<02:04,  1.58s/it]
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Epoch 41 Training:  19%|█▉        | 18/96 [00:28<02:03,  1.58s/it]
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Epoch 41 Training:  20%|█▉        | 19/96 [00:29<02:01,  1.58s/it]
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Epoch 41 Training:  21%|██        | 20/96 [00:31<02:00,  1.58s/it]
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Epoch 41 Training:  22%|██▏       | 21/96 [00:33<01:58,  1.58s/it]
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Epoch 41 Training:  23%|██▎       | 22/96 [00:34<01:56,  1.58s/it]
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Epoch 41 Training:  24%|██▍       | 23/96 [00:36<01:55,  1.58s/it]
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Epoch 41 Training:  25%|██▌       | 24/96 [00:37<01:53,  1.58s/it]
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Epoch 41 Training:  26%|██▌       | 25/96 [00:39<01:52,  1.58s/it]
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Epoch 41 Training:  27%|██▋       | 26/96 [00:40<01:50,  1.58s/it]
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Epoch 41 Training:  28%|██▊       | 27/96 [00:42<01:49,  1.59s/it]
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Epoch 41 Training:  29%|██▉       | 28/96 [00:44<01:47,  1.58s/it]
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Epoch 41 Training:  30%|███       | 29/96 [00:45<01:46,  1.58s/it]
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Epoch 41 Training:  31%|███▏      | 30/96 [00:47<01:44,  1.58s/it]
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Epoch 41 Training:  32%|███▏      | 31/96 [00:48<01:42,  1.58s/it]
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Epoch 41 Training:  33%|███▎      | 32/96 [00:50<01:41,  1.58s/it]
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Epoch 41 Training:  34%|███▍      | 33/96 [00:52<01:39,  1.58s/it]
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Epoch 41 Training:  35%|███▌      | 34/96 [00:53<01:37,  1.58s/it]
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Epoch 41 Training:  36%|███▋      | 35/96 [00:55<01:36,  1.58s/it]
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Epoch 41 Training:  38%|███▊      | 36/96 [00:56<01:34,  1.58s/it]
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Epoch 41 Training:  39%|███▊      | 37/96 [00:58<01:32,  1.57s/it]
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Epoch 41 Training:  40%|███▉      | 38/96 [00:59<01:31,  1.57s/it]
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Epoch 41 Training:  41%|████      | 39/96 [01:01<01:29,  1.57s/it]
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Epoch 41 Training:  42%|████▏     | 40/96 [01:03<01:28,  1.57s/it]
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Epoch 41 Training:  43%|████▎     | 41/96 [01:04<01:26,  1.57s/it]
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Epoch 41 Training:  44%|████▍     | 42/96 [01:06<01:24,  1.57s/it]
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Epoch 41 Training:  45%|████▍     | 43/96 [01:07<01:23,  1.57s/it]
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Epoch 41 Training:  46%|████▌     | 44/96 [01:09<01:21,  1.57s/it]
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Epoch 41 Training:  47%|████▋     | 45/96 [01:10<01:20,  1.57s/it]
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Epoch 41 Training:  48%|████▊     | 46/96 [01:12<01:18,  1.57s/it]
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Epoch 41 Training:  49%|████▉     | 47/96 [01:14<01:17,  1.57s/it]
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Epoch 41 Training:  51%|█████     | 49/96 [01:17<01:14,  1.57s/it]
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Epoch 41 Training:  52%|█████▏    | 50/96 [01:18<01:12,  1.57s/it]
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Epoch 41 Training:  53%|█████▎    | 51/96 [01:20<01:10,  1.57s/it]
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Epoch 41 Training:  54%|█████▍    | 52/96 [01:21<01:09,  1.57s/it]
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Epoch 41 Training:  55%|█████▌    | 53/96 [01:23<01:07,  1.57s/it]
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Epoch 41 Training:  56%|█████▋    | 54/96 [01:25<01:06,  1.57s/it]
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Epoch 41 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.15it/s][2026-10-01 17:06:07] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0041 | Epoch 041] train/policy_loss=24.7029 train/unweighted_policy_loss=4.9406 train/loss=24.7029 train/grad_norm=0.0857
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+[2026-10-01 17:06:07] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0042 | Epoch 041] timing/rollout_seconds=179.4675 timing/feedback_seconds=5.5527 timing/optimization_seconds=152.1000 timing/cycle_seconds=334.6052
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+
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+[2026-10-01 17:09:10] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0042 | Epoch 042] train/reward_dists_mean=0.8413 train/reward_dists_std=0.0266 train/reward_ssim_mean=0.6396 train/reward_ssim_std=0.0907 train/reward_dists_group_std_mean=0.0077 train/reward_dists_group_std_max=0.0215 train/reward_dists_group_std_min=0.0045 train/reward_dists_group_mean_std=0.0253 train/reward_ssim_group_std_mean=0.0084 train/reward_ssim_group_std_max=0.0183 train/reward_ssim_group_std_min=0.0031 train/reward_ssim_group_mean_std=0.0902 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.3331 train/adv_max=4.1000 train/adv_abs_mean=1.1846
+
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Epoch 42 Training:  32%|███▏      | 31/96 [00:48<01:42,  1.57s/it]
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Epoch 42 Training:  33%|███▎      | 32/96 [00:50<01:40,  1.57s/it]
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Epoch 42 Training:  34%|███▍      | 33/96 [00:51<01:39,  1.58s/it]
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Epoch 42 Training:  35%|███▌      | 34/96 [00:53<01:37,  1.58s/it]
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Epoch 42 Training:  36%|███▋      | 35/96 [00:55<01:36,  1.57s/it]
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Epoch 42 Training:  38%|███▊      | 36/96 [00:56<01:34,  1.58s/it]
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Epoch 42 Training:  39%|███▊      | 37/96 [00:58<01:32,  1.58s/it]
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Epoch 42 Training:  40%|███▉      | 38/96 [00:59<01:31,  1.57s/it]
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Epoch 42 Training:  41%|████      | 39/96 [01:01<01:29,  1.58s/it]
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Epoch 42 Training:  42%|████▏     | 40/96 [01:02<01:28,  1.58s/it]
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Epoch 42 Training:  43%|████▎     | 41/96 [01:04<01:26,  1.58s/it]
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Epoch 42 Training:  44%|████▍     | 42/96 [01:06<01:25,  1.58s/it]
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Epoch 42 Training:  45%|████▍     | 43/96 [01:07<01:23,  1.58s/it]
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Epoch 42 Training:  46%|████▌     | 44/96 [01:09<01:22,  1.58s/it]
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Epoch 42 Training:  47%|████▋     | 45/96 [01:10<01:20,  1.58s/it]
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Epoch 42 Training:  48%|████▊     | 46/96 [01:12<01:19,  1.58s/it]
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Epoch 42 Training:  49%|████▉     | 47/96 [01:14<01:17,  1.59s/it]
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Epoch 42 Training:  50%|█████     | 48/96 [01:15<01:16,  1.59s/it]
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Epoch 42 Training:  51%|█████     | 49/96 [01:17<01:14,  1.58s/it]
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Epoch 42 Training:  52%|█████▏    | 50/96 [01:18<01:12,  1.58s/it]
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Epoch 42 Training:  53%|█████▎    | 51/96 [01:20<01:11,  1.58s/it]
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Epoch 42 Training:  54%|█████▍    | 52/96 [01:21<01:09,  1.58s/it]
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Epoch 42 Training:  55%|█████▌    | 53/96 [01:23<01:08,  1.58s/it]
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Epoch 42 Training:  56%|█████▋    | 54/96 [01:25<01:06,  1.59s/it]
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Epoch 42 Training:  57%|█████▋    | 55/96 [01:26<01:04,  1.59s/it]
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Epoch 42 Training:  58%|█████▊    | 56/96 [01:28<01:03,  1.58s/it]
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Epoch 42 Training:  59%|█████▉    | 57/96 [01:29<01:01,  1.58s/it]
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Epoch 42 Training:  60%|██████    | 58/96 [01:31<00:59,  1.58s/it]
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Epoch 42 Training:  61%|██████▏   | 59/96 [01:33<00:58,  1.58s/it]
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Epoch 42 Training:  62%|██████▎   | 60/96 [01:34<00:56,  1.58s/it]
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Epoch 42 Training:  64%|██████▎   | 61/96 [01:36<00:55,  1.58s/it]
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Epoch 42 Training:  65%|██████▍   | 62/96 [01:37<00:53,  1.58s/it]
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Epoch 42 Training:  66%|██████▌   | 63/96 [01:39<00:52,  1.58s/it]
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Epoch 42 Training:  67%|██████▋   | 64/96 [01:40<00:50,  1.58s/it]
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Epoch 42 Training:  68%|██████▊   | 65/96 [01:42<00:48,  1.58s/it]
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Epoch 42 Training:  69%|██████▉   | 66/96 [01:44<00:47,  1.58s/it]
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Epoch 42 Training:  70%|██████▉   | 67/96 [01:45<00:45,  1.58s/it]
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Epoch 42 Training:  71%|███████   | 68/96 [01:47<00:44,  1.58s/it]
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Epoch 42 Training:  72%|███████▏  | 69/96 [01:48<00:42,  1.59s/it]
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Epoch 42 Training:  73%|███████▎  | 70/96 [01:50<00:41,  1.59s/it]
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Epoch 42 Training:  74%|███████▍  | 71/96 [01:52<00:39,  1.59s/it]
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Epoch 42 Training:  75%|███████▌  | 72/96 [01:53<00:38,  1.59s/it]
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Epoch 42 Training:  76%|███████▌  | 73/96 [01:55<00:36,  1.58s/it]
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Epoch 42 Training:  77%|███████▋  | 74/96 [01:56<00:34,  1.59s/it]
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Epoch 42 Training:  78%|███████▊  | 75/96 [01:58<00:33,  1.59s/it]
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Epoch 42 Training:  79%|███████▉  | 76/96 [02:00<00:31,  1.59s/it]
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Epoch 42 Training:  80%|████████  | 77/96 [02:01<00:30,  1.59s/it]
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Epoch 42 Training:  81%|████████▏ | 78/96 [02:03<00:28,  1.58s/it]
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Epoch 42 Training:  82%|████████▏ | 79/96 [02:04<00:26,  1.59s/it]
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Epoch 42 Training:  83%|████████▎ | 80/96 [02:06<00:25,  1.58s/it]
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Epoch 42 Training:  84%|████████▍ | 81/96 [02:07<00:23,  1.58s/it]
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Epoch 42 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s][2026-10-01 17:11:42] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0042 | Epoch 042] train/policy_loss=25.2397 train/unweighted_policy_loss=5.0479 train/loss=25.2397 train/grad_norm=0.0643
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+[2026-10-01 17:11:42] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0043 | Epoch 042] timing/rollout_seconds=179.7226 timing/feedback_seconds=4.9668 timing/optimization_seconds=152.3949 timing/cycle_seconds=335.0471
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+[2026-10-01 17:14:45] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0043 | Epoch 043] train/reward_dists_mean=0.8481 train/reward_dists_std=0.0276 train/reward_ssim_mean=0.6457 train/reward_ssim_std=0.1039 train/reward_dists_group_std_mean=0.0079 train/reward_dists_group_std_max=0.0234 train/reward_dists_group_std_min=0.0031 train/reward_dists_group_mean_std=0.0263 train/reward_ssim_group_std_mean=0.0083 train/reward_ssim_group_std_max=0.0176 train/reward_ssim_group_std_min=0.0030 train/reward_ssim_group_mean_std=0.1036 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.4869 train/adv_max=4.7431 train/adv_abs_mean=1.1843
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Epoch 43 Training:   7%|▋         | 7/96 [00:10<02:20,  1.58s/it]
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Epoch 43 Training:   8%|▊         | 8/96 [00:12<02:19,  1.58s/it]
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Epoch 43 Training:   9%|▉         | 9/96 [00:14<02:17,  1.58s/it]
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Epoch 43 Training:  10%|█         | 10/96 [00:15<02:15,  1.58s/it]
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Epoch 43 Training:  11%|█▏        | 11/96 [00:17<02:14,  1.58s/it]
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Epoch 43 Training:  12%|█▎        | 12/96 [00:18<02:12,  1.58s/it]
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Epoch 43 Training:  14%|█▎        | 13/96 [00:20<02:10,  1.58s/it]
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Epoch 43 Training:  15%|█▍        | 14/96 [00:22<02:09,  1.58s/it]
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Epoch 43 Training:  16%|█▌        | 15/96 [00:23<02:07,  1.58s/it]
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Epoch 43 Training:  17%|█▋        | 16/96 [00:25<02:06,  1.58s/it]
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Epoch 43 Training:  18%|█▊        | 17/96 [00:26<02:04,  1.58s/it]
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Epoch 43 Training:  19%|█▉        | 18/96 [00:28<02:03,  1.58s/it]
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Epoch 43 Training:  20%|█▉        | 19/96 [00:29<02:01,  1.58s/it]
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Epoch 43 Training:  21%|██        | 20/96 [00:31<02:00,  1.59s/it]
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Epoch 43 Training:  22%|██▏       | 21/96 [00:33<01:58,  1.58s/it]
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Epoch 43 Training:  23%|██▎       | 22/96 [00:34<01:57,  1.58s/it]
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Epoch 43 Training:  24%|██▍       | 23/96 [00:36<01:55,  1.58s/it]
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Epoch 43 Training:  25%|██▌       | 24/96 [00:37<01:53,  1.58s/it]
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Epoch 43 Training:  26%|██▌       | 25/96 [00:39<01:52,  1.58s/it]
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Epoch 43 Training:  27%|██▋       | 26/96 [00:40<01:50,  1.58s/it]
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Epoch 43 Training:  28%|██▊       | 27/96 [00:42<01:48,  1.58s/it]
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Epoch 43 Training:  29%|██▉       | 28/96 [00:44<01:47,  1.58s/it]
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Epoch 43 Training:  30%|███       | 29/96 [00:45<01:45,  1.58s/it]
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Epoch 43 Training:  31%|███▏      | 30/96 [00:47<01:44,  1.58s/it]
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Epoch 43 Training:  32%|███▏      | 31/96 [00:48<01:42,  1.58s/it]
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Epoch 43 Training:  33%|███▎      | 32/96 [00:50<01:40,  1.58s/it]
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Epoch 43 Training:  34%|███▍      | 33/96 [00:52<01:39,  1.59s/it]
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Epoch 43 Training:  35%|███▌      | 34/96 [00:53<01:38,  1.58s/it]
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Epoch 43 Training:  36%|███▋      | 35/96 [00:55<01:36,  1.59s/it]
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Epoch 43 Training:  38%|███▊      | 36/96 [00:56<01:34,  1.58s/it]
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Epoch 43 Training:  39%|███▊      | 37/96 [00:58<01:33,  1.58s/it]
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Epoch 43 Training:  40%|███▉      | 38/96 [00:59<01:31,  1.58s/it]
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Epoch 43 Training:  41%|████      | 39/96 [01:01<01:30,  1.58s/it]
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Epoch 43 Training:  42%|████▏     | 40/96 [01:03<01:28,  1.58s/it]
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Epoch 43 Training:  43%|████▎     | 41/96 [01:04<01:26,  1.58s/it]
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Epoch 43 Training:  44%|████▍     | 42/96 [01:06<01:25,  1.58s/it]
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Epoch 43 Training:  45%|████▍     | 43/96 [01:07<01:23,  1.58s/it]
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Epoch 43 Training:  46%|████▌     | 44/96 [01:09<01:22,  1.58s/it]
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Epoch 43 Training:  47%|████▋     | 45/96 [01:11<01:20,  1.58s/it]
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Epoch 43 Training:  48%|████▊     | 46/96 [01:12<01:19,  1.58s/it]
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Epoch 43 Training:  49%|████▉     | 47/96 [01:14<01:17,  1.58s/it]
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Epoch 43 Training:  50%|█████     | 48/96 [01:15<01:16,  1.58s/it]
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Epoch 43 Training:  51%|█████     | 49/96 [01:17<01:14,  1.58s/it]
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Epoch 43 Training:  52%|█████▏    | 50/96 [01:18<01:13,  1.59s/it]
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Epoch 43 Training:  53%|█████▎    | 51/96 [01:20<01:14,  1.66s/it]
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Epoch 43 Training:  54%|█████▍    | 52/96 [01:22<01:11,  1.64s/it]
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Epoch 43 Training:  55%|█████▌    | 53/96 [01:23<01:09,  1.62s/it]
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Epoch 43 Training:  56%|█████▋    | 54/96 [01:25<01:07,  1.61s/it]
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Epoch 43 Training:  57%|█████▋    | 55/96 [01:27<01:05,  1.61s/it]
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Epoch 43 Training:  58%|█████▊    | 56/96 [01:28<01:03,  1.60s/it]
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Epoch 43 Training:  59%|█████▉    | 57/96 [01:30<01:02,  1.60s/it]
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Epoch 43 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s][2026-10-01 17:17:17] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0043 | Epoch 043] train/policy_loss=24.4292 train/unweighted_policy_loss=4.8858 train/loss=24.4292 train/grad_norm=0.0614
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+[2026-10-01 17:17:18] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0044 | Epoch 043] timing/rollout_seconds=179.6314 timing/feedback_seconds=5.2107 timing/optimization_seconds=152.9588 timing/cycle_seconds=335.5835
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+
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+
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+[2026-10-01 17:20:21] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0044 | Epoch 044] train/reward_dists_mean=0.8475 train/reward_dists_std=0.0313 train/reward_ssim_mean=0.6821 train/reward_ssim_std=0.0829 train/reward_dists_group_std_mean=0.0085 train/reward_dists_group_std_max=0.0200 train/reward_dists_group_std_min=0.0030 train/reward_dists_group_mean_std=0.0300 train/reward_ssim_group_std_mean=0.0082 train/reward_ssim_group_std_max=0.0240 train/reward_ssim_group_std_min=0.0034 train/reward_ssim_group_mean_std=0.0823 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.3917 train/adv_max=4.1658 train/adv_abs_mean=1.2102
+
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Epoch 44 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 44 Training:  35%|███▌      | 34/96 [00:53<01:38,  1.58s/it]
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Epoch 44 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 44 Training:  36%|███▋      | 35/96 [00:55<01:36,  1.58s/it]
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Epoch 44 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 44 Training:  38%|███▊      | 36/96 [00:57<01:34,  1.58s/it]
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Epoch 44 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 44 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 44 Training:  39%|███▊      | 37/96 [00:58<01:33,  1.58s/it]
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Epoch 44 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 44 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 44 Training:  40%|███▉      | 38/96 [01:00<01:32,  1.59s/it]
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Epoch 44 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 44 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 44 Training:  41%|████      | 39/96 [01:01<01:30,  1.59s/it]
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Epoch 44 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 44 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 44 Training:  42%|████▏     | 40/96 [01:03<01:28,  1.58s/it]
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Epoch 44 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 44 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 44 Training:  43%|████▎     | 41/96 [01:04<01:26,  1.58s/it]
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Epoch 44 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 44 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 44 Training:  44%|████▍     | 42/96 [01:06<01:25,  1.58s/it]
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Epoch 44 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 44 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 44 Training:  45%|████▍     | 43/96 [01:08<01:23,  1.58s/it]
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Epoch 44 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 44 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 44 Training:  46%|████▌     | 44/96 [01:09<01:22,  1.58s/it]
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Epoch 44 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 44 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 44 Training:  47%|████▋     | 45/96 [01:11<01:20,  1.58s/it]
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Epoch 44 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 44 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 44 Training:  48%|████▊     | 46/96 [01:12<01:19,  1.58s/it]
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Epoch 44 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 44 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 44 Training:  49%|████▉     | 47/96 [01:14<01:17,  1.58s/it]
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Epoch 44 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 44 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 44 Training:  50%|█████     | 48/96 [01:15<01:15,  1.58s/it]
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Epoch 44 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 44 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 44 Training:  51%|█████     | 49/96 [01:17<01:14,  1.58s/it]
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Epoch 44 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 44 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 44 Training:  52%|█████▏    | 50/96 [01:19<01:12,  1.58s/it]
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Epoch 44 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 44 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 44 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 44 Training:  53%|█████▎    | 51/96 [01:20<01:11,  1.58s/it]
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Epoch 44 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 44 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 44 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 44 Training:  54%|█████▍    | 52/96 [01:22<01:09,  1.59s/it]
+
Epoch 44 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 44 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 44 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 44 Training:  55%|█████▌    | 53/96 [01:23<01:08,  1.59s/it]
+
Epoch 44 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 44 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 44 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 44 Training:  56%|█████▋    | 54/96 [01:25<01:06,  1.59s/it]
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Epoch 44 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 44 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 44 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 44 Training:  57%|█████▋    | 55/96 [01:27<01:05,  1.59s/it]
+
Epoch 44 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 44 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 44 Training:  58%|█████▊    | 56/96 [01:28<01:03,  1.58s/it]
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Epoch 44 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 44 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 44 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 44 Training:  59%|█████▉    | 57/96 [01:30<01:01,  1.58s/it]
+
Epoch 44 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 44 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 44 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 44 Training:  60%|██████    | 58/96 [01:31<00:59,  1.58s/it]
+
Epoch 44 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 44 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 44 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 44 Training:  61%|██████▏   | 59/96 [01:33<00:58,  1.58s/it]
+
Epoch 44 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 44 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 44 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 44 Training:  62%|██████▎   | 60/96 [01:34<00:56,  1.58s/it]
+
Epoch 44 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 44 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 44 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 44 Training:  64%|██████▎   | 61/96 [01:36<00:55,  1.58s/it]
+
Epoch 44 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 44 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 44 Training:  65%|██████▍   | 62/96 [01:38<00:53,  1.58s/it]
+
Epoch 44 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 44 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 44 Training:  66%|██████▌   | 63/96 [01:39<00:52,  1.58s/it]
+
Epoch 44 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 44 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 44 Training:  67%|██████▋   | 64/96 [01:41<00:50,  1.58s/it]
+
Epoch 44 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 44 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 44 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 44 Training:  68%|██████▊   | 65/96 [01:42<00:49,  1.58s/it]
+
Epoch 44 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 44 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 44 Training:  69%|██████▉   | 66/96 [01:44<00:47,  1.59s/it]
+
Epoch 44 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 44 Training:  70%|██████▉   | 67/96 [01:46<00:46,  1.59s/it]
+
Epoch 44 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 44 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 44 Training:  71%|███████   | 68/96 [01:47<00:44,  1.58s/it]
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Epoch 44 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 44 Training:  72%|███████▏  | 69/96 [01:49<00:42,  1.59s/it]
+
Epoch 44 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 44 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 44 Training:  73%|███████▎  | 70/96 [01:50<00:41,  1.59s/it]
+
Epoch 44 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 44 Training:  74%|███████▍  | 71/96 [01:52<00:39,  1.59s/it]
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Epoch 44 Training:  75%|███████▌  | 72/96 [01:54<00:38,  1.59s/it]
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Epoch 44 Training:  76%|███████▌  | 73/96 [01:55<00:36,  1.59s/it]
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Epoch 44 Training:  77%|███████▋  | 74/96 [01:57<00:34,  1.59s/it]
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Epoch 44 Training:  78%|███████▊  | 75/96 [01:58<00:33,  1.59s/it]
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Epoch 44 Training:  79%|███████▉  | 76/96 [02:00<00:31,  1.58s/it]
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Epoch 44 Training:  80%|████████  | 77/96 [02:01<00:30,  1.58s/it]
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Epoch 44 Training:  81%|████████▏ | 78/96 [02:03<00:28,  1.59s/it]
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Epoch 44 Training:  82%|████████▏ | 79/96 [02:05<00:26,  1.59s/it]
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Epoch 44 Training:  83%|████████▎ | 80/96 [02:06<00:25,  1.59s/it]
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Epoch 44 Training:  84%|████████▍ | 81/96 [02:08<00:23,  1.58s/it]
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Epoch 44 Training:  85%|████████▌ | 82/96 [02:09<00:22,  1.59s/it]
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Epoch 44 Training:  86%|████████▋ | 83/96 [02:11<00:20,  1.58s/it]
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Epoch 44 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 44 Training:  88%|████████▊ | 84/96 [02:13<00:19,  1.61s/it]
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Epoch 44 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s][2026-10-01 17:22:53] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0044 | Epoch 044] train/policy_loss=24.2055 train/unweighted_policy_loss=4.8411 train/loss=24.2055 train/grad_norm=0.0538
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+[2026-10-01 17:22:53] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0045 | Epoch 044] timing/rollout_seconds=179.9377 timing/feedback_seconds=4.7817 timing/optimization_seconds=153.0138 timing/cycle_seconds=335.8226
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+[2026-10-01 17:25:57] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0045 | Epoch 045] train/reward_dists_mean=0.8504 train/reward_dists_std=0.0291 train/reward_ssim_mean=0.6603 train/reward_ssim_std=0.1046 train/reward_dists_group_std_mean=0.0080 train/reward_dists_group_std_max=0.0191 train/reward_dists_group_std_min=0.0030 train/reward_dists_group_mean_std=0.0277 train/reward_ssim_group_std_mean=0.0087 train/reward_ssim_group_std_max=0.0245 train/reward_ssim_group_std_min=0.0024 train/reward_ssim_group_mean_std=0.1041 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.6413 train/adv_max=4.3274 train/adv_abs_mean=1.1968
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Epoch 45 Training:  27%|██▋       | 26/96 [00:40<01:49,  1.56s/it]
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Epoch 45 Training:  33%|███▎      | 32/96 [00:50<01:40,  1.57s/it]
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Epoch 45 Training:  34%|███▍      | 33/96 [00:51<01:39,  1.57s/it]
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Epoch 45 Training:  38%|███▊      | 36/96 [00:56<01:39,  1.65s/it]
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Epoch 45 Training:  39%|███▊      | 37/96 [00:58<01:35,  1.62s/it]
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Epoch 45 Training:  41%|████      | 39/96 [01:01<01:30,  1.59s/it]
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Epoch 45 Training:  42%|████▏     | 40/96 [01:02<01:28,  1.58s/it]
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Epoch 45 Training:  43%|████▎     | 41/96 [01:04<01:26,  1.58s/it]
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Epoch 45 Training:  44%|████▍     | 42/96 [01:06<01:25,  1.58s/it]
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Epoch 45 Training:  45%|████▍     | 43/96 [01:07<01:23,  1.57s/it]
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Epoch 45 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s][2026-10-01 17:28:28] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0045 | Epoch 045] train/policy_loss=24.2966 train/unweighted_policy_loss=4.8593 train/loss=24.2966 train/grad_norm=0.0605
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+[2026-10-01 17:28:28] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0046 | Epoch 045] timing/rollout_seconds=179.5407 timing/feedback_seconds=4.9599 timing/optimization_seconds=151.6751 timing/cycle_seconds=334.2157
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+[2026-10-01 17:31:31] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0046 | Epoch 046] train/reward_dists_mean=0.8507 train/reward_dists_std=0.0255 train/reward_ssim_mean=0.6487 train/reward_ssim_std=0.1021 train/reward_dists_group_std_mean=0.0074 train/reward_dists_group_std_max=0.0136 train/reward_dists_group_std_min=0.0040 train/reward_dists_group_mean_std=0.0242 train/reward_ssim_group_std_mean=0.0092 train/reward_ssim_group_std_max=0.0267 train/reward_ssim_group_std_min=0.0038 train/reward_ssim_group_mean_std=0.1016 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.3605 train/adv_max=4.0281 train/adv_abs_mean=1.2513
+
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Epoch 46 Training:  39%|███▊      | 37/96 [00:57<01:32,  1.56s/it]
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Epoch 46 Training:  40%|███▉      | 38/96 [00:59<01:30,  1.56s/it]
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Epoch 46 Training:  41%|████      | 39/96 [01:00<01:29,  1.56s/it]
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Epoch 46 Training:  42%|████▏     | 40/96 [01:02<01:27,  1.56s/it]
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Epoch 46 Training:  43%|████▎     | 41/96 [01:03<01:25,  1.56s/it]
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Epoch 46 Training:  44%|████▍     | 42/96 [01:05<01:24,  1.57s/it]
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Epoch 46 Training:  45%|████▍     | 43/96 [01:07<01:22,  1.56s/it]
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Epoch 46 Training:  46%|████▌     | 44/96 [01:08<01:21,  1.56s/it]
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Epoch 46 Training:  47%|████▋     | 45/96 [01:10<01:19,  1.56s/it]
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Epoch 46 Training:  48%|████▊     | 46/96 [01:11<01:18,  1.56s/it]
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Epoch 46 Training:  49%|████▉     | 47/96 [01:13<01:16,  1.57s/it]
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Epoch 46 Training:  50%|█████     | 48/96 [01:14<01:15,  1.57s/it]
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Epoch 46 Training:  51%|█████     | 49/96 [01:16<01:13,  1.57s/it]
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Epoch 46 Training:  52%|█████▏    | 50/96 [01:18<01:12,  1.57s/it]
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Epoch 46 Training:  53%|█████▎    | 51/96 [01:19<01:10,  1.56s/it]
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Epoch 46 Training:  54%|█████▍    | 52/96 [01:21<01:08,  1.57s/it]
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Epoch 46 Training:  55%|█████▌    | 53/96 [01:22<01:07,  1.57s/it]
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Epoch 46 Training:  56%|█████▋    | 54/96 [01:24<01:05,  1.57s/it]
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Epoch 46 Training:  57%|█████▋    | 55/96 [01:25<01:04,  1.57s/it]
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Epoch 46 Training:  58%|█████▊    | 56/96 [01:27<01:02,  1.57s/it]
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Epoch 46 Training:  59%|█████▉    | 57/96 [01:28<01:01,  1.57s/it]
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Epoch 46 Training:  60%|██████    | 58/96 [01:30<00:59,  1.57s/it]
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Epoch 46 Training:  61%|██████▏   | 59/96 [01:32<00:57,  1.56s/it]
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Epoch 46 Training:  62%|██████▎   | 60/96 [01:33<00:56,  1.56s/it]
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Epoch 46 Training:  64%|██████▎   | 61/96 [01:35<00:54,  1.57s/it]
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Epoch 46 Training:  65%|██████▍   | 62/96 [01:36<00:53,  1.56s/it]
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Epoch 46 Training:  66%|██████▌   | 63/96 [01:38<00:51,  1.56s/it]
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Epoch 46 Training:  67%|██████▋   | 64/96 [01:39<00:50,  1.57s/it]
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Epoch 46 Training:  68%|██████▊   | 65/96 [01:41<00:48,  1.56s/it]
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Epoch 46 Training:  69%|██████▉   | 66/96 [01:43<00:46,  1.56s/it]
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Epoch 46 Training:  70%|██████▉   | 67/96 [01:44<00:45,  1.56s/it]
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Epoch 46 Training:  71%|███████   | 68/96 [01:46<00:43,  1.56s/it]
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Epoch 46 Training:  72%|███████▏  | 69/96 [01:47<00:42,  1.56s/it]
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Epoch 46 Training:  73%|███████▎  | 70/96 [01:49<00:40,  1.56s/it]
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Epoch 46 Training:  74%|███████▍  | 71/96 [01:50<00:39,  1.57s/it]
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Epoch 46 Training:  75%|███████▌  | 72/96 [01:52<00:37,  1.56s/it]
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Epoch 46 Training:  76%|███████▌  | 73/96 [01:53<00:35,  1.56s/it]
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Epoch 46 Training:  77%|███████▋  | 74/96 [01:55<00:34,  1.56s/it]
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Epoch 46 Training:  78%|███████▊  | 75/96 [01:57<00:32,  1.56s/it]
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Epoch 46 Training:  79%|███████▉  | 76/96 [01:58<00:31,  1.56s/it]
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Epoch 46 Training:  80%|████████  | 77/96 [02:00<00:29,  1.56s/it]
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Epoch 46 Training:  81%|████████▏ | 78/96 [02:01<00:28,  1.56s/it]
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Epoch 46 Training:  82%|████████▏ | 79/96 [02:03<00:26,  1.56s/it]
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Epoch 46 Training:  83%|████████▎ | 80/96 [02:05<00:25,  1.59s/it]
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Epoch 46 Training:  84%|████████▍ | 81/96 [02:06<00:23,  1.59s/it]
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Epoch 46 Training:  85%|████████▌ | 82/96 [02:08<00:22,  1.59s/it]
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Epoch 46 Training:  86%|████████▋ | 83/96 [02:09<00:20,  1.58s/it]
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Epoch 46 Training:  88%|████████▊ | 84/96 [02:11<00:18,  1.57s/it]
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Epoch 46 Training:  89%|████████▊ | 85/96 [02:12<00:17,  1.57s/it]
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Epoch 46 Training:  90%|████████▉ | 86/96 [02:14<00:15,  1.57s/it]
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Epoch 46 Training:  91%|█████████ | 87/96 [02:15<00:14,  1.57s/it]
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Epoch 46 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s][2026-10-01 17:34:02] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0046 | Epoch 046] train/policy_loss=24.4732 train/unweighted_policy_loss=4.8946 train/loss=24.4732 train/grad_norm=0.0681
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+[2026-10-01 17:34:02] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0047 | Epoch 046] timing/rollout_seconds=180.1723 timing/feedback_seconds=5.6539 timing/optimization_seconds=150.8120 timing/cycle_seconds=333.9635
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+[2026-10-01 17:37:07] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0047 | Epoch 047] train/reward_dists_mean=0.8504 train/reward_dists_std=0.0289 train/reward_ssim_mean=0.6761 train/reward_ssim_std=0.0961 train/reward_dists_group_std_mean=0.0081 train/reward_dists_group_std_max=0.0144 train/reward_dists_group_std_min=0.0018 train/reward_dists_group_mean_std=0.0277 train/reward_ssim_group_std_mean=0.0081 train/reward_ssim_group_std_max=0.0165 train/reward_ssim_group_std_min=0.0025 train/reward_ssim_group_mean_std=0.0957 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.3304 train/adv_max=4.3435 train/adv_abs_mean=1.1955
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Epoch 47 Training:  14%|█▎        | 13/96 [00:20<02:12,  1.60s/it]
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Epoch 47 Training:  15%|█▍        | 14/96 [00:22<02:11,  1.60s/it]
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Epoch 47 Training:  16%|█▌        | 15/96 [00:23<02:09,  1.60s/it]
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Epoch 47 Training:  17%|█▋        | 16/96 [00:25<02:07,  1.60s/it]
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Epoch 47 Training:  18%|█▊        | 17/96 [00:27<02:06,  1.60s/it]
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Epoch 47 Training:  19%|█▉        | 18/96 [00:28<02:04,  1.60s/it]
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Epoch 47 Training:  20%|█▉        | 19/96 [00:30<02:03,  1.60s/it]
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Epoch 47 Training:  21%|██        | 20/96 [00:31<02:01,  1.60s/it]
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Epoch 47 Training:  22%|██▏       | 21/96 [00:33<02:00,  1.60s/it]
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Epoch 47 Training:  23%|██▎       | 22/96 [00:35<01:58,  1.60s/it]
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Epoch 47 Training:  24%|██▍       | 23/96 [00:36<01:56,  1.60s/it]
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Epoch 47 Training:  25%|██▌       | 24/96 [00:38<01:55,  1.60s/it]
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Epoch 47 Training:  26%|██▌       | 25/96 [00:39<01:53,  1.60s/it]
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Epoch 47 Training:  27%|██▋       | 26/96 [00:41<01:52,  1.60s/it]
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Epoch 47 Training:  28%|██▊       | 27/96 [00:43<01:50,  1.60s/it]
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Epoch 47 Training:  29%|██▉       | 28/96 [00:44<01:49,  1.60s/it]
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Epoch 47 Training:  30%|███       | 29/96 [00:46<01:47,  1.60s/it]
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Epoch 47 Training:  31%|███▏      | 30/96 [00:47<01:45,  1.60s/it]
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Epoch 47 Training:  32%|███▏      | 31/96 [00:49<01:44,  1.60s/it]
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Epoch 47 Training:  33%|███▎      | 32/96 [00:51<01:42,  1.60s/it]
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Epoch 47 Training:  34%|███▍      | 33/96 [00:52<01:40,  1.60s/it]
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Epoch 47 Training:  35%|███▌      | 34/96 [00:54<01:39,  1.60s/it]
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Epoch 47 Training:  36%|███▋      | 35/96 [00:55<01:37,  1.60s/it]
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Epoch 47 Training:  38%|███▊      | 36/96 [00:57<01:36,  1.60s/it]
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Epoch 47 Training:  39%|███▊      | 37/96 [00:59<01:34,  1.60s/it]
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Epoch 47 Training:  40%|███▉      | 38/96 [01:00<01:32,  1.60s/it]
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Epoch 47 Training:  41%|████      | 39/96 [01:02<01:31,  1.60s/it]
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Epoch 47 Training:  42%|████▏     | 40/96 [01:03<01:29,  1.60s/it]
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Epoch 47 Training:  43%|████▎     | 41/96 [01:05<01:28,  1.60s/it]
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Epoch 47 Training:  44%|████▍     | 42/96 [01:07<01:26,  1.60s/it]
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Epoch 47 Training:  45%|████▍     | 43/96 [01:08<01:24,  1.60s/it]
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Epoch 47 Training:  46%|████▌     | 44/96 [01:10<01:23,  1.60s/it]
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Epoch 47 Training:  47%|████▋     | 45/96 [01:11<01:21,  1.60s/it]
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Epoch 47 Training:  48%|████▊     | 46/96 [01:13<01:20,  1.60s/it]
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Epoch 47 Training:  49%|████▉     | 47/96 [01:15<01:18,  1.60s/it]
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Epoch 47 Training:  50%|█████     | 48/96 [01:16<01:16,  1.60s/it]
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Epoch 47 Training:  51%|█████     | 49/96 [01:18<01:15,  1.60s/it]
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Epoch 47 Training:  52%|█████▏    | 50/96 [01:20<01:13,  1.60s/it]
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Epoch 47 Training:  53%|█████▎    | 51/96 [01:21<01:12,  1.60s/it]
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Epoch 47 Training:  54%|█████▍    | 52/96 [01:23<01:10,  1.60s/it]
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Epoch 47 Training:  55%|█████▌    | 53/96 [01:24<01:08,  1.60s/it]
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Epoch 47 Training:  56%|█████▋    | 54/96 [01:26<01:07,  1.60s/it]
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Epoch 47 Training:  57%|█████▋    | 55/96 [01:28<01:05,  1.60s/it]
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Epoch 47 Training:  58%|█████▊    | 56/96 [01:29<01:04,  1.60s/it]
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Epoch 47 Training:  59%|█████▉    | 57/96 [01:31<01:02,  1.60s/it]
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Epoch 47 Training:  60%|██████    | 58/96 [01:32<01:00,  1.60s/it]
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Epoch 47 Training:  61%|██████▏   | 59/96 [01:34<00:59,  1.60s/it]
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Epoch 47 Training:  62%|██████▎   | 60/96 [01:36<00:57,  1.60s/it]
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Epoch 47 Training:  64%|██████▎   | 61/96 [01:37<00:55,  1.60s/it]
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Epoch 47 Training:  65%|██████▍   | 62/96 [01:39<00:54,  1.60s/it]
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Epoch 47 Training:  66%|██████▌   | 63/96 [01:40<00:52,  1.60s/it]
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Epoch 47 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s][2026-10-01 17:39:41] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0047 | Epoch 047] train/policy_loss=24.1680 train/unweighted_policy_loss=4.8336 train/loss=24.1680 train/grad_norm=0.0582
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+[2026-10-01 17:39:41] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0048 | Epoch 047] timing/rollout_seconds=180.0379 timing/feedback_seconds=7.4197 timing/optimization_seconds=154.4128 timing/cycle_seconds=339.2361
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Epoch 48 Sampling:  85%|████████▌ | 82/96 [02:31<00:25,  1.84s/it]
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Epoch 48 Sampling:  91%|█████████ | 87/96 [02:40<00:16,  1.84s/it]
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Epoch 48 Sampling: 100%|██████████| 96/96 [02:56<00:00,  1.84s/it]
+
Pointwise Rewards: ssim:   0%|          | 0/6 [00:00<?, ?it/s]
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Pointwise Rewards: ssim:  67%|██████▋   | 4/6 [00:00<00:00,  5.52it/s]
Pointwise Rewards: ssim:  83%|████████▎ | 5/6 [00:00<00:00,  5.73it/s]
Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:01<00:00,  5.65it/s]
Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:01<00:00,  5.63it/s]
+
Pointwise Rewards: dists:   0%|          | 0/6 [00:00<?, ?it/s]
Pointwise Rewards: dists:  17%|█▋        | 1/6 [00:00<00:01,  3.27it/s]
Pointwise Rewards: dists:  33%|███▎      | 2/6 [00:00<00:01,  3.26it/s]
Pointwise Rewards: dists:  50%|█████     | 3/6 [00:00<00:00,  3.19it/s]
Pointwise Rewards: dists:  67%|██████▋   | 4/6 [00:01<00:00,  3.20it/s]
Pointwise Rewards: dists:  83%|████████▎ | 5/6 [00:01<00:00,  3.24it/s]
Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.23it/s]
Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.23it/s]
+[2026-10-01 17:42:44] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0048 | Epoch 048] train/reward_dists_mean=0.8403 train/reward_dists_std=0.0303 train/reward_ssim_mean=0.6455 train/reward_ssim_std=0.1017 train/reward_dists_group_std_mean=0.0087 train/reward_dists_group_std_max=0.0271 train/reward_dists_group_std_min=0.0039 train/reward_dists_group_mean_std=0.0287 train/reward_ssim_group_std_mean=0.0095 train/reward_ssim_group_std_max=0.0221 train/reward_ssim_group_std_min=0.0027 train/reward_ssim_group_mean_std=0.1012 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.0706 train/adv_max=4.3604 train/adv_abs_mean=1.1975
+
Epoch 48 Training:   0%|          | 0/96 [00:00<?, ?it/s]
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Epoch 48 Training:   1%|          | 1/96 [00:01<02:21,  1.49s/it]
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Epoch 48 Training:   2%|▏         | 2/96 [00:03<02:23,  1.53s/it]
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Epoch 48 Training:   3%|▎         | 3/96 [00:04<02:24,  1.55s/it]
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Epoch 48 Training:   4%|▍         | 4/96 [00:06<02:23,  1.56s/it]
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Epoch 48 Training:   5%|▌         | 5/96 [00:07<02:23,  1.57s/it]
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Epoch 48 Training:   6%|▋         | 6/96 [00:09<02:22,  1.58s/it]
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Epoch 48 Training:   7%|▋         | 7/96 [00:10<02:20,  1.58s/it]
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Epoch 48 Training:   8%|▊         | 8/96 [00:12<02:19,  1.59s/it]
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Epoch 48 Training:   9%|▉         | 9/96 [00:14<02:18,  1.59s/it]
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Epoch 48 Training:  10%|█         | 10/96 [00:15<02:16,  1.59s/it]
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Epoch 48 Training:  11%|█▏        | 11/96 [00:17<02:15,  1.59s/it]
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Epoch 48 Training:  12%|█▎        | 12/96 [00:18<02:13,  1.59s/it]
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Epoch 48 Training:  14%|█▎        | 13/96 [00:20<02:12,  1.59s/it]
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Epoch 48 Training:  15%|█▍        | 14/96 [00:22<02:10,  1.59s/it]
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Epoch 48 Training:  16%|█▌        | 15/96 [00:23<02:08,  1.59s/it]
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Epoch 48 Training:  17%|█▋        | 16/96 [00:25<02:07,  1.59s/it]
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Epoch 48 Training:  18%|█▊        | 17/96 [00:26<02:05,  1.59s/it]
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Epoch 48 Training:  19%|█▉        | 18/96 [00:28<02:04,  1.59s/it]
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Epoch 48 Training:  20%|█▉        | 19/96 [00:30<02:02,  1.59s/it]
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Epoch 48 Training:  21%|██        | 20/96 [00:31<02:00,  1.59s/it]
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Epoch 48 Training:  22%|██▏       | 21/96 [00:33<01:59,  1.59s/it]
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Epoch 48 Training:  23%|██▎       | 22/96 [00:34<01:57,  1.59s/it]
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Epoch 48 Training:  24%|██▍       | 23/96 [00:36<01:56,  1.59s/it]
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Epoch 48 Training:  25%|██▌       | 24/96 [00:38<01:54,  1.59s/it]
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Epoch 48 Training:  26%|██▌       | 25/96 [00:39<01:53,  1.59s/it]
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Epoch 48 Training:  27%|██▋       | 26/96 [00:41<01:51,  1.59s/it]
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Epoch 48 Training:  28%|██▊       | 27/96 [00:42<01:49,  1.59s/it]
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Epoch 48 Training:  29%|██▉       | 28/96 [00:44<01:47,  1.59s/it]
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Epoch 48 Training:  30%|███       | 29/96 [00:45<01:46,  1.59s/it]
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Epoch 48 Training:  31%|███▏      | 30/96 [00:47<01:44,  1.59s/it]
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Epoch 48 Training:  32%|███▏      | 31/96 [00:49<01:43,  1.59s/it]
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Epoch 48 Training:  33%|███▎      | 32/96 [00:50<01:41,  1.59s/it]
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Epoch 48 Training:  34%|███▍      | 33/96 [00:52<01:40,  1.59s/it]
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Epoch 48 Training:  35%|███▌      | 34/96 [00:53<01:38,  1.59s/it]
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Epoch 48 Training:  36%|███▋      | 35/96 [00:55<01:36,  1.59s/it]
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Epoch 48 Training:  38%|███▊      | 36/96 [00:57<01:35,  1.59s/it]
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Epoch 48 Training:  39%|███▊      | 37/96 [00:58<01:33,  1.59s/it]
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Epoch 48 Training:  41%|████      | 39/96 [01:01<01:30,  1.59s/it]
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Epoch 48 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 48 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 48 Training:  42%|████▏     | 40/96 [01:03<01:28,  1.59s/it]
+
Epoch 48 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 48 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 48 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 48 Training:  43%|████▎     | 41/96 [01:05<01:27,  1.59s/it]
+
Epoch 48 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 48 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 48 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 48 Training:  44%|████▍     | 42/96 [01:06<01:25,  1.59s/it]
+
Epoch 48 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 48 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 48 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 48 Training:  45%|████▍     | 43/96 [01:08<01:24,  1.59s/it]
+
Epoch 48 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 48 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 48 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 48 Training:  46%|████▌     | 44/96 [01:09<01:22,  1.59s/it]
+
Epoch 48 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 48 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 48 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 48 Training:  47%|████▋     | 45/96 [01:11<01:20,  1.59s/it]
+
Epoch 48 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 48 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 48 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 48 Training:  48%|████▊     | 46/96 [01:12<01:19,  1.59s/it]
+
Epoch 48 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 48 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 48 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 48 Training:  49%|████▉     | 47/96 [01:14<01:17,  1.59s/it]
+
Epoch 48 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 48 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.15it/s]
+
Epoch 48 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 48 Training:  50%|█████     | 48/96 [01:16<01:16,  1.59s/it]
+
Epoch 48 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 48 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 48 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 48 Training:  51%|█████     | 49/96 [01:17<01:14,  1.59s/it]
+
Epoch 48 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 48 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 48 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 48 Training:  52%|█████▏    | 50/96 [01:19<01:12,  1.59s/it]
+
Epoch 48 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 48 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 48 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 48 Training:  53%|█████▎    | 51/96 [01:20<01:11,  1.59s/it]
+
Epoch 48 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 48 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 48 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 48 Training:  54%|█████▍    | 52/96 [01:22<01:09,  1.59s/it]
+
Epoch 48 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 48 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 48 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.02it/s]
+
                                                                
Epoch 48 Training:  55%|█████▌    | 53/96 [01:24<01:08,  1.59s/it]
+
Epoch 48 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 48 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.15it/s]
+
Epoch 48 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.02it/s]
+
                                                                
Epoch 48 Training:  56%|█████▋    | 54/96 [01:25<01:06,  1.59s/it]
+
Epoch 48 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 48 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 48 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 48 Training:  57%|█████▋    | 55/96 [01:27<01:05,  1.59s/it]
+
Epoch 48 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 48 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 48 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 48 Training:  58%|█████▊    | 56/96 [01:28<01:03,  1.59s/it]
+
Epoch 48 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 48 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 48 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 48 Training:  59%|█████▉    | 57/96 [01:30<01:01,  1.59s/it]
+
Epoch 48 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 48 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 48 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 48 Training:  60%|██████    | 58/96 [01:32<01:00,  1.59s/it]
+
Epoch 48 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 48 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 48 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 48 Training:  61%|██████▏   | 59/96 [01:33<00:58,  1.59s/it]
+
Epoch 48 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 48 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.15it/s]
+
Epoch 48 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 48 Training:  62%|██████▎   | 60/96 [01:35<00:57,  1.59s/it]
+
Epoch 48 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 48 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 48 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 48 Training:  64%|██████▎   | 61/96 [01:36<00:55,  1.59s/it]
+
Epoch 48 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 48 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.15it/s]
+
Epoch 48 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 48 Training:  65%|██████▍   | 62/96 [01:38<00:54,  1.59s/it]
+
Epoch 48 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 48 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 48 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 48 Training:  66%|██████▌   | 63/96 [01:39<00:52,  1.59s/it]
+
Epoch 48 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 48 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 48 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 48 Training:  67%|██████▋   | 64/96 [01:41<00:50,  1.59s/it]
+
Epoch 48 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 48 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 48 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 48 Training:  68%|██████▊   | 65/96 [01:43<00:49,  1.59s/it]
+
Epoch 48 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 48 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 48 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 48 Training:  69%|██████▉   | 66/96 [01:44<00:47,  1.59s/it]
+
Epoch 48 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 48 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 48 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 48 Training:  70%|██████▉   | 67/96 [01:46<00:46,  1.59s/it]
+
Epoch 48 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 48 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 48 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 48 Training:  71%|███████   | 68/96 [01:47<00:44,  1.59s/it]
+
Epoch 48 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 48 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.15it/s]
+
Epoch 48 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 48 Training:  72%|███████▏  | 69/96 [01:49<00:42,  1.59s/it]
+
Epoch 48 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 48 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.15it/s]
+
Epoch 48 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 48 Training:  73%|███████▎  | 70/96 [01:51<00:41,  1.59s/it]
+
Epoch 48 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 48 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 48 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 48 Training:  74%|███████▍  | 71/96 [01:52<00:39,  1.59s/it]
+
Epoch 48 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 48 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 48 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 48 Training:  75%|███████▌  | 72/96 [01:54<00:38,  1.59s/it]
+
Epoch 48 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 48 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 48 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 48 Training:  76%|███████▌  | 73/96 [01:55<00:36,  1.59s/it]
+
Epoch 48 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 48 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 48 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 48 Training:  77%|███████▋  | 74/96 [01:57<00:34,  1.59s/it]
+
Epoch 48 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 48 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 48 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 48 Training:  78%|███████▊  | 75/96 [01:59<00:33,  1.59s/it]
+
Epoch 48 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 48 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.15it/s]
+
Epoch 48 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 48 Training:  79%|███████▉  | 76/96 [02:00<00:31,  1.59s/it]
+
Epoch 48 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 48 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 48 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 48 Training:  80%|████████  | 77/96 [02:02<00:30,  1.59s/it]
+
Epoch 48 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 48 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 48 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 48 Training:  81%|████████▏ | 78/96 [02:03<00:28,  1.59s/it]
+
Epoch 48 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 48 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 48 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 48 Training:  82%|████████▏ | 79/96 [02:05<00:27,  1.59s/it]
+
Epoch 48 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 48 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 48 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 48 Training:  83%|████████▎ | 80/96 [02:06<00:25,  1.59s/it]
+
Epoch 48 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 48 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 48 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 48 Training:  84%|████████▍ | 81/96 [02:08<00:23,  1.59s/it]
+
Epoch 48 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 48 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 48 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 48 Training:  85%|████████▌ | 82/96 [02:10<00:22,  1.59s/it]
+
Epoch 48 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 48 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 48 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 48 Training:  86%|████████▋ | 83/96 [02:11<00:20,  1.59s/it]
+
Epoch 48 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 48 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 48 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 48 Training:  88%|████████▊ | 84/96 [02:13<00:19,  1.59s/it]
+
Epoch 48 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 48 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 48 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 48 Training:  89%|████████▊ | 85/96 [02:14<00:17,  1.59s/it]
+
Epoch 48 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 48 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 48 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 48 Training:  90%|████████▉ | 86/96 [02:16<00:15,  1.59s/it]
+
Epoch 48 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 48 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.15it/s]
+
Epoch 48 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 48 Training:  91%|█████████ | 87/96 [02:18<00:14,  1.59s/it]
+
Epoch 48 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 48 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 48 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.02it/s]
+
                                                                
Epoch 48 Training:  92%|█████████▏| 88/96 [02:19<00:12,  1.59s/it]
+
Epoch 48 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 48 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.15it/s]
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Epoch 48 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.02it/s]
+
                                                                
Epoch 48 Training:  93%|█████████▎| 89/96 [02:21<00:11,  1.59s/it]
+
Epoch 48 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 48 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 48 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 48 Training:  94%|█████████▍| 90/96 [02:22<00:09,  1.59s/it]
+
Epoch 48 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 48 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 48 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s][2026-10-01 17:45:17] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0048 | Epoch 048] train/policy_loss=24.8203 train/unweighted_policy_loss=4.9641 train/loss=24.8203 train/grad_norm=0.0614
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+[2026-10-01 17:45:17] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0049 | Epoch 048] timing/rollout_seconds=180.0338 timing/feedback_seconds=5.8628 timing/optimization_seconds=153.0722 timing/cycle_seconds=335.9626
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+
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+[2026-10-01 17:48:21] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0049 | Epoch 049] train/reward_dists_mean=0.8436 train/reward_dists_std=0.0260 train/reward_ssim_mean=0.6470 train/reward_ssim_std=0.1157 train/reward_dists_group_std_mean=0.0082 train/reward_dists_group_std_max=0.0155 train/reward_dists_group_std_min=0.0039 train/reward_dists_group_mean_std=0.0246 train/reward_ssim_group_std_mean=0.0087 train/reward_ssim_group_std_max=0.0267 train/reward_ssim_group_std_min=0.0040 train/reward_ssim_group_mean_std=0.1153 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.0668 train/adv_max=4.4894 train/adv_abs_mean=1.2075
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Epoch 49 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 49 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 49 Training:  17%|█▋        | 16/96 [00:25<02:06,  1.59s/it]
+
Epoch 49 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 49 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 49 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 49 Training:  18%|█▊        | 17/96 [00:26<02:05,  1.59s/it]
+
Epoch 49 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 49 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 49 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 49 Training:  19%|█▉        | 18/96 [00:28<02:03,  1.59s/it]
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Epoch 49 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 49 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 49 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 49 Training:  20%|█▉        | 19/96 [00:30<02:02,  1.59s/it]
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Epoch 49 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 49 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 49 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 49 Training:  21%|██        | 20/96 [00:31<02:00,  1.59s/it]
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Epoch 49 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 49 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 49 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 49 Training:  22%|██▏       | 21/96 [00:33<01:58,  1.59s/it]
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Epoch 49 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 49 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 49 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 49 Training:  23%|██▎       | 22/96 [00:34<01:57,  1.59s/it]
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Epoch 49 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 49 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 49 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 49 Training:  24%|██▍       | 23/96 [00:36<01:56,  1.59s/it]
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Epoch 49 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 49 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 49 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 49 Training:  25%|██▌       | 24/96 [00:38<01:54,  1.59s/it]
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Epoch 49 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 49 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 49 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 49 Training:  26%|██▌       | 25/96 [00:39<01:52,  1.59s/it]
+
Epoch 49 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 49 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 49 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 49 Training:  27%|██▋       | 26/96 [00:41<01:51,  1.59s/it]
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Epoch 49 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 49 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 49 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 49 Training:  28%|██▊       | 27/96 [00:42<01:49,  1.59s/it]
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Epoch 49 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 49 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 49 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 49 Training:  29%|██▉       | 28/96 [00:44<01:48,  1.59s/it]
+
Epoch 49 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 49 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 49 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 49 Training:  30%|███       | 29/96 [00:45<01:46,  1.59s/it]
+
Epoch 49 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 49 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 49 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 49 Training:  31%|███▏      | 30/96 [00:47<01:44,  1.59s/it]
+
Epoch 49 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 49 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 49 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 49 Training:  32%|███▏      | 31/96 [00:49<01:43,  1.59s/it]
+
Epoch 49 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 49 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 49 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 49 Training:  33%|███▎      | 32/96 [00:50<01:41,  1.59s/it]
+
Epoch 49 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 49 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 49 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 49 Training:  34%|███▍      | 33/96 [00:52<01:39,  1.59s/it]
+
Epoch 49 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 49 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 49 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.95it/s]
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Epoch 49 Training:  35%|███▌      | 34/96 [00:53<01:38,  1.60s/it]
+
Epoch 49 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 49 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 49 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 49 Training:  36%|███▋      | 35/96 [00:55<01:37,  1.59s/it]
+
Epoch 49 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 49 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 49 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 49 Training:  38%|███▊      | 36/96 [00:57<01:35,  1.59s/it]
+
Epoch 49 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 49 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 49 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 49 Training:  39%|███▊      | 37/96 [00:58<01:33,  1.59s/it]
+
Epoch 49 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 49 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 49 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 49 Training:  40%|███▉      | 38/96 [01:00<01:32,  1.59s/it]
+
Epoch 49 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 49 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.15it/s]
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Epoch 49 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 49 Training:  41%|████      | 39/96 [01:01<01:30,  1.59s/it]
+
Epoch 49 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 49 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 49 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 49 Training:  42%|████▏     | 40/96 [01:03<01:28,  1.59s/it]
+
Epoch 49 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 49 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 49 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 49 Training:  43%|████▎     | 41/96 [01:05<01:27,  1.59s/it]
+
Epoch 49 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 49 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 49 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 49 Training:  44%|████▍     | 42/96 [01:06<01:25,  1.59s/it]
+
Epoch 49 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 49 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 49 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 49 Training:  45%|████▍     | 43/96 [01:08<01:24,  1.59s/it]
+
Epoch 49 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 49 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 49 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 49 Training:  46%|████▌     | 44/96 [01:09<01:22,  1.59s/it]
+
Epoch 49 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 49 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 49 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 49 Training:  47%|████▋     | 45/96 [01:11<01:21,  1.59s/it]
+
Epoch 49 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 49 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 49 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 49 Training:  48%|████▊     | 46/96 [01:13<01:19,  1.59s/it]
+
Epoch 49 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 49 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 49 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 49 Training:  49%|████▉     | 47/96 [01:14<01:17,  1.59s/it]
+
Epoch 49 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 49 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 49 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 49 Training:  50%|█████     | 48/96 [01:16<01:16,  1.59s/it]
+
Epoch 49 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 49 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 49 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 49 Training:  51%|█████     | 49/96 [01:17<01:14,  1.59s/it]
+
Epoch 49 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 49 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 49 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 49 Training:  52%|█████▏    | 50/96 [01:19<01:13,  1.59s/it]
+
Epoch 49 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 49 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 49 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 49 Training:  53%|█████▎    | 51/96 [01:20<01:11,  1.59s/it]
+
Epoch 49 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 49 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 49 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 49 Training:  54%|█████▍    | 52/96 [01:22<01:09,  1.59s/it]
+
Epoch 49 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 49 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 49 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 49 Training:  55%|█████▌    | 53/96 [01:24<01:08,  1.59s/it]
+
Epoch 49 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 49 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 49 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 49 Training:  56%|█████▋    | 54/96 [01:25<01:06,  1.59s/it]
+
Epoch 49 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 49 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 49 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 49 Training:  57%|█████▋    | 55/96 [01:27<01:05,  1.59s/it]
+
Epoch 49 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 49 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 49 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 49 Training:  58%|█████▊    | 56/96 [01:28<01:03,  1.59s/it]
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Epoch 49 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 49 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 49 Training:  59%|█████▉    | 57/96 [01:30<01:01,  1.59s/it]
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Epoch 49 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 49 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 49 Training:  60%|██████    | 58/96 [01:32<01:00,  1.59s/it]
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Epoch 49 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 49 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.02it/s]
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Epoch 49 Training:  61%|██████▏   | 59/96 [01:33<00:58,  1.59s/it]
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Epoch 49 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 49 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 49 Training:  62%|██████▎   | 60/96 [01:35<00:57,  1.59s/it]
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Epoch 49 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 49 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 49 Training:  64%|██████▎   | 61/96 [01:36<00:55,  1.59s/it]
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Epoch 49 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 49 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 49 Training:  65%|██████▍   | 62/96 [01:38<00:54,  1.59s/it]
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Epoch 49 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 49 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 49 Training:  66%|██████▌   | 63/96 [01:40<00:52,  1.59s/it]
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Epoch 49 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 49 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 49 Training:  67%|██████▋   | 64/96 [01:41<00:50,  1.59s/it]
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Epoch 49 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 49 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 49 Training:  68%|██████▊   | 65/96 [01:43<00:49,  1.59s/it]
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Epoch 49 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 49 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 49 Training:  69%|██████▉   | 66/96 [01:44<00:47,  1.59s/it]
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Epoch 49 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 49 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s][2026-10-01 17:50:53] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0049 | Epoch 049] train/policy_loss=24.4853 train/unweighted_policy_loss=4.8971 train/loss=24.4853 train/grad_norm=0.0572
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+[2026-10-01 17:50:53] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0050 | Epoch 049] timing/rollout_seconds=180.3108 timing/feedback_seconds=6.3568 timing/optimization_seconds=153.2022 timing/cycle_seconds=336.5696
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+[2026-10-01 17:51:46] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0050 | Epoch 050] eval/flir_rl_full/reward_dists_mean=0.8358 eval/flir_rl_full/reward_dists_std=0.0303 eval/flir_rl_full/reward_psnr_mean=19.6719 eval/flir_rl_full/reward_psnr_std=2.6096 eval/flir_rl_full/reward_ssim_mean=0.6264 eval/flir_rl_full/reward_ssim_std=0.0823
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+
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+[2026-10-01 17:54:49] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0050 | Epoch 050] train/reward_dists_mean=0.8485 train/reward_dists_std=0.0300 train/reward_ssim_mean=0.6734 train/reward_ssim_std=0.1134 train/reward_dists_group_std_mean=0.0085 train/reward_dists_group_std_max=0.0215 train/reward_dists_group_std_min=0.0047 train/reward_dists_group_mean_std=0.0285 train/reward_ssim_group_std_mean=0.0090 train/reward_ssim_group_std_max=0.0219 train/reward_ssim_group_std_min=0.0024 train/reward_ssim_group_mean_std=0.1130 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.7106 train/adv_max=5.1838 train/adv_abs_mean=1.2326
+
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Epoch 50 Training:  40%|███▉      | 38/96 [00:59<01:31,  1.57s/it]
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Epoch 50 Training:  41%|████      | 39/96 [01:01<01:29,  1.57s/it]
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Epoch 50 Training:  42%|████▏     | 40/96 [01:02<01:28,  1.57s/it]
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Epoch 50 Training:  43%|████▎     | 41/96 [01:04<01:26,  1.57s/it]
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Epoch 50 Training:  44%|████▍     | 42/96 [01:06<01:24,  1.57s/it]
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Epoch 50 Training:  45%|████▍     | 43/96 [01:07<01:23,  1.57s/it]
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Epoch 50 Training:  46%|████▌     | 44/96 [01:09<01:21,  1.57s/it]
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Epoch 50 Training:  47%|████▋     | 45/96 [01:10<01:20,  1.57s/it]
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Epoch 50 Training:  48%|████▊     | 46/96 [01:12<01:18,  1.58s/it]
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Epoch 50 Training:  49%|████▉     | 47/96 [01:13<01:17,  1.58s/it]
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Epoch 50 Training:  50%|█████     | 48/96 [01:15<01:15,  1.58s/it]
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Epoch 50 Training:  51%|█████     | 49/96 [01:17<01:14,  1.58s/it]
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Epoch 50 Training:  52%|█████▏    | 50/96 [01:18<01:12,  1.58s/it]
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Epoch 50 Training:  53%|█████▎    | 51/96 [01:20<01:10,  1.58s/it]
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Epoch 50 Training:  54%|█████▍    | 52/96 [01:21<01:09,  1.58s/it]
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Epoch 50 Training:  55%|█████▌    | 53/96 [01:23<01:07,  1.58s/it]
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Epoch 50 Training:  56%|█████▋    | 54/96 [01:25<01:06,  1.58s/it]
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Epoch 50 Training:  57%|█████▋    | 55/96 [01:26<01:04,  1.58s/it]
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Epoch 50 Training:  58%|█████▊    | 56/96 [01:28<01:02,  1.57s/it]
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Epoch 50 Training:  59%|█████▉    | 57/96 [01:29<01:01,  1.57s/it]
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Epoch 50 Training:  60%|██████    | 58/96 [01:31<00:59,  1.57s/it]
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Epoch 50 Training:  61%|██████▏   | 59/96 [01:32<00:58,  1.57s/it]
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Epoch 50 Training:  62%|██████▎   | 60/96 [01:34<00:56,  1.58s/it]
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Epoch 50 Training:  64%|██████▎   | 61/96 [01:36<00:55,  1.57s/it]
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Epoch 50 Training:  65%|██████▍   | 62/96 [01:37<00:53,  1.57s/it]
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Epoch 50 Training:  66%|██████▌   | 63/96 [01:39<00:51,  1.57s/it]
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Epoch 50 Training:  67%|██████▋   | 64/96 [01:40<00:50,  1.57s/it]
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Epoch 50 Training:  68%|██████▊   | 65/96 [01:42<00:48,  1.57s/it]
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Epoch 50 Training:  69%|██████▉   | 66/96 [01:43<00:47,  1.57s/it]
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Epoch 50 Training:  70%|██████▉   | 67/96 [01:45<00:45,  1.57s/it]
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Epoch 50 Training:  71%|███████   | 68/96 [01:47<00:44,  1.57s/it]
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Epoch 50 Training:  72%|███████▏  | 69/96 [01:48<00:42,  1.57s/it]
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Epoch 50 Training:  73%|███████▎  | 70/96 [01:50<00:40,  1.57s/it]
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Epoch 50 Training:  74%|███████▍  | 71/96 [01:51<00:39,  1.57s/it]
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Epoch 50 Training:  75%|███████▌  | 72/96 [01:53<00:37,  1.58s/it]
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Epoch 50 Training:  76%|███████▌  | 73/96 [01:54<00:36,  1.58s/it]
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Epoch 50 Training:  77%|███████▋  | 74/96 [01:56<00:34,  1.57s/it]
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Epoch 50 Training:  78%|███████▊  | 75/96 [01:58<00:33,  1.57s/it]
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Epoch 50 Training:  79%|███████▉  | 76/96 [01:59<00:31,  1.57s/it]
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Epoch 50 Training:  80%|████████  | 77/96 [02:01<00:29,  1.57s/it]
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Epoch 50 Training:  81%|████████▏ | 78/96 [02:02<00:28,  1.57s/it]
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Epoch 50 Training:  82%|████████▏ | 79/96 [02:04<00:26,  1.57s/it]
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Epoch 50 Training:  83%|████████▎ | 80/96 [02:05<00:25,  1.57s/it]
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Epoch 50 Training:  84%|████████▍ | 81/96 [02:07<00:23,  1.57s/it]
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Epoch 50 Training:  85%|████████▌ | 82/96 [02:09<00:22,  1.57s/it]
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Epoch 50 Training:  86%|████████▋ | 83/96 [02:10<00:20,  1.57s/it]
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Epoch 50 Training:  88%|████████▊ | 84/96 [02:12<00:18,  1.57s/it]
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Epoch 50 Training:  89%|████████▊ | 85/96 [02:13<00:17,  1.57s/it]
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Epoch 50 Training:  90%|████████▉ | 86/96 [02:15<00:15,  1.57s/it]
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Epoch 50 Training:  91%|█████████ | 87/96 [02:16<00:14,  1.57s/it]
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Epoch 50 Training:  92%|█████████▏| 88/96 [02:18<00:12,  1.57s/it]
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Epoch 50 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s][2026-10-01 17:57:20] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0050 | Epoch 050] train/policy_loss=24.7557 train/unweighted_policy_loss=4.9511 train/loss=24.7557 train/grad_norm=0.0536
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+[2026-10-01 17:57:20] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0051 | Epoch 050] timing/rollout_seconds=179.5395 timing/feedback_seconds=4.9275 timing/optimization_seconds=151.7428 timing/cycle_seconds=334.1634
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+[2026-10-01 18:00:24] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0051 | Epoch 051] train/reward_dists_mean=0.8321 train/reward_dists_std=0.0397 train/reward_ssim_mean=0.6441 train/reward_ssim_std=0.1115 train/reward_dists_group_std_mean=0.0111 train/reward_dists_group_std_max=0.0394 train/reward_dists_group_std_min=0.0037 train/reward_dists_group_mean_std=0.0376 train/reward_ssim_group_std_mean=0.0085 train/reward_ssim_group_std_max=0.0238 train/reward_ssim_group_std_min=0.0034 train/reward_ssim_group_mean_std=0.1111 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.0698 train/adv_max=4.1142 train/adv_abs_mean=1.1717
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Epoch 51 Training:  14%|█▎        | 13/96 [00:20<02:11,  1.59s/it]
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Epoch 51 Training:  15%|█▍        | 14/96 [00:22<02:09,  1.58s/it]
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Epoch 51 Training:  16%|█▌        | 15/96 [00:23<02:07,  1.58s/it]
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Epoch 51 Training:  17%|█▋        | 16/96 [00:25<02:06,  1.58s/it]
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Epoch 51 Training:  18%|█▊        | 17/96 [00:26<02:05,  1.59s/it]
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Epoch 51 Training:  19%|█▉        | 18/96 [00:28<02:03,  1.58s/it]
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Epoch 51 Training:  20%|█▉        | 19/96 [00:29<02:01,  1.58s/it]
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Epoch 51 Training:  21%|██        | 20/96 [00:31<01:59,  1.58s/it]
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Epoch 51 Training:  22%|██▏       | 21/96 [00:33<01:58,  1.58s/it]
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Epoch 51 Training:  23%|██▎       | 22/96 [00:34<01:57,  1.58s/it]
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Epoch 51 Training:  24%|██▍       | 23/96 [00:36<01:55,  1.58s/it]
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Epoch 51 Training:  25%|██▌       | 24/96 [00:37<01:53,  1.58s/it]
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Epoch 51 Training:  26%|██▌       | 25/96 [00:39<01:52,  1.58s/it]
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Epoch 51 Training:  27%|██▋       | 26/96 [00:41<01:50,  1.58s/it]
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Epoch 51 Training:  28%|██▊       | 27/96 [00:42<01:49,  1.59s/it]
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Epoch 51 Training:  29%|██▉       | 28/96 [00:44<01:47,  1.58s/it]
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Epoch 51 Training:  30%|███       | 29/96 [00:45<01:45,  1.58s/it]
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Epoch 51 Training:  31%|███▏      | 30/96 [00:47<01:44,  1.58s/it]
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Epoch 51 Training:  32%|███▏      | 31/96 [00:48<01:42,  1.58s/it]
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Epoch 51 Training:  33%|███▎      | 32/96 [00:50<01:41,  1.58s/it]
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Epoch 51 Training:  34%|███▍      | 33/96 [00:52<01:39,  1.59s/it]
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Epoch 51 Training:  35%|███▌      | 34/96 [00:53<01:38,  1.58s/it]
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Epoch 51 Training:  36%|███▋      | 35/96 [00:55<01:36,  1.59s/it]
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Epoch 51 Training:  38%|███▊      | 36/96 [00:56<01:35,  1.59s/it]
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Epoch 51 Training:  39%|███▊      | 37/96 [00:58<01:33,  1.59s/it]
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Epoch 51 Training:  40%|███▉      | 38/96 [01:00<01:32,  1.59s/it]
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Epoch 51 Training:  41%|████      | 39/96 [01:01<01:30,  1.59s/it]
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Epoch 51 Training:  42%|████▏     | 40/96 [01:03<01:28,  1.58s/it]
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Epoch 51 Training:  43%|████▎     | 41/96 [01:04<01:26,  1.58s/it]
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Epoch 51 Training:  44%|████▍     | 42/96 [01:06<01:25,  1.59s/it]
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Epoch 51 Training:  45%|████▍     | 43/96 [01:07<01:24,  1.59s/it]
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Epoch 51 Training:  46%|████▌     | 44/96 [01:09<01:22,  1.59s/it]
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Epoch 51 Training:  47%|████▋     | 45/96 [01:11<01:21,  1.59s/it]
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Epoch 51 Training:  48%|████▊     | 46/96 [01:12<01:22,  1.65s/it]
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Epoch 51 Training:  49%|████▉     | 47/96 [01:14<01:19,  1.63s/it]
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Epoch 51 Training:  50%|█████     | 48/96 [01:16<01:17,  1.62s/it]
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Epoch 51 Training:  51%|█████     | 49/96 [01:17<01:15,  1.61s/it]
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Epoch 51 Training:  52%|█████▏    | 50/96 [01:19<01:13,  1.60s/it]
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Epoch 51 Training:  53%|█████▎    | 51/96 [01:20<01:11,  1.60s/it]
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Epoch 51 Training:  54%|█████▍    | 52/96 [01:22<01:10,  1.60s/it]
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Epoch 51 Training:  55%|█████▌    | 53/96 [01:24<01:08,  1.59s/it]
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Epoch 51 Training:  56%|█████▋    | 54/96 [01:25<01:06,  1.59s/it]
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Epoch 51 Training:  57%|█████▋    | 55/96 [01:27<01:05,  1.59s/it]
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Epoch 51 Training:  58%|█████▊    | 56/96 [01:28<01:03,  1.59s/it]
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Epoch 51 Training:  59%|█████▉    | 57/96 [01:30<01:02,  1.59s/it]
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Epoch 51 Training:  60%|██████    | 58/96 [01:32<01:00,  1.59s/it]
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Epoch 51 Training:  61%|██████▏   | 59/96 [01:33<00:58,  1.59s/it]
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Epoch 51 Training:  62%|██████▎   | 60/96 [01:35<00:57,  1.59s/it]
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Epoch 51 Training:  64%|██████▎   | 61/96 [01:36<00:55,  1.59s/it]
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Epoch 51 Training:  65%|██████▍   | 62/96 [01:38<00:54,  1.59s/it]
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Epoch 51 Training:  66%|██████▌   | 63/96 [01:39<00:52,  1.59s/it]
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Epoch 51 Training:  67%|██████▋   | 64/96 [01:41<00:50,  1.58s/it]
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Epoch 51 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s][2026-10-01 18:02:56] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0051 | Epoch 051] train/policy_loss=24.8477 train/unweighted_policy_loss=4.9695 train/loss=24.8477 train/grad_norm=0.0551
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+[2026-10-01 18:02:56] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0052 | Epoch 051] timing/rollout_seconds=179.6779 timing/feedback_seconds=5.4596 timing/optimization_seconds=153.1727 timing/cycle_seconds=335.7442
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+
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+[2026-10-01 18:05:59] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0052 | Epoch 052] train/reward_dists_mean=0.8348 train/reward_dists_std=0.0337 train/reward_ssim_mean=0.6513 train/reward_ssim_std=0.1099 train/reward_dists_group_std_mean=0.0093 train/reward_dists_group_std_max=0.0227 train/reward_dists_group_std_min=0.0043 train/reward_dists_group_mean_std=0.0322 train/reward_ssim_group_std_mean=0.0071 train/reward_ssim_group_std_max=0.0155 train/reward_ssim_group_std_min=0.0032 train/reward_ssim_group_mean_std=0.1096 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.7453 train/adv_max=4.4442 train/adv_abs_mean=1.2183
+
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Epoch 52 Training:  43%|████▎     | 41/96 [01:04<01:27,  1.59s/it]
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Epoch 52 Training:  44%|████▍     | 42/96 [01:06<01:25,  1.58s/it]
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Epoch 52 Training:  45%|████▍     | 43/96 [01:08<01:24,  1.59s/it]
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Epoch 52 Training:  46%|████▌     | 44/96 [01:09<01:22,  1.58s/it]
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Epoch 52 Training:  47%|████▋     | 45/96 [01:11<01:20,  1.59s/it]
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Epoch 52 Training:  48%|████▊     | 46/96 [01:12<01:19,  1.58s/it]
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Epoch 52 Training:  49%|████▉     | 47/96 [01:14<01:17,  1.58s/it]
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Epoch 52 Training:  50%|█████     | 48/96 [01:16<01:15,  1.58s/it]
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Epoch 52 Training:  51%|█████     | 49/96 [01:17<01:14,  1.58s/it]
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Epoch 52 Training:  52%|█████▏    | 50/96 [01:19<01:12,  1.58s/it]
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Epoch 52 Training:  53%|█████▎    | 51/96 [01:20<01:11,  1.58s/it]
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Epoch 52 Training:  54%|█████▍    | 52/96 [01:22<01:09,  1.59s/it]
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Epoch 52 Training:  55%|█████▌    | 53/96 [01:23<01:08,  1.59s/it]
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Epoch 52 Training:  56%|█████▋    | 54/96 [01:25<01:06,  1.59s/it]
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Epoch 52 Training:  57%|█████▋    | 55/96 [01:27<01:05,  1.59s/it]
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Epoch 52 Training:  58%|█████▊    | 56/96 [01:28<01:03,  1.59s/it]
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Epoch 52 Training:  59%|█████▉    | 57/96 [01:30<01:02,  1.59s/it]
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Epoch 52 Training:  60%|██████    | 58/96 [01:31<01:00,  1.59s/it]
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Epoch 52 Training:  61%|██████▏   | 59/96 [01:33<00:58,  1.59s/it]
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Epoch 52 Training:  62%|██████▎   | 60/96 [01:35<00:57,  1.59s/it]
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Epoch 52 Training:  64%|██████▎   | 61/96 [01:36<00:55,  1.59s/it]
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Epoch 52 Training:  65%|██████▍   | 62/96 [01:38<00:54,  1.59s/it]
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Epoch 52 Training:  66%|██████▌   | 63/96 [01:39<00:52,  1.59s/it]
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Epoch 52 Training:  67%|██████▋   | 64/96 [01:41<00:50,  1.59s/it]
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Epoch 52 Training:  68%|██████▊   | 65/96 [01:43<00:49,  1.59s/it]
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Epoch 52 Training:  69%|██████▉   | 66/96 [01:44<00:47,  1.59s/it]
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Epoch 52 Training:  70%|██████▉   | 67/96 [01:46<00:46,  1.59s/it]
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Epoch 52 Training:  71%|███████   | 68/96 [01:47<00:44,  1.59s/it]
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Epoch 52 Training:  72%|███████▏  | 69/96 [01:49<00:42,  1.59s/it]
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Epoch 52 Training:  73%|███████▎  | 70/96 [01:51<00:41,  1.59s/it]
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Epoch 52 Training:  74%|███████▍  | 71/96 [01:52<00:39,  1.59s/it]
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Epoch 52 Training:  75%|███████▌  | 72/96 [01:54<00:38,  1.59s/it]
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Epoch 52 Training:  76%|███████▌  | 73/96 [01:55<00:36,  1.59s/it]
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Epoch 52 Training:  77%|███████▋  | 74/96 [01:57<00:35,  1.60s/it]
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Epoch 52 Training:  78%|███████▊  | 75/96 [01:59<00:33,  1.60s/it]
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Epoch 52 Training:  79%|███████▉  | 76/96 [02:00<00:31,  1.60s/it]
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Epoch 52 Training:  80%|████████  | 77/96 [02:02<00:30,  1.59s/it]
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Epoch 52 Training:  81%|████████▏ | 78/96 [02:03<00:28,  1.59s/it]
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Epoch 52 Training:  82%|████████▏ | 79/96 [02:05<00:27,  1.59s/it]
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Epoch 52 Training:  83%|████████▎ | 80/96 [02:06<00:25,  1.59s/it]
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Epoch 52 Training:  84%|████████▍ | 81/96 [02:08<00:23,  1.59s/it]
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Epoch 52 Training:  85%|████████▌ | 82/96 [02:10<00:22,  1.59s/it]
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Epoch 52 Training:  86%|████████▋ | 83/96 [02:11<00:20,  1.59s/it]
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Epoch 52 Training:  88%|████████▊ | 84/96 [02:13<00:19,  1.59s/it]
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Epoch 52 Training:  89%|████████▊ | 85/96 [02:14<00:17,  1.59s/it]
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Epoch 52 Training:  90%|████████▉ | 86/96 [02:16<00:15,  1.59s/it]
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Epoch 52 Training:  91%|█████████ | 87/96 [02:18<00:14,  1.58s/it]
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Epoch 52 Training:  92%|█████████▏| 88/96 [02:19<00:12,  1.58s/it]
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Epoch 52 Training:  93%|█████████▎| 89/96 [02:21<00:11,  1.59s/it]
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Epoch 52 Training:  94%|█████████▍| 90/96 [02:22<00:09,  1.62s/it]
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Epoch 52 Training:  95%|█████████▍| 91/96 [02:24<00:08,  1.61s/it]
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Epoch 52 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s][2026-10-01 18:08:32] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0052 | Epoch 052] train/policy_loss=24.8127 train/unweighted_policy_loss=4.9625 train/loss=24.8127 train/grad_norm=0.0680
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+[2026-10-01 18:08:32] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0053 | Epoch 052] timing/rollout_seconds=179.4118 timing/feedback_seconds=5.2636 timing/optimization_seconds=153.4170 timing/cycle_seconds=335.7618
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+[2026-10-01 18:11:35] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0053 | Epoch 053] train/reward_dists_mean=0.8465 train/reward_dists_std=0.0344 train/reward_ssim_mean=0.6539 train/reward_ssim_std=0.1166 train/reward_dists_group_std_mean=0.0088 train/reward_dists_group_std_max=0.0329 train/reward_dists_group_std_min=0.0042 train/reward_dists_group_mean_std=0.0329 train/reward_ssim_group_std_mean=0.0078 train/reward_ssim_group_std_max=0.0255 train/reward_ssim_group_std_min=0.0033 train/reward_ssim_group_mean_std=0.1163 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.2034 train/adv_max=4.1047 train/adv_abs_mean=1.1479
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Epoch 53 Training:  17%|█▋        | 16/96 [00:25<02:06,  1.58s/it]
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Epoch 53 Training:  18%|█▊        | 17/96 [00:26<02:04,  1.58s/it]
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Epoch 53 Training:  19%|█▉        | 18/96 [00:28<02:03,  1.58s/it]
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Epoch 53 Training:  20%|█▉        | 19/96 [00:29<02:01,  1.58s/it]
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Epoch 53 Training:  21%|██        | 20/96 [00:31<02:00,  1.58s/it]
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Epoch 53 Training:  22%|██▏       | 21/96 [00:33<01:58,  1.58s/it]
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Epoch 53 Training:  23%|██▎       | 22/96 [00:34<01:57,  1.58s/it]
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Epoch 53 Training:  24%|██▍       | 23/96 [00:36<01:55,  1.59s/it]
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Epoch 53 Training:  25%|██▌       | 24/96 [00:37<01:54,  1.59s/it]
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Epoch 53 Training:  26%|██▌       | 25/96 [00:39<01:52,  1.59s/it]
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Epoch 53 Training:  27%|██▋       | 26/96 [00:41<01:51,  1.59s/it]
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Epoch 53 Training:  28%|██▊       | 27/96 [00:42<01:49,  1.59s/it]
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Epoch 53 Training:  29%|██▉       | 28/96 [00:44<01:47,  1.59s/it]
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Epoch 53 Training:  30%|███       | 29/96 [00:45<01:46,  1.59s/it]
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Epoch 53 Training:  31%|███▏      | 30/96 [00:47<01:50,  1.67s/it]
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Epoch 53 Training:  32%|███▏      | 31/96 [00:49<01:46,  1.64s/it]
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Epoch 53 Training:  33%|███▎      | 32/96 [00:50<01:43,  1.62s/it]
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Epoch 53 Training:  34%|███▍      | 33/96 [00:52<01:43,  1.64s/it]
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Epoch 53 Training:  35%|███▌      | 34/96 [00:54<01:40,  1.62s/it]
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Epoch 53 Training:  36%|███▋      | 35/96 [00:55<01:38,  1.61s/it]
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Epoch 53 Training:  38%|███▊      | 36/96 [00:57<01:36,  1.60s/it]
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Epoch 53 Training:  39%|███▊      | 37/96 [00:58<01:34,  1.60s/it]
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Epoch 53 Training:  40%|███▉      | 38/96 [01:00<01:32,  1.59s/it]
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Epoch 53 Training:  41%|████      | 39/96 [01:01<01:30,  1.59s/it]
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Epoch 53 Training:  42%|████▏     | 40/96 [01:03<01:28,  1.59s/it]
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Epoch 53 Training:  43%|████▎     | 41/96 [01:05<01:27,  1.59s/it]
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Epoch 53 Training:  44%|████▍     | 42/96 [01:06<01:25,  1.59s/it]
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Epoch 53 Training:  45%|████▍     | 43/96 [01:08<01:23,  1.58s/it]
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Epoch 53 Training:  46%|████▌     | 44/96 [01:09<01:22,  1.58s/it]
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Epoch 53 Training:  47%|████▋     | 45/96 [01:11<01:20,  1.58s/it]
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Epoch 53 Training:  48%|████▊     | 46/96 [01:13<01:19,  1.58s/it]
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Epoch 53 Training:  49%|████▉     | 47/96 [01:14<01:17,  1.59s/it]
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Epoch 53 Training:  50%|█████     | 48/96 [01:16<01:16,  1.59s/it]
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Epoch 53 Training:  51%|█████     | 49/96 [01:17<01:14,  1.59s/it]
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Epoch 53 Training:  52%|█████▏    | 50/96 [01:19<01:12,  1.58s/it]
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Epoch 53 Training:  53%|█████▎    | 51/96 [01:20<01:11,  1.58s/it]
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Epoch 53 Training:  54%|█████▍    | 52/96 [01:22<01:09,  1.58s/it]
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Epoch 53 Training:  55%|█████▌    | 53/96 [01:24<01:08,  1.58s/it]
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Epoch 53 Training:  56%|█████▋    | 54/96 [01:25<01:06,  1.58s/it]
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Epoch 53 Training:  57%|█████▋    | 55/96 [01:27<01:04,  1.58s/it]
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Epoch 53 Training:  58%|█████▊    | 56/96 [01:28<01:02,  1.57s/it]
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Epoch 53 Training:  59%|█████▉    | 57/96 [01:30<01:02,  1.60s/it]
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Epoch 53 Training:  60%|██████    | 58/96 [01:32<01:00,  1.59s/it]
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Epoch 53 Training:  61%|██████▏   | 59/96 [01:33<00:58,  1.59s/it]
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Epoch 53 Training:  62%|██████▎   | 60/96 [01:35<00:56,  1.58s/it]
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Epoch 53 Training:  64%|██████▎   | 61/96 [01:36<00:55,  1.58s/it]
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Epoch 53 Training:  65%|██████▍   | 62/96 [01:38<00:53,  1.58s/it]
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Epoch 53 Training:  66%|██████▌   | 63/96 [01:39<00:52,  1.58s/it]
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Epoch 53 Training:  67%|██████▋   | 64/96 [01:41<00:50,  1.58s/it]
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Epoch 53 Training:  68%|██████▊   | 65/96 [01:43<00:48,  1.58s/it]
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Epoch 53 Training:  69%|██████▉   | 66/96 [01:44<00:47,  1.58s/it]
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Epoch 53 Training:  70%|██████▉   | 67/96 [01:46<00:45,  1.58s/it]
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Epoch 53 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s][2026-10-01 18:14:07] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0053 | Epoch 053] train/policy_loss=24.3726 train/unweighted_policy_loss=4.8745 train/loss=24.3726 train/grad_norm=0.0557
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+[2026-10-01 18:14:07] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0054 | Epoch 053] timing/rollout_seconds=179.3678 timing/feedback_seconds=5.0659 timing/optimization_seconds=152.7601 timing/cycle_seconds=335.0389
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+
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+
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+[2026-10-01 18:17:10] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0054 | Epoch 054] train/reward_dists_mean=0.8464 train/reward_dists_std=0.0296 train/reward_ssim_mean=0.6394 train/reward_ssim_std=0.1106 train/reward_dists_group_std_mean=0.0082 train/reward_dists_group_std_max=0.0143 train/reward_dists_group_std_min=0.0042 train/reward_dists_group_mean_std=0.0283 train/reward_ssim_group_std_mean=0.0078 train/reward_ssim_group_std_max=0.0157 train/reward_ssim_group_std_min=0.0029 train/reward_ssim_group_mean_std=0.1103 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.5467 train/adv_max=4.6960 train/adv_abs_mean=1.1722
+
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Epoch 54 Training:  45%|████▍     | 43/96 [01:08<01:24,  1.59s/it]
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Epoch 54 Training:  46%|████▌     | 44/96 [01:09<01:22,  1.58s/it]
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Epoch 54 Training:  47%|████▋     | 45/96 [01:11<01:20,  1.58s/it]
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Epoch 54 Training:  48%|████▊     | 46/96 [01:12<01:19,  1.58s/it]
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Epoch 54 Training:  49%|████▉     | 47/96 [01:14<01:17,  1.58s/it]
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Epoch 54 Training:  50%|█████     | 48/96 [01:16<01:15,  1.58s/it]
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Epoch 54 Training:  51%|█████     | 49/96 [01:17<01:14,  1.58s/it]
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Epoch 54 Training:  52%|█████▏    | 50/96 [01:19<01:12,  1.57s/it]
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Epoch 54 Training:  53%|█████▎    | 51/96 [01:20<01:11,  1.58s/it]
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Epoch 54 Training:  54%|█████▍    | 52/96 [01:22<01:09,  1.58s/it]
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Epoch 54 Training:  55%|█████▌    | 53/96 [01:24<01:08,  1.58s/it]
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Epoch 54 Training:  56%|█████▋    | 54/96 [01:25<01:06,  1.59s/it]
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Epoch 54 Training:  57%|█████▋    | 55/96 [01:27<01:04,  1.58s/it]
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Epoch 54 Training:  58%|█████▊    | 56/96 [01:28<01:03,  1.59s/it]
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Epoch 54 Training:  59%|█████▉    | 57/96 [01:30<01:01,  1.59s/it]
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Epoch 54 Training:  60%|██████    | 58/96 [01:31<01:00,  1.59s/it]
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Epoch 54 Training:  61%|██████▏   | 59/96 [01:33<00:58,  1.58s/it]
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Epoch 54 Training:  62%|██████▎   | 60/96 [01:35<00:56,  1.58s/it]
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Epoch 54 Training:  64%|██████▎   | 61/96 [01:36<00:55,  1.58s/it]
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Epoch 54 Training:  65%|██████▍   | 62/96 [01:38<00:53,  1.58s/it]
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Epoch 54 Training:  66%|██████▌   | 63/96 [01:39<00:52,  1.58s/it]
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Epoch 54 Training:  67%|██████▋   | 64/96 [01:41<00:50,  1.58s/it]
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Epoch 54 Training:  68%|██████▊   | 65/96 [01:43<00:49,  1.58s/it]
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Epoch 54 Training:  69%|██████▉   | 66/96 [01:44<00:47,  1.58s/it]
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Epoch 54 Training:  70%|██████▉   | 67/96 [01:46<00:45,  1.58s/it]
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Epoch 54 Training:  71%|███████   | 68/96 [01:47<00:44,  1.59s/it]
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Epoch 54 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.97it/s]
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Epoch 54 Training:  72%|███████▏  | 69/96 [01:49<00:42,  1.58s/it]
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Epoch 54 Training:  73%|███████▎  | 70/96 [01:50<00:41,  1.58s/it]
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Epoch 54 Training:  74%|███████▍  | 71/96 [01:52<00:39,  1.58s/it]
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Epoch 54 Training:  75%|███████▌  | 72/96 [01:54<00:37,  1.58s/it]
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Epoch 54 Training:  76%|███████▌  | 73/96 [01:55<00:36,  1.58s/it]
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Epoch 54 Training:  77%|███████▋  | 74/96 [01:57<00:34,  1.58s/it]
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Epoch 54 Training:  78%|███████▊  | 75/96 [01:58<00:33,  1.58s/it]
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Epoch 54 Training:  79%|███████▉  | 76/96 [02:00<00:31,  1.58s/it]
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Epoch 54 Training:  80%|████████  | 77/96 [02:02<00:29,  1.58s/it]
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Epoch 54 Training:  81%|████████▏ | 78/96 [02:03<00:28,  1.58s/it]
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Epoch 54 Training:  82%|████████▏ | 79/96 [02:05<00:26,  1.58s/it]
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Epoch 54 Training:  83%|████████▎ | 80/96 [02:06<00:25,  1.58s/it]
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Epoch 54 Training:  84%|████████▍ | 81/96 [02:08<00:23,  1.58s/it]
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Epoch 54 Training:  85%|████████▌ | 82/96 [02:09<00:22,  1.58s/it]
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Epoch 54 Training:  86%|████████▋ | 83/96 [02:11<00:20,  1.59s/it]
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Epoch 54 Training:  88%|████████▊ | 84/96 [02:13<00:18,  1.58s/it]
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Epoch 54 Training:  89%|████████▊ | 85/96 [02:14<00:17,  1.58s/it]
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Epoch 54 Training:  90%|████████▉ | 86/96 [02:16<00:16,  1.60s/it]
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Epoch 54 Training:  91%|█████████ | 87/96 [02:17<00:14,  1.60s/it]
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Epoch 54 Training:  92%|█████████▏| 88/96 [02:19<00:12,  1.59s/it]
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Epoch 54 Training:  93%|█████████▎| 89/96 [02:21<00:11,  1.58s/it]
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Epoch 54 Training:  94%|█████████▍| 90/96 [02:22<00:09,  1.58s/it]
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Epoch 54 Training:  95%|█████████▍| 91/96 [02:24<00:07,  1.58s/it]
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Epoch 54 Training:  96%|█████████▌| 92/96 [02:25<00:06,  1.58s/it]
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Epoch 54 Training:  97%|█████████▋| 93/96 [02:27<00:04,  1.57s/it]
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Epoch 54 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s][2026-10-01 18:19:43] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0054 | Epoch 054] train/policy_loss=24.5638 train/unweighted_policy_loss=4.9128 train/loss=24.5638 train/grad_norm=0.0577
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+[2026-10-01 18:19:43] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0055 | Epoch 054] timing/rollout_seconds=179.8493 timing/feedback_seconds=5.3997 timing/optimization_seconds=152.7912 timing/cycle_seconds=335.5779
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+
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+[2026-10-01 18:22:46] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0055 | Epoch 055] train/reward_dists_mean=0.8535 train/reward_dists_std=0.0239 train/reward_ssim_mean=0.6566 train/reward_ssim_std=0.1087 train/reward_dists_group_std_mean=0.0079 train/reward_dists_group_std_max=0.0158 train/reward_dists_group_std_min=0.0030 train/reward_dists_group_mean_std=0.0224 train/reward_ssim_group_std_mean=0.0082 train/reward_ssim_group_std_max=0.0176 train/reward_ssim_group_std_min=0.0028 train/reward_ssim_group_mean_std=0.1083 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.8616 train/adv_max=3.8584 train/adv_abs_mean=1.2357
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Epoch 55 Training:  20%|█▉        | 19/96 [00:30<02:02,  1.59s/it]
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Epoch 55 Training:  21%|██        | 20/96 [00:31<02:00,  1.59s/it]
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Epoch 55 Training:  22%|██▏       | 21/96 [00:33<01:59,  1.59s/it]
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Epoch 55 Training:  23%|██▎       | 22/96 [00:34<01:57,  1.59s/it]
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Epoch 55 Training:  24%|██▍       | 23/96 [00:36<01:56,  1.59s/it]
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Epoch 55 Training:  25%|██▌       | 24/96 [00:38<01:54,  1.59s/it]
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Epoch 55 Training:  26%|██▌       | 25/96 [00:39<01:53,  1.59s/it]
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Epoch 55 Training:  27%|██▋       | 26/96 [00:41<01:51,  1.59s/it]
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Epoch 55 Training:  28%|██▊       | 27/96 [00:42<01:49,  1.59s/it]
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Epoch 55 Training:  29%|██▉       | 28/96 [00:44<01:48,  1.59s/it]
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Epoch 55 Training:  30%|███       | 29/96 [00:45<01:46,  1.59s/it]
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Epoch 55 Training:  31%|███▏      | 30/96 [00:47<01:44,  1.59s/it]
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Epoch 55 Training:  32%|███▏      | 31/96 [00:49<01:43,  1.59s/it]
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Epoch 55 Training:  33%|███▎      | 32/96 [00:50<01:41,  1.59s/it]
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Epoch 55 Training:  34%|███▍      | 33/96 [00:52<01:40,  1.59s/it]
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Epoch 55 Training:  35%|███▌      | 34/96 [00:53<01:38,  1.59s/it]
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Epoch 55 Training:  36%|███▋      | 35/96 [00:55<01:36,  1.59s/it]
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Epoch 55 Training:  38%|███▊      | 36/96 [00:57<01:35,  1.59s/it]
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Epoch 55 Training:  39%|███▊      | 37/96 [00:58<01:33,  1.59s/it]
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Epoch 55 Training:  40%|███▉      | 38/96 [01:00<01:32,  1.59s/it]
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Epoch 55 Training:  41%|████      | 39/96 [01:01<01:30,  1.59s/it]
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Epoch 55 Training:  42%|████▏     | 40/96 [01:03<01:28,  1.59s/it]
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Epoch 55 Training:  43%|████▎     | 41/96 [01:05<01:27,  1.59s/it]
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Epoch 55 Training:  44%|████▍     | 42/96 [01:06<01:25,  1.59s/it]
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Epoch 55 Training:  45%|████▍     | 43/96 [01:08<01:24,  1.59s/it]
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Epoch 55 Training:  46%|████▌     | 44/96 [01:09<01:22,  1.58s/it]
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Epoch 55 Training:  47%|████▋     | 45/96 [01:11<01:20,  1.58s/it]
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Epoch 55 Training:  48%|████▊     | 46/96 [01:12<01:19,  1.58s/it]
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Epoch 55 Training:  49%|████▉     | 47/96 [01:14<01:17,  1.59s/it]
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Epoch 55 Training:  50%|█████     | 48/96 [01:16<01:16,  1.59s/it]
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Epoch 55 Training:  51%|█████     | 49/96 [01:17<01:14,  1.59s/it]
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Epoch 55 Training:  52%|█████▏    | 50/96 [01:19<01:12,  1.59s/it]
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Epoch 55 Training:  53%|█████▎    | 51/96 [01:20<01:11,  1.59s/it]
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Epoch 55 Training:  54%|█████▍    | 52/96 [01:22<01:09,  1.59s/it]
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Epoch 55 Training:  55%|█████▌    | 53/96 [01:24<01:08,  1.59s/it]
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Epoch 55 Training:  56%|█████▋    | 54/96 [01:25<01:06,  1.59s/it]
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Epoch 55 Training:  57%|█████▋    | 55/96 [01:27<01:05,  1.59s/it]
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Epoch 55 Training:  58%|█████▊    | 56/96 [01:28<01:03,  1.59s/it]
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Epoch 55 Training:  59%|█████▉    | 57/96 [01:30<01:01,  1.59s/it]
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Epoch 55 Training:  60%|██████    | 58/96 [01:32<01:00,  1.58s/it]
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Epoch 55 Training:  61%|██████▏   | 59/96 [01:33<00:58,  1.58s/it]
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Epoch 55 Training:  62%|██████▎   | 60/96 [01:35<00:56,  1.58s/it]
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Epoch 55 Training:  64%|██████▎   | 61/96 [01:36<00:55,  1.58s/it]
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Epoch 55 Training:  65%|██████▍   | 62/96 [01:38<00:53,  1.58s/it]
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Epoch 55 Training:  66%|██████▌   | 63/96 [01:39<00:52,  1.58s/it]
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Epoch 55 Training:  67%|██████▋   | 64/96 [01:41<00:50,  1.59s/it]
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Epoch 55 Training:  68%|██████▊   | 65/96 [01:43<00:49,  1.59s/it]
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Epoch 55 Training:  69%|██████▉   | 66/96 [01:44<00:47,  1.59s/it]
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Epoch 55 Training:  70%|██████▉   | 67/96 [01:46<00:45,  1.59s/it]
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Epoch 55 Training:  71%|███████   | 68/96 [01:47<00:44,  1.59s/it]
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Epoch 55 Training:  72%|███████▏  | 69/96 [01:49<00:42,  1.59s/it]
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Epoch 55 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s][2026-10-01 18:25:19] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0055 | Epoch 055] train/policy_loss=24.6209 train/unweighted_policy_loss=4.9242 train/loss=24.6209 train/grad_norm=0.0577
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+[2026-10-01 18:25:19] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0056 | Epoch 055] timing/rollout_seconds=180.0561 timing/feedback_seconds=5.9483 timing/optimization_seconds=153.0653 timing/cycle_seconds=336.2127
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+[2026-10-01 18:28:22] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0056 | Epoch 056] train/reward_dists_mean=0.8542 train/reward_dists_std=0.0249 train/reward_ssim_mean=0.6663 train/reward_ssim_std=0.0904 train/reward_dists_group_std_mean=0.0072 train/reward_dists_group_std_max=0.0167 train/reward_dists_group_std_min=0.0033 train/reward_dists_group_mean_std=0.0237 train/reward_ssim_group_std_mean=0.0083 train/reward_ssim_group_std_max=0.0181 train/reward_ssim_group_std_min=0.0038 train/reward_ssim_group_mean_std=0.0899 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.3423 train/adv_max=3.9216 train/adv_abs_mean=1.2228
+
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Epoch 56 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s][2026-10-01 18:30:54] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0056 | Epoch 056] train/policy_loss=24.0914 train/unweighted_policy_loss=4.8183 train/loss=24.0914 train/grad_norm=0.0567
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+[2026-10-01 18:30:54] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0057 | Epoch 056] timing/rollout_seconds=179.6176 timing/feedback_seconds=5.3608 timing/optimization_seconds=152.9609 timing/cycle_seconds=335.4517
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+
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+[2026-10-01 18:33:57] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0057 | Epoch 057] train/reward_dists_mean=0.8516 train/reward_dists_std=0.0301 train/reward_ssim_mean=0.6555 train/reward_ssim_std=0.0865 train/reward_dists_group_std_mean=0.0074 train/reward_dists_group_std_max=0.0172 train/reward_dists_group_std_min=0.0030 train/reward_dists_group_mean_std=0.0291 train/reward_ssim_group_std_mean=0.0090 train/reward_ssim_group_std_max=0.0238 train/reward_ssim_group_std_min=0.0028 train/reward_ssim_group_mean_std=0.0859 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.4529 train/adv_max=4.6092 train/adv_abs_mean=1.1763
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Epoch 57 Training:  23%|██▎       | 22/96 [00:34<01:58,  1.60s/it]
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Epoch 57 Training:  24%|██▍       | 23/96 [00:36<01:56,  1.59s/it]
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Epoch 57 Training:  25%|██▌       | 24/96 [00:37<01:54,  1.59s/it]
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Epoch 57 Training:  26%|██▌       | 25/96 [00:39<01:52,  1.59s/it]
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Epoch 57 Training:  27%|██▋       | 26/96 [00:41<01:50,  1.58s/it]
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Epoch 57 Training:  28%|██▊       | 27/96 [00:42<01:49,  1.58s/it]
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Epoch 57 Training:  29%|██▉       | 28/96 [00:44<01:47,  1.58s/it]
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Epoch 57 Training:  30%|███       | 29/96 [00:45<01:45,  1.58s/it]
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Epoch 57 Training:  31%|███▏      | 30/96 [00:47<01:44,  1.58s/it]
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Epoch 57 Training:  32%|███▏      | 31/96 [00:49<01:42,  1.58s/it]
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Epoch 57 Training:  33%|███▎      | 32/96 [00:50<01:41,  1.58s/it]
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Epoch 57 Training:  34%|███▍      | 33/96 [00:52<01:39,  1.58s/it]
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Epoch 57 Training:  35%|███▌      | 34/96 [00:53<01:38,  1.58s/it]
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Epoch 57 Training:  36%|███▋      | 35/96 [00:55<01:36,  1.58s/it]
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Epoch 57 Training:  38%|███▊      | 36/96 [00:56<01:34,  1.58s/it]
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Epoch 57 Training:  39%|███▊      | 37/96 [00:58<01:33,  1.58s/it]
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Epoch 57 Training:  40%|███▉      | 38/96 [01:00<01:31,  1.58s/it]
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Epoch 57 Training:  41%|████      | 39/96 [01:01<01:29,  1.58s/it]
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Epoch 57 Training:  42%|████▏     | 40/96 [01:03<01:28,  1.58s/it]
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Epoch 57 Training:  43%|████▎     | 41/96 [01:04<01:26,  1.58s/it]
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Epoch 57 Training:  44%|████▍     | 42/96 [01:06<01:25,  1.58s/it]
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Epoch 57 Training:  45%|████▍     | 43/96 [01:07<01:23,  1.58s/it]
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Epoch 57 Training:  46%|████▌     | 44/96 [01:09<01:21,  1.58s/it]
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Epoch 57 Training:  47%|████▋     | 45/96 [01:11<01:20,  1.57s/it]
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Epoch 57 Training:  48%|████▊     | 46/96 [01:12<01:18,  1.57s/it]
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Epoch 57 Training:  49%|████▉     | 47/96 [01:14<01:17,  1.58s/it]
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Epoch 57 Training:  50%|█████     | 48/96 [01:15<01:15,  1.58s/it]
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Epoch 57 Training:  51%|█████     | 49/96 [01:17<01:14,  1.58s/it]
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Epoch 57 Training:  52%|█████▏    | 50/96 [01:19<01:12,  1.58s/it]
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Epoch 57 Training:  53%|█████▎    | 51/96 [01:20<01:11,  1.58s/it]
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Epoch 57 Training:  54%|█████▍    | 52/96 [01:22<01:09,  1.58s/it]
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Epoch 57 Training:  55%|█████▌    | 53/96 [01:23<01:07,  1.58s/it]
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Epoch 57 Training:  56%|█████▋    | 54/96 [01:25<01:06,  1.58s/it]
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Epoch 57 Training:  57%|█████▋    | 55/96 [01:26<01:04,  1.58s/it]
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Epoch 57 Training:  58%|█████▊    | 56/96 [01:28<01:03,  1.58s/it]
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Epoch 57 Training:  59%|█████▉    | 57/96 [01:30<01:01,  1.58s/it]
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Epoch 57 Training:  60%|██████    | 58/96 [01:31<00:59,  1.58s/it]
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Epoch 57 Training:  61%|██████▏   | 59/96 [01:33<00:58,  1.58s/it]
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Epoch 57 Training:  62%|██████▎   | 60/96 [01:34<00:56,  1.58s/it]
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Epoch 57 Training:  64%|██████▎   | 61/96 [01:36<00:55,  1.58s/it]
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Epoch 57 Training:  65%|██████▍   | 62/96 [01:37<00:53,  1.58s/it]
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Epoch 57 Training:  66%|██████▌   | 63/96 [01:39<00:52,  1.58s/it]
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Epoch 57 Training:  67%|██████▋   | 64/96 [01:41<00:50,  1.58s/it]
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Epoch 57 Training:  68%|██████▊   | 65/96 [01:42<00:48,  1.58s/it]
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Epoch 57 Training:  69%|██████▉   | 66/96 [01:44<00:47,  1.58s/it]
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Epoch 57 Training:  70%|██████▉   | 67/96 [01:45<00:45,  1.58s/it]
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Epoch 57 Training:  71%|███████   | 68/96 [01:47<00:44,  1.58s/it]
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Epoch 57 Training:  72%|███████▏  | 69/96 [01:49<00:42,  1.58s/it]
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Epoch 57 Training:  73%|███████▎  | 70/96 [01:50<00:41,  1.58s/it]
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Epoch 57 Training:  74%|███████▍  | 71/96 [01:52<00:39,  1.58s/it]
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Epoch 57 Training:  75%|███████▌  | 72/96 [01:53<00:37,  1.58s/it]
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Epoch 57 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s][2026-10-01 18:36:29] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0057 | Epoch 057] train/policy_loss=24.4061 train/unweighted_policy_loss=4.8812 train/loss=24.4061 train/grad_norm=0.0530
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+[2026-10-01 18:36:29] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0058 | Epoch 057] timing/rollout_seconds=179.4804 timing/feedback_seconds=5.3188 timing/optimization_seconds=152.4651 timing/cycle_seconds=334.8978
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+[2026-10-01 18:39:33] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0058 | Epoch 058] train/reward_dists_mean=0.8533 train/reward_dists_std=0.0234 train/reward_ssim_mean=0.6396 train/reward_ssim_std=0.0945 train/reward_dists_group_std_mean=0.0079 train/reward_dists_group_std_max=0.0188 train/reward_dists_group_std_min=0.0039 train/reward_dists_group_mean_std=0.0219 train/reward_ssim_group_std_mean=0.0085 train/reward_ssim_group_std_max=0.0207 train/reward_ssim_group_std_min=0.0035 train/reward_ssim_group_mean_std=0.0941 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.0200 train/adv_max=4.9603 train/adv_abs_mean=1.1605
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Epoch 58 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s][2026-10-01 18:42:05] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0058 | Epoch 058] train/policy_loss=24.4883 train/unweighted_policy_loss=4.8977 train/loss=24.4883 train/grad_norm=0.0669
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+[2026-10-01 18:42:05] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0059 | Epoch 058] timing/rollout_seconds=180.3720 timing/feedback_seconds=6.0992 timing/optimization_seconds=152.9337 timing/cycle_seconds=336.2244
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+
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Pointwise Rewards: dists:  67%|██████▋   | 4/6 [00:01<00:00,  3.15it/s]
Pointwise Rewards: dists:  83%|████████▎ | 5/6 [00:01<00:00,  3.18it/s]
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+[2026-10-01 18:45:09] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0059 | Epoch 059] train/reward_dists_mean=0.8495 train/reward_dists_std=0.0244 train/reward_ssim_mean=0.6607 train/reward_ssim_std=0.1116 train/reward_dists_group_std_mean=0.0084 train/reward_dists_group_std_max=0.0180 train/reward_dists_group_std_min=0.0028 train/reward_dists_group_mean_std=0.0227 train/reward_ssim_group_std_mean=0.0085 train/reward_ssim_group_std_max=0.0187 train/reward_ssim_group_std_min=0.0035 train/reward_ssim_group_mean_std=0.1112 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.2696 train/adv_max=5.5309 train/adv_abs_mean=1.2596
+
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Epoch 59 Training:  25%|██▌       | 24/96 [00:37<01:52,  1.57s/it]
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Epoch 59 Training:  26%|██▌       | 25/96 [00:39<01:51,  1.57s/it]
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Epoch 59 Training:  27%|██▋       | 26/96 [00:40<01:49,  1.56s/it]
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Epoch 59 Training:  28%|██▊       | 27/96 [00:42<01:48,  1.57s/it]
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Epoch 59 Training:  29%|██▉       | 28/96 [00:43<01:46,  1.57s/it]
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Epoch 59 Training:  30%|███       | 29/96 [00:45<01:45,  1.57s/it]
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Epoch 59 Training:  31%|███▏      | 30/96 [00:47<01:43,  1.57s/it]
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Epoch 59 Training:  32%|███▏      | 31/96 [00:48<01:41,  1.57s/it]
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Epoch 59 Training:  33%|███▎      | 32/96 [00:50<01:40,  1.57s/it]
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Epoch 59 Training:  34%|███▍      | 33/96 [00:51<01:38,  1.57s/it]
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Epoch 59 Training:  35%|███▌      | 34/96 [00:53<01:37,  1.57s/it]
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Epoch 59 Training:  36%|███▋      | 35/96 [00:54<01:35,  1.57s/it]
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Epoch 59 Training:  38%|███▊      | 36/96 [00:56<01:38,  1.64s/it]
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Epoch 59 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 59 Training:  39%|███▊      | 37/96 [00:58<01:35,  1.62s/it]
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Epoch 59 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 59 Training:  40%|███▉      | 38/96 [00:59<01:32,  1.60s/it]
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Epoch 59 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 59 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 59 Training:  41%|████      | 39/96 [01:01<01:30,  1.59s/it]
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Epoch 59 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 59 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 59 Training:  42%|████▏     | 40/96 [01:02<01:28,  1.58s/it]
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Epoch 59 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 59 Training:  43%|████▎     | 41/96 [01:04<01:26,  1.58s/it]
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Epoch 59 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 59 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 59 Training:  44%|████▍     | 42/96 [01:06<01:25,  1.58s/it]
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Epoch 59 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 59 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.98it/s]
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Epoch 59 Training:  45%|████▍     | 43/96 [01:07<01:23,  1.58s/it]
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Epoch 59 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 59 Training:  46%|████▌     | 44/96 [01:09<01:21,  1.58s/it]
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Epoch 59 Training:  47%|████▋     | 45/96 [01:10<01:20,  1.58s/it]
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Epoch 59 Training:  48%|████▊     | 46/96 [01:12<01:18,  1.58s/it]
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Epoch 59 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 59 Training:  49%|████▉     | 47/96 [01:13<01:17,  1.58s/it]
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Epoch 59 Training:  50%|█████     | 48/96 [01:15<01:15,  1.57s/it]
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Epoch 59 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 59 Training:  51%|█████     | 49/96 [01:17<01:13,  1.57s/it]
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Epoch 59 Training:  52%|█████▏    | 50/96 [01:18<01:12,  1.57s/it]
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Epoch 59 Training:  53%|█████▎    | 51/96 [01:20<01:10,  1.57s/it]
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Epoch 59 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 59 Training:  54%|█████▍    | 52/96 [01:21<01:09,  1.57s/it]
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Epoch 59 Training:  55%|█████▌    | 53/96 [01:23<01:07,  1.57s/it]
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Epoch 59 Training:  56%|█████▋    | 54/96 [01:24<01:06,  1.57s/it]
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Epoch 59 Training:  57%|█████▋    | 55/96 [01:26<01:04,  1.57s/it]
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Epoch 59 Training:  58%|█████▊    | 56/96 [01:28<01:03,  1.58s/it]
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Epoch 59 Training:  59%|█████▉    | 57/96 [01:29<01:01,  1.58s/it]
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Epoch 59 Training:  60%|██████    | 58/96 [01:31<00:59,  1.57s/it]
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Epoch 59 Training:  61%|██████▏   | 59/96 [01:32<00:58,  1.57s/it]
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Epoch 59 Training:  62%|██████▎   | 60/96 [01:34<00:56,  1.58s/it]
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Epoch 59 Training:  64%|██████▎   | 61/96 [01:35<00:55,  1.57s/it]
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Epoch 59 Training:  65%|██████▍   | 62/96 [01:37<00:53,  1.58s/it]
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Epoch 59 Training:  66%|██████▌   | 63/96 [01:39<00:52,  1.58s/it]
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Epoch 59 Training:  67%|██████▋   | 64/96 [01:40<00:50,  1.57s/it]
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Epoch 59 Training:  68%|██████▊   | 65/96 [01:42<00:48,  1.57s/it]
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Epoch 59 Training:  69%|██████▉   | 66/96 [01:43<00:47,  1.57s/it]
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Epoch 59 Training:  70%|██████▉   | 67/96 [01:45<00:45,  1.57s/it]
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Epoch 59 Training:  71%|███████   | 68/96 [01:46<00:43,  1.57s/it]
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Epoch 59 Training:  72%|███████▏  | 69/96 [01:48<00:42,  1.57s/it]
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Epoch 59 Training:  73%|███████▎  | 70/96 [01:50<00:40,  1.57s/it]
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Epoch 59 Training:  74%|███████▍  | 71/96 [01:51<00:39,  1.57s/it]
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Epoch 59 Training:  75%|███████▌  | 72/96 [01:53<00:37,  1.58s/it]
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Epoch 59 Training:  76%|███████▌  | 73/96 [01:54<00:36,  1.58s/it]
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Epoch 59 Training:  77%|███████▋  | 74/96 [01:56<00:34,  1.57s/it]
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Epoch 59 Training:  78%|███████▊  | 75/96 [01:57<00:32,  1.57s/it]
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Epoch 59 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s][2026-10-01 18:47:40] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0059 | Epoch 059] train/policy_loss=24.5458 train/unweighted_policy_loss=4.9092 train/loss=24.5458 train/grad_norm=0.0573
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+[2026-10-01 18:47:40] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0060 | Epoch 059] timing/rollout_seconds=180.0439 timing/feedback_seconds=5.7294 timing/optimization_seconds=151.6587 timing/cycle_seconds=334.5643
+[2026-10-01 18:47:40] [Rank 0] [INFO] [flow_factory.models.abc]: Saving LoRA weights for transformer role=base to /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/runs/klein4b_nft_flir_full_ssim_dists/saves/klein4b_nft_flir_full_ssim_dists/checkpoints/checkpoint-60
+[2026-10-01 18:47:40] [Rank 0] [INFO] [flow_factory.models.abc]: Checkpoint saved successfully to /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/runs/klein4b_nft_flir_full_ssim_dists/saves/klein4b_nft_flir_full_ssim_dists/checkpoints/checkpoint-60
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+[2026-10-01 18:48:33] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0060 | Epoch 060] eval/flir_rl_full/reward_dists_mean=0.8449 eval/flir_rl_full/reward_dists_std=0.0317 eval/flir_rl_full/reward_psnr_mean=20.0777 eval/flir_rl_full/reward_psnr_std=2.8127 eval/flir_rl_full/reward_ssim_mean=0.6224 eval/flir_rl_full/reward_ssim_std=0.0816
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+
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Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:01<00:00,  5.56it/s]
Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:01<00:00,  5.50it/s]
+
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Pointwise Rewards: dists:  50%|█████     | 3/6 [00:00<00:00,  3.30it/s]
Pointwise Rewards: dists:  67%|██████▋   | 4/6 [00:01<00:00,  3.20it/s]
Pointwise Rewards: dists:  83%|████████▎ | 5/6 [00:01<00:00,  3.14it/s]
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Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.14it/s]
+[2026-10-01 18:51:37] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0060 | Epoch 060] train/reward_dists_mean=0.8537 train/reward_dists_std=0.0267 train/reward_ssim_mean=0.6837 train/reward_ssim_std=0.1002 train/reward_dists_group_std_mean=0.0085 train/reward_dists_group_std_max=0.0185 train/reward_dists_group_std_min=0.0037 train/reward_dists_group_mean_std=0.0251 train/reward_ssim_group_std_mean=0.0081 train/reward_ssim_group_std_max=0.0174 train/reward_ssim_group_std_min=0.0039 train/reward_ssim_group_mean_std=0.0998 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.5359 train/adv_max=3.9949 train/adv_abs_mean=1.2022
+
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Epoch 60 Training:  47%|████▋     | 45/96 [01:10<01:20,  1.57s/it]
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Epoch 60 Training:  48%|████▊     | 46/96 [01:11<01:18,  1.57s/it]
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Epoch 60 Training:  49%|████▉     | 47/96 [01:13<01:16,  1.57s/it]
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Epoch 60 Training:  50%|█████     | 48/96 [01:15<01:15,  1.57s/it]
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Epoch 60 Training:  51%|█████     | 49/96 [01:16<01:13,  1.57s/it]
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Epoch 60 Training:  52%|█████▏    | 50/96 [01:18<01:12,  1.57s/it]
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Epoch 60 Training:  53%|█████▎    | 51/96 [01:19<01:10,  1.57s/it]
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Epoch 60 Training:  54%|█████▍    | 52/96 [01:21<01:08,  1.57s/it]
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Epoch 60 Training:  55%|█████▌    | 53/96 [01:22<01:07,  1.57s/it]
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Epoch 60 Training:  56%|█████▋    | 54/96 [01:24<01:05,  1.57s/it]
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Epoch 60 Training:  57%|█████▋    | 55/96 [01:25<01:04,  1.57s/it]
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Epoch 60 Training:  58%|█████▊    | 56/96 [01:27<01:02,  1.57s/it]
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Epoch 60 Training:  59%|█████▉    | 57/96 [01:29<01:01,  1.57s/it]
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Epoch 60 Training:  60%|██████    | 58/96 [01:30<00:59,  1.57s/it]
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Epoch 60 Training:  61%|██████▏   | 59/96 [01:32<00:58,  1.57s/it]
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Epoch 60 Training:  62%|██████▎   | 60/96 [01:33<00:56,  1.57s/it]
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Epoch 60 Training:  64%|██████▎   | 61/96 [01:35<00:54,  1.57s/it]
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Epoch 60 Training:  65%|██████▍   | 62/96 [01:36<00:53,  1.57s/it]
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Epoch 60 Training:  66%|██████▌   | 63/96 [01:38<00:51,  1.57s/it]
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Epoch 60 Training:  67%|██████▋   | 64/96 [01:40<00:50,  1.57s/it]
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Epoch 60 Training:  68%|██████▊   | 65/96 [01:41<00:48,  1.57s/it]
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Epoch 60 Training:  69%|██████▉   | 66/96 [01:43<00:46,  1.57s/it]
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Epoch 60 Training:  70%|██████▉   | 67/96 [01:44<00:45,  1.57s/it]
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Epoch 60 Training:  71%|███████   | 68/96 [01:46<00:43,  1.57s/it]
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Epoch 60 Training:  72%|███████▏  | 69/96 [01:47<00:42,  1.57s/it]
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Epoch 60 Training:  73%|███████▎  | 70/96 [01:49<00:40,  1.57s/it]
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Epoch 60 Training:  74%|███████▍  | 71/96 [01:51<00:39,  1.57s/it]
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Epoch 60 Training:  75%|███████▌  | 72/96 [01:52<00:37,  1.57s/it]
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Epoch 60 Training:  76%|███████▌  | 73/96 [01:54<00:36,  1.57s/it]
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Epoch 60 Training:  77%|███████▋  | 74/96 [01:55<00:34,  1.57s/it]
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Epoch 60 Training:  78%|███████▊  | 75/96 [01:57<00:32,  1.57s/it]
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Epoch 60 Training:  79%|███████▉  | 76/96 [01:58<00:31,  1.57s/it]
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Epoch 60 Training:  80%|████████  | 77/96 [02:00<00:29,  1.57s/it]
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Epoch 60 Training:  81%|████████▏ | 78/96 [02:02<00:28,  1.57s/it]
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Epoch 60 Training:  82%|████████▏ | 79/96 [02:03<00:26,  1.57s/it]
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Epoch 60 Training:  83%|████████▎ | 80/96 [02:05<00:25,  1.57s/it]
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Epoch 60 Training:  84%|████████▍ | 81/96 [02:06<00:23,  1.57s/it]
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Epoch 60 Training:  85%|████████▌ | 82/96 [02:08<00:21,  1.57s/it]
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Epoch 60 Training:  86%|████████▋ | 83/96 [02:09<00:20,  1.57s/it]
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Epoch 60 Training:  88%|████████▊ | 84/96 [02:11<00:18,  1.57s/it]
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Epoch 60 Training:  89%|████████▊ | 85/96 [02:12<00:17,  1.57s/it]
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Epoch 60 Training:  90%|████████▉ | 86/96 [02:14<00:15,  1.57s/it]
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Epoch 60 Training:  91%|█████████ | 87/96 [02:16<00:14,  1.58s/it]
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Epoch 60 Training:  92%|█████████▏| 88/96 [02:17<00:12,  1.57s/it]
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Epoch 60 Training:  93%|█████████▎| 89/96 [02:19<00:11,  1.66s/it]
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Epoch 60 Training:  94%|█████████▍| 90/96 [02:21<00:09,  1.63s/it]
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Epoch 60 Training:  95%|█████████▍| 91/96 [02:22<00:08,  1.61s/it]
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Epoch 60 Training:  96%|█████████▌| 92/96 [02:24<00:06,  1.60s/it]
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Epoch 60 Training:  97%|█████████▋| 93/96 [02:25<00:04,  1.59s/it]
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Epoch 60 Training:  98%|█████████▊| 94/96 [02:27<00:03,  1.58s/it]
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Epoch 60 Training:  99%|█████████▉| 95/96 [02:28<00:01,  1.58s/it]
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+[2026-10-01 18:54:07] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0061 | Epoch 060] timing/rollout_seconds=179.6825 timing/feedback_seconds=4.9589 timing/optimization_seconds=151.2775 timing/cycle_seconds=333.9237
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+[2026-10-01 18:57:11] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0061 | Epoch 061] train/reward_dists_mean=0.8447 train/reward_dists_std=0.0291 train/reward_ssim_mean=0.6626 train/reward_ssim_std=0.1139 train/reward_dists_group_std_mean=0.0084 train/reward_dists_group_std_max=0.0158 train/reward_dists_group_std_min=0.0038 train/reward_dists_group_mean_std=0.0277 train/reward_ssim_group_std_mean=0.0078 train/reward_ssim_group_std_max=0.0194 train/reward_ssim_group_std_min=0.0036 train/reward_ssim_group_mean_std=0.1136 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.7083 train/adv_max=4.0884 train/adv_abs_mean=1.1482
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Epoch 61 Training:  22%|██▏       | 21/96 [00:32<01:57,  1.56s/it]
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Epoch 61 Training:  23%|██▎       | 22/96 [00:34<01:55,  1.56s/it]
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Epoch 61 Training:  24%|██▍       | 23/96 [00:35<01:54,  1.56s/it]
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Epoch 61 Training:  25%|██▌       | 24/96 [00:37<01:52,  1.56s/it]
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Epoch 61 Training:  26%|██▌       | 25/96 [00:39<01:50,  1.56s/it]
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Epoch 61 Training:  27%|██▋       | 26/96 [00:40<01:49,  1.56s/it]
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Epoch 61 Training:  28%|██▊       | 27/96 [00:42<01:47,  1.56s/it]
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Epoch 61 Training:  29%|██▉       | 28/96 [00:43<01:46,  1.57s/it]
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Epoch 61 Training:  30%|███       | 29/96 [00:45<01:44,  1.56s/it]
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Epoch 61 Training:  31%|███▏      | 30/96 [00:46<01:43,  1.56s/it]
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Epoch 61 Training:  32%|███▏      | 31/96 [00:48<01:41,  1.56s/it]
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Epoch 61 Training:  33%|███▎      | 32/96 [00:50<01:40,  1.57s/it]
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Epoch 61 Training:  34%|███▍      | 33/96 [00:51<01:38,  1.56s/it]
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Epoch 61 Training:  35%|███▌      | 34/96 [00:53<01:38,  1.59s/it]
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Epoch 61 Training:  36%|███▋      | 35/96 [00:54<01:36,  1.58s/it]
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Epoch 61 Training:  38%|███▊      | 36/96 [00:56<01:34,  1.58s/it]
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Epoch 61 Training:  39%|███▊      | 37/96 [00:57<01:32,  1.57s/it]
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Epoch 61 Training:  40%|███▉      | 38/96 [00:59<01:31,  1.57s/it]
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Epoch 61 Training:  41%|████      | 39/96 [01:01<01:29,  1.57s/it]
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Epoch 61 Training:  42%|████▏     | 40/96 [01:02<01:28,  1.57s/it]
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Epoch 61 Training:  43%|████▎     | 41/96 [01:04<01:26,  1.57s/it]
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Epoch 61 Training:  44%|████▍     | 42/96 [01:05<01:24,  1.57s/it]
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Epoch 61 Training:  45%|████▍     | 43/96 [01:07<01:23,  1.57s/it]
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Epoch 61 Training:  46%|████▌     | 44/96 [01:08<01:21,  1.57s/it]
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Epoch 61 Training:  47%|████▋     | 45/96 [01:10<01:19,  1.57s/it]
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Epoch 61 Training:  48%|████▊     | 46/96 [01:12<01:18,  1.57s/it]
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Epoch 61 Training:  49%|████▉     | 47/96 [01:13<01:17,  1.59s/it]
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Epoch 61 Training:  51%|█████     | 49/96 [01:16<01:14,  1.58s/it]
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Epoch 61 Training:  52%|█████▏    | 50/96 [01:18<01:12,  1.57s/it]
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Epoch 61 Training:  53%|█████▎    | 51/96 [01:19<01:10,  1.57s/it]
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Epoch 61 Training:  54%|█████▍    | 52/96 [01:21<01:08,  1.57s/it]
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Epoch 61 Training:  55%|█████▌    | 53/96 [01:23<01:07,  1.57s/it]
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Epoch 61 Training:  56%|█████▋    | 54/96 [01:24<01:05,  1.57s/it]
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Epoch 61 Training:  57%|█████▋    | 55/96 [01:26<01:04,  1.57s/it]
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Epoch 61 Training:  58%|█████▊    | 56/96 [01:27<01:02,  1.57s/it]
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Epoch 61 Training:  59%|█████▉    | 57/96 [01:29<01:01,  1.57s/it]
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Epoch 61 Training:  60%|██████    | 58/96 [01:30<00:59,  1.57s/it]
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Epoch 61 Training:  61%|██████▏   | 59/96 [01:32<00:57,  1.56s/it]
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Epoch 61 Training:  62%|██████▎   | 60/96 [01:33<00:56,  1.56s/it]
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Epoch 61 Training:  64%|██████▎   | 61/96 [01:35<00:57,  1.64s/it]
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Epoch 61 Training:  65%|██████▍   | 62/96 [01:37<00:54,  1.62s/it]
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Epoch 61 Training:  66%|██████▌   | 63/96 [01:38<00:52,  1.60s/it]
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Epoch 61 Training:  67%|██████▋   | 64/96 [01:40<00:50,  1.59s/it]
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Epoch 61 Training:  68%|██████▊   | 65/96 [01:42<00:49,  1.58s/it]
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Epoch 61 Training:  69%|██████▉   | 66/96 [01:43<00:47,  1.58s/it]
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Epoch 61 Training:  70%|██████▉   | 67/96 [01:45<00:45,  1.57s/it]
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Epoch 61 Training:  71%|███████   | 68/96 [01:46<00:43,  1.57s/it]
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Epoch 61 Training:  72%|███████▏  | 69/96 [01:48<00:42,  1.57s/it]
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Epoch 61 Training:  73%|███████▎  | 70/96 [01:49<00:40,  1.57s/it]
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Epoch 61 Training:  74%|███████▍  | 71/96 [01:51<00:39,  1.57s/it]
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Epoch 61 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.15it/s][2026-10-01 18:59:41] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0061 | Epoch 061] train/policy_loss=24.6158 train/unweighted_policy_loss=4.9232 train/loss=24.6158 train/grad_norm=0.0488
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+[2026-10-01 18:59:41] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0062 | Epoch 061] timing/rollout_seconds=179.9848 timing/feedback_seconds=4.3709 timing/optimization_seconds=151.1994 timing/cycle_seconds=334.2938
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+[2026-10-01 19:02:45] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0062 | Epoch 062] train/reward_dists_mean=0.8462 train/reward_dists_std=0.0317 train/reward_ssim_mean=0.6611 train/reward_ssim_std=0.0972 train/reward_dists_group_std_mean=0.0097 train/reward_dists_group_std_max=0.0324 train/reward_dists_group_std_min=0.0039 train/reward_dists_group_mean_std=0.0297 train/reward_ssim_group_std_mean=0.0094 train/reward_ssim_group_std_max=0.0212 train/reward_ssim_group_std_min=0.0014 train/reward_ssim_group_mean_std=0.0967 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.5951 train/adv_max=4.6913 train/adv_abs_mean=1.2447
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Epoch 62 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s][2026-10-01 19:05:15] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0062 | Epoch 062] train/policy_loss=24.6968 train/unweighted_policy_loss=4.9394 train/loss=24.6968 train/grad_norm=0.0552
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+[2026-10-01 19:05:15] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0063 | Epoch 062] timing/rollout_seconds=179.4758 timing/feedback_seconds=4.6792 timing/optimization_seconds=150.8858 timing/cycle_seconds=333.3136
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+
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+[2026-10-01 19:08:19] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0063 | Epoch 063] train/reward_dists_mean=0.8477 train/reward_dists_std=0.0353 train/reward_ssim_mean=0.6463 train/reward_ssim_std=0.1032 train/reward_dists_group_std_mean=0.0085 train/reward_dists_group_std_max=0.0219 train/reward_dists_group_std_min=0.0041 train/reward_dists_group_mean_std=0.0341 train/reward_ssim_group_std_mean=0.0076 train/reward_ssim_group_std_max=0.0152 train/reward_ssim_group_std_min=0.0037 train/reward_ssim_group_mean_std=0.1029 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.7118 train/adv_max=4.2802 train/adv_abs_mean=1.1722
+
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Epoch 63 Training:  25%|██▌       | 24/96 [00:38<01:54,  1.59s/it]
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Epoch 63 Training:  26%|██▌       | 25/96 [00:39<01:53,  1.59s/it]
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Epoch 63 Training:  27%|██▋       | 26/96 [00:41<01:51,  1.59s/it]
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Epoch 63 Training:  28%|██▊       | 27/96 [00:43<01:49,  1.59s/it]
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Epoch 63 Training:  29%|██▉       | 28/96 [00:44<01:48,  1.60s/it]
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Epoch 63 Training:  30%|███       | 29/96 [00:46<01:46,  1.60s/it]
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Epoch 63 Training:  31%|███▏      | 30/96 [00:47<01:45,  1.60s/it]
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Epoch 63 Training:  32%|███▏      | 31/96 [00:49<01:44,  1.60s/it]
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Epoch 63 Training:  33%|███▎      | 32/96 [00:51<01:42,  1.60s/it]
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Epoch 63 Training:  34%|███▍      | 33/96 [00:52<01:40,  1.60s/it]
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Epoch 63 Training:  35%|███▌      | 34/96 [00:54<01:38,  1.60s/it]
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Epoch 63 Training:  36%|███▋      | 35/96 [00:55<01:37,  1.59s/it]
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Epoch 63 Training:  38%|███▊      | 36/96 [00:57<01:35,  1.60s/it]
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Epoch 63 Training:  39%|███▊      | 37/96 [00:59<01:34,  1.60s/it]
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Epoch 63 Training:  40%|███▉      | 38/96 [01:00<01:32,  1.60s/it]
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Epoch 63 Training:  41%|████      | 39/96 [01:02<01:31,  1.60s/it]
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Epoch 63 Training:  42%|████▏     | 40/96 [01:03<01:29,  1.60s/it]
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Epoch 63 Training:  43%|████▎     | 41/96 [01:05<01:28,  1.60s/it]
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Epoch 63 Training:  44%|████▍     | 42/96 [01:07<01:26,  1.60s/it]
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Epoch 63 Training:  45%|████▍     | 43/96 [01:08<01:24,  1.60s/it]
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Epoch 63 Training:  46%|████▌     | 44/96 [01:10<01:23,  1.60s/it]
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Epoch 63 Training:  47%|████▋     | 45/96 [01:11<01:21,  1.60s/it]
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Epoch 63 Training:  48%|████▊     | 46/96 [01:13<01:19,  1.60s/it]
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Epoch 63 Training:  49%|████▉     | 47/96 [01:15<01:18,  1.60s/it]
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Epoch 63 Training:  50%|█████     | 48/96 [01:16<01:16,  1.60s/it]
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Epoch 63 Training:  51%|█████     | 49/96 [01:18<01:15,  1.60s/it]
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Epoch 63 Training:  52%|█████▏    | 50/96 [01:19<01:13,  1.59s/it]
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Epoch 63 Training:  53%|█████▎    | 51/96 [01:21<01:11,  1.59s/it]
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Epoch 63 Training:  54%|█████▍    | 52/96 [01:23<01:10,  1.59s/it]
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Epoch 63 Training:  55%|█████▌    | 53/96 [01:24<01:08,  1.59s/it]
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Epoch 63 Training:  56%|█████▋    | 54/96 [01:26<01:06,  1.59s/it]
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Epoch 63 Training:  57%|█████▋    | 55/96 [01:27<01:05,  1.60s/it]
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Epoch 63 Training:  58%|█████▊    | 56/96 [01:29<01:03,  1.60s/it]
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Epoch 63 Training:  59%|█████▉    | 57/96 [01:31<01:02,  1.60s/it]
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Epoch 63 Training:  60%|██████    | 58/96 [01:32<01:00,  1.60s/it]
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Epoch 63 Training:  61%|██████▏   | 59/96 [01:34<00:59,  1.60s/it]
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Epoch 63 Training:  62%|██████▎   | 60/96 [01:35<00:57,  1.60s/it]
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Epoch 63 Training:  64%|██████▎   | 61/96 [01:37<00:55,  1.60s/it]
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Epoch 63 Training:  65%|██████▍   | 62/96 [01:38<00:54,  1.60s/it]
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Epoch 63 Training:  66%|██████▌   | 63/96 [01:40<00:52,  1.60s/it]
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Epoch 63 Training:  67%|██████▋   | 64/96 [01:42<00:51,  1.60s/it]
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Epoch 63 Training:  68%|██████▊   | 65/96 [01:43<00:49,  1.60s/it]
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Epoch 63 Training:  69%|██████▉   | 66/96 [01:45<00:47,  1.60s/it]
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Epoch 63 Training:  70%|██████▉   | 67/96 [01:46<00:46,  1.60s/it]
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Epoch 63 Training:  71%|███████   | 68/96 [01:48<00:44,  1.60s/it]
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Epoch 63 Training:  72%|███████▏  | 69/96 [01:50<00:43,  1.60s/it]
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Epoch 63 Training:  73%|███████▎  | 70/96 [01:51<00:41,  1.60s/it]
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Epoch 63 Training:  74%|███████▍  | 71/96 [01:53<00:39,  1.59s/it]
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Epoch 63 Training:  75%|███████▌  | 72/96 [01:54<00:38,  1.60s/it]
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Epoch 63 Training:  76%|███████▌  | 73/96 [01:56<00:36,  1.60s/it]
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Epoch 63 Training:  77%|███████▋  | 74/96 [01:58<00:35,  1.60s/it]
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Epoch 63 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s][2026-10-01 19:10:52] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0063 | Epoch 063] train/policy_loss=24.6416 train/unweighted_policy_loss=4.9283 train/loss=24.6416 train/grad_norm=0.0528
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+[2026-10-01 19:10:52] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0064 | Epoch 063] timing/rollout_seconds=180.1505 timing/feedback_seconds=5.5244 timing/optimization_seconds=154.0092 timing/cycle_seconds=337.1807
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+[2026-10-01 19:13:56] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0064 | Epoch 064] train/reward_dists_mean=0.8508 train/reward_dists_std=0.0296 train/reward_ssim_mean=0.6705 train/reward_ssim_std=0.1003 train/reward_dists_group_std_mean=0.0086 train/reward_dists_group_std_max=0.0171 train/reward_dists_group_std_min=0.0031 train/reward_dists_group_mean_std=0.0281 train/reward_ssim_group_std_mean=0.0092 train/reward_ssim_group_std_max=0.0242 train/reward_ssim_group_std_min=0.0040 train/reward_ssim_group_mean_std=0.0998 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.0706 train/adv_max=4.6190 train/adv_abs_mean=1.2096
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Epoch 64 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.15it/s][2026-10-01 19:16:27] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0064 | Epoch 064] train/policy_loss=24.4353 train/unweighted_policy_loss=4.8871 train/loss=24.4353 train/grad_norm=0.0498
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+[2026-10-01 19:16:27] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0065 | Epoch 064] timing/rollout_seconds=180.1518 timing/feedback_seconds=5.4591 timing/optimization_seconds=151.9099 timing/cycle_seconds=334.9774
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+[2026-10-01 19:19:31] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0065 | Epoch 065] train/reward_dists_mean=0.8445 train/reward_dists_std=0.0294 train/reward_ssim_mean=0.6422 train/reward_ssim_std=0.0839 train/reward_dists_group_std_mean=0.0080 train/reward_dists_group_std_max=0.0194 train/reward_dists_group_std_min=0.0032 train/reward_dists_group_mean_std=0.0281 train/reward_ssim_group_std_mean=0.0079 train/reward_ssim_group_std_max=0.0185 train/reward_ssim_group_std_min=0.0037 train/reward_ssim_group_mean_std=0.0835 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.3264 train/adv_max=4.3571 train/adv_abs_mean=1.1675
+
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Epoch 65 Training:  27%|██▋       | 26/96 [00:40<01:49,  1.57s/it]
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Epoch 65 Training:  28%|██▊       | 27/96 [00:42<01:48,  1.57s/it]
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Epoch 65 Training:  29%|██▉       | 28/96 [00:43<01:46,  1.57s/it]
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Epoch 65 Training:  30%|███       | 29/96 [00:45<01:45,  1.57s/it]
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Epoch 65 Training:  31%|███▏      | 30/96 [00:47<01:43,  1.57s/it]
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Epoch 65 Training:  32%|███▏      | 31/96 [00:48<01:41,  1.57s/it]
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Epoch 65 Training:  33%|███▎      | 32/96 [00:50<01:40,  1.57s/it]
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Epoch 65 Training:  34%|███▍      | 33/96 [00:51<01:38,  1.57s/it]
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Epoch 65 Training:  35%|███▌      | 34/96 [00:53<01:36,  1.56s/it]
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Epoch 65 Training:  36%|███▋      | 35/96 [00:54<01:35,  1.56s/it]
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Epoch 65 Training:  38%|███▊      | 36/96 [00:56<01:33,  1.56s/it]
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Epoch 65 Training:  39%|███▊      | 37/96 [00:57<01:32,  1.56s/it]
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Epoch 65 Training:  40%|███▉      | 38/96 [00:59<01:30,  1.57s/it]
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Epoch 65 Training:  41%|████      | 39/96 [01:01<01:29,  1.57s/it]
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Epoch 65 Training:  42%|████▏     | 40/96 [01:02<01:27,  1.57s/it]
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Epoch 65 Training:  43%|████▎     | 41/96 [01:04<01:26,  1.56s/it]
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Epoch 65 Training:  44%|████▍     | 42/96 [01:05<01:24,  1.56s/it]
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Epoch 65 Training:  45%|████▍     | 43/96 [01:07<01:23,  1.57s/it]
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Epoch 65 Training:  46%|████▌     | 44/96 [01:08<01:21,  1.57s/it]
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Epoch 65 Training:  47%|████▋     | 45/96 [01:10<01:20,  1.57s/it]
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Epoch 65 Training:  48%|████▊     | 46/96 [01:12<01:18,  1.57s/it]
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Epoch 65 Training:  49%|████▉     | 47/96 [01:13<01:16,  1.57s/it]
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Epoch 65 Training:  51%|█████     | 49/96 [01:16<01:13,  1.56s/it]
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Epoch 65 Training:  52%|█████▏    | 50/96 [01:18<01:12,  1.57s/it]
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Epoch 65 Training:  53%|█████▎    | 51/96 [01:19<01:10,  1.57s/it]
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Epoch 65 Training:  54%|█████▍    | 52/96 [01:21<01:08,  1.57s/it]
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Epoch 65 Training:  55%|█████▌    | 53/96 [01:23<01:07,  1.57s/it]
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Epoch 65 Training:  56%|█████▋    | 54/96 [01:24<01:05,  1.57s/it]
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Epoch 65 Training:  57%|█████▋    | 55/96 [01:26<01:04,  1.57s/it]
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Epoch 65 Training:  58%|█████▊    | 56/96 [01:27<01:02,  1.57s/it]
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Epoch 65 Training:  59%|█████▉    | 57/96 [01:29<01:01,  1.56s/it]
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Epoch 65 Training:  60%|██████    | 58/96 [01:30<00:59,  1.56s/it]
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Epoch 65 Training:  61%|██████▏   | 59/96 [01:32<00:57,  1.56s/it]
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Epoch 65 Training:  62%|██████▎   | 60/96 [01:33<00:56,  1.56s/it]
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Epoch 65 Training:  64%|██████▎   | 61/96 [01:35<00:54,  1.57s/it]
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Epoch 65 Training:  65%|██████▍   | 62/96 [01:37<00:53,  1.57s/it]
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Epoch 65 Training:  66%|██████▌   | 63/96 [01:38<00:51,  1.56s/it]
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Epoch 65 Training:  67%|██████▋   | 64/96 [01:40<00:49,  1.56s/it]
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Epoch 65 Training:  68%|██████▊   | 65/96 [01:41<00:48,  1.56s/it]
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Epoch 65 Training:  69%|██████▉   | 66/96 [01:43<00:46,  1.56s/it]
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Epoch 65 Training:  70%|██████▉   | 67/96 [01:44<00:45,  1.56s/it]
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Epoch 65 Training:  71%|███████   | 68/96 [01:46<00:43,  1.56s/it]
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Epoch 65 Training:  72%|███████▏  | 69/96 [01:48<00:42,  1.56s/it]
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Epoch 65 Training:  73%|███████▎  | 70/96 [01:49<00:40,  1.57s/it]
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Epoch 65 Training:  74%|███████▍  | 71/96 [01:51<00:39,  1.57s/it]
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Epoch 65 Training:  75%|███████▌  | 72/96 [01:52<00:37,  1.56s/it]
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Epoch 65 Training:  76%|███████▌  | 73/96 [01:54<00:35,  1.56s/it]
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Epoch 65 Training:  77%|███████▋  | 74/96 [01:55<00:34,  1.56s/it]
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Epoch 65 Training:  78%|███████▊  | 75/96 [01:57<00:32,  1.56s/it]
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Epoch 65 Training:  79%|███████▉  | 76/96 [01:58<00:31,  1.56s/it]
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Epoch 65 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s][2026-10-01 19:22:01] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0065 | Epoch 065] train/policy_loss=25.0827 train/unweighted_policy_loss=5.0165 train/loss=25.0827 train/grad_norm=0.0494
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+[2026-10-01 19:22:01] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0066 | Epoch 065] timing/rollout_seconds=180.1674 timing/feedback_seconds=5.7334 timing/optimization_seconds=150.8999 timing/cycle_seconds=334.0219
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+[2026-10-01 19:25:06] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0066 | Epoch 066] train/reward_dists_mean=0.8578 train/reward_dists_std=0.0263 train/reward_ssim_mean=0.6501 train/reward_ssim_std=0.0913 train/reward_dists_group_std_mean=0.0075 train/reward_dists_group_std_max=0.0140 train/reward_dists_group_std_min=0.0033 train/reward_dists_group_mean_std=0.0251 train/reward_ssim_group_std_mean=0.0088 train/reward_ssim_group_std_max=0.0205 train/reward_ssim_group_std_min=0.0032 train/reward_ssim_group_mean_std=0.0908 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.4205 train/adv_max=5.4166 train/adv_abs_mean=1.1761
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Epoch 66 Training:   1%|          | 1/96 [00:01<02:25,  1.53s/it]
+
Epoch 66 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 66 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 66 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 66 Training:   2%|▏         | 2/96 [00:03<02:27,  1.57s/it]
+
Epoch 66 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 66 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 66 Training:   3%|▎         | 3/96 [00:04<02:27,  1.58s/it]
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Epoch 66 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 66 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 66 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 66 Training:   4%|▍         | 4/96 [00:06<02:25,  1.59s/it]
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Epoch 66 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 66 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 66 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 66 Training:   5%|▌         | 5/96 [00:07<02:24,  1.59s/it]
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Epoch 66 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 66 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 66 Training:   6%|▋         | 6/96 [00:09<02:23,  1.59s/it]
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Epoch 66 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 66 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 66 Training:   7%|▋         | 7/96 [00:11<02:21,  1.59s/it]
+
Epoch 66 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 66 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 66 Training:   8%|▊         | 8/96 [00:12<02:20,  1.59s/it]
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Epoch 66 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 66 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 66 Training:   9%|▉         | 9/96 [00:14<02:18,  1.59s/it]
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Epoch 66 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 66 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 66 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 66 Training:  10%|█         | 10/96 [00:15<02:17,  1.59s/it]
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Epoch 66 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 66 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 66 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 66 Training:  11%|█▏        | 11/96 [00:17<02:15,  1.59s/it]
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Epoch 66 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 66 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 66 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 66 Training:  12%|█▎        | 12/96 [00:19<02:13,  1.59s/it]
+
Epoch 66 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 66 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 66 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 66 Training:  14%|█▎        | 13/96 [00:20<02:12,  1.59s/it]
+
Epoch 66 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 66 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 66 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 66 Training:  15%|█▍        | 14/96 [00:22<02:10,  1.59s/it]
+
Epoch 66 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 66 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 66 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 66 Training:  16%|█▌        | 15/96 [00:23<02:08,  1.59s/it]
+
Epoch 66 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 66 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 66 Training:  17%|█▋        | 16/96 [00:25<02:07,  1.59s/it]
+
Epoch 66 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 66 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 66 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 66 Training:  18%|█▊        | 17/96 [00:27<02:05,  1.59s/it]
+
Epoch 66 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 66 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 66 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 66 Training:  19%|█▉        | 18/96 [00:28<02:04,  1.59s/it]
+
Epoch 66 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 66 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 66 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 66 Training:  20%|█▉        | 19/96 [00:30<02:02,  1.59s/it]
+
Epoch 66 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 66 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 66 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 66 Training:  21%|██        | 20/96 [00:31<02:01,  1.59s/it]
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Epoch 66 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 66 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 66 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 66 Training:  22%|██▏       | 21/96 [00:33<01:59,  1.59s/it]
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Epoch 66 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 66 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 66 Training:  23%|██▎       | 22/96 [00:34<01:57,  1.59s/it]
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Epoch 66 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 66 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 66 Training:  24%|██▍       | 23/96 [00:36<01:56,  1.59s/it]
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Epoch 66 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 66 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 66 Training:  25%|██▌       | 24/96 [00:38<01:54,  1.59s/it]
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Epoch 66 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 66 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 66 Training:  26%|██▌       | 25/96 [00:39<01:53,  1.59s/it]
+
Epoch 66 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 66 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 66 Training:  27%|██▋       | 26/96 [00:41<01:51,  1.59s/it]
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Epoch 66 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 66 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 66 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 66 Training:  28%|██▊       | 27/96 [00:42<01:49,  1.59s/it]
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Epoch 66 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 66 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 66 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 66 Training:  29%|██▉       | 28/96 [00:44<01:48,  1.59s/it]
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Epoch 66 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 66 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 66 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 66 Training:  30%|███       | 29/96 [00:46<01:46,  1.59s/it]
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Epoch 66 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 66 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 66 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 66 Training:  31%|███▏      | 30/96 [00:47<01:45,  1.59s/it]
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Epoch 66 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 66 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 66 Training:  32%|███▏      | 31/96 [00:49<01:43,  1.59s/it]
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Epoch 66 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 66 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 66 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 66 Training:  33%|███▎      | 32/96 [00:50<01:41,  1.59s/it]
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Epoch 66 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 66 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 66 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 66 Training:  34%|███▍      | 33/96 [00:52<01:40,  1.59s/it]
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Epoch 66 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 66 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 66 Training:  35%|███▌      | 34/96 [00:54<01:38,  1.59s/it]
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Epoch 66 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 66 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 66 Training:  36%|███▋      | 35/96 [00:55<01:37,  1.60s/it]
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Epoch 66 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 66 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 66 Training:  38%|███▊      | 36/96 [00:57<01:35,  1.60s/it]
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Epoch 66 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 66 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 66 Training:  39%|███▊      | 37/96 [00:58<01:34,  1.60s/it]
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Epoch 66 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 66 Training:  40%|███▉      | 38/96 [01:00<01:32,  1.60s/it]
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Epoch 66 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 66 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 66 Training:  41%|████      | 39/96 [01:02<01:30,  1.60s/it]
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Epoch 66 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 66 Training:  42%|████▏     | 40/96 [01:03<01:29,  1.59s/it]
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Epoch 66 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 66 Training:  43%|████▎     | 41/96 [01:05<01:27,  1.59s/it]
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Epoch 66 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 66 Training:  44%|████▍     | 42/96 [01:06<01:26,  1.59s/it]
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Epoch 66 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 66 Training:  45%|████▍     | 43/96 [01:08<01:24,  1.59s/it]
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Epoch 66 Training:  46%|████▌     | 44/96 [01:10<01:22,  1.59s/it]
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Epoch 66 Training:  47%|████▋     | 45/96 [01:11<01:21,  1.59s/it]
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Epoch 66 Training:  48%|████▊     | 46/96 [01:13<01:19,  1.59s/it]
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Epoch 66 Training:  49%|████▉     | 47/96 [01:14<01:17,  1.59s/it]
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Epoch 66 Training:  50%|█████     | 48/96 [01:16<01:16,  1.59s/it]
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Epoch 66 Training:  51%|█████     | 49/96 [01:18<01:14,  1.59s/it]
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Epoch 66 Training:  52%|█████▏    | 50/96 [01:19<01:13,  1.59s/it]
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Epoch 66 Training:  53%|█████▎    | 51/96 [01:21<01:11,  1.59s/it]
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Epoch 66 Training:  54%|█████▍    | 52/96 [01:22<01:10,  1.59s/it]
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Epoch 66 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s][2026-10-01 19:27:39] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0066 | Epoch 066] train/policy_loss=25.0922 train/unweighted_policy_loss=5.0184 train/loss=25.0922 train/grad_norm=0.0617
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+[2026-10-01 19:27:39] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0067 | Epoch 066] timing/rollout_seconds=180.2377 timing/feedback_seconds=8.0040 timing/optimization_seconds=153.7142 timing/cycle_seconds=338.4126
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+
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+
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+[2026-10-01 19:30:43] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0067 | Epoch 067] train/reward_dists_mean=0.8612 train/reward_dists_std=0.0273 train/reward_ssim_mean=0.6758 train/reward_ssim_std=0.1136 train/reward_dists_group_std_mean=0.0072 train/reward_dists_group_std_max=0.0178 train/reward_dists_group_std_min=0.0034 train/reward_dists_group_mean_std=0.0262 train/reward_ssim_group_std_mean=0.0082 train/reward_ssim_group_std_max=0.0236 train/reward_ssim_group_std_min=0.0030 train/reward_ssim_group_mean_std=0.1132 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.3184 train/adv_max=4.6774 train/adv_abs_mean=1.2351
+
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Epoch 67 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 67 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 67 Training:  30%|███       | 29/96 [00:46<01:46,  1.59s/it]
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Epoch 67 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 67 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 67 Training:  31%|███▏      | 30/96 [00:47<01:45,  1.60s/it]
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Epoch 67 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 67 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.67it/s]
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Epoch 67 Training:  32%|███▏      | 31/96 [00:49<01:47,  1.65s/it]
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Epoch 67 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 67 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 67 Training:  33%|███▎      | 32/96 [00:51<01:44,  1.63s/it]
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Epoch 67 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 67 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 67 Training:  34%|███▍      | 33/96 [00:52<01:42,  1.62s/it]
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Epoch 67 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 67 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 67 Training:  35%|███▌      | 34/96 [00:54<01:40,  1.61s/it]
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Epoch 67 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 67 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 67 Training:  36%|███▋      | 35/96 [00:55<01:38,  1.61s/it]
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Epoch 67 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 67 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 67 Training:  38%|███▊      | 36/96 [00:57<01:36,  1.60s/it]
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Epoch 67 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 67 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 67 Training:  39%|███▊      | 37/96 [00:59<01:34,  1.60s/it]
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Epoch 67 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 67 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 67 Training:  40%|███▉      | 38/96 [01:00<01:32,  1.60s/it]
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Epoch 67 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 67 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 67 Training:  41%|████      | 39/96 [01:02<01:31,  1.60s/it]
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Epoch 67 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 67 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 67 Training:  42%|████▏     | 40/96 [01:03<01:29,  1.60s/it]
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Epoch 67 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 67 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 67 Training:  43%|████▎     | 41/96 [01:05<01:27,  1.60s/it]
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Epoch 67 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 67 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 67 Training:  44%|████▍     | 42/96 [01:07<01:26,  1.60s/it]
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Epoch 67 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 67 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 67 Training:  45%|████▍     | 43/96 [01:08<01:24,  1.60s/it]
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Epoch 67 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 67 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 67 Training:  46%|████▌     | 44/96 [01:10<01:23,  1.60s/it]
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Epoch 67 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 67 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 67 Training:  47%|████▋     | 45/96 [01:11<01:21,  1.60s/it]
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Epoch 67 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 67 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 67 Training:  48%|████▊     | 46/96 [01:13<01:19,  1.60s/it]
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Epoch 67 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 67 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 67 Training:  49%|████▉     | 47/96 [01:15<01:18,  1.60s/it]
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Epoch 67 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 67 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 67 Training:  50%|█████     | 48/96 [01:16<01:16,  1.60s/it]
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Epoch 67 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 67 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 67 Training:  51%|█████     | 49/96 [01:18<01:15,  1.60s/it]
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Epoch 67 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 67 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 67 Training:  52%|█████▏    | 50/96 [01:19<01:13,  1.60s/it]
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Epoch 67 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 67 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 67 Training:  53%|█████▎    | 51/96 [01:21<01:12,  1.60s/it]
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Epoch 67 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 67 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 67 Training:  54%|█████▍    | 52/96 [01:23<01:10,  1.60s/it]
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Epoch 67 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 67 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.15it/s]
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Epoch 67 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 67 Training:  55%|█████▌    | 53/96 [01:24<01:08,  1.60s/it]
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Epoch 67 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 67 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 67 Training:  56%|█████▋    | 54/96 [01:26<01:07,  1.60s/it]
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Epoch 67 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 67 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 67 Training:  57%|█████▋    | 55/96 [01:27<01:05,  1.60s/it]
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Epoch 67 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 67 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 67 Training:  58%|█████▊    | 56/96 [01:29<01:03,  1.60s/it]
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Epoch 67 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 67 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 67 Training:  59%|█████▉    | 57/96 [01:31<01:02,  1.60s/it]
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Epoch 67 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 67 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 67 Training:  60%|██████    | 58/96 [01:32<01:00,  1.60s/it]
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Epoch 67 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 67 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 67 Training:  61%|██████▏   | 59/96 [01:34<00:59,  1.60s/it]
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Epoch 67 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 67 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 67 Training:  62%|██████▎   | 60/96 [01:35<00:57,  1.60s/it]
+
Epoch 67 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 67 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 67 Training:  64%|██████▎   | 61/96 [01:37<00:55,  1.60s/it]
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Epoch 67 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 67 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 67 Training:  65%|██████▍   | 62/96 [01:39<00:54,  1.60s/it]
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Epoch 67 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 67 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 67 Training:  66%|██████▌   | 63/96 [01:40<00:52,  1.60s/it]
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Epoch 67 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 67 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 67 Training:  67%|██████▋   | 64/96 [01:42<00:51,  1.60s/it]
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Epoch 67 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 67 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 67 Training:  68%|██████▊   | 65/96 [01:43<00:49,  1.60s/it]
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Epoch 67 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 67 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 67 Training:  69%|██████▉   | 66/96 [01:45<00:47,  1.59s/it]
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Epoch 67 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 67 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 67 Training:  70%|██████▉   | 67/96 [01:47<00:46,  1.59s/it]
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Epoch 67 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 67 Training:  71%|███████   | 68/96 [01:48<00:44,  1.59s/it]
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Epoch 67 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 67 Training:  72%|███████▏  | 69/96 [01:50<00:42,  1.59s/it]
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Epoch 67 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 67 Training:  73%|███████▎  | 70/96 [01:51<00:41,  1.59s/it]
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Epoch 67 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 67 Training:  74%|███████▍  | 71/96 [01:53<00:39,  1.59s/it]
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Epoch 67 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 67 Training:  75%|███████▌  | 72/96 [01:55<00:38,  1.59s/it]
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Epoch 67 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.02it/s]
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Epoch 67 Training:  76%|███████▌  | 73/96 [01:56<00:36,  1.59s/it]
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Epoch 67 Training:  77%|███████▋  | 74/96 [01:58<00:35,  1.59s/it]
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Epoch 67 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 67 Training:  78%|███████▊  | 75/96 [01:59<00:33,  1.59s/it]
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Epoch 67 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 67 Training:  79%|███████▉  | 76/96 [02:01<00:31,  1.59s/it]
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Epoch 67 Training:  80%|████████  | 77/96 [02:02<00:30,  1.59s/it]
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Epoch 67 Training:  81%|████████▏ | 78/96 [02:04<00:28,  1.59s/it]
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Epoch 67 Training:  82%|████████▏ | 79/96 [02:06<00:27,  1.59s/it]
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Epoch 67 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.15it/s][2026-10-01 19:33:16] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0067 | Epoch 067] train/policy_loss=24.4319 train/unweighted_policy_loss=4.8864 train/loss=24.4319 train/grad_norm=0.0543
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+[2026-10-01 19:33:16] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0068 | Epoch 067] timing/rollout_seconds=179.8970 timing/feedback_seconds=5.4345 timing/optimization_seconds=154.0238 timing/cycle_seconds=336.8957
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+[2026-10-01 19:36:20] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0068 | Epoch 068] train/reward_dists_mean=0.8590 train/reward_dists_std=0.0212 train/reward_ssim_mean=0.6503 train/reward_ssim_std=0.0925 train/reward_dists_group_std_mean=0.0074 train/reward_dists_group_std_max=0.0134 train/reward_dists_group_std_min=0.0030 train/reward_dists_group_mean_std=0.0197 train/reward_ssim_group_std_mean=0.0098 train/reward_ssim_group_std_max=0.0200 train/reward_ssim_group_std_min=0.0032 train/reward_ssim_group_mean_std=0.0919 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.0718 train/adv_max=4.1997 train/adv_abs_mean=1.1898
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Epoch 68 Training:   4%|▍         | 4/96 [00:06<02:26,  1.59s/it]
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Epoch 68 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 68 Training:   5%|▌         | 5/96 [00:07<02:25,  1.59s/it]
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Epoch 68 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 68 Training:   6%|▋         | 6/96 [00:09<02:23,  1.60s/it]
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Epoch 68 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 68 Training:   7%|▋         | 7/96 [00:11<02:22,  1.60s/it]
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Epoch 68 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 68 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 68 Training:   8%|▊         | 8/96 [00:12<02:20,  1.60s/it]
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Epoch 68 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 68 Training:   9%|▉         | 9/96 [00:14<02:18,  1.60s/it]
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Epoch 68 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 68 Training:  10%|█         | 10/96 [00:15<02:17,  1.60s/it]
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Epoch 68 Training:  11%|█▏        | 11/96 [00:17<02:15,  1.60s/it]
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Epoch 68 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 68 Training:  12%|█▎        | 12/96 [00:19<02:14,  1.60s/it]
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Epoch 68 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 68 Training:  14%|█▎        | 13/96 [00:20<02:12,  1.60s/it]
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Epoch 68 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 68 Training:  15%|█▍        | 14/96 [00:22<02:11,  1.60s/it]
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Epoch 68 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 68 Training:  16%|█▌        | 15/96 [00:23<02:09,  1.60s/it]
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Epoch 68 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 68 Training:  17%|█▋        | 16/96 [00:25<02:07,  1.60s/it]
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Epoch 68 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 68 Training:  18%|█▊        | 17/96 [00:27<02:06,  1.60s/it]
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Epoch 68 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 68 Training:  19%|█▉        | 18/96 [00:28<02:04,  1.60s/it]
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Epoch 68 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 68 Training:  20%|█▉        | 19/96 [00:30<02:03,  1.60s/it]
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Epoch 68 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 68 Training:  21%|██        | 20/96 [00:31<02:01,  1.60s/it]
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Epoch 68 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 68 Training:  22%|██▏       | 21/96 [00:33<01:59,  1.60s/it]
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Epoch 68 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 68 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 68 Training:  23%|██▎       | 22/96 [00:35<01:58,  1.60s/it]
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Epoch 68 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 68 Training:  24%|██▍       | 23/96 [00:36<01:56,  1.60s/it]
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Epoch 68 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 68 Training:  25%|██▌       | 24/96 [00:38<01:55,  1.60s/it]
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Epoch 68 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 68 Training:  26%|██▌       | 25/96 [00:39<01:53,  1.60s/it]
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Epoch 68 Training:  27%|██▋       | 26/96 [00:41<01:51,  1.60s/it]
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Epoch 68 Training:  28%|██▊       | 27/96 [00:43<01:50,  1.60s/it]
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Epoch 68 Training:  29%|██▉       | 28/96 [00:44<01:48,  1.60s/it]
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Epoch 68 Training:  30%|███       | 29/96 [00:46<01:47,  1.60s/it]
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Epoch 68 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 68 Training:  31%|███▏      | 30/96 [00:47<01:45,  1.60s/it]
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Epoch 68 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 68 Training:  32%|███▏      | 31/96 [00:49<01:43,  1.60s/it]
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Epoch 68 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 68 Training:  33%|███▎      | 32/96 [00:51<01:42,  1.60s/it]
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Epoch 68 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 68 Training:  34%|███▍      | 33/96 [00:52<01:40,  1.60s/it]
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Epoch 68 Training:  35%|███▌      | 34/96 [00:54<01:39,  1.60s/it]
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Epoch 68 Training:  36%|███▋      | 35/96 [00:55<01:37,  1.60s/it]
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Epoch 68 Training:  38%|███▊      | 36/96 [00:57<01:35,  1.60s/it]
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Epoch 68 Training:  39%|███▊      | 37/96 [00:59<01:34,  1.60s/it]
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Epoch 68 Training:  40%|███▉      | 38/96 [01:00<01:32,  1.60s/it]
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Epoch 68 Training:  41%|████      | 39/96 [01:02<01:31,  1.60s/it]
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Epoch 68 Training:  42%|████▏     | 40/96 [01:03<01:29,  1.60s/it]
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Epoch 68 Training:  43%|████▎     | 41/96 [01:05<01:27,  1.60s/it]
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Epoch 68 Training:  44%|████▍     | 42/96 [01:07<01:26,  1.60s/it]
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Epoch 68 Training:  45%|████▍     | 43/96 [01:08<01:24,  1.60s/it]
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Epoch 68 Training:  46%|████▌     | 44/96 [01:10<01:23,  1.60s/it]
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Epoch 68 Training:  47%|████▋     | 45/96 [01:11<01:21,  1.60s/it]
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Epoch 68 Training:  48%|████▊     | 46/96 [01:13<01:19,  1.60s/it]
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Epoch 68 Training:  49%|████▉     | 47/96 [01:15<01:18,  1.60s/it]
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Epoch 68 Training:  50%|█████     | 48/96 [01:16<01:16,  1.60s/it]
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Epoch 68 Training:  51%|█████     | 49/96 [01:18<01:15,  1.60s/it]
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Epoch 68 Training:  52%|█████▏    | 50/96 [01:19<01:13,  1.60s/it]
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Epoch 68 Training:  53%|█████▎    | 51/96 [01:21<01:11,  1.60s/it]
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Epoch 68 Training:  54%|█████▍    | 52/96 [01:23<01:10,  1.60s/it]
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Epoch 68 Training:  55%|█████▌    | 53/96 [01:24<01:08,  1.60s/it]
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Epoch 68 Training:  56%|█████▋    | 54/96 [01:26<01:07,  1.60s/it]
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Epoch 68 Training:  57%|█████▋    | 55/96 [01:27<01:05,  1.60s/it]
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Epoch 68 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s][2026-10-01 19:38:54] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0068 | Epoch 068] train/policy_loss=24.7014 train/unweighted_policy_loss=4.9403 train/loss=24.7014 train/grad_norm=0.0584
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+[2026-10-01 19:38:54] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0069 | Epoch 068] timing/rollout_seconds=180.1957 timing/feedback_seconds=5.8712 timing/optimization_seconds=154.3860 timing/cycle_seconds=337.5165
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+
Pointwise Rewards: ssim:   0%|          | 0/6 [00:00<?, ?it/s]
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Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:01<00:00,  5.56it/s]
+
Pointwise Rewards: dists:   0%|          | 0/6 [00:00<?, ?it/s]
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Pointwise Rewards: dists:  67%|██████▋   | 4/6 [00:01<00:00,  3.31it/s]
Pointwise Rewards: dists:  83%|████████▎ | 5/6 [00:01<00:00,  3.29it/s]
Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.25it/s]
Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.26it/s]
+[2026-10-01 19:41:58] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0069 | Epoch 069] train/reward_dists_mean=0.8513 train/reward_dists_std=0.0307 train/reward_ssim_mean=0.6321 train/reward_ssim_std=0.1034 train/reward_dists_group_std_mean=0.0075 train/reward_dists_group_std_max=0.0150 train/reward_dists_group_std_min=0.0028 train/reward_dists_group_mean_std=0.0296 train/reward_ssim_group_std_mean=0.0089 train/reward_ssim_group_std_max=0.0182 train/reward_ssim_group_std_min=0.0035 train/reward_ssim_group_mean_std=0.1030 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.8293 train/adv_max=4.4608 train/adv_abs_mean=1.1877
+
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Epoch 69 Training:   3%|▎         | 3/96 [00:04<02:26,  1.58s/it]
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Epoch 69 Training:   5%|▌         | 5/96 [00:07<02:24,  1.59s/it]
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Epoch 69 Training:   6%|▋         | 6/96 [00:09<02:22,  1.59s/it]
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Epoch 69 Training:   7%|▋         | 7/96 [00:11<02:21,  1.59s/it]
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Epoch 69 Training:   8%|▊         | 8/96 [00:12<02:19,  1.59s/it]
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Epoch 69 Training:   9%|▉         | 9/96 [00:14<02:18,  1.59s/it]
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Epoch 69 Training:  10%|█         | 10/96 [00:15<02:16,  1.59s/it]
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Epoch 69 Training:  11%|█▏        | 11/96 [00:17<02:15,  1.59s/it]
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Epoch 69 Training:  12%|█▎        | 12/96 [00:19<02:13,  1.59s/it]
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Epoch 69 Training:  14%|█▎        | 13/96 [00:20<02:11,  1.59s/it]
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Epoch 69 Training:  15%|█▍        | 14/96 [00:22<02:10,  1.59s/it]
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Epoch 69 Training:  16%|█▌        | 15/96 [00:23<02:08,  1.59s/it]
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Epoch 69 Training:  18%|█▊        | 17/96 [00:26<02:05,  1.59s/it]
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Epoch 69 Training:  19%|█▉        | 18/96 [00:28<02:04,  1.59s/it]
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Epoch 69 Training:  21%|██        | 20/96 [00:31<02:00,  1.59s/it]
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Epoch 69 Training:  22%|██▏       | 21/96 [00:33<01:59,  1.59s/it]
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Epoch 69 Training:  23%|██▎       | 22/96 [00:34<01:57,  1.59s/it]
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Epoch 69 Training:  24%|██▍       | 23/96 [00:36<01:56,  1.59s/it]
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Epoch 69 Training:  25%|██▌       | 24/96 [00:38<01:54,  1.59s/it]
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Epoch 69 Training:  26%|██▌       | 25/96 [00:39<01:52,  1.59s/it]
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Epoch 69 Training:  28%|██▊       | 27/96 [00:42<01:49,  1.59s/it]
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Epoch 69 Training:  29%|██▉       | 28/96 [00:44<01:48,  1.59s/it]
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Epoch 69 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 69 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 69 Training:  33%|███▎      | 32/96 [00:50<01:41,  1.59s/it]
+
Epoch 69 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 69 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 69 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 69 Training:  34%|███▍      | 33/96 [00:52<01:40,  1.59s/it]
+
Epoch 69 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 69 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 69 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 69 Training:  35%|███▌      | 34/96 [00:54<01:38,  1.59s/it]
+
Epoch 69 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 69 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 69 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 69 Training:  36%|███▋      | 35/96 [00:55<01:37,  1.59s/it]
+
Epoch 69 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 69 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 69 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 69 Training:  38%|███▊      | 36/96 [00:57<01:35,  1.59s/it]
+
Epoch 69 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 69 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.15it/s]
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Epoch 69 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.65it/s]
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Epoch 69 Training:  39%|███▊      | 37/96 [00:59<01:37,  1.66s/it]
+
Epoch 69 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 69 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 69 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 69 Training:  40%|███▉      | 38/96 [01:00<01:34,  1.64s/it]
+
Epoch 69 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 69 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 69 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 69 Training:  41%|████      | 39/96 [01:02<01:32,  1.62s/it]
+
Epoch 69 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 69 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 69 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 69 Training:  42%|████▏     | 40/96 [01:03<01:30,  1.61s/it]
+
Epoch 69 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 69 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 69 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 69 Training:  43%|████▎     | 41/96 [01:05<01:28,  1.61s/it]
+
Epoch 69 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 69 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 69 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 69 Training:  44%|████▍     | 42/96 [01:06<01:26,  1.60s/it]
+
Epoch 69 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 69 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 69 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 69 Training:  45%|████▍     | 43/96 [01:08<01:24,  1.60s/it]
+
Epoch 69 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 69 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 69 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 69 Training:  46%|████▌     | 44/96 [01:10<01:22,  1.60s/it]
+
Epoch 69 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 69 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 69 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 69 Training:  47%|████▋     | 45/96 [01:11<01:21,  1.60s/it]
+
Epoch 69 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 69 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 69 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 69 Training:  48%|████▊     | 46/96 [01:13<01:19,  1.59s/it]
+
Epoch 69 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 69 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 69 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 69 Training:  49%|████▉     | 47/96 [01:14<01:18,  1.59s/it]
+
Epoch 69 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 69 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 69 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 69 Training:  50%|█████     | 48/96 [01:16<01:16,  1.59s/it]
+
Epoch 69 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 69 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 69 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 69 Training:  51%|█████     | 49/96 [01:18<01:14,  1.59s/it]
+
Epoch 69 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 69 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 69 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
+
                                                                
Epoch 69 Training:  52%|█████▏    | 50/96 [01:19<01:13,  1.59s/it]
+
Epoch 69 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 69 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 69 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 69 Training:  53%|█████▎    | 51/96 [01:21<01:15,  1.68s/it]
+
Epoch 69 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 69 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 69 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 69 Training:  54%|█████▍    | 52/96 [01:23<01:12,  1.65s/it]
+
Epoch 69 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 69 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 69 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 69 Training:  55%|█████▌    | 53/96 [01:24<01:10,  1.63s/it]
+
Epoch 69 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 69 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 69 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 69 Training:  56%|█████▋    | 54/96 [01:26<01:08,  1.62s/it]
+
Epoch 69 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 69 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 69 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 69 Training:  57%|█████▋    | 55/96 [01:27<01:06,  1.61s/it]
+
Epoch 69 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 69 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 69 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 69 Training:  58%|█████▊    | 56/96 [01:29<01:04,  1.60s/it]
+
Epoch 69 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 69 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 69 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 69 Training:  59%|█████▉    | 57/96 [01:31<01:02,  1.60s/it]
+
Epoch 69 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 69 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 69 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 69 Training:  60%|██████    | 58/96 [01:32<01:00,  1.60s/it]
+
Epoch 69 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 69 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 69 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 69 Training:  61%|██████▏   | 59/96 [01:34<00:59,  1.60s/it]
+
Epoch 69 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 69 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 69 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 69 Training:  62%|██████▎   | 60/96 [01:35<00:57,  1.60s/it]
+
Epoch 69 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 69 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 69 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 69 Training:  64%|██████▎   | 61/96 [01:37<00:55,  1.59s/it]
+
Epoch 69 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 69 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 69 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 69 Training:  65%|██████▍   | 62/96 [01:39<00:54,  1.59s/it]
+
Epoch 69 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 69 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 69 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.60it/s]
+
                                                                
Epoch 69 Training:  66%|██████▌   | 63/96 [01:40<00:54,  1.66s/it]
+
Epoch 69 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 69 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 69 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 69 Training:  67%|██████▋   | 64/96 [01:42<00:52,  1.64s/it]
+
Epoch 69 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 69 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 69 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 69 Training:  68%|██████▊   | 65/96 [01:44<00:50,  1.62s/it]
+
Epoch 69 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 69 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 69 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 69 Training:  69%|██████▉   | 66/96 [01:45<00:48,  1.61s/it]
+
Epoch 69 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 69 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 69 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 69 Training:  70%|██████▉   | 67/96 [01:47<00:46,  1.61s/it]
+
Epoch 69 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 69 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 69 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 69 Training:  71%|███████   | 68/96 [01:48<00:45,  1.64s/it]
+
Epoch 69 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 69 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 69 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 69 Training:  72%|███████▏  | 69/96 [01:50<00:43,  1.62s/it]
+
Epoch 69 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 69 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 69 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 69 Training:  73%|███████▎  | 70/96 [01:52<00:41,  1.61s/it]
+
Epoch 69 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 69 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 69 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 69 Training:  74%|███████▍  | 71/96 [01:53<00:40,  1.61s/it]
+
Epoch 69 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 69 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 69 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 69 Training:  75%|███████▌  | 72/96 [01:55<00:38,  1.61s/it]
+
Epoch 69 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 69 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 69 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 69 Training:  76%|███████▌  | 73/96 [01:56<00:36,  1.60s/it]
+
Epoch 69 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 69 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 69 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 69 Training:  77%|███████▋  | 74/96 [01:58<00:35,  1.60s/it]
+
Epoch 69 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 69 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 69 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 69 Training:  78%|███████▊  | 75/96 [02:00<00:33,  1.60s/it]
+
Epoch 69 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 69 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 69 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 69 Training:  79%|███████▉  | 76/96 [02:01<00:31,  1.59s/it]
+
Epoch 69 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 69 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 69 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 69 Training:  80%|████████  | 77/96 [02:03<00:30,  1.59s/it]
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Epoch 69 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 69 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 69 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 69 Training:  81%|████████▏ | 78/96 [02:04<00:28,  1.59s/it]
+
Epoch 69 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 69 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 69 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 69 Training:  82%|████████▏ | 79/96 [02:06<00:27,  1.59s/it]
+
Epoch 69 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 69 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 69 Training:  83%|████████▎ | 80/96 [02:08<00:25,  1.59s/it]
+
Epoch 69 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 69 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 69 Training:  84%|████████▍ | 81/96 [02:09<00:23,  1.59s/it]
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Epoch 69 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 69 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 69 Training:  85%|████████▌ | 82/96 [02:11<00:22,  1.59s/it]
+
Epoch 69 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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+[2026-10-01 19:44:31] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0070 | Epoch 069] timing/rollout_seconds=180.2537 timing/feedback_seconds=6.2539 timing/optimization_seconds=154.1944 timing/cycle_seconds=337.4312
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+[2026-10-01 19:45:24] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0070 | Epoch 070] eval/flir_rl_full/reward_dists_mean=0.8483 eval/flir_rl_full/reward_dists_std=0.0344 eval/flir_rl_full/reward_psnr_mean=19.7932 eval/flir_rl_full/reward_psnr_std=2.6144 eval/flir_rl_full/reward_ssim_mean=0.6169 eval/flir_rl_full/reward_ssim_std=0.0838
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+
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+[2026-10-01 19:48:28] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0070 | Epoch 070] train/reward_dists_mean=0.8579 train/reward_dists_std=0.0275 train/reward_ssim_mean=0.6659 train/reward_ssim_std=0.0811 train/reward_dists_group_std_mean=0.0068 train/reward_dists_group_std_max=0.0134 train/reward_dists_group_std_min=0.0033 train/reward_dists_group_mean_std=0.0265 train/reward_ssim_group_std_mean=0.0093 train/reward_ssim_group_std_max=0.0216 train/reward_ssim_group_std_min=0.0033 train/reward_ssim_group_mean_std=0.0805 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.5254 train/adv_max=4.2656 train/adv_abs_mean=1.2195
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Epoch 70 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
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Epoch 70 Training:   2%|▏         | 2/96 [00:03<02:24,  1.54s/it]
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Epoch 70 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 70 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.95it/s]
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Epoch 70 Training:   3%|▎         | 3/96 [00:04<02:24,  1.55s/it]
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Epoch 70 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 70 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.94it/s]
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Epoch 70 Training:   4%|▍         | 4/96 [00:06<02:23,  1.56s/it]
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Epoch 70 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 70 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.91it/s]
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Epoch 70 Training:   5%|▌         | 5/96 [00:07<02:22,  1.56s/it]
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Epoch 70 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
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Epoch 70 Training:   6%|▋         | 6/96 [00:09<02:21,  1.57s/it]
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Epoch 70 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 70 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
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Epoch 70 Training:   7%|▋         | 7/96 [00:10<02:19,  1.57s/it]
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Epoch 70 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 70 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 70 Training:   8%|▊         | 8/96 [00:12<02:18,  1.57s/it]
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Epoch 70 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 70 Training:   9%|▉         | 9/96 [00:14<02:16,  1.57s/it]
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Epoch 70 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 70 Training:  10%|█         | 10/96 [00:15<02:15,  1.57s/it]
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Epoch 70 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.94it/s]
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Epoch 70 Training:  11%|█▏        | 11/96 [00:17<02:13,  1.57s/it]
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Epoch 70 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 70 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.94it/s]
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Epoch 70 Training:  12%|█▎        | 12/96 [00:18<02:12,  1.57s/it]
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Epoch 70 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 70 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.94it/s]
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Epoch 70 Training:  14%|█▎        | 13/96 [00:20<02:10,  1.57s/it]
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Epoch 70 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 70 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
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Epoch 70 Training:  15%|█▍        | 14/96 [00:21<02:08,  1.57s/it]
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Epoch 70 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 70 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 70 Training:  16%|█▌        | 15/96 [00:23<02:07,  1.57s/it]
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Epoch 70 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 70 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 70 Training:  17%|█▋        | 16/96 [00:25<02:05,  1.57s/it]
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Epoch 70 Training:  18%|█▊        | 17/96 [00:26<02:04,  1.57s/it]
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Epoch 70 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 70 Training:  19%|█▉        | 18/96 [00:28<02:02,  1.57s/it]
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Epoch 70 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 70 Training:  20%|█▉        | 19/96 [00:29<02:00,  1.57s/it]
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Epoch 70 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 70 Training:  21%|██        | 20/96 [00:31<01:59,  1.57s/it]
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Epoch 70 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 70 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.94it/s]
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Epoch 70 Training:  22%|██▏       | 21/96 [00:32<01:57,  1.57s/it]
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Epoch 70 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 70 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 70 Training:  23%|██▎       | 22/96 [00:34<01:56,  1.57s/it]
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Epoch 70 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 70 Training:  24%|██▍       | 23/96 [00:36<01:54,  1.57s/it]
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Epoch 70 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 70 Training:  25%|██▌       | 24/96 [00:37<01:53,  1.57s/it]
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Epoch 70 Training:  26%|██▌       | 25/96 [00:39<01:51,  1.57s/it]
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Epoch 70 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 70 Training:  27%|██▋       | 26/96 [00:40<01:49,  1.57s/it]
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Epoch 70 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 70 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 70 Training:  28%|██▊       | 27/96 [00:42<01:48,  1.57s/it]
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Epoch 70 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 70 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.91it/s]
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Epoch 70 Training:  29%|██▉       | 28/96 [00:43<01:47,  1.58s/it]
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Epoch 70 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 70 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 70 Training:  30%|███       | 29/96 [00:45<01:45,  1.58s/it]
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Epoch 70 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 70 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 70 Training:  31%|███▏      | 30/96 [00:47<01:43,  1.57s/it]
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Epoch 70 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 70 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 70 Training:  32%|███▏      | 31/96 [00:48<01:42,  1.57s/it]
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Epoch 70 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 70 Training:  33%|███▎      | 32/96 [00:50<01:40,  1.57s/it]
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Epoch 70 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 70 Training:  34%|███▍      | 33/96 [00:51<01:39,  1.57s/it]
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Epoch 70 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 70 Training:  35%|███▌      | 34/96 [00:53<01:37,  1.57s/it]
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Epoch 70 Training:  36%|███▋      | 35/96 [00:54<01:35,  1.57s/it]
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Epoch 70 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 70 Training:  38%|███▊      | 36/96 [00:56<01:34,  1.57s/it]
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Epoch 70 Training:  39%|███▊      | 37/96 [00:58<01:32,  1.57s/it]
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Epoch 70 Training:  40%|███▉      | 38/96 [00:59<01:31,  1.57s/it]
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Epoch 70 Training:  41%|████      | 39/96 [01:01<01:29,  1.57s/it]
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Epoch 70 Training:  42%|████▏     | 40/96 [01:02<01:28,  1.57s/it]
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Epoch 70 Training:  43%|████▎     | 41/96 [01:04<01:26,  1.57s/it]
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Epoch 70 Training:  44%|████▍     | 42/96 [01:05<01:24,  1.57s/it]
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Epoch 70 Training:  45%|████▍     | 43/96 [01:07<01:23,  1.57s/it]
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Epoch 70 Training:  46%|████▌     | 44/96 [01:09<01:21,  1.57s/it]
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Epoch 70 Training:  47%|████▋     | 45/96 [01:10<01:20,  1.57s/it]
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Epoch 70 Training:  48%|████▊     | 46/96 [01:12<01:18,  1.57s/it]
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Epoch 70 Training:  49%|████▉     | 47/96 [01:13<01:16,  1.57s/it]
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Epoch 70 Training:  50%|█████     | 48/96 [01:15<01:15,  1.57s/it]
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Epoch 70 Training:  51%|█████     | 49/96 [01:16<01:13,  1.57s/it]
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Epoch 70 Training:  52%|█████▏    | 50/96 [01:18<01:12,  1.57s/it]
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Epoch 70 Training:  53%|█████▎    | 51/96 [01:20<01:10,  1.57s/it]
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Epoch 70 Training:  54%|█████▍    | 52/96 [01:21<01:09,  1.57s/it]
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Epoch 70 Training:  55%|█████▌    | 53/96 [01:23<01:07,  1.57s/it]
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Epoch 70 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s][2026-10-01 19:50:59] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0070 | Epoch 070] train/policy_loss=25.3707 train/unweighted_policy_loss=5.0741 train/loss=25.3707 train/grad_norm=0.0694
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+[2026-10-01 19:50:59] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0071 | Epoch 070] timing/rollout_seconds=180.1530 timing/feedback_seconds=6.0746 timing/optimization_seconds=151.5706 timing/cycle_seconds=334.8549
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+
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+
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+[2026-10-01 19:54:03] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0071 | Epoch 071] train/reward_dists_mean=0.8523 train/reward_dists_std=0.0342 train/reward_ssim_mean=0.6701 train/reward_ssim_std=0.1005 train/reward_dists_group_std_mean=0.0070 train/reward_dists_group_std_max=0.0172 train/reward_dists_group_std_min=0.0034 train/reward_dists_group_mean_std=0.0334 train/reward_ssim_group_std_mean=0.0080 train/reward_ssim_group_std_max=0.0228 train/reward_ssim_group_std_min=0.0031 train/reward_ssim_group_mean_std=0.1001 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.5381 train/adv_max=4.0549 train/adv_abs_mean=1.1600
+
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Epoch 71 Training:  30%|███       | 29/96 [00:45<01:45,  1.57s/it]
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Epoch 71 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 71 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
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Epoch 71 Training:  31%|███▏      | 30/96 [00:47<01:43,  1.57s/it]
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Epoch 71 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 71 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.94it/s]
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Epoch 71 Training:  32%|███▏      | 31/96 [00:48<01:41,  1.57s/it]
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Epoch 71 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 71 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
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Epoch 71 Training:  33%|███▎      | 32/96 [00:50<01:40,  1.56s/it]
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Epoch 71 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 71 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
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Epoch 71 Training:  34%|███▍      | 33/96 [00:51<01:38,  1.56s/it]
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Epoch 71 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 71 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
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Epoch 71 Training:  35%|███▌      | 34/96 [00:53<01:36,  1.56s/it]
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Epoch 71 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 71 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.94it/s]
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Epoch 71 Training:  36%|███▋      | 35/96 [00:55<01:35,  1.56s/it]
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Epoch 71 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 71 Training:  38%|███▊      | 36/96 [00:56<01:33,  1.56s/it]
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Epoch 71 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 71 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 71 Training:  39%|███▊      | 37/96 [00:58<01:32,  1.57s/it]
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Epoch 71 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 71 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.94it/s]
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Epoch 71 Training:  40%|███▉      | 38/96 [00:59<01:30,  1.57s/it]
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Epoch 71 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 71 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
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Epoch 71 Training:  41%|████      | 39/96 [01:01<01:29,  1.56s/it]
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Epoch 71 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 71 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 71 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
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Epoch 71 Training:  42%|████▏     | 40/96 [01:02<01:27,  1.56s/it]
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Epoch 71 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 71 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 71 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
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Epoch 71 Training:  43%|████▎     | 41/96 [01:04<01:26,  1.56s/it]
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Epoch 71 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 71 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 71 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.94it/s]
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Epoch 71 Training:  44%|████▍     | 42/96 [01:06<01:24,  1.56s/it]
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Epoch 71 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 71 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 71 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
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Epoch 71 Training:  45%|████▍     | 43/96 [01:07<01:22,  1.56s/it]
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Epoch 71 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 71 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 71 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
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Epoch 71 Training:  46%|████▌     | 44/96 [01:09<01:21,  1.56s/it]
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Epoch 71 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 71 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 71 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.89it/s]
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Epoch 71 Training:  47%|████▋     | 45/96 [01:10<01:20,  1.57s/it]
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Epoch 71 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 71 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 71 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
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Epoch 71 Training:  48%|████▊     | 46/96 [01:12<01:18,  1.57s/it]
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Epoch 71 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 71 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 71 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.97it/s]
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Epoch 71 Training:  49%|████▉     | 47/96 [01:13<01:16,  1.57s/it]
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Epoch 71 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 71 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 71 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 71 Training:  50%|█████     | 48/96 [01:15<01:15,  1.57s/it]
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Epoch 71 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 71 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 71 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
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Epoch 71 Training:  51%|█████     | 49/96 [01:17<01:13,  1.57s/it]
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Epoch 71 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 71 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 71 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
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Epoch 71 Training:  52%|█████▏    | 50/96 [01:18<01:12,  1.57s/it]
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Epoch 71 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 71 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
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Epoch 71 Training:  53%|█████▎    | 51/96 [01:20<01:10,  1.57s/it]
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Epoch 71 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 71 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 71 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 71 Training:  54%|█████▍    | 52/96 [01:21<01:08,  1.57s/it]
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Epoch 71 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 71 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
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Epoch 71 Training:  55%|█████▌    | 53/96 [01:23<01:07,  1.57s/it]
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Epoch 71 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 71 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
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Epoch 71 Training:  56%|█████▋    | 54/96 [01:24<01:05,  1.57s/it]
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Epoch 71 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 71 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
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Epoch 71 Training:  57%|█████▋    | 55/96 [01:26<01:04,  1.57s/it]
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Epoch 71 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 71 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 71 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
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Epoch 71 Training:  58%|█████▊    | 56/96 [01:27<01:02,  1.56s/it]
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Epoch 71 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 71 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.15it/s]
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Epoch 71 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.94it/s]
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Epoch 71 Training:  59%|█████▉    | 57/96 [01:29<01:01,  1.57s/it]
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Epoch 71 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 71 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.97it/s]
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Epoch 71 Training:  60%|██████    | 58/96 [01:31<00:59,  1.56s/it]
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Epoch 71 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 71 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
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Epoch 71 Training:  61%|██████▏   | 59/96 [01:32<00:57,  1.56s/it]
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Epoch 71 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 71 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
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Epoch 71 Training:  62%|██████▎   | 60/96 [01:34<00:56,  1.56s/it]
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Epoch 71 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 71 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 71 Training:  64%|██████▎   | 61/96 [01:35<00:54,  1.56s/it]
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Epoch 71 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 71 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.94it/s]
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Epoch 71 Training:  65%|██████▍   | 62/96 [01:37<00:53,  1.56s/it]
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Epoch 71 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 71 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 71 Training:  66%|██████▌   | 63/96 [01:38<00:51,  1.56s/it]
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Epoch 71 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 71 Training:  67%|██████▋   | 64/96 [01:40<00:50,  1.56s/it]
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Epoch 71 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
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Epoch 71 Training:  68%|██████▊   | 65/96 [01:42<00:48,  1.57s/it]
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Epoch 71 Training:  69%|██████▉   | 66/96 [01:43<00:47,  1.57s/it]
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Epoch 71 Training:  70%|██████▉   | 67/96 [01:45<00:45,  1.57s/it]
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Epoch 71 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 71 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.91it/s]
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Epoch 71 Training:  71%|███████   | 68/96 [01:46<00:43,  1.57s/it]
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Epoch 71 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 71 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 71 Training:  72%|███████▏  | 69/96 [01:48<00:42,  1.57s/it]
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Epoch 71 Training:  73%|███████▎  | 70/96 [01:49<00:40,  1.57s/it]
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Epoch 71 Training:  74%|███████▍  | 71/96 [01:51<00:39,  1.57s/it]
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Epoch 71 Training:  75%|███████▌  | 72/96 [01:53<00:37,  1.57s/it]
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Epoch 71 Training:  76%|███████▌  | 73/96 [01:54<00:36,  1.57s/it]
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Epoch 71 Training:  77%|███████▋  | 74/96 [01:56<00:34,  1.57s/it]
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Epoch 71 Training:  78%|███████▊  | 75/96 [01:57<00:32,  1.57s/it]
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Epoch 71 Training:  79%|███████▉  | 76/96 [01:59<00:31,  1.57s/it]
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Epoch 71 Training:  80%|████████  | 77/96 [02:00<00:29,  1.57s/it]
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Epoch 71 Training:  81%|████████▏ | 78/96 [02:02<00:28,  1.57s/it]
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Epoch 71 Training:  82%|████████▏ | 79/96 [02:04<00:26,  1.57s/it]
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Epoch 71 Training:  83%|████████▎ | 80/96 [02:05<00:25,  1.57s/it]
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Epoch 71 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s][2026-10-01 19:56:34] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0071 | Epoch 071] train/policy_loss=24.8977 train/unweighted_policy_loss=4.9795 train/loss=24.8977 train/grad_norm=0.0492
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+[2026-10-01 19:56:34] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0072 | Epoch 071] timing/rollout_seconds=180.4113 timing/feedback_seconds=6.4202 timing/optimization_seconds=151.5911 timing/cycle_seconds=334.9652
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+[2026-10-01 19:59:37] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0072 | Epoch 072] train/reward_dists_mean=0.8648 train/reward_dists_std=0.0208 train/reward_ssim_mean=0.6937 train/reward_ssim_std=0.0992 train/reward_dists_group_std_mean=0.0073 train/reward_dists_group_std_max=0.0126 train/reward_dists_group_std_min=0.0037 train/reward_dists_group_mean_std=0.0193 train/reward_ssim_group_std_mean=0.0086 train/reward_ssim_group_std_max=0.0202 train/reward_ssim_group_std_min=0.0039 train/reward_ssim_group_mean_std=0.0988 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.9649 train/adv_max=5.4437 train/adv_abs_mean=1.1720
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Epoch 72 Training:   5%|▌         | 5/96 [00:07<02:21,  1.56s/it]
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Epoch 72 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 72 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.94it/s]
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Epoch 72 Training:   6%|▋         | 6/96 [00:09<02:20,  1.56s/it]
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Epoch 72 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 72 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
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Epoch 72 Training:   7%|▋         | 7/96 [00:10<02:19,  1.56s/it]
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Epoch 72 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 72 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.91it/s]
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Epoch 72 Training:   8%|▊         | 8/96 [00:12<02:17,  1.57s/it]
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Epoch 72 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 72 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
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Epoch 72 Training:   9%|▉         | 9/96 [00:14<02:16,  1.57s/it]
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Epoch 72 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 72 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
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Epoch 72 Training:  10%|█         | 10/96 [00:15<02:14,  1.57s/it]
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Epoch 72 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.94it/s]
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Epoch 72 Training:  11%|█▏        | 11/96 [00:17<02:13,  1.57s/it]
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Epoch 72 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 72 Training:  12%|█▎        | 12/96 [00:18<02:11,  1.57s/it]
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Epoch 72 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 72 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
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Epoch 72 Training:  14%|█▎        | 13/96 [00:20<02:10,  1.57s/it]
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Epoch 72 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 72 Training:  15%|█▍        | 14/96 [00:21<02:08,  1.57s/it]
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Epoch 72 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 72 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
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Epoch 72 Training:  16%|█▌        | 15/96 [00:23<02:06,  1.57s/it]
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Epoch 72 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 72 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.94it/s]
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Epoch 72 Training:  17%|█▋        | 16/96 [00:24<02:05,  1.57s/it]
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Epoch 72 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 72 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.94it/s]
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Epoch 72 Training:  18%|█▊        | 17/96 [00:26<02:03,  1.57s/it]
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Epoch 72 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 72 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.94it/s]
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Epoch 72 Training:  19%|█▉        | 18/96 [00:28<02:02,  1.57s/it]
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Epoch 72 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 72 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
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Epoch 72 Training:  20%|█▉        | 19/96 [00:29<02:00,  1.57s/it]
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Epoch 72 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 72 Training:  21%|██        | 20/96 [00:31<01:59,  1.57s/it]
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Epoch 72 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 72 Training:  22%|██▏       | 21/96 [00:32<01:57,  1.57s/it]
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Epoch 72 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 72 Training:  23%|██▎       | 22/96 [00:34<01:55,  1.57s/it]
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Epoch 72 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 72 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.94it/s]
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Epoch 72 Training:  24%|██▍       | 23/96 [00:35<01:54,  1.57s/it]
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Epoch 72 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 72 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
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Epoch 72 Training:  25%|██▌       | 24/96 [00:37<01:52,  1.57s/it]
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Epoch 72 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 72 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
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Epoch 72 Training:  26%|██▌       | 25/96 [00:39<01:51,  1.57s/it]
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Epoch 72 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 72 Training:  27%|██▋       | 26/96 [00:40<01:49,  1.57s/it]
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Epoch 72 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 72 Training:  28%|██▊       | 27/96 [00:42<01:48,  1.57s/it]
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Epoch 72 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 72 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.94it/s]
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Epoch 72 Training:  29%|██▉       | 28/96 [00:43<01:46,  1.57s/it]
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Epoch 72 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 72 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
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Epoch 72 Training:  30%|███       | 29/96 [00:45<01:44,  1.57s/it]
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Epoch 72 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 72 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.94it/s]
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Epoch 72 Training:  31%|███▏      | 30/96 [00:46<01:43,  1.57s/it]
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Epoch 72 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 72 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
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Epoch 72 Training:  32%|███▏      | 31/96 [00:48<01:41,  1.57s/it]
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Epoch 72 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 72 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.94it/s]
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Epoch 72 Training:  33%|███▎      | 32/96 [00:50<01:40,  1.57s/it]
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Epoch 72 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 72 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 72 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
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Epoch 72 Training:  34%|███▍      | 33/96 [00:51<01:38,  1.57s/it]
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Epoch 72 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 72 Training:  35%|███▌      | 34/96 [00:53<01:37,  1.57s/it]
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Epoch 72 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 72 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
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Epoch 72 Training:  36%|███▋      | 35/96 [00:54<01:35,  1.57s/it]
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Epoch 72 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 72 Training:  38%|███▊      | 36/96 [00:56<01:34,  1.57s/it]
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Epoch 72 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 72 Training:  39%|███▊      | 37/96 [00:57<01:32,  1.57s/it]
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Epoch 72 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 72 Training:  40%|███▉      | 38/96 [00:59<01:30,  1.57s/it]
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Epoch 72 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 72 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
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Epoch 72 Training:  41%|████      | 39/96 [01:01<01:29,  1.57s/it]
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Epoch 72 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 72 Training:  42%|████▏     | 40/96 [01:02<01:27,  1.57s/it]
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Epoch 72 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 72 Training:  43%|████▎     | 41/96 [01:04<01:26,  1.57s/it]
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Epoch 72 Training:  44%|████▍     | 42/96 [01:05<01:24,  1.57s/it]
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Epoch 72 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 72 Training:  45%|████▍     | 43/96 [01:07<01:23,  1.57s/it]
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Epoch 72 Training:  46%|████▌     | 44/96 [01:08<01:21,  1.57s/it]
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Epoch 72 Training:  47%|████▋     | 45/96 [01:10<01:19,  1.57s/it]
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Epoch 72 Training:  48%|████▊     | 46/96 [01:11<01:18,  1.57s/it]
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Epoch 72 Training:  49%|████▉     | 47/96 [01:13<01:16,  1.57s/it]
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Epoch 72 Training:  50%|█████     | 48/96 [01:15<01:15,  1.57s/it]
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Epoch 72 Training:  51%|█████     | 49/96 [01:16<01:13,  1.57s/it]
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Epoch 72 Training:  52%|█████▏    | 50/96 [01:18<01:12,  1.57s/it]
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Epoch 72 Training:  53%|█████▎    | 51/96 [01:19<01:10,  1.57s/it]
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Epoch 72 Training:  54%|█████▍    | 52/96 [01:21<01:08,  1.57s/it]
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Epoch 72 Training:  55%|█████▌    | 53/96 [01:22<01:07,  1.57s/it]
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Epoch 72 Training:  56%|█████▋    | 54/96 [01:24<01:05,  1.57s/it]
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Epoch 72 Training:  57%|█████▋    | 55/96 [01:26<01:04,  1.57s/it]
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Epoch 72 Training:  58%|█████▊    | 56/96 [01:27<01:02,  1.57s/it]
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Epoch 72 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s][2026-10-01 20:02:07] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0072 | Epoch 072] train/policy_loss=24.1363 train/unweighted_policy_loss=4.8273 train/loss=24.1363 train/grad_norm=0.0583
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+[2026-10-01 20:02:07] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0073 | Epoch 072] timing/rollout_seconds=179.5391 timing/feedback_seconds=5.4642 timing/optimization_seconds=150.9412 timing/cycle_seconds=333.3562
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+
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+[2026-10-01 20:05:11] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0073 | Epoch 073] train/reward_dists_mean=0.8509 train/reward_dists_std=0.0248 train/reward_ssim_mean=0.6606 train/reward_ssim_std=0.0993 train/reward_dists_group_std_mean=0.0090 train/reward_dists_group_std_max=0.0204 train/reward_dists_group_std_min=0.0031 train/reward_dists_group_mean_std=0.0228 train/reward_ssim_group_std_mean=0.0082 train/reward_ssim_group_std_max=0.0213 train/reward_ssim_group_std_min=0.0038 train/reward_ssim_group_mean_std=0.0989 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-6.7424 train/adv_max=4.2237 train/adv_abs_mean=1.1430
+
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Epoch 73 Training:  34%|███▍      | 33/96 [00:52<01:39,  1.58s/it]
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Epoch 73 Training:  35%|███▌      | 34/96 [00:53<01:37,  1.57s/it]
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Epoch 73 Training:  36%|███▋      | 35/96 [00:55<01:36,  1.58s/it]
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Epoch 73 Training:  38%|███▊      | 36/96 [00:56<01:34,  1.58s/it]
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Epoch 73 Training:  39%|███▊      | 37/96 [00:58<01:32,  1.58s/it]
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Epoch 73 Training:  40%|███▉      | 38/96 [00:59<01:31,  1.57s/it]
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Epoch 73 Training:  41%|████      | 39/96 [01:01<01:30,  1.58s/it]
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Epoch 73 Training:  42%|████▏     | 40/96 [01:03<01:28,  1.58s/it]
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Epoch 73 Training:  43%|████▎     | 41/96 [01:04<01:26,  1.58s/it]
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Epoch 73 Training:  44%|████▍     | 42/96 [01:06<01:25,  1.57s/it]
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Epoch 73 Training:  45%|████▍     | 43/96 [01:07<01:23,  1.58s/it]
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Epoch 73 Training:  46%|████▌     | 44/96 [01:09<01:22,  1.58s/it]
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Epoch 73 Training:  47%|████▋     | 45/96 [01:10<01:20,  1.58s/it]
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Epoch 73 Training:  48%|████▊     | 46/96 [01:12<01:18,  1.57s/it]
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Epoch 73 Training:  49%|████▉     | 47/96 [01:14<01:17,  1.57s/it]
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Epoch 73 Training:  50%|█████     | 48/96 [01:15<01:15,  1.57s/it]
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Epoch 73 Training:  51%|█████     | 49/96 [01:17<01:13,  1.57s/it]
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Epoch 73 Training:  52%|█████▏    | 50/96 [01:18<01:12,  1.58s/it]
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Epoch 73 Training:  53%|█████▎    | 51/96 [01:20<01:10,  1.58s/it]
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Epoch 73 Training:  54%|█████▍    | 52/96 [01:21<01:09,  1.57s/it]
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Epoch 73 Training:  55%|█████▌    | 53/96 [01:23<01:07,  1.58s/it]
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Epoch 73 Training:  56%|█████▋    | 54/96 [01:25<01:06,  1.57s/it]
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Epoch 73 Training:  57%|█████▋    | 55/96 [01:26<01:04,  1.57s/it]
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Epoch 73 Training:  58%|█████▊    | 56/96 [01:28<01:02,  1.57s/it]
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Epoch 73 Training:  59%|█████▉    | 57/96 [01:29<01:01,  1.57s/it]
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Epoch 73 Training:  60%|██████    | 58/96 [01:31<00:59,  1.58s/it]
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Epoch 73 Training:  61%|██████▏   | 59/96 [01:33<00:58,  1.58s/it]
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Epoch 73 Training:  62%|██████▎   | 60/96 [01:34<00:57,  1.59s/it]
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Epoch 73 Training:  64%|██████▎   | 61/96 [01:36<00:55,  1.59s/it]
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Epoch 73 Training:  65%|██████▍   | 62/96 [01:37<00:54,  1.59s/it]
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Epoch 73 Training:  66%|██████▌   | 63/96 [01:39<00:52,  1.59s/it]
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Epoch 73 Training:  67%|██████▋   | 64/96 [01:40<00:50,  1.59s/it]
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Epoch 73 Training:  68%|██████▊   | 65/96 [01:42<00:49,  1.59s/it]
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Epoch 73 Training:  69%|██████▉   | 66/96 [01:44<00:47,  1.59s/it]
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Epoch 73 Training:  70%|██████▉   | 67/96 [01:45<00:46,  1.59s/it]
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Epoch 73 Training:  71%|███████   | 68/96 [01:47<00:44,  1.59s/it]
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Epoch 73 Training:  72%|███████▏  | 69/96 [01:48<00:42,  1.59s/it]
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Epoch 73 Training:  73%|███████▎  | 70/96 [01:50<00:41,  1.59s/it]
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Epoch 73 Training:  74%|███████▍  | 71/96 [01:52<00:39,  1.59s/it]
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Epoch 73 Training:  75%|███████▌  | 72/96 [01:53<00:38,  1.59s/it]
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Epoch 73 Training:  76%|███████▌  | 73/96 [01:55<00:36,  1.59s/it]
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Epoch 73 Training:  77%|███████▋  | 74/96 [01:56<00:34,  1.59s/it]
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Epoch 73 Training:  78%|███████▊  | 75/96 [01:58<00:33,  1.59s/it]
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Epoch 73 Training:  79%|███████▉  | 76/96 [02:00<00:31,  1.59s/it]
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Epoch 73 Training:  80%|████████  | 77/96 [02:01<00:30,  1.59s/it]
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Epoch 73 Training:  81%|████████▏ | 78/96 [02:03<00:28,  1.60s/it]
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Epoch 73 Training:  82%|████████▏ | 79/96 [02:04<00:27,  1.60s/it]
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Epoch 73 Training:  83%|████████▎ | 80/96 [02:06<00:25,  1.59s/it]
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Epoch 73 Training:  84%|████████▍ | 81/96 [02:08<00:23,  1.59s/it]
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Epoch 73 Training:  85%|████████▌ | 82/96 [02:09<00:22,  1.59s/it]
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Epoch 73 Training:  86%|████████▋ | 83/96 [02:11<00:20,  1.58s/it]
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Epoch 73 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.15it/s][2026-10-01 20:07:43] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0073 | Epoch 073] train/policy_loss=24.7983 train/unweighted_policy_loss=4.9597 train/loss=24.7983 train/grad_norm=0.0593
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+[2026-10-01 20:07:43] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0074 | Epoch 073] timing/rollout_seconds=180.0591 timing/feedback_seconds=5.6479 timing/optimization_seconds=152.4662 timing/cycle_seconds=335.4631
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+[2026-10-01 20:10:47] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0074 | Epoch 074] train/reward_dists_mean=0.8508 train/reward_dists_std=0.0362 train/reward_ssim_mean=0.6617 train/reward_ssim_std=0.1272 train/reward_dists_group_std_mean=0.0078 train/reward_dists_group_std_max=0.0224 train/reward_dists_group_std_min=0.0038 train/reward_dists_group_mean_std=0.0351 train/reward_ssim_group_std_mean=0.0081 train/reward_ssim_group_std_max=0.0154 train/reward_ssim_group_std_min=0.0028 train/reward_ssim_group_mean_std=0.1269 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.7524 train/adv_max=4.9389 train/adv_abs_mean=1.2085
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Epoch 74 Training:   8%|▊         | 8/96 [00:12<02:18,  1.57s/it]
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Epoch 74 Training:   9%|▉         | 9/96 [00:14<02:16,  1.57s/it]
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Epoch 74 Training:  10%|█         | 10/96 [00:15<02:15,  1.57s/it]
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Epoch 74 Training:  11%|█▏        | 11/96 [00:17<02:13,  1.57s/it]
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Epoch 74 Training:  12%|█▎        | 12/96 [00:18<02:12,  1.58s/it]
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Epoch 74 Training:  14%|█▎        | 13/96 [00:20<02:10,  1.57s/it]
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Epoch 74 Training:  15%|█▍        | 14/96 [00:21<02:08,  1.57s/it]
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Epoch 74 Training:  16%|█▌        | 15/96 [00:23<02:06,  1.57s/it]
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Epoch 74 Training:  17%|█▋        | 16/96 [00:25<02:05,  1.57s/it]
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Epoch 74 Training:  18%|█▊        | 17/96 [00:26<02:04,  1.57s/it]
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Epoch 74 Training:  19%|█▉        | 18/96 [00:28<02:02,  1.57s/it]
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Epoch 74 Training:  20%|█▉        | 19/96 [00:29<02:01,  1.57s/it]
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Epoch 74 Training:  21%|██        | 20/96 [00:31<01:59,  1.58s/it]
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Epoch 74 Training:  22%|██▏       | 21/96 [00:32<01:58,  1.58s/it]
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Epoch 74 Training:  23%|██▎       | 22/96 [00:34<01:56,  1.58s/it]
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Epoch 74 Training:  24%|██▍       | 23/96 [00:36<01:55,  1.58s/it]
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Epoch 74 Training:  25%|██▌       | 24/96 [00:37<01:53,  1.58s/it]
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Epoch 74 Training:  26%|██▌       | 25/96 [00:39<01:52,  1.58s/it]
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Epoch 74 Training:  27%|██▋       | 26/96 [00:40<01:50,  1.58s/it]
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Epoch 74 Training:  28%|██▊       | 27/96 [00:42<01:48,  1.57s/it]
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Epoch 74 Training:  29%|██▉       | 28/96 [00:43<01:47,  1.58s/it]
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Epoch 74 Training:  30%|███       | 29/96 [00:45<01:45,  1.58s/it]
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Epoch 74 Training:  31%|███▏      | 30/96 [00:47<01:43,  1.57s/it]
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Epoch 74 Training:  32%|███▏      | 31/96 [00:48<01:42,  1.57s/it]
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Epoch 74 Training:  33%|███▎      | 32/96 [00:50<01:40,  1.58s/it]
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Epoch 74 Training:  34%|███▍      | 33/96 [00:51<01:39,  1.57s/it]
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Epoch 74 Training:  35%|███▌      | 34/96 [00:53<01:37,  1.57s/it]
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Epoch 74 Training:  36%|███▋      | 35/96 [00:54<01:35,  1.57s/it]
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Epoch 74 Training:  38%|███▊      | 36/96 [00:56<01:34,  1.57s/it]
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Epoch 74 Training:  39%|███▊      | 37/96 [00:58<01:32,  1.58s/it]
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Epoch 74 Training:  40%|███▉      | 38/96 [00:59<01:31,  1.57s/it]
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Epoch 74 Training:  41%|████      | 39/96 [01:01<01:29,  1.57s/it]
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Epoch 74 Training:  42%|████▏     | 40/96 [01:02<01:28,  1.57s/it]
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Epoch 74 Training:  43%|████▎     | 41/96 [01:04<01:26,  1.57s/it]
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Epoch 74 Training:  44%|████▍     | 42/96 [01:05<01:24,  1.57s/it]
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Epoch 74 Training:  45%|████▍     | 43/96 [01:07<01:23,  1.57s/it]
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Epoch 74 Training:  46%|████▌     | 44/96 [01:09<01:22,  1.58s/it]
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Epoch 74 Training:  47%|████▋     | 45/96 [01:10<01:20,  1.57s/it]
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Epoch 74 Training:  48%|████▊     | 46/96 [01:12<01:18,  1.57s/it]
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Epoch 74 Training:  49%|████▉     | 47/96 [01:13<01:17,  1.58s/it]
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Epoch 74 Training:  50%|█████     | 48/96 [01:15<01:15,  1.58s/it]
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Epoch 74 Training:  51%|█████     | 49/96 [01:17<01:14,  1.58s/it]
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Epoch 74 Training:  52%|█████▏    | 50/96 [01:18<01:12,  1.57s/it]
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Epoch 74 Training:  53%|█████▎    | 51/96 [01:20<01:10,  1.57s/it]
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Epoch 74 Training:  54%|█████▍    | 52/96 [01:21<01:09,  1.57s/it]
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Epoch 74 Training:  55%|█████▌    | 53/96 [01:23<01:07,  1.57s/it]
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Epoch 74 Training:  56%|█████▋    | 54/96 [01:24<01:05,  1.57s/it]
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Epoch 74 Training:  57%|█████▋    | 55/96 [01:26<01:04,  1.57s/it]
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Epoch 74 Training:  58%|█████▊    | 56/96 [01:27<01:02,  1.57s/it]
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Epoch 74 Training:  59%|█████▉    | 57/96 [01:29<01:01,  1.57s/it]
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Epoch 74 Training:  60%|██████    | 58/96 [01:31<00:59,  1.57s/it]
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Epoch 74 Training:  61%|██████▏   | 59/96 [01:32<00:58,  1.57s/it]
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Epoch 74 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s][2026-10-01 20:13:18] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0074 | Epoch 074] train/policy_loss=24.8263 train/unweighted_policy_loss=4.9653 train/loss=24.8263 train/grad_norm=0.0606
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+[2026-10-01 20:13:18] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0075 | Epoch 074] timing/rollout_seconds=180.1031 timing/feedback_seconds=5.6008 timing/optimization_seconds=151.5554 timing/cycle_seconds=334.6760
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+
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+[2026-10-01 20:16:21] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0075 | Epoch 075] train/reward_dists_mean=0.8490 train/reward_dists_std=0.0304 train/reward_ssim_mean=0.6829 train/reward_ssim_std=0.1115 train/reward_dists_group_std_mean=0.0097 train/reward_dists_group_std_max=0.0185 train/reward_dists_group_std_min=0.0042 train/reward_dists_group_mean_std=0.0286 train/reward_ssim_group_std_mean=0.0085 train/reward_ssim_group_std_max=0.0146 train/reward_ssim_group_std_min=0.0037 train/reward_ssim_group_mean_std=0.1111 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.9700 train/adv_max=4.4587 train/adv_abs_mean=1.2687
+
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Epoch 75 Training:  38%|███▊      | 36/96 [00:56<01:34,  1.57s/it]
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Epoch 75 Training:  39%|███▊      | 37/96 [00:58<01:32,  1.57s/it]
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Epoch 75 Training:  40%|███▉      | 38/96 [00:59<01:30,  1.57s/it]
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Epoch 75 Training:  41%|████      | 39/96 [01:01<01:29,  1.56s/it]
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Epoch 75 Training:  42%|████▏     | 40/96 [01:02<01:27,  1.56s/it]
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Epoch 75 Training:  43%|████▎     | 41/96 [01:04<01:26,  1.57s/it]
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Epoch 75 Training:  44%|████▍     | 42/96 [01:05<01:24,  1.56s/it]
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Epoch 75 Training:  45%|████▍     | 43/96 [01:07<01:22,  1.56s/it]
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Epoch 75 Training:  46%|████▌     | 44/96 [01:09<01:21,  1.56s/it]
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Epoch 75 Training:  47%|████▋     | 45/96 [01:10<01:19,  1.56s/it]
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Epoch 75 Training:  48%|████▊     | 46/96 [01:12<01:18,  1.56s/it]
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Epoch 75 Training:  49%|████▉     | 47/96 [01:13<01:16,  1.56s/it]
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Epoch 75 Training:  50%|█████     | 48/96 [01:15<01:15,  1.57s/it]
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Epoch 75 Training:  51%|█████     | 49/96 [01:16<01:13,  1.56s/it]
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Epoch 75 Training:  52%|█████▏    | 50/96 [01:18<01:12,  1.57s/it]
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Epoch 75 Training:  53%|█████▎    | 51/96 [01:19<01:10,  1.57s/it]
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Epoch 75 Training:  54%|█████▍    | 52/96 [01:21<01:08,  1.56s/it]
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Epoch 75 Training:  55%|█████▌    | 53/96 [01:23<01:07,  1.56s/it]
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Epoch 75 Training:  56%|█████▋    | 54/96 [01:24<01:05,  1.56s/it]
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Epoch 75 Training:  57%|█████▋    | 55/96 [01:26<01:04,  1.56s/it]
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Epoch 75 Training:  58%|█████▊    | 56/96 [01:27<01:02,  1.56s/it]
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Epoch 75 Training:  59%|█████▉    | 57/96 [01:29<01:00,  1.56s/it]
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Epoch 75 Training:  60%|██████    | 58/96 [01:30<00:59,  1.56s/it]
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Epoch 75 Training:  61%|██████▏   | 59/96 [01:32<00:57,  1.56s/it]
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Epoch 75 Training:  62%|██████▎   | 60/96 [01:34<00:56,  1.56s/it]
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Epoch 75 Training:  64%|██████▎   | 61/96 [01:35<00:54,  1.56s/it]
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Epoch 75 Training:  65%|██████▍   | 62/96 [01:37<00:53,  1.56s/it]
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Epoch 75 Training:  66%|██████▌   | 63/96 [01:38<00:51,  1.56s/it]
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Epoch 75 Training:  67%|██████▋   | 64/96 [01:40<00:50,  1.56s/it]
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Epoch 75 Training:  68%|██████▊   | 65/96 [01:41<00:48,  1.56s/it]
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Epoch 75 Training:  69%|██████▉   | 66/96 [01:43<00:46,  1.56s/it]
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Epoch 75 Training:  70%|██████▉   | 67/96 [01:44<00:45,  1.57s/it]
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Epoch 75 Training:  71%|███████   | 68/96 [01:46<00:43,  1.57s/it]
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Epoch 75 Training:  72%|███████▏  | 69/96 [01:48<00:42,  1.57s/it]
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Epoch 75 Training:  73%|███████▎  | 70/96 [01:49<00:40,  1.57s/it]
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Epoch 75 Training:  74%|███████▍  | 71/96 [01:51<00:39,  1.57s/it]
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Epoch 75 Training:  75%|███████▌  | 72/96 [01:52<00:37,  1.57s/it]
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Epoch 75 Training:  76%|███████▌  | 73/96 [01:54<00:36,  1.57s/it]
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Epoch 75 Training:  77%|███████▋  | 74/96 [01:55<00:34,  1.57s/it]
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Epoch 75 Training:  78%|███████▊  | 75/96 [01:57<00:32,  1.57s/it]
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Epoch 75 Training:  79%|███████▉  | 76/96 [01:59<00:31,  1.57s/it]
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Epoch 75 Training:  80%|████████  | 77/96 [02:00<00:29,  1.57s/it]
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Epoch 75 Training:  81%|████████▏ | 78/96 [02:02<00:28,  1.57s/it]
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Epoch 75 Training:  82%|████████▏ | 79/96 [02:03<00:26,  1.56s/it]
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Epoch 75 Training:  83%|████████▎ | 80/96 [02:05<00:25,  1.57s/it]
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Epoch 75 Training:  84%|████████▍ | 81/96 [02:06<00:23,  1.56s/it]
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Epoch 75 Training:  85%|████████▌ | 82/96 [02:08<00:21,  1.57s/it]
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Epoch 75 Training:  86%|████████▋ | 83/96 [02:10<00:20,  1.57s/it]
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Epoch 75 Training:  88%|████████▊ | 84/96 [02:11<00:18,  1.57s/it]
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Epoch 75 Training:  89%|████████▊ | 85/96 [02:13<00:17,  1.57s/it]
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Epoch 75 Training:  90%|████████▉ | 86/96 [02:14<00:15,  1.56s/it]
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Epoch 75 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s][2026-10-01 20:18:52] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0075 | Epoch 075] train/policy_loss=24.7355 train/unweighted_policy_loss=4.9471 train/loss=24.7355 train/grad_norm=0.0604
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+[2026-10-01 20:18:52] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0076 | Epoch 075] timing/rollout_seconds=180.0312 timing/feedback_seconds=5.3689 timing/optimization_seconds=151.2051 timing/cycle_seconds=334.1321
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+
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+[2026-10-01 20:21:55] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0076 | Epoch 076] train/reward_dists_mean=0.8511 train/reward_dists_std=0.0288 train/reward_ssim_mean=0.6694 train/reward_ssim_std=0.0860 train/reward_dists_group_std_mean=0.0084 train/reward_dists_group_std_max=0.0195 train/reward_dists_group_std_min=0.0031 train/reward_dists_group_mean_std=0.0272 train/reward_ssim_group_std_mean=0.0083 train/reward_ssim_group_std_max=0.0233 train/reward_ssim_group_std_min=0.0034 train/reward_ssim_group_mean_std=0.0855 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.2914 train/adv_max=5.9795 train/adv_abs_mean=1.2267
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Epoch 76 Training:  11%|█▏        | 11/96 [00:17<02:14,  1.58s/it]
+
Epoch 76 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 76 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 76 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 76 Training:  12%|█▎        | 12/96 [00:18<02:12,  1.58s/it]
+
Epoch 76 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 76 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 76 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 76 Training:  14%|█▎        | 13/96 [00:20<02:10,  1.58s/it]
+
Epoch 76 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 76 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 76 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 76 Training:  15%|█▍        | 14/96 [00:22<02:09,  1.58s/it]
+
Epoch 76 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 76 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 76 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 76 Training:  16%|█▌        | 15/96 [00:23<02:07,  1.58s/it]
+
Epoch 76 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 76 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 76 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 76 Training:  17%|█▋        | 16/96 [00:25<02:05,  1.57s/it]
+
Epoch 76 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 76 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 76 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 76 Training:  18%|█▊        | 17/96 [00:26<02:04,  1.57s/it]
+
Epoch 76 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 76 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 76 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 76 Training:  19%|█▉        | 18/96 [00:28<02:02,  1.58s/it]
+
Epoch 76 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 76 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 76 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 76 Training:  20%|█▉        | 19/96 [00:29<02:01,  1.58s/it]
+
Epoch 76 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 76 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 76 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 76 Training:  21%|██        | 20/96 [00:31<01:59,  1.58s/it]
+
Epoch 76 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 76 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 76 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 76 Training:  22%|██▏       | 21/96 [00:33<01:58,  1.58s/it]
+
Epoch 76 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 76 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 76 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 76 Training:  23%|██▎       | 22/96 [00:34<01:56,  1.58s/it]
+
Epoch 76 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 76 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 76 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 76 Training:  24%|██▍       | 23/96 [00:36<01:55,  1.58s/it]
+
Epoch 76 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 76 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 76 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 76 Training:  25%|██▌       | 24/96 [00:37<01:53,  1.58s/it]
+
Epoch 76 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 76 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 76 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 76 Training:  26%|██▌       | 25/96 [00:39<01:51,  1.57s/it]
+
Epoch 76 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 76 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 76 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 76 Training:  27%|██▋       | 26/96 [00:40<01:50,  1.57s/it]
+
Epoch 76 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 76 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 76 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.02it/s]
+
                                                                
Epoch 76 Training:  28%|██▊       | 27/96 [00:42<01:48,  1.58s/it]
+
Epoch 76 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 76 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 76 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 76 Training:  29%|██▉       | 28/96 [00:44<01:47,  1.57s/it]
+
Epoch 76 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 76 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 76 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 76 Training:  30%|███       | 29/96 [00:45<01:45,  1.58s/it]
+
Epoch 76 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 76 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
+
Epoch 76 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 76 Training:  31%|███▏      | 30/96 [00:47<01:43,  1.57s/it]
+
Epoch 76 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 76 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.15it/s]
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Epoch 76 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 76 Training:  32%|███▏      | 31/96 [00:48<01:42,  1.58s/it]
+
Epoch 76 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 76 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 76 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 76 Training:  33%|███▎      | 32/96 [00:50<01:41,  1.58s/it]
+
Epoch 76 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 76 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 76 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 76 Training:  34%|███▍      | 33/96 [00:51<01:39,  1.58s/it]
+
Epoch 76 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 76 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 76 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 76 Training:  35%|███▌      | 34/96 [00:53<01:37,  1.58s/it]
+
Epoch 76 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 76 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 76 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 76 Training:  36%|███▋      | 35/96 [00:55<01:36,  1.58s/it]
+
Epoch 76 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 76 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 76 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 76 Training:  38%|███▊      | 36/96 [00:56<01:34,  1.58s/it]
+
Epoch 76 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 76 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 76 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 76 Training:  39%|███▊      | 37/96 [00:58<01:32,  1.58s/it]
+
Epoch 76 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 76 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 76 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 76 Training:  40%|███▉      | 38/96 [00:59<01:31,  1.57s/it]
+
Epoch 76 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 76 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 76 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 76 Training:  41%|████      | 39/96 [01:01<01:29,  1.57s/it]
+
Epoch 76 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 76 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 76 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 76 Training:  42%|████▏     | 40/96 [01:02<01:28,  1.57s/it]
+
Epoch 76 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 76 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 76 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 76 Training:  43%|████▎     | 41/96 [01:04<01:26,  1.57s/it]
+
Epoch 76 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 76 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 76 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 76 Training:  44%|████▍     | 42/96 [01:06<01:24,  1.57s/it]
+
Epoch 76 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 76 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 76 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 76 Training:  45%|████▍     | 43/96 [01:07<01:23,  1.57s/it]
+
Epoch 76 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 76 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 76 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 76 Training:  46%|████▌     | 44/96 [01:09<01:21,  1.57s/it]
+
Epoch 76 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 76 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 76 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 76 Training:  47%|████▋     | 45/96 [01:10<01:20,  1.57s/it]
+
Epoch 76 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 76 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 76 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 76 Training:  48%|████▊     | 46/96 [01:12<01:18,  1.58s/it]
+
Epoch 76 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 76 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 76 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 76 Training:  49%|████▉     | 47/96 [01:14<01:17,  1.58s/it]
+
Epoch 76 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 76 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 76 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 76 Training:  50%|█████     | 48/96 [01:15<01:15,  1.57s/it]
+
Epoch 76 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 76 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 76 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 76 Training:  51%|█████     | 49/96 [01:17<01:14,  1.58s/it]
+
Epoch 76 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 76 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 76 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 76 Training:  52%|█████▏    | 50/96 [01:18<01:12,  1.58s/it]
+
Epoch 76 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 76 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 76 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 76 Training:  53%|█████▎    | 51/96 [01:20<01:10,  1.58s/it]
+
Epoch 76 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 76 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 76 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 76 Training:  54%|█████▍    | 52/96 [01:21<01:09,  1.58s/it]
+
Epoch 76 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 76 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 76 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 76 Training:  55%|█████▌    | 53/96 [01:23<01:07,  1.58s/it]
+
Epoch 76 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 76 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 76 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 76 Training:  56%|█████▋    | 54/96 [01:25<01:06,  1.57s/it]
+
Epoch 76 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 76 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 76 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 76 Training:  57%|█████▋    | 55/96 [01:26<01:04,  1.58s/it]
+
Epoch 76 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 76 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 76 Training:  58%|█████▊    | 56/96 [01:28<01:02,  1.57s/it]
+
Epoch 76 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 76 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 76 Training:  59%|█████▉    | 57/96 [01:29<01:01,  1.57s/it]
+
Epoch 76 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 76 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 76 Training:  60%|██████    | 58/96 [01:31<00:59,  1.57s/it]
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Epoch 76 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 76 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 76 Training:  61%|██████▏   | 59/96 [01:32<00:58,  1.58s/it]
+
Epoch 76 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 76 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 76 Training:  62%|██████▎   | 60/96 [01:34<00:56,  1.57s/it]
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Epoch 76 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 76 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 76 Training:  64%|██████▎   | 61/96 [01:36<00:55,  1.57s/it]
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Epoch 76 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 76 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 76 Training:  65%|██████▍   | 62/96 [01:37<00:53,  1.57s/it]
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Epoch 76 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 76 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s][2026-10-01 20:24:27] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0076 | Epoch 076] train/policy_loss=24.8330 train/unweighted_policy_loss=4.9666 train/loss=24.8330 train/grad_norm=0.0675
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+[2026-10-01 20:24:27] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0077 | Epoch 076] timing/rollout_seconds=180.0256 timing/feedback_seconds=5.9515 timing/optimization_seconds=151.9499 timing/cycle_seconds=334.9509
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+[2026-10-01 20:27:30] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0077 | Epoch 077] train/reward_dists_mean=0.8486 train/reward_dists_std=0.0348 train/reward_ssim_mean=0.6715 train/reward_ssim_std=0.1129 train/reward_dists_group_std_mean=0.0092 train/reward_dists_group_std_max=0.0245 train/reward_dists_group_std_min=0.0037 train/reward_dists_group_mean_std=0.0333 train/reward_ssim_group_std_mean=0.0081 train/reward_ssim_group_std_max=0.0163 train/reward_ssim_group_std_min=0.0032 train/reward_ssim_group_mean_std=0.1126 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.9938 train/adv_max=3.7269 train/adv_abs_mean=1.2226
+
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Epoch 77 Training:  41%|████      | 39/96 [01:01<01:30,  1.58s/it]
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Epoch 77 Training:  42%|████▏     | 40/96 [01:03<01:28,  1.58s/it]
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Epoch 77 Training:  43%|████▎     | 41/96 [01:05<01:26,  1.58s/it]
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Epoch 77 Training:  44%|████▍     | 42/96 [01:06<01:25,  1.58s/it]
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Epoch 77 Training:  45%|████▍     | 43/96 [01:08<01:24,  1.59s/it]
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Epoch 77 Training:  46%|████▌     | 44/96 [01:09<01:22,  1.58s/it]
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Epoch 77 Training:  47%|████▋     | 45/96 [01:11<01:20,  1.59s/it]
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Epoch 77 Training:  48%|████▊     | 46/96 [01:12<01:19,  1.59s/it]
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Epoch 77 Training:  49%|████▉     | 47/96 [01:14<01:21,  1.67s/it]
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Epoch 77 Training:  50%|█████     | 48/96 [01:16<01:18,  1.64s/it]
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Epoch 77 Training:  51%|█████     | 49/96 [01:17<01:16,  1.63s/it]
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Epoch 77 Training:  52%|█████▏    | 50/96 [01:19<01:14,  1.61s/it]
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Epoch 77 Training:  53%|█████▎    | 51/96 [01:21<01:12,  1.60s/it]
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Epoch 77 Training:  54%|█████▍    | 52/96 [01:22<01:10,  1.60s/it]
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Epoch 77 Training:  55%|█████▌    | 53/96 [01:24<01:08,  1.60s/it]
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Epoch 77 Training:  56%|█████▋    | 54/96 [01:25<01:06,  1.59s/it]
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Epoch 77 Training:  57%|█████▋    | 55/96 [01:27<01:05,  1.59s/it]
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Epoch 77 Training:  58%|█████▊    | 56/96 [01:29<01:03,  1.59s/it]
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Epoch 77 Training:  59%|█████▉    | 57/96 [01:30<01:01,  1.59s/it]
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Epoch 77 Training:  60%|██████    | 58/96 [01:32<01:00,  1.59s/it]
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Epoch 77 Training:  61%|██████▏   | 59/96 [01:34<01:01,  1.66s/it]
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Epoch 77 Training:  62%|██████▎   | 60/96 [01:35<00:58,  1.64s/it]
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Epoch 77 Training:  64%|██████▎   | 61/96 [01:37<00:56,  1.62s/it]
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Epoch 77 Training:  65%|██████▍   | 62/96 [01:38<00:54,  1.61s/it]
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Epoch 77 Training:  66%|██████▌   | 63/96 [01:40<00:52,  1.60s/it]
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Epoch 77 Training:  67%|██████▋   | 64/96 [01:42<00:51,  1.60s/it]
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Epoch 77 Training:  68%|██████▊   | 65/96 [01:43<00:49,  1.60s/it]
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Epoch 77 Training:  69%|██████▉   | 66/96 [01:45<00:47,  1.60s/it]
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Epoch 77 Training:  70%|██████▉   | 67/96 [01:46<00:46,  1.59s/it]
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Epoch 77 Training:  71%|███████   | 68/96 [01:48<00:44,  1.59s/it]
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Epoch 77 Training:  72%|███████▏  | 69/96 [01:50<00:45,  1.68s/it]
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Epoch 77 Training:  73%|███████▎  | 70/96 [01:51<00:42,  1.65s/it]
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Epoch 77 Training:  74%|███████▍  | 71/96 [01:53<00:40,  1.63s/it]
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Epoch 77 Training:  75%|███████▌  | 72/96 [01:55<00:38,  1.62s/it]
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Epoch 77 Training:  76%|███████▌  | 73/96 [01:56<00:37,  1.61s/it]
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Epoch 77 Training:  77%|███████▋  | 74/96 [01:58<00:35,  1.60s/it]
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Epoch 77 Training:  78%|███████▊  | 75/96 [01:59<00:33,  1.60s/it]
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Epoch 77 Training:  79%|███████▉  | 76/96 [02:01<00:32,  1.61s/it]
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Epoch 77 Training:  80%|████████  | 77/96 [02:02<00:30,  1.60s/it]
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Epoch 77 Training:  81%|████████▏ | 78/96 [02:04<00:28,  1.60s/it]
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Epoch 77 Training:  82%|████████▏ | 79/96 [02:06<00:27,  1.59s/it]
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Epoch 77 Training:  83%|████████▎ | 80/96 [02:07<00:25,  1.59s/it]
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Epoch 77 Training:  84%|████████▍ | 81/96 [02:09<00:23,  1.59s/it]
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Epoch 77 Training:  85%|████████▌ | 82/96 [02:10<00:22,  1.59s/it]
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Epoch 77 Training:  86%|████████▋ | 83/96 [02:12<00:20,  1.59s/it]
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Epoch 77 Training:  88%|████████▊ | 84/96 [02:14<00:19,  1.59s/it]
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Epoch 77 Training:  89%|████████▊ | 85/96 [02:15<00:17,  1.59s/it]
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Epoch 77 Training:  90%|████████▉ | 86/96 [02:17<00:15,  1.59s/it]
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Epoch 77 Training:  91%|█████████ | 87/96 [02:18<00:14,  1.59s/it]
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Epoch 77 Training:  92%|█████████▏| 88/96 [02:20<00:12,  1.59s/it]
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Epoch 77 Training:  93%|█████████▎| 89/96 [02:22<00:11,  1.59s/it]
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Epoch 77 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s][2026-10-01 20:30:04] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0077 | Epoch 077] train/policy_loss=24.5922 train/unweighted_policy_loss=4.9184 train/loss=24.5922 train/grad_norm=0.0703
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+[2026-10-01 20:30:04] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0078 | Epoch 077] timing/rollout_seconds=180.0708 timing/feedback_seconds=5.9512 timing/optimization_seconds=153.9603 timing/cycle_seconds=337.0277
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+
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+[2026-10-01 20:33:07] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0078 | Epoch 078] train/reward_dists_mean=0.8575 train/reward_dists_std=0.0300 train/reward_ssim_mean=0.6952 train/reward_ssim_std=0.1129 train/reward_dists_group_std_mean=0.0087 train/reward_dists_group_std_max=0.0172 train/reward_dists_group_std_min=0.0039 train/reward_dists_group_mean_std=0.0285 train/reward_ssim_group_std_mean=0.0093 train/reward_ssim_group_std_max=0.0209 train/reward_ssim_group_std_min=0.0034 train/reward_ssim_group_mean_std=0.1124 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.1914 train/adv_max=4.8388 train/adv_abs_mean=1.2743
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Epoch 78 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 78 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 78 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.02it/s]
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Epoch 78 Training:  16%|█▌        | 15/96 [00:23<02:08,  1.59s/it]
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Epoch 78 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 78 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.02it/s]
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Epoch 78 Training:  17%|█▋        | 16/96 [00:25<02:07,  1.59s/it]
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Epoch 78 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 78 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.02it/s]
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Epoch 78 Training:  18%|█▊        | 17/96 [00:26<02:05,  1.59s/it]
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Epoch 78 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 78 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 78 Training:  19%|█▉        | 18/96 [00:28<02:04,  1.59s/it]
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Epoch 78 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 78 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 78 Training:  20%|█▉        | 19/96 [00:30<02:02,  1.59s/it]
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Epoch 78 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 78 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 78 Training:  21%|██        | 20/96 [00:31<02:00,  1.59s/it]
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Epoch 78 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 78 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 78 Training:  22%|██▏       | 21/96 [00:33<01:59,  1.59s/it]
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Epoch 78 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 78 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 78 Training:  23%|██▎       | 22/96 [00:34<01:57,  1.59s/it]
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Epoch 78 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 78 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 78 Training:  24%|██▍       | 23/96 [00:36<01:56,  1.59s/it]
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Epoch 78 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 78 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 78 Training:  25%|██▌       | 24/96 [00:38<01:54,  1.59s/it]
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Epoch 78 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 78 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 78 Training:  26%|██▌       | 25/96 [00:39<01:53,  1.59s/it]
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Epoch 78 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 78 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 78 Training:  27%|██▋       | 26/96 [00:41<01:51,  1.59s/it]
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Epoch 78 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 78 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 78 Training:  28%|██▊       | 27/96 [00:42<01:49,  1.59s/it]
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Epoch 78 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 78 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 78 Training:  29%|██▉       | 28/96 [00:44<01:47,  1.59s/it]
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Epoch 78 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 78 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 78 Training:  30%|███       | 29/96 [00:46<01:46,  1.59s/it]
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Epoch 78 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 78 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 78 Training:  31%|███▏      | 30/96 [00:47<01:44,  1.59s/it]
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Epoch 78 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 78 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 78 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.02it/s]
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Epoch 78 Training:  32%|███▏      | 31/96 [00:49<01:43,  1.59s/it]
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Epoch 78 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 78 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 78 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 78 Training:  33%|███▎      | 32/96 [00:50<01:41,  1.59s/it]
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Epoch 78 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 78 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 78 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 78 Training:  34%|███▍      | 33/96 [00:52<01:39,  1.59s/it]
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Epoch 78 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 78 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 78 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 78 Training:  35%|███▌      | 34/96 [00:53<01:38,  1.59s/it]
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Epoch 78 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 78 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 78 Training:  36%|███▋      | 35/96 [00:55<01:37,  1.59s/it]
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Epoch 78 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 78 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.98it/s]
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Epoch 78 Training:  38%|███▊      | 36/96 [00:57<01:35,  1.60s/it]
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Epoch 78 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 78 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 78 Training:  39%|███▊      | 37/96 [00:58<01:34,  1.60s/it]
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Epoch 78 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 78 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 78 Training:  40%|███▉      | 38/96 [01:00<01:32,  1.60s/it]
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Epoch 78 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 78 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 78 Training:  41%|████      | 39/96 [01:01<01:30,  1.59s/it]
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Epoch 78 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 78 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 78 Training:  42%|████▏     | 40/96 [01:03<01:29,  1.59s/it]
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Epoch 78 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 78 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 78 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.02it/s]
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Epoch 78 Training:  43%|████▎     | 41/96 [01:05<01:27,  1.59s/it]
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Epoch 78 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 78 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 78 Training:  44%|████▍     | 42/96 [01:06<01:26,  1.59s/it]
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Epoch 78 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 78 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 78 Training:  45%|████▍     | 43/96 [01:08<01:24,  1.59s/it]
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Epoch 78 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 78 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.02it/s]
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Epoch 78 Training:  46%|████▌     | 44/96 [01:09<01:22,  1.59s/it]
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Epoch 78 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 78 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 78 Training:  47%|████▋     | 45/96 [01:11<01:21,  1.59s/it]
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Epoch 78 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 78 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 78 Training:  48%|████▊     | 46/96 [01:13<01:19,  1.59s/it]
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Epoch 78 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 78 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 78 Training:  49%|████▉     | 47/96 [01:14<01:17,  1.59s/it]
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Epoch 78 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 78 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 78 Training:  50%|█████     | 48/96 [01:16<01:16,  1.59s/it]
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Epoch 78 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 78 Training:  51%|█████     | 49/96 [01:17<01:14,  1.59s/it]
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Epoch 78 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 78 Training:  52%|█████▏    | 50/96 [01:19<01:13,  1.59s/it]
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Epoch 78 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 78 Training:  53%|█████▎    | 51/96 [01:21<01:11,  1.59s/it]
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Epoch 78 Training:  54%|█████▍    | 52/96 [01:22<01:10,  1.59s/it]
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Epoch 78 Training:  55%|█████▌    | 53/96 [01:24<01:08,  1.59s/it]
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Epoch 78 Training:  56%|█████▋    | 54/96 [01:25<01:06,  1.59s/it]
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Epoch 78 Training:  57%|█████▋    | 55/96 [01:27<01:05,  1.59s/it]
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Epoch 78 Training:  58%|█████▊    | 56/96 [01:29<01:03,  1.59s/it]
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Epoch 78 Training:  59%|█████▉    | 57/96 [01:30<01:02,  1.59s/it]
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Epoch 78 Training:  60%|██████    | 58/96 [01:32<01:00,  1.59s/it]
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Epoch 78 Training:  61%|██████▏   | 59/96 [01:33<00:58,  1.59s/it]
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Epoch 78 Training:  62%|██████▎   | 60/96 [01:35<00:57,  1.59s/it]
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Epoch 78 Training:  64%|██████▎   | 61/96 [01:36<00:55,  1.59s/it]
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Epoch 78 Training:  65%|██████▍   | 62/96 [01:38<00:54,  1.59s/it]
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Epoch 78 Training:  66%|██████▌   | 63/96 [01:40<00:52,  1.59s/it]
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Epoch 78 Training:  67%|██████▋   | 64/96 [01:41<00:51,  1.59s/it]
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Epoch 78 Training:  68%|██████▊   | 65/96 [01:43<00:49,  1.59s/it]
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Epoch 78 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s][2026-10-01 20:35:40] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0078 | Epoch 078] train/policy_loss=24.8119 train/unweighted_policy_loss=4.9624 train/loss=24.8119 train/grad_norm=0.0674
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+[2026-10-01 20:35:40] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0079 | Epoch 078] timing/rollout_seconds=180.0988 timing/feedback_seconds=5.9006 timing/optimization_seconds=153.4954 timing/cycle_seconds=336.5067
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+
Pointwise Rewards: ssim:   0%|          | 0/6 [00:00<?, ?it/s]
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Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:01<00:00,  5.49it/s]
+
Pointwise Rewards: dists:   0%|          | 0/6 [00:00<?, ?it/s]
Pointwise Rewards: dists:  17%|█▋        | 1/6 [00:00<00:01,  3.23it/s]
Pointwise Rewards: dists:  33%|███▎      | 2/6 [00:00<00:01,  3.22it/s]
Pointwise Rewards: dists:  50%|█████     | 3/6 [00:00<00:00,  3.11it/s]
Pointwise Rewards: dists:  67%|██████▋   | 4/6 [00:01<00:00,  3.20it/s]
Pointwise Rewards: dists:  83%|████████▎ | 5/6 [00:01<00:00,  3.22it/s]
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Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.19it/s]
+[2026-10-01 20:38:44] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0079 | Epoch 079] train/reward_dists_mean=0.8535 train/reward_dists_std=0.0400 train/reward_ssim_mean=0.6896 train/reward_ssim_std=0.1141 train/reward_dists_group_std_mean=0.0080 train/reward_dists_group_std_max=0.0189 train/reward_dists_group_std_min=0.0023 train/reward_dists_group_mean_std=0.0391 train/reward_ssim_group_std_mean=0.0091 train/reward_ssim_group_std_max=0.0238 train/reward_ssim_group_std_min=0.0025 train/reward_ssim_group_mean_std=0.1136 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.0867 train/adv_max=4.5196 train/adv_abs_mean=1.1623
+
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Epoch 79 Training:   3%|▎         | 3/96 [00:04<02:27,  1.59s/it]
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Epoch 79 Training:   5%|▌         | 5/96 [00:07<02:24,  1.59s/it]
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Epoch 79 Training:   6%|▋         | 6/96 [00:09<02:23,  1.60s/it]
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Epoch 79 Training:   7%|▋         | 7/96 [00:11<02:22,  1.60s/it]
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Epoch 79 Training:   8%|▊         | 8/96 [00:12<02:20,  1.60s/it]
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Epoch 79 Training:   9%|▉         | 9/96 [00:14<02:19,  1.60s/it]
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Epoch 79 Training:  10%|█         | 10/96 [00:15<02:17,  1.60s/it]
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Epoch 79 Training:  11%|█▏        | 11/96 [00:17<02:15,  1.60s/it]
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Epoch 79 Training:  12%|█▎        | 12/96 [00:19<02:17,  1.63s/it]
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Epoch 79 Training:  14%|█▎        | 13/96 [00:20<02:14,  1.62s/it]
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Epoch 79 Training:  15%|█▍        | 14/96 [00:22<02:12,  1.62s/it]
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Epoch 79 Training:  16%|█▌        | 15/96 [00:24<02:10,  1.61s/it]
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Epoch 79 Training:  17%|█▋        | 16/96 [00:25<02:08,  1.61s/it]
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Epoch 79 Training:  18%|█▊        | 17/96 [00:27<02:06,  1.61s/it]
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Epoch 79 Training:  19%|█▉        | 18/96 [00:28<02:05,  1.60s/it]
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Epoch 79 Training:  21%|██        | 20/96 [00:32<02:01,  1.60s/it]
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Epoch 79 Training:  22%|██▏       | 21/96 [00:33<02:00,  1.60s/it]
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Epoch 79 Training:  23%|██▎       | 22/96 [00:35<01:58,  1.61s/it]
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Epoch 79 Training:  24%|██▍       | 23/96 [00:36<01:57,  1.60s/it]
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Epoch 79 Training:  25%|██▌       | 24/96 [00:38<01:55,  1.60s/it]
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Epoch 79 Training:  26%|██▌       | 25/96 [00:40<01:53,  1.60s/it]
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Epoch 79 Training:  27%|██▋       | 26/96 [00:41<01:51,  1.60s/it]
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Epoch 79 Training:  29%|██▉       | 28/96 [00:44<01:49,  1.62s/it]
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Epoch 79 Training:  33%|███▎      | 32/96 [00:51<01:42,  1.60s/it]
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Epoch 79 Training:  34%|███▍      | 33/96 [00:52<01:40,  1.60s/it]
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Epoch 79 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 79 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 79 Training:  44%|████▍     | 42/96 [01:07<01:26,  1.60s/it]
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Epoch 79 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 79 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 79 Training:  45%|████▍     | 43/96 [01:08<01:24,  1.60s/it]
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Epoch 79 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 79 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 79 Training:  46%|████▌     | 44/96 [01:10<01:23,  1.60s/it]
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Epoch 79 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 79 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 79 Training:  47%|████▋     | 45/96 [01:12<01:21,  1.60s/it]
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Epoch 79 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 79 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 79 Training:  48%|████▊     | 46/96 [01:13<01:20,  1.60s/it]
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Epoch 79 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 79 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 79 Training:  49%|████▉     | 47/96 [01:15<01:18,  1.60s/it]
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Epoch 79 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 79 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 79 Training:  50%|█████     | 48/96 [01:16<01:16,  1.60s/it]
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Epoch 79 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 79 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 79 Training:  51%|█████     | 49/96 [01:18<01:15,  1.60s/it]
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Epoch 79 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 79 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 79 Training:  52%|█████▏    | 50/96 [01:20<01:13,  1.60s/it]
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Epoch 79 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 79 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 79 Training:  53%|█████▎    | 51/96 [01:21<01:12,  1.60s/it]
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Epoch 79 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 79 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 79 Training:  54%|█████▍    | 52/96 [01:23<01:10,  1.60s/it]
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Epoch 79 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 79 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 79 Training:  55%|█████▌    | 53/96 [01:24<01:08,  1.60s/it]
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Epoch 79 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 79 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 79 Training:  56%|█████▋    | 54/96 [01:26<01:07,  1.60s/it]
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Epoch 79 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 79 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 79 Training:  57%|█████▋    | 55/96 [01:28<01:05,  1.60s/it]
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Epoch 79 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 79 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 79 Training:  58%|█████▊    | 56/96 [01:29<01:03,  1.60s/it]
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Epoch 79 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 79 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 79 Training:  59%|█████▉    | 57/96 [01:31<01:02,  1.60s/it]
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Epoch 79 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 79 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 79 Training:  60%|██████    | 58/96 [01:32<01:00,  1.60s/it]
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Epoch 79 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 79 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 79 Training:  61%|██████▏   | 59/96 [01:34<00:59,  1.60s/it]
+
Epoch 79 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 79 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 79 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 79 Training:  62%|██████▎   | 60/96 [01:36<00:57,  1.60s/it]
+
Epoch 79 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 79 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 79 Training:  64%|██████▎   | 61/96 [01:37<00:56,  1.60s/it]
+
Epoch 79 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 79 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 79 Training:  65%|██████▍   | 62/96 [01:39<00:54,  1.60s/it]
+
Epoch 79 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 79 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 79 Training:  66%|██████▌   | 63/96 [01:40<00:52,  1.60s/it]
+
Epoch 79 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 79 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 79 Training:  67%|██████▋   | 64/96 [01:42<00:51,  1.60s/it]
+
Epoch 79 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 79 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 79 Training:  68%|██████▊   | 65/96 [01:44<00:49,  1.60s/it]
+
Epoch 79 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 79 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 79 Training:  69%|██████▉   | 66/96 [01:45<00:48,  1.60s/it]
+
Epoch 79 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 79 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 79 Training:  70%|██████▉   | 67/96 [01:47<00:46,  1.60s/it]
+
Epoch 79 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 79 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 79 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 79 Training:  71%|███████   | 68/96 [01:48<00:44,  1.60s/it]
+
Epoch 79 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 79 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 79 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 79 Training:  72%|███████▏  | 69/96 [01:50<00:43,  1.60s/it]
+
Epoch 79 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 79 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 79 Training:  73%|███████▎  | 70/96 [01:52<00:41,  1.60s/it]
+
Epoch 79 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 79 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 79 Training:  74%|███████▍  | 71/96 [01:53<00:40,  1.60s/it]
+
Epoch 79 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 79 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 79 Training:  75%|███████▌  | 72/96 [01:55<00:38,  1.60s/it]
+
Epoch 79 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 79 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 79 Training:  76%|███████▌  | 73/96 [01:56<00:36,  1.60s/it]
+
Epoch 79 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 79 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 79 Training:  77%|███████▋  | 74/96 [01:58<00:35,  1.60s/it]
+
Epoch 79 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 79 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 79 Training:  78%|███████▊  | 75/96 [02:00<00:33,  1.60s/it]
+
Epoch 79 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 79 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 79 Training:  79%|███████▉  | 76/96 [02:01<00:31,  1.60s/it]
+
Epoch 79 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 79 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 79 Training:  80%|████████  | 77/96 [02:03<00:30,  1.60s/it]
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Epoch 79 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 79 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 79 Training:  81%|████████▏ | 78/96 [02:04<00:28,  1.60s/it]
+
Epoch 79 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 79 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 79 Training:  82%|████████▏ | 79/96 [02:06<00:27,  1.60s/it]
+
Epoch 79 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 79 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 79 Training:  83%|████████▎ | 80/96 [02:08<00:25,  1.60s/it]
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Epoch 79 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 79 Training:  84%|████████▍ | 81/96 [02:09<00:23,  1.60s/it]
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Epoch 79 Training:  85%|████████▌ | 82/96 [02:11<00:22,  1.60s/it]
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Epoch 79 Training:  86%|████████▋ | 83/96 [02:12<00:20,  1.60s/it]
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Epoch 79 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 79 Training:  88%|████████▊ | 84/96 [02:14<00:19,  1.60s/it]
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Epoch 79 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 79 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 79 Training:  89%|████████▊ | 85/96 [02:16<00:17,  1.60s/it]
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Epoch 79 Training:  90%|████████▉ | 86/96 [02:17<00:16,  1.60s/it]
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Epoch 79 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 79 Training:  91%|█████████ | 87/96 [02:19<00:14,  1.60s/it]
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Epoch 79 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 79 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 79 Training:  92%|█████████▏| 88/96 [02:20<00:12,  1.60s/it]
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Epoch 79 Training:  93%|█████████▎| 89/96 [02:22<00:11,  1.60s/it]
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Epoch 79 Training:  94%|█████████▍| 90/96 [02:24<00:09,  1.60s/it]
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Epoch 79 Training:  95%|█████████▍| 91/96 [02:25<00:08,  1.60s/it]
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Epoch 79 Training:  96%|█████████▌| 92/96 [02:27<00:06,  1.60s/it]
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+[2026-10-01 20:41:18] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0080 | Epoch 079] timing/rollout_seconds=180.1180 timing/feedback_seconds=5.2029 timing/optimization_seconds=154.3974 timing/cycle_seconds=337.3619
+[2026-10-01 20:41:18] [Rank 0] [INFO] [flow_factory.models.abc]: Saving LoRA weights for transformer role=base to /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/runs/klein4b_nft_flir_full_ssim_dists/saves/klein4b_nft_flir_full_ssim_dists/checkpoints/checkpoint-80
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+[2026-10-01 20:42:11] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0080 | Epoch 080] eval/flir_rl_full/reward_dists_mean=0.8286 eval/flir_rl_full/reward_dists_std=0.0418 eval/flir_rl_full/reward_psnr_mean=19.4237 eval/flir_rl_full/reward_psnr_std=2.5589 eval/flir_rl_full/reward_ssim_mean=0.6194 eval/flir_rl_full/reward_ssim_std=0.0851
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+[2026-10-01 20:45:14] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0080 | Epoch 080] train/reward_dists_mean=0.8490 train/reward_dists_std=0.0350 train/reward_ssim_mean=0.6636 train/reward_ssim_std=0.1053 train/reward_dists_group_std_mean=0.0077 train/reward_dists_group_std_max=0.0172 train/reward_dists_group_std_min=0.0038 train/reward_dists_group_mean_std=0.0340 train/reward_ssim_group_std_mean=0.0075 train/reward_ssim_group_std_max=0.0177 train/reward_ssim_group_std_min=0.0031 train/reward_ssim_group_mean_std=0.1050 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-6.2678 train/adv_max=4.5619 train/adv_abs_mean=1.2599
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Epoch 80 Training:  11%|█▏        | 11/96 [00:17<02:14,  1.58s/it]
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Epoch 80 Training:  12%|█▎        | 12/96 [00:18<02:12,  1.58s/it]
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Epoch 80 Training:  14%|█▎        | 13/96 [00:20<02:10,  1.57s/it]
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Epoch 80 Training:  15%|█▍        | 14/96 [00:21<02:09,  1.58s/it]
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Epoch 80 Training:  16%|█▌        | 15/96 [00:23<02:07,  1.57s/it]
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Epoch 80 Training:  17%|█▋        | 16/96 [00:25<02:06,  1.58s/it]
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Epoch 80 Training:  18%|█▊        | 17/96 [00:26<02:04,  1.58s/it]
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Epoch 80 Training:  19%|█▉        | 18/96 [00:28<02:03,  1.58s/it]
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Epoch 80 Training:  20%|█▉        | 19/96 [00:29<02:01,  1.58s/it]
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Epoch 80 Training:  21%|██        | 20/96 [00:31<02:00,  1.58s/it]
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Epoch 80 Training:  22%|██▏       | 21/96 [00:33<01:59,  1.59s/it]
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Epoch 80 Training:  23%|██▎       | 22/96 [00:34<01:57,  1.59s/it]
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Epoch 80 Training:  24%|██▍       | 23/96 [00:36<01:55,  1.59s/it]
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Epoch 80 Training:  25%|██▌       | 24/96 [00:37<01:54,  1.59s/it]
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Epoch 80 Training:  26%|██▌       | 25/96 [00:39<01:52,  1.58s/it]
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Epoch 80 Training:  27%|██▋       | 26/96 [00:40<01:50,  1.58s/it]
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Epoch 80 Training:  28%|██▊       | 27/96 [00:42<01:49,  1.58s/it]
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Epoch 80 Training:  29%|██▉       | 28/96 [00:44<01:47,  1.58s/it]
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Epoch 80 Training:  30%|███       | 29/96 [00:45<01:46,  1.58s/it]
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Epoch 80 Training:  31%|███▏      | 30/96 [00:47<01:44,  1.58s/it]
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Epoch 80 Training:  32%|███▏      | 31/96 [00:48<01:42,  1.58s/it]
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Epoch 80 Training:  33%|███▎      | 32/96 [00:50<01:41,  1.58s/it]
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Epoch 80 Training:  34%|███▍      | 33/96 [00:52<01:39,  1.58s/it]
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Epoch 80 Training:  35%|███▌      | 34/96 [00:53<01:38,  1.58s/it]
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Epoch 80 Training:  36%|███▋      | 35/96 [00:55<01:36,  1.58s/it]
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Epoch 80 Training:  38%|███▊      | 36/96 [00:56<01:34,  1.58s/it]
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Epoch 80 Training:  39%|███▊      | 37/96 [00:58<01:32,  1.58s/it]
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Epoch 80 Training:  40%|███▉      | 38/96 [00:59<01:31,  1.57s/it]
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Epoch 80 Training:  41%|████      | 39/96 [01:01<01:29,  1.57s/it]
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Epoch 80 Training:  42%|████▏     | 40/96 [01:03<01:27,  1.57s/it]
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Epoch 80 Training:  43%|████▎     | 41/96 [01:04<01:26,  1.57s/it]
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Epoch 80 Training:  44%|████▍     | 42/96 [01:06<01:24,  1.57s/it]
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Epoch 80 Training:  45%|████▍     | 43/96 [01:07<01:23,  1.57s/it]
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Epoch 80 Training:  46%|████▌     | 44/96 [01:09<01:21,  1.58s/it]
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Epoch 80 Training:  47%|████▋     | 45/96 [01:10<01:20,  1.58s/it]
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Epoch 80 Training:  48%|████▊     | 46/96 [01:12<01:18,  1.58s/it]
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Epoch 80 Training:  49%|████▉     | 47/96 [01:14<01:17,  1.58s/it]
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Epoch 80 Training:  50%|█████     | 48/96 [01:15<01:15,  1.58s/it]
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Epoch 80 Training:  51%|█████     | 49/96 [01:17<01:14,  1.58s/it]
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Epoch 80 Training:  52%|█████▏    | 50/96 [01:18<01:12,  1.58s/it]
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Epoch 80 Training:  53%|█████▎    | 51/96 [01:20<01:10,  1.58s/it]
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Epoch 80 Training:  54%|█████▍    | 52/96 [01:22<01:09,  1.58s/it]
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Epoch 80 Training:  55%|█████▌    | 53/96 [01:23<01:07,  1.58s/it]
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Epoch 80 Training:  56%|█████▋    | 54/96 [01:25<01:06,  1.58s/it]
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Epoch 80 Training:  57%|█████▋    | 55/96 [01:26<01:04,  1.58s/it]
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Epoch 80 Training:  58%|█████▊    | 56/96 [01:28<01:03,  1.58s/it]
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Epoch 80 Training:  59%|█████▉    | 57/96 [01:29<01:01,  1.58s/it]
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Epoch 80 Training:  60%|██████    | 58/96 [01:31<00:59,  1.58s/it]
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Epoch 80 Training:  61%|██████▏   | 59/96 [01:33<00:58,  1.58s/it]
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Epoch 80 Training:  62%|██████▎   | 60/96 [01:34<00:56,  1.58s/it]
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Epoch 80 Training:  64%|██████▎   | 61/96 [01:36<00:55,  1.58s/it]
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Epoch 80 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s][2026-10-01 20:47:46] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0080 | Epoch 080] train/policy_loss=24.7415 train/unweighted_policy_loss=4.9483 train/loss=24.7415 train/grad_norm=0.0916
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+[2026-10-01 20:47:46] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0081 | Epoch 080] timing/rollout_seconds=180.2312 timing/feedback_seconds=5.4720 timing/optimization_seconds=152.4323 timing/cycle_seconds=335.5984
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+
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+[2026-10-01 20:50:50] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0081 | Epoch 081] train/reward_dists_mean=0.8518 train/reward_dists_std=0.0355 train/reward_ssim_mean=0.6662 train/reward_ssim_std=0.1227 train/reward_dists_group_std_mean=0.0076 train/reward_dists_group_std_max=0.0183 train/reward_dists_group_std_min=0.0034 train/reward_dists_group_mean_std=0.0346 train/reward_ssim_group_std_mean=0.0083 train/reward_ssim_group_std_max=0.0186 train/reward_ssim_group_std_min=0.0028 train/reward_ssim_group_mean_std=0.1223 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.5364 train/adv_max=4.5394 train/adv_abs_mean=1.1902
+
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Epoch 81 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.90it/s]
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Epoch 81 Training:  40%|███▉      | 38/96 [01:00<01:32,  1.59s/it]
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Epoch 81 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 81 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.91it/s]
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Epoch 81 Training:  41%|████      | 39/96 [01:01<01:30,  1.59s/it]
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Epoch 81 Training:  42%|████▏     | 40/96 [01:03<01:28,  1.59s/it]
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Epoch 81 Training:  43%|████▎     | 41/96 [01:04<01:27,  1.59s/it]
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Epoch 81 Training:  44%|████▍     | 42/96 [01:06<01:25,  1.59s/it]
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Epoch 81 Training:  45%|████▍     | 43/96 [01:07<01:23,  1.58s/it]
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Epoch 81 Training:  46%|████▌     | 44/96 [01:09<01:22,  1.58s/it]
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Epoch 81 Training:  47%|████▋     | 45/96 [01:11<01:20,  1.58s/it]
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Epoch 81 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.91it/s]
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Epoch 81 Training:  48%|████▊     | 46/96 [01:12<01:19,  1.58s/it]
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Epoch 81 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.88it/s]
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Epoch 81 Training:  49%|████▉     | 47/96 [01:14<01:17,  1.58s/it]
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Epoch 81 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.89it/s]
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Epoch 81 Training:  50%|█████     | 48/96 [01:15<01:16,  1.59s/it]
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Epoch 81 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.91it/s]
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Epoch 81 Training:  51%|█████     | 49/96 [01:17<01:14,  1.59s/it]
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Epoch 81 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.89it/s]
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Epoch 81 Training:  52%|█████▏    | 50/96 [01:19<01:13,  1.59s/it]
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Epoch 81 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 81 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.90it/s]
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Epoch 81 Training:  53%|█████▎    | 51/96 [01:20<01:11,  1.59s/it]
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Epoch 81 Training:  54%|█████▍    | 52/96 [01:22<01:09,  1.59s/it]
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Epoch 81 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 81 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.89it/s]
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Epoch 81 Training:  55%|█████▌    | 53/96 [01:23<01:08,  1.59s/it]
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Epoch 81 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 81 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.89it/s]
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Epoch 81 Training:  56%|█████▋    | 54/96 [01:25<01:06,  1.59s/it]
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Epoch 81 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.88it/s]
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Epoch 81 Training:  57%|█████▋    | 55/96 [01:27<01:05,  1.59s/it]
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Epoch 81 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 81 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.87it/s]
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Epoch 81 Training:  58%|█████▊    | 56/96 [01:28<01:03,  1.59s/it]
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Epoch 81 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 81 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.89it/s]
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Epoch 81 Training:  59%|█████▉    | 57/96 [01:30<01:02,  1.59s/it]
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Epoch 81 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 81 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 81 Training:  60%|██████    | 58/96 [01:31<01:00,  1.59s/it]
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Epoch 81 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.90it/s]
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Epoch 81 Training:  61%|██████▏   | 59/96 [01:33<00:58,  1.59s/it]
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Epoch 81 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.89it/s]
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Epoch 81 Training:  62%|██████▎   | 60/96 [01:34<00:57,  1.59s/it]
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Epoch 81 Training:  64%|██████▎   | 61/96 [01:36<00:55,  1.59s/it]
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Epoch 81 Training:  65%|██████▍   | 62/96 [01:38<00:54,  1.59s/it]
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Epoch 81 Training:  66%|██████▌   | 63/96 [01:39<00:52,  1.59s/it]
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Epoch 81 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.88it/s]
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Epoch 81 Training:  67%|██████▋   | 64/96 [01:41<00:50,  1.59s/it]
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Epoch 81 Training:  68%|██████▊   | 65/96 [01:42<00:49,  1.59s/it]
+
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Epoch 81 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.89it/s]
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Epoch 81 Training:  69%|██████▉   | 66/96 [01:44<00:47,  1.59s/it]
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Epoch 81 Training:  70%|██████▉   | 67/96 [01:46<00:46,  1.59s/it]
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Epoch 81 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.90it/s]
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Epoch 81 Training:  71%|███████   | 68/96 [01:47<00:44,  1.59s/it]
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Epoch 81 Training:  72%|███████▏  | 69/96 [01:49<00:42,  1.59s/it]
+
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Epoch 81 Training:  73%|███████▎  | 70/96 [01:50<00:41,  1.59s/it]
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Epoch 81 Training:  74%|███████▍  | 71/96 [01:52<00:39,  1.59s/it]
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Epoch 81 Training:  75%|███████▌  | 72/96 [01:54<00:38,  1.59s/it]
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Epoch 81 Training:  76%|███████▌  | 73/96 [01:55<00:36,  1.59s/it]
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Epoch 81 Training:  77%|███████▋  | 74/96 [01:57<00:34,  1.59s/it]
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Epoch 81 Training:  78%|███████▊  | 75/96 [01:58<00:33,  1.59s/it]
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Epoch 81 Training:  79%|███████▉  | 76/96 [02:00<00:31,  1.59s/it]
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Epoch 81 Training:  80%|████████  | 77/96 [02:01<00:30,  1.59s/it]
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Epoch 81 Training:  81%|████████▏ | 78/96 [02:03<00:28,  1.59s/it]
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Epoch 81 Training:  82%|████████▏ | 79/96 [02:05<00:27,  1.59s/it]
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Epoch 81 Training:  83%|████████▎ | 80/96 [02:06<00:25,  1.59s/it]
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Epoch 81 Training:  84%|████████▍ | 81/96 [02:08<00:23,  1.59s/it]
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Epoch 81 Training:  85%|████████▌ | 82/96 [02:09<00:22,  1.59s/it]
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Epoch 81 Training:  86%|████████▋ | 83/96 [02:11<00:20,  1.59s/it]
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Epoch 81 Training:  88%|████████▊ | 84/96 [02:13<00:19,  1.59s/it]
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Epoch 81 Training:  89%|████████▊ | 85/96 [02:14<00:17,  1.59s/it]
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Epoch 81 Training:  90%|████████▉ | 86/96 [02:16<00:15,  1.59s/it]
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Epoch 81 Training:  91%|█████████ | 87/96 [02:17<00:14,  1.58s/it]
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Epoch 81 Training:  92%|█████████▏| 88/96 [02:19<00:12,  1.59s/it]
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Epoch 81 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s][2026-10-01 20:53:22] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0081 | Epoch 081] train/policy_loss=24.6945 train/unweighted_policy_loss=4.9389 train/loss=24.6945 train/grad_norm=0.0696
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+[2026-10-01 20:53:22] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0082 | Epoch 081] timing/rollout_seconds=180.0543 timing/feedback_seconds=5.4971 timing/optimization_seconds=152.7669 timing/cycle_seconds=335.7961
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+[2026-10-01 20:56:26] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0082 | Epoch 082] train/reward_dists_mean=0.8436 train/reward_dists_std=0.0334 train/reward_ssim_mean=0.6474 train/reward_ssim_std=0.1066 train/reward_dists_group_std_mean=0.0084 train/reward_dists_group_std_max=0.0227 train/reward_dists_group_std_min=0.0035 train/reward_dists_group_mean_std=0.0321 train/reward_ssim_group_std_mean=0.0084 train/reward_ssim_group_std_max=0.0203 train/reward_ssim_group_std_min=0.0039 train/reward_ssim_group_mean_std=0.1062 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-6.0511 train/adv_max=6.6140 train/adv_abs_mean=1.2266
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Epoch 82 Training:  15%|█▍        | 14/96 [00:21<02:08,  1.57s/it]
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Epoch 82 Training:  16%|█▌        | 15/96 [00:23<02:06,  1.57s/it]
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Epoch 82 Training:  17%|█▋        | 16/96 [00:25<02:05,  1.57s/it]
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Epoch 82 Training:  18%|█▊        | 17/96 [00:26<02:03,  1.57s/it]
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Epoch 82 Training:  19%|█▉        | 18/96 [00:28<02:02,  1.57s/it]
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Epoch 82 Training:  20%|█▉        | 19/96 [00:29<02:01,  1.58s/it]
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Epoch 82 Training:  21%|██        | 20/96 [00:31<01:59,  1.58s/it]
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Epoch 82 Training:  22%|██▏       | 21/96 [00:32<01:57,  1.57s/it]
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Epoch 82 Training:  23%|██▎       | 22/96 [00:34<01:56,  1.57s/it]
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Epoch 82 Training:  24%|██▍       | 23/96 [00:36<01:54,  1.57s/it]
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Epoch 82 Training:  25%|██▌       | 24/96 [00:37<01:52,  1.57s/it]
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Epoch 82 Training:  26%|██▌       | 25/96 [00:39<01:51,  1.57s/it]
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Epoch 82 Training:  27%|██▋       | 26/96 [00:40<01:49,  1.57s/it]
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Epoch 82 Training:  28%|██▊       | 27/96 [00:42<01:48,  1.57s/it]
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Epoch 82 Training:  29%|██▉       | 28/96 [00:43<01:46,  1.57s/it]
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Epoch 82 Training:  30%|███       | 29/96 [00:45<01:45,  1.57s/it]
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Epoch 82 Training:  31%|███▏      | 30/96 [00:47<01:43,  1.57s/it]
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Epoch 82 Training:  32%|███▏      | 31/96 [00:48<01:41,  1.57s/it]
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Epoch 82 Training:  33%|███▎      | 32/96 [00:50<01:40,  1.57s/it]
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Epoch 82 Training:  34%|███▍      | 33/96 [00:51<01:38,  1.57s/it]
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Epoch 82 Training:  35%|███▌      | 34/96 [00:53<01:37,  1.57s/it]
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Epoch 82 Training:  36%|███▋      | 35/96 [00:54<01:35,  1.57s/it]
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Epoch 82 Training:  38%|███▊      | 36/96 [00:56<01:34,  1.58s/it]
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Epoch 82 Training:  39%|███▊      | 37/96 [00:58<01:32,  1.57s/it]
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Epoch 82 Training:  40%|███▉      | 38/96 [00:59<01:30,  1.57s/it]
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Epoch 82 Training:  41%|████      | 39/96 [01:01<01:29,  1.57s/it]
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Epoch 82 Training:  42%|████▏     | 40/96 [01:02<01:27,  1.57s/it]
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Epoch 82 Training:  43%|████▎     | 41/96 [01:04<01:26,  1.57s/it]
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Epoch 82 Training:  44%|████▍     | 42/96 [01:05<01:24,  1.57s/it]
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Epoch 82 Training:  45%|████▍     | 43/96 [01:07<01:23,  1.57s/it]
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Epoch 82 Training:  46%|████▌     | 44/96 [01:09<01:21,  1.57s/it]
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Epoch 82 Training:  47%|████▋     | 45/96 [01:10<01:19,  1.57s/it]
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Epoch 82 Training:  48%|████▊     | 46/96 [01:12<01:18,  1.57s/it]
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Epoch 82 Training:  49%|████▉     | 47/96 [01:13<01:16,  1.57s/it]
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Epoch 82 Training:  50%|█████     | 48/96 [01:15<01:15,  1.57s/it]
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Epoch 82 Training:  51%|█████     | 49/96 [01:16<01:13,  1.57s/it]
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Epoch 82 Training:  52%|█████▏    | 50/96 [01:18<01:12,  1.57s/it]
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Epoch 82 Training:  53%|█████▎    | 51/96 [01:20<01:10,  1.57s/it]
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Epoch 82 Training:  54%|█████▍    | 52/96 [01:21<01:09,  1.57s/it]
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Epoch 82 Training:  55%|█████▌    | 53/96 [01:23<01:07,  1.57s/it]
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Epoch 82 Training:  56%|█████▋    | 54/96 [01:24<01:05,  1.57s/it]
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Epoch 82 Training:  57%|█████▋    | 55/96 [01:26<01:04,  1.57s/it]
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Epoch 82 Training:  58%|█████▊    | 56/96 [01:27<01:02,  1.57s/it]
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Epoch 82 Training:  59%|█████▉    | 57/96 [01:29<01:01,  1.57s/it]
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Epoch 82 Training:  61%|██████▏   | 59/96 [01:32<00:58,  1.57s/it]
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Epoch 82 Training:  62%|██████▎   | 60/96 [01:34<00:56,  1.57s/it]
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Epoch 82 Training:  64%|██████▎   | 61/96 [01:35<00:54,  1.57s/it]
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Epoch 82 Training:  65%|██████▍   | 62/96 [01:37<00:53,  1.57s/it]
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Epoch 82 Training:  66%|██████▌   | 63/96 [01:38<00:51,  1.57s/it]
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Epoch 82 Training:  67%|██████▋   | 64/96 [01:40<00:50,  1.57s/it]
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Epoch 82 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s][2026-10-01 20:58:56] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0082 | Epoch 082] train/policy_loss=25.3233 train/unweighted_policy_loss=5.0647 train/loss=25.3233 train/grad_norm=0.0700
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+[2026-10-01 20:58:56] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0083 | Epoch 082] timing/rollout_seconds=179.9934 timing/feedback_seconds=5.1893 timing/optimization_seconds=151.3861 timing/cycle_seconds=334.2789
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+
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Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.25it/s]
+[2026-10-01 21:01:59] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0083 | Epoch 083] train/reward_dists_mean=0.8550 train/reward_dists_std=0.0283 train/reward_ssim_mean=0.6568 train/reward_ssim_std=0.0787 train/reward_dists_group_std_mean=0.0079 train/reward_dists_group_std_max=0.0231 train/reward_dists_group_std_min=0.0037 train/reward_dists_group_mean_std=0.0268 train/reward_ssim_group_std_mean=0.0086 train/reward_ssim_group_std_max=0.0215 train/reward_ssim_group_std_min=0.0029 train/reward_ssim_group_mean_std=0.0781 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.7430 train/adv_max=4.0447 train/adv_abs_mean=1.1009
+
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Epoch 83 Training:  43%|████▎     | 41/96 [01:04<01:26,  1.57s/it]
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Epoch 83 Training:  44%|████▍     | 42/96 [01:06<01:24,  1.57s/it]
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Epoch 83 Training:  45%|████▍     | 43/96 [01:07<01:23,  1.57s/it]
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Epoch 83 Training:  46%|████▌     | 44/96 [01:09<01:21,  1.57s/it]
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Epoch 83 Training:  47%|████▋     | 45/96 [01:10<01:20,  1.57s/it]
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Epoch 83 Training:  48%|████▊     | 46/96 [01:12<01:18,  1.57s/it]
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Epoch 83 Training:  49%|████▉     | 47/96 [01:13<01:17,  1.57s/it]
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Epoch 83 Training:  50%|█████     | 48/96 [01:15<01:15,  1.58s/it]
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Epoch 83 Training:  51%|█████     | 49/96 [01:17<01:14,  1.58s/it]
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Epoch 83 Training:  52%|█████▏    | 50/96 [01:18<01:12,  1.57s/it]
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Epoch 83 Training:  53%|█████▎    | 51/96 [01:20<01:10,  1.57s/it]
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Epoch 83 Training:  54%|█████▍    | 52/96 [01:21<01:09,  1.58s/it]
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Epoch 83 Training:  55%|█████▌    | 53/96 [01:23<01:07,  1.58s/it]
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Epoch 83 Training:  56%|█████▋    | 54/96 [01:24<01:06,  1.58s/it]
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Epoch 83 Training:  57%|█████▋    | 55/96 [01:26<01:04,  1.57s/it]
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Epoch 83 Training:  58%|█████▊    | 56/96 [01:28<01:02,  1.57s/it]
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Epoch 83 Training:  59%|█████▉    | 57/96 [01:29<01:01,  1.57s/it]
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Epoch 83 Training:  60%|██████    | 58/96 [01:31<00:59,  1.58s/it]
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Epoch 83 Training:  61%|██████▏   | 59/96 [01:32<00:58,  1.58s/it]
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Epoch 83 Training:  62%|██████▎   | 60/96 [01:34<00:56,  1.58s/it]
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Epoch 83 Training:  64%|██████▎   | 61/96 [01:35<00:55,  1.58s/it]
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Epoch 83 Training:  65%|██████▍   | 62/96 [01:37<00:53,  1.58s/it]
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Epoch 83 Training:  66%|██████▌   | 63/96 [01:39<00:52,  1.58s/it]
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Epoch 83 Training:  67%|██████▋   | 64/96 [01:40<00:50,  1.58s/it]
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Epoch 83 Training:  68%|██████▊   | 65/96 [01:42<00:48,  1.58s/it]
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Epoch 83 Training:  69%|██████▉   | 66/96 [01:43<00:47,  1.58s/it]
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Epoch 83 Training:  70%|██████▉   | 67/96 [01:45<00:45,  1.58s/it]
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Epoch 83 Training:  71%|███████   | 68/96 [01:46<00:44,  1.58s/it]
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Epoch 83 Training:  72%|███████▏  | 69/96 [01:48<00:42,  1.58s/it]
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Epoch 83 Training:  73%|███████▎  | 70/96 [01:50<00:40,  1.58s/it]
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Epoch 83 Training:  74%|███████▍  | 71/96 [01:51<00:39,  1.57s/it]
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Epoch 83 Training:  75%|███████▌  | 72/96 [01:53<00:37,  1.57s/it]
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Epoch 83 Training:  76%|███████▌  | 73/96 [01:54<00:36,  1.57s/it]
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Epoch 83 Training:  77%|███████▋  | 74/96 [01:56<00:34,  1.58s/it]
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Epoch 83 Training:  78%|███████▊  | 75/96 [01:58<00:33,  1.57s/it]
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Epoch 83 Training:  79%|███████▉  | 76/96 [01:59<00:31,  1.57s/it]
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Epoch 83 Training:  80%|████████  | 77/96 [02:01<00:29,  1.58s/it]
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Epoch 83 Training:  81%|████████▏ | 78/96 [02:02<00:28,  1.58s/it]
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Epoch 83 Training:  82%|████████▏ | 79/96 [02:04<00:26,  1.58s/it]
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Epoch 83 Training:  83%|████████▎ | 80/96 [02:05<00:25,  1.58s/it]
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Epoch 83 Training:  84%|████████▍ | 81/96 [02:07<00:23,  1.58s/it]
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Epoch 83 Training:  85%|████████▌ | 82/96 [02:09<00:22,  1.58s/it]
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Epoch 83 Training:  86%|████████▋ | 83/96 [02:10<00:20,  1.58s/it]
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Epoch 83 Training:  88%|████████▊ | 84/96 [02:12<00:18,  1.58s/it]
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Epoch 83 Training:  89%|████████▊ | 85/96 [02:13<00:17,  1.58s/it]
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Epoch 83 Training:  90%|████████▉ | 86/96 [02:15<00:15,  1.58s/it]
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Epoch 83 Training:  91%|█████████ | 87/96 [02:16<00:14,  1.58s/it]
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Epoch 83 Training:  92%|█████████▏| 88/96 [02:18<00:12,  1.58s/it]
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Epoch 83 Training:  93%|█████████▎| 89/96 [02:20<00:11,  1.57s/it]
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Epoch 83 Training:  94%|█████████▍| 90/96 [02:21<00:09,  1.57s/it]
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Epoch 83 Training:  95%|█████████▍| 91/96 [02:23<00:07,  1.58s/it]
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Epoch 83 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s][2026-10-01 21:04:30] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0083 | Epoch 083] train/policy_loss=25.6102 train/unweighted_policy_loss=5.1220 train/loss=25.6102 train/grad_norm=0.0691
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+[2026-10-01 21:04:31] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0084 | Epoch 083] timing/rollout_seconds=179.4520 timing/feedback_seconds=5.0816 timing/optimization_seconds=151.7911 timing/cycle_seconds=334.1452
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+
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+[2026-10-01 21:07:34] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0084 | Epoch 084] train/reward_dists_mean=0.8598 train/reward_dists_std=0.0272 train/reward_ssim_mean=0.6497 train/reward_ssim_std=0.1014 train/reward_dists_group_std_mean=0.0066 train/reward_dists_group_std_max=0.0186 train/reward_dists_group_std_min=0.0031 train/reward_dists_group_mean_std=0.0262 train/reward_ssim_group_std_mean=0.0094 train/reward_ssim_group_std_max=0.0253 train/reward_ssim_group_std_min=0.0034 train/reward_ssim_group_mean_std=0.1009 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.7088 train/adv_max=4.0812 train/adv_abs_mean=1.2446
+
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Epoch 84 Training:  18%|█▊        | 17/96 [00:26<02:03,  1.56s/it]
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Epoch 84 Training:  19%|█▉        | 18/96 [00:28<02:01,  1.56s/it]
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Epoch 84 Training:  20%|█▉        | 19/96 [00:29<02:00,  1.56s/it]
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Epoch 84 Training:  21%|██        | 20/96 [00:31<01:58,  1.56s/it]
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Epoch 84 Training:  22%|██▏       | 21/96 [00:32<01:56,  1.56s/it]
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Epoch 84 Training:  23%|██▎       | 22/96 [00:34<01:55,  1.56s/it]
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Epoch 84 Training:  24%|██▍       | 23/96 [00:35<01:54,  1.57s/it]
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Epoch 84 Training:  25%|██▌       | 24/96 [00:37<01:52,  1.56s/it]
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Epoch 84 Training:  26%|██▌       | 25/96 [00:39<01:51,  1.57s/it]
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Epoch 84 Training:  27%|██▋       | 26/96 [00:40<01:49,  1.57s/it]
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Epoch 84 Training:  28%|██▊       | 27/96 [00:42<01:48,  1.57s/it]
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Epoch 84 Training:  29%|██▉       | 28/96 [00:43<01:46,  1.57s/it]
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Epoch 84 Training:  30%|███       | 29/96 [00:45<01:45,  1.57s/it]
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Epoch 84 Training:  31%|███▏      | 30/96 [00:46<01:43,  1.56s/it]
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Epoch 84 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 84 Training:  32%|███▏      | 31/96 [00:48<01:41,  1.57s/it]
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Epoch 84 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 84 Training:  33%|███▎      | 32/96 [00:50<01:40,  1.56s/it]
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Epoch 84 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 84 Training:  34%|███▍      | 33/96 [00:51<01:38,  1.57s/it]
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Epoch 84 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 84 Training:  35%|███▌      | 34/96 [00:53<01:37,  1.57s/it]
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Epoch 84 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.96it/s]
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Epoch 84 Training:  36%|███▋      | 35/96 [00:54<01:36,  1.58s/it]
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Epoch 84 Training:  38%|███▊      | 36/96 [00:56<01:34,  1.57s/it]
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Epoch 84 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 84 Training:  39%|███▊      | 37/96 [00:57<01:32,  1.57s/it]
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Epoch 84 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
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Epoch 84 Training:  40%|███▉      | 38/96 [00:59<01:31,  1.57s/it]
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Epoch 84 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.95it/s]
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Epoch 84 Training:  41%|████      | 39/96 [01:01<01:29,  1.58s/it]
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Epoch 84 Training:  42%|████▏     | 40/96 [01:02<01:27,  1.57s/it]
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Epoch 84 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 84 Training:  43%|████▎     | 41/96 [01:04<01:26,  1.57s/it]
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Epoch 84 Training:  44%|████▍     | 42/96 [01:05<01:24,  1.57s/it]
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Epoch 84 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 84 Training:  45%|████▍     | 43/96 [01:07<01:22,  1.56s/it]
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Epoch 84 Training:  46%|████▌     | 44/96 [01:08<01:21,  1.57s/it]
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Epoch 84 Training:  47%|████▋     | 45/96 [01:10<01:19,  1.56s/it]
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Epoch 84 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 84 Training:  48%|████▊     | 46/96 [01:12<01:18,  1.57s/it]
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Epoch 84 Training:  49%|████▉     | 47/96 [01:13<01:16,  1.57s/it]
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Epoch 84 Training:  50%|█████     | 48/96 [01:15<01:15,  1.57s/it]
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Epoch 84 Training:  51%|█████     | 49/96 [01:16<01:13,  1.57s/it]
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Epoch 84 Training:  52%|█████▏    | 50/96 [01:18<01:11,  1.57s/it]
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Epoch 84 Training:  53%|█████▎    | 51/96 [01:19<01:10,  1.56s/it]
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Epoch 84 Training:  54%|█████▍    | 52/96 [01:21<01:08,  1.56s/it]
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Epoch 84 Training:  55%|█████▌    | 53/96 [01:22<01:07,  1.57s/it]
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Epoch 84 Training:  56%|█████▋    | 54/96 [01:24<01:05,  1.56s/it]
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Epoch 84 Training:  57%|█████▋    | 55/96 [01:26<01:04,  1.57s/it]
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Epoch 84 Training:  58%|█████▊    | 56/96 [01:27<01:02,  1.56s/it]
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Epoch 84 Training:  59%|█████▉    | 57/96 [01:29<01:00,  1.56s/it]
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Epoch 84 Training:  60%|██████    | 58/96 [01:30<00:59,  1.57s/it]
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Epoch 84 Training:  61%|██████▏   | 59/96 [01:32<00:57,  1.56s/it]
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Epoch 84 Training:  62%|██████▎   | 60/96 [01:33<00:56,  1.57s/it]
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Epoch 84 Training:  64%|██████▎   | 61/96 [01:35<00:54,  1.57s/it]
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Epoch 84 Training:  65%|██████▍   | 62/96 [01:37<00:53,  1.57s/it]
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Epoch 84 Training:  66%|██████▌   | 63/96 [01:38<00:51,  1.57s/it]
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Epoch 84 Training:  67%|██████▋   | 64/96 [01:40<00:50,  1.57s/it]
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Epoch 84 Training:  68%|██████▊   | 65/96 [01:41<00:48,  1.57s/it]
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Epoch 84 Training:  69%|██████▉   | 66/96 [01:43<00:46,  1.56s/it]
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Epoch 84 Training:  70%|██████▉   | 67/96 [01:44<00:45,  1.56s/it]
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Epoch 84 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s][2026-10-01 21:10:05] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0084 | Epoch 084] train/policy_loss=25.4707 train/unweighted_policy_loss=5.0941 train/loss=25.4707 train/grad_norm=0.0708
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+[2026-10-01 21:10:05] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0085 | Epoch 084] timing/rollout_seconds=179.9689 timing/feedback_seconds=5.7955 timing/optimization_seconds=151.1883 timing/cycle_seconds=334.1870
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+
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Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.17it/s]
+[2026-10-01 21:13:08] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0085 | Epoch 085] train/reward_dists_mean=0.8639 train/reward_dists_std=0.0254 train/reward_ssim_mean=0.6507 train/reward_ssim_std=0.0966 train/reward_dists_group_std_mean=0.0059 train/reward_dists_group_std_max=0.0113 train/reward_dists_group_std_min=0.0027 train/reward_dists_group_mean_std=0.0246 train/reward_ssim_group_std_mean=0.0088 train/reward_ssim_group_std_max=0.0224 train/reward_ssim_group_std_min=0.0030 train/reward_ssim_group_mean_std=0.0961 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.6229 train/adv_max=4.5774 train/adv_abs_mean=1.1961
+
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Epoch 85 Training:  46%|████▌     | 44/96 [01:08<01:20,  1.56s/it]
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Epoch 85 Training:  47%|████▋     | 45/96 [01:10<01:19,  1.56s/it]
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Epoch 85 Training:  48%|████▊     | 46/96 [01:12<01:18,  1.56s/it]
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Epoch 85 Training:  49%|████▉     | 47/96 [01:13<01:16,  1.56s/it]
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Epoch 85 Training:  50%|█████     | 48/96 [01:15<01:14,  1.56s/it]
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Epoch 85 Training:  51%|█████     | 49/96 [01:16<01:13,  1.56s/it]
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Epoch 85 Training:  52%|█████▏    | 50/96 [01:18<01:11,  1.56s/it]
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Epoch 85 Training:  53%|█████▎    | 51/96 [01:19<01:10,  1.56s/it]
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Epoch 85 Training:  54%|█████▍    | 52/96 [01:21<01:08,  1.56s/it]
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Epoch 85 Training:  55%|█████▌    | 53/96 [01:23<01:06,  1.56s/it]
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Epoch 85 Training:  56%|█████▋    | 54/96 [01:24<01:05,  1.56s/it]
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Epoch 85 Training:  57%|█████▋    | 55/96 [01:26<01:03,  1.56s/it]
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Epoch 85 Training:  58%|█████▊    | 56/96 [01:27<01:02,  1.56s/it]
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Epoch 85 Training:  59%|█████▉    | 57/96 [01:29<01:00,  1.56s/it]
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Epoch 85 Training:  60%|██████    | 58/96 [01:30<00:59,  1.56s/it]
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Epoch 85 Training:  61%|██████▏   | 59/96 [01:32<00:57,  1.56s/it]
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Epoch 85 Training:  62%|██████▎   | 60/96 [01:33<00:56,  1.56s/it]
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Epoch 85 Training:  64%|██████▎   | 61/96 [01:35<00:57,  1.63s/it]
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Epoch 85 Training:  65%|██████▍   | 62/96 [01:37<00:54,  1.62s/it]
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Epoch 85 Training:  66%|██████▌   | 63/96 [01:38<00:52,  1.60s/it]
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Epoch 85 Training:  67%|██████▋   | 64/96 [01:40<00:50,  1.59s/it]
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Epoch 85 Training:  68%|██████▊   | 65/96 [01:42<00:49,  1.58s/it]
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Epoch 85 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 85 Training:  69%|██████▉   | 66/96 [01:43<00:47,  1.58s/it]
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Epoch 85 Training:  70%|██████▉   | 67/96 [01:45<00:45,  1.57s/it]
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Epoch 85 Training:  71%|███████   | 68/96 [01:46<00:43,  1.57s/it]
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Epoch 85 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 85 Training:  72%|███████▏  | 69/96 [01:48<00:42,  1.57s/it]
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Epoch 85 Training:  73%|███████▎  | 70/96 [01:49<00:40,  1.56s/it]
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Epoch 85 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.91it/s]
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Epoch 85 Training:  74%|███████▍  | 71/96 [01:51<00:40,  1.60s/it]
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Epoch 85 Training:  75%|███████▌  | 72/96 [01:53<00:38,  1.59s/it]
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Epoch 85 Training:  76%|███████▌  | 73/96 [01:54<00:36,  1.58s/it]
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Epoch 85 Training:  77%|███████▋  | 74/96 [01:56<00:36,  1.66s/it]
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Epoch 85 Training:  78%|███████▊  | 75/96 [01:58<00:34,  1.63s/it]
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Epoch 85 Training:  79%|███████▉  | 76/96 [01:59<00:32,  1.64s/it]
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Epoch 85 Training:  80%|████████  | 77/96 [02:01<00:30,  1.61s/it]
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Epoch 85 Training:  81%|████████▏ | 78/96 [02:02<00:28,  1.60s/it]
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Epoch 85 Training:  82%|████████▏ | 79/96 [02:04<00:26,  1.59s/it]
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Epoch 85 Training:  83%|████████▎ | 80/96 [02:05<00:25,  1.58s/it]
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Epoch 85 Training:  84%|████████▍ | 81/96 [02:07<00:23,  1.58s/it]
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Epoch 85 Training:  85%|████████▌ | 82/96 [02:09<00:21,  1.57s/it]
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Epoch 85 Training:  86%|████████▋ | 83/96 [02:10<00:20,  1.57s/it]
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Epoch 85 Training:  88%|████████▊ | 84/96 [02:12<00:18,  1.57s/it]
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Epoch 85 Training:  89%|████████▊ | 85/96 [02:13<00:17,  1.56s/it]
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Epoch 85 Training:  90%|████████▉ | 86/96 [02:15<00:15,  1.56s/it]
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Epoch 85 Training:  91%|█████████ | 87/96 [02:16<00:14,  1.56s/it]
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Epoch 85 Training:  92%|█████████▏| 88/96 [02:18<00:12,  1.56s/it]
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Epoch 85 Training:  93%|█████████▎| 89/96 [02:20<00:10,  1.56s/it]
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Epoch 85 Training:  94%|█████████▍| 90/96 [02:21<00:09,  1.56s/it]
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Epoch 85 Training:  95%|█████████▍| 91/96 [02:23<00:07,  1.56s/it]
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Epoch 85 Training:  96%|█████████▌| 92/96 [02:24<00:06,  1.56s/it]
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Epoch 85 Training:  97%|█████████▋| 93/96 [02:26<00:04,  1.56s/it]
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Epoch 85 Training:  98%|█████████▊| 94/96 [02:27<00:03,  1.56s/it]
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Epoch 85 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s][2026-10-01 21:15:39] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0085 | Epoch 085] train/policy_loss=25.2070 train/unweighted_policy_loss=5.0414 train/loss=25.2070 train/grad_norm=0.0619
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+[2026-10-01 21:15:39] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0086 | Epoch 085] timing/rollout_seconds=180.1358 timing/feedback_seconds=5.9182 timing/optimization_seconds=151.6822 timing/cycle_seconds=334.7581
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+[2026-10-01 21:18:43] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0086 | Epoch 086] train/reward_dists_mean=0.8504 train/reward_dists_std=0.0276 train/reward_ssim_mean=0.6510 train/reward_ssim_std=0.1066 train/reward_dists_group_std_mean=0.0079 train/reward_dists_group_std_max=0.0165 train/reward_dists_group_std_min=0.0030 train/reward_dists_group_mean_std=0.0262 train/reward_ssim_group_std_mean=0.0089 train/reward_ssim_group_std_max=0.0229 train/reward_ssim_group_std_min=0.0032 train/reward_ssim_group_mean_std=0.1062 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.9914 train/adv_max=4.0730 train/adv_abs_mean=1.1810
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Epoch 86 Training:  21%|██        | 20/96 [00:31<01:58,  1.56s/it]
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Epoch 86 Training:  22%|██▏       | 21/96 [00:32<01:57,  1.56s/it]
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Epoch 86 Training:  23%|██▎       | 22/96 [00:34<01:56,  1.57s/it]
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Epoch 86 Training:  24%|██▍       | 23/96 [00:35<01:54,  1.56s/it]
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Epoch 86 Training:  25%|██▌       | 24/96 [00:37<01:52,  1.57s/it]
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Epoch 86 Training:  26%|██▌       | 25/96 [00:38<01:51,  1.56s/it]
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Epoch 86 Training:  27%|██▋       | 26/96 [00:40<01:49,  1.57s/it]
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Epoch 86 Training:  28%|██▊       | 27/96 [00:42<01:47,  1.56s/it]
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Epoch 86 Training:  29%|██▉       | 28/96 [00:43<01:46,  1.57s/it]
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Epoch 86 Training:  30%|███       | 29/96 [00:45<01:45,  1.57s/it]
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Epoch 86 Training:  31%|███▏      | 30/96 [00:46<01:43,  1.57s/it]
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Epoch 86 Training:  32%|███▏      | 31/96 [00:48<01:42,  1.57s/it]
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Epoch 86 Training:  33%|███▎      | 32/96 [00:49<01:40,  1.57s/it]
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Epoch 86 Training:  34%|███▍      | 33/96 [00:51<01:38,  1.57s/it]
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Epoch 86 Training:  35%|███▌      | 34/96 [00:53<01:37,  1.57s/it]
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Epoch 86 Training:  36%|███▋      | 35/96 [00:54<01:35,  1.57s/it]
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Epoch 86 Training:  38%|███▊      | 36/96 [00:56<01:34,  1.57s/it]
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Epoch 86 Training:  39%|███▊      | 37/96 [00:57<01:32,  1.57s/it]
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Epoch 86 Training:  40%|███▉      | 38/96 [00:59<01:31,  1.57s/it]
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Epoch 86 Training:  41%|████      | 39/96 [01:00<01:29,  1.57s/it]
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Epoch 86 Training:  42%|████▏     | 40/96 [01:02<01:28,  1.57s/it]
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Epoch 86 Training:  43%|████▎     | 41/96 [01:04<01:26,  1.57s/it]
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Epoch 86 Training:  44%|████▍     | 42/96 [01:05<01:24,  1.57s/it]
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Epoch 86 Training:  45%|████▍     | 43/96 [01:07<01:23,  1.57s/it]
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Epoch 86 Training:  46%|████▌     | 44/96 [01:08<01:21,  1.57s/it]
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Epoch 86 Training:  47%|████▋     | 45/96 [01:10<01:20,  1.57s/it]
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Epoch 86 Training:  48%|████▊     | 46/96 [01:11<01:18,  1.57s/it]
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Epoch 86 Training:  49%|████▉     | 47/96 [01:13<01:16,  1.57s/it]
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Epoch 86 Training:  50%|█████     | 48/96 [01:15<01:15,  1.57s/it]
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Epoch 86 Training:  51%|█████     | 49/96 [01:16<01:13,  1.57s/it]
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Epoch 86 Training:  52%|█████▏    | 50/96 [01:18<01:12,  1.57s/it]
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Epoch 86 Training:  53%|█████▎    | 51/96 [01:19<01:10,  1.57s/it]
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Epoch 86 Training:  54%|█████▍    | 52/96 [01:21<01:09,  1.57s/it]
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Epoch 86 Training:  55%|█████▌    | 53/96 [01:22<01:07,  1.57s/it]
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Epoch 86 Training:  56%|█████▋    | 54/96 [01:24<01:05,  1.57s/it]
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Epoch 86 Training:  57%|█████▋    | 55/96 [01:26<01:04,  1.57s/it]
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Epoch 86 Training:  58%|█████▊    | 56/96 [01:27<01:02,  1.57s/it]
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Epoch 86 Training:  59%|█████▉    | 57/96 [01:29<01:01,  1.57s/it]
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Epoch 86 Training:  60%|██████    | 58/96 [01:30<00:59,  1.57s/it]
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Epoch 86 Training:  61%|██████▏   | 59/96 [01:32<00:57,  1.57s/it]
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Epoch 86 Training:  62%|██████▎   | 60/96 [01:33<00:56,  1.57s/it]
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Epoch 86 Training:  64%|██████▎   | 61/96 [01:35<00:54,  1.56s/it]
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Epoch 86 Training:  65%|██████▍   | 62/96 [01:37<00:53,  1.57s/it]
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Epoch 86 Training:  66%|██████▌   | 63/96 [01:38<00:51,  1.57s/it]
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Epoch 86 Training:  67%|██████▋   | 64/96 [01:40<00:50,  1.57s/it]
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Epoch 86 Training:  68%|██████▊   | 65/96 [01:41<00:48,  1.57s/it]
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Epoch 86 Training:  69%|██████▉   | 66/96 [01:43<00:47,  1.57s/it]
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Epoch 86 Training:  70%|██████▉   | 67/96 [01:44<00:45,  1.57s/it]
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Epoch 86 Training:  71%|███████   | 68/96 [01:46<00:44,  1.57s/it]
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Epoch 86 Training:  72%|███████▏  | 69/96 [01:48<00:42,  1.57s/it]
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Epoch 86 Training:  73%|███████▎  | 70/96 [01:49<00:40,  1.57s/it]
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Epoch 86 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s][2026-10-01 21:21:14] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0086 | Epoch 086] train/policy_loss=25.1712 train/unweighted_policy_loss=5.0342 train/loss=25.1712 train/grad_norm=0.0637
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+[2026-10-01 21:21:14] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0087 | Epoch 086] timing/rollout_seconds=180.1709 timing/feedback_seconds=5.8939 timing/optimization_seconds=151.2118 timing/cycle_seconds=334.2607
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Pointwise Rewards: ssim:  83%|████████▎ | 5/6 [00:00<00:00,  5.53it/s]
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Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:01<00:00,  5.46it/s]
+
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Pointwise Rewards: dists:  83%|████████▎ | 5/6 [00:01<00:00,  3.18it/s]
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Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.18it/s]
+[2026-10-01 21:24:17] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0087 | Epoch 087] train/reward_dists_mean=0.8499 train/reward_dists_std=0.0292 train/reward_ssim_mean=0.6534 train/reward_ssim_std=0.1058 train/reward_dists_group_std_mean=0.0072 train/reward_dists_group_std_max=0.0139 train/reward_dists_group_std_min=0.0031 train/reward_dists_group_mean_std=0.0282 train/reward_ssim_group_std_mean=0.0072 train/reward_ssim_group_std_max=0.0175 train/reward_ssim_group_std_min=0.0027 train/reward_ssim_group_mean_std=0.1056 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.4436 train/adv_max=4.2793 train/adv_abs_mean=1.2112
+
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Epoch 87 Training:  51%|█████     | 49/96 [01:16<01:13,  1.57s/it]
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Epoch 87 Training:  52%|█████▏    | 50/96 [01:18<01:12,  1.57s/it]
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Epoch 87 Training:  53%|█████▎    | 51/96 [01:20<01:10,  1.57s/it]
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Epoch 87 Training:  54%|█████▍    | 52/96 [01:21<01:09,  1.57s/it]
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Epoch 87 Training:  55%|█████▌    | 53/96 [01:23<01:07,  1.57s/it]
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Epoch 87 Training:  56%|█████▋    | 54/96 [01:24<01:06,  1.57s/it]
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Epoch 87 Training:  57%|█████▋    | 55/96 [01:26<01:04,  1.57s/it]
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Epoch 87 Training:  58%|█████▊    | 56/96 [01:27<01:02,  1.57s/it]
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Epoch 87 Training:  59%|█████▉    | 57/96 [01:29<01:01,  1.56s/it]
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Epoch 87 Training:  60%|██████    | 58/96 [01:31<00:59,  1.56s/it]
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Epoch 87 Training:  61%|██████▏   | 59/96 [01:32<00:57,  1.56s/it]
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Epoch 87 Training:  62%|██████▎   | 60/96 [01:34<00:56,  1.56s/it]
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Epoch 87 Training:  64%|██████▎   | 61/96 [01:35<00:54,  1.56s/it]
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Epoch 87 Training:  65%|██████▍   | 62/96 [01:37<00:53,  1.57s/it]
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Epoch 87 Training:  66%|██████▌   | 63/96 [01:38<00:51,  1.57s/it]
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Epoch 87 Training:  67%|██████▋   | 64/96 [01:40<00:50,  1.57s/it]
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Epoch 87 Training:  68%|██████▊   | 65/96 [01:41<00:48,  1.57s/it]
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Epoch 87 Training:  69%|██████▉   | 66/96 [01:43<00:46,  1.57s/it]
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Epoch 87 Training:  70%|██████▉   | 67/96 [01:45<00:45,  1.56s/it]
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Epoch 87 Training:  71%|███████   | 68/96 [01:46<00:43,  1.56s/it]
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Epoch 87 Training:  72%|███████▏  | 69/96 [01:48<00:42,  1.57s/it]
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Epoch 87 Training:  73%|███████▎  | 70/96 [01:49<00:40,  1.57s/it]
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Epoch 87 Training:  74%|███████▍  | 71/96 [01:51<00:39,  1.57s/it]
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Epoch 87 Training:  75%|███████▌  | 72/96 [01:52<00:37,  1.57s/it]
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Epoch 87 Training:  76%|███████▌  | 73/96 [01:54<00:36,  1.60s/it]
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Epoch 87 Training:  77%|███████▋  | 74/96 [01:56<00:35,  1.59s/it]
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Epoch 87 Training:  78%|███████▊  | 75/96 [01:57<00:33,  1.59s/it]
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Epoch 87 Training:  79%|███████▉  | 76/96 [01:59<00:31,  1.59s/it]
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Epoch 87 Training:  80%|████████  | 77/96 [02:00<00:29,  1.58s/it]
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Epoch 87 Training:  81%|████████▏ | 78/96 [02:02<00:28,  1.57s/it]
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Epoch 87 Training:  82%|████████▏ | 79/96 [02:04<00:26,  1.57s/it]
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Epoch 87 Training:  83%|████████▎ | 80/96 [02:05<00:25,  1.57s/it]
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Epoch 87 Training:  84%|████████▍ | 81/96 [02:07<00:23,  1.57s/it]
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Epoch 87 Training:  85%|████████▌ | 82/96 [02:08<00:21,  1.57s/it]
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Epoch 87 Training:  86%|████████▋ | 83/96 [02:10<00:20,  1.57s/it]
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Epoch 87 Training:  88%|████████▊ | 84/96 [02:11<00:18,  1.57s/it]
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Epoch 87 Training:  89%|████████▊ | 85/96 [02:13<00:17,  1.56s/it]
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Epoch 87 Training:  90%|████████▉ | 86/96 [02:14<00:15,  1.57s/it]
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Epoch 87 Training:  91%|█████████ | 87/96 [02:16<00:14,  1.57s/it]
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Epoch 87 Training:  92%|█████████▏| 88/96 [02:18<00:12,  1.57s/it]
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Epoch 87 Training:  93%|█████████▎| 89/96 [02:19<00:11,  1.57s/it]
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Epoch 87 Training:  94%|█████████▍| 90/96 [02:21<00:09,  1.57s/it]
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Epoch 87 Training:  95%|█████████▍| 91/96 [02:22<00:07,  1.57s/it]
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Epoch 87 Training:  96%|█████████▌| 92/96 [02:24<00:06,  1.57s/it]
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Epoch 87 Training:  97%|█████████▋| 93/96 [02:25<00:04,  1.57s/it]
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Epoch 87 Training:  98%|█████████▊| 94/96 [02:27<00:03,  1.57s/it]
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Epoch 87 Training:  99%|█████████▉| 95/96 [02:29<00:01,  1.57s/it]
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Epoch 87 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s][2026-10-01 21:26:48] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0087 | Epoch 087] train/policy_loss=25.4493 train/unweighted_policy_loss=5.0899 train/loss=25.4493 train/grad_norm=0.0584
+
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Epoch 87 Training: 100%|██████████| 96/96 [02:30<00:00,  1.59s/it]
Epoch 87 Training: 100%|██████████| 96/96 [02:30<00:00,  1.57s/it]
+[2026-10-01 21:26:48] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0088 | Epoch 087] timing/rollout_seconds=180.0588 timing/feedback_seconds=5.9620 timing/optimization_seconds=151.3675 timing/cycle_seconds=334.3724
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+
Pointwise Rewards: ssim:   0%|          | 0/6 [00:00<?, ?it/s]
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Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:01<00:00,  5.71it/s]
+
Pointwise Rewards: dists:   0%|          | 0/6 [00:00<?, ?it/s]
Pointwise Rewards: dists:  17%|█▋        | 1/6 [00:00<00:01,  3.48it/s]
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Pointwise Rewards: dists:  50%|█████     | 3/6 [00:00<00:00,  3.26it/s]
Pointwise Rewards: dists:  67%|██████▋   | 4/6 [00:01<00:00,  3.25it/s]
Pointwise Rewards: dists:  83%|████████▎ | 5/6 [00:01<00:00,  3.15it/s]
Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.17it/s]
Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.21it/s]
+[2026-10-01 21:29:52] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0088 | Epoch 088] train/reward_dists_mean=0.8472 train/reward_dists_std=0.0328 train/reward_ssim_mean=0.6842 train/reward_ssim_std=0.1075 train/reward_dists_group_std_mean=0.0082 train/reward_dists_group_std_max=0.0182 train/reward_dists_group_std_min=0.0025 train/reward_dists_group_mean_std=0.0315 train/reward_ssim_group_std_mean=0.0080 train/reward_ssim_group_std_max=0.0190 train/reward_ssim_group_std_min=0.0031 train/reward_ssim_group_mean_std=0.1072 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.5656 train/adv_max=4.4981 train/adv_abs_mean=1.2200
+
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Epoch 88 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 88 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 88 Training:  24%|██▍       | 23/96 [00:36<01:56,  1.59s/it]
+
Epoch 88 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 88 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 88 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 88 Training:  25%|██▌       | 24/96 [00:38<01:54,  1.59s/it]
+
Epoch 88 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 88 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 88 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 88 Training:  26%|██▌       | 25/96 [00:39<01:52,  1.59s/it]
+
Epoch 88 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 88 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 88 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 88 Training:  27%|██▋       | 26/96 [00:41<01:51,  1.59s/it]
+
Epoch 88 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 88 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 88 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 88 Training:  28%|██▊       | 27/96 [00:42<01:49,  1.59s/it]
+
Epoch 88 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 88 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 88 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 88 Training:  29%|██▉       | 28/96 [00:44<01:48,  1.59s/it]
+
Epoch 88 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 88 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 88 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 88 Training:  30%|███       | 29/96 [00:46<01:46,  1.59s/it]
+
Epoch 88 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 88 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 88 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.04it/s]
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Epoch 88 Training:  31%|███▏      | 30/96 [00:47<01:44,  1.59s/it]
+
Epoch 88 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 88 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 88 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 88 Training:  32%|███▏      | 31/96 [00:49<01:43,  1.59s/it]
+
Epoch 88 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 88 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 88 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 88 Training:  33%|███▎      | 32/96 [00:50<01:41,  1.59s/it]
+
Epoch 88 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 88 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 88 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 88 Training:  34%|███▍      | 33/96 [00:52<01:40,  1.59s/it]
+
Epoch 88 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 88 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 88 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 88 Training:  35%|███▌      | 34/96 [00:54<01:38,  1.59s/it]
+
Epoch 88 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 88 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 88 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 88 Training:  36%|███▋      | 35/96 [00:55<01:36,  1.59s/it]
+
Epoch 88 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 88 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 88 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.04it/s]
+
                                                                
Epoch 88 Training:  38%|███▊      | 36/96 [00:57<01:35,  1.59s/it]
+
Epoch 88 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 88 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 88 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 88 Training:  39%|███▊      | 37/96 [00:58<01:33,  1.59s/it]
+
Epoch 88 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 88 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 88 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.04it/s]
+
                                                                
Epoch 88 Training:  40%|███▉      | 38/96 [01:00<01:31,  1.59s/it]
+
Epoch 88 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 88 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 88 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.04it/s]
+
                                                                
Epoch 88 Training:  41%|████      | 39/96 [01:01<01:30,  1.59s/it]
+
Epoch 88 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 88 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 88 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 88 Training:  42%|████▏     | 40/96 [01:03<01:28,  1.59s/it]
+
Epoch 88 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 88 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 88 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 88 Training:  43%|████▎     | 41/96 [01:05<01:27,  1.59s/it]
+
Epoch 88 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 88 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 88 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 88 Training:  44%|████▍     | 42/96 [01:06<01:25,  1.59s/it]
+
Epoch 88 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 88 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 88 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 88 Training:  45%|████▍     | 43/96 [01:08<01:24,  1.59s/it]
+
Epoch 88 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 88 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 88 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 88 Training:  46%|████▌     | 44/96 [01:09<01:22,  1.59s/it]
+
Epoch 88 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 88 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 88 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 88 Training:  47%|████▋     | 45/96 [01:11<01:21,  1.59s/it]
+
Epoch 88 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 88 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 88 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 88 Training:  48%|████▊     | 46/96 [01:13<01:19,  1.59s/it]
+
Epoch 88 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 88 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 88 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 88 Training:  49%|████▉     | 47/96 [01:14<01:17,  1.59s/it]
+
Epoch 88 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 88 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 88 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 88 Training:  50%|█████     | 48/96 [01:16<01:16,  1.59s/it]
+
Epoch 88 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 88 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 88 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 88 Training:  51%|█████     | 49/96 [01:17<01:14,  1.59s/it]
+
Epoch 88 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 88 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 88 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 88 Training:  52%|█████▏    | 50/96 [01:19<01:13,  1.59s/it]
+
Epoch 88 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 88 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
+
Epoch 88 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 88 Training:  53%|█████▎    | 51/96 [01:21<01:11,  1.59s/it]
+
Epoch 88 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 88 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 88 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 88 Training:  54%|█████▍    | 52/96 [01:22<01:09,  1.59s/it]
+
Epoch 88 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 88 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 88 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 88 Training:  55%|█████▌    | 53/96 [01:24<01:08,  1.59s/it]
+
Epoch 88 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 88 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 88 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 88 Training:  56%|█████▋    | 54/96 [01:25<01:06,  1.59s/it]
+
Epoch 88 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 88 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 88 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 88 Training:  57%|█████▋    | 55/96 [01:27<01:05,  1.59s/it]
+
Epoch 88 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 88 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 88 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 88 Training:  58%|█████▊    | 56/96 [01:28<01:03,  1.59s/it]
+
Epoch 88 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 88 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 88 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 88 Training:  59%|█████▉    | 57/96 [01:30<01:01,  1.59s/it]
+
Epoch 88 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 88 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 88 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
+
                                                                
Epoch 88 Training:  60%|██████    | 58/96 [01:32<01:00,  1.59s/it]
+
Epoch 88 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 88 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 88 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 88 Training:  61%|██████▏   | 59/96 [01:33<00:58,  1.59s/it]
+
Epoch 88 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 88 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 88 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 88 Training:  62%|██████▎   | 60/96 [01:35<00:57,  1.59s/it]
+
Epoch 88 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 88 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 88 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 88 Training:  64%|██████▎   | 61/96 [01:36<00:55,  1.59s/it]
+
Epoch 88 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 88 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 88 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 88 Training:  65%|██████▍   | 62/96 [01:38<00:53,  1.59s/it]
+
Epoch 88 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 88 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 88 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 88 Training:  66%|██████▌   | 63/96 [01:40<00:52,  1.59s/it]
+
Epoch 88 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 88 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 88 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 88 Training:  67%|██████▋   | 64/96 [01:41<00:50,  1.59s/it]
+
Epoch 88 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 88 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 88 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 88 Training:  68%|██████▊   | 65/96 [01:43<00:49,  1.59s/it]
+
Epoch 88 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 88 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 88 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 88 Training:  69%|██████▉   | 66/96 [01:44<00:47,  1.59s/it]
+
Epoch 88 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 88 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 88 Training:  70%|██████▉   | 67/96 [01:46<00:45,  1.59s/it]
+
Epoch 88 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 88 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 88 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 88 Training:  71%|███████   | 68/96 [01:47<00:44,  1.59s/it]
+
Epoch 88 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 88 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 88 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 88 Training:  72%|███████▏  | 69/96 [01:49<00:42,  1.59s/it]
+
Epoch 88 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 88 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 88 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 88 Training:  73%|███████▎  | 70/96 [01:51<00:41,  1.59s/it]
+
Epoch 88 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 88 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 88 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 88 Training:  74%|███████▍  | 71/96 [01:52<00:39,  1.59s/it]
+
Epoch 88 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 88 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s]
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Epoch 88 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 88 Training:  75%|███████▌  | 72/96 [01:54<00:38,  1.59s/it]
+
Epoch 88 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 88 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 88 Training:  76%|███████▌  | 73/96 [01:55<00:36,  1.59s/it]
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Epoch 88 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 88 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s][2026-10-01 21:32:24] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0088 | Epoch 088] train/policy_loss=25.4712 train/unweighted_policy_loss=5.0942 train/loss=25.4712 train/grad_norm=0.0753
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+[2026-10-01 21:32:24] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0089 | Epoch 088] timing/rollout_seconds=180.0346 timing/feedback_seconds=5.9068 timing/optimization_seconds=153.2197 timing/cycle_seconds=336.1591
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Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.23it/s]
+[2026-10-01 21:35:27] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0089 | Epoch 089] train/reward_dists_mean=0.8338 train/reward_dists_std=0.0371 train/reward_ssim_mean=0.6192 train/reward_ssim_std=0.1112 train/reward_dists_group_std_mean=0.0091 train/reward_dists_group_std_max=0.0195 train/reward_dists_group_std_min=0.0031 train/reward_dists_group_mean_std=0.0358 train/reward_ssim_group_std_mean=0.0072 train/reward_ssim_group_std_max=0.0143 train/reward_ssim_group_std_min=0.0031 train/reward_ssim_group_mean_std=0.1109 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.2636 train/adv_max=4.5051 train/adv_abs_mean=1.2189
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Epoch 89 Training:  88%|████████▊ | 84/96 [02:13<00:18,  1.58s/it]
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Epoch 89 Training:  89%|████████▊ | 85/96 [02:14<00:17,  1.58s/it]
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Epoch 89 Training:  93%|█████████▎| 89/96 [02:20<00:11,  1.58s/it]
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Epoch 89 Training:  97%|█████████▋| 93/96 [02:27<00:04,  1.58s/it]
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Epoch 89 Training:  98%|█████████▊| 94/96 [02:28<00:03,  1.59s/it]
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Epoch 89 Training:  99%|█████████▉| 95/96 [02:30<00:01,  1.59s/it]
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Epoch 89 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s][2026-10-01 21:37:59] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0089 | Epoch 089] train/policy_loss=25.8622 train/unweighted_policy_loss=5.1724 train/loss=25.8622 train/grad_norm=0.0787
+
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Epoch 89 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.88it/s]
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Epoch 89 Training: 100%|██████████| 96/96 [02:32<00:00,  1.61s/it]
Epoch 89 Training: 100%|██████████| 96/96 [02:32<00:00,  1.58s/it]
+[2026-10-01 21:37:59] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0090 | Epoch 089] timing/rollout_seconds=179.5045 timing/feedback_seconds=5.1581 timing/optimization_seconds=152.7236 timing/cycle_seconds=335.0669
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Eval/flir_rl_full: 100%|██████████| 8/8 [00:51<00:00,  6.42s/it]
Eval/flir_rl_full: 100%|██████████| 8/8 [00:51<00:00,  6.46s/it]
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+
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+[2026-10-01 21:38:52] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0090 | Epoch 090] eval/flir_rl_full/reward_dists_mean=0.8340 eval/flir_rl_full/reward_dists_std=0.0368 eval/flir_rl_full/reward_psnr_mean=19.4202 eval/flir_rl_full/reward_psnr_std=2.7541 eval/flir_rl_full/reward_ssim_mean=0.6337 eval/flir_rl_full/reward_ssim_std=0.0840
+
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+
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+[2026-10-01 21:41:55] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0090 | Epoch 090] train/reward_dists_mean=0.8385 train/reward_dists_std=0.0379 train/reward_ssim_mean=0.6920 train/reward_ssim_std=0.0912 train/reward_dists_group_std_mean=0.0106 train/reward_dists_group_std_max=0.0274 train/reward_dists_group_std_min=0.0036 train/reward_dists_group_mean_std=0.0360 train/reward_ssim_group_std_mean=0.0073 train/reward_ssim_group_std_max=0.0120 train/reward_ssim_group_std_min=0.0033 train/reward_ssim_group_mean_std=0.0909 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.8096 train/adv_max=4.6405 train/adv_abs_mean=1.2507
+
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Epoch 90 Training:  21%|██        | 20/96 [00:31<01:59,  1.58s/it]
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Epoch 90 Training:  23%|██▎       | 22/96 [00:34<01:56,  1.57s/it]
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Epoch 90 Training:  24%|██▍       | 23/96 [00:36<01:54,  1.57s/it]
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Epoch 90 Training:  27%|██▋       | 26/96 [00:40<01:50,  1.57s/it]
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Epoch 90 Training:  29%|██▉       | 28/96 [00:43<01:46,  1.57s/it]
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Epoch 90 Training:  32%|███▏      | 31/96 [00:48<01:41,  1.57s/it]
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Epoch 90 Training:  33%|███▎      | 32/96 [00:50<01:40,  1.57s/it]
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Epoch 90 Training:  34%|███▍      | 33/96 [00:51<01:38,  1.57s/it]
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Epoch 90 Training:  35%|███▌      | 34/96 [00:53<01:37,  1.58s/it]
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Epoch 90 Training:  36%|███▋      | 35/96 [00:54<01:36,  1.58s/it]
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Epoch 90 Training:  38%|███▊      | 36/96 [00:56<01:34,  1.57s/it]
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Epoch 90 Training:  39%|███▊      | 37/96 [00:58<01:32,  1.57s/it]
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Epoch 90 Training:  40%|███▉      | 38/96 [00:59<01:31,  1.58s/it]
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Epoch 90 Training:  41%|████      | 39/96 [01:01<01:29,  1.57s/it]
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Epoch 90 Training:  42%|████▏     | 40/96 [01:02<01:28,  1.58s/it]
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Epoch 90 Training:  43%|████▎     | 41/96 [01:04<01:26,  1.58s/it]
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Epoch 90 Training:  44%|████▍     | 42/96 [01:05<01:25,  1.58s/it]
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Epoch 90 Training:  45%|████▍     | 43/96 [01:07<01:23,  1.57s/it]
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Epoch 90 Training:  46%|████▌     | 44/96 [01:09<01:21,  1.57s/it]
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Epoch 90 Training:  47%|████▋     | 45/96 [01:10<01:19,  1.57s/it]
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Epoch 90 Training:  48%|████▊     | 46/96 [01:12<01:18,  1.57s/it]
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Epoch 90 Training:  49%|████▉     | 47/96 [01:13<01:16,  1.57s/it]
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Epoch 90 Training:  51%|█████     | 49/96 [01:16<01:13,  1.56s/it]
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Epoch 90 Training:  52%|█████▏    | 50/96 [01:18<01:11,  1.56s/it]
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Epoch 90 Training:  53%|█████▎    | 51/96 [01:20<01:10,  1.56s/it]
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Epoch 90 Training:  54%|█████▍    | 52/96 [01:21<01:08,  1.56s/it]
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Epoch 90 Training:  55%|█████▌    | 53/96 [01:23<01:07,  1.56s/it]
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Epoch 90 Training:  56%|█████▋    | 54/96 [01:24<01:05,  1.56s/it]
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Epoch 90 Training:  57%|█████▋    | 55/96 [01:26<01:04,  1.56s/it]
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Epoch 90 Training:  58%|█████▊    | 56/96 [01:27<01:02,  1.57s/it]
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Epoch 90 Training:  59%|█████▉    | 57/96 [01:29<01:01,  1.57s/it]
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Epoch 90 Training:  61%|██████▏   | 59/96 [01:32<00:57,  1.56s/it]
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Epoch 90 Training:  64%|██████▎   | 61/96 [01:35<00:54,  1.57s/it]
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Epoch 90 Training:  65%|██████▍   | 62/96 [01:37<00:53,  1.56s/it]
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Epoch 90 Training:  66%|██████▌   | 63/96 [01:38<00:51,  1.56s/it]
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Epoch 90 Training:  67%|██████▋   | 64/96 [01:40<00:50,  1.56s/it]
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Epoch 90 Training:  68%|██████▊   | 65/96 [01:41<00:48,  1.57s/it]
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Epoch 90 Training:  69%|██████▉   | 66/96 [01:43<00:47,  1.57s/it]
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Epoch 90 Training:  70%|██████▉   | 67/96 [01:45<00:45,  1.56s/it]
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Epoch 90 Training:  71%|███████   | 68/96 [01:46<00:43,  1.57s/it]
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Epoch 90 Training:  72%|███████▏  | 69/96 [01:48<00:42,  1.57s/it]
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Epoch 90 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s][2026-10-01 21:44:26] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0090 | Epoch 090] train/policy_loss=24.5340 train/unweighted_policy_loss=4.9068 train/loss=24.5340 train/grad_norm=0.1007
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Epoch 90 Training: 100%|██████████| 96/96 [02:30<00:00,  1.57s/it]
+[2026-10-01 21:44:26] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0091 | Epoch 090] timing/rollout_seconds=179.7454 timing/feedback_seconds=5.2464 timing/optimization_seconds=151.4870 timing/cycle_seconds=334.0766
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+
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+[2026-10-01 21:47:29] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0091 | Epoch 091] train/reward_dists_mean=0.8513 train/reward_dists_std=0.0282 train/reward_ssim_mean=0.6453 train/reward_ssim_std=0.1239 train/reward_dists_group_std_mean=0.0080 train/reward_dists_group_std_max=0.0143 train/reward_dists_group_std_min=0.0040 train/reward_dists_group_mean_std=0.0269 train/reward_ssim_group_std_mean=0.0086 train/reward_ssim_group_std_max=0.0220 train/reward_ssim_group_std_min=0.0041 train/reward_ssim_group_mean_std=0.1235 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.8565 train/adv_max=4.2043 train/adv_abs_mean=1.1347
+
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Epoch 91 Training:  99%|█████████▉| 95/96 [02:29<00:01,  1.57s/it]
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Epoch 91 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s][2026-10-01 21:50:01] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0091 | Epoch 091] train/policy_loss=25.9661 train/unweighted_policy_loss=5.1932 train/loss=25.9661 train/grad_norm=0.0699
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Epoch 91 Training: 100%|██████████| 96/96 [02:31<00:00,  1.58s/it]
+[2026-10-01 21:50:01] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0092 | Epoch 091] timing/rollout_seconds=179.4685 timing/feedback_seconds=5.3714 timing/optimization_seconds=152.2267 timing/cycle_seconds=334.5563
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+[2026-10-01 21:53:05] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0092 | Epoch 092] train/reward_dists_mean=0.8608 train/reward_dists_std=0.0234 train/reward_ssim_mean=0.6853 train/reward_ssim_std=0.1003 train/reward_dists_group_std_mean=0.0073 train/reward_dists_group_std_max=0.0137 train/reward_dists_group_std_min=0.0030 train/reward_dists_group_mean_std=0.0221 train/reward_ssim_group_std_mean=0.0097 train/reward_ssim_group_std_max=0.0214 train/reward_ssim_group_std_min=0.0034 train/reward_ssim_group_mean_std=0.0998 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.9322 train/adv_max=4.5128 train/adv_abs_mean=1.2509
+
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Epoch 92 Training:  24%|██▍       | 23/96 [00:36<01:55,  1.58s/it]
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Epoch 92 Training:  25%|██▌       | 24/96 [00:37<01:54,  1.59s/it]
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Epoch 92 Training:  26%|██▌       | 25/96 [00:39<01:52,  1.59s/it]
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Epoch 92 Training:  27%|██▋       | 26/96 [00:41<01:50,  1.58s/it]
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Epoch 92 Training:  28%|██▊       | 27/96 [00:42<01:49,  1.58s/it]
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Epoch 92 Training:  29%|██▉       | 28/96 [00:44<01:47,  1.58s/it]
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Epoch 92 Training:  30%|███       | 29/96 [00:45<01:45,  1.58s/it]
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Epoch 92 Training:  31%|███▏      | 30/96 [00:47<01:44,  1.58s/it]
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Epoch 92 Training:  32%|███▏      | 31/96 [00:48<01:42,  1.58s/it]
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Epoch 92 Training:  33%|███▎      | 32/96 [00:50<01:41,  1.58s/it]
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Epoch 92 Training:  34%|███▍      | 33/96 [00:52<01:39,  1.58s/it]
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Epoch 92 Training:  35%|███▌      | 34/96 [00:53<01:38,  1.58s/it]
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Epoch 92 Training:  36%|███▋      | 35/96 [00:55<01:36,  1.58s/it]
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Epoch 92 Training:  38%|███▊      | 36/96 [00:56<01:35,  1.58s/it]
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Epoch 92 Training:  39%|███▊      | 37/96 [00:58<01:33,  1.58s/it]
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Epoch 92 Training:  40%|███▉      | 38/96 [00:59<01:31,  1.58s/it]
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Epoch 92 Training:  41%|████      | 39/96 [01:01<01:30,  1.58s/it]
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Epoch 92 Training:  42%|████▏     | 40/96 [01:03<01:28,  1.58s/it]
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Epoch 92 Training:  43%|████▎     | 41/96 [01:04<01:26,  1.58s/it]
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Epoch 92 Training:  44%|████▍     | 42/96 [01:06<01:25,  1.58s/it]
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Epoch 92 Training:  45%|████▍     | 43/96 [01:07<01:24,  1.59s/it]
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Epoch 92 Training:  46%|████▌     | 44/96 [01:09<01:22,  1.59s/it]
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Epoch 92 Training:  47%|████▋     | 45/96 [01:11<01:20,  1.58s/it]
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Epoch 92 Training:  48%|████▊     | 46/96 [01:12<01:19,  1.58s/it]
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Epoch 92 Training:  49%|████▉     | 47/96 [01:14<01:17,  1.58s/it]
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Epoch 92 Training:  50%|█████     | 48/96 [01:15<01:15,  1.58s/it]
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Epoch 92 Training:  51%|█████     | 49/96 [01:17<01:14,  1.58s/it]
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Epoch 92 Training:  52%|█████▏    | 50/96 [01:18<01:12,  1.58s/it]
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Epoch 92 Training:  53%|█████▎    | 51/96 [01:20<01:11,  1.58s/it]
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Epoch 92 Training:  54%|█████▍    | 52/96 [01:22<01:09,  1.58s/it]
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Epoch 92 Training:  55%|█████▌    | 53/96 [01:23<01:07,  1.58s/it]
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Epoch 92 Training:  56%|█████▋    | 54/96 [01:25<01:06,  1.58s/it]
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Epoch 92 Training:  57%|█████▋    | 55/96 [01:26<01:04,  1.58s/it]
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Epoch 92 Training:  58%|█████▊    | 56/96 [01:28<01:03,  1.58s/it]
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Epoch 92 Training:  59%|█████▉    | 57/96 [01:30<01:01,  1.58s/it]
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Epoch 92 Training:  60%|██████    | 58/96 [01:31<01:00,  1.58s/it]
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Epoch 92 Training:  61%|██████▏   | 59/96 [01:33<00:58,  1.58s/it]
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Epoch 92 Training:  62%|██████▎   | 60/96 [01:34<00:57,  1.58s/it]
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Epoch 92 Training:  64%|██████▎   | 61/96 [01:36<00:55,  1.58s/it]
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Epoch 92 Training:  65%|██████▍   | 62/96 [01:37<00:53,  1.58s/it]
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Epoch 92 Training:  66%|██████▌   | 63/96 [01:39<00:52,  1.58s/it]
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Epoch 92 Training:  67%|██████▋   | 64/96 [01:41<00:50,  1.58s/it]
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Epoch 92 Training:  68%|██████▊   | 65/96 [01:42<00:48,  1.58s/it]
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Epoch 92 Training:  69%|██████▉   | 66/96 [01:44<00:47,  1.58s/it]
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Epoch 92 Training:  70%|██████▉   | 67/96 [01:45<00:45,  1.58s/it]
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Epoch 92 Training:  71%|███████   | 68/96 [01:47<00:44,  1.58s/it]
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Epoch 92 Training:  72%|███████▏  | 69/96 [01:48<00:42,  1.58s/it]
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Epoch 92 Training:  73%|███████▎  | 70/96 [01:50<00:40,  1.58s/it]
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Epoch 92 Training:  74%|███████▍  | 71/96 [01:52<00:39,  1.58s/it]
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Epoch 92 Training:  75%|███████▌  | 72/96 [01:53<00:37,  1.58s/it]
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Epoch 92 Training:  76%|███████▌  | 73/96 [01:55<00:36,  1.58s/it]
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Epoch 92 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s][2026-10-01 21:55:37] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0092 | Epoch 092] train/policy_loss=25.6115 train/unweighted_policy_loss=5.1223 train/loss=25.6115 train/grad_norm=0.0733
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+[2026-10-01 21:55:37] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0093 | Epoch 092] timing/rollout_seconds=179.7291 timing/feedback_seconds=6.7214 timing/optimization_seconds=152.1791 timing/cycle_seconds=336.1980
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+[2026-10-01 21:58:41] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0093 | Epoch 093] train/reward_dists_mean=0.8545 train/reward_dists_std=0.0267 train/reward_ssim_mean=0.6338 train/reward_ssim_std=0.1108 train/reward_dists_group_std_mean=0.0067 train/reward_dists_group_std_max=0.0138 train/reward_dists_group_std_min=0.0033 train/reward_dists_group_mean_std=0.0257 train/reward_ssim_group_std_mean=0.0096 train/reward_ssim_group_std_max=0.0165 train/reward_ssim_group_std_min=0.0032 train/reward_ssim_group_mean_std=0.1103 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.7710 train/adv_max=4.6081 train/adv_abs_mean=1.2564
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Epoch 93 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s][2026-10-01 22:01:14] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0093 | Epoch 093] train/policy_loss=25.8697 train/unweighted_policy_loss=5.1739 train/loss=25.8697 train/grad_norm=0.0786
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+[2026-10-01 22:01:14] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0094 | Epoch 093] timing/rollout_seconds=180.1505 timing/feedback_seconds=5.8232 timing/optimization_seconds=153.9130 timing/cycle_seconds=336.8966
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+
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+
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Pointwise Rewards: dists:  67%|██████▋   | 4/6 [00:01<00:00,  3.17it/s]
Pointwise Rewards: dists:  83%|████████▎ | 5/6 [00:01<00:00,  3.18it/s]
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Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.19it/s]
+[2026-10-01 22:04:18] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0094 | Epoch 094] train/reward_dists_mean=0.8591 train/reward_dists_std=0.0267 train/reward_ssim_mean=0.6704 train/reward_ssim_std=0.1089 train/reward_dists_group_std_mean=0.0068 train/reward_dists_group_std_max=0.0187 train/reward_dists_group_std_min=0.0029 train/reward_dists_group_mean_std=0.0256 train/reward_ssim_group_std_mean=0.0108 train/reward_ssim_group_std_max=0.0207 train/reward_ssim_group_std_min=0.0028 train/reward_ssim_group_mean_std=0.1083 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.8173 train/adv_max=4.3252 train/adv_abs_mean=1.2292
+
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Epoch 94 Training:  27%|██▋       | 26/96 [00:40<01:50,  1.58s/it]
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Epoch 94 Training:  28%|██▊       | 27/96 [00:42<01:49,  1.58s/it]
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Epoch 94 Training:  29%|██▉       | 28/96 [00:44<01:47,  1.58s/it]
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Epoch 94 Training:  30%|███       | 29/96 [00:45<01:45,  1.58s/it]
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Epoch 94 Training:  31%|███▏      | 30/96 [00:47<01:43,  1.58s/it]
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Epoch 94 Training:  32%|███▏      | 31/96 [00:48<01:42,  1.57s/it]
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Epoch 94 Training:  33%|███▎      | 32/96 [00:50<01:40,  1.57s/it]
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Epoch 94 Training:  34%|███▍      | 33/96 [00:51<01:39,  1.58s/it]
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Epoch 94 Training:  35%|███▌      | 34/96 [00:53<01:37,  1.58s/it]
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Epoch 94 Training:  36%|███▋      | 35/96 [00:55<01:36,  1.58s/it]
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Epoch 94 Training:  38%|███▊      | 36/96 [00:56<01:34,  1.58s/it]
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Epoch 94 Training:  39%|███▊      | 37/96 [00:58<01:33,  1.58s/it]
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Epoch 94 Training:  40%|███▉      | 38/96 [00:59<01:31,  1.58s/it]
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Epoch 94 Training:  41%|████      | 39/96 [01:01<01:29,  1.58s/it]
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Epoch 94 Training:  42%|████▏     | 40/96 [01:03<01:28,  1.58s/it]
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Epoch 94 Training:  43%|████▎     | 41/96 [01:04<01:26,  1.58s/it]
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Epoch 94 Training:  44%|████▍     | 42/96 [01:06<01:25,  1.58s/it]
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Epoch 94 Training:  45%|████▍     | 43/96 [01:07<01:23,  1.58s/it]
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Epoch 94 Training:  46%|████▌     | 44/96 [01:09<01:22,  1.58s/it]
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Epoch 94 Training:  47%|████▋     | 45/96 [01:10<01:20,  1.58s/it]
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Epoch 94 Training:  48%|████▊     | 46/96 [01:12<01:19,  1.59s/it]
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Epoch 94 Training:  49%|████▉     | 47/96 [01:14<01:17,  1.59s/it]
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Epoch 94 Training:  50%|█████     | 48/96 [01:15<01:16,  1.59s/it]
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Epoch 94 Training:  51%|█████     | 49/96 [01:17<01:14,  1.59s/it]
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Epoch 94 Training:  52%|█████▏    | 50/96 [01:18<01:13,  1.59s/it]
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Epoch 94 Training:  53%|█████▎    | 51/96 [01:20<01:11,  1.59s/it]
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Epoch 94 Training:  54%|█████▍    | 52/96 [01:22<01:09,  1.59s/it]
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Epoch 94 Training:  55%|█████▌    | 53/96 [01:23<01:08,  1.59s/it]
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Epoch 94 Training:  56%|█████▋    | 54/96 [01:25<01:06,  1.59s/it]
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Epoch 94 Training:  57%|█████▋    | 55/96 [01:26<01:05,  1.59s/it]
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Epoch 94 Training:  58%|█████▊    | 56/96 [01:28<01:03,  1.59s/it]
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Epoch 94 Training:  59%|█████▉    | 57/96 [01:30<01:01,  1.59s/it]
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Epoch 94 Training:  60%|██████    | 58/96 [01:31<01:00,  1.59s/it]
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Epoch 94 Training:  61%|██████▏   | 59/96 [01:33<00:58,  1.59s/it]
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Epoch 94 Training:  62%|██████▎   | 60/96 [01:34<00:57,  1.59s/it]
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Epoch 94 Training:  64%|██████▎   | 61/96 [01:36<00:55,  1.59s/it]
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Epoch 94 Training:  65%|██████▍   | 62/96 [01:37<00:54,  1.59s/it]
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Epoch 94 Training:  66%|██████▌   | 63/96 [01:39<00:52,  1.59s/it]
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Epoch 94 Training:  67%|██████▋   | 64/96 [01:41<00:50,  1.59s/it]
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Epoch 94 Training:  68%|██████▊   | 65/96 [01:42<00:49,  1.58s/it]
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Epoch 94 Training:  69%|██████▉   | 66/96 [01:44<00:47,  1.59s/it]
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Epoch 94 Training:  70%|██████▉   | 67/96 [01:45<00:46,  1.59s/it]
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Epoch 94 Training:  71%|███████   | 68/96 [01:47<00:44,  1.59s/it]
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Epoch 94 Training:  72%|███████▏  | 69/96 [01:49<00:42,  1.59s/it]
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Epoch 94 Training:  73%|███████▎  | 70/96 [01:50<00:41,  1.59s/it]
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Epoch 94 Training:  74%|███████▍  | 71/96 [01:52<00:39,  1.59s/it]
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Epoch 94 Training:  75%|███████▌  | 72/96 [01:53<00:38,  1.59s/it]
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Epoch 94 Training:  76%|███████▌  | 73/96 [01:55<00:36,  1.59s/it]
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Epoch 94 Training:  77%|███████▋  | 74/96 [01:57<00:34,  1.59s/it]
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Epoch 94 Training:  78%|███████▊  | 75/96 [01:58<00:33,  1.59s/it]
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Epoch 94 Training:  79%|███████▉  | 76/96 [02:00<00:31,  1.59s/it]
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Epoch 94 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.17it/s][2026-10-01 22:06:50] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0094 | Epoch 094] train/policy_loss=25.7966 train/unweighted_policy_loss=5.1593 train/loss=25.7966 train/grad_norm=0.0965
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+[2026-10-01 22:06:50] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0095 | Epoch 094] timing/rollout_seconds=180.1578 timing/feedback_seconds=5.9762 timing/optimization_seconds=152.6206 timing/cycle_seconds=335.7066
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+
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+[2026-10-01 22:09:53] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0095 | Epoch 095] train/reward_dists_mean=0.8569 train/reward_dists_std=0.0270 train/reward_ssim_mean=0.6538 train/reward_ssim_std=0.1003 train/reward_dists_group_std_mean=0.0066 train/reward_dists_group_std_max=0.0149 train/reward_dists_group_std_min=0.0030 train/reward_dists_group_mean_std=0.0260 train/reward_ssim_group_std_mean=0.0095 train/reward_ssim_group_std_max=0.0208 train/reward_ssim_group_std_min=0.0029 train/reward_ssim_group_mean_std=0.0998 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.1719 train/adv_max=4.8021 train/adv_abs_mean=1.2130
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Epoch 95 Training:   1%|          | 1/96 [00:01<02:23,  1.51s/it]
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Epoch 95 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 95 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 95 Training:   2%|▏         | 2/96 [00:03<02:26,  1.56s/it]
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Epoch 95 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 95 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.94it/s]
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Epoch 95 Training:   3%|▎         | 3/96 [00:04<02:25,  1.57s/it]
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Epoch 95 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 95 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.96it/s]
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Epoch 95 Training:   4%|▍         | 4/96 [00:06<02:24,  1.57s/it]
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Epoch 95 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 95 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.97it/s]
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Epoch 95 Training:   5%|▌         | 5/96 [00:07<02:23,  1.58s/it]
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Epoch 95 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 95 Training:   6%|▋         | 6/96 [00:09<02:22,  1.58s/it]
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Epoch 95 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 95 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.95it/s]
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Epoch 95 Training:   7%|▋         | 7/96 [00:11<02:20,  1.58s/it]
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Epoch 95 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 95 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.97it/s]
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Epoch 95 Training:   8%|▊         | 8/96 [00:12<02:18,  1.58s/it]
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Epoch 95 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 95 Training:   9%|▉         | 9/96 [00:14<02:16,  1.57s/it]
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Epoch 95 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 95 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 95 Training:  10%|█         | 10/96 [00:15<02:15,  1.58s/it]
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Epoch 95 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
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Epoch 95 Training:  11%|█▏        | 11/96 [00:17<02:14,  1.58s/it]
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Epoch 95 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 95 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
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Epoch 95 Training:  12%|█▎        | 12/96 [00:18<02:12,  1.58s/it]
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Epoch 95 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 95 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.91it/s]
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Epoch 95 Training:  14%|█▎        | 13/96 [00:20<02:11,  1.58s/it]
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Epoch 95 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 95 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.96it/s]
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Epoch 95 Training:  15%|█▍        | 14/96 [00:22<02:09,  1.58s/it]
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Epoch 95 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 95 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.02it/s]
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Epoch 95 Training:  16%|█▌        | 15/96 [00:23<02:07,  1.58s/it]
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Epoch 95 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 95 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.94it/s]
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Epoch 95 Training:  17%|█▋        | 16/96 [00:25<02:06,  1.58s/it]
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Epoch 95 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 95 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.96it/s]
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Epoch 95 Training:  18%|█▊        | 17/96 [00:26<02:04,  1.58s/it]
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Epoch 95 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 95 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.96it/s]
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Epoch 95 Training:  19%|█▉        | 18/96 [00:28<02:02,  1.58s/it]
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Epoch 95 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 95 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.96it/s]
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Epoch 95 Training:  20%|█▉        | 19/96 [00:29<02:01,  1.57s/it]
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Epoch 95 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 95 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.95it/s]
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Epoch 95 Training:  21%|██        | 20/96 [00:31<01:59,  1.57s/it]
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Epoch 95 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 95 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.96it/s]
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Epoch 95 Training:  22%|██▏       | 21/96 [00:33<01:57,  1.57s/it]
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Epoch 95 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 95 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.94it/s]
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Epoch 95 Training:  23%|██▎       | 22/96 [00:34<01:56,  1.57s/it]
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Epoch 95 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 95 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 95 Training:  24%|██▍       | 23/96 [00:36<01:55,  1.58s/it]
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Epoch 95 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 95 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.96it/s]
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Epoch 95 Training:  25%|██▌       | 24/96 [00:37<01:53,  1.57s/it]
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Epoch 95 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 95 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.94it/s]
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Epoch 95 Training:  26%|██▌       | 25/96 [00:39<01:51,  1.57s/it]
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Epoch 95 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 95 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.96it/s]
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Epoch 95 Training:  27%|██▋       | 26/96 [00:40<01:50,  1.57s/it]
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Epoch 95 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 95 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.96it/s]
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Epoch 95 Training:  28%|██▊       | 27/96 [00:42<01:48,  1.57s/it]
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Epoch 95 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 95 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.96it/s]
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Epoch 95 Training:  29%|██▉       | 28/96 [00:44<01:46,  1.57s/it]
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Epoch 95 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 95 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.77it/s]
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Epoch 95 Training:  30%|███       | 29/96 [00:45<01:46,  1.60s/it]
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Epoch 95 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 95 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.96it/s]
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Epoch 95 Training:  31%|███▏      | 30/96 [00:47<01:44,  1.59s/it]
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Epoch 95 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 95 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.96it/s]
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Epoch 95 Training:  32%|███▏      | 31/96 [00:48<01:42,  1.58s/it]
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Epoch 95 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 95 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 95 Training:  33%|███▎      | 32/96 [00:50<01:41,  1.58s/it]
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Epoch 95 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 95 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.95it/s]
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Epoch 95 Training:  34%|███▍      | 33/96 [00:52<01:39,  1.58s/it]
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Epoch 95 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 95 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.96it/s]
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Epoch 95 Training:  35%|███▌      | 34/96 [00:53<01:37,  1.58s/it]
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Epoch 95 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 95 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
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Epoch 95 Training:  36%|███▋      | 35/96 [00:55<01:36,  1.58s/it]
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Epoch 95 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 95 Training:  38%|███▊      | 36/96 [00:56<01:34,  1.58s/it]
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Epoch 95 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 95 Training:  39%|███▊      | 37/96 [00:58<01:33,  1.58s/it]
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Epoch 95 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 95 Training:  40%|███▉      | 38/96 [00:59<01:31,  1.58s/it]
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Epoch 95 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 95 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.91it/s]
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Epoch 95 Training:  41%|████      | 39/96 [01:01<01:30,  1.58s/it]
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Epoch 95 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 95 Training:  42%|████▏     | 40/96 [01:03<01:28,  1.58s/it]
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Epoch 95 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 95 Training:  43%|████▎     | 41/96 [01:04<01:26,  1.58s/it]
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Epoch 95 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 95 Training:  44%|████▍     | 42/96 [01:06<01:25,  1.58s/it]
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Epoch 95 Training:  45%|████▍     | 43/96 [01:07<01:23,  1.58s/it]
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Epoch 95 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 95 Training:  46%|████▌     | 44/96 [01:09<01:22,  1.58s/it]
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Epoch 95 Training:  47%|████▋     | 45/96 [01:10<01:20,  1.58s/it]
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Epoch 95 Training:  48%|████▊     | 46/96 [01:12<01:18,  1.58s/it]
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Epoch 95 Training:  49%|████▉     | 47/96 [01:14<01:17,  1.58s/it]
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Epoch 95 Training:  50%|█████     | 48/96 [01:15<01:15,  1.58s/it]
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Epoch 95 Training:  51%|█████     | 49/96 [01:17<01:14,  1.58s/it]
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Epoch 95 Training:  52%|█████▏    | 50/96 [01:18<01:12,  1.58s/it]
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Epoch 95 Training:  53%|█████▎    | 51/96 [01:20<01:11,  1.58s/it]
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Epoch 95 Training:  54%|█████▍    | 52/96 [01:22<01:09,  1.58s/it]
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Epoch 95 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s][2026-10-01 22:12:25] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0095 | Epoch 095] train/policy_loss=26.1343 train/unweighted_policy_loss=5.2269 train/loss=26.1343 train/grad_norm=0.0779
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+[2026-10-01 22:12:25] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0096 | Epoch 095] timing/rollout_seconds=180.3288 timing/feedback_seconds=6.3010 timing/optimization_seconds=152.6039 timing/cycle_seconds=335.9072
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+
Pointwise Rewards: ssim:   0%|          | 0/6 [00:00<?, ?it/s]
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Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:01<00:00,  5.63it/s]
+
Pointwise Rewards: dists:   0%|          | 0/6 [00:00<?, ?it/s]
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Pointwise Rewards: dists:  33%|███▎      | 2/6 [00:00<00:01,  3.25it/s]
Pointwise Rewards: dists:  50%|█████     | 3/6 [00:00<00:00,  3.27it/s]
Pointwise Rewards: dists:  67%|██████▋   | 4/6 [00:01<00:00,  3.22it/s]
Pointwise Rewards: dists:  83%|████████▎ | 5/6 [00:01<00:00,  3.21it/s]
Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.25it/s]
Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.24it/s]
+[2026-10-01 22:15:29] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0096 | Epoch 096] train/reward_dists_mean=0.8557 train/reward_dists_std=0.0285 train/reward_ssim_mean=0.6688 train/reward_ssim_std=0.1087 train/reward_dists_group_std_mean=0.0075 train/reward_dists_group_std_max=0.0173 train/reward_dists_group_std_min=0.0022 train/reward_dists_group_mean_std=0.0274 train/reward_ssim_group_std_mean=0.0093 train/reward_ssim_group_std_max=0.0206 train/reward_ssim_group_std_min=0.0034 train/reward_ssim_group_mean_std=0.1082 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.6742 train/adv_max=4.5502 train/adv_abs_mean=1.2438
+
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Epoch 96 Training:   3%|▎         | 3/96 [00:04<02:25,  1.56s/it]
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Epoch 96 Training:   4%|▍         | 4/96 [00:06<02:24,  1.57s/it]
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Epoch 96 Training:   5%|▌         | 5/96 [00:07<02:22,  1.57s/it]
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Epoch 96 Training:   6%|▋         | 6/96 [00:09<02:21,  1.57s/it]
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Epoch 96 Training:   7%|▋         | 7/96 [00:10<02:19,  1.57s/it]
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Epoch 96 Training:   8%|▊         | 8/96 [00:12<02:17,  1.57s/it]
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Epoch 96 Training:   9%|▉         | 9/96 [00:14<02:16,  1.57s/it]
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Epoch 96 Training:  10%|█         | 10/96 [00:15<02:15,  1.57s/it]
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Epoch 96 Training:  11%|█▏        | 11/96 [00:17<02:14,  1.58s/it]
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Epoch 96 Training:  12%|█▎        | 12/96 [00:18<02:12,  1.57s/it]
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Epoch 96 Training:  14%|█▎        | 13/96 [00:20<02:10,  1.58s/it]
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Epoch 96 Training:  15%|█▍        | 14/96 [00:21<02:09,  1.57s/it]
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Epoch 96 Training:  16%|█▌        | 15/96 [00:23<02:07,  1.57s/it]
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Epoch 96 Training:  17%|█▋        | 16/96 [00:25<02:06,  1.58s/it]
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Epoch 96 Training:  18%|█▊        | 17/96 [00:26<02:04,  1.57s/it]
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Epoch 96 Training:  19%|█▉        | 18/96 [00:28<02:02,  1.57s/it]
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Epoch 96 Training:  21%|██        | 20/96 [00:31<01:59,  1.58s/it]
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Epoch 96 Training:  22%|██▏       | 21/96 [00:33<01:58,  1.58s/it]
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Epoch 96 Training:  23%|██▎       | 22/96 [00:34<01:57,  1.58s/it]
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Epoch 96 Training:  24%|██▍       | 23/96 [00:36<01:55,  1.58s/it]
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Epoch 96 Training:  25%|██▌       | 24/96 [00:37<01:53,  1.58s/it]
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Epoch 96 Training:  26%|██▌       | 25/96 [00:39<01:51,  1.58s/it]
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Epoch 96 Training:  27%|██▋       | 26/96 [00:40<01:50,  1.58s/it]
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Epoch 96 Training:  28%|██▊       | 27/96 [00:42<01:48,  1.58s/it]
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Epoch 96 Training:  29%|██▉       | 28/96 [00:44<01:46,  1.57s/it]
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Epoch 96 Training:  30%|███       | 29/96 [00:45<01:45,  1.57s/it]
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Epoch 96 Training:  31%|███▏      | 30/96 [00:47<01:43,  1.57s/it]
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Epoch 96 Training:  32%|███▏      | 31/96 [00:48<01:42,  1.58s/it]
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Epoch 96 Training:  33%|███▎      | 32/96 [00:50<01:41,  1.58s/it]
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Epoch 96 Training:  34%|███▍      | 33/96 [00:51<01:39,  1.58s/it]
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Epoch 96 Training:  35%|███▌      | 34/96 [00:53<01:37,  1.58s/it]
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Epoch 96 Training:  36%|███▋      | 35/96 [00:55<01:36,  1.58s/it]
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Epoch 96 Training:  38%|███▊      | 36/96 [00:56<01:34,  1.58s/it]
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Epoch 96 Training:  39%|███▊      | 37/96 [00:58<01:32,  1.58s/it]
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Epoch 96 Training:  40%|███▉      | 38/96 [00:59<01:31,  1.57s/it]
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Epoch 96 Training:  41%|████      | 39/96 [01:01<01:29,  1.57s/it]
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Epoch 96 Training:  42%|████▏     | 40/96 [01:02<01:27,  1.57s/it]
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Epoch 96 Training:  43%|████▎     | 41/96 [01:04<01:26,  1.58s/it]
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Epoch 96 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 96 Training:  44%|████▍     | 42/96 [01:06<01:25,  1.57s/it]
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Epoch 96 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 96 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.89it/s]
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Epoch 96 Training:  45%|████▍     | 43/96 [01:07<01:23,  1.58s/it]
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Epoch 96 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 96 Training:  46%|████▌     | 44/96 [01:09<01:22,  1.58s/it]
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Epoch 96 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 96 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.91it/s]
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Epoch 96 Training:  47%|████▋     | 45/96 [01:10<01:20,  1.58s/it]
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Epoch 96 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 96 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.91it/s]
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Epoch 96 Training:  48%|████▊     | 46/96 [01:12<01:19,  1.58s/it]
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Epoch 96 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 96 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
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Epoch 96 Training:  49%|████▉     | 47/96 [01:14<01:17,  1.58s/it]
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Epoch 96 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 96 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.94it/s]
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Epoch 96 Training:  50%|█████     | 48/96 [01:15<01:15,  1.58s/it]
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Epoch 96 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 96 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.91it/s]
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Epoch 96 Training:  51%|█████     | 49/96 [01:17<01:14,  1.58s/it]
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Epoch 96 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 96 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.95it/s]
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Epoch 96 Training:  52%|█████▏    | 50/96 [01:18<01:12,  1.57s/it]
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Epoch 96 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 96 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.91it/s]
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Epoch 96 Training:  53%|█████▎    | 51/96 [01:20<01:10,  1.58s/it]
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Epoch 96 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.94it/s]
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Epoch 96 Training:  54%|█████▍    | 52/96 [01:21<01:09,  1.57s/it]
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Epoch 96 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 96 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 96 Training:  55%|█████▌    | 53/96 [01:23<01:07,  1.57s/it]
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Epoch 96 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
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Epoch 96 Training:  56%|█████▋    | 54/96 [01:25<01:06,  1.57s/it]
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Epoch 96 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 96 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.91it/s]
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Epoch 96 Training:  57%|█████▋    | 55/96 [01:26<01:04,  1.58s/it]
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Epoch 96 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 96 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.95it/s]
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Epoch 96 Training:  58%|█████▊    | 56/96 [01:28<01:02,  1.57s/it]
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Epoch 96 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 96 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.91it/s]
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Epoch 96 Training:  59%|█████▉    | 57/96 [01:29<01:01,  1.58s/it]
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Epoch 96 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 96 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
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Epoch 96 Training:  60%|██████    | 58/96 [01:31<00:59,  1.57s/it]
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Epoch 96 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 96 Training:  61%|██████▏   | 59/96 [01:32<00:58,  1.58s/it]
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Epoch 96 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 96 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
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Epoch 96 Training:  62%|██████▎   | 60/96 [01:34<00:56,  1.58s/it]
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Epoch 96 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 96 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.89it/s]
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Epoch 96 Training:  64%|██████▎   | 61/96 [01:36<00:55,  1.58s/it]
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Epoch 96 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.90it/s]
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Epoch 96 Training:  65%|██████▍   | 62/96 [01:37<00:53,  1.58s/it]
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Epoch 96 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 96 Training:  66%|██████▌   | 63/96 [01:39<00:52,  1.58s/it]
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Epoch 96 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 96 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.91it/s]
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Epoch 96 Training:  67%|██████▋   | 64/96 [01:40<00:50,  1.58s/it]
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Epoch 96 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.94it/s]
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Epoch 96 Training:  68%|██████▊   | 65/96 [01:42<00:48,  1.58s/it]
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Epoch 96 Training:  69%|██████▉   | 66/96 [01:43<00:47,  1.58s/it]
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Epoch 96 Training:  70%|██████▉   | 67/96 [01:45<00:45,  1.58s/it]
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Epoch 96 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
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Epoch 96 Training:  71%|███████   | 68/96 [01:47<00:44,  1.58s/it]
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Epoch 96 Training:  72%|███████▏  | 69/96 [01:48<00:42,  1.58s/it]
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Epoch 96 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 96 Training:  73%|███████▎  | 70/96 [01:50<00:41,  1.58s/it]
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Epoch 96 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.94it/s]
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Epoch 96 Training:  74%|███████▍  | 71/96 [01:51<00:39,  1.57s/it]
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Epoch 96 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.96it/s]
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Epoch 96 Training:  75%|███████▌  | 72/96 [01:53<00:37,  1.57s/it]
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Epoch 96 Training:  76%|███████▌  | 73/96 [01:55<00:36,  1.57s/it]
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Epoch 96 Training:  77%|███████▋  | 74/96 [01:56<00:34,  1.57s/it]
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Epoch 96 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.95it/s]
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Epoch 96 Training:  78%|███████▊  | 75/96 [01:58<00:33,  1.58s/it]
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Epoch 96 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.91it/s]
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Epoch 96 Training:  79%|███████▉  | 76/96 [01:59<00:31,  1.58s/it]
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Epoch 96 Training:  80%|████████  | 77/96 [02:01<00:29,  1.58s/it]
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Epoch 96 Training:  81%|████████▏ | 78/96 [02:02<00:28,  1.57s/it]
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Epoch 96 Training:  82%|████████▏ | 79/96 [02:04<00:26,  1.57s/it]
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Epoch 96 Timestep:  50%|█████     | 1/2 [00:00<00:00,  1.66it/s][2026-10-01 22:18:01] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0096 | Epoch 096] train/policy_loss=26.1976 train/unweighted_policy_loss=5.2395 train/loss=26.1976 train/grad_norm=0.0636
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+[2026-10-01 22:18:01] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0097 | Epoch 096] timing/rollout_seconds=180.1930 timing/feedback_seconds=5.8922 timing/optimization_seconds=152.1253 timing/cycle_seconds=335.2089
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+[2026-10-01 22:21:06] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0097 | Epoch 097] train/reward_dists_mean=0.8495 train/reward_dists_std=0.0297 train/reward_ssim_mean=0.6416 train/reward_ssim_std=0.0889 train/reward_dists_group_std_mean=0.0084 train/reward_dists_group_std_max=0.0151 train/reward_dists_group_std_min=0.0038 train/reward_dists_group_mean_std=0.0283 train/reward_ssim_group_std_mean=0.0084 train/reward_ssim_group_std_max=0.0149 train/reward_ssim_group_std_min=0.0041 train/reward_ssim_group_mean_std=0.0885 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.9425 train/adv_max=4.0280 train/adv_abs_mean=1.2045
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Epoch 97 Training:   4%|▍         | 4/96 [00:06<02:24,  1.57s/it]
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Epoch 97 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 97 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.90it/s]
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Epoch 97 Training:   5%|▌         | 5/96 [00:07<02:23,  1.58s/it]
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Epoch 97 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 97 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.90it/s]
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Epoch 97 Training:   6%|▋         | 6/96 [00:09<02:22,  1.58s/it]
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Epoch 97 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 97 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.98it/s]
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Epoch 97 Training:   7%|▋         | 7/96 [00:11<02:20,  1.58s/it]
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Epoch 97 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 97 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 97 Training:   8%|▊         | 8/96 [00:12<02:18,  1.58s/it]
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Epoch 97 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 97 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.98it/s]
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Epoch 97 Training:   9%|▉         | 9/96 [00:14<02:17,  1.58s/it]
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Epoch 97 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 97 Training:  10%|█         | 10/96 [00:15<02:15,  1.57s/it]
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Epoch 97 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 97 Training:  11%|█▏        | 11/96 [00:17<02:14,  1.58s/it]
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Epoch 97 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 97 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.97it/s]
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Epoch 97 Training:  12%|█▎        | 12/96 [00:18<02:13,  1.58s/it]
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Epoch 97 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 97 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.94it/s]
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Epoch 97 Training:  14%|█▎        | 13/96 [00:20<02:11,  1.58s/it]
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Epoch 97 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 97 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.99it/s]
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Epoch 97 Training:  15%|█▍        | 14/96 [00:22<02:09,  1.58s/it]
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Epoch 97 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 97 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.95it/s]
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Epoch 97 Training:  16%|█▌        | 15/96 [00:23<02:07,  1.58s/it]
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Epoch 97 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 97 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.97it/s]
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Epoch 97 Training:  17%|█▋        | 16/96 [00:25<02:06,  1.58s/it]
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Epoch 97 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 97 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 97 Training:  18%|█▊        | 17/96 [00:26<02:04,  1.58s/it]
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Epoch 97 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 97 Training:  19%|█▉        | 18/96 [00:28<02:03,  1.58s/it]
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Epoch 97 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 97 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 97 Training:  20%|█▉        | 19/96 [00:29<02:01,  1.58s/it]
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Epoch 97 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 97 Training:  21%|██        | 20/96 [00:31<01:59,  1.58s/it]
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Epoch 97 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 97 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
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Epoch 97 Training:  22%|██▏       | 21/96 [00:33<01:58,  1.58s/it]
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Epoch 97 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 97 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.00it/s]
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Epoch 97 Training:  23%|██▎       | 22/96 [00:34<01:56,  1.58s/it]
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Epoch 97 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 97 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 97 Training:  24%|██▍       | 23/96 [00:36<01:55,  1.58s/it]
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Epoch 97 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 97 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 97 Training:  25%|██▌       | 24/96 [00:37<01:53,  1.58s/it]
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Epoch 97 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 97 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.95it/s]
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Epoch 97 Training:  26%|██▌       | 25/96 [00:39<01:51,  1.58s/it]
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Epoch 97 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 97 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.90it/s]
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Epoch 97 Training:  27%|██▋       | 26/96 [00:40<01:50,  1.58s/it]
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Epoch 97 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 97 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.01it/s]
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Epoch 97 Training:  28%|██▊       | 27/96 [00:42<01:48,  1.58s/it]
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Epoch 97 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 97 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.96it/s]
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Epoch 97 Training:  29%|██▉       | 28/96 [00:44<01:47,  1.58s/it]
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Epoch 97 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 97 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.95it/s]
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Epoch 97 Training:  30%|███       | 29/96 [00:45<01:45,  1.57s/it]
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Epoch 97 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 97 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
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Epoch 97 Training:  31%|███▏      | 30/96 [00:47<01:43,  1.57s/it]
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Epoch 97 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 97 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
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Epoch 97 Training:  32%|███▏      | 31/96 [00:48<01:42,  1.58s/it]
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Epoch 97 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 97 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 97 Training:  33%|███▎      | 32/96 [00:50<01:40,  1.58s/it]
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Epoch 97 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 97 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.97it/s]
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Epoch 97 Training:  34%|███▍      | 33/96 [00:52<01:39,  1.58s/it]
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Epoch 97 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 97 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.90it/s]
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Epoch 97 Training:  35%|███▌      | 34/96 [00:53<01:38,  1.58s/it]
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Epoch 97 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 97 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.96it/s]
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Epoch 97 Training:  36%|███▋      | 35/96 [00:55<01:36,  1.58s/it]
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Epoch 97 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 97 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.90it/s]
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Epoch 97 Training:  38%|███▊      | 36/96 [00:56<01:35,  1.59s/it]
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Epoch 97 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 97 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.89it/s]
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Epoch 97 Training:  39%|███▊      | 37/96 [00:58<01:33,  1.59s/it]
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Epoch 97 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 97 Training:  40%|███▉      | 38/96 [00:59<01:32,  1.59s/it]
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Epoch 97 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 97 Training:  41%|████      | 39/96 [01:01<01:30,  1.59s/it]
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Epoch 97 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 97 Training:  42%|████▏     | 40/96 [01:03<01:28,  1.59s/it]
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Epoch 97 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 97 Training:  43%|████▎     | 41/96 [01:04<01:27,  1.59s/it]
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Epoch 97 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 97 Training:  44%|████▍     | 42/96 [01:06<01:25,  1.59s/it]
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Epoch 97 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 97 Training:  45%|████▍     | 43/96 [01:07<01:24,  1.59s/it]
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Epoch 97 Training:  46%|████▌     | 44/96 [01:09<01:22,  1.59s/it]
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Epoch 97 Training:  47%|████▋     | 45/96 [01:11<01:21,  1.59s/it]
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Epoch 97 Training:  48%|████▊     | 46/96 [01:12<01:19,  1.59s/it]
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Epoch 97 Training:  49%|████▉     | 47/96 [01:14<01:18,  1.59s/it]
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Epoch 97 Training:  50%|█████     | 48/96 [01:15<01:16,  1.59s/it]
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Epoch 97 Training:  51%|█████     | 49/96 [01:17<01:14,  1.59s/it]
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Epoch 97 Training:  52%|█████▏    | 50/96 [01:19<01:13,  1.59s/it]
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Epoch 97 Training:  53%|█████▎    | 51/96 [01:20<01:11,  1.59s/it]
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Epoch 97 Training:  54%|█████▍    | 52/96 [01:22<01:09,  1.59s/it]
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Epoch 97 Training:  55%|█████▌    | 53/96 [01:23<01:08,  1.59s/it]
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Epoch 97 Training:  56%|█████▋    | 54/96 [01:25<01:06,  1.59s/it]
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Epoch 97 Training:  57%|█████▋    | 55/96 [01:27<01:05,  1.59s/it]
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Epoch 97 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s][2026-10-01 22:23:38] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0097 | Epoch 097] train/policy_loss=26.7217 train/unweighted_policy_loss=5.3443 train/loss=26.7217 train/grad_norm=0.0674
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+[2026-10-01 22:23:38] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0098 | Epoch 097] timing/rollout_seconds=180.0504 timing/feedback_seconds=7.3524 timing/optimization_seconds=152.9877 timing/cycle_seconds=337.6339
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+
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+
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+[2026-10-01 22:26:42] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0098 | Epoch 098] train/reward_dists_mean=0.8488 train/reward_dists_std=0.0326 train/reward_ssim_mean=0.6781 train/reward_ssim_std=0.1033 train/reward_dists_group_std_mean=0.0082 train/reward_dists_group_std_max=0.0304 train/reward_dists_group_std_min=0.0033 train/reward_dists_group_mean_std=0.0313 train/reward_ssim_group_std_mean=0.0087 train/reward_ssim_group_std_max=0.0216 train/reward_ssim_group_std_min=0.0036 train/reward_ssim_group_mean_std=0.1029 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.2820 train/adv_max=4.4155 train/adv_abs_mean=1.1945
+
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Epoch 98 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 98 Training:  33%|███▎      | 32/96 [00:50<01:41,  1.59s/it]
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Epoch 98 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 98 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 98 Training:  34%|███▍      | 33/96 [00:52<01:40,  1.59s/it]
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Epoch 98 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 98 Training:  35%|███▌      | 34/96 [00:53<01:38,  1.59s/it]
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Epoch 98 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.96it/s]
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Epoch 98 Training:  36%|███▋      | 35/96 [00:55<01:36,  1.59s/it]
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Epoch 98 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.91it/s]
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Epoch 98 Training:  38%|███▊      | 36/96 [00:57<01:35,  1.59s/it]
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Epoch 98 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 98 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.94it/s]
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Epoch 98 Training:  39%|███▊      | 37/96 [00:58<01:33,  1.59s/it]
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Epoch 98 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 98 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.90it/s]
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Epoch 98 Training:  40%|███▉      | 38/96 [01:00<01:32,  1.59s/it]
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Epoch 98 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.94it/s]
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Epoch 98 Training:  41%|████      | 39/96 [01:01<01:30,  1.59s/it]
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Epoch 98 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.91it/s]
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Epoch 98 Training:  42%|████▏     | 40/96 [01:03<01:28,  1.59s/it]
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Epoch 98 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 98 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
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Epoch 98 Training:  43%|████▎     | 41/96 [01:04<01:27,  1.59s/it]
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Epoch 98 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 98 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 98 Training:  44%|████▍     | 42/96 [01:06<01:25,  1.59s/it]
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Epoch 98 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 98 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 98 Training:  45%|████▍     | 43/96 [01:08<01:24,  1.59s/it]
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Epoch 98 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 98 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 98 Training:  46%|████▌     | 44/96 [01:09<01:22,  1.59s/it]
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Epoch 98 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 98 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.95it/s]
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Epoch 98 Training:  47%|████▋     | 45/96 [01:11<01:20,  1.59s/it]
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Epoch 98 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 98 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.95it/s]
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Epoch 98 Training:  48%|████▊     | 46/96 [01:12<01:19,  1.58s/it]
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Epoch 98 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 98 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
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Epoch 98 Training:  49%|████▉     | 47/96 [01:14<01:17,  1.59s/it]
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Epoch 98 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 98 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.94it/s]
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Epoch 98 Training:  50%|█████     | 48/96 [01:16<01:15,  1.58s/it]
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Epoch 98 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 98 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
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Epoch 98 Training:  51%|█████     | 49/96 [01:17<01:14,  1.58s/it]
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Epoch 98 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 98 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 98 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 98 Training:  52%|█████▏    | 50/96 [01:19<01:12,  1.59s/it]
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Epoch 98 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 98 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 98 Training:  53%|█████▎    | 51/96 [01:20<01:11,  1.59s/it]
+
Epoch 98 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 98 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
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Epoch 98 Training:  54%|█████▍    | 52/96 [01:22<01:09,  1.58s/it]
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Epoch 98 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 98 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
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Epoch 98 Training:  55%|█████▌    | 53/96 [01:23<01:08,  1.58s/it]
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Epoch 98 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 98 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.94it/s]
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Epoch 98 Training:  56%|█████▋    | 54/96 [01:25<01:06,  1.58s/it]
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Epoch 98 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 98 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.94it/s]
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Epoch 98 Training:  57%|█████▋    | 55/96 [01:27<01:04,  1.58s/it]
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Epoch 98 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 98 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.95it/s]
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Epoch 98 Training:  58%|█████▊    | 56/96 [01:28<01:03,  1.58s/it]
+
Epoch 98 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 98 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.89it/s]
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Epoch 98 Training:  59%|█████▉    | 57/96 [01:30<01:01,  1.59s/it]
+
Epoch 98 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 98 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 98 Training:  60%|██████    | 58/96 [01:31<01:00,  1.59s/it]
+
Epoch 98 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 98 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.94it/s]
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Epoch 98 Training:  61%|██████▏   | 59/96 [01:33<00:58,  1.59s/it]
+
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Epoch 98 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
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Epoch 98 Training:  62%|██████▎   | 60/96 [01:35<00:57,  1.59s/it]
+
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Epoch 98 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 98 Training:  64%|██████▎   | 61/96 [01:36<00:55,  1.58s/it]
+
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Epoch 98 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.91it/s]
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Epoch 98 Training:  65%|██████▍   | 62/96 [01:38<00:53,  1.59s/it]
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Epoch 98 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.90it/s]
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Epoch 98 Training:  66%|██████▌   | 63/96 [01:39<00:52,  1.59s/it]
+
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Epoch 98 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.91it/s]
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Epoch 98 Training:  67%|██████▋   | 64/96 [01:41<00:50,  1.59s/it]
+
Epoch 98 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 98 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.90it/s]
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Epoch 98 Training:  68%|██████▊   | 65/96 [01:43<00:49,  1.59s/it]
+
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Epoch 98 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.91it/s]
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Epoch 98 Training:  69%|██████▉   | 66/96 [01:44<00:47,  1.59s/it]
+
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Epoch 98 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.90it/s]
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Epoch 98 Training:  70%|██████▉   | 67/96 [01:46<00:46,  1.59s/it]
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Epoch 98 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.89it/s]
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Epoch 98 Training:  71%|███████   | 68/96 [01:47<00:44,  1.59s/it]
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Epoch 98 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.91it/s]
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Epoch 98 Training:  72%|███████▏  | 69/96 [01:49<00:42,  1.59s/it]
+
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Epoch 98 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.90it/s]
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Epoch 98 Training:  73%|███████▎  | 70/96 [01:50<00:41,  1.59s/it]
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Epoch 98 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.90it/s]
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Epoch 98 Training:  74%|███████▍  | 71/96 [01:52<00:39,  1.59s/it]
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Epoch 98 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
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Epoch 98 Training:  75%|███████▌  | 72/96 [01:54<00:38,  1.59s/it]
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Epoch 98 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.91it/s]
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Epoch 98 Training:  76%|███████▌  | 73/96 [01:55<00:36,  1.59s/it]
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Epoch 98 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.91it/s]
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Epoch 98 Training:  77%|███████▋  | 74/96 [01:57<00:34,  1.59s/it]
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Epoch 98 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.94it/s]
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Epoch 98 Training:  78%|███████▊  | 75/96 [01:58<00:33,  1.58s/it]
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Epoch 98 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 98 Training:  79%|███████▉  | 76/96 [02:00<00:31,  1.58s/it]
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Epoch 98 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.91it/s]
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Epoch 98 Training:  80%|████████  | 77/96 [02:02<00:30,  1.58s/it]
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Epoch 98 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.91it/s]
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Epoch 98 Training:  81%|████████▏ | 78/96 [02:03<00:28,  1.58s/it]
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Epoch 98 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 98 Training:  82%|████████▏ | 79/96 [02:05<00:26,  1.58s/it]
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Epoch 98 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
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Epoch 98 Training:  83%|████████▎ | 80/96 [02:06<00:25,  1.58s/it]
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Epoch 98 Training:  84%|████████▍ | 81/96 [02:08<00:23,  1.58s/it]
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Epoch 98 Training:  85%|████████▌ | 82/96 [02:09<00:22,  1.58s/it]
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Epoch 98 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s][2026-10-01 22:29:14] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0098 | Epoch 098] train/policy_loss=26.5446 train/unweighted_policy_loss=5.3089 train/loss=26.5446 train/grad_norm=0.0697
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+[2026-10-01 22:29:14] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0099 | Epoch 098] timing/rollout_seconds=179.7679 timing/feedback_seconds=5.5727 timing/optimization_seconds=152.8342 timing/cycle_seconds=335.5156
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+
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+[2026-10-01 22:32:18] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0099 | Epoch 099] train/reward_dists_mean=0.8453 train/reward_dists_std=0.0312 train/reward_ssim_mean=0.6614 train/reward_ssim_std=0.1330 train/reward_dists_group_std_mean=0.0086 train/reward_dists_group_std_max=0.0186 train/reward_dists_group_std_min=0.0030 train/reward_dists_group_mean_std=0.0298 train/reward_ssim_group_std_mean=0.0081 train/reward_ssim_group_std_max=0.0242 train/reward_ssim_group_std_min=0.0028 train/reward_ssim_group_mean_std=0.1327 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.4231 train/adv_max=4.5914 train/adv_abs_mean=1.2232
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Epoch 99 Training:   7%|▋         | 7/96 [00:10<02:20,  1.58s/it]
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Epoch 99 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 99 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.15it/s]
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Epoch 99 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.91it/s]
+
                                                                
Epoch 99 Training:   8%|▊         | 8/96 [00:12<02:19,  1.58s/it]
+
Epoch 99 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 99 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 99 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
+
                                                                
Epoch 99 Training:   9%|▉         | 9/96 [00:14<02:17,  1.58s/it]
+
Epoch 99 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 99 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.15it/s]
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Epoch 99 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
+
                                                                
Epoch 99 Training:  10%|█         | 10/96 [00:15<02:15,  1.58s/it]
+
Epoch 99 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 99 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 99 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
+
                                                                
Epoch 99 Training:  11%|█▏        | 11/96 [00:17<02:14,  1.58s/it]
+
Epoch 99 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 99 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.15it/s]
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Epoch 99 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
+
                                                                
Epoch 99 Training:  12%|█▎        | 12/96 [00:18<02:12,  1.58s/it]
+
Epoch 99 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 99 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.15it/s]
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Epoch 99 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
+
                                                                
Epoch 99 Training:  14%|█▎        | 13/96 [00:20<02:10,  1.58s/it]
+
Epoch 99 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 99 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.15it/s]
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Epoch 99 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.98it/s]
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Epoch 99 Training:  15%|█▍        | 14/96 [00:22<02:09,  1.58s/it]
+
Epoch 99 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 99 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.15it/s]
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Epoch 99 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.89it/s]
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Epoch 99 Training:  16%|█▌        | 15/96 [00:23<02:08,  1.58s/it]
+
Epoch 99 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 99 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.15it/s]
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Epoch 99 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.91it/s]
+
                                                                
Epoch 99 Training:  17%|█▋        | 16/96 [00:25<02:06,  1.58s/it]
+
Epoch 99 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 99 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.15it/s]
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Epoch 99 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.96it/s]
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Epoch 99 Training:  18%|█▊        | 17/96 [00:26<02:05,  1.58s/it]
+
Epoch 99 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 99 Timestep:  50%|█████     | 1/2 [00:00<00:00,  1.32it/s]
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Epoch 99 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.63it/s]
+
                                                                
Epoch 99 Training:  19%|█▉        | 18/96 [00:28<02:10,  1.67s/it]
+
Epoch 99 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 99 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.15it/s]
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Epoch 99 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.98it/s]
+
                                                                
Epoch 99 Training:  20%|█▉        | 19/96 [00:30<02:06,  1.64s/it]
+
Epoch 99 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 99 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.15it/s]
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Epoch 99 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
+
                                                                
Epoch 99 Training:  21%|██        | 20/96 [00:31<02:03,  1.62s/it]
+
Epoch 99 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 99 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.15it/s]
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Epoch 99 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
+
                                                                
Epoch 99 Training:  22%|██▏       | 21/96 [00:33<02:01,  1.61s/it]
+
Epoch 99 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 99 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.15it/s]
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Epoch 99 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
+
                                                                
Epoch 99 Training:  23%|██▎       | 22/96 [00:35<01:58,  1.60s/it]
+
Epoch 99 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 99 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.15it/s]
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Epoch 99 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.97it/s]
+
                                                                
Epoch 99 Training:  24%|██▍       | 23/96 [00:36<01:56,  1.60s/it]
+
Epoch 99 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 99 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.15it/s]
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Epoch 99 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.88it/s]
+
                                                                
Epoch 99 Training:  25%|██▌       | 24/96 [00:38<01:55,  1.60s/it]
+
Epoch 99 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 99 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.15it/s]
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Epoch 99 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.91it/s]
+
                                                                
Epoch 99 Training:  26%|██▌       | 25/96 [00:39<01:53,  1.59s/it]
+
Epoch 99 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 99 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 99 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
+
                                                                
Epoch 99 Training:  27%|██▋       | 26/96 [00:41<01:51,  1.60s/it]
+
Epoch 99 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 99 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.15it/s]
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Epoch 99 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
+
                                                                
Epoch 99 Training:  28%|██▊       | 27/96 [00:42<01:50,  1.59s/it]
+
Epoch 99 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 99 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.15it/s]
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Epoch 99 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.90it/s]
+
                                                                
Epoch 99 Training:  29%|██▉       | 28/96 [00:44<01:48,  1.59s/it]
+
Epoch 99 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 99 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.15it/s]
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Epoch 99 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.87it/s]
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Epoch 99 Training:  30%|███       | 29/96 [00:46<01:46,  1.60s/it]
+
Epoch 99 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 99 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 99 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
+
                                                                
Epoch 99 Training:  31%|███▏      | 30/96 [00:47<01:45,  1.59s/it]
+
Epoch 99 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 99 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.15it/s]
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Epoch 99 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.89it/s]
+
                                                                
Epoch 99 Training:  32%|███▏      | 31/96 [00:49<01:43,  1.59s/it]
+
Epoch 99 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 99 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.15it/s]
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Epoch 99 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
+
                                                                
Epoch 99 Training:  33%|███▎      | 32/96 [00:50<01:42,  1.60s/it]
+
Epoch 99 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 99 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 99 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.91it/s]
+
                                                                
Epoch 99 Training:  34%|███▍      | 33/96 [00:52<01:40,  1.59s/it]
+
Epoch 99 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 99 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 99 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.88it/s]
+
                                                                
Epoch 99 Training:  35%|███▌      | 34/96 [00:54<01:38,  1.59s/it]
+
Epoch 99 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 99 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.15it/s]
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Epoch 99 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
+
                                                                
Epoch 99 Training:  36%|███▋      | 35/96 [00:55<01:37,  1.60s/it]
+
Epoch 99 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 99 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.15it/s]
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Epoch 99 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
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Epoch 99 Training:  38%|███▊      | 36/96 [00:57<01:35,  1.59s/it]
+
Epoch 99 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 99 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.15it/s]
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Epoch 99 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.88it/s]
+
                                                                
Epoch 99 Training:  39%|███▊      | 37/96 [00:58<01:33,  1.59s/it]
+
Epoch 99 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 99 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.15it/s]
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Epoch 99 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.95it/s]
+
                                                                
Epoch 99 Training:  40%|███▉      | 38/96 [01:00<01:32,  1.59s/it]
+
Epoch 99 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 99 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.15it/s]
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Epoch 99 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.91it/s]
+
                                                                
Epoch 99 Training:  41%|████      | 39/96 [01:02<01:30,  1.59s/it]
+
Epoch 99 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 99 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.15it/s]
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Epoch 99 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.89it/s]
+
                                                                
Epoch 99 Training:  42%|████▏     | 40/96 [01:03<01:29,  1.59s/it]
+
Epoch 99 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 99 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.15it/s]
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Epoch 99 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.88it/s]
+
                                                                
Epoch 99 Training:  43%|████▎     | 41/96 [01:05<01:27,  1.59s/it]
+
Epoch 99 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 99 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.15it/s]
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Epoch 99 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.90it/s]
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Epoch 99 Training:  44%|████▍     | 42/96 [01:06<01:25,  1.59s/it]
+
Epoch 99 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 99 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.15it/s]
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Epoch 99 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
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Epoch 99 Training:  45%|████▍     | 43/96 [01:08<01:24,  1.59s/it]
+
Epoch 99 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 99 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.15it/s]
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Epoch 99 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.88it/s]
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Epoch 99 Training:  46%|████▌     | 44/96 [01:10<01:22,  1.59s/it]
+
Epoch 99 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 99 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.15it/s]
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Epoch 99 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
+
                                                                
Epoch 99 Training:  47%|████▋     | 45/96 [01:11<01:21,  1.59s/it]
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Epoch 99 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 99 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.15it/s]
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Epoch 99 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
+
                                                                
Epoch 99 Training:  48%|████▊     | 46/96 [01:13<01:19,  1.59s/it]
+
Epoch 99 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 99 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.15it/s]
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Epoch 99 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.87it/s]
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Epoch 99 Training:  49%|████▉     | 47/96 [01:14<01:17,  1.59s/it]
+
Epoch 99 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 99 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.15it/s]
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Epoch 99 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 99 Training:  50%|█████     | 48/96 [01:16<01:16,  1.59s/it]
+
Epoch 99 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 99 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 99 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.89it/s]
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Epoch 99 Training:  51%|█████     | 49/96 [01:17<01:14,  1.59s/it]
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Epoch 99 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 99 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 99 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.91it/s]
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Epoch 99 Training:  52%|█████▏    | 50/96 [01:19<01:13,  1.59s/it]
+
Epoch 99 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 99 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 99 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.89it/s]
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Epoch 99 Training:  53%|█████▎    | 51/96 [01:21<01:11,  1.59s/it]
+
Epoch 99 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 99 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 99 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
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Epoch 99 Training:  54%|█████▍    | 52/96 [01:22<01:09,  1.59s/it]
+
Epoch 99 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 99 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 99 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
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Epoch 99 Training:  55%|█████▌    | 53/96 [01:24<01:08,  1.58s/it]
+
Epoch 99 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 99 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
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Epoch 99 Training:  56%|█████▋    | 54/96 [01:25<01:06,  1.58s/it]
+
Epoch 99 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 99 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 99 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.92it/s]
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Epoch 99 Training:  57%|█████▋    | 55/96 [01:27<01:04,  1.58s/it]
+
Epoch 99 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 99 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s]
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Epoch 99 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.93it/s]
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Epoch 99 Training:  58%|█████▊    | 56/96 [01:29<01:03,  1.58s/it]
+
Epoch 99 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 99 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.90it/s]
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Epoch 99 Training:  59%|█████▉    | 57/96 [01:30<01:01,  1.58s/it]
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Epoch 99 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 99 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.03it/s]
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Epoch 99 Training:  60%|██████    | 58/96 [01:32<01:00,  1.59s/it]
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Epoch 99 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 99 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.16it/s][2026-10-01 22:34:51] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0099 | Epoch 099] train/policy_loss=27.1105 train/unweighted_policy_loss=5.4221 train/loss=27.1105 train/grad_norm=0.0726
+
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+[2026-10-01 22:34:51] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0100 | Epoch 099] timing/rollout_seconds=180.2269 timing/feedback_seconds=6.1273 timing/optimization_seconds=153.4621 timing/cycle_seconds=336.7312
+[2026-10-01 22:34:51] [Rank 0] [INFO] [flow_factory.models.abc]: Saving LoRA weights for transformer role=base to /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/runs/klein4b_nft_flir_full_ssim_dists/saves/klein4b_nft_flir_full_ssim_dists/checkpoints/checkpoint-100
+[2026-10-01 22:34:51] [Rank 0] [INFO] [flow_factory.models.abc]: Checkpoint saved successfully to /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/runs/klein4b_nft_flir_full_ssim_dists/saves/klein4b_nft_flir_full_ssim_dists/checkpoints/checkpoint-100
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+
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+[2026-10-01 22:35:44] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0100 | Epoch 100] eval/flir_rl_full/reward_dists_mean=0.8406 eval/flir_rl_full/reward_dists_std=0.0347 eval/flir_rl_full/reward_psnr_mean=19.7968 eval/flir_rl_full/reward_psnr_std=2.6775 eval/flir_rl_full/reward_ssim_mean=0.6270 eval/flir_rl_full/reward_ssim_std=0.0796
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+W1001 22:36:19.022000 4088694 site-packages/torch/distributed/elastic/multiprocessing/api.py:1010] Sending process 4089302 closing signal SIGTERM
+W1001 22:36:19.023000 4088694 site-packages/torch/distributed/elastic/multiprocessing/api.py:1010] Sending process 4089303 closing signal SIGTERM
+W1001 22:36:19.024000 4088694 site-packages/torch/distributed/elastic/multiprocessing/api.py:1010] Sending process 4089304 closing signal SIGTERM
+W1001 22:36:19.024000 4088694 site-packages/torch/distributed/elastic/multiprocessing/api.py:1010] Sending process 4089305 closing signal SIGTERM
+W1001 22:36:19.024000 4088694 site-packages/torch/distributed/elastic/multiprocessing/api.py:1010] Sending process 4089306 closing signal SIGTERM
+W1001 22:36:19.025000 4088694 site-packages/torch/distributed/elastic/multiprocessing/api.py:1010] Sending process 4089307 closing signal SIGTERM
+W1001 22:36:19.025000 4088694 site-packages/torch/distributed/elastic/multiprocessing/api.py:1010] Sending process 4089308 closing signal SIGTERM
+W1001 22:36:19.025000 4088694 site-packages/torch/distributed/elastic/multiprocessing/api.py:1010] Sending process 4089309 closing signal SIGTERM
+Traceback (most recent call last):
+  File "/opt/conda/envs/ff-qi21/bin/accelerate", line 6, in <module>
+    sys.exit(main())
+  File "/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/accelerate/commands/accelerate_cli.py", line 50, in main
+    args.func(args)
+  File "/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/accelerate/commands/launch.py", line 1392, in launch_command
+    deepspeed_launcher(args)
+  File "/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/accelerate/commands/launch.py", line 1076, in deepspeed_launcher
+    distrib_run.run(args)
+  File "/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torch/distributed/run.py", line 982, in run
+    elastic_launch(
+  File "/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torch/distributed/launcher/api.py", line 170, in __call__
+    return launch_agent(self._config, self._entrypoint, list(args))
+  File "/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torch/distributed/launcher/api.py", line 308, in launch_agent
+    result = agent.run()
+  File "/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torch/distributed/elastic/metrics/api.py", line 134, in wrapper
+    result = f(*args, **kwargs)
+  File "/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torch/distributed/elastic/agent/server/api.py", line 731, in run
+    result = self._invoke_run(role)
+  File "/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torch/distributed/elastic/agent/server/api.py", line 908, in _invoke_run
+    time.sleep(monitor_interval)
+  File "/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torch/distributed/elastic/multiprocessing/api.py", line 86, in _terminate_process_handler
+    raise SignalException(f"Process {os.getpid()} got signal: {sigval}", sigval=sigval)
+torch.distributed.elastic.multiprocessing.api.SignalException: Process 4088694 got signal: 15
diff --git a/runs/klein4b_nft_flir_full_ssim_dists_from_ep72/saves/klein4b_nft_flir_full_ssim_dists_from_ep72/checkpoints/checkpoint-0/README.md b/runs/klein4b_nft_flir_full_ssim_dists_from_ep72/saves/klein4b_nft_flir_full_ssim_dists_from_ep72/checkpoints/checkpoint-0/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..2b78a7a995873d5db3090fca56885fe89cdf97e1
--- /dev/null
+++ b/runs/klein4b_nft_flir_full_ssim_dists_from_ep72/saves/klein4b_nft_flir_full_ssim_dists_from_ep72/checkpoints/checkpoint-0/README.md
@@ -0,0 +1,204 @@
+---
+base_model: /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/flow_factory_gpu_gate_models/FLUX.2-klein-base-4B/transformer
+library_name: peft
+tags:
+- lora
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]
+### Framework versions
+
+- PEFT 0.20.0
\ No newline at end of file
diff --git a/runs/klein4b_nft_flir_full_ssim_dists_from_ep72/saves/klein4b_nft_flir_full_ssim_dists_from_ep72/checkpoints/checkpoint-0/adapter_config.json b/runs/klein4b_nft_flir_full_ssim_dists_from_ep72/saves/klein4b_nft_flir_full_ssim_dists_from_ep72/checkpoints/checkpoint-0/adapter_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..5f9cf3a62b2c39b4a6732da0fe5cc18764b942af
--- /dev/null
+++ b/runs/klein4b_nft_flir_full_ssim_dists_from_ep72/saves/klein4b_nft_flir_full_ssim_dists_from_ep72/checkpoints/checkpoint-0/adapter_config.json
@@ -0,0 +1,59 @@
+{
+  "alora_invocation_tokens": null,
+  "alpha_pattern": {},
+  "arrow_config": null,
+  "auto_mapping": {
+    "base_model_class": "Flux2Transformer2DModel",
+    "parent_library": "diffusers.models.transformers.transformer_flux2"
+  },
+  "base_model_name_or_path": null,
+  "bias": "none",
+  "corda_config": null,
+  "ensure_weight_tying": false,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": "gaussian",
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 128,
+  "lora_bias": false,
+  "lora_dropout": 0.0,
+  "lora_ga_config": null,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "monteclora_config": null,
+  "peft_type": "LORA",
+  "peft_version": "0.20.0",
+  "qalora_group_size": 16,
+  "r": 64,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "attn.add_k_proj",
+    "attn.add_q_proj",
+    "attn.to_v",
+    "attn.add_v_proj",
+    "ff.linear_out",
+    "attn.to_q",
+    "ff_context.linear_in",
+    "attn.to_qkv_mlp_proj",
+    "ff.linear_in",
+    "ff_context.linear_out",
+    "attn.to_add_out",
+    "attn.to_k",
+    "attn.to_out.0"
+  ],
+  "target_parameters": null,
+  "task_type": null,
+  "trainable_token_indices": null,
+  "use_bdlora": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false,
+  "velora_config": null
+}
\ No newline at end of file
diff --git a/runs/klein4b_nft_flir_full_ssim_dists_from_ep72/saves/klein4b_nft_flir_full_ssim_dists_from_ep72/checkpoints/checkpoint-0/manifest.json b/runs/klein4b_nft_flir_full_ssim_dists_from_ep72/saves/klein4b_nft_flir_full_ssim_dists_from_ep72/checkpoints/checkpoint-0/manifest.json
new file mode 100644
index 0000000000000000000000000000000000000000..4443fd995e0f6f3bd9b7e3dacea93df8d345ecfb
--- /dev/null
+++ b/runs/klein4b_nft_flir_full_ssim_dists_from_ep72/saves/klein4b_nft_flir_full_ssim_dists_from_ep72/checkpoints/checkpoint-0/manifest.json
@@ -0,0 +1,16 @@
+{
+  "base_role": "base",
+  "entries": [
+    {
+      "component": "transformer",
+      "path": ".",
+      "role": "base"
+    }
+  ],
+  "finetune_type": "lora",
+  "format_version": 1,
+  "primary": {
+    "component": "transformer",
+    "path": "."
+  }
+}
diff --git a/runs/klein4b_nft_flir_full_ssim_dists_from_ep72/saves/klein4b_nft_flir_full_ssim_dists_from_ep72/checkpoints/checkpoint-80/README.md b/runs/klein4b_nft_flir_full_ssim_dists_from_ep72/saves/klein4b_nft_flir_full_ssim_dists_from_ep72/checkpoints/checkpoint-80/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..2b78a7a995873d5db3090fca56885fe89cdf97e1
--- /dev/null
+++ b/runs/klein4b_nft_flir_full_ssim_dists_from_ep72/saves/klein4b_nft_flir_full_ssim_dists_from_ep72/checkpoints/checkpoint-80/README.md
@@ -0,0 +1,204 @@
+---
+base_model: /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/flow_factory_gpu_gate_models/FLUX.2-klein-base-4B/transformer
+library_name: peft
+tags:
+- lora
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]
+### Framework versions
+
+- PEFT 0.20.0
\ No newline at end of file
diff --git a/runs/klein4b_nft_flir_full_ssim_dists_from_ep72/saves/klein4b_nft_flir_full_ssim_dists_from_ep72/checkpoints/checkpoint-80/adapter_config.json b/runs/klein4b_nft_flir_full_ssim_dists_from_ep72/saves/klein4b_nft_flir_full_ssim_dists_from_ep72/checkpoints/checkpoint-80/adapter_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..5f9cf3a62b2c39b4a6732da0fe5cc18764b942af
--- /dev/null
+++ b/runs/klein4b_nft_flir_full_ssim_dists_from_ep72/saves/klein4b_nft_flir_full_ssim_dists_from_ep72/checkpoints/checkpoint-80/adapter_config.json
@@ -0,0 +1,59 @@
+{
+  "alora_invocation_tokens": null,
+  "alpha_pattern": {},
+  "arrow_config": null,
+  "auto_mapping": {
+    "base_model_class": "Flux2Transformer2DModel",
+    "parent_library": "diffusers.models.transformers.transformer_flux2"
+  },
+  "base_model_name_or_path": null,
+  "bias": "none",
+  "corda_config": null,
+  "ensure_weight_tying": false,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": "gaussian",
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 128,
+  "lora_bias": false,
+  "lora_dropout": 0.0,
+  "lora_ga_config": null,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "monteclora_config": null,
+  "peft_type": "LORA",
+  "peft_version": "0.20.0",
+  "qalora_group_size": 16,
+  "r": 64,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "attn.add_k_proj",
+    "attn.add_q_proj",
+    "attn.to_v",
+    "attn.add_v_proj",
+    "ff.linear_out",
+    "attn.to_q",
+    "ff_context.linear_in",
+    "attn.to_qkv_mlp_proj",
+    "ff.linear_in",
+    "ff_context.linear_out",
+    "attn.to_add_out",
+    "attn.to_k",
+    "attn.to_out.0"
+  ],
+  "target_parameters": null,
+  "task_type": null,
+  "trainable_token_indices": null,
+  "use_bdlora": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false,
+  "velora_config": null
+}
\ No newline at end of file
diff --git a/runs/klein4b_nft_flir_full_ssim_dists_from_ep72/saves/klein4b_nft_flir_full_ssim_dists_from_ep72/checkpoints/checkpoint-80/manifest.json b/runs/klein4b_nft_flir_full_ssim_dists_from_ep72/saves/klein4b_nft_flir_full_ssim_dists_from_ep72/checkpoints/checkpoint-80/manifest.json
new file mode 100644
index 0000000000000000000000000000000000000000..4443fd995e0f6f3bd9b7e3dacea93df8d345ecfb
--- /dev/null
+++ b/runs/klein4b_nft_flir_full_ssim_dists_from_ep72/saves/klein4b_nft_flir_full_ssim_dists_from_ep72/checkpoints/checkpoint-80/manifest.json
@@ -0,0 +1,16 @@
+{
+  "base_role": "base",
+  "entries": [
+    {
+      "component": "transformer",
+      "path": ".",
+      "role": "base"
+    }
+  ],
+  "finetune_type": "lora",
+  "format_version": 1,
+  "primary": {
+    "component": "transformer",
+    "path": "."
+  }
+}
diff --git a/runs/klein4b_nft_flir_full_ssim_dists_psnr_from_ep72/saves/klein4b_nft_flir_full_ssim_dists_psnr_from_ep72/checkpoints/checkpoint-0/README.md b/runs/klein4b_nft_flir_full_ssim_dists_psnr_from_ep72/saves/klein4b_nft_flir_full_ssim_dists_psnr_from_ep72/checkpoints/checkpoint-0/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..2b78a7a995873d5db3090fca56885fe89cdf97e1
--- /dev/null
+++ b/runs/klein4b_nft_flir_full_ssim_dists_psnr_from_ep72/saves/klein4b_nft_flir_full_ssim_dists_psnr_from_ep72/checkpoints/checkpoint-0/README.md
@@ -0,0 +1,204 @@
+---
+base_model: /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/flow_factory_gpu_gate_models/FLUX.2-klein-base-4B/transformer
+library_name: peft
+tags:
+- lora
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]
+### Framework versions
+
+- PEFT 0.20.0
\ No newline at end of file
diff --git a/runs/klein4b_nft_flir_full_ssim_dists_psnr_from_ep72/saves/klein4b_nft_flir_full_ssim_dists_psnr_from_ep72/checkpoints/checkpoint-0/adapter_config.json b/runs/klein4b_nft_flir_full_ssim_dists_psnr_from_ep72/saves/klein4b_nft_flir_full_ssim_dists_psnr_from_ep72/checkpoints/checkpoint-0/adapter_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..414f5bc8ea47e00b5599283c5094ef634196b095
--- /dev/null
+++ b/runs/klein4b_nft_flir_full_ssim_dists_psnr_from_ep72/saves/klein4b_nft_flir_full_ssim_dists_psnr_from_ep72/checkpoints/checkpoint-0/adapter_config.json
@@ -0,0 +1,59 @@
+{
+  "alora_invocation_tokens": null,
+  "alpha_pattern": {},
+  "arrow_config": null,
+  "auto_mapping": {
+    "base_model_class": "Flux2Transformer2DModel",
+    "parent_library": "diffusers.models.transformers.transformer_flux2"
+  },
+  "base_model_name_or_path": null,
+  "bias": "none",
+  "corda_config": null,
+  "ensure_weight_tying": false,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": "gaussian",
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 128,
+  "lora_bias": false,
+  "lora_dropout": 0.0,
+  "lora_ga_config": null,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "monteclora_config": null,
+  "peft_type": "LORA",
+  "peft_version": "0.20.0",
+  "qalora_group_size": 16,
+  "r": 64,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "attn.to_q",
+    "attn.to_out.0",
+    "attn.to_qkv_mlp_proj",
+    "ff.linear_out",
+    "attn.add_v_proj",
+    "ff_context.linear_out",
+    "attn.to_k",
+    "ff_context.linear_in",
+    "attn.to_v",
+    "attn.add_q_proj",
+    "attn.add_k_proj",
+    "ff.linear_in",
+    "attn.to_add_out"
+  ],
+  "target_parameters": null,
+  "task_type": null,
+  "trainable_token_indices": null,
+  "use_bdlora": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false,
+  "velora_config": null
+}
\ No newline at end of file
diff --git a/runs/klein4b_nft_flir_full_ssim_dists_psnr_from_ep72/saves/klein4b_nft_flir_full_ssim_dists_psnr_from_ep72/checkpoints/checkpoint-0/manifest.json b/runs/klein4b_nft_flir_full_ssim_dists_psnr_from_ep72/saves/klein4b_nft_flir_full_ssim_dists_psnr_from_ep72/checkpoints/checkpoint-0/manifest.json
new file mode 100644
index 0000000000000000000000000000000000000000..4443fd995e0f6f3bd9b7e3dacea93df8d345ecfb
--- /dev/null
+++ b/runs/klein4b_nft_flir_full_ssim_dists_psnr_from_ep72/saves/klein4b_nft_flir_full_ssim_dists_psnr_from_ep72/checkpoints/checkpoint-0/manifest.json
@@ -0,0 +1,16 @@
+{
+  "base_role": "base",
+  "entries": [
+    {
+      "component": "transformer",
+      "path": ".",
+      "role": "base"
+    }
+  ],
+  "finetune_type": "lora",
+  "format_version": 1,
+  "primary": {
+    "component": "transformer",
+    "path": "."
+  }
+}
diff --git a/runs/klein4b_nft_flir_full_ssim_dists_psnr_from_ep72/saves/klein4b_nft_flir_full_ssim_dists_psnr_from_ep72/checkpoints/checkpoint-80/README.md b/runs/klein4b_nft_flir_full_ssim_dists_psnr_from_ep72/saves/klein4b_nft_flir_full_ssim_dists_psnr_from_ep72/checkpoints/checkpoint-80/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..2b78a7a995873d5db3090fca56885fe89cdf97e1
--- /dev/null
+++ b/runs/klein4b_nft_flir_full_ssim_dists_psnr_from_ep72/saves/klein4b_nft_flir_full_ssim_dists_psnr_from_ep72/checkpoints/checkpoint-80/README.md
@@ -0,0 +1,204 @@
+---
+base_model: /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/flow_factory_gpu_gate_models/FLUX.2-klein-base-4B/transformer
+library_name: peft
+tags:
+- lora
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]
+### Framework versions
+
+- PEFT 0.20.0
\ No newline at end of file
diff --git a/runs/klein4b_nft_flir_full_ssim_dists_psnr_from_ep72/saves/klein4b_nft_flir_full_ssim_dists_psnr_from_ep72/checkpoints/checkpoint-80/adapter_config.json b/runs/klein4b_nft_flir_full_ssim_dists_psnr_from_ep72/saves/klein4b_nft_flir_full_ssim_dists_psnr_from_ep72/checkpoints/checkpoint-80/adapter_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..414f5bc8ea47e00b5599283c5094ef634196b095
--- /dev/null
+++ b/runs/klein4b_nft_flir_full_ssim_dists_psnr_from_ep72/saves/klein4b_nft_flir_full_ssim_dists_psnr_from_ep72/checkpoints/checkpoint-80/adapter_config.json
@@ -0,0 +1,59 @@
+{
+  "alora_invocation_tokens": null,
+  "alpha_pattern": {},
+  "arrow_config": null,
+  "auto_mapping": {
+    "base_model_class": "Flux2Transformer2DModel",
+    "parent_library": "diffusers.models.transformers.transformer_flux2"
+  },
+  "base_model_name_or_path": null,
+  "bias": "none",
+  "corda_config": null,
+  "ensure_weight_tying": false,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": "gaussian",
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 128,
+  "lora_bias": false,
+  "lora_dropout": 0.0,
+  "lora_ga_config": null,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "monteclora_config": null,
+  "peft_type": "LORA",
+  "peft_version": "0.20.0",
+  "qalora_group_size": 16,
+  "r": 64,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "attn.to_q",
+    "attn.to_out.0",
+    "attn.to_qkv_mlp_proj",
+    "ff.linear_out",
+    "attn.add_v_proj",
+    "ff_context.linear_out",
+    "attn.to_k",
+    "ff_context.linear_in",
+    "attn.to_v",
+    "attn.add_q_proj",
+    "attn.add_k_proj",
+    "ff.linear_in",
+    "attn.to_add_out"
+  ],
+  "target_parameters": null,
+  "task_type": null,
+  "trainable_token_indices": null,
+  "use_bdlora": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false,
+  "velora_config": null
+}
\ No newline at end of file
diff --git a/runs/klein4b_nft_flir_full_ssim_dists_psnr_from_ep72/saves/klein4b_nft_flir_full_ssim_dists_psnr_from_ep72/checkpoints/checkpoint-80/manifest.json b/runs/klein4b_nft_flir_full_ssim_dists_psnr_from_ep72/saves/klein4b_nft_flir_full_ssim_dists_psnr_from_ep72/checkpoints/checkpoint-80/manifest.json
new file mode 100644
index 0000000000000000000000000000000000000000..4443fd995e0f6f3bd9b7e3dacea93df8d345ecfb
--- /dev/null
+++ b/runs/klein4b_nft_flir_full_ssim_dists_psnr_from_ep72/saves/klein4b_nft_flir_full_ssim_dists_psnr_from_ep72/checkpoints/checkpoint-80/manifest.json
@@ -0,0 +1,16 @@
+{
+  "base_role": "base",
+  "entries": [
+    {
+      "component": "transformer",
+      "path": ".",
+      "role": "base"
+    }
+  ],
+  "finetune_type": "lora",
+  "format_version": 1,
+  "primary": {
+    "component": "transformer",
+    "path": "."
+  }
+}
diff --git a/runs/klein4b_nft_vedai_full_ssim_dists/train.log b/runs/klein4b_nft_vedai_full_ssim_dists/train.log
new file mode 100644
index 0000000000000000000000000000000000000000..e38344c49c2195b4ee4ce296f8df4a62e01bca01
--- /dev/null
+++ b/runs/klein4b_nft_vedai_full_ssim_dists/train.log
@@ -0,0 +1,8680 @@
+[2026-10-01 14:18:41,977] [INFO] [flow_factory.cli]: ============================================================
+[2026-10-01 14:18:41,978] [INFO] [flow_factory.cli]: Flow-Factory Launch Configuration
+[2026-10-01 14:18:41,978] [INFO] [flow_factory.cli]:   Num machines:       1
+[2026-10-01 14:18:41,978] [INFO] [flow_factory.cli]:   Machine rank:       0
+[2026-10-01 14:18:41,978] [INFO] [flow_factory.cli]:   Num processes:      8
+[2026-10-01 14:18:41,979] [INFO] [flow_factory.cli]:   Master node:        localhost:29512
+[2026-10-01 14:18:41,979] [INFO] [flow_factory.cli]:   Mixed precision:    bf16
+[2026-10-01 14:18:41,979] [INFO] [flow_factory.cli]:   Accelerate config:  config/deepspeed/deepspeed_zero2.yaml
+[2026-10-01 14:18:41,979] [INFO] [flow_factory.cli]: ============================================================
+W1001 14:18:49.971000 2452102 site-packages/torch/distributed/run.py:852] 
+W1001 14:18:49.971000 2452102 site-packages/torch/distributed/run.py:852] *****************************************
+W1001 14:18:49.971000 2452102 site-packages/torch/distributed/run.py:852] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
+W1001 14:18:49.971000 2452102 site-packages/torch/distributed/run.py:852] *****************************************
+[2026-10-01 14:18:55] [Rank 0] [WARNING] [flow_factory.hparams.abc]: NFTTrainingArguments.from_dict captured 3 unknown key(s) into extra_kwargs: ['condition_image_size', 'flat_steps', 'ramp_rate']. Verify these are intentional (e.g., adapter-specific kwargs); typos against declared fields will be silently accepted otherwise.
+[2026-10-01 14:18:55] [Rank 0] [INFO] [flow_factory.hparams.training_args._base]: World Size: 8
+[2026-10-01 14:18:55] [Rank 0] [WARNING] [flow_factory.hparams.abc]: EvaluationArguments.from_dict captured 1 unknown key(s) into extra_kwargs: ['condition_image_size']. Verify these are intentional (e.g., adapter-specific kwargs); typos against declared fields will be silently accepted otherwise.
+[2026-10-01 14:18:55,709] [INFO] [flow_factory.train]: ====================================================================================================
+[2026-10-01 14:18:55,709] [INFO] [flow_factory.train]: Flow-Factory Training Initialized
+[2026-10-01 14:18:55,709] [INFO] [flow_factory.train]: World Size: 8
+[2026-10-01 14:18:55,710] [INFO] [flow_factory.train]: ====================================================================================================
+[2026-10-01 14:18:55,710] [INFO] [flow_factory.train]: Config: /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/runs/klein4b_nft_vedai_full_ssim_dists/train.yaml
+[2026-10-01 14:18:55,717] [INFO] [flow_factory.train]: 
+launcher: accelerate
+config_file: config/deepspeed/deepspeed_zero2.yaml
+num_processes: 8
+main_process_port: 29512
+mixed_precision: bf16
+data:
+  dataset_dir: data
+  cache_dir: ~/.cache/flow_factory/datasets
+  image_dir: null
+  video_dir: null
+  audio_dir: null
+  preprocessing_batch_size: 8
+  dataloader_num_workers: 8
+  enable_preprocess: true
+  force_reprocess: false
+  max_dataset_size: null
+  preprocess_parallelism: local
+  sampler_type: group_contiguous
+  sampler_subgroup_size: null
+  datasets:
+  - extra_kwargs: {}
+    name: vedai_rl_full
+    dataset_dir: /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/vedai_rl_full
+    image_dir: null
+    video_dir: null
+    audio_dir: null
+    train:
+      extra_kwargs: {}
+      enabled: true
+      split: train
+      weight: 1
+      max_dataset_size: null
+      unique_sample_num_per_epoch: 48
+      num_batches_per_epoch: null
+    eval:
+      extra_kwargs: {}
+      enabled: true
+      split: test
+      max_dataset_size: null
+      resolution: null
+      num_inference_steps: null
+      guidance_scale: null
+    source_id: 0
+model:
+  model_name_or_path: /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/flow_factory_gpu_gate_models/FLUX.2-klein-base-4B
+  finetune_type: lora
+  trainable_parameters_dtype: bfloat16
+  component_load_dtypes: null
+  frozen_parameters_dtype: null
+  target_components:
+  - transformer
+  target_modules: default
+  model_type: flux2-klein
+  lora_rank: 64
+  lora_alpha: 128
+  resume_path: /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/runs/klein4b_sft_vedai_full_r64a128_lr1e-4/saves/klein4b_sft_vedai_full_r64a128_lr1e-4/checkpoints/checkpoint-48
+  resume_type: lora
+scheduler:
+  dynamics_type: ODE
+  noise_level: 0.0
+  num_sde_steps: 0
+  sde_steps: []
+  seed: 42
+training:
+  trainer_type: nft
+  resolution:
+  - 512
+  - 512
+  height: 512
+  width: 512
+  num_frames: null
+  frame_rate: null
+  max_epochs: 200
+  per_device_batch_size: 1
+  gradient_step_per_epoch: 1
+  max_grad_norm: 1.0
+  num_batches_per_epoch: 96
+  gradient_accumulation_steps: 192
+  num_inner_epochs: 1
+  group_size: 16
+  unique_sample_num_per_epoch: 48
+  num_inference_steps: 8
+  guidance_scale: 1.0
+  ref_param_device: cuda
+  seed: 42
+  learning_rate: 1.0e-05
+  adam_weight_decay: 0.0001
+  adam_betas:
+  - 0.9
+  - 0.999
+  adam_epsilon: 1.0e-08
+  enable_gradient_checkpointing: false
+  offload_samples_to_cpu: false
+  ema_decay: 0.5
+  ema_update_interval: 1
+  ema_device: cuda
+  ema_decay_schedule: piecewise_linear
+  latent_storage_dtype: fp16
+  shuffle_samples: true
+  reward_optimization_overlap: false
+  reward_optimization_overlap_mode: ready
+  reward_optimization_overlap_poll_interval: 0.05
+  global_std: false
+  advantage_aggregation: gdpo
+  nft_beta: 0.1
+  off_policy: true
+  adv_clip_range:
+  - -5.0
+  - 5.0
+  kl_type: v-based
+  kl_beta: 0.0
+  num_train_timesteps: 2
+  time_sampling_strategy: discrete
+  time_shift: 3.0
+  timestep_range:
+  - 0.0
+  - 0.5
+  flat_steps: 0
+  ramp_rate: 0.001
+  condition_image_size: 1024
+eval:
+  resolution:
+  - 512
+  - 512
+  height: 512
+  width: 512
+  num_frames: null
+  frame_rate: null
+  per_device_batch_size: 1
+  seed: 42
+  guidance_scale: 1.0
+  num_inference_steps: 28
+  eval_freq: 10
+  condition_image_size: 1024
+log:
+  run_name: klein4b_nft_vedai_full_ssim_dists
+  project: IR-Engine
+  logging_backend: wandb
+  save_dir: /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/runs/klein4b_nft_vedai_full_ssim_dists/saves
+  save_freq: 20
+  save_model_only: true
+  verbose: true
+acceleration:
+  shared: []
+  rollout: []
+reward:
+  reward_0:
+    name: ssim
+    reward_model: ir_rewards.fidelity.SSIMReward
+    weight:
+      vedai_rl_full: 1.0
+    dtype: float32
+    device: cuda
+    batch_size: 16
+    async_reward: false
+    num_workers: 1
+    applicable_datasets:
+    - vedai_rl_full
+  reward_1:
+    name: dists
+    reward_model: ir_rewards.fidelity.DISTSReward
+    weight:
+      vedai_rl_full: 1.0
+    dtype: float32
+    device: cuda
+    batch_size: 16
+    async_reward: false
+    num_workers: 1
+    applicable_datasets:
+    - vedai_rl_full
+eval_reward:
+  reward_0:
+    name: ssim
+    reward_model: ir_rewards.fidelity.SSIMReward
+    weight:
+      vedai_rl_full: 1.0
+    dtype: float32
+    device: cuda
+    batch_size: 16
+    async_reward: false
+    num_workers: 1
+    applicable_datasets:
+    - vedai_rl_full
+  reward_1:
+    name: psnr
+    reward_model: ir_rewards.fidelity.PSNRReward
+    weight:
+      vedai_rl_full: 1.0
+    dtype: float32
+    device: cuda
+    batch_size: 16
+    async_reward: false
+    num_workers: 1
+    applicable_datasets:
+    - vedai_rl_full
+  reward_2:
+    name: dists
+    reward_model: ir_rewards.fidelity.DISTSReward
+    weight:
+      vedai_rl_full: 1.0
+    dtype: float32
+    device: cuda
+    batch_size: 16
+    async_reward: false
+    num_workers: 1
+    applicable_datasets:
+    - vedai_rl_full
+optimizer:
+  optimizer_configs:
+  - extra_kwargs: {}
+    name: default
+    optimizer: adamw
+    learning_rate: 5.0e-05
+    weight_decay: 0.0001
+    max_grad_norm: 1.0
+    update_frequency: 1
+    betas:
+    - 0.9
+    - 0.999
+    eps: 1.0e-08
+
+[2026-10-01 14:18:55,717] [INFO] [flow_factory.train]: ====================================================================================================
+[2026-10-01 14:19:06] [Rank 7] [INFO] [flow_factory.models.loader]: Loading model architecture: flux2-klein...
+[2026-10-01 14:19:06] [Rank 3] [INFO] [flow_factory.models.loader]: Loading model architecture: flux2-klein...
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+[2026-10-01 14:19:06] [Rank 1] [INFO] [flow_factory.models.loader]: Loading model architecture: flux2-klein...
+[2026-10-01 14:19:06,357] [WARNING] [accelerate.utils.other]: [RANK 0] Detected kernel version 5.4.241, which is below the recommended minimum of 5.5.0; this can cause the process to hang. It is recommended to upgrade the kernel to the minimum version or higher.
+[2026-10-01 14:19:06] [Rank 2] [INFO] [flow_factory.models.loader]: Loading model architecture: flux2-klein...
+[2026-10-01 14:19:06] [Rank 0] [INFO] [flow_factory.models.loader]: Loading model architecture: flux2-klein...
+
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+
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+[2026-10-01 14:19:30] [Rank 0] [INFO] [flow_factory.scheduler.loader]: Loaded SDE scheduler: FlowMatchEulerDiscreteSDEScheduler
+
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+[2026-10-01 14:19:30] [Rank 3] [INFO] [flow_factory.scheduler.loader]: Loaded SDE scheduler: FlowMatchEulerDiscreteSDEScheduler
+
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+[2026-10-01 14:19:30] [Rank 7] [INFO] [flow_factory.scheduler.loader]: Loaded SDE scheduler: FlowMatchEulerDiscreteSDEScheduler
+[2026-10-01 14:19:31] [Rank 0] [INFO] [flow_factory.models.abc]: LoRA adapter loaded for transformer role=base from /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/runs/klein4b_sft_vedai_full_r64a128_lr1e-4/saves/klein4b_sft_vedai_full_r64a128_lr1e-4/checkpoints/checkpoint-48
+
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+
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+[2026-10-01 14:19:31] [Rank 6] [INFO] [flow_factory.scheduler.loader]: Loaded SDE scheduler: FlowMatchEulerDiscreteSDEScheduler
+[2026-10-01 14:19:31] [Rank 4] [INFO] [flow_factory.scheduler.loader]: Loaded SDE scheduler: FlowMatchEulerDiscreteSDEScheduler
+[2026-10-01 14:19:31] [Rank 2] [INFO] [flow_factory.scheduler.loader]: Loaded SDE scheduler: FlowMatchEulerDiscreteSDEScheduler
+[2026-10-01 14:19:31] [Rank 1] [INFO] [flow_factory.scheduler.loader]: Loaded SDE scheduler: FlowMatchEulerDiscreteSDEScheduler
+[2026-10-01 14:19:32] [Rank 0] [INFO] [flow_factory.models.abc]: Checkpoint loaded successfully from /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/runs/klein4b_sft_vedai_full_r64a128_lr1e-4/saves/klein4b_sft_vedai_full_r64a128_lr1e-4/checkpoints/checkpoint-48 (type=lora)
+[2026-10-01 14:19:32] [Rank 1] [INFO] [flow_factory.models.abc]: Froze ALL transformer parameters
+[2026-10-01 14:19:32] [Rank 5] [INFO] [flow_factory.models.abc]: Froze ALL transformer parameters
+[2026-10-01 14:19:32] [Rank 0] [INFO] [flow_factory.models.abc]: Froze ALL transformer parameters
+[2026-10-01 14:19:32] [Rank 1] [INFO] [flow_factory.models.abc]: Component transformer already has 'default' adapter. Skipping initialization but enabling gradients.
+[2026-10-01 14:19:32] [Rank 5] [INFO] [flow_factory.models.abc]: Component transformer already has 'default' adapter. Skipping initialization but enabling gradients.
+[2026-10-01 14:19:32] [Rank 6] [INFO] [flow_factory.models.abc]: Froze ALL transformer parameters
+[2026-10-01 14:19:32] [Rank 0] [INFO] [flow_factory.models.abc]: Component transformer already has 'default' adapter. Skipping initialization but enabling gradients.
+[2026-10-01 14:19:32] [Rank 6] [INFO] [flow_factory.models.abc]: Component transformer already has 'default' adapter. Skipping initialization but enabling gradients.
+[2026-10-01 14:19:32] [Rank 4] [INFO] [flow_factory.models.abc]: Froze ALL transformer parameters
+[2026-10-01 14:19:32] [Rank 3] [INFO] [flow_factory.models.abc]: Froze ALL transformer parameters
+[2026-10-01 14:19:32] [Rank 7] [INFO] [flow_factory.models.abc]: Froze ALL transformer parameters
+[2026-10-01 14:19:32] [Rank 4] [INFO] [flow_factory.models.abc]: Component transformer already has 'default' adapter. Skipping initialization but enabling gradients.
+[2026-10-01 14:19:32] [Rank 3] [INFO] [flow_factory.models.abc]: Component transformer already has 'default' adapter. Skipping initialization but enabling gradients.
+[2026-10-01 14:19:32] [Rank 7] [INFO] [flow_factory.models.abc]: Component transformer already has 'default' adapter. Skipping initialization but enabling gradients.
+[2026-10-01 14:19:32] [Rank 2] [INFO] [flow_factory.models.abc]: Froze ALL transformer parameters
+[2026-10-01 14:19:32] [Rank 2] [INFO] [flow_factory.models.abc]: Component transformer already has 'default' adapter. Skipping initialization but enabling gradients.
+[2026-10-01 14:19:32] [Rank 0] [INFO] [flow_factory.models.abc]: Set 160 trainable parameters to torch.bfloat16; frozen component policy -> {'text_encoder': None, 'transformer': None, 'vae': None}
+[2026-10-01 14:19:32] [Rank 5] [INFO] [flow_factory.models.abc]: Set 160 trainable parameters to torch.bfloat16; frozen component policy -> {'text_encoder': None, 'transformer': None, 'vae': None}
+[2026-10-01 14:19:32] [Rank 5] [INFO] [flow_factory.models.loader]: Successfully loaded Flux2KleinAdapter
+[2026-10-01 14:19:32] [Rank 0] [INFO] [flow_factory.models.abc]: Precision inventory stage=initialize component=text_encoder {'trainable': {}, 'frozen': {'torch.bfloat16': 4022468096}}
+[2026-10-01 14:19:32] [Rank 6] [INFO] [flow_factory.models.abc]: Set 160 trainable parameters to torch.bfloat16; frozen component policy -> {'text_encoder': None, 'transformer': None, 'vae': None}
+[2026-10-01 14:19:32] [Rank 6] [INFO] [flow_factory.models.loader]: Successfully loaded Flux2KleinAdapter
+[2026-10-01 14:19:32] [Rank 1] [INFO] [flow_factory.models.abc]: Set 160 trainable parameters to torch.bfloat16; frozen component policy -> {'text_encoder': None, 'transformer': None, 'vae': None}
+[2026-10-01 14:19:32] [Rank 1] [INFO] [flow_factory.models.loader]: Successfully loaded Flux2KleinAdapter
+[2026-10-01 14:19:32] [Rank 4] [INFO] [flow_factory.models.abc]: Set 160 trainable parameters to torch.bfloat16; frozen component policy -> {'text_encoder': None, 'transformer': None, 'vae': None}
+[2026-10-01 14:19:32] [Rank 4] [INFO] [flow_factory.models.loader]: Successfully loaded Flux2KleinAdapter
+[2026-10-01 14:19:32] [Rank 7] [INFO] [flow_factory.models.abc]: Set 160 trainable parameters to torch.bfloat16; frozen component policy -> {'text_encoder': None, 'transformer': None, 'vae': None}
+[2026-10-01 14:19:32] [Rank 7] [INFO] [flow_factory.models.loader]: Successfully loaded Flux2KleinAdapter
+[2026-10-01 14:19:32] [Rank 0] [INFO] [flow_factory.models.abc]: Precision inventory stage=initialize component=transformer {'trainable': {'torch.bfloat16': 76677120}, 'frozen': {'torch.float32': 3875544576}}
+[2026-10-01 14:19:32] [Rank 3] [INFO] [flow_factory.models.abc]: Set 160 trainable parameters to torch.bfloat16; frozen component policy -> {'text_encoder': None, 'transformer': None, 'vae': None}
+[2026-10-01 14:19:32] [Rank 3] [INFO] [flow_factory.models.loader]: Successfully loaded Flux2KleinAdapter
+[2026-10-01 14:19:32] [Rank 0] [INFO] [flow_factory.models.abc]: Precision inventory stage=initialize component=vae {'trainable': {}, 'frozen': {'torch.float32': 84046115}}
+[2026-10-01 14:19:32] [Rank 2] [INFO] [flow_factory.models.abc]: Set 160 trainable parameters to torch.bfloat16; frozen component policy -> {'text_encoder': None, 'transformer': None, 'vae': None}
+[2026-10-01 14:19:32] [Rank 0] [INFO] [flow_factory.models.loader]: Successfully loaded Flux2KleinAdapter
+[2026-10-01 14:19:32] [Rank 2] [INFO] [flow_factory.models.loader]: Successfully loaded Flux2KleinAdapter
+[2026-10-01 14:19:55] [Rank 0] [INFO] [flow_factory.data_utils.loader]: Preprocessing train dataset shard 0000/0007 -> /root/.cache/flow_factory/datasets/1e68492a08871f0f6ebe621929ff234a.tmp/_parts/rank_0000_of_0008/cache-1e68492a08871f0f6ebe621929ff234a_shard0000of0007.arrow
+[2026-10-01 14:19:55] [Rank 3] [INFO] [flow_factory.data_utils.loader]: Preprocessing train dataset shard 0003/0007 -> /root/.cache/flow_factory/datasets/1e68492a08871f0f6ebe621929ff234a.tmp/_parts/rank_0003_of_0008/cache-1e68492a08871f0f6ebe621929ff234a_shard0003of0007.arrow
+[2026-10-01 14:19:55] [Rank 4] [INFO] [flow_factory.data_utils.loader]: Preprocessing train dataset shard 0004/0007 -> /root/.cache/flow_factory/datasets/1e68492a08871f0f6ebe621929ff234a.tmp/_parts/rank_0004_of_0008/cache-1e68492a08871f0f6ebe621929ff234a_shard0004of0007.arrow
+[2026-10-01 14:19:55] [Rank 7] [INFO] [flow_factory.data_utils.loader]: Preprocessing train dataset shard 0007/0007 -> /root/.cache/flow_factory/datasets/1e68492a08871f0f6ebe621929ff234a.tmp/_parts/rank_0007_of_0008/cache-1e68492a08871f0f6ebe621929ff234a_shard0007of0007.arrow
+[2026-10-01 14:19:55] [Rank 1] [INFO] [flow_factory.data_utils.loader]: Preprocessing train dataset shard 0001/0007 -> /root/.cache/flow_factory/datasets/1e68492a08871f0f6ebe621929ff234a.tmp/_parts/rank_0001_of_0008/cache-1e68492a08871f0f6ebe621929ff234a_shard0001of0007.arrow
+[2026-10-01 14:19:55] [Rank 5] [INFO] [flow_factory.data_utils.loader]: Preprocessing train dataset shard 0005/0007 -> /root/.cache/flow_factory/datasets/1e68492a08871f0f6ebe621929ff234a.tmp/_parts/rank_0005_of_0008/cache-1e68492a08871f0f6ebe621929ff234a_shard0005of0007.arrow
+[2026-10-01 14:19:55] [Rank 2] [INFO] [flow_factory.data_utils.loader]: Preprocessing train dataset shard 0002/0007 -> /root/.cache/flow_factory/datasets/1e68492a08871f0f6ebe621929ff234a.tmp/_parts/rank_0002_of_0008/cache-1e68492a08871f0f6ebe621929ff234a_shard0002of0007.arrow
+[2026-10-01 14:19:55] [Rank 6] [INFO] [flow_factory.data_utils.loader]: Preprocessing train dataset shard 0006/0007 -> /root/.cache/flow_factory/datasets/1e68492a08871f0f6ebe621929ff234a.tmp/_parts/rank_0006_of_0008/cache-1e68492a08871f0f6ebe621929ff234a_shard0006of0007.arrow
+/opt/conda/envs/ff-qi21/lib/python3.10/contextlib.py:135: UserWarning: CUDA reports that you have 8 available devices, and you have used fork_rng without explicitly specifying which devices are being used. For safety, we initialize *every* CUDA device by default, which can be quite slow if you have a lot of CUDAs. If you know that you are only making use of a few CUDA devices, set the environment variable CUDA_VISIBLE_DEVICES or the 'devices' keyword argument of fork_rng with the set of devices you are actually using. For example, if you are using CPU only, set device.upper()_VISIBLE_DEVICES= or devices=[]; if you are using device 0 only, set CUDA_VISIBLE_DEVICES=0 or devices=[0].  To initialize all devices and suppress this warning, set the 'devices' keyword argument to `range(torch.cuda.device_count())`.
+  return next(self.gen)
+/opt/conda/envs/ff-qi21/lib/python3.10/contextlib.py:135: UserWarning: CUDA reports that you have 8 available devices, and you have used fork_rng without explicitly specifying which devices are being used. For safety, we initialize *every* CUDA device by default, which can be quite slow if you have a lot of CUDAs. If you know that you are only making use of a few CUDA devices, set the environment variable CUDA_VISIBLE_DEVICES or the 'devices' keyword argument of fork_rng with the set of devices you are actually using. For example, if you are using CPU only, set device.upper()_VISIBLE_DEVICES= or devices=[]; if you are using device 0 only, set CUDA_VISIBLE_DEVICES=0 or devices=[0].  To initialize all devices and suppress this warning, set the 'devices' keyword argument to `range(torch.cuda.device_count())`.
+  return next(self.gen)
+/opt/conda/envs/ff-qi21/lib/python3.10/contextlib.py:135: UserWarning: CUDA reports that you have 8 available devices, and you have used fork_rng without explicitly specifying which devices are being used. For safety, we initialize *every* CUDA device by default, which can be quite slow if you have a lot of CUDAs. If you know that you are only making use of a few CUDA devices, set the environment variable CUDA_VISIBLE_DEVICES or the 'devices' keyword argument of fork_rng with the set of devices you are actually using. For example, if you are using CPU only, set device.upper()_VISIBLE_DEVICES= or devices=[]; if you are using device 0 only, set CUDA_VISIBLE_DEVICES=0 or devices=[0].  To initialize all devices and suppress this warning, set the 'devices' keyword argument to `range(torch.cuda.device_count())`.
+  return next(self.gen)
+/opt/conda/envs/ff-qi21/lib/python3.10/contextlib.py:135: UserWarning: CUDA reports that you have 8 available devices, and you have used fork_rng without explicitly specifying which devices are being used. For safety, we initialize *every* CUDA device by default, which can be quite slow if you have a lot of CUDAs. If you know that you are only making use of a few CUDA devices, set the environment variable CUDA_VISIBLE_DEVICES or the 'devices' keyword argument of fork_rng with the set of devices you are actually using. For example, if you are using CPU only, set device.upper()_VISIBLE_DEVICES= or devices=[]; if you are using device 0 only, set CUDA_VISIBLE_DEVICES=0 or devices=[0].  To initialize all devices and suppress this warning, set the 'devices' keyword argument to `range(torch.cuda.device_count())`.
+  return next(self.gen)
+/opt/conda/envs/ff-qi21/lib/python3.10/contextlib.py:135: UserWarning: CUDA reports that you have 8 available devices, and you have used fork_rng without explicitly specifying which devices are being used. For safety, we initialize *every* CUDA device by default, which can be quite slow if you have a lot of CUDAs. If you know that you are only making use of a few CUDA devices, set the environment variable CUDA_VISIBLE_DEVICES or the 'devices' keyword argument of fork_rng with the set of devices you are actually using. For example, if you are using CPU only, set device.upper()_VISIBLE_DEVICES= or devices=[]; if you are using device 0 only, set CUDA_VISIBLE_DEVICES=0 or devices=[0].  To initialize all devices and suppress this warning, set the 'devices' keyword argument to `range(torch.cuda.device_count())`.
+  return next(self.gen)
+/opt/conda/envs/ff-qi21/lib/python3.10/contextlib.py:135: UserWarning: CUDA reports that you have 8 available devices, and you have used fork_rng without explicitly specifying which devices are being used. For safety, we initialize *every* CUDA device by default, which can be quite slow if you have a lot of CUDAs. If you know that you are only making use of a few CUDA devices, set the environment variable CUDA_VISIBLE_DEVICES or the 'devices' keyword argument of fork_rng with the set of devices you are actually using. For example, if you are using CPU only, set device.upper()_VISIBLE_DEVICES= or devices=[]; if you are using device 0 only, set CUDA_VISIBLE_DEVICES=0 or devices=[0].  To initialize all devices and suppress this warning, set the 'devices' keyword argument to `range(torch.cuda.device_count())`.
+  return next(self.gen)
+/opt/conda/envs/ff-qi21/lib/python3.10/contextlib.py:135: UserWarning: CUDA reports that you have 8 available devices, and you have used fork_rng without explicitly specifying which devices are being used. For safety, we initialize *every* CUDA device by default, which can be quite slow if you have a lot of CUDAs. If you know that you are only making use of a few CUDA devices, set the environment variable CUDA_VISIBLE_DEVICES or the 'devices' keyword argument of fork_rng with the set of devices you are actually using. For example, if you are using CPU only, set device.upper()_VISIBLE_DEVICES= or devices=[]; if you are using device 0 only, set CUDA_VISIBLE_DEVICES=0 or devices=[0].  To initialize all devices and suppress this warning, set the 'devices' keyword argument to `range(torch.cuda.device_count())`.
+  return next(self.gen)
+/opt/conda/envs/ff-qi21/lib/python3.10/contextlib.py:135: UserWarning: CUDA reports that you have 8 available devices, and you have used fork_rng without explicitly specifying which devices are being used. For safety, we initialize *every* CUDA device by default, which can be quite slow if you have a lot of CUDAs. If you know that you are only making use of a few CUDA devices, set the environment variable CUDA_VISIBLE_DEVICES or the 'devices' keyword argument of fork_rng with the set of devices you are actually using. For example, if you are using CPU only, set device.upper()_VISIBLE_DEVICES= or devices=[]; if you are using device 0 only, set CUDA_VISIBLE_DEVICES=0 or devices=[0].  To initialize all devices and suppress this warning, set the 'devices' keyword argument to `range(torch.cuda.device_count())`.
+  return next(self.gen)
+
[Preprocessing train dataset] Shard 0000/0007:   0%|          | 0/109 [00:00<?, ? examples/s]
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[Preprocessing train dataset] Shard 0000/0007:  73%|███████▎  | 80/109 [00:21<00:08,  3.57 examples/s]
[Preprocessing train dataset] Shard 0000/0007:  81%|████████  | 88/109 [00:24<00:05,  3.53 examples/s]
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[Preprocessing train dataset] Shard 0000/0007:  95%|█████████▌| 104/109 [00:28<00:01,  3.85 examples/s]
[Preprocessing train dataset] Shard 0000/0007: 100%|██████████| 109/109 [00:29<00:00,  3.96 examples/s]
[Preprocessing train dataset] Shard 0000/0007: 100%|██████████| 109/109 [00:29<00:00,  3.74 examples/s]
+[2026-10-01 14:20:31] [Rank 0] [INFO] [flow_factory.data_utils.loader]: [local] Consolidated 8 part(s) for train split -> /root/.cache/flow_factory/datasets/1e68492a08871f0f6ebe621929ff234a
+[2026-10-01 14:20:31] [Rank 5] [INFO] [flow_factory.data_utils.loader]: Multi-source: built DataLoader for 'vedai_rl_full' (dataset_size=872, unique_sample_num_per_epoch=48, num_batches_per_epoch=96)
+[2026-10-01 14:20:31] [Rank 6] [INFO] [flow_factory.data_utils.loader]: Multi-source: built DataLoader for 'vedai_rl_full' (dataset_size=872, unique_sample_num_per_epoch=48, num_batches_per_epoch=96)
+[2026-10-01 14:20:31] [Rank 7] [INFO] [flow_factory.data_utils.loader]: Multi-source: built DataLoader for 'vedai_rl_full' (dataset_size=872, unique_sample_num_per_epoch=48, num_batches_per_epoch=96)
+[2026-10-01 14:20:31] [Rank 0] [INFO] [flow_factory.data_utils.loader]: Multi-source: built DataLoader for 'vedai_rl_full' (dataset_size=872, unique_sample_num_per_epoch=48, num_batches_per_epoch=96)
+[2026-10-01 14:20:31] [Rank 4] [INFO] [flow_factory.data_utils.loader]: Multi-source: built DataLoader for 'vedai_rl_full' (dataset_size=872, unique_sample_num_per_epoch=48, num_batches_per_epoch=96)
+[2026-10-01 14:20:31] [Rank 2] [INFO] [flow_factory.data_utils.loader]: Multi-source: built DataLoader for 'vedai_rl_full' (dataset_size=872, unique_sample_num_per_epoch=48, num_batches_per_epoch=96)
+[2026-10-01 14:20:31] [Rank 3] [INFO] [flow_factory.data_utils.loader]: Multi-source: built DataLoader for 'vedai_rl_full' (dataset_size=872, unique_sample_num_per_epoch=48, num_batches_per_epoch=96)
+[2026-10-01 14:20:31] [Rank 1] [INFO] [flow_factory.data_utils.loader]: Multi-source: built DataLoader for 'vedai_rl_full' (dataset_size=872, unique_sample_num_per_epoch=48, num_batches_per_epoch=96)
+[2026-10-01 14:20:31] [Rank 5] [INFO] [flow_factory.data_utils.loader]: Preprocessing test dataset shard 0005/0007 -> /root/.cache/flow_factory/datasets/c624060e1ad3d97604e0883a86585d87.tmp/_parts/rank_0005_of_0008/cache-c624060e1ad3d97604e0883a86585d87_shard0005of0007.arrow
+[2026-10-01 14:20:31] [Rank 6] [INFO] [flow_factory.data_utils.loader]: Preprocessing test dataset shard 0006/0007 -> /root/.cache/flow_factory/datasets/c624060e1ad3d97604e0883a86585d87.tmp/_parts/rank_0006_of_0008/cache-c624060e1ad3d97604e0883a86585d87_shard0006of0007.arrow
+[2026-10-01 14:20:31] [Rank 2] [INFO] [flow_factory.data_utils.loader]: Preprocessing test dataset shard 0002/0007 -> /root/.cache/flow_factory/datasets/c624060e1ad3d97604e0883a86585d87.tmp/_parts/rank_0002_of_0008/cache-c624060e1ad3d97604e0883a86585d87_shard0002of0007.arrow
+[2026-10-01 14:20:31] [Rank 0] [INFO] [flow_factory.data_utils.loader]: Preprocessing test dataset shard 0000/0007 -> /root/.cache/flow_factory/datasets/c624060e1ad3d97604e0883a86585d87.tmp/_parts/rank_0000_of_0008/cache-c624060e1ad3d97604e0883a86585d87_shard0000of0007.arrow
+[2026-10-01 14:20:31] [Rank 3] [INFO] [flow_factory.data_utils.loader]: Preprocessing test dataset shard 0003/0007 -> /root/.cache/flow_factory/datasets/c624060e1ad3d97604e0883a86585d87.tmp/_parts/rank_0003_of_0008/cache-c624060e1ad3d97604e0883a86585d87_shard0003of0007.arrow
+[2026-10-01 14:20:31] [Rank 4] [INFO] [flow_factory.data_utils.loader]: Preprocessing test dataset shard 0004/0007 -> /root/.cache/flow_factory/datasets/c624060e1ad3d97604e0883a86585d87.tmp/_parts/rank_0004_of_0008/cache-c624060e1ad3d97604e0883a86585d87_shard0004of0007.arrow
+[2026-10-01 14:20:31] [Rank 1] [INFO] [flow_factory.data_utils.loader]: Preprocessing test dataset shard 0001/0007 -> /root/.cache/flow_factory/datasets/c624060e1ad3d97604e0883a86585d87.tmp/_parts/rank_0001_of_0008/cache-c624060e1ad3d97604e0883a86585d87_shard0001of0007.arrow
+[2026-10-01 14:20:31] [Rank 7] [INFO] [flow_factory.data_utils.loader]: Preprocessing test dataset shard 0007/0007 -> /root/.cache/flow_factory/datasets/c624060e1ad3d97604e0883a86585d87.tmp/_parts/rank_0007_of_0008/cache-c624060e1ad3d97604e0883a86585d87_shard0007of0007.arrow
+
[Preprocessing test dataset] Shard 0000/0007:   0%|          | 0/8 [00:00<?, ? examples/s]
[Preprocessing test dataset] Shard 0000/0007: 100%|██████████| 8/8 [00:01<00:00,  4.43 examples/s]
[Preprocessing test dataset] Shard 0000/0007: 100%|██████████| 8/8 [00:01<00:00,  4.42 examples/s]
+[2026-10-01 14:20:35] [Rank 0] [INFO] [flow_factory.data_utils.loader]: [local] Consolidated 8 part(s) for test split -> /root/.cache/flow_factory/datasets/c624060e1ad3d97604e0883a86585d87
+/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.
+  warnings.warn(
+/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.
+  warnings.warn(
+/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.
+  warnings.warn(
+/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=VGG16_Weights.IMAGENET1K_V1`. You can also use `weights=VGG16_Weights.DEFAULT` to get the most up-to-date weights.
+  warnings.warn(msg)
+/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.
+  warnings.warn(
+/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.
+  warnings.warn(
+/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=VGG16_Weights.IMAGENET1K_V1`. You can also use `weights=VGG16_Weights.DEFAULT` to get the most up-to-date weights.
+  warnings.warn(msg)
+/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.
+  warnings.warn(
+/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=VGG16_Weights.IMAGENET1K_V1`. You can also use `weights=VGG16_Weights.DEFAULT` to get the most up-to-date weights.
+  warnings.warn(msg)
+/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.
+  warnings.warn(
+/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=VGG16_Weights.IMAGENET1K_V1`. You can also use `weights=VGG16_Weights.DEFAULT` to get the most up-to-date weights.
+  warnings.warn(msg)
+/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=VGG16_Weights.IMAGENET1K_V1`. You can also use `weights=VGG16_Weights.DEFAULT` to get the most up-to-date weights.
+  warnings.warn(msg)
+/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.
+  warnings.warn(
+/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=VGG16_Weights.IMAGENET1K_V1`. You can also use `weights=VGG16_Weights.DEFAULT` to get the most up-to-date weights.
+  warnings.warn(msg)
+/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=VGG16_Weights.IMAGENET1K_V1`. You can also use `weights=VGG16_Weights.DEFAULT` to get the most up-to-date weights.
+  warnings.warn(msg)
+/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=VGG16_Weights.IMAGENET1K_V1`. You can also use `weights=VGG16_Weights.DEFAULT` to get the most up-to-date weights.
+  warnings.warn(msg)
+wandb: [wandb.login()] Loaded credentials for https://api.wandb.ai from /root/.netrc.
+wandb: W&B API key is configured. Use `wandb login --relogin` to force relogin
+wandb: setting up run 51cagjhz
+wandb: Tracking run with wandb version 0.28.2
+wandb: Run data is saved locally in /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/runs/klein4b_nft_vedai_full_ssim_dists/wandb/run-20261001_142058-51cagjhz
+wandb: Run `wandb offline` to turn off syncing.
+wandb: Syncing run klein4b_nft_vedai_full_ssim_dists
+wandb: ⭐️ View project at https://wandb.ai/315229706-xi-an-jiaotong-university-/IR-Engine
+wandb: 🚀 View run at https://wandb.ai/315229706-xi-an-jiaotong-university-/IR-Engine/runs/51cagjhz
+[2026-10-01 14:21:00] [Rank 0] [INFO] [flow_factory.models.abc]: ======================================================================
+[2026-10-01 14:21:00] [Rank 0] [INFO] [flow_factory.models.abc]: Transformer Trainable Parameters:
+[2026-10-01 14:21:00] [Rank 0] [INFO] [flow_factory.models.abc]:   Total:        3,952,221,696 (  7.36 GB)
+[2026-10-01 14:21:00] [Rank 0] [INFO] [flow_factory.models.abc]:   Trainable:       76,677,120 (  0.14 GB)
+[2026-10-01 14:21:00] [Rank 0] [INFO] [flow_factory.models.abc]:   Percentage:           1.94%
+[2026-10-01 14:21:00] [Rank 0] [INFO] [flow_factory.models.abc]: ======================================================================
+[2026-10-01 14:21:00] [Rank 0] [INFO] [flow_factory.models.abc]: Saving LoRA weights for transformer role=base to /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/runs/klein4b_nft_vedai_full_ssim_dists/saves/klein4b_nft_vedai_full_ssim_dists/checkpoints/checkpoint-0
+[2026-10-01 14:21:00] [Rank 0] [INFO] [flow_factory.models.abc]: Checkpoint saved successfully to /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/runs/klein4b_nft_vedai_full_ssim_dists/saves/klein4b_nft_vedai_full_ssim_dists/checkpoints/checkpoint-0
+
Eval/vedai_rl_full:   0%|          | 0/8 [00:00<?, ?it/s][2026-10-01 14:21:01] [Rank 6] [WARNING] [flow_factory.models.flux.flux2_klein]: FLUX.2 does not support batch inference with varying condition images per sample. Falling back to single-sample inference. This warning will only appear once.
+[2026-10-01 14:21:01] [Rank 5] [WARNING] [flow_factory.models.flux.flux2_klein]: FLUX.2 does not support batch inference with varying condition images per sample. Falling back to single-sample inference. This warning will only appear once.
+[2026-10-01 14:21:01] [Rank 0] [WARNING] [flow_factory.models.flux.flux2_klein]: FLUX.2 does not support batch inference with varying condition images per sample. Falling back to single-sample inference. This warning will only appear once.
+[2026-10-01 14:21:01] [Rank 7] [WARNING] [flow_factory.models.flux.flux2_klein]: FLUX.2 does not support batch inference with varying condition images per sample. Falling back to single-sample inference. This warning will only appear once.
+[2026-10-01 14:21:01] [Rank 3] [WARNING] [flow_factory.models.flux.flux2_klein]: FLUX.2 does not support batch inference with varying condition images per sample. Falling back to single-sample inference. This warning will only appear once.
+[2026-10-01 14:21:01] [Rank 4] [WARNING] [flow_factory.models.flux.flux2_klein]: FLUX.2 does not support batch inference with varying condition images per sample. Falling back to single-sample inference. This warning will only appear once.
+[2026-10-01 14:21:01] [Rank 1] [WARNING] [flow_factory.models.flux.flux2_klein]: FLUX.2 does not support batch inference with varying condition images per sample. Falling back to single-sample inference. This warning will only appear once.
+[2026-10-01 14:21:01] [Rank 2] [WARNING] [flow_factory.models.flux.flux2_klein]: FLUX.2 does not support batch inference with varying condition images per sample. Falling back to single-sample inference. This warning will only appear once.
+
Eval/vedai_rl_full:  12%|█▎        | 1/8 [00:06<00:44,  6.37s/it]
Eval/vedai_rl_full:  25%|██▌       | 2/8 [00:11<00:33,  5.66s/it]
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Eval/vedai_rl_full: 100%|██████████| 8/8 [00:42<00:00,  5.35s/it]
+
Pointwise Rewards: ssim:   0%|          | 0/1 [00:00<?, ?it/s]
Pointwise Rewards: ssim: 100%|██████████| 1/1 [00:00<00:00,  6.82it/s]
Pointwise Rewards: ssim: 100%|██████████| 1/1 [00:00<00:00,  6.80it/s]
+
Pointwise Rewards: psnr:   0%|          | 0/1 [00:00<?, ?it/s]
Pointwise Rewards: psnr: 100%|██████████| 1/1 [00:00<00:00, 12.39it/s]
+
Pointwise Rewards: dists:   0%|          | 0/1 [00:00<?, ?it/s]
Pointwise Rewards: dists: 100%|██████████| 1/1 [00:00<00:00,  7.68it/s]
Pointwise Rewards: dists: 100%|██████████| 1/1 [00:00<00:00,  7.64it/s]
+[2026-10-01 14:21:44] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0000 | Epoch 000] eval/vedai_rl_full/reward_dists_mean=0.9292 eval/vedai_rl_full/reward_dists_std=0.0183 eval/vedai_rl_full/reward_psnr_mean=29.0025 eval/vedai_rl_full/reward_psnr_std=2.6570 eval/vedai_rl_full/reward_ssim_mean=0.9194 eval/vedai_rl_full/reward_ssim_std=0.0282
+
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+
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Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:00<00:00,  6.25it/s]
+
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Pointwise Rewards: dists:  83%|████████▎ | 5/6 [00:01<00:00,  3.59it/s]
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Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.60it/s]
+[2026-10-01 14:24:15] [Rank 6] [WARNING] [flow_factory.models.flux.flux2_klein]: Flux.2: Ragged I2I detected (varying condition image sizes). Processing samples individually (warning shown once).
+[2026-10-01 14:24:15] [Rank 7] [WARNING] [flow_factory.models.flux.flux2_klein]: Flux.2: Ragged I2I detected (varying condition image sizes). Processing samples individually (warning shown once).
+[2026-10-01 14:24:15] [Rank 5] [WARNING] [flow_factory.models.flux.flux2_klein]: Flux.2: Ragged I2I detected (varying condition image sizes). Processing samples individually (warning shown once).
+[2026-10-01 14:24:15] [Rank 3] [WARNING] [flow_factory.models.flux.flux2_klein]: Flux.2: Ragged I2I detected (varying condition image sizes). Processing samples individually (warning shown once).
+[2026-10-01 14:24:15] [Rank 1] [WARNING] [flow_factory.models.flux.flux2_klein]: Flux.2: Ragged I2I detected (varying condition image sizes). Processing samples individually (warning shown once).
+[2026-10-01 14:24:15] [Rank 2] [WARNING] [flow_factory.models.flux.flux2_klein]: Flux.2: Ragged I2I detected (varying condition image sizes). Processing samples individually (warning shown once).
+[2026-10-01 14:24:15] [Rank 4] [WARNING] [flow_factory.models.flux.flux2_klein]: Flux.2: Ragged I2I detected (varying condition image sizes). Processing samples individually (warning shown once).
+[2026-10-01 14:24:15] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0000 | Epoch 000] train/reward_dists_mean=0.9192 train/reward_dists_std=0.0256 train/reward_ssim_mean=0.9326 train/reward_ssim_std=0.0204 train/reward_dists_group_std_mean=0.0045 train/reward_dists_group_std_max=0.0096 train/reward_dists_group_std_min=0.0019 train/reward_dists_group_mean_std=0.0252 train/reward_ssim_group_std_mean=0.0021 train/reward_ssim_group_std_max=0.0179 train/reward_ssim_group_std_min=0.0006 train/reward_ssim_group_mean_std=0.0202 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.8645 train/adv_max=4.5537 train/adv_abs_mean=1.3875
+
Epoch 0 Training:   0%|          | 0/96 [00:00<?, ?it/s][2026-10-01 14:24:15] [Rank 0] [WARNING] [flow_factory.models.flux.flux2_klein]: Flux.2: Ragged I2I detected (varying condition image sizes). Processing samples individually (warning shown once).
+
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Epoch 0 Training:  24%|██▍       | 23/96 [00:29<01:34,  1.29s/it]
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Epoch 0 Training:  25%|██▌       | 24/96 [00:30<01:32,  1.29s/it]
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Epoch 0 Training:  26%|██▌       | 25/96 [00:32<01:31,  1.29s/it]
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Epoch 0 Training:  27%|██▋       | 26/96 [00:33<01:30,  1.29s/it]
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Epoch 0 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.49it/s]
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Epoch 0 Training:  28%|██▊       | 27/96 [00:34<01:29,  1.29s/it]
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Epoch 0 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 0 Training:  29%|██▉       | 28/96 [00:36<01:27,  1.29s/it]
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Epoch 0 Training:  30%|███       | 29/96 [00:37<01:26,  1.29s/it]
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Epoch 0 Training:  31%|███▏      | 30/96 [00:38<01:25,  1.29s/it]
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Epoch 0 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 0 Training:  32%|███▏      | 31/96 [00:39<01:23,  1.29s/it]
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Epoch 0 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 0 Training:  33%|███▎      | 32/96 [00:41<01:22,  1.29s/it]
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Epoch 0 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 0 Training:  34%|███▍      | 33/96 [00:42<01:21,  1.29s/it]
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Epoch 0 Training:  35%|███▌      | 34/96 [00:43<01:19,  1.29s/it]
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Epoch 0 Training:  36%|███▋      | 35/96 [00:45<01:18,  1.29s/it]
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Epoch 0 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 0 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.40it/s]
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Epoch 0 Training:  38%|███▊      | 36/96 [00:46<01:17,  1.29s/it]
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Epoch 0 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 0 Training:  39%|███▊      | 37/96 [00:47<01:15,  1.29s/it]
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Epoch 0 Training:  40%|███▉      | 38/96 [00:48<01:14,  1.29s/it]
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Epoch 0 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 0 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.63it/s]
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Epoch 0 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.47it/s]
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Epoch 0 Training:  41%|████      | 39/96 [00:50<01:13,  1.29s/it]
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Epoch 0 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 0 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 0 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.45it/s]
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Epoch 0 Training:  42%|████▏     | 40/96 [00:51<01:11,  1.28s/it]
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Epoch 0 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 0 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 0 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.43it/s]
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Epoch 0 Training:  43%|████▎     | 41/96 [00:52<01:10,  1.28s/it]
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Epoch 0 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 0 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 0 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.46it/s]
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Epoch 0 Training:  44%|████▍     | 42/96 [00:54<01:08,  1.28s/it]
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Epoch 0 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 0 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 0 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.48it/s]
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Epoch 0 Training:  45%|████▍     | 43/96 [00:55<01:07,  1.28s/it]
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Epoch 0 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 0 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.48it/s]
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Epoch 0 Training:  46%|████▌     | 44/96 [00:56<01:06,  1.28s/it]
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Epoch 0 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.43it/s]
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Epoch 0 Training:  47%|████▋     | 45/96 [00:57<01:05,  1.28s/it]
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Epoch 0 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 0 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.50it/s]
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Epoch 0 Training:  48%|████▊     | 46/96 [00:59<01:03,  1.28s/it]
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Epoch 0 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 0 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.45it/s]
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Epoch 0 Training:  49%|████▉     | 47/96 [01:00<01:02,  1.28s/it]
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Epoch 0 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.44it/s]
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Epoch 0 Training:  50%|█████     | 48/96 [01:01<01:01,  1.28s/it]
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Epoch 0 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.47it/s]
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Epoch 0 Training:  51%|█████     | 49/96 [01:02<00:59,  1.28s/it]
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Epoch 0 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.45it/s]
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Epoch 0 Training:  52%|█████▏    | 50/96 [01:04<00:58,  1.28s/it]
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Epoch 0 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.48it/s]
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Epoch 0 Training:  53%|█████▎    | 51/96 [01:05<00:57,  1.28s/it]
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Epoch 0 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.42it/s]
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Epoch 0 Training:  54%|█████▍    | 52/96 [01:06<00:56,  1.28s/it]
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Epoch 0 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.42it/s]
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Epoch 0 Training:  55%|█████▌    | 53/96 [01:08<00:54,  1.28s/it]
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Epoch 0 Training:  56%|█████▋    | 54/96 [01:09<00:53,  1.28s/it]
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Epoch 0 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.43it/s]
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Epoch 0 Training:  57%|█████▋    | 55/96 [01:10<00:52,  1.28s/it]
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Epoch 0 Training:  58%|█████▊    | 56/96 [01:11<00:51,  1.28s/it]
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Epoch 0 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.42it/s]
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Epoch 0 Training:  59%|█████▉    | 57/96 [01:13<00:49,  1.28s/it]
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Epoch 0 Training:  60%|██████    | 58/96 [01:14<00:48,  1.28s/it]
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Epoch 0 Training:  61%|██████▏   | 59/96 [01:15<00:47,  1.28s/it]
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Epoch 0 Training:  62%|██████▎   | 60/96 [01:17<00:46,  1.28s/it]
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Epoch 0 Training:  64%|██████▎   | 61/96 [01:18<00:44,  1.28s/it]
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Epoch 0 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.43it/s]
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Epoch 0 Training:  65%|██████▍   | 62/96 [01:19<00:43,  1.28s/it]
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Epoch 0 Training:  66%|██████▌   | 63/96 [01:20<00:42,  1.28s/it]
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Epoch 0 Training:  67%|██████▋   | 64/96 [01:22<00:40,  1.28s/it]
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Epoch 0 Training:  68%|██████▊   | 65/96 [01:23<00:39,  1.28s/it]
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Epoch 0 Training:  69%|██████▉   | 66/96 [01:24<00:38,  1.28s/it]
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Epoch 0 Training:  70%|██████▉   | 67/96 [01:25<00:37,  1.28s/it]
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Epoch 0 Training:  71%|███████   | 68/96 [01:27<00:35,  1.28s/it]
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Epoch 0 Training:  72%|███████▏  | 69/96 [01:28<00:34,  1.28s/it]
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Epoch 0 Training:  73%|███████▎  | 70/96 [01:29<00:33,  1.28s/it]
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Epoch 0 Training:  74%|███████▍  | 71/96 [01:31<00:31,  1.28s/it]
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Epoch 0 Training:  75%|███████▌  | 72/96 [01:32<00:30,  1.28s/it]
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Epoch 0 Training:  76%|███████▌  | 73/96 [01:33<00:29,  1.28s/it]
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Epoch 0 Training:  77%|███████▋  | 74/96 [01:34<00:28,  1.28s/it]
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Epoch 0 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s][2026-10-01 14:26:19] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0000 | Epoch 000] train/policy_loss=13.9958 train/unweighted_policy_loss=2.7992 train/loss=13.9958 train/grad_norm=0.0881
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+[2026-10-01 14:26:19] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0001 | Epoch 000] timing/rollout_seconds=147.7342 timing/feedback_seconds=4.6105 timing/optimization_seconds=123.6725 timing/cycle_seconds=274.0641
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+[2026-10-01 14:28:50] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0001 | Epoch 001] train/reward_dists_mean=0.8969 train/reward_dists_std=0.0287 train/reward_ssim_mean=0.8735 train/reward_ssim_std=0.0481 train/reward_dists_group_std_mean=0.0063 train/reward_dists_group_std_max=0.0194 train/reward_dists_group_std_min=0.0016 train/reward_dists_group_mean_std=0.0278 train/reward_ssim_group_std_mean=0.0092 train/reward_ssim_group_std_max=0.0306 train/reward_ssim_group_std_min=0.0013 train/reward_ssim_group_mean_std=0.0469 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.3618 train/adv_max=5.8717 train/adv_abs_mean=1.5259
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Epoch 1 Training:  99%|█████████▉| 95/96 [02:05<00:01,  1.31s/it]
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Epoch 1 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s][2026-10-01 14:30:56] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0001 | Epoch 001] train/policy_loss=12.8279 train/unweighted_policy_loss=2.5656 train/loss=12.8279 train/grad_norm=0.1273
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+[2026-10-01 14:30:56] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0002 | Epoch 001] timing/rollout_seconds=147.6678 timing/feedback_seconds=4.6681 timing/optimization_seconds=127.2236 timing/cycle_seconds=277.7886
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+
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+[2026-10-01 14:33:27] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0002 | Epoch 002] train/reward_dists_mean=0.9205 train/reward_dists_std=0.0316 train/reward_ssim_mean=0.9092 train/reward_ssim_std=0.0384 train/reward_dists_group_std_mean=0.0047 train/reward_dists_group_std_max=0.0292 train/reward_dists_group_std_min=0.0014 train/reward_dists_group_mean_std=0.0310 train/reward_ssim_group_std_mean=0.0058 train/reward_ssim_group_std_max=0.0371 train/reward_ssim_group_std_min=0.0012 train/reward_ssim_group_mean_std=0.0375 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.3202 train/adv_max=4.9396 train/adv_abs_mean=1.4317
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Epoch 2 Training:  27%|██▋       | 26/96 [00:34<01:32,  1.32s/it]
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Epoch 2 Training:  28%|██▊       | 27/96 [00:35<01:31,  1.33s/it]
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Epoch 2 Training:  29%|██▉       | 28/96 [00:36<01:30,  1.33s/it]
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Epoch 2 Training:  30%|███       | 29/96 [00:38<01:28,  1.33s/it]
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Epoch 2 Training:  31%|███▏      | 30/96 [00:39<01:27,  1.32s/it]
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Epoch 2 Training:  32%|███▏      | 31/96 [00:40<01:26,  1.32s/it]
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Epoch 2 Training:  33%|███▎      | 32/96 [00:42<01:24,  1.32s/it]
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Epoch 2 Training:  34%|███▍      | 33/96 [00:43<01:23,  1.32s/it]
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Epoch 2 Training:  35%|███▌      | 34/96 [00:44<01:21,  1.32s/it]
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Epoch 2 Training:  36%|███▋      | 35/96 [00:46<01:20,  1.32s/it]
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Epoch 2 Training:  38%|███▊      | 36/96 [00:47<01:19,  1.32s/it]
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Epoch 2 Training:  39%|███▊      | 37/96 [00:48<01:17,  1.32s/it]
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Epoch 2 Training:  40%|███▉      | 38/96 [00:50<01:16,  1.32s/it]
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Epoch 2 Training:  41%|████      | 39/96 [00:51<01:15,  1.32s/it]
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Epoch 2 Training:  42%|████▏     | 40/96 [00:52<01:14,  1.32s/it]
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Epoch 2 Training:  43%|████▎     | 41/96 [00:54<01:12,  1.33s/it]
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Epoch 2 Training:  44%|████▍     | 42/96 [00:55<01:11,  1.33s/it]
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Epoch 2 Training:  45%|████▍     | 43/96 [00:56<01:10,  1.32s/it]
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Epoch 2 Training:  46%|████▌     | 44/96 [00:58<01:08,  1.32s/it]
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Epoch 2 Training:  47%|████▋     | 45/96 [00:59<01:07,  1.32s/it]
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Epoch 2 Training:  48%|████▊     | 46/96 [01:00<01:05,  1.32s/it]
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Epoch 2 Training:  49%|████▉     | 47/96 [01:02<01:04,  1.31s/it]
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Epoch 2 Training:  50%|█████     | 48/96 [01:03<01:03,  1.32s/it]
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Epoch 2 Training:  51%|█████     | 49/96 [01:04<01:01,  1.32s/it]
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Epoch 2 Training:  52%|█████▏    | 50/96 [01:06<01:00,  1.32s/it]
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Epoch 2 Training:  53%|█████▎    | 51/96 [01:07<00:59,  1.32s/it]
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Epoch 2 Training:  54%|█████▍    | 52/96 [01:08<00:57,  1.31s/it]
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Epoch 2 Training:  55%|█████▌    | 53/96 [01:09<00:56,  1.31s/it]
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Epoch 2 Training:  56%|█████▋    | 54/96 [01:11<00:54,  1.31s/it]
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Epoch 2 Training:  57%|█████▋    | 55/96 [01:12<00:53,  1.31s/it]
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Epoch 2 Training:  58%|█████▊    | 56/96 [01:13<00:52,  1.31s/it]
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Epoch 2 Training:  59%|█████▉    | 57/96 [01:15<00:51,  1.31s/it]
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Epoch 2 Training:  60%|██████    | 58/96 [01:16<00:49,  1.32s/it]
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Epoch 2 Training:  61%|██████▏   | 59/96 [01:17<00:48,  1.32s/it]
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Epoch 2 Training:  62%|██████▎   | 60/96 [01:19<00:47,  1.32s/it]
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Epoch 2 Training:  64%|██████▎   | 61/96 [01:20<00:46,  1.32s/it]
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Epoch 2 Training:  65%|██████▍   | 62/96 [01:21<00:44,  1.32s/it]
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Epoch 2 Training:  66%|██████▌   | 63/96 [01:23<00:43,  1.32s/it]
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Epoch 2 Training:  67%|██████▋   | 64/96 [01:24<00:42,  1.32s/it]
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Epoch 2 Training:  68%|██████▊   | 65/96 [01:25<00:40,  1.32s/it]
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Epoch 2 Training:  69%|██████▉   | 66/96 [01:27<00:39,  1.32s/it]
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Epoch 2 Training:  70%|██████▉   | 67/96 [01:28<00:38,  1.32s/it]
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Epoch 2 Training:  71%|███████   | 68/96 [01:29<00:36,  1.32s/it]
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Epoch 2 Training:  72%|███████▏  | 69/96 [01:31<00:35,  1.32s/it]
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Epoch 2 Training:  73%|███████▎  | 70/96 [01:32<00:34,  1.32s/it]
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Epoch 2 Training:  74%|███████▍  | 71/96 [01:33<00:32,  1.32s/it]
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Epoch 2 Training:  75%|███████▌  | 72/96 [01:34<00:31,  1.32s/it]
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Epoch 2 Training:  76%|███████▌  | 73/96 [01:36<00:30,  1.32s/it]
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Epoch 2 Training:  77%|███████▋  | 74/96 [01:37<00:29,  1.32s/it]
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Epoch 2 Training:  78%|███████▊  | 75/96 [01:38<00:27,  1.32s/it]
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Epoch 2 Training:  79%|███████▉  | 76/96 [01:40<00:26,  1.32s/it]
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Epoch 2 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s][2026-10-01 14:35:34] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0002 | Epoch 002] train/policy_loss=13.6121 train/unweighted_policy_loss=2.7224 train/loss=13.6121 train/grad_norm=0.0706
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+[2026-10-01 14:35:34] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0003 | Epoch 002] timing/rollout_seconds=147.8821 timing/feedback_seconds=4.5978 timing/optimization_seconds=127.2417 timing/cycle_seconds=277.7671
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+[2026-10-01 14:38:06] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0003 | Epoch 003] train/reward_dists_mean=0.9262 train/reward_dists_std=0.0161 train/reward_ssim_mean=0.9266 train/reward_ssim_std=0.0208 train/reward_dists_group_std_mean=0.0047 train/reward_dists_group_std_max=0.0073 train/reward_dists_group_std_min=0.0025 train/reward_dists_group_mean_std=0.0154 train/reward_ssim_group_std_mean=0.0028 train/reward_ssim_group_std_max=0.0165 train/reward_ssim_group_std_min=0.0009 train/reward_ssim_group_mean_std=0.0205 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.7920 train/adv_max=4.4510 train/adv_abs_mean=1.4208
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Epoch 3 Training:   1%|          | 1/96 [00:01<02:01,  1.28s/it]
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Epoch 3 Training:   2%|▏         | 2/96 [00:02<02:03,  1.31s/it]
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Epoch 3 Training:   3%|▎         | 3/96 [00:03<02:02,  1.32s/it]
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Epoch 3 Training:   4%|▍         | 4/96 [00:05<02:01,  1.32s/it]
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Epoch 3 Training:   5%|▌         | 5/96 [00:06<02:00,  1.32s/it]
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Epoch 3 Training:   6%|▋         | 6/96 [00:07<01:59,  1.33s/it]
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Epoch 3 Training:   7%|▋         | 7/96 [00:09<01:58,  1.33s/it]
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Epoch 3 Training:   8%|▊         | 8/96 [00:10<01:56,  1.33s/it]
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Epoch 3 Training:   9%|▉         | 9/96 [00:11<01:55,  1.33s/it]
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Epoch 3 Training:  10%|█         | 10/96 [00:13<01:54,  1.33s/it]
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Epoch 3 Training:  11%|█▏        | 11/96 [00:14<01:53,  1.33s/it]
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Epoch 3 Training:  12%|█▎        | 12/96 [00:15<01:51,  1.33s/it]
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Epoch 3 Training:  14%|█▎        | 13/96 [00:17<01:50,  1.33s/it]
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Epoch 3 Training:  15%|█▍        | 14/96 [00:18<01:49,  1.33s/it]
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Epoch 3 Training:  16%|█▌        | 15/96 [00:19<01:47,  1.33s/it]
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Epoch 3 Training:  17%|█▋        | 16/96 [00:21<01:46,  1.33s/it]
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Epoch 3 Training:  18%|█▊        | 17/96 [00:22<01:44,  1.33s/it]
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Epoch 3 Training:  19%|█▉        | 18/96 [00:23<01:43,  1.33s/it]
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Epoch 3 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 3 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.48it/s]
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Epoch 3 Training:  20%|█▉        | 19/96 [00:25<01:41,  1.32s/it]
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Epoch 3 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 3 Training:  21%|██        | 20/96 [00:26<01:40,  1.33s/it]
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Epoch 3 Training:  22%|██▏       | 21/96 [00:27<01:39,  1.33s/it]
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Epoch 3 Training:  23%|██▎       | 22/96 [00:29<01:38,  1.33s/it]
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Epoch 3 Training:  24%|██▍       | 23/96 [00:30<01:36,  1.32s/it]
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Epoch 3 Training:  25%|██▌       | 24/96 [00:31<01:35,  1.33s/it]
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Epoch 3 Training:  26%|██▌       | 25/96 [00:33<01:34,  1.33s/it]
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Epoch 3 Training:  27%|██▋       | 26/96 [00:34<01:32,  1.33s/it]
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Epoch 3 Training:  28%|██▊       | 27/96 [00:35<01:31,  1.33s/it]
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Epoch 3 Training:  29%|██▉       | 28/96 [00:37<01:30,  1.33s/it]
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Epoch 3 Training:  30%|███       | 29/96 [00:38<01:28,  1.33s/it]
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Epoch 3 Training:  31%|███▏      | 30/96 [00:39<01:27,  1.33s/it]
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Epoch 3 Training:  32%|███▏      | 31/96 [00:41<01:26,  1.33s/it]
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Epoch 3 Training:  33%|███▎      | 32/96 [00:42<01:24,  1.33s/it]
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Epoch 3 Training:  34%|███▍      | 33/96 [00:43<01:23,  1.33s/it]
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Epoch 3 Training:  35%|███▌      | 34/96 [00:45<01:22,  1.33s/it]
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Epoch 3 Training:  36%|███▋      | 35/96 [00:46<01:20,  1.33s/it]
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Epoch 3 Training:  38%|███▊      | 36/96 [00:47<01:19,  1.33s/it]
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Epoch 3 Training:  39%|███▊      | 37/96 [00:49<01:18,  1.33s/it]
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Epoch 3 Training:  40%|███▉      | 38/96 [00:50<01:16,  1.33s/it]
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Epoch 3 Training:  41%|████      | 39/96 [00:51<01:15,  1.33s/it]
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Epoch 3 Training:  42%|████▏     | 40/96 [00:53<01:14,  1.33s/it]
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Epoch 3 Training:  43%|████▎     | 41/96 [00:54<01:13,  1.33s/it]
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Epoch 3 Training:  44%|████▍     | 42/96 [00:55<01:11,  1.33s/it]
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Epoch 3 Training:  45%|████▍     | 43/96 [00:57<01:10,  1.33s/it]
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Epoch 3 Training:  46%|████▌     | 44/96 [00:58<01:09,  1.33s/it]
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Epoch 3 Training:  47%|████▋     | 45/96 [00:59<01:07,  1.33s/it]
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Epoch 3 Training:  48%|████▊     | 46/96 [01:01<01:06,  1.33s/it]
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Epoch 3 Training:  49%|████▉     | 47/96 [01:02<01:05,  1.33s/it]
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Epoch 3 Training:  50%|█████     | 48/96 [01:03<01:03,  1.32s/it]
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Epoch 3 Training:  51%|█████     | 49/96 [01:05<01:02,  1.32s/it]
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Epoch 3 Training:  52%|█████▏    | 50/96 [01:06<01:00,  1.32s/it]
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Epoch 3 Training:  53%|█████▎    | 51/96 [01:07<00:59,  1.32s/it]
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Epoch 3 Training:  54%|█████▍    | 52/96 [01:08<00:58,  1.32s/it]
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Epoch 3 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s][2026-10-01 14:40:14] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0003 | Epoch 003] train/policy_loss=13.7488 train/unweighted_policy_loss=2.7498 train/loss=13.7488 train/grad_norm=0.0985
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+[2026-10-01 14:40:14] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0004 | Epoch 003] timing/rollout_seconds=147.7725 timing/feedback_seconds=5.6607 timing/optimization_seconds=128.0990 timing/cycle_seconds=279.6834
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+
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+[2026-10-01 14:42:45] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0004 | Epoch 004] train/reward_dists_mean=0.9261 train/reward_dists_std=0.0145 train/reward_ssim_mean=0.9245 train/reward_ssim_std=0.0231 train/reward_dists_group_std_mean=0.0044 train/reward_dists_group_std_max=0.0078 train/reward_dists_group_std_min=0.0021 train/reward_dists_group_mean_std=0.0137 train/reward_ssim_group_std_mean=0.0023 train/reward_ssim_group_std_max=0.0105 train/reward_ssim_group_std_min=0.0008 train/reward_ssim_group_mean_std=0.0230 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-6.1292 train/adv_max=4.4847 train/adv_abs_mean=1.4547
+
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Epoch 4 Training:  30%|███       | 29/96 [00:38<01:29,  1.33s/it]
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Epoch 4 Training:  33%|███▎      | 32/96 [00:42<01:24,  1.33s/it]
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Epoch 4 Training:  34%|███▍      | 33/96 [00:43<01:23,  1.33s/it]
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Epoch 4 Training:  35%|███▌      | 34/96 [00:45<01:22,  1.33s/it]
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Epoch 4 Training:  36%|███▋      | 35/96 [00:46<01:20,  1.33s/it]
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Epoch 4 Training:  39%|███▊      | 37/96 [00:49<01:23,  1.42s/it]
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Epoch 4 Training:  40%|███▉      | 38/96 [00:51<01:23,  1.44s/it]
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Epoch 4 Training:  41%|████      | 39/96 [00:52<01:20,  1.41s/it]
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Epoch 4 Training:  42%|████▏     | 40/96 [00:53<01:17,  1.39s/it]
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Epoch 4 Training:  43%|████▎     | 41/96 [00:55<01:15,  1.37s/it]
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Epoch 4 Training:  44%|████▍     | 42/96 [00:56<01:13,  1.36s/it]
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Epoch 4 Training:  45%|████▍     | 43/96 [00:57<01:12,  1.36s/it]
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Epoch 4 Training:  46%|████▌     | 44/96 [00:59<01:10,  1.35s/it]
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Epoch 4 Training:  47%|████▋     | 45/96 [01:00<01:08,  1.34s/it]
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Epoch 4 Training:  48%|████▊     | 46/96 [01:01<01:06,  1.34s/it]
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Epoch 4 Training:  49%|████▉     | 47/96 [01:03<01:05,  1.33s/it]
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Epoch 4 Training:  50%|█████     | 48/96 [01:04<01:04,  1.33s/it]
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Epoch 4 Training:  51%|█████     | 49/96 [01:05<01:02,  1.33s/it]
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Epoch 4 Training:  52%|█████▏    | 50/96 [01:07<01:00,  1.33s/it]
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Epoch 4 Training:  53%|█████▎    | 51/96 [01:08<00:59,  1.32s/it]
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Epoch 4 Training:  54%|█████▍    | 52/96 [01:09<00:58,  1.32s/it]
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Epoch 4 Training:  55%|█████▌    | 53/96 [01:10<00:56,  1.32s/it]
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Epoch 4 Training:  56%|█████▋    | 54/96 [01:12<00:55,  1.32s/it]
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Epoch 4 Training:  57%|█████▋    | 55/96 [01:13<00:54,  1.32s/it]
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Epoch 4 Training:  58%|█████▊    | 56/96 [01:14<00:52,  1.32s/it]
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Epoch 4 Training:  59%|█████▉    | 57/96 [01:16<00:51,  1.32s/it]
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Epoch 4 Training:  60%|██████    | 58/96 [01:17<00:50,  1.33s/it]
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Epoch 4 Training:  61%|██████▏   | 59/96 [01:18<00:49,  1.32s/it]
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Epoch 4 Training:  62%|██████▎   | 60/96 [01:20<00:47,  1.33s/it]
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Epoch 4 Training:  64%|██████▎   | 61/96 [01:21<00:46,  1.33s/it]
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Epoch 4 Training:  65%|██████▍   | 62/96 [01:22<00:45,  1.33s/it]
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Epoch 4 Training:  66%|██████▌   | 63/96 [01:24<00:43,  1.32s/it]
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Epoch 4 Training:  67%|██████▋   | 64/96 [01:25<00:42,  1.32s/it]
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Epoch 4 Training:  68%|██████▊   | 65/96 [01:26<00:41,  1.33s/it]
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Epoch 4 Training:  69%|██████▉   | 66/96 [01:28<00:39,  1.33s/it]
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Epoch 4 Training:  70%|██████▉   | 67/96 [01:29<00:38,  1.33s/it]
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Epoch 4 Training:  71%|███████   | 68/96 [01:30<00:37,  1.33s/it]
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Epoch 4 Training:  72%|███████▏  | 69/96 [01:32<00:35,  1.33s/it]
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Epoch 4 Training:  73%|███████▎  | 70/96 [01:33<00:34,  1.32s/it]
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Epoch 4 Training:  74%|███████▍  | 71/96 [01:34<00:33,  1.32s/it]
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Epoch 4 Training:  75%|███████▌  | 72/96 [01:36<00:31,  1.32s/it]
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Epoch 4 Training:  76%|███████▌  | 73/96 [01:37<00:30,  1.33s/it]
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Epoch 4 Training:  77%|███████▋  | 74/96 [01:38<00:29,  1.33s/it]
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Epoch 4 Training:  78%|███████▊  | 75/96 [01:40<00:27,  1.33s/it]
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Epoch 4 Training:  79%|███████▉  | 76/96 [01:41<00:26,  1.33s/it]
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Epoch 4 Training:  80%|████████  | 77/96 [01:42<00:25,  1.33s/it]
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Epoch 4 Training:  81%|████████▏ | 78/96 [01:44<00:23,  1.33s/it]
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Epoch 4 Training:  82%|████████▏ | 79/96 [01:45<00:22,  1.34s/it]
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Epoch 4 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s][2026-10-01 14:44:53] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0004 | Epoch 004] train/policy_loss=13.4972 train/unweighted_policy_loss=2.6994 train/loss=13.4972 train/grad_norm=0.0718
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+[2026-10-01 14:44:53] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0005 | Epoch 004] timing/rollout_seconds=147.7356 timing/feedback_seconds=4.5997 timing/optimization_seconds=128.9339 timing/cycle_seconds=279.4249
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+
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+[2026-10-01 14:47:24] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0005 | Epoch 005] train/reward_dists_mean=0.9331 train/reward_dists_std=0.0165 train/reward_ssim_mean=0.9312 train/reward_ssim_std=0.0211 train/reward_dists_group_std_mean=0.0034 train/reward_dists_group_std_max=0.0064 train/reward_dists_group_std_min=0.0016 train/reward_dists_group_mean_std=0.0161 train/reward_ssim_group_std_mean=0.0016 train/reward_ssim_group_std_max=0.0037 train/reward_ssim_group_std_min=0.0004 train/reward_ssim_group_mean_std=0.0210 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.4312 train/adv_max=4.6139 train/adv_abs_mean=1.3919
+
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Epoch 5 Training:   4%|▍         | 4/96 [00:05<02:01,  1.32s/it]
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Epoch 5 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 5 Training:   5%|▌         | 5/96 [00:06<02:00,  1.32s/it]
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Epoch 5 Training:   6%|▋         | 6/96 [00:07<01:58,  1.32s/it]
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Epoch 5 Training:   7%|▋         | 7/96 [00:09<01:57,  1.32s/it]
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Epoch 5 Training:   8%|▊         | 8/96 [00:10<01:56,  1.32s/it]
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Epoch 5 Training:   9%|▉         | 9/96 [00:11<01:55,  1.32s/it]
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Epoch 5 Training:  10%|█         | 10/96 [00:13<01:54,  1.33s/it]
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Epoch 5 Training:  11%|█▏        | 11/96 [00:14<01:52,  1.33s/it]
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Epoch 5 Training:  12%|█▎        | 12/96 [00:15<01:51,  1.33s/it]
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Epoch 5 Training:  14%|█▎        | 13/96 [00:17<01:50,  1.33s/it]
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Epoch 5 Training:  15%|█▍        | 14/96 [00:18<01:48,  1.33s/it]
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Epoch 5 Training:  16%|█▌        | 15/96 [00:19<01:47,  1.33s/it]
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Epoch 5 Training:  17%|█▋        | 16/96 [00:21<01:46,  1.33s/it]
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Epoch 5 Training:  18%|█▊        | 17/96 [00:22<01:44,  1.33s/it]
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Epoch 5 Training:  19%|█▉        | 18/96 [00:23<01:43,  1.33s/it]
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Epoch 5 Training:  20%|█▉        | 19/96 [00:25<01:42,  1.33s/it]
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Epoch 5 Training:  21%|██        | 20/96 [00:26<01:40,  1.32s/it]
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Epoch 5 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 5 Training:  22%|██▏       | 21/96 [00:27<01:39,  1.32s/it]
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Epoch 5 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 5 Training:  23%|██▎       | 22/96 [00:29<01:37,  1.32s/it]
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Epoch 5 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 5 Training:  24%|██▍       | 23/96 [00:30<01:36,  1.32s/it]
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Epoch 5 Training:  25%|██▌       | 24/96 [00:31<01:35,  1.33s/it]
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Epoch 5 Training:  26%|██▌       | 25/96 [00:33<01:34,  1.33s/it]
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Epoch 5 Training:  27%|██▋       | 26/96 [00:34<01:32,  1.33s/it]
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Epoch 5 Training:  28%|██▊       | 27/96 [00:35<01:31,  1.33s/it]
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Epoch 5 Training:  29%|██▉       | 28/96 [00:37<01:30,  1.33s/it]
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Epoch 5 Training:  30%|███       | 29/96 [00:38<01:28,  1.33s/it]
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Epoch 5 Training:  31%|███▏      | 30/96 [00:39<01:27,  1.33s/it]
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Epoch 5 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 5 Training:  32%|███▏      | 31/96 [00:41<01:26,  1.32s/it]
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Epoch 5 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 5 Training:  33%|███▎      | 32/96 [00:42<01:24,  1.32s/it]
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Epoch 5 Training:  34%|███▍      | 33/96 [00:43<01:23,  1.32s/it]
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Epoch 5 Training:  35%|███▌      | 34/96 [00:45<01:22,  1.32s/it]
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Epoch 5 Training:  36%|███▋      | 35/96 [00:46<01:20,  1.32s/it]
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Epoch 5 Training:  38%|███▊      | 36/96 [00:47<01:19,  1.32s/it]
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Epoch 5 Training:  39%|███▊      | 37/96 [00:48<01:18,  1.32s/it]
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Epoch 5 Training:  40%|███▉      | 38/96 [00:50<01:16,  1.32s/it]
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Epoch 5 Training:  41%|████      | 39/96 [00:51<01:15,  1.33s/it]
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Epoch 5 Training:  42%|████▏     | 40/96 [00:52<01:14,  1.33s/it]
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Epoch 5 Training:  43%|████▎     | 41/96 [00:54<01:12,  1.33s/it]
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Epoch 5 Training:  44%|████▍     | 42/96 [00:55<01:11,  1.33s/it]
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Epoch 5 Training:  45%|████▍     | 43/96 [00:56<01:10,  1.32s/it]
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Epoch 5 Training:  46%|████▌     | 44/96 [00:58<01:08,  1.32s/it]
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Epoch 5 Training:  47%|████▋     | 45/96 [00:59<01:07,  1.32s/it]
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Epoch 5 Training:  48%|████▊     | 46/96 [01:00<01:06,  1.32s/it]
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Epoch 5 Training:  49%|████▉     | 47/96 [01:02<01:04,  1.32s/it]
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Epoch 5 Training:  50%|█████     | 48/96 [01:03<01:03,  1.32s/it]
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Epoch 5 Training:  51%|█████     | 49/96 [01:04<01:01,  1.32s/it]
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Epoch 5 Training:  52%|█████▏    | 50/96 [01:06<01:00,  1.32s/it]
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Epoch 5 Training:  53%|█████▎    | 51/96 [01:07<00:59,  1.32s/it]
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Epoch 5 Training:  54%|█████▍    | 52/96 [01:08<00:58,  1.32s/it]
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Epoch 5 Training:  55%|█████▌    | 53/96 [01:10<00:56,  1.32s/it]
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Epoch 5 Training:  56%|█████▋    | 54/96 [01:11<00:55,  1.32s/it]
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Epoch 5 Training:  57%|█████▋    | 55/96 [01:12<00:53,  1.32s/it]
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Epoch 5 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s][2026-10-01 14:49:31] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0005 | Epoch 005] train/policy_loss=12.9805 train/unweighted_policy_loss=2.5961 train/loss=12.9805 train/grad_norm=0.0573
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+[2026-10-01 14:49:31] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0006 | Epoch 005] timing/rollout_seconds=147.7758 timing/feedback_seconds=4.6129 timing/optimization_seconds=127.6267 timing/cycle_seconds=278.1862
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+
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+
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+[2026-10-01 14:52:04] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0006 | Epoch 006] train/reward_dists_mean=0.9278 train/reward_dists_std=0.0204 train/reward_ssim_mean=0.9344 train/reward_ssim_std=0.0203 train/reward_dists_group_std_mean=0.0032 train/reward_dists_group_std_max=0.0067 train/reward_dists_group_std_min=0.0015 train/reward_dists_group_mean_std=0.0201 train/reward_ssim_group_std_mean=0.0015 train/reward_ssim_group_std_max=0.0045 train/reward_ssim_group_std_min=0.0006 train/reward_ssim_group_mean_std=0.0203 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.4996 train/adv_max=5.3330 train/adv_abs_mean=1.3568
+
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Epoch 6 Training:  33%|███▎      | 32/96 [00:42<01:24,  1.32s/it]
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Epoch 6 Training:  34%|███▍      | 33/96 [00:43<01:29,  1.41s/it]
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Epoch 6 Training:  35%|███▌      | 34/96 [00:45<01:25,  1.39s/it]
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Epoch 6 Training:  36%|███▋      | 35/96 [00:46<01:23,  1.36s/it]
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Epoch 6 Training:  38%|███▊      | 36/96 [00:47<01:20,  1.35s/it]
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Epoch 6 Training:  39%|███▊      | 37/96 [00:49<01:19,  1.34s/it]
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Epoch 6 Training:  40%|███▉      | 38/96 [00:50<01:17,  1.33s/it]
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Epoch 6 Training:  41%|████      | 39/96 [00:51<01:15,  1.33s/it]
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Epoch 6 Training:  42%|████▏     | 40/96 [00:53<01:15,  1.35s/it]
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Epoch 6 Training:  43%|████▎     | 41/96 [00:54<01:13,  1.34s/it]
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Epoch 6 Training:  44%|████▍     | 42/96 [00:55<01:11,  1.33s/it]
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Epoch 6 Training:  45%|████▍     | 43/96 [00:57<01:10,  1.32s/it]
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Epoch 6 Training:  46%|████▌     | 44/96 [00:58<01:08,  1.32s/it]
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Epoch 6 Training:  47%|████▋     | 45/96 [00:59<01:07,  1.32s/it]
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Epoch 6 Training:  48%|████▊     | 46/96 [01:01<01:05,  1.32s/it]
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Epoch 6 Training:  49%|████▉     | 47/96 [01:02<01:04,  1.32s/it]
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Epoch 6 Training:  50%|█████     | 48/96 [01:03<01:03,  1.32s/it]
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Epoch 6 Training:  51%|█████     | 49/96 [01:05<01:02,  1.32s/it]
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Epoch 6 Training:  52%|█████▏    | 50/96 [01:06<01:00,  1.32s/it]
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Epoch 6 Training:  53%|█████▎    | 51/96 [01:07<00:59,  1.32s/it]
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Epoch 6 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.47it/s]
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Epoch 6 Training:  54%|█████▍    | 52/96 [01:09<00:58,  1.32s/it]
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Epoch 6 Training:  55%|█████▌    | 53/96 [01:10<00:56,  1.32s/it]
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Epoch 6 Training:  56%|█████▋    | 54/96 [01:11<00:55,  1.32s/it]
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Epoch 6 Training:  57%|█████▋    | 55/96 [01:13<00:54,  1.32s/it]
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Epoch 6 Training:  58%|█████▊    | 56/96 [01:14<00:52,  1.32s/it]
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Epoch 6 Training:  59%|█████▉    | 57/96 [01:15<00:51,  1.32s/it]
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Epoch 6 Training:  60%|██████    | 58/96 [01:17<00:51,  1.34s/it]
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Epoch 6 Training:  61%|██████▏   | 59/96 [01:18<00:49,  1.34s/it]
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Epoch 6 Training:  62%|██████▎   | 60/96 [01:19<00:48,  1.33s/it]
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Epoch 6 Training:  64%|██████▎   | 61/96 [01:21<00:46,  1.33s/it]
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Epoch 6 Training:  65%|██████▍   | 62/96 [01:22<00:45,  1.33s/it]
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Epoch 6 Training:  66%|██████▌   | 63/96 [01:23<00:43,  1.33s/it]
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Epoch 6 Training:  67%|██████▋   | 64/96 [01:25<00:42,  1.33s/it]
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Epoch 6 Training:  68%|██████▊   | 65/96 [01:26<00:41,  1.33s/it]
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Epoch 6 Training:  69%|██████▉   | 66/96 [01:27<00:39,  1.33s/it]
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Epoch 6 Training:  70%|██████▉   | 67/96 [01:29<00:38,  1.33s/it]
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Epoch 6 Training:  71%|███████   | 68/96 [01:30<00:37,  1.33s/it]
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Epoch 6 Training:  72%|███████▏  | 69/96 [01:31<00:35,  1.33s/it]
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Epoch 6 Training:  73%|███████▎  | 70/96 [01:33<00:34,  1.32s/it]
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Epoch 6 Training:  74%|███████▍  | 71/96 [01:34<00:33,  1.32s/it]
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Epoch 6 Training:  75%|███████▌  | 72/96 [01:35<00:31,  1.33s/it]
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Epoch 6 Training:  76%|███████▌  | 73/96 [01:37<00:30,  1.33s/it]
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Epoch 6 Training:  77%|███████▋  | 74/96 [01:38<00:29,  1.33s/it]
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Epoch 6 Training:  78%|███████▊  | 75/96 [01:39<00:27,  1.33s/it]
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Epoch 6 Training:  79%|███████▉  | 76/96 [01:40<00:26,  1.33s/it]
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Epoch 6 Training:  80%|████████  | 77/96 [01:42<00:25,  1.32s/it]
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Epoch 6 Training:  81%|████████▏ | 78/96 [01:43<00:23,  1.32s/it]
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Epoch 6 Training:  82%|████████▏ | 79/96 [01:44<00:22,  1.32s/it]
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Epoch 6 Training:  83%|████████▎ | 80/96 [01:46<00:21,  1.32s/it]
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Epoch 6 Training:  84%|████████▍ | 81/96 [01:47<00:19,  1.32s/it]
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Epoch 6 Training:  85%|████████▌ | 82/96 [01:48<00:18,  1.32s/it]
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Epoch 6 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s][2026-10-01 14:54:11] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0006 | Epoch 006] train/policy_loss=12.7965 train/unweighted_policy_loss=2.5593 train/loss=12.7965 train/grad_norm=0.0382
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+[2026-10-01 14:54:11] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0007 | Epoch 006] timing/rollout_seconds=148.1218 timing/feedback_seconds=5.5569 timing/optimization_seconds=128.8672 timing/cycle_seconds=279.6488
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+[2026-10-01 14:56:42] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0007 | Epoch 007] train/reward_dists_mean=0.9300 train/reward_dists_std=0.0173 train/reward_ssim_mean=0.9355 train/reward_ssim_std=0.0182 train/reward_dists_group_std_mean=0.0033 train/reward_dists_group_std_max=0.0061 train/reward_dists_group_std_min=0.0012 train/reward_dists_group_mean_std=0.0170 train/reward_ssim_group_std_mean=0.0021 train/reward_ssim_group_std_max=0.0073 train/reward_ssim_group_std_min=0.0005 train/reward_ssim_group_mean_std=0.0180 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.1040 train/adv_max=3.9115 train/adv_abs_mean=1.3621
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Epoch 7 Training:   8%|▊         | 8/96 [00:10<01:57,  1.33s/it]
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Epoch 7 Training:   9%|▉         | 9/96 [00:11<01:55,  1.33s/it]
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Epoch 7 Training:  10%|█         | 10/96 [00:13<01:54,  1.33s/it]
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Epoch 7 Training:  11%|█▏        | 11/96 [00:14<01:52,  1.33s/it]
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Epoch 7 Training:  12%|█▎        | 12/96 [00:15<01:51,  1.32s/it]
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Epoch 7 Training:  14%|█▎        | 13/96 [00:17<01:49,  1.32s/it]
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Epoch 7 Training:  15%|█▍        | 14/96 [00:18<01:48,  1.32s/it]
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Epoch 7 Training:  16%|█▌        | 15/96 [00:19<01:47,  1.32s/it]
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Epoch 7 Training:  17%|█▋        | 16/96 [00:21<01:45,  1.32s/it]
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Epoch 7 Training:  18%|█▊        | 17/96 [00:22<01:44,  1.32s/it]
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Epoch 7 Training:  19%|█▉        | 18/96 [00:23<01:43,  1.33s/it]
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Epoch 7 Training:  20%|█▉        | 19/96 [00:25<01:42,  1.32s/it]
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Epoch 7 Training:  21%|██        | 20/96 [00:26<01:40,  1.32s/it]
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Epoch 7 Training:  22%|██▏       | 21/96 [00:27<01:39,  1.32s/it]
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Epoch 7 Training:  23%|██▎       | 22/96 [00:29<01:38,  1.33s/it]
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Epoch 7 Training:  24%|██▍       | 23/96 [00:30<01:36,  1.32s/it]
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Epoch 7 Training:  25%|██▌       | 24/96 [00:31<01:35,  1.32s/it]
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Epoch 7 Training:  26%|██▌       | 25/96 [00:33<01:33,  1.32s/it]
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Epoch 7 Training:  27%|██▋       | 26/96 [00:34<01:32,  1.33s/it]
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Epoch 7 Training:  28%|██▊       | 27/96 [00:35<01:31,  1.33s/it]
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Epoch 7 Training:  29%|██▉       | 28/96 [00:37<01:30,  1.32s/it]
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Epoch 7 Training:  30%|███       | 29/96 [00:38<01:28,  1.33s/it]
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Epoch 7 Training:  31%|███▏      | 30/96 [00:39<01:27,  1.33s/it]
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Epoch 7 Training:  32%|███▏      | 31/96 [00:41<01:26,  1.33s/it]
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Epoch 7 Training:  33%|███▎      | 32/96 [00:42<01:24,  1.32s/it]
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Epoch 7 Training:  34%|███▍      | 33/96 [00:43<01:23,  1.33s/it]
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Epoch 7 Training:  35%|███▌      | 34/96 [00:45<01:22,  1.33s/it]
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Epoch 7 Training:  36%|███▋      | 35/96 [00:46<01:20,  1.33s/it]
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Epoch 7 Training:  38%|███▊      | 36/96 [00:47<01:19,  1.33s/it]
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Epoch 7 Training:  39%|███▊      | 37/96 [00:48<01:18,  1.33s/it]
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Epoch 7 Training:  40%|███▉      | 38/96 [00:50<01:16,  1.33s/it]
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Epoch 7 Training:  41%|████      | 39/96 [00:51<01:15,  1.33s/it]
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Epoch 7 Training:  42%|████▏     | 40/96 [00:52<01:14,  1.32s/it]
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Epoch 7 Training:  43%|████▎     | 41/96 [00:54<01:12,  1.32s/it]
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Epoch 7 Training:  44%|████▍     | 42/96 [00:55<01:11,  1.32s/it]
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Epoch 7 Training:  45%|████▍     | 43/96 [00:56<01:09,  1.32s/it]
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Epoch 7 Training:  46%|████▌     | 44/96 [00:58<01:08,  1.32s/it]
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Epoch 7 Training:  47%|████▋     | 45/96 [00:59<01:07,  1.32s/it]
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Epoch 7 Training:  48%|████▊     | 46/96 [01:00<01:05,  1.32s/it]
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Epoch 7 Training:  49%|████▉     | 47/96 [01:02<01:04,  1.32s/it]
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Epoch 7 Training:  51%|█████     | 49/96 [01:04<01:01,  1.32s/it]
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Epoch 7 Training:  52%|█████▏    | 50/96 [01:06<01:00,  1.32s/it]
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Epoch 7 Training:  53%|█████▎    | 51/96 [01:07<00:59,  1.32s/it]
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Epoch 7 Training:  54%|█████▍    | 52/96 [01:08<00:57,  1.32s/it]
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Epoch 7 Training:  55%|█████▌    | 53/96 [01:10<00:56,  1.32s/it]
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Epoch 7 Training:  56%|█████▋    | 54/96 [01:11<00:55,  1.32s/it]
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Epoch 7 Training:  57%|█████▋    | 55/96 [01:12<00:54,  1.32s/it]
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Epoch 7 Training:  58%|█████▊    | 56/96 [01:14<00:52,  1.32s/it]
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Epoch 7 Training:  59%|█████▉    | 57/96 [01:15<00:51,  1.32s/it]
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Epoch 7 Training:  60%|██████    | 58/96 [01:16<00:50,  1.32s/it]
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Epoch 7 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s][2026-10-01 14:58:49] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0007 | Epoch 007] train/policy_loss=12.6585 train/unweighted_policy_loss=2.5317 train/loss=12.6585 train/grad_norm=0.0440
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+[2026-10-01 14:58:49] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0008 | Epoch 007] timing/rollout_seconds=148.0179 timing/feedback_seconds=4.7996 timing/optimization_seconds=127.4830 timing/cycle_seconds=278.1042
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+
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+
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Pointwise Rewards: dists:  83%|████████▎ | 5/6 [00:01<00:00,  3.66it/s]
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Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.62it/s]
+[2026-10-01 15:01:20] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0008 | Epoch 008] train/reward_dists_mean=0.9278 train/reward_dists_std=0.0200 train/reward_ssim_mean=0.9327 train/reward_ssim_std=0.0219 train/reward_dists_group_std_mean=0.0031 train/reward_dists_group_std_max=0.0060 train/reward_dists_group_std_min=0.0016 train/reward_dists_group_mean_std=0.0198 train/reward_ssim_group_std_mean=0.0020 train/reward_ssim_group_std_max=0.0165 train/reward_ssim_group_std_min=0.0007 train/reward_ssim_group_mean_std=0.0217 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.9698 train/adv_max=5.4554 train/adv_abs_mean=1.3695
+
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Epoch 8 Training:  11%|█▏        | 11/96 [00:14<01:52,  1.33s/it]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.49it/s]
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Epoch 8 Training:  36%|███▋      | 35/96 [00:46<01:20,  1.32s/it]
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Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.49it/s]
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Epoch 8 Training:  38%|███▊      | 36/96 [00:47<01:19,  1.32s/it]
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Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.47it/s]
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Epoch 8 Training:  39%|███▊      | 37/96 [00:48<01:18,  1.32s/it]
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Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.48it/s]
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Epoch 8 Training:  40%|███▉      | 38/96 [00:50<01:16,  1.32s/it]
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Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.47it/s]
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Epoch 8 Training:  41%|████      | 39/96 [00:51<01:15,  1.32s/it]
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Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.48it/s]
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Epoch 8 Training:  42%|████▏     | 40/96 [00:52<01:14,  1.32s/it]
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Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.49it/s]
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Epoch 8 Training:  43%|████▎     | 41/96 [00:54<01:12,  1.32s/it]
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Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.49it/s]
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Epoch 8 Training:  44%|████▍     | 42/96 [00:55<01:11,  1.32s/it]
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Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Training:  45%|████▍     | 43/96 [00:56<01:09,  1.32s/it]
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Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.49it/s]
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Epoch 8 Training:  46%|████▌     | 44/96 [00:58<01:08,  1.32s/it]
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Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Training:  47%|████▋     | 45/96 [00:59<01:07,  1.32s/it]
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Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.48it/s]
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Epoch 8 Training:  48%|████▊     | 46/96 [01:00<01:06,  1.32s/it]
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Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.50it/s]
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Epoch 8 Training:  49%|████▉     | 47/96 [01:02<01:04,  1.32s/it]
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Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.48it/s]
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Epoch 8 Training:  50%|█████     | 48/96 [01:03<01:03,  1.32s/it]
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Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.48it/s]
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Epoch 8 Training:  51%|█████     | 49/96 [01:04<01:02,  1.33s/it]
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Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.49it/s]
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Epoch 8 Training:  52%|█████▏    | 50/96 [01:06<01:00,  1.33s/it]
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Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.49it/s]
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Epoch 8 Training:  53%|█████▎    | 51/96 [01:07<00:59,  1.33s/it]
+
Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.48it/s]
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Epoch 8 Training:  54%|█████▍    | 52/96 [01:08<00:58,  1.33s/it]
+
Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.49it/s]
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Epoch 8 Training:  55%|█████▌    | 53/96 [01:10<00:57,  1.33s/it]
+
Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.49it/s]
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Epoch 8 Training:  56%|█████▋    | 54/96 [01:11<00:55,  1.33s/it]
+
Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.50it/s]
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Epoch 8 Training:  57%|█████▋    | 55/96 [01:12<00:54,  1.32s/it]
+
Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.49it/s]
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Epoch 8 Training:  58%|█████▊    | 56/96 [01:14<00:52,  1.32s/it]
+
Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.48it/s]
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Epoch 8 Training:  59%|█████▉    | 57/96 [01:15<00:51,  1.33s/it]
+
Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.49it/s]
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Epoch 8 Training:  60%|██████    | 58/96 [01:16<00:50,  1.32s/it]
+
Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.49it/s]
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Epoch 8 Training:  61%|██████▏   | 59/96 [01:18<00:49,  1.32s/it]
+
Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.49it/s]
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Epoch 8 Training:  62%|██████▎   | 60/96 [01:19<00:47,  1.33s/it]
+
Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.48it/s]
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Epoch 8 Training:  64%|██████▎   | 61/96 [01:20<00:46,  1.33s/it]
+
Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.50it/s]
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Epoch 8 Training:  65%|██████▍   | 62/96 [01:22<00:45,  1.33s/it]
+
Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.42it/s]
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Epoch 8 Training:  66%|██████▌   | 63/96 [01:23<00:44,  1.34s/it]
+
Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.49it/s]
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Epoch 8 Training:  67%|██████▋   | 64/96 [01:24<00:42,  1.33s/it]
+
Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.47it/s]
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Epoch 8 Training:  68%|██████▊   | 65/96 [01:26<00:41,  1.33s/it]
+
Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.43it/s]
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Epoch 8 Training:  69%|██████▉   | 66/96 [01:27<00:40,  1.33s/it]
+
Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.49it/s]
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Epoch 8 Training:  70%|██████▉   | 67/96 [01:28<00:38,  1.33s/it]
+
Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.49it/s]
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Epoch 8 Training:  71%|███████   | 68/96 [01:30<00:37,  1.33s/it]
+
Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Training:  72%|███████▏  | 69/96 [01:31<00:35,  1.32s/it]
+
Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.49it/s]
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Epoch 8 Training:  73%|███████▎  | 70/96 [01:32<00:34,  1.33s/it]
+
Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Training:  74%|███████▍  | 71/96 [01:34<00:33,  1.32s/it]
+
Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Training:  75%|███████▌  | 72/96 [01:35<00:31,  1.32s/it]
+
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Epoch 8 Training:  76%|███████▌  | 73/96 [01:36<00:30,  1.32s/it]
+
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.48it/s]
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Epoch 8 Training:  77%|███████▋  | 74/96 [01:38<00:29,  1.33s/it]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.43it/s]
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Epoch 8 Training:  78%|███████▊  | 75/96 [01:39<00:27,  1.33s/it]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.48it/s]
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Epoch 8 Training:  79%|███████▉  | 76/96 [01:40<00:26,  1.33s/it]
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Epoch 8 Training:  80%|████████  | 77/96 [01:42<00:25,  1.33s/it]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.47it/s]
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Epoch 8 Training:  81%|████████▏ | 78/96 [01:43<00:23,  1.33s/it]
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Epoch 8 Training:  82%|████████▏ | 79/96 [01:44<00:22,  1.33s/it]
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Epoch 8 Training:  83%|████████▎ | 80/96 [01:46<00:21,  1.33s/it]
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Epoch 8 Training:  84%|████████▍ | 81/96 [01:47<00:19,  1.33s/it]
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Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Training:  85%|████████▌ | 82/96 [01:48<00:18,  1.33s/it]
+
Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Training:  86%|████████▋ | 83/96 [01:50<00:17,  1.33s/it]
+
Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Training:  88%|████████▊ | 84/96 [01:51<00:15,  1.33s/it]
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Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Training:  89%|████████▊ | 85/96 [01:52<00:14,  1.33s/it]
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Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s][2026-10-01 15:03:28] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0008 | Epoch 008] train/policy_loss=12.3523 train/unweighted_policy_loss=2.4705 train/loss=12.3523 train/grad_norm=0.0426
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+[2026-10-01 15:03:28] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0009 | Epoch 008] timing/rollout_seconds=147.9945 timing/feedback_seconds=4.9588 timing/optimization_seconds=127.9036 timing/cycle_seconds=278.5520
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+
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+
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+[2026-10-01 15:05:59] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0009 | Epoch 009] train/reward_dists_mean=0.9304 train/reward_dists_std=0.0184 train/reward_ssim_mean=0.9352 train/reward_ssim_std=0.0211 train/reward_dists_group_std_mean=0.0030 train/reward_dists_group_std_max=0.0071 train/reward_dists_group_std_min=0.0018 train/reward_dists_group_mean_std=0.0181 train/reward_ssim_group_std_mean=0.0015 train/reward_ssim_group_std_max=0.0053 train/reward_ssim_group_std_min=0.0005 train/reward_ssim_group_mean_std=0.0211 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.6660 train/adv_max=4.3230 train/adv_abs_mean=1.3156
+
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Epoch 9 Training:  11%|█▏        | 11/96 [00:14<01:52,  1.33s/it]
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Epoch 9 Training:  12%|█▎        | 12/96 [00:15<01:51,  1.33s/it]
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Epoch 9 Training:  14%|█▎        | 13/96 [00:17<01:50,  1.33s/it]
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Epoch 9 Training:  15%|█▍        | 14/96 [00:18<01:48,  1.33s/it]
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Epoch 9 Training:  16%|█▌        | 15/96 [00:19<01:47,  1.33s/it]
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Epoch 9 Training:  17%|█▋        | 16/96 [00:21<01:46,  1.33s/it]
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Epoch 9 Training:  18%|█▊        | 17/96 [00:22<01:45,  1.33s/it]
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Epoch 9 Training:  19%|█▉        | 18/96 [00:23<01:43,  1.33s/it]
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Epoch 9 Training:  20%|█▉        | 19/96 [00:25<01:42,  1.33s/it]
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Epoch 9 Training:  21%|██        | 20/96 [00:26<01:40,  1.33s/it]
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Epoch 9 Training:  22%|██▏       | 21/96 [00:27<01:39,  1.33s/it]
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Epoch 9 Training:  23%|██▎       | 22/96 [00:29<01:38,  1.33s/it]
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Epoch 9 Training:  24%|██▍       | 23/96 [00:30<01:36,  1.33s/it]
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Epoch 9 Training:  25%|██▌       | 24/96 [00:31<01:35,  1.33s/it]
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Epoch 9 Training:  26%|██▌       | 25/96 [00:33<01:34,  1.33s/it]
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Epoch 9 Training:  27%|██▋       | 26/96 [00:34<01:32,  1.33s/it]
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Epoch 9 Training:  28%|██▊       | 27/96 [00:35<01:31,  1.33s/it]
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Epoch 9 Training:  29%|██▉       | 28/96 [00:37<01:30,  1.33s/it]
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Epoch 9 Training:  30%|███       | 29/96 [00:38<01:28,  1.33s/it]
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Epoch 9 Training:  31%|███▏      | 30/96 [00:39<01:27,  1.32s/it]
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Epoch 9 Training:  32%|███▏      | 31/96 [00:41<01:25,  1.32s/it]
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Epoch 9 Training:  33%|███▎      | 32/96 [00:42<01:24,  1.32s/it]
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Epoch 9 Training:  34%|███▍      | 33/96 [00:43<01:23,  1.32s/it]
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Epoch 9 Training:  35%|███▌      | 34/96 [00:45<01:22,  1.32s/it]
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Epoch 9 Training:  36%|███▋      | 35/96 [00:46<01:20,  1.32s/it]
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Epoch 9 Training:  38%|███▊      | 36/96 [00:47<01:19,  1.32s/it]
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Epoch 9 Training:  39%|███▊      | 37/96 [00:49<01:18,  1.32s/it]
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Epoch 9 Training:  40%|███▉      | 38/96 [00:50<01:16,  1.32s/it]
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Epoch 9 Training:  41%|████      | 39/96 [00:51<01:15,  1.32s/it]
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Epoch 9 Training:  42%|████▏     | 40/96 [00:53<01:14,  1.32s/it]
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Epoch 9 Training:  43%|████▎     | 41/96 [00:54<01:12,  1.33s/it]
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Epoch 9 Training:  44%|████▍     | 42/96 [00:55<01:11,  1.33s/it]
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Epoch 9 Training:  45%|████▍     | 43/96 [00:57<01:10,  1.33s/it]
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Epoch 9 Training:  46%|████▌     | 44/96 [00:58<01:08,  1.33s/it]
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Epoch 9 Training:  47%|████▋     | 45/96 [00:59<01:07,  1.33s/it]
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Epoch 9 Training:  48%|████▊     | 46/96 [01:01<01:06,  1.33s/it]
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Epoch 9 Training:  49%|████▉     | 47/96 [01:02<01:05,  1.33s/it]
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Epoch 9 Training:  50%|█████     | 48/96 [01:03<01:03,  1.33s/it]
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Epoch 9 Training:  51%|█████     | 49/96 [01:05<01:02,  1.33s/it]
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Epoch 9 Training:  52%|█████▏    | 50/96 [01:06<01:01,  1.33s/it]
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Epoch 9 Training:  53%|█████▎    | 51/96 [01:07<00:59,  1.33s/it]
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Epoch 9 Training:  54%|█████▍    | 52/96 [01:09<00:58,  1.33s/it]
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Epoch 9 Training:  55%|█████▌    | 53/96 [01:10<00:57,  1.33s/it]
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Epoch 9 Training:  56%|█████▋    | 54/96 [01:11<00:55,  1.33s/it]
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Epoch 9 Training:  57%|█████▋    | 55/96 [01:12<00:54,  1.33s/it]
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Epoch 9 Training:  58%|█████▊    | 56/96 [01:14<00:52,  1.32s/it]
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Epoch 9 Training:  59%|█████▉    | 57/96 [01:15<00:51,  1.32s/it]
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Epoch 9 Training:  60%|██████    | 58/96 [01:16<00:50,  1.32s/it]
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Epoch 9 Training:  61%|██████▏   | 59/96 [01:18<00:48,  1.32s/it]
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Epoch 9 Training:  62%|██████▎   | 60/96 [01:19<00:47,  1.33s/it]
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Epoch 9 Training:  64%|██████▎   | 61/96 [01:20<00:46,  1.33s/it]
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Epoch 9 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s][2026-10-01 15:08:06] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0009 | Epoch 009] train/policy_loss=12.1637 train/unweighted_policy_loss=2.4327 train/loss=12.1637 train/grad_norm=0.0500
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+[2026-10-01 15:08:06] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0010 | Epoch 009] timing/rollout_seconds=148.0050 timing/feedback_seconds=4.5162 timing/optimization_seconds=127.9497 timing/cycle_seconds=278.6187
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+[2026-10-01 15:08:51] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0010 | Epoch 010] eval/vedai_rl_full/reward_dists_mean=0.9274 eval/vedai_rl_full/reward_dists_std=0.0202 eval/vedai_rl_full/reward_psnr_mean=27.7459 eval/vedai_rl_full/reward_psnr_std=3.2870 eval/vedai_rl_full/reward_ssim_mean=0.9209 eval/vedai_rl_full/reward_ssim_std=0.0261
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Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:00<00:00,  6.23it/s]
+
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Pointwise Rewards: dists:  83%|████████▎ | 5/6 [00:01<00:00,  3.54it/s]
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+[2026-10-01 15:11:22] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0010 | Epoch 010] train/reward_dists_mean=0.9262 train/reward_dists_std=0.0279 train/reward_ssim_mean=0.9365 train/reward_ssim_std=0.0241 train/reward_dists_group_std_mean=0.0033 train/reward_dists_group_std_max=0.0167 train/reward_dists_group_std_min=0.0013 train/reward_dists_group_mean_std=0.0277 train/reward_ssim_group_std_mean=0.0014 train/reward_ssim_group_std_max=0.0115 train/reward_ssim_group_std_min=0.0004 train/reward_ssim_group_mean_std=0.0240 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.8452 train/adv_max=6.8679 train/adv_abs_mean=1.3422
+
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Epoch 10 Training:  33%|███▎      | 32/96 [00:42<01:24,  1.33s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 10 Training:  34%|███▍      | 33/96 [00:43<01:23,  1.33s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 10 Training:  35%|███▌      | 34/96 [00:45<01:22,  1.33s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.23it/s]
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Epoch 10 Training:  36%|███▋      | 35/96 [00:46<01:21,  1.33s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 10 Training:  38%|███▊      | 36/96 [00:47<01:19,  1.33s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 10 Training:  39%|███▊      | 37/96 [00:48<01:18,  1.33s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 10 Training:  40%|███▉      | 38/96 [00:50<01:16,  1.33s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 10 Training:  41%|████      | 39/96 [00:51<01:15,  1.33s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 10 Training:  42%|████▏     | 40/96 [00:52<01:14,  1.33s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 10 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 10 Training:  43%|████▎     | 41/96 [00:54<01:13,  1.33s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 10 Training:  44%|████▍     | 42/96 [00:55<01:11,  1.33s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 10 Training:  45%|████▍     | 43/96 [00:56<01:10,  1.33s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 10 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 10 Training:  46%|████▌     | 44/96 [00:58<01:08,  1.33s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 10 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 10 Training:  47%|████▋     | 45/96 [00:59<01:07,  1.33s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 10 Training:  48%|████▊     | 46/96 [01:00<01:06,  1.33s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 10 Training:  49%|████▉     | 47/96 [01:02<01:04,  1.33s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 10 Training:  50%|█████     | 48/96 [01:03<01:03,  1.33s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 10 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 10 Training:  51%|█████     | 49/96 [01:04<01:02,  1.32s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 10 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 10 Training:  52%|█████▏    | 50/96 [01:06<01:01,  1.33s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 10 Training:  53%|█████▎    | 51/96 [01:07<00:59,  1.33s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 10 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 10 Training:  54%|█████▍    | 52/96 [01:08<00:58,  1.33s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 10 Training:  55%|█████▌    | 53/96 [01:10<00:56,  1.32s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 10 Training:  56%|█████▋    | 54/96 [01:11<00:55,  1.33s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 10 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.29it/s]
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Epoch 10 Training:  57%|█████▋    | 55/96 [01:12<00:54,  1.33s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 10 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.42it/s]
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Epoch 10 Training:  58%|█████▊    | 56/96 [01:14<00:53,  1.33s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 10 Training:  59%|█████▉    | 57/96 [01:15<00:51,  1.33s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 10 Training:  60%|██████    | 58/96 [01:16<00:50,  1.33s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 10 Training:  61%|██████▏   | 59/96 [01:18<00:49,  1.33s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 10 Training:  62%|██████▎   | 60/96 [01:19<00:47,  1.33s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.22it/s]
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Epoch 10 Training:  64%|██████▎   | 61/96 [01:20<00:46,  1.33s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.23it/s]
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Epoch 10 Training:  65%|██████▍   | 62/96 [01:22<00:45,  1.33s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 10 Training:  66%|██████▌   | 63/96 [01:23<00:43,  1.33s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 10 Training:  67%|██████▋   | 64/96 [01:24<00:42,  1.33s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 10 Training:  68%|██████▊   | 65/96 [01:26<00:41,  1.33s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 10 Training:  69%|██████▉   | 66/96 [01:27<00:39,  1.33s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 10 Training:  70%|██████▉   | 67/96 [01:28<00:38,  1.33s/it]
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Epoch 10 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 10 Training:  71%|███████   | 68/96 [01:30<00:37,  1.32s/it]
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Epoch 10 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 10 Training:  72%|███████▏  | 69/96 [01:31<00:35,  1.32s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 10 Training:  73%|███████▎  | 70/96 [01:32<00:34,  1.32s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 10 Training:  74%|███████▍  | 71/96 [01:34<00:33,  1.32s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 10 Training:  75%|███████▌  | 72/96 [01:35<00:31,  1.33s/it]
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Epoch 10 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 10 Training:  76%|███████▌  | 73/96 [01:36<00:30,  1.33s/it]
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Epoch 10 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.23it/s]
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Epoch 10 Training:  77%|███████▋  | 74/96 [01:38<00:29,  1.33s/it]
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Epoch 10 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 10 Training:  78%|███████▊  | 75/96 [01:39<00:27,  1.32s/it]
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Epoch 10 Training:  79%|███████▉  | 76/96 [01:40<00:26,  1.32s/it]
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Epoch 10 Training:  80%|████████  | 77/96 [01:42<00:25,  1.32s/it]
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Epoch 10 Training:  81%|████████▏ | 78/96 [01:43<00:23,  1.33s/it]
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Epoch 10 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 10 Training:  82%|████████▏ | 79/96 [01:44<00:22,  1.33s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 10 Training:  83%|████████▎ | 80/96 [01:46<00:21,  1.32s/it]
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Epoch 10 Training:  84%|████████▍ | 81/96 [01:47<00:19,  1.32s/it]
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Epoch 10 Training:  85%|████████▌ | 82/96 [01:48<00:18,  1.32s/it]
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Epoch 10 Training:  86%|████████▋ | 83/96 [01:49<00:17,  1.32s/it]
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Epoch 10 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s][2026-10-01 15:13:30] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0010 | Epoch 010] train/policy_loss=12.2111 train/unweighted_policy_loss=2.4422 train/loss=12.2111 train/grad_norm=0.0447
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+[2026-10-01 15:13:30] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0011 | Epoch 010] timing/rollout_seconds=148.4486 timing/feedback_seconds=4.9055 timing/optimization_seconds=127.8263 timing/cycle_seconds=278.8686
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+
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+[2026-10-01 15:16:01] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0011 | Epoch 011] train/reward_dists_mean=0.9280 train/reward_dists_std=0.0201 train/reward_ssim_mean=0.9396 train/reward_ssim_std=0.0236 train/reward_dists_group_std_mean=0.0031 train/reward_dists_group_std_max=0.0058 train/reward_dists_group_std_min=0.0013 train/reward_dists_group_mean_std=0.0198 train/reward_ssim_group_std_mean=0.0011 train/reward_ssim_group_std_max=0.0032 train/reward_ssim_group_std_min=0.0004 train/reward_ssim_group_mean_std=0.0236 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.5764 train/adv_max=4.4965 train/adv_abs_mean=1.3678
+
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 11 Training:   8%|▊         | 8/96 [00:10<01:56,  1.32s/it]
+
Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
+
                                                                
Epoch 11 Training:   9%|▉         | 9/96 [00:11<01:55,  1.32s/it]
+
Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
+
                                                                
Epoch 11 Training:  10%|█         | 10/96 [00:13<01:53,  1.32s/it]
+
Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
+
                                                                
Epoch 11 Training:  11%|█▏        | 11/96 [00:14<01:52,  1.32s/it]
+
Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
+
                                                                
Epoch 11 Training:  12%|█▎        | 12/96 [00:15<01:51,  1.32s/it]
+
Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
+
                                                                
Epoch 11 Training:  14%|█▎        | 13/96 [00:17<01:49,  1.32s/it]
+
Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
+
                                                                
Epoch 11 Training:  15%|█▍        | 14/96 [00:18<01:48,  1.32s/it]
+
Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
+
                                                                
Epoch 11 Training:  16%|█▌        | 15/96 [00:19<01:47,  1.32s/it]
+
Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
+
                                                                
Epoch 11 Training:  17%|█▋        | 16/96 [00:21<01:45,  1.32s/it]
+
Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
+
                                                                
Epoch 11 Training:  18%|█▊        | 17/96 [00:22<01:44,  1.32s/it]
+
Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
+
                                                                
Epoch 11 Training:  19%|█▉        | 18/96 [00:23<01:43,  1.32s/it]
+
Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
+
                                                                
Epoch 11 Training:  20%|█▉        | 19/96 [00:25<01:41,  1.32s/it]
+
Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
+
                                                                
Epoch 11 Training:  21%|██        | 20/96 [00:26<01:40,  1.32s/it]
+
Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
+
                                                                
Epoch 11 Training:  22%|██▏       | 21/96 [00:27<01:39,  1.32s/it]
+
Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
+
                                                                
Epoch 11 Training:  23%|██▎       | 22/96 [00:29<01:37,  1.32s/it]
+
Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
+
                                                                
Epoch 11 Training:  24%|██▍       | 23/96 [00:30<01:36,  1.32s/it]
+
Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
+
                                                                
Epoch 11 Training:  25%|██▌       | 24/96 [00:31<01:35,  1.32s/it]
+
Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
+
                                                                
Epoch 11 Training:  26%|██▌       | 25/96 [00:32<01:33,  1.32s/it]
+
Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
+
                                                                
Epoch 11 Training:  27%|██▋       | 26/96 [00:34<01:32,  1.32s/it]
+
Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
+
                                                                
Epoch 11 Training:  28%|██▊       | 27/96 [00:35<01:31,  1.32s/it]
+
Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
+
                                                                
Epoch 11 Training:  29%|██▉       | 28/96 [00:36<01:30,  1.32s/it]
+
Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
+
                                                                
Epoch 11 Training:  30%|███       | 29/96 [00:38<01:28,  1.32s/it]
+
Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
+
                                                                
Epoch 11 Training:  31%|███▏      | 30/96 [00:39<01:27,  1.32s/it]
+
Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
+
                                                                
Epoch 11 Training:  32%|███▏      | 31/96 [00:40<01:25,  1.32s/it]
+
Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.63it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.29it/s]
+
                                                                
Epoch 11 Training:  33%|███▎      | 32/96 [00:42<01:24,  1.32s/it]
+
Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
+
                                                                
Epoch 11 Training:  34%|███▍      | 33/96 [00:43<01:23,  1.32s/it]
+
Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
+
                                                                
Epoch 11 Training:  35%|███▌      | 34/96 [00:44<01:21,  1.32s/it]
+
Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
+
                                                                
Epoch 11 Training:  36%|███▋      | 35/96 [00:46<01:20,  1.32s/it]
+
Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
+
                                                                
Epoch 11 Training:  38%|███▊      | 36/96 [00:47<01:19,  1.32s/it]
+
Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
+
                                                                
Epoch 11 Training:  39%|███▊      | 37/96 [00:48<01:18,  1.32s/it]
+
Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.23it/s]
+
                                                                
Epoch 11 Training:  40%|███▉      | 38/96 [00:50<01:17,  1.33s/it]
+
Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
+
                                                                
Epoch 11 Training:  41%|████      | 39/96 [00:51<01:15,  1.33s/it]
+
Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.29it/s]
+
                                                                
Epoch 11 Training:  42%|████▏     | 40/96 [00:52<01:14,  1.32s/it]
+
Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
+
                                                                
Epoch 11 Training:  43%|████▎     | 41/96 [00:54<01:12,  1.32s/it]
+
Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
+
                                                                
Epoch 11 Training:  44%|████▍     | 42/96 [00:55<01:11,  1.32s/it]
+
Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
+
                                                                
Epoch 11 Training:  45%|████▍     | 43/96 [00:56<01:10,  1.32s/it]
+
Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.30it/s]
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Epoch 11 Training:  46%|████▌     | 44/96 [00:58<01:08,  1.32s/it]
+
Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.20it/s]
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Epoch 11 Training:  47%|████▋     | 45/96 [00:59<01:07,  1.33s/it]
+
Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 11 Training:  48%|████▊     | 46/96 [01:00<01:06,  1.33s/it]
+
Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 11 Training:  49%|████▉     | 47/96 [01:02<01:05,  1.33s/it]
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Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 11 Training:  50%|█████     | 48/96 [01:03<01:03,  1.33s/it]
+
Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 11 Training:  51%|█████     | 49/96 [01:04<01:02,  1.33s/it]
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Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 11 Training:  52%|█████▏    | 50/96 [01:06<01:00,  1.33s/it]
+
Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 11 Training:  53%|█████▎    | 51/96 [01:07<00:59,  1.33s/it]
+
Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.29it/s]
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Epoch 11 Training:  54%|█████▍    | 52/96 [01:08<00:58,  1.32s/it]
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Epoch 11 Training:  55%|█████▌    | 53/96 [01:10<00:56,  1.32s/it]
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Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 11 Training:  56%|█████▋    | 54/96 [01:11<00:55,  1.32s/it]
+
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 11 Training:  57%|█████▋    | 55/96 [01:12<00:54,  1.32s/it]
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Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 11 Training:  58%|█████▊    | 56/96 [01:14<00:52,  1.32s/it]
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Epoch 11 Training:  59%|█████▉    | 57/96 [01:15<00:51,  1.32s/it]
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Epoch 11 Training:  60%|██████    | 58/96 [01:16<00:50,  1.32s/it]
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Epoch 11 Training:  61%|██████▏   | 59/96 [01:18<00:49,  1.33s/it]
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Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.63it/s][2026-10-01 15:18:08] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0011 | Epoch 011] train/policy_loss=13.0225 train/unweighted_policy_loss=2.6045 train/loss=13.0225 train/grad_norm=0.0379
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+[2026-10-01 15:18:08] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0012 | Epoch 011] timing/rollout_seconds=147.9937 timing/feedback_seconds=4.6099 timing/optimization_seconds=127.7099 timing/cycle_seconds=278.5057
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+
Pointwise Rewards: ssim:   0%|          | 0/6 [00:00<?, ?it/s]
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Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:00<00:00,  6.46it/s]
+
Pointwise Rewards: dists:   0%|          | 0/6 [00:00<?, ?it/s]
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Pointwise Rewards: dists:  50%|█████     | 3/6 [00:00<00:00,  3.63it/s]
Pointwise Rewards: dists:  67%|██████▋   | 4/6 [00:01<00:00,  3.63it/s]
Pointwise Rewards: dists:  83%|████████▎ | 5/6 [00:01<00:00,  3.69it/s]
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Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.68it/s]
+[2026-10-01 15:20:39] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0012 | Epoch 012] train/reward_dists_mean=0.9208 train/reward_dists_std=0.0230 train/reward_ssim_mean=0.9395 train/reward_ssim_std=0.0227 train/reward_dists_group_std_mean=0.0036 train/reward_dists_group_std_max=0.0084 train/reward_dists_group_std_min=0.0021 train/reward_dists_group_mean_std=0.0227 train/reward_ssim_group_std_mean=0.0015 train/reward_ssim_group_std_max=0.0072 train/reward_ssim_group_std_min=0.0004 train/reward_ssim_group_mean_std=0.0226 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.8897 train/adv_max=4.6265 train/adv_abs_mean=1.3854
+
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Epoch 12 Training:   3%|▎         | 3/96 [00:03<02:02,  1.32s/it]
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Epoch 12 Training:   5%|▌         | 5/96 [00:06<02:00,  1.33s/it]
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Epoch 12 Training:   6%|▋         | 6/96 [00:07<01:59,  1.33s/it]
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Epoch 12 Training:   7%|▋         | 7/96 [00:09<01:57,  1.32s/it]
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Epoch 12 Training:   8%|▊         | 8/96 [00:10<01:56,  1.33s/it]
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Epoch 12 Training:   9%|▉         | 9/96 [00:11<01:55,  1.33s/it]
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Epoch 12 Training:  10%|█         | 10/96 [00:13<01:54,  1.33s/it]
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Epoch 12 Training:  11%|█▏        | 11/96 [00:14<01:53,  1.33s/it]
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Epoch 12 Training:  12%|█▎        | 12/96 [00:15<01:51,  1.33s/it]
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Epoch 12 Training:  14%|█▎        | 13/96 [00:17<01:50,  1.33s/it]
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Epoch 12 Training:  15%|█▍        | 14/96 [00:18<01:49,  1.33s/it]
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Epoch 12 Training:  16%|█▌        | 15/96 [00:19<01:47,  1.33s/it]
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Epoch 12 Training:  18%|█▊        | 17/96 [00:22<01:45,  1.33s/it]
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Epoch 12 Training:  19%|█▉        | 18/96 [00:23<01:43,  1.33s/it]
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Epoch 12 Training:  20%|█▉        | 19/96 [00:25<01:48,  1.41s/it]
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Epoch 12 Training:  21%|██        | 20/96 [00:26<01:45,  1.38s/it]
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Epoch 12 Training:  22%|██▏       | 21/96 [00:28<01:42,  1.37s/it]
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Epoch 12 Training:  23%|██▎       | 22/96 [00:29<01:40,  1.35s/it]
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Epoch 12 Training:  24%|██▍       | 23/96 [00:30<01:38,  1.35s/it]
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Epoch 12 Training:  25%|██▌       | 24/96 [00:32<01:36,  1.34s/it]
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Epoch 12 Training:  26%|██▌       | 25/96 [00:33<01:35,  1.34s/it]
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Epoch 12 Training:  28%|██▊       | 27/96 [00:36<01:32,  1.33s/it]
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Epoch 12 Training:  29%|██▉       | 28/96 [00:37<01:30,  1.34s/it]
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Epoch 12 Training:  30%|███       | 29/96 [00:38<01:29,  1.33s/it]
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Epoch 12 Training:  33%|███▎      | 32/96 [00:42<01:25,  1.33s/it]
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Epoch 12 Training:  34%|███▍      | 33/96 [00:44<01:23,  1.33s/it]
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Epoch 12 Training:  35%|███▌      | 34/96 [00:45<01:22,  1.33s/it]
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Epoch 12 Training:  36%|███▋      | 35/96 [00:46<01:21,  1.33s/it]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.31it/s]
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Epoch 12 Training:  38%|███▊      | 36/96 [00:48<01:19,  1.33s/it]
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Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.31it/s]
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Epoch 12 Training:  39%|███▊      | 37/96 [00:49<01:18,  1.33s/it]
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Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.22it/s]
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Epoch 12 Training:  40%|███▉      | 38/96 [00:50<01:17,  1.34s/it]
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Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.32it/s]
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Epoch 12 Training:  41%|████      | 39/96 [00:52<01:16,  1.34s/it]
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Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.55it/s]
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Epoch 12 Training:  42%|████▏     | 40/96 [00:53<01:20,  1.44s/it]
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Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.32it/s]
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Epoch 12 Training:  43%|████▎     | 41/96 [00:55<01:23,  1.52s/it]
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Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.63it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.31it/s]
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Epoch 12 Training:  44%|████▍     | 42/96 [00:56<01:18,  1.46s/it]
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Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.30it/s]
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Epoch 12 Training:  45%|████▍     | 43/96 [00:58<01:15,  1.42s/it]
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Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.33it/s]
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Epoch 12 Training:  46%|████▌     | 44/96 [00:59<01:12,  1.39s/it]
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Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.32it/s]
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Epoch 12 Training:  47%|████▋     | 45/96 [01:00<01:10,  1.37s/it]
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Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.31it/s]
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Epoch 12 Training:  48%|████▊     | 46/96 [01:02<01:07,  1.36s/it]
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Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.32it/s]
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Epoch 12 Training:  49%|████▉     | 47/96 [01:03<01:06,  1.35s/it]
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Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.31it/s]
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Epoch 12 Training:  50%|█████     | 48/96 [01:04<01:04,  1.34s/it]
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Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.30it/s]
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Epoch 12 Training:  51%|█████     | 49/96 [01:06<01:02,  1.34s/it]
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Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.48it/s]
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Epoch 12 Training:  52%|█████▏    | 50/96 [01:07<01:01,  1.34s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 12 Training:  53%|█████▎    | 51/96 [01:08<01:00,  1.34s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.63it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 12 Training:  54%|█████▍    | 52/96 [01:10<00:58,  1.33s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 12 Training:  55%|█████▌    | 53/96 [01:11<00:57,  1.33s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 12 Training:  56%|█████▋    | 54/96 [01:12<00:55,  1.33s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.29it/s]
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Epoch 12 Training:  57%|█████▋    | 55/96 [01:14<00:54,  1.33s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 12 Training:  58%|█████▊    | 56/96 [01:15<00:53,  1.33s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 12 Training:  59%|█████▉    | 57/96 [01:16<00:51,  1.33s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 12 Training:  60%|██████    | 58/96 [01:18<00:50,  1.33s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.29it/s]
+
                                                                
Epoch 12 Training:  61%|██████▏   | 59/96 [01:19<00:49,  1.33s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.29it/s]
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Epoch 12 Training:  62%|██████▎   | 60/96 [01:20<00:47,  1.32s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.29it/s]
+
                                                                
Epoch 12 Training:  64%|██████▎   | 61/96 [01:22<00:46,  1.33s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.22it/s]
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Epoch 12 Training:  65%|██████▍   | 62/96 [01:23<00:45,  1.33s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.63it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 12 Training:  66%|██████▌   | 63/96 [01:24<00:43,  1.33s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.23it/s]
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Epoch 12 Training:  67%|██████▋   | 64/96 [01:26<00:42,  1.33s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 12 Training:  68%|██████▊   | 65/96 [01:27<00:41,  1.33s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.29it/s]
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Epoch 12 Training:  69%|██████▉   | 66/96 [01:28<00:39,  1.33s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 12 Training:  70%|██████▉   | 67/96 [01:30<00:38,  1.33s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 12 Training:  71%|███████   | 68/96 [01:31<00:37,  1.33s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.29it/s]
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Epoch 12 Training:  72%|███████▏  | 69/96 [01:32<00:35,  1.33s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 12 Training:  73%|███████▎  | 70/96 [01:33<00:34,  1.33s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 12 Training:  74%|███████▍  | 71/96 [01:35<00:33,  1.33s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 12 Training:  75%|███████▌  | 72/96 [01:36<00:31,  1.33s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 12 Training:  76%|███████▌  | 73/96 [01:37<00:30,  1.33s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.30it/s]
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Epoch 12 Training:  77%|███████▋  | 74/96 [01:39<00:29,  1.32s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 12 Training:  78%|███████▊  | 75/96 [01:40<00:27,  1.33s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 12 Training:  79%|███████▉  | 76/96 [01:41<00:26,  1.33s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 12 Training:  80%|████████  | 77/96 [01:43<00:25,  1.33s/it]
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Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.29it/s]
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Epoch 12 Training:  81%|████████▏ | 78/96 [01:44<00:23,  1.33s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 12 Training:  82%|████████▏ | 79/96 [01:45<00:22,  1.33s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 12 Training:  83%|████████▎ | 80/96 [01:47<00:21,  1.33s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 12 Training:  84%|████████▍ | 81/96 [01:48<00:19,  1.33s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.29it/s]
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Epoch 12 Training:  85%|████████▌ | 82/96 [01:49<00:18,  1.32s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 12 Training:  86%|████████▋ | 83/96 [01:51<00:17,  1.33s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.30it/s]
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Epoch 12 Training:  88%|████████▊ | 84/96 [01:52<00:15,  1.33s/it]
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Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.29it/s]
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Epoch 12 Training:  89%|████████▊ | 85/96 [01:53<00:14,  1.33s/it]
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Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.29it/s]
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Epoch 12 Training:  90%|████████▉ | 86/96 [01:55<00:13,  1.32s/it]
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Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s][2026-10-01 15:22:48] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0012 | Epoch 012] train/policy_loss=13.5664 train/unweighted_policy_loss=2.7133 train/loss=13.5664 train/grad_norm=0.0510
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+[2026-10-01 15:22:48] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0013 | Epoch 012] timing/rollout_seconds=147.9446 timing/feedback_seconds=4.5124 timing/optimization_seconds=129.0961 timing/cycle_seconds=279.8601
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+
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+[2026-10-01 15:25:20] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0013 | Epoch 013] train/reward_dists_mean=0.9229 train/reward_dists_std=0.0202 train/reward_ssim_mean=0.9392 train/reward_ssim_std=0.0200 train/reward_dists_group_std_mean=0.0035 train/reward_dists_group_std_max=0.0063 train/reward_dists_group_std_min=0.0015 train/reward_dists_group_mean_std=0.0198 train/reward_ssim_group_std_mean=0.0014 train/reward_ssim_group_std_max=0.0070 train/reward_ssim_group_std_min=0.0005 train/reward_ssim_group_mean_std=0.0199 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.8026 train/adv_max=4.4502 train/adv_abs_mean=1.3883
+
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Epoch 13 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 13 Training:  12%|█▎        | 12/96 [00:16<01:51,  1.33s/it]
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Epoch 13 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 13 Training:  14%|█▎        | 13/96 [00:17<01:50,  1.33s/it]
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Epoch 13 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.29it/s]
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Epoch 13 Training:  15%|█▍        | 14/96 [00:18<01:48,  1.33s/it]
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Epoch 13 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.30it/s]
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Epoch 13 Training:  16%|█▌        | 15/96 [00:20<01:47,  1.33s/it]
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Epoch 13 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 13 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 13 Training:  17%|█▋        | 16/96 [00:21<01:46,  1.33s/it]
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Epoch 13 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 13 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 13 Training:  18%|█▊        | 17/96 [00:22<01:44,  1.33s/it]
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Epoch 13 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 13 Training:  19%|█▉        | 18/96 [00:24<01:43,  1.33s/it]
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Epoch 13 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 13 Training:  20%|█▉        | 19/96 [00:25<01:41,  1.32s/it]
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Epoch 13 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 13 Training:  21%|██        | 20/96 [00:26<01:40,  1.32s/it]
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Epoch 13 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 13 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 13 Training:  22%|██▏       | 21/96 [00:28<01:39,  1.33s/it]
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Epoch 13 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 13 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 13 Training:  23%|██▎       | 22/96 [00:29<01:38,  1.33s/it]
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Epoch 13 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 13 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 13 Training:  24%|██▍       | 23/96 [00:30<01:36,  1.33s/it]
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Epoch 13 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 13 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 13 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 13 Training:  25%|██▌       | 24/96 [00:31<01:35,  1.33s/it]
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Epoch 13 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 13 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 13 Training:  26%|██▌       | 25/96 [00:33<01:34,  1.33s/it]
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Epoch 13 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 13 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 13 Training:  27%|██▋       | 26/96 [00:34<01:32,  1.33s/it]
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Epoch 13 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 13 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 13 Training:  28%|██▊       | 27/96 [00:35<01:31,  1.33s/it]
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Epoch 13 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 13 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 13 Training:  29%|██▉       | 28/96 [00:37<01:30,  1.33s/it]
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Epoch 13 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 13 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 13 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 13 Training:  30%|███       | 29/96 [00:38<01:28,  1.33s/it]
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Epoch 13 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 13 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 13 Training:  31%|███▏      | 30/96 [00:39<01:27,  1.32s/it]
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Epoch 13 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 13 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.29it/s]
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Epoch 13 Training:  32%|███▏      | 31/96 [00:41<01:26,  1.32s/it]
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Epoch 13 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 13 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 13 Training:  33%|███▎      | 32/96 [00:42<01:24,  1.33s/it]
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Epoch 13 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 13 Training:  34%|███▍      | 33/96 [00:43<01:23,  1.33s/it]
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Epoch 13 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 13 Training:  35%|███▌      | 34/96 [00:45<01:22,  1.33s/it]
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Epoch 13 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 13 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 13 Training:  36%|███▋      | 35/96 [00:46<01:20,  1.33s/it]
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Epoch 13 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 13 Training:  38%|███▊      | 36/96 [00:47<01:19,  1.33s/it]
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Epoch 13 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 13 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 13 Training:  39%|███▊      | 37/96 [00:49<01:18,  1.33s/it]
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Epoch 13 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 13 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.30it/s]
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Epoch 13 Training:  40%|███▉      | 38/96 [00:50<01:16,  1.33s/it]
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Epoch 13 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 13 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 13 Training:  41%|████      | 39/96 [00:51<01:15,  1.33s/it]
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Epoch 13 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 13 Training:  42%|████▏     | 40/96 [00:53<01:14,  1.33s/it]
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Epoch 13 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 13 Training:  43%|████▎     | 41/96 [00:54<01:12,  1.32s/it]
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Epoch 13 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 13 Training:  44%|████▍     | 42/96 [00:55<01:11,  1.33s/it]
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Epoch 13 Training:  45%|████▍     | 43/96 [00:57<01:10,  1.33s/it]
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Epoch 13 Training:  46%|████▌     | 44/96 [00:58<01:09,  1.33s/it]
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Epoch 13 Training:  47%|████▋     | 45/96 [00:59<01:07,  1.33s/it]
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Epoch 13 Training:  48%|████▊     | 46/96 [01:01<01:06,  1.33s/it]
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Epoch 13 Training:  49%|████▉     | 47/96 [01:02<01:05,  1.33s/it]
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Epoch 13 Training:  50%|█████     | 48/96 [01:03<01:03,  1.33s/it]
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Epoch 13 Training:  51%|█████     | 49/96 [01:05<01:02,  1.32s/it]
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Epoch 13 Training:  52%|█████▏    | 50/96 [01:06<01:00,  1.32s/it]
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Epoch 13 Training:  53%|█████▎    | 51/96 [01:07<00:59,  1.32s/it]
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Epoch 13 Training:  54%|█████▍    | 52/96 [01:09<00:58,  1.32s/it]
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Epoch 13 Training:  55%|█████▌    | 53/96 [01:10<00:56,  1.32s/it]
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Epoch 13 Training:  56%|█████▋    | 54/96 [01:11<00:55,  1.32s/it]
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Epoch 13 Training:  57%|█████▋    | 55/96 [01:13<00:54,  1.32s/it]
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Epoch 13 Training:  58%|█████▊    | 56/96 [01:14<00:52,  1.32s/it]
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Epoch 13 Training:  59%|█████▉    | 57/96 [01:15<00:51,  1.32s/it]
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Epoch 13 Training:  60%|██████    | 58/96 [01:17<00:50,  1.32s/it]
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Epoch 13 Training:  61%|██████▏   | 59/96 [01:18<00:48,  1.32s/it]
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Epoch 13 Training:  62%|██████▎   | 60/96 [01:19<00:47,  1.32s/it]
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Epoch 13 Training:  64%|██████▎   | 61/96 [01:21<00:46,  1.32s/it]
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Epoch 13 Training:  65%|██████▍   | 62/96 [01:22<00:44,  1.32s/it]
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Epoch 13 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s][2026-10-01 15:27:27] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0013 | Epoch 013] train/policy_loss=14.0407 train/unweighted_policy_loss=2.8081 train/loss=14.0407 train/grad_norm=0.0555
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+[2026-10-01 15:27:27] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0014 | Epoch 013] timing/rollout_seconds=148.5307 timing/feedback_seconds=5.1075 timing/optimization_seconds=127.9006 timing/cycle_seconds=279.0978
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+
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+
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+[2026-10-01 15:29:58] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0014 | Epoch 014] train/reward_dists_mean=0.9255 train/reward_dists_std=0.0233 train/reward_ssim_mean=0.9378 train/reward_ssim_std=0.0201 train/reward_dists_group_std_mean=0.0033 train/reward_dists_group_std_max=0.0068 train/reward_dists_group_std_min=0.0017 train/reward_dists_group_mean_std=0.0230 train/reward_ssim_group_std_mean=0.0012 train/reward_ssim_group_std_max=0.0057 train/reward_ssim_group_std_min=0.0003 train/reward_ssim_group_mean_std=0.0201 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.2608 train/adv_max=4.0276 train/adv_abs_mean=1.3443
+
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Epoch 14 Training:  14%|█▎        | 13/96 [00:17<01:49,  1.32s/it]
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Epoch 14 Training:  23%|██▎       | 22/96 [00:29<01:37,  1.32s/it]
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Epoch 14 Training:  41%|████      | 39/96 [00:51<01:15,  1.32s/it]
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Epoch 14 Training:  42%|████▏     | 40/96 [00:52<01:13,  1.32s/it]
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Epoch 14 Training:  43%|████▎     | 41/96 [00:54<01:12,  1.32s/it]
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Epoch 14 Training:  44%|████▍     | 42/96 [00:55<01:11,  1.32s/it]
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Epoch 14 Training:  45%|████▍     | 43/96 [00:56<01:10,  1.32s/it]
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Epoch 14 Training:  46%|████▌     | 44/96 [00:58<01:08,  1.32s/it]
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Epoch 14 Training:  47%|████▋     | 45/96 [00:59<01:07,  1.32s/it]
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Epoch 14 Training:  48%|████▊     | 46/96 [01:00<01:06,  1.32s/it]
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Epoch 14 Training:  49%|████▉     | 47/96 [01:02<01:04,  1.32s/it]
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Epoch 14 Training:  50%|█████     | 48/96 [01:03<01:04,  1.35s/it]
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Epoch 14 Training:  51%|█████     | 49/96 [01:04<01:03,  1.34s/it]
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Epoch 14 Training:  52%|█████▏    | 50/96 [01:06<01:01,  1.34s/it]
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Epoch 14 Training:  53%|█████▎    | 51/96 [01:07<00:59,  1.33s/it]
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Epoch 14 Training:  54%|█████▍    | 52/96 [01:08<00:58,  1.33s/it]
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Epoch 14 Training:  55%|█████▌    | 53/96 [01:10<00:57,  1.33s/it]
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Epoch 14 Training:  56%|█████▋    | 54/96 [01:11<00:55,  1.33s/it]
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Epoch 14 Training:  57%|█████▋    | 55/96 [01:13<00:58,  1.43s/it]
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Epoch 14 Training:  58%|█████▊    | 56/96 [01:14<00:55,  1.39s/it]
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Epoch 14 Training:  59%|█████▉    | 57/96 [01:15<00:53,  1.37s/it]
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Epoch 14 Training:  60%|██████    | 58/96 [01:17<00:51,  1.36s/it]
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Epoch 14 Training:  61%|██████▏   | 59/96 [01:18<00:49,  1.35s/it]
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Epoch 14 Training:  62%|██████▎   | 60/96 [01:19<00:48,  1.34s/it]
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Epoch 14 Training:  64%|██████▎   | 61/96 [01:21<00:46,  1.34s/it]
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Epoch 14 Training:  65%|██████▍   | 62/96 [01:22<00:45,  1.33s/it]
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Epoch 14 Training:  66%|██████▌   | 63/96 [01:23<00:43,  1.33s/it]
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Epoch 14 Training:  67%|██████▋   | 64/96 [01:24<00:42,  1.33s/it]
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Epoch 14 Training:  68%|██████▊   | 65/96 [01:26<00:41,  1.32s/it]
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Epoch 14 Training:  69%|██████▉   | 66/96 [01:27<00:39,  1.32s/it]
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Epoch 14 Training:  70%|██████▉   | 67/96 [01:29<00:41,  1.42s/it]
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Epoch 14 Training:  71%|███████   | 68/96 [01:30<00:38,  1.39s/it]
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Epoch 14 Training:  72%|███████▏  | 69/96 [01:31<00:36,  1.37s/it]
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Epoch 14 Training:  73%|███████▎  | 70/96 [01:33<00:35,  1.35s/it]
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Epoch 14 Training:  74%|███████▍  | 71/96 [01:34<00:33,  1.35s/it]
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Epoch 14 Training:  75%|███████▌  | 72/96 [01:35<00:32,  1.34s/it]
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Epoch 14 Training:  76%|███████▌  | 73/96 [01:37<00:30,  1.33s/it]
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Epoch 14 Training:  77%|███████▋  | 74/96 [01:38<00:29,  1.33s/it]
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Epoch 14 Training:  78%|███████▊  | 75/96 [01:39<00:27,  1.33s/it]
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Epoch 14 Training:  79%|███████▉  | 76/96 [01:41<00:26,  1.33s/it]
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Epoch 14 Training:  80%|████████  | 77/96 [01:42<00:25,  1.33s/it]
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Epoch 14 Training:  81%|████████▏ | 78/96 [01:43<00:23,  1.32s/it]
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Epoch 14 Training:  82%|████████▏ | 79/96 [01:45<00:22,  1.32s/it]
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Epoch 14 Training:  83%|████████▎ | 80/96 [01:46<00:21,  1.32s/it]
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Epoch 14 Training:  84%|████████▍ | 81/96 [01:47<00:19,  1.32s/it]
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Epoch 14 Training:  85%|████████▌ | 82/96 [01:49<00:18,  1.32s/it]
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Epoch 14 Training:  86%|████████▋ | 83/96 [01:50<00:17,  1.32s/it]
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Epoch 14 Training:  88%|████████▊ | 84/96 [01:51<00:15,  1.32s/it]
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Epoch 14 Training:  89%|████████▊ | 85/96 [01:53<00:14,  1.32s/it]
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Epoch 14 Training:  90%|████████▉ | 86/96 [01:54<00:13,  1.32s/it]
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Epoch 14 Training:  91%|█████████ | 87/96 [01:55<00:11,  1.32s/it]
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Epoch 14 Training:  92%|█████████▏| 88/96 [01:57<00:10,  1.32s/it]
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Epoch 14 Training:  93%|█████████▎| 89/96 [01:58<00:09,  1.32s/it]
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Epoch 14 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s][2026-10-01 15:32:06] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0014 | Epoch 014] train/policy_loss=13.9697 train/unweighted_policy_loss=2.7939 train/loss=13.9697 train/grad_norm=0.0499
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+[2026-10-01 15:32:06] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0015 | Epoch 014] timing/rollout_seconds=147.9731 timing/feedback_seconds=4.7012 timing/optimization_seconds=128.1856 timing/cycle_seconds=278.8142
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+
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+[2026-10-01 15:34:37] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0015 | Epoch 015] train/reward_dists_mean=0.9363 train/reward_dists_std=0.0223 train/reward_ssim_mean=0.9414 train/reward_ssim_std=0.0213 train/reward_dists_group_std_mean=0.0029 train/reward_dists_group_std_max=0.0081 train/reward_dists_group_std_min=0.0014 train/reward_dists_group_mean_std=0.0221 train/reward_ssim_group_std_mean=0.0010 train/reward_ssim_group_std_max=0.0034 train/reward_ssim_group_std_min=0.0003 train/reward_ssim_group_mean_std=0.0212 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.1042 train/adv_max=4.3118 train/adv_abs_mean=1.3188
+
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Epoch 15 Training:  15%|█▍        | 14/96 [00:18<01:48,  1.32s/it]
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Epoch 15 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 15 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.36it/s]
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Epoch 15 Training:  16%|█▌        | 15/96 [00:19<01:47,  1.32s/it]
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Epoch 15 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 15 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.31it/s]
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Epoch 15 Training:  17%|█▋        | 16/96 [00:21<01:45,  1.32s/it]
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Epoch 15 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 15 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.35it/s]
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Epoch 15 Training:  18%|█▊        | 17/96 [00:22<01:44,  1.32s/it]
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Epoch 15 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 15 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.29it/s]
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Epoch 15 Training:  19%|█▉        | 18/96 [00:23<01:43,  1.32s/it]
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Epoch 15 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 15 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.37it/s]
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Epoch 15 Training:  20%|█▉        | 19/96 [00:25<01:41,  1.32s/it]
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Epoch 15 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 15 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.33it/s]
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Epoch 15 Training:  21%|██        | 20/96 [00:26<01:40,  1.32s/it]
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Epoch 15 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 15 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.36it/s]
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Epoch 15 Training:  22%|██▏       | 21/96 [00:27<01:39,  1.32s/it]
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Epoch 15 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 15 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 15 Training:  23%|██▎       | 22/96 [00:29<01:37,  1.32s/it]
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Epoch 15 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 15 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.29it/s]
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Epoch 15 Training:  24%|██▍       | 23/96 [00:30<01:36,  1.32s/it]
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Epoch 15 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 15 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 15 Training:  25%|██▌       | 24/96 [00:31<01:35,  1.32s/it]
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Epoch 15 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 15 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.30it/s]
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Epoch 15 Training:  26%|██▌       | 25/96 [00:32<01:33,  1.32s/it]
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Epoch 15 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 15 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 15 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 15 Training:  27%|██▋       | 26/96 [00:34<01:32,  1.32s/it]
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Epoch 15 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 15 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 15 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 15 Training:  28%|██▊       | 27/96 [00:35<01:31,  1.33s/it]
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Epoch 15 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 15 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.37it/s]
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Epoch 15 Training:  29%|██▉       | 28/96 [00:36<01:30,  1.32s/it]
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Epoch 15 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 15 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.29it/s]
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Epoch 15 Training:  30%|███       | 29/96 [00:38<01:28,  1.32s/it]
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Epoch 15 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 15 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 15 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.38it/s]
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Epoch 15 Training:  31%|███▏      | 30/96 [00:39<01:27,  1.33s/it]
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Epoch 15 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 15 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 15 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 15 Training:  32%|███▏      | 31/96 [00:40<01:26,  1.33s/it]
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Epoch 15 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 15 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 15 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.35it/s]
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Epoch 15 Training:  33%|███▎      | 32/96 [00:42<01:25,  1.33s/it]
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Epoch 15 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 15 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.63it/s]
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Epoch 15 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 15 Training:  34%|███▍      | 33/96 [00:43<01:23,  1.33s/it]
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Epoch 15 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 15 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 15 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.36it/s]
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Epoch 15 Training:  35%|███▌      | 34/96 [00:44<01:22,  1.33s/it]
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Epoch 15 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 15 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 15 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.33it/s]
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Epoch 15 Training:  36%|███▋      | 35/96 [00:46<01:20,  1.32s/it]
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Epoch 15 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 15 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.29it/s]
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Epoch 15 Training:  38%|███▊      | 36/96 [00:47<01:19,  1.33s/it]
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Epoch 15 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 15 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 15 Training:  39%|███▊      | 37/96 [00:48<01:18,  1.33s/it]
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Epoch 15 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 15 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 15 Training:  40%|███▉      | 38/96 [00:50<01:17,  1.33s/it]
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Epoch 15 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 15 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.34it/s]
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Epoch 15 Training:  41%|████      | 39/96 [00:51<01:15,  1.33s/it]
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Epoch 15 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 15 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 15 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.32it/s]
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Epoch 15 Training:  42%|████▏     | 40/96 [00:52<01:14,  1.32s/it]
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Epoch 15 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 15 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 15 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.38it/s]
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Epoch 15 Training:  43%|████▎     | 41/96 [00:54<01:12,  1.33s/it]
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Epoch 15 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 15 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.48it/s]
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Epoch 15 Training:  44%|████▍     | 42/96 [00:55<01:11,  1.33s/it]
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Epoch 15 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 15 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.38it/s]
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Epoch 15 Training:  45%|████▍     | 43/96 [00:56<01:10,  1.33s/it]
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Epoch 15 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 15 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.32it/s]
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Epoch 15 Training:  46%|████▌     | 44/96 [00:58<01:08,  1.32s/it]
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Epoch 15 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.29it/s]
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Epoch 15 Training:  47%|████▋     | 45/96 [00:59<01:07,  1.32s/it]
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Epoch 15 Training:  48%|████▊     | 46/96 [01:00<01:06,  1.32s/it]
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Epoch 15 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.29it/s]
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Epoch 15 Training:  49%|████▉     | 47/96 [01:02<01:04,  1.32s/it]
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Epoch 15 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 15 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.33it/s]
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Epoch 15 Training:  50%|█████     | 48/96 [01:03<01:03,  1.32s/it]
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Epoch 15 Training:  51%|█████     | 49/96 [01:04<01:02,  1.32s/it]
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Epoch 15 Training:  52%|█████▏    | 50/96 [01:06<01:00,  1.32s/it]
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Epoch 15 Training:  53%|█████▎    | 51/96 [01:07<00:59,  1.32s/it]
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Epoch 15 Training:  54%|█████▍    | 52/96 [01:08<00:58,  1.32s/it]
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Epoch 15 Training:  55%|█████▌    | 53/96 [01:10<00:56,  1.32s/it]
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Epoch 15 Training:  56%|█████▋    | 54/96 [01:11<00:55,  1.32s/it]
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Epoch 15 Training:  57%|█████▋    | 55/96 [01:12<00:54,  1.32s/it]
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Epoch 15 Training:  58%|█████▊    | 56/96 [01:14<00:52,  1.32s/it]
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Epoch 15 Training:  59%|█████▉    | 57/96 [01:15<00:51,  1.32s/it]
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Epoch 15 Training:  60%|██████    | 58/96 [01:16<00:50,  1.32s/it]
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Epoch 15 Training:  61%|██████▏   | 59/96 [01:17<00:48,  1.32s/it]
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Epoch 15 Training:  62%|██████▎   | 60/96 [01:19<00:47,  1.32s/it]
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Epoch 15 Training:  64%|██████▎   | 61/96 [01:20<00:46,  1.32s/it]
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Epoch 15 Training:  65%|██████▍   | 62/96 [01:21<00:44,  1.32s/it]
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Epoch 15 Training:  66%|██████▌   | 63/96 [01:23<00:43,  1.32s/it]
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Epoch 15 Training:  67%|██████▋   | 64/96 [01:24<00:42,  1.32s/it]
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Epoch 15 Training:  68%|██████▊   | 65/96 [01:25<00:41,  1.32s/it]
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Epoch 15 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s][2026-10-01 15:36:44] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0015 | Epoch 015] train/policy_loss=13.9476 train/unweighted_policy_loss=2.7895 train/loss=13.9476 train/grad_norm=0.0332
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+[2026-10-01 15:36:44] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0016 | Epoch 015] timing/rollout_seconds=148.0125 timing/feedback_seconds=4.6443 timing/optimization_seconds=127.5895 timing/cycle_seconds=278.2218
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Epoch 16 Sampling:  91%|█████████ | 87/96 [02:11<00:13,  1.51s/it]
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Epoch 16 Sampling:  95%|█████████▍| 91/96 [02:17<00:07,  1.51s/it]
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Epoch 16 Sampling:  99%|█████████▉| 95/96 [02:23<00:01,  1.51s/it]
Epoch 16 Sampling: 100%|██████████| 96/96 [02:25<00:00,  1.51s/it]
Epoch 16 Sampling: 100%|██████████| 96/96 [02:25<00:00,  1.51s/it]
+
Pointwise Rewards: ssim:   0%|          | 0/6 [00:00<?, ?it/s]
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Pointwise Rewards: ssim:  50%|█████     | 3/6 [00:00<00:00,  6.07it/s]
Pointwise Rewards: ssim:  67%|██████▋   | 4/6 [00:00<00:00,  6.08it/s]
Pointwise Rewards: ssim:  83%|████████▎ | 5/6 [00:00<00:00,  6.17it/s]
Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:01<00:00,  5.50it/s]
Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:01<00:00,  5.79it/s]
+
Pointwise Rewards: dists:   0%|          | 0/6 [00:00<?, ?it/s]
Pointwise Rewards: dists:  17%|█▋        | 1/6 [00:00<00:01,  3.56it/s]
Pointwise Rewards: dists:  33%|███▎      | 2/6 [00:00<00:01,  3.82it/s]
Pointwise Rewards: dists:  50%|█████     | 3/6 [00:00<00:00,  3.73it/s]
Pointwise Rewards: dists:  67%|██████▋   | 4/6 [00:01<00:00,  3.66it/s]
Pointwise Rewards: dists:  83%|████████▎ | 5/6 [00:01<00:00,  3.71it/s]
Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.68it/s]
Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.69it/s]
+[2026-10-01 15:39:16] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0016 | Epoch 016] train/reward_dists_mean=0.9282 train/reward_dists_std=0.0213 train/reward_ssim_mean=0.9370 train/reward_ssim_std=0.0214 train/reward_dists_group_std_mean=0.0031 train/reward_dists_group_std_max=0.0069 train/reward_dists_group_std_min=0.0011 train/reward_dists_group_mean_std=0.0211 train/reward_ssim_group_std_mean=0.0013 train/reward_ssim_group_std_max=0.0095 train/reward_ssim_group_std_min=0.0004 train/reward_ssim_group_mean_std=0.0213 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.7103 train/adv_max=4.2645 train/adv_abs_mean=1.3099
+
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Epoch 16 Training:   1%|          | 1/96 [00:01<01:57,  1.23s/it]
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Epoch 16 Training:   2%|▏         | 2/96 [00:02<02:01,  1.29s/it]
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Epoch 16 Training:   3%|▎         | 3/96 [00:03<02:02,  1.32s/it]
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Epoch 16 Training:   4%|▍         | 4/96 [00:05<02:01,  1.33s/it]
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Epoch 16 Training:   5%|▌         | 5/96 [00:06<02:00,  1.32s/it]
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Epoch 16 Training:   6%|▋         | 6/96 [00:07<01:59,  1.32s/it]
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Epoch 16 Training:   7%|▋         | 7/96 [00:09<01:57,  1.32s/it]
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Epoch 16 Training:   8%|▊         | 8/96 [00:10<01:56,  1.33s/it]
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Epoch 16 Training:   9%|▉         | 9/96 [00:11<01:55,  1.33s/it]
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Epoch 16 Training:  10%|█         | 10/96 [00:13<01:54,  1.33s/it]
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Epoch 16 Training:  11%|█▏        | 11/96 [00:14<01:52,  1.32s/it]
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Epoch 16 Training:  12%|█▎        | 12/96 [00:15<01:51,  1.32s/it]
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Epoch 16 Training:  14%|█▎        | 13/96 [00:17<01:49,  1.32s/it]
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Epoch 16 Training:  15%|█▍        | 14/96 [00:18<01:48,  1.32s/it]
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Epoch 16 Training:  16%|█▌        | 15/96 [00:19<01:47,  1.32s/it]
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Epoch 16 Training:  17%|█▋        | 16/96 [00:21<01:46,  1.33s/it]
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Epoch 16 Training:  18%|█▊        | 17/96 [00:22<01:44,  1.33s/it]
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Epoch 16 Training:  19%|█▉        | 18/96 [00:23<01:43,  1.33s/it]
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Epoch 16 Training:  20%|█▉        | 19/96 [00:25<01:42,  1.33s/it]
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Epoch 16 Training:  21%|██        | 20/96 [00:26<01:41,  1.33s/it]
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Epoch 16 Training:  22%|██▏       | 21/96 [00:27<01:39,  1.33s/it]
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Epoch 16 Training:  23%|██▎       | 22/96 [00:29<01:38,  1.33s/it]
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Epoch 16 Training:  24%|██▍       | 23/96 [00:30<01:36,  1.33s/it]
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Epoch 16 Training:  25%|██▌       | 24/96 [00:31<01:35,  1.33s/it]
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Epoch 16 Training:  26%|██▌       | 25/96 [00:33<01:34,  1.33s/it]
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Epoch 16 Training:  27%|██▋       | 26/96 [00:34<01:32,  1.33s/it]
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Epoch 16 Training:  28%|██▊       | 27/96 [00:35<01:31,  1.33s/it]
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Epoch 16 Training:  29%|██▉       | 28/96 [00:37<01:30,  1.33s/it]
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Epoch 16 Training:  30%|███       | 29/96 [00:38<01:28,  1.33s/it]
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Epoch 16 Training:  31%|███▏      | 30/96 [00:39<01:27,  1.33s/it]
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Epoch 16 Training:  32%|███▏      | 31/96 [00:41<01:26,  1.33s/it]
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Epoch 16 Training:  33%|███▎      | 32/96 [00:42<01:25,  1.33s/it]
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Epoch 16 Training:  34%|███▍      | 33/96 [00:43<01:23,  1.33s/it]
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Epoch 16 Training:  35%|███▌      | 34/96 [00:45<01:22,  1.33s/it]
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Epoch 16 Training:  36%|███▋      | 35/96 [00:46<01:21,  1.33s/it]
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Epoch 16 Training:  38%|███▊      | 36/96 [00:47<01:19,  1.33s/it]
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Epoch 16 Training:  39%|███▊      | 37/96 [00:49<01:18,  1.33s/it]
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Epoch 16 Training:  40%|███▉      | 38/96 [00:50<01:17,  1.33s/it]
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Epoch 16 Training:  41%|████      | 39/96 [00:51<01:15,  1.33s/it]
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Epoch 16 Training:  42%|████▏     | 40/96 [00:53<01:14,  1.33s/it]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 16 Training:  44%|████▍     | 42/96 [00:55<01:11,  1.33s/it]
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Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 16 Training:  45%|████▍     | 43/96 [00:57<01:10,  1.33s/it]
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Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 16 Training:  46%|████▌     | 44/96 [00:58<01:09,  1.33s/it]
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Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 16 Training:  47%|████▋     | 45/96 [00:59<01:07,  1.33s/it]
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Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 16 Training:  48%|████▊     | 46/96 [01:01<01:06,  1.33s/it]
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Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.31it/s]
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Epoch 16 Training:  49%|████▉     | 47/96 [01:02<01:05,  1.33s/it]
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Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.22it/s]
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Epoch 16 Training:  50%|█████     | 48/96 [01:03<01:03,  1.33s/it]
+
Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.29it/s]
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Epoch 16 Training:  51%|█████     | 49/96 [01:04<01:02,  1.33s/it]
+
Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 16 Training:  52%|█████▏    | 50/96 [01:06<01:01,  1.33s/it]
+
Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 16 Training:  53%|█████▎    | 51/96 [01:07<00:59,  1.33s/it]
+
Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 16 Training:  54%|█████▍    | 52/96 [01:08<00:58,  1.33s/it]
+
Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
+
                                                                
Epoch 16 Training:  55%|█████▌    | 53/96 [01:10<00:57,  1.33s/it]
+
Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
+
                                                                
Epoch 16 Training:  56%|█████▋    | 54/96 [01:11<00:55,  1.33s/it]
+
Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 16 Training:  57%|█████▋    | 55/96 [01:12<00:54,  1.32s/it]
+
Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.29it/s]
+
                                                                
Epoch 16 Training:  58%|█████▊    | 56/96 [01:14<00:52,  1.32s/it]
+
Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
+
                                                                
Epoch 16 Training:  59%|█████▉    | 57/96 [01:15<00:51,  1.33s/it]
+
Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
+
                                                                
Epoch 16 Training:  60%|██████    | 58/96 [01:16<00:50,  1.33s/it]
+
Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
+
                                                                
Epoch 16 Training:  61%|██████▏   | 59/96 [01:18<00:49,  1.33s/it]
+
Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
+
                                                                
Epoch 16 Training:  62%|██████▎   | 60/96 [01:19<00:47,  1.33s/it]
+
Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
+
                                                                
Epoch 16 Training:  64%|██████▎   | 61/96 [01:20<00:46,  1.33s/it]
+
Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
+
                                                                
Epoch 16 Training:  65%|██████▍   | 62/96 [01:22<00:45,  1.33s/it]
+
Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
+
                                                                
Epoch 16 Training:  66%|██████▌   | 63/96 [01:23<00:43,  1.33s/it]
+
Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
+
                                                                
Epoch 16 Training:  67%|██████▋   | 64/96 [01:24<00:42,  1.33s/it]
+
Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.30it/s]
+
                                                                
Epoch 16 Training:  68%|██████▊   | 65/96 [01:26<00:41,  1.32s/it]
+
Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.30it/s]
+
                                                                
Epoch 16 Training:  69%|██████▉   | 66/96 [01:27<00:39,  1.32s/it]
+
Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
+
                                                                
Epoch 16 Training:  70%|██████▉   | 67/96 [01:28<00:38,  1.32s/it]
+
Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.29it/s]
+
                                                                
Epoch 16 Training:  71%|███████   | 68/96 [01:30<00:37,  1.32s/it]
+
Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.30it/s]
+
                                                                
Epoch 16 Training:  72%|███████▏  | 69/96 [01:31<00:35,  1.32s/it]
+
Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
+
                                                                
Epoch 16 Training:  73%|███████▎  | 70/96 [01:32<00:34,  1.33s/it]
+
Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
+
                                                                
Epoch 16 Training:  74%|███████▍  | 71/96 [01:34<00:33,  1.33s/it]
+
Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.29it/s]
+
                                                                
Epoch 16 Training:  75%|███████▌  | 72/96 [01:35<00:31,  1.32s/it]
+
Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.29it/s]
+
                                                                
Epoch 16 Training:  76%|███████▌  | 73/96 [01:36<00:30,  1.32s/it]
+
Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
+
                                                                
Epoch 16 Training:  77%|███████▋  | 74/96 [01:38<00:29,  1.33s/it]
+
Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
+
                                                                
Epoch 16 Training:  78%|███████▊  | 75/96 [01:39<00:27,  1.32s/it]
+
Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
+
                                                                
Epoch 16 Training:  79%|███████▉  | 76/96 [01:40<00:26,  1.33s/it]
+
Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
+
                                                                
Epoch 16 Training:  80%|████████  | 77/96 [01:42<00:25,  1.33s/it]
+
Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 16 Training:  81%|████████▏ | 78/96 [01:43<00:23,  1.33s/it]
+
Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 16 Training:  82%|████████▏ | 79/96 [01:44<00:22,  1.33s/it]
+
Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 16 Training:  83%|████████▎ | 80/96 [01:46<00:21,  1.33s/it]
+
Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 16 Training:  84%|████████▍ | 81/96 [01:47<00:19,  1.33s/it]
+
Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 16 Training:  85%|████████▌ | 82/96 [01:48<00:18,  1.33s/it]
+
Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 16 Training:  86%|████████▋ | 83/96 [01:50<00:17,  1.33s/it]
+
Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 16 Training:  88%|████████▊ | 84/96 [01:51<00:15,  1.33s/it]
+
Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 16 Training:  89%|████████▊ | 85/96 [01:52<00:14,  1.33s/it]
+
Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 16 Training:  90%|████████▉ | 86/96 [01:54<00:13,  1.33s/it]
+
Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 16 Training:  91%|█████████ | 87/96 [01:55<00:11,  1.33s/it]
+
Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 16 Training:  92%|█████████▏| 88/96 [01:56<00:10,  1.33s/it]
+
Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 16 Training:  93%|█████████▎| 89/96 [01:58<00:09,  1.33s/it]
+
Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 16 Training:  94%|█████████▍| 90/96 [01:59<00:07,  1.33s/it]
+
Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 16 Training:  95%|█████████▍| 91/96 [02:00<00:06,  1.33s/it]
+
Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 16 Training:  96%|█████████▌| 92/96 [02:02<00:05,  1.33s/it]
+
Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.63it/s][2026-10-01 15:41:23] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0016 | Epoch 016] train/policy_loss=14.3230 train/unweighted_policy_loss=2.8646 train/loss=14.3230 train/grad_norm=0.0599
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+[2026-10-01 15:41:23] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0017 | Epoch 016] timing/rollout_seconds=148.4127 timing/feedback_seconds=5.0343 timing/optimization_seconds=127.9490 timing/cycle_seconds=278.9034
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+
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+[2026-10-01 15:43:54] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0017 | Epoch 017] train/reward_dists_mean=0.9266 train/reward_dists_std=0.0262 train/reward_ssim_mean=0.9346 train/reward_ssim_std=0.0231 train/reward_dists_group_std_mean=0.0035 train/reward_dists_group_std_max=0.0217 train/reward_dists_group_std_min=0.0016 train/reward_dists_group_mean_std=0.0259 train/reward_ssim_group_std_mean=0.0024 train/reward_ssim_group_std_max=0.0435 train/reward_ssim_group_std_min=0.0002 train/reward_ssim_group_mean_std=0.0221 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.9200 train/adv_max=4.1382 train/adv_abs_mean=1.3121
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Epoch 17 Training:  19%|█▉        | 18/96 [00:23<01:44,  1.34s/it]
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Epoch 17 Training:  20%|█▉        | 19/96 [00:25<01:42,  1.34s/it]
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Epoch 17 Training:  21%|██        | 20/96 [00:26<01:41,  1.34s/it]
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Epoch 17 Training:  22%|██▏       | 21/96 [00:27<01:40,  1.34s/it]
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Epoch 17 Training:  23%|██▎       | 22/96 [00:29<01:39,  1.34s/it]
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Epoch 17 Training:  24%|██▍       | 23/96 [00:30<01:37,  1.34s/it]
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Epoch 17 Training:  25%|██▌       | 24/96 [00:31<01:36,  1.34s/it]
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Epoch 17 Training:  26%|██▌       | 25/96 [00:33<01:34,  1.34s/it]
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Epoch 17 Training:  27%|██▋       | 26/96 [00:34<01:33,  1.34s/it]
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Epoch 17 Training:  28%|██▊       | 27/96 [00:35<01:32,  1.33s/it]
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Epoch 17 Training:  29%|██▉       | 28/96 [00:37<01:30,  1.33s/it]
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Epoch 17 Training:  30%|███       | 29/96 [00:38<01:29,  1.33s/it]
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Epoch 17 Training:  31%|███▏      | 30/96 [00:39<01:27,  1.33s/it]
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Epoch 17 Training:  32%|███▏      | 31/96 [00:41<01:26,  1.33s/it]
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Epoch 17 Training:  33%|███▎      | 32/96 [00:42<01:25,  1.33s/it]
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Epoch 17 Training:  34%|███▍      | 33/96 [00:43<01:24,  1.33s/it]
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Epoch 17 Training:  35%|███▌      | 34/96 [00:45<01:22,  1.33s/it]
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Epoch 17 Training:  36%|███▋      | 35/96 [00:46<01:21,  1.34s/it]
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Epoch 17 Training:  38%|███▊      | 36/96 [00:47<01:19,  1.33s/it]
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Epoch 17 Training:  39%|███▊      | 37/96 [00:49<01:18,  1.33s/it]
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Epoch 17 Training:  40%|███▉      | 38/96 [00:50<01:17,  1.33s/it]
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Epoch 17 Training:  41%|████      | 39/96 [00:51<01:16,  1.33s/it]
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Epoch 17 Training:  42%|████▏     | 40/96 [00:53<01:14,  1.34s/it]
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Epoch 17 Training:  43%|████▎     | 41/96 [00:54<01:13,  1.34s/it]
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Epoch 17 Training:  44%|████▍     | 42/96 [00:56<01:12,  1.34s/it]
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Epoch 17 Training:  45%|████▍     | 43/96 [00:57<01:10,  1.34s/it]
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Epoch 17 Training:  46%|████▌     | 44/96 [00:58<01:09,  1.34s/it]
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Epoch 17 Training:  47%|████▋     | 45/96 [01:00<01:08,  1.34s/it]
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Epoch 17 Training:  48%|████▊     | 46/96 [01:01<01:06,  1.34s/it]
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Epoch 17 Training:  49%|████▉     | 47/96 [01:02<01:05,  1.34s/it]
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Epoch 17 Training:  50%|█████     | 48/96 [01:04<01:04,  1.34s/it]
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Epoch 17 Training:  51%|█████     | 49/96 [01:05<01:02,  1.34s/it]
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Epoch 17 Training:  52%|█████▏    | 50/96 [01:06<01:01,  1.34s/it]
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Epoch 17 Training:  53%|█████▎    | 51/96 [01:08<01:00,  1.34s/it]
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Epoch 17 Training:  54%|█████▍    | 52/96 [01:09<00:59,  1.34s/it]
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Epoch 17 Training:  55%|█████▌    | 53/96 [01:10<00:57,  1.34s/it]
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Epoch 17 Training:  56%|█████▋    | 54/96 [01:12<00:56,  1.34s/it]
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Epoch 17 Training:  57%|█████▋    | 55/96 [01:13<00:54,  1.34s/it]
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Epoch 17 Training:  58%|█████▊    | 56/96 [01:14<00:53,  1.34s/it]
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Epoch 17 Training:  59%|█████▉    | 57/96 [01:16<00:52,  1.34s/it]
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Epoch 17 Training:  60%|██████    | 58/96 [01:17<00:50,  1.33s/it]
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Epoch 17 Training:  61%|██████▏   | 59/96 [01:18<00:49,  1.33s/it]
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Epoch 17 Training:  62%|██████▎   | 60/96 [01:20<00:47,  1.33s/it]
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Epoch 17 Training:  64%|██████▎   | 61/96 [01:21<00:46,  1.33s/it]
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Epoch 17 Training:  65%|██████▍   | 62/96 [01:22<00:45,  1.33s/it]
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Epoch 17 Training:  66%|██████▌   | 63/96 [01:24<00:43,  1.33s/it]
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Epoch 17 Training:  67%|██████▋   | 64/96 [01:25<00:42,  1.33s/it]
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Epoch 17 Training:  68%|██████▊   | 65/96 [01:26<00:41,  1.33s/it]
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Epoch 17 Training:  69%|██████▉   | 66/96 [01:28<00:39,  1.33s/it]
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Epoch 17 Training:  70%|██████▉   | 67/96 [01:29<00:38,  1.33s/it]
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Epoch 17 Training:  71%|███████   | 68/96 [01:30<00:37,  1.33s/it]
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Epoch 17 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s][2026-10-01 15:46:02] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0017 | Epoch 017] train/policy_loss=15.1622 train/unweighted_policy_loss=3.0324 train/loss=15.1622 train/grad_norm=0.0415
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+[2026-10-01 15:46:02] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0018 | Epoch 017] timing/rollout_seconds=148.0595 timing/feedback_seconds=4.7817 timing/optimization_seconds=128.5161 timing/cycle_seconds=279.3900
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Epoch 18 Sampling: 100%|██████████| 96/96 [02:25<00:00,  1.51s/it]
Epoch 18 Sampling: 100%|██████████| 96/96 [02:25<00:00,  1.51s/it]
+
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Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:01<00:00,  5.73it/s]
+
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Pointwise Rewards: dists:  33%|███▎      | 2/6 [00:00<00:01,  3.63it/s]
Pointwise Rewards: dists:  50%|█████     | 3/6 [00:00<00:00,  3.60it/s]
Pointwise Rewards: dists:  67%|██████▋   | 4/6 [00:01<00:00,  3.64it/s]
Pointwise Rewards: dists:  83%|████████▎ | 5/6 [00:01<00:00,  3.65it/s]
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Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.63it/s]
+[2026-10-01 15:48:34] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0018 | Epoch 018] train/reward_dists_mean=0.9326 train/reward_dists_std=0.0226 train/reward_ssim_mean=0.9322 train/reward_ssim_std=0.0229 train/reward_dists_group_std_mean=0.0036 train/reward_dists_group_std_max=0.0181 train/reward_dists_group_std_min=0.0014 train/reward_dists_group_mean_std=0.0221 train/reward_ssim_group_std_mean=0.0018 train/reward_ssim_group_std_max=0.0157 train/reward_ssim_group_std_min=0.0005 train/reward_ssim_group_mean_std=0.0227 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-5.4462 train/adv_max=4.4914 train/adv_abs_mean=1.3510
+
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Epoch 18 Training:   2%|▏         | 2/96 [00:02<02:00,  1.28s/it]
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Epoch 18 Training:   5%|▌         | 5/96 [00:06<01:59,  1.32s/it]
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Epoch 18 Training:   6%|▋         | 6/96 [00:07<01:59,  1.32s/it]
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Epoch 18 Training:   7%|▋         | 7/96 [00:09<01:57,  1.32s/it]
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Epoch 18 Training:   8%|▊         | 8/96 [00:10<01:56,  1.33s/it]
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Epoch 18 Training:   9%|▉         | 9/96 [00:11<01:55,  1.33s/it]
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Epoch 18 Training:  10%|█         | 10/96 [00:13<01:53,  1.32s/it]
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Epoch 18 Training:  11%|█▏        | 11/96 [00:14<01:52,  1.33s/it]
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Epoch 18 Training:  12%|█▎        | 12/96 [00:15<01:51,  1.33s/it]
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Epoch 18 Training:  14%|█▎        | 13/96 [00:17<01:50,  1.33s/it]
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Epoch 18 Training:  15%|█▍        | 14/96 [00:18<01:49,  1.33s/it]
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Epoch 18 Training:  16%|█▌        | 15/96 [00:19<01:47,  1.33s/it]
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Epoch 18 Training:  17%|█▋        | 16/96 [00:21<01:46,  1.33s/it]
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Epoch 18 Training:  18%|█▊        | 17/96 [00:22<01:45,  1.33s/it]
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Epoch 18 Training:  19%|█▉        | 18/96 [00:23<01:43,  1.33s/it]
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Epoch 18 Training:  20%|█▉        | 19/96 [00:25<01:42,  1.33s/it]
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Epoch 18 Training:  21%|██        | 20/96 [00:26<01:40,  1.33s/it]
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Epoch 18 Training:  22%|██▏       | 21/96 [00:27<01:39,  1.33s/it]
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Epoch 18 Training:  23%|██▎       | 22/96 [00:29<01:38,  1.33s/it]
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Epoch 18 Training:  24%|██▍       | 23/96 [00:30<01:36,  1.33s/it]
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Epoch 18 Training:  25%|██▌       | 24/96 [00:31<01:35,  1.33s/it]
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Epoch 18 Training:  26%|██▌       | 25/96 [00:33<01:34,  1.33s/it]
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Epoch 18 Training:  27%|██▋       | 26/96 [00:34<01:32,  1.33s/it]
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Epoch 18 Training:  28%|██▊       | 27/96 [00:35<01:31,  1.33s/it]
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Epoch 18 Training:  29%|██▉       | 28/96 [00:37<01:30,  1.33s/it]
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Epoch 18 Training:  30%|███       | 29/96 [00:38<01:28,  1.33s/it]
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Epoch 18 Training:  31%|███▏      | 30/96 [00:39<01:27,  1.33s/it]
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Epoch 18 Training:  32%|███▏      | 31/96 [00:41<01:26,  1.33s/it]
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Epoch 18 Training:  33%|███▎      | 32/96 [00:42<01:25,  1.33s/it]
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Epoch 18 Training:  34%|███▍      | 33/96 [00:43<01:23,  1.33s/it]
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Epoch 18 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 18 Training:  47%|████▋     | 45/96 [00:59<01:07,  1.33s/it]
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Epoch 18 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 18 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 18 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 18 Training:  48%|████▊     | 46/96 [01:00<01:06,  1.33s/it]
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Epoch 18 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 18 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.22it/s]
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Epoch 18 Training:  49%|████▉     | 47/96 [01:02<01:05,  1.33s/it]
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Epoch 18 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 18 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 18 Training:  50%|█████     | 48/96 [01:03<01:03,  1.33s/it]
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Epoch 18 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 18 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 18 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 18 Training:  51%|█████     | 49/96 [01:04<01:02,  1.33s/it]
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Epoch 18 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 18 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 18 Training:  52%|█████▏    | 50/96 [01:06<01:01,  1.33s/it]
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Epoch 18 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 18 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.22it/s]
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Epoch 18 Training:  53%|█████▎    | 51/96 [01:07<00:59,  1.33s/it]
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Epoch 18 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 18 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 18 Training:  54%|█████▍    | 52/96 [01:08<00:58,  1.33s/it]
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Epoch 18 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 18 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 18 Training:  55%|█████▌    | 53/96 [01:10<00:57,  1.33s/it]
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Epoch 18 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 18 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 18 Training:  56%|█████▋    | 54/96 [01:11<00:55,  1.33s/it]
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Epoch 18 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 18 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 18 Training:  57%|█████▋    | 55/96 [01:12<00:54,  1.33s/it]
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Epoch 18 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 18 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 18 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 18 Training:  58%|█████▊    | 56/96 [01:14<00:53,  1.33s/it]
+
Epoch 18 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 18 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 18 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 18 Training:  59%|█████▉    | 57/96 [01:15<00:51,  1.33s/it]
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Epoch 18 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 18 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 18 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 18 Training:  60%|██████    | 58/96 [01:16<00:50,  1.33s/it]
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Epoch 18 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 18 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 18 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 18 Training:  61%|██████▏   | 59/96 [01:18<00:49,  1.33s/it]
+
Epoch 18 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 18 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 18 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 18 Training:  62%|██████▎   | 60/96 [01:19<00:47,  1.33s/it]
+
Epoch 18 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 18 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 18 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 18 Training:  64%|██████▎   | 61/96 [01:20<00:46,  1.33s/it]
+
Epoch 18 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 18 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 18 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 18 Training:  65%|██████▍   | 62/96 [01:22<00:45,  1.33s/it]
+
Epoch 18 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 18 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 18 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 18 Training:  66%|██████▌   | 63/96 [01:23<00:43,  1.33s/it]
+
Epoch 18 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 18 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 18 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 18 Training:  67%|██████▋   | 64/96 [01:24<00:42,  1.33s/it]
+
Epoch 18 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 18 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 18 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.22it/s]
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Epoch 18 Training:  68%|██████▊   | 65/96 [01:26<00:41,  1.33s/it]
+
Epoch 18 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 18 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 18 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.20it/s]
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Epoch 18 Training:  69%|██████▉   | 66/96 [01:27<00:40,  1.34s/it]
+
Epoch 18 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 18 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 18 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.20it/s]
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Epoch 18 Training:  70%|██████▉   | 67/96 [01:28<00:38,  1.34s/it]
+
Epoch 18 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 18 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 18 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 18 Training:  71%|███████   | 68/96 [01:30<00:37,  1.33s/it]
+
Epoch 18 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 18 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 18 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.23it/s]
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Epoch 18 Training:  72%|███████▏  | 69/96 [01:31<00:36,  1.33s/it]
+
Epoch 18 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 18 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 18 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.23it/s]
+
                                                                
Epoch 18 Training:  73%|███████▎  | 70/96 [01:32<00:34,  1.33s/it]
+
Epoch 18 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 18 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 18 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 18 Training:  74%|███████▍  | 71/96 [01:34<00:33,  1.33s/it]
+
Epoch 18 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 18 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 18 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.23it/s]
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Epoch 18 Training:  75%|███████▌  | 72/96 [01:35<00:31,  1.33s/it]
+
Epoch 18 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 18 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 18 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.23it/s]
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Epoch 18 Training:  76%|███████▌  | 73/96 [01:36<00:30,  1.33s/it]
+
Epoch 18 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 18 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 18 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 18 Training:  77%|███████▋  | 74/96 [01:38<00:29,  1.33s/it]
+
Epoch 18 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 18 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 18 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 18 Training:  78%|███████▊  | 75/96 [01:39<00:27,  1.33s/it]
+
Epoch 18 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 18 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 18 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 18 Training:  79%|███████▉  | 76/96 [01:40<00:26,  1.33s/it]
+
Epoch 18 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 18 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 18 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 18 Training:  80%|████████  | 77/96 [01:42<00:25,  1.33s/it]
+
Epoch 18 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 18 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 18 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 18 Training:  81%|████████▏ | 78/96 [01:43<00:23,  1.33s/it]
+
Epoch 18 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 18 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 18 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.23it/s]
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Epoch 18 Training:  82%|████████▏ | 79/96 [01:44<00:22,  1.33s/it]
+
Epoch 18 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 18 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.19it/s]
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Epoch 18 Training:  83%|████████▎ | 80/96 [01:46<00:21,  1.34s/it]
+
Epoch 18 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 18 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 18 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 18 Training:  84%|████████▍ | 81/96 [01:47<00:20,  1.33s/it]
+
Epoch 18 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 18 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 18 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.23it/s]
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Epoch 18 Training:  85%|████████▌ | 82/96 [01:48<00:18,  1.33s/it]
+
Epoch 18 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 18 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 18 Training:  86%|████████▋ | 83/96 [01:50<00:17,  1.33s/it]
+
Epoch 18 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 18 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 18 Training:  88%|████████▊ | 84/96 [01:51<00:15,  1.33s/it]
+
Epoch 18 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 18 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 18 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 18 Training:  89%|████████▊ | 85/96 [01:52<00:14,  1.32s/it]
+
Epoch 18 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 18 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 18 Training:  90%|████████▉ | 86/96 [01:54<00:13,  1.32s/it]
+
Epoch 18 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 18 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 18 Training:  91%|█████████ | 87/96 [01:55<00:11,  1.33s/it]
+
Epoch 18 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 18 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 18 Training:  92%|█████████▏| 88/96 [01:56<00:10,  1.33s/it]
+
Epoch 18 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 18 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 18 Training:  93%|█████████▎| 89/96 [01:58<00:09,  1.33s/it]
+
Epoch 18 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 18 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 18 Training:  94%|█████████▍| 90/96 [01:59<00:07,  1.32s/it]
+
Epoch 18 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 18 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.63it/s]
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Epoch 18 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 18 Training:  95%|█████████▍| 91/96 [02:00<00:06,  1.32s/it]
+
Epoch 18 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 18 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.20it/s]
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Epoch 18 Training:  96%|█████████▌| 92/96 [02:02<00:05,  1.33s/it]
+
Epoch 18 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 18 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 18 Training:  97%|█████████▋| 93/96 [02:03<00:03,  1.33s/it]
+
Epoch 18 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 18 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 18 Training:  98%|█████████▊| 94/96 [02:04<00:02,  1.33s/it]
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Epoch 18 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 18 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 18 Training:  99%|█████████▉| 95/96 [02:06<00:01,  1.33s/it]
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+[2026-10-01 15:50:42] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0019 | Epoch 018] timing/rollout_seconds=148.1230 timing/feedback_seconds=4.9486 timing/optimization_seconds=128.1884 timing/cycle_seconds=279.1732
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+
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+[2026-10-01 15:53:13] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0019 | Epoch 019] train/reward_dists_mean=0.9329 train/reward_dists_std=0.0219 train/reward_ssim_mean=0.9373 train/reward_ssim_std=0.0190 train/reward_dists_group_std_mean=0.0028 train/reward_dists_group_std_max=0.0061 train/reward_dists_group_std_min=0.0014 train/reward_dists_group_mean_std=0.0217 train/reward_ssim_group_std_mean=0.0016 train/reward_ssim_group_std_max=0.0139 train/reward_ssim_group_std_min=0.0005 train/reward_ssim_group_mean_std=0.0188 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.9903 train/adv_max=4.2020 train/adv_abs_mean=1.3394
+
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Epoch 19 Training:  21%|██        | 20/96 [00:26<01:40,  1.33s/it]
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Epoch 19 Training:  22%|██▏       | 21/96 [00:27<01:39,  1.33s/it]
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Epoch 19 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 19 Training:  23%|██▎       | 22/96 [00:29<01:38,  1.33s/it]
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Epoch 19 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 19 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 19 Training:  24%|██▍       | 23/96 [00:30<01:36,  1.32s/it]
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Epoch 19 Training:  25%|██▌       | 24/96 [00:31<01:35,  1.32s/it]
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Epoch 19 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 19 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 19 Training:  26%|██▌       | 25/96 [00:33<01:33,  1.32s/it]
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Epoch 19 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 19 Training:  27%|██▋       | 26/96 [00:34<01:32,  1.33s/it]
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Epoch 19 Training:  28%|██▊       | 27/96 [00:35<01:31,  1.32s/it]
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Epoch 19 Training:  29%|██▉       | 28/96 [00:36<01:29,  1.32s/it]
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Epoch 19 Training:  30%|███       | 29/96 [00:38<01:28,  1.32s/it]
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Epoch 19 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.42it/s]
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Epoch 19 Training:  31%|███▏      | 30/96 [00:39<01:27,  1.33s/it]
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Epoch 19 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 19 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.31it/s]
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Epoch 19 Training:  32%|███▏      | 31/96 [00:40<01:26,  1.32s/it]
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Epoch 19 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 19 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 19 Training:  33%|███▎      | 32/96 [00:42<01:24,  1.32s/it]
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Epoch 19 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 19 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 19 Training:  34%|███▍      | 33/96 [00:43<01:23,  1.32s/it]
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Epoch 19 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 19 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 19 Training:  35%|███▌      | 34/96 [00:44<01:22,  1.32s/it]
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Epoch 19 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 19 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 19 Training:  36%|███▋      | 35/96 [00:46<01:20,  1.32s/it]
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Epoch 19 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 19 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.34it/s]
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Epoch 19 Training:  38%|███▊      | 36/96 [00:47<01:19,  1.32s/it]
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Epoch 19 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 19 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.33it/s]
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Epoch 19 Training:  39%|███▊      | 37/96 [00:48<01:18,  1.32s/it]
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Epoch 19 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 19 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.30it/s]
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Epoch 19 Training:  40%|███▉      | 38/96 [00:50<01:16,  1.32s/it]
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Epoch 19 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 19 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.35it/s]
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Epoch 19 Training:  41%|████      | 39/96 [00:51<01:15,  1.32s/it]
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Epoch 19 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 19 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.32it/s]
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Epoch 19 Training:  42%|████▏     | 40/96 [00:52<01:14,  1.33s/it]
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Epoch 19 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 19 Training:  43%|████▎     | 41/96 [00:54<01:12,  1.33s/it]
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Epoch 19 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 19 Training:  44%|████▍     | 42/96 [00:55<01:11,  1.32s/it]
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Epoch 19 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 19 Training:  45%|████▍     | 43/96 [00:56<01:10,  1.32s/it]
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Epoch 19 Training:  46%|████▌     | 44/96 [00:58<01:08,  1.32s/it]
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Epoch 19 Training:  47%|████▋     | 45/96 [00:59<01:07,  1.32s/it]
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Epoch 19 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 19 Training:  48%|████▊     | 46/96 [01:00<01:06,  1.33s/it]
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Epoch 19 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 19 Training:  49%|████▉     | 47/96 [01:02<01:04,  1.33s/it]
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Epoch 19 Training:  50%|█████     | 48/96 [01:03<01:03,  1.32s/it]
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Epoch 19 Training:  51%|█████     | 49/96 [01:04<01:02,  1.32s/it]
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Epoch 19 Training:  52%|█████▏    | 50/96 [01:06<01:01,  1.33s/it]
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Epoch 19 Training:  53%|█████▎    | 51/96 [01:07<00:59,  1.33s/it]
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Epoch 19 Training:  54%|█████▍    | 52/96 [01:08<00:58,  1.33s/it]
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Epoch 19 Training:  55%|█████▌    | 53/96 [01:10<00:56,  1.32s/it]
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Epoch 19 Training:  56%|█████▋    | 54/96 [01:11<00:55,  1.33s/it]
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Epoch 19 Training:  57%|█████▋    | 55/96 [01:12<00:54,  1.33s/it]
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Epoch 19 Training:  58%|█████▊    | 56/96 [01:14<00:53,  1.33s/it]
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Epoch 19 Training:  59%|█████▉    | 57/96 [01:15<00:51,  1.33s/it]
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Epoch 19 Training:  60%|██████    | 58/96 [01:16<00:50,  1.33s/it]
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Epoch 19 Training:  61%|██████▏   | 59/96 [01:18<00:49,  1.33s/it]
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Epoch 19 Training:  62%|██████▎   | 60/96 [01:19<00:47,  1.32s/it]
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Epoch 19 Training:  64%|██████▎   | 61/96 [01:20<00:46,  1.32s/it]
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Epoch 19 Training:  65%|██████▍   | 62/96 [01:22<00:44,  1.32s/it]
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Epoch 19 Training:  66%|██████▌   | 63/96 [01:23<00:43,  1.32s/it]
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Epoch 19 Training:  67%|██████▋   | 64/96 [01:24<00:42,  1.33s/it]
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Epoch 19 Training:  68%|██████▊   | 65/96 [01:26<00:41,  1.33s/it]
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Epoch 19 Training:  69%|██████▉   | 66/96 [01:27<00:39,  1.33s/it]
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Epoch 19 Training:  70%|██████▉   | 67/96 [01:28<00:38,  1.33s/it]
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Epoch 19 Training:  71%|███████   | 68/96 [01:29<00:37,  1.33s/it]
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Epoch 19 Training:  72%|███████▏  | 69/96 [01:31<00:35,  1.33s/it]
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Epoch 19 Training:  73%|███████▎  | 70/96 [01:32<00:34,  1.33s/it]
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Epoch 19 Training:  74%|███████▍  | 71/96 [01:33<00:33,  1.33s/it]
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Epoch 19 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s][2026-10-01 15:55:20] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0019 | Epoch 019] train/policy_loss=15.4415 train/unweighted_policy_loss=3.0883 train/loss=15.4415 train/grad_norm=0.0460
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+[2026-10-01 15:55:20] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0020 | Epoch 019] timing/rollout_seconds=148.1550 timing/feedback_seconds=4.8522 timing/optimization_seconds=127.7028 timing/cycle_seconds=278.4948
+[2026-10-01 15:55:20] [Rank 0] [INFO] [flow_factory.models.abc]: Saving LoRA weights for transformer role=base to /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/runs/klein4b_nft_vedai_full_ssim_dists/saves/klein4b_nft_vedai_full_ssim_dists/checkpoints/checkpoint-20
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+[2026-10-01 15:56:04] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0020 | Epoch 020] eval/vedai_rl_full/reward_dists_mean=0.9295 eval/vedai_rl_full/reward_dists_std=0.0199 eval/vedai_rl_full/reward_psnr_mean=28.0045 eval/vedai_rl_full/reward_psnr_std=2.8771 eval/vedai_rl_full/reward_ssim_mean=0.9185 eval/vedai_rl_full/reward_ssim_std=0.0268
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+
Pointwise Rewards: ssim:   0%|          | 0/6 [00:00<?, ?it/s]
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Pointwise Rewards: ssim:  83%|████████▎ | 5/6 [00:00<00:00,  6.45it/s]
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Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:00<00:00,  6.22it/s]
+
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Pointwise Rewards: dists:  83%|████████▎ | 5/6 [00:01<00:00,  3.67it/s]
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Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.62it/s]
+[2026-10-01 15:58:35] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0020 | Epoch 020] train/reward_dists_mean=0.9316 train/reward_dists_std=0.0165 train/reward_ssim_mean=0.9330 train/reward_ssim_std=0.0152 train/reward_dists_group_std_mean=0.0031 train/reward_dists_group_std_max=0.0056 train/reward_dists_group_std_min=0.0018 train/reward_dists_group_mean_std=0.0161 train/reward_ssim_group_std_mean=0.0013 train/reward_ssim_group_std_max=0.0040 train/reward_ssim_group_std_min=0.0004 train/reward_ssim_group_mean_std=0.0151 train/reward_dists_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-4.8187 train/adv_max=4.4528 train/adv_abs_mean=1.3058
+
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Epoch 20 Training:   6%|▋         | 6/96 [00:07<01:55,  1.28s/it]
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Epoch 20 Training:   7%|▋         | 7/96 [00:08<01:53,  1.28s/it]
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Epoch 20 Training:   8%|▊         | 8/96 [00:10<01:52,  1.28s/it]
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Epoch 20 Training:  11%|█▏        | 11/96 [00:14<01:54,  1.34s/it]
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Epoch 20 Training:  12%|█▎        | 12/96 [00:15<01:51,  1.32s/it]
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Epoch 20 Training:  14%|█▎        | 13/96 [00:16<01:48,  1.31s/it]
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Epoch 20 Training:  15%|█▍        | 14/96 [00:18<01:46,  1.30s/it]
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Epoch 20 Training:  16%|█▌        | 15/96 [00:19<01:44,  1.29s/it]
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Epoch 20 Training:  17%|█▋        | 16/96 [00:20<01:42,  1.29s/it]
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Epoch 20 Training:  18%|█▊        | 17/96 [00:21<01:41,  1.28s/it]
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Epoch 20 Training:  19%|█▉        | 18/96 [00:23<01:40,  1.28s/it]
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Epoch 20 Training:  21%|██        | 20/96 [00:25<01:37,  1.28s/it]
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Epoch 20 Training:  22%|██▏       | 21/96 [00:27<01:35,  1.28s/it]
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Epoch 20 Training:  23%|██▎       | 22/96 [00:28<01:34,  1.28s/it]
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Epoch 20 Training:  24%|██▍       | 23/96 [00:29<01:33,  1.28s/it]
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Epoch 20 Training:  25%|██▌       | 24/96 [00:30<01:32,  1.28s/it]
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Epoch 20 Training:  26%|██▌       | 25/96 [00:32<01:30,  1.28s/it]
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Epoch 20 Training:  27%|██▋       | 26/96 [00:33<01:29,  1.28s/it]
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Epoch 20 Training:  28%|██▊       | 27/96 [00:34<01:28,  1.28s/it]
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Epoch 20 Training:  29%|██▉       | 28/96 [00:36<01:26,  1.27s/it]
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Epoch 20 Training:  32%|███▏      | 31/96 [00:39<01:22,  1.28s/it]
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Epoch 20 Training:  33%|███▎      | 32/96 [00:41<01:21,  1.27s/it]
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Epoch 20 Training:  34%|███▍      | 33/96 [00:42<01:20,  1.27s/it]
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Epoch 20 Training:  43%|████▎     | 41/96 [00:52<01:10,  1.27s/it]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.42it/s]
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Epoch 20 Training:  44%|████▍     | 42/96 [00:53<01:08,  1.27s/it]
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Epoch 20 Training:  45%|████▍     | 43/96 [00:55<01:07,  1.27s/it]
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Epoch 20 Training:  46%|████▌     | 44/96 [00:56<01:06,  1.27s/it]
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Epoch 20 Training:  47%|████▋     | 45/96 [00:57<01:04,  1.27s/it]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.42it/s]
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Epoch 20 Training:  48%|████▊     | 46/96 [00:58<01:03,  1.27s/it]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.42it/s]
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Epoch 20 Training:  49%|████▉     | 47/96 [01:00<01:02,  1.27s/it]
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Epoch 20 Training:  50%|█████     | 48/96 [01:01<01:05,  1.37s/it]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.44it/s]
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Epoch 20 Training:  51%|█████     | 49/96 [01:03<01:03,  1.34s/it]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.37it/s]
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Epoch 20 Training:  52%|█████▏    | 50/96 [01:04<01:00,  1.33s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Training:  53%|█████▎    | 51/96 [01:05<00:59,  1.31s/it]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.38it/s]
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Epoch 20 Training:  54%|█████▍    | 52/96 [01:06<00:57,  1.30s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.42it/s]
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Epoch 20 Training:  55%|█████▌    | 53/96 [01:08<00:55,  1.29s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.41it/s]
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Epoch 20 Training:  56%|█████▋    | 54/96 [01:09<00:54,  1.29s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.42it/s]
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Epoch 20 Training:  57%|█████▋    | 55/96 [01:10<00:52,  1.29s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.42it/s]
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Epoch 20 Training:  58%|█████▊    | 56/96 [01:12<00:51,  1.28s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.42it/s]
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Epoch 20 Training:  59%|█████▉    | 57/96 [01:13<00:53,  1.38s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.43it/s]
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Epoch 20 Training:  60%|██████    | 58/96 [01:14<00:51,  1.35s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.42it/s]
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Epoch 20 Training:  61%|██████▏   | 59/96 [01:16<00:49,  1.32s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.42it/s]
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Epoch 20 Training:  62%|██████▎   | 60/96 [01:17<00:47,  1.31s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.42it/s]
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Epoch 20 Training:  64%|██████▎   | 61/96 [01:18<00:45,  1.30s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.42it/s]
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Epoch 20 Training:  65%|██████▍   | 62/96 [01:20<00:43,  1.29s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.44it/s]
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Epoch 20 Training:  66%|██████▌   | 63/96 [01:21<00:42,  1.29s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.42it/s]
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Epoch 20 Training:  67%|██████▋   | 64/96 [01:22<00:41,  1.28s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.41it/s]
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Epoch 20 Training:  68%|██████▊   | 65/96 [01:23<00:39,  1.28s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.42it/s]
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Epoch 20 Training:  69%|██████▉   | 66/96 [01:25<00:38,  1.28s/it]
+
Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.44it/s]
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Epoch 20 Training:  70%|██████▉   | 67/96 [01:26<00:37,  1.28s/it]
+
Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.40it/s]
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Epoch 20 Training:  71%|███████   | 68/96 [01:27<00:35,  1.28s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.42it/s]
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Epoch 20 Training:  72%|███████▏  | 69/96 [01:28<00:34,  1.28s/it]
+
Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.44it/s]
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Epoch 20 Training:  73%|███████▎  | 70/96 [01:30<00:33,  1.28s/it]
+
Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.42it/s]
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Epoch 20 Training:  74%|███████▍  | 71/96 [01:31<00:31,  1.28s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.44it/s]
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Epoch 20 Training:  75%|███████▌  | 72/96 [01:32<00:30,  1.28s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.40it/s]
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Epoch 20 Training:  76%|███████▌  | 73/96 [01:34<00:29,  1.28s/it]
+
Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.42it/s]
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Epoch 20 Training:  77%|███████▋  | 74/96 [01:35<00:28,  1.28s/it]
+
Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.43it/s]
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Epoch 20 Training:  78%|███████▊  | 75/96 [01:36<00:26,  1.27s/it]
+
Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.43it/s]
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Epoch 20 Training:  79%|███████▉  | 76/96 [01:37<00:25,  1.27s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.44it/s]
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Epoch 20 Training:  80%|████████  | 77/96 [01:39<00:24,  1.27s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.43it/s]
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Epoch 20 Training:  81%|████████▏ | 78/96 [01:40<00:22,  1.27s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.44it/s]
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Epoch 20 Training:  82%|████████▏ | 79/96 [01:41<00:21,  1.27s/it]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.43it/s]
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Epoch 20 Training:  83%|████████▎ | 80/96 [01:42<00:20,  1.27s/it]
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Epoch 20 Training:  84%|████████▍ | 81/96 [01:44<00:19,  1.27s/it]
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Epoch 20 Training:  85%|████████▌ | 82/96 [01:45<00:17,  1.27s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.40it/s]
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Epoch 20 Training:  86%|████████▋ | 83/96 [01:46<00:16,  1.27s/it]
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Epoch 20 Training:  88%|████████▊ | 84/96 [01:48<00:15,  1.28s/it]
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Epoch 20 Training:  89%|████████▊ | 85/96 [01:49<00:14,  1.27s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.44it/s]
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Epoch 20 Training:  90%|████████▉ | 86/96 [01:50<00:12,  1.28s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.43it/s]
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Epoch 20 Training:  91%|█████████ | 87/96 [01:51<00:11,  1.28s/it]
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Epoch 20 Training:  92%|█████████▏| 88/96 [01:53<00:10,  1.27s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Training:  93%|█████████▎| 89/96 [01:54<00:08,  1.27s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Training:  94%|█████████▍| 90/96 [01:55<00:07,  1.27s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Training:  95%|█████████▍| 91/96 [01:56<00:06,  1.28s/it]
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+
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+
                                                                
Epoch 20 Training:  96%|█████████▌| 92/96 [01:58<00:05,  1.28s/it]
+
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+
                                                                
Epoch 20 Training:  97%|█████████▋| 93/96 [01:59<00:03,  1.27s/it]
+
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+
                                                                
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+
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+
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+
                                                                
Epoch 20 Training:  99%|█████████▉| 95/96 [02:02<00:01,  1.28s/it]
+
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+
Epoch 20 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s][2026-10-01 16:00:39] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0020 | Epoch 020] train/policy_loss=15.4130 train/unweighted_policy_loss=3.0826 train/loss=15.4130 train/grad_norm=0.0487
+
+
Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.23it/s]
+
                                                                
Epoch 20 Training: 100%|██████████| 96/96 [02:03<00:00,  1.30s/it]
Epoch 20 Training: 100%|██████████| 96/96 [02:03<00:00,  1.29s/it]
+[2026-10-01 16:00:39] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0021 | Epoch 020] timing/rollout_seconds=147.7837 timing/feedback_seconds=4.7614 timing/optimization_seconds=124.1006 timing/cycle_seconds=274.5700
+
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Epoch 21 Sampling:  35%|███▌      | 34/96 [00:51<01:33,  1.51s/it]W1001 16:01:32.459000 2452102 site-packages/torch/distributed/elastic/agent/server/api.py:739] Received Signals.SIGTERM death signal, shutting down workers
+W1001 16:01:32.518000 2452102 site-packages/torch/distributed/elastic/multiprocessing/api.py:1010] Sending process 2452287 closing signal SIGTERM
+W1001 16:01:32.519000 2452102 site-packages/torch/distributed/elastic/multiprocessing/api.py:1010] Sending process 2452288 closing signal SIGTERM
+W1001 16:01:32.520000 2452102 site-packages/torch/distributed/elastic/multiprocessing/api.py:1010] Sending process 2452289 closing signal SIGTERM
+W1001 16:01:32.520000 2452102 site-packages/torch/distributed/elastic/multiprocessing/api.py:1010] Sending process 2452290 closing signal SIGTERM
+W1001 16:01:32.521000 2452102 site-packages/torch/distributed/elastic/multiprocessing/api.py:1010] Sending process 2452291 closing signal SIGTERM
+W1001 16:01:32.521000 2452102 site-packages/torch/distributed/elastic/multiprocessing/api.py:1010] Sending process 2452292 closing signal SIGTERM
+W1001 16:01:32.521000 2452102 site-packages/torch/distributed/elastic/multiprocessing/api.py:1010] Sending process 2452293 closing signal SIGTERM
+W1001 16:01:32.521000 2452102 site-packages/torch/distributed/elastic/multiprocessing/api.py:1010] Sending process 2452294 closing signal SIGTERM
+Traceback (most recent call last):
+  File "/opt/conda/envs/ff-qi21/bin/accelerate", line 6, in <module>
+    sys.exit(main())
+  File "/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/accelerate/commands/accelerate_cli.py", line 50, in main
+    args.func(args)
+  File "/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/accelerate/commands/launch.py", line 1392, in launch_command
+    deepspeed_launcher(args)
+  File "/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/accelerate/commands/launch.py", line 1076, in deepspeed_launcher
+    distrib_run.run(args)
+  File "/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torch/distributed/run.py", line 982, in run
+    elastic_launch(
+  File "/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torch/distributed/launcher/api.py", line 170, in __call__
+    return launch_agent(self._config, self._entrypoint, list(args))
+  File "/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torch/distributed/launcher/api.py", line 308, in launch_agent
+    result = agent.run()
+  File "/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torch/distributed/elastic/metrics/api.py", line 134, in wrapper
+    result = f(*args, **kwargs)
+  File "/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torch/distributed/elastic/agent/server/api.py", line 731, in run
+    result = self._invoke_run(role)
+  File "/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torch/distributed/elastic/agent/server/api.py", line 908, in _invoke_run
+    time.sleep(monitor_interval)
+  File "/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torch/distributed/elastic/multiprocessing/api.py", line 86, in _terminate_process_handler
+    raise SignalException(f"Process {os.getpid()} got signal: {sigval}", sigval=sigval)
+torch.distributed.elastic.multiprocessing.api.SignalException: Process 2452102 got signal: 15
diff --git a/runs/klein4b_nft_vedai_full_ssim_dists_psnr/saves/klein4b_nft_vedai_full_ssim_dists_psnr/checkpoints/checkpoint-100/README.md b/runs/klein4b_nft_vedai_full_ssim_dists_psnr/saves/klein4b_nft_vedai_full_ssim_dists_psnr/checkpoints/checkpoint-100/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..2b78a7a995873d5db3090fca56885fe89cdf97e1
--- /dev/null
+++ b/runs/klein4b_nft_vedai_full_ssim_dists_psnr/saves/klein4b_nft_vedai_full_ssim_dists_psnr/checkpoints/checkpoint-100/README.md
@@ -0,0 +1,204 @@
+---
+base_model: /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/flow_factory_gpu_gate_models/FLUX.2-klein-base-4B/transformer
+library_name: peft
+tags:
+- lora
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]
+### Framework versions
+
+- PEFT 0.20.0
\ No newline at end of file
diff --git a/runs/klein4b_nft_vedai_full_ssim_dists_psnr/saves/klein4b_nft_vedai_full_ssim_dists_psnr/checkpoints/checkpoint-100/adapter_config.json b/runs/klein4b_nft_vedai_full_ssim_dists_psnr/saves/klein4b_nft_vedai_full_ssim_dists_psnr/checkpoints/checkpoint-100/adapter_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..fcfa8ded1f5cf7419a2fa9deaebc9fb92bd4b313
--- /dev/null
+++ b/runs/klein4b_nft_vedai_full_ssim_dists_psnr/saves/klein4b_nft_vedai_full_ssim_dists_psnr/checkpoints/checkpoint-100/adapter_config.json
@@ -0,0 +1,59 @@
+{
+  "alora_invocation_tokens": null,
+  "alpha_pattern": {},
+  "arrow_config": null,
+  "auto_mapping": {
+    "base_model_class": "Flux2Transformer2DModel",
+    "parent_library": "diffusers.models.transformers.transformer_flux2"
+  },
+  "base_model_name_or_path": null,
+  "bias": "none",
+  "corda_config": null,
+  "ensure_weight_tying": false,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": "gaussian",
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 128,
+  "lora_bias": false,
+  "lora_dropout": 0.0,
+  "lora_ga_config": null,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "monteclora_config": null,
+  "peft_type": "LORA",
+  "peft_version": "0.20.0",
+  "qalora_group_size": 16,
+  "r": 64,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "attn.to_q",
+    "attn.to_out.0",
+    "attn.add_q_proj",
+    "ff.linear_out",
+    "attn.to_qkv_mlp_proj",
+    "ff.linear_in",
+    "attn.add_v_proj",
+    "attn.to_add_out",
+    "attn.to_k",
+    "attn.to_v",
+    "ff_context.linear_in",
+    "attn.add_k_proj",
+    "ff_context.linear_out"
+  ],
+  "target_parameters": null,
+  "task_type": null,
+  "trainable_token_indices": null,
+  "use_bdlora": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false,
+  "velora_config": null
+}
\ No newline at end of file
diff --git a/runs/klein4b_nft_vedai_full_ssim_dists_psnr/saves/klein4b_nft_vedai_full_ssim_dists_psnr/checkpoints/checkpoint-100/manifest.json b/runs/klein4b_nft_vedai_full_ssim_dists_psnr/saves/klein4b_nft_vedai_full_ssim_dists_psnr/checkpoints/checkpoint-100/manifest.json
new file mode 100644
index 0000000000000000000000000000000000000000..4443fd995e0f6f3bd9b7e3dacea93df8d345ecfb
--- /dev/null
+++ b/runs/klein4b_nft_vedai_full_ssim_dists_psnr/saves/klein4b_nft_vedai_full_ssim_dists_psnr/checkpoints/checkpoint-100/manifest.json
@@ -0,0 +1,16 @@
+{
+  "base_role": "base",
+  "entries": [
+    {
+      "component": "transformer",
+      "path": ".",
+      "role": "base"
+    }
+  ],
+  "finetune_type": "lora",
+  "format_version": 1,
+  "primary": {
+    "component": "transformer",
+    "path": "."
+  }
+}
diff --git a/runs/klein4b_nft_vedai_full_ssim_dists_psnr/train.log b/runs/klein4b_nft_vedai_full_ssim_dists_psnr/train.log
new file mode 100644
index 0000000000000000000000000000000000000000..97cb1156bef587674a25db8e0ce0511fbfcb270f
--- /dev/null
+++ b/runs/klein4b_nft_vedai_full_ssim_dists_psnr/train.log
@@ -0,0 +1,39693 @@
+[2026-10-01 16:01:59,661] [INFO] [flow_factory.cli]: ============================================================
+[2026-10-01 16:01:59,662] [INFO] [flow_factory.cli]: Flow-Factory Launch Configuration
+[2026-10-01 16:01:59,662] [INFO] [flow_factory.cli]:   Num machines:       1
+[2026-10-01 16:01:59,662] [INFO] [flow_factory.cli]:   Machine rank:       0
+[2026-10-01 16:01:59,662] [INFO] [flow_factory.cli]:   Num processes:      8
+[2026-10-01 16:01:59,663] [INFO] [flow_factory.cli]:   Master node:        localhost:29512
+[2026-10-01 16:01:59,663] [INFO] [flow_factory.cli]:   Mixed precision:    bf16
+[2026-10-01 16:01:59,663] [INFO] [flow_factory.cli]:   Accelerate config:  config/deepspeed/deepspeed_zero2.yaml
+[2026-10-01 16:01:59,663] [INFO] [flow_factory.cli]: ============================================================
+W1001 16:02:15.965000 2490833 site-packages/torch/distributed/run.py:852] 
+W1001 16:02:15.965000 2490833 site-packages/torch/distributed/run.py:852] *****************************************
+W1001 16:02:15.965000 2490833 site-packages/torch/distributed/run.py:852] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
+W1001 16:02:15.965000 2490833 site-packages/torch/distributed/run.py:852] *****************************************
+[2026-10-01 16:02:24] [Rank 0] [WARNING] [flow_factory.hparams.abc]: NFTTrainingArguments.from_dict captured 3 unknown key(s) into extra_kwargs: ['condition_image_size', 'flat_steps', 'ramp_rate']. Verify these are intentional (e.g., adapter-specific kwargs); typos against declared fields will be silently accepted otherwise.
+[2026-10-01 16:02:24] [Rank 0] [INFO] [flow_factory.hparams.training_args._base]: World Size: 8
+[2026-10-01 16:02:24] [Rank 0] [WARNING] [flow_factory.hparams.abc]: EvaluationArguments.from_dict captured 1 unknown key(s) into extra_kwargs: ['condition_image_size']. Verify these are intentional (e.g., adapter-specific kwargs); typos against declared fields will be silently accepted otherwise.
+[2026-10-01 16:02:24,371] [INFO] [flow_factory.train]: ====================================================================================================
+[2026-10-01 16:02:24,371] [INFO] [flow_factory.train]: Flow-Factory Training Initialized
+[2026-10-01 16:02:24,371] [INFO] [flow_factory.train]: World Size: 8
+[2026-10-01 16:02:24,371] [INFO] [flow_factory.train]: ====================================================================================================
+[2026-10-01 16:02:24,371] [INFO] [flow_factory.train]: Config: /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/runs/klein4b_nft_vedai_full_ssim_dists_psnr/train.yaml
+[2026-10-01 16:02:24,379] [INFO] [flow_factory.train]: 
+launcher: accelerate
+config_file: config/deepspeed/deepspeed_zero2.yaml
+num_processes: 8
+main_process_port: 29512
+mixed_precision: bf16
+data:
+  dataset_dir: data
+  cache_dir: ~/.cache/flow_factory/datasets
+  image_dir: null
+  video_dir: null
+  audio_dir: null
+  preprocessing_batch_size: 8
+  dataloader_num_workers: 8
+  enable_preprocess: true
+  force_reprocess: false
+  max_dataset_size: null
+  preprocess_parallelism: local
+  sampler_type: group_contiguous
+  sampler_subgroup_size: null
+  datasets:
+  - extra_kwargs: {}
+    name: vedai_rl_full
+    dataset_dir: /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/data/ff/vedai_rl_full
+    image_dir: null
+    video_dir: null
+    audio_dir: null
+    train:
+      extra_kwargs: {}
+      enabled: true
+      split: train
+      weight: 1
+      max_dataset_size: null
+      unique_sample_num_per_epoch: 48
+      num_batches_per_epoch: null
+    eval:
+      extra_kwargs: {}
+      enabled: true
+      split: test
+      max_dataset_size: null
+      resolution: null
+      num_inference_steps: null
+      guidance_scale: null
+    source_id: 0
+model:
+  model_name_or_path: /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/flow_factory_gpu_gate_models/FLUX.2-klein-base-4B
+  finetune_type: lora
+  trainable_parameters_dtype: bfloat16
+  component_load_dtypes: null
+  frozen_parameters_dtype: null
+  target_components:
+  - transformer
+  target_modules: default
+  model_type: flux2-klein
+  lora_rank: 64
+  lora_alpha: 128
+  resume_path: /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/runs/klein4b_sft_vedai_full_r64a128_lr1e-4/saves/klein4b_sft_vedai_full_r64a128_lr1e-4/checkpoints/checkpoint-48
+  resume_type: lora
+scheduler:
+  dynamics_type: ODE
+  noise_level: 0.0
+  num_sde_steps: 0
+  sde_steps: []
+  seed: 42
+training:
+  trainer_type: nft
+  resolution:
+  - 512
+  - 512
+  height: 512
+  width: 512
+  num_frames: null
+  frame_rate: null
+  max_epochs: 200
+  per_device_batch_size: 1
+  gradient_step_per_epoch: 1
+  max_grad_norm: 1.0
+  num_batches_per_epoch: 96
+  gradient_accumulation_steps: 192
+  num_inner_epochs: 1
+  group_size: 16
+  unique_sample_num_per_epoch: 48
+  num_inference_steps: 8
+  guidance_scale: 1.0
+  ref_param_device: cuda
+  seed: 42
+  learning_rate: 1.0e-05
+  adam_weight_decay: 0.0001
+  adam_betas:
+  - 0.9
+  - 0.999
+  adam_epsilon: 1.0e-08
+  enable_gradient_checkpointing: false
+  offload_samples_to_cpu: false
+  ema_decay: 0.5
+  ema_update_interval: 1
+  ema_device: cuda
+  ema_decay_schedule: piecewise_linear
+  latent_storage_dtype: fp16
+  shuffle_samples: true
+  reward_optimization_overlap: false
+  reward_optimization_overlap_mode: ready
+  reward_optimization_overlap_poll_interval: 0.05
+  global_std: false
+  advantage_aggregation: gdpo
+  nft_beta: 0.1
+  off_policy: true
+  adv_clip_range:
+  - -5.0
+  - 5.0
+  kl_type: v-based
+  kl_beta: 0.0
+  num_train_timesteps: 2
+  time_sampling_strategy: discrete
+  time_shift: 3.0
+  timestep_range:
+  - 0.0
+  - 0.5
+  flat_steps: 0
+  ramp_rate: 0.001
+  condition_image_size: 1024
+eval:
+  resolution:
+  - 512
+  - 512
+  height: 512
+  width: 512
+  num_frames: null
+  frame_rate: null
+  per_device_batch_size: 1
+  seed: 42
+  guidance_scale: 1.0
+  num_inference_steps: 28
+  eval_freq: 10
+  condition_image_size: 1024
+log:
+  run_name: klein4b_nft_vedai_full_ssim_dists_psnr
+  project: IR-Engine
+  logging_backend: wandb
+  save_dir: /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/runs/klein4b_nft_vedai_full_ssim_dists_psnr/saves
+  save_freq: 20
+  save_model_only: true
+  verbose: true
+acceleration:
+  shared: []
+  rollout: []
+reward:
+  reward_0:
+    name: ssim
+    reward_model: ir_rewards.fidelity.SSIMReward
+    weight:
+      vedai_rl_full: 1.0
+    dtype: float32
+    device: cuda
+    batch_size: 16
+    async_reward: false
+    num_workers: 1
+    applicable_datasets:
+    - vedai_rl_full
+  reward_1:
+    name: dists
+    reward_model: ir_rewards.fidelity.DISTSReward
+    weight:
+      vedai_rl_full: 1.0
+    dtype: float32
+    device: cuda
+    batch_size: 16
+    async_reward: false
+    num_workers: 1
+    applicable_datasets:
+    - vedai_rl_full
+  reward_2:
+    name: psnr
+    reward_model: ir_rewards.fidelity.PSNRReward
+    weight:
+      vedai_rl_full: 1.0
+    dtype: float32
+    device: cuda
+    batch_size: 16
+    async_reward: false
+    num_workers: 1
+    applicable_datasets:
+    - vedai_rl_full
+eval_reward:
+  reward_0:
+    name: ssim
+    reward_model: ir_rewards.fidelity.SSIMReward
+    weight:
+      vedai_rl_full: 1.0
+    dtype: float32
+    device: cuda
+    batch_size: 16
+    async_reward: false
+    num_workers: 1
+    applicable_datasets:
+    - vedai_rl_full
+  reward_1:
+    name: psnr
+    reward_model: ir_rewards.fidelity.PSNRReward
+    weight:
+      vedai_rl_full: 1.0
+    dtype: float32
+    device: cuda
+    batch_size: 16
+    async_reward: false
+    num_workers: 1
+    applicable_datasets:
+    - vedai_rl_full
+  reward_2:
+    name: dists
+    reward_model: ir_rewards.fidelity.DISTSReward
+    weight:
+      vedai_rl_full: 1.0
+    dtype: float32
+    device: cuda
+    batch_size: 16
+    async_reward: false
+    num_workers: 1
+    applicable_datasets:
+    - vedai_rl_full
+optimizer:
+  optimizer_configs:
+  - extra_kwargs: {}
+    name: default
+    optimizer: adamw
+    learning_rate: 5.0e-05
+    weight_decay: 0.0001
+    max_grad_norm: 1.0
+    update_frequency: 1
+    betas:
+    - 0.9
+    - 0.999
+    eps: 1.0e-08
+
+[2026-10-01 16:02:24,379] [INFO] [flow_factory.train]: ====================================================================================================
+[2026-10-01 16:02:33] [Rank 4] [INFO] [flow_factory.models.loader]: Loading model architecture: flux2-klein...
+[2026-10-01 16:02:33] [Rank 2] [INFO] [flow_factory.models.loader]: Loading model architecture: flux2-klein...
+[2026-10-01 16:02:33] [Rank 7] [INFO] [flow_factory.models.loader]: Loading model architecture: flux2-klein...
+[2026-10-01 16:02:33] [Rank 5] [INFO] [flow_factory.models.loader]: Loading model architecture: flux2-klein...
+[2026-10-01 16:02:33] [Rank 6] [INFO] [flow_factory.models.loader]: Loading model architecture: flux2-klein...
+[2026-10-01 16:02:33] [Rank 1] [INFO] [flow_factory.models.loader]: Loading model architecture: flux2-klein...
+[2026-10-01 16:02:33,875] [WARNING] [accelerate.utils.other]: [RANK 0] Detected kernel version 5.4.241, which is below the recommended minimum of 5.5.0; this can cause the process to hang. It is recommended to upgrade the kernel to the minimum version or higher.
+[2026-10-01 16:02:33] [Rank 3] [INFO] [flow_factory.models.loader]: Loading model architecture: flux2-klein...
+[2026-10-01 16:02:33] [Rank 0] [INFO] [flow_factory.models.loader]: Loading model architecture: flux2-klein...
+
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+
Loading pipeline components...:  40%|████      | 2/5 [00:22<00:34, 11.42s/it][2026-10-01 16:02:56] [Rank 5] [INFO] [flow_factory.scheduler.loader]: Loaded SDE scheduler: FlowMatchEulerDiscreteSDEScheduler
+
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+[2026-10-01 16:02:57] [Rank 4] [INFO] [flow_factory.scheduler.loader]: Loaded SDE scheduler: FlowMatchEulerDiscreteSDEScheduler
+[2026-10-01 16:02:57] [Rank 3] [INFO] [flow_factory.scheduler.loader]: Loaded SDE scheduler: FlowMatchEulerDiscreteSDEScheduler
+
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+
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+
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+[2026-10-01 16:02:57] [Rank 0] [INFO] [flow_factory.scheduler.loader]: Loaded SDE scheduler: FlowMatchEulerDiscreteSDEScheduler
+[2026-10-01 16:02:57] [Rank 7] [INFO] [flow_factory.scheduler.loader]: Loaded SDE scheduler: FlowMatchEulerDiscreteSDEScheduler
+[2026-10-01 16:02:58] [Rank 1] [INFO] [flow_factory.scheduler.loader]: Loaded SDE scheduler: FlowMatchEulerDiscreteSDEScheduler
+[2026-10-01 16:02:58] [Rank 2] [INFO] [flow_factory.scheduler.loader]: Loaded SDE scheduler: FlowMatchEulerDiscreteSDEScheduler
+[2026-10-01 16:02:58] [Rank 0] [INFO] [flow_factory.models.abc]: LoRA adapter loaded for transformer role=base from /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/runs/klein4b_sft_vedai_full_r64a128_lr1e-4/saves/klein4b_sft_vedai_full_r64a128_lr1e-4/checkpoints/checkpoint-48
+[2026-10-01 16:02:59] [Rank 0] [INFO] [flow_factory.models.abc]: Checkpoint loaded successfully from /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/runs/klein4b_sft_vedai_full_r64a128_lr1e-4/saves/klein4b_sft_vedai_full_r64a128_lr1e-4/checkpoints/checkpoint-48 (type=lora)
+[2026-10-01 16:02:59] [Rank 2] [INFO] [flow_factory.models.abc]: Froze ALL transformer parameters
+[2026-10-01 16:02:59] [Rank 2] [INFO] [flow_factory.models.abc]: Component transformer already has 'default' adapter. Skipping initialization but enabling gradients.
+[2026-10-01 16:02:59] [Rank 1] [INFO] [flow_factory.models.abc]: Froze ALL transformer parameters
+[2026-10-01 16:02:59] [Rank 7] [INFO] [flow_factory.models.abc]: Froze ALL transformer parameters
+[2026-10-01 16:02:59] [Rank 1] [INFO] [flow_factory.models.abc]: Component transformer already has 'default' adapter. Skipping initialization but enabling gradients.
+[2026-10-01 16:02:59] [Rank 7] [INFO] [flow_factory.models.abc]: Component transformer already has 'default' adapter. Skipping initialization but enabling gradients.
+[2026-10-01 16:02:59] [Rank 6] [INFO] [flow_factory.models.abc]: Froze ALL transformer parameters
+[2026-10-01 16:02:59] [Rank 6] [INFO] [flow_factory.models.abc]: Component transformer already has 'default' adapter. Skipping initialization but enabling gradients.
+[2026-10-01 16:02:59] [Rank 4] [INFO] [flow_factory.models.abc]: Froze ALL transformer parameters
+[2026-10-01 16:02:59] [Rank 0] [INFO] [flow_factory.models.abc]: Froze ALL transformer parameters
+[2026-10-01 16:02:59] [Rank 4] [INFO] [flow_factory.models.abc]: Component transformer already has 'default' adapter. Skipping initialization but enabling gradients.
+[2026-10-01 16:02:59] [Rank 3] [INFO] [flow_factory.models.abc]: Froze ALL transformer parameters
+[2026-10-01 16:02:59] [Rank 0] [INFO] [flow_factory.models.abc]: Component transformer already has 'default' adapter. Skipping initialization but enabling gradients.
+[2026-10-01 16:02:59] [Rank 3] [INFO] [flow_factory.models.abc]: Component transformer already has 'default' adapter. Skipping initialization but enabling gradients.
+[2026-10-01 16:02:59] [Rank 5] [INFO] [flow_factory.models.abc]: Froze ALL transformer parameters
+[2026-10-01 16:02:59] [Rank 5] [INFO] [flow_factory.models.abc]: Component transformer already has 'default' adapter. Skipping initialization but enabling gradients.
+[2026-10-01 16:02:59] [Rank 1] [INFO] [flow_factory.models.abc]: Set 160 trainable parameters to torch.bfloat16; frozen component policy -> {'text_encoder': None, 'transformer': None, 'vae': None}
+[2026-10-01 16:02:59] [Rank 2] [INFO] [flow_factory.models.abc]: Set 160 trainable parameters to torch.bfloat16; frozen component policy -> {'text_encoder': None, 'transformer': None, 'vae': None}
+[2026-10-01 16:02:59] [Rank 1] [INFO] [flow_factory.models.loader]: Successfully loaded Flux2KleinAdapter
+[2026-10-01 16:02:59] [Rank 2] [INFO] [flow_factory.models.loader]: Successfully loaded Flux2KleinAdapter
+[2026-10-01 16:02:59] [Rank 6] [INFO] [flow_factory.models.abc]: Set 160 trainable parameters to torch.bfloat16; frozen component policy -> {'text_encoder': None, 'transformer': None, 'vae': None}
+[2026-10-01 16:02:59] [Rank 7] [INFO] [flow_factory.models.abc]: Set 160 trainable parameters to torch.bfloat16; frozen component policy -> {'text_encoder': None, 'transformer': None, 'vae': None}
+[2026-10-01 16:02:59] [Rank 0] [INFO] [flow_factory.models.abc]: Set 160 trainable parameters to torch.bfloat16; frozen component policy -> {'text_encoder': None, 'transformer': None, 'vae': None}
+[2026-10-01 16:02:59] [Rank 6] [INFO] [flow_factory.models.loader]: Successfully loaded Flux2KleinAdapter
+[2026-10-01 16:02:59] [Rank 7] [INFO] [flow_factory.models.loader]: Successfully loaded Flux2KleinAdapter
+[2026-10-01 16:02:59] [Rank 3] [INFO] [flow_factory.models.abc]: Set 160 trainable parameters to torch.bfloat16; frozen component policy -> {'text_encoder': None, 'transformer': None, 'vae': None}
+[2026-10-01 16:02:59] [Rank 3] [INFO] [flow_factory.models.loader]: Successfully loaded Flux2KleinAdapter
+[2026-10-01 16:02:59] [Rank 4] [INFO] [flow_factory.models.abc]: Set 160 trainable parameters to torch.bfloat16; frozen component policy -> {'text_encoder': None, 'transformer': None, 'vae': None}
+[2026-10-01 16:02:59] [Rank 4] [INFO] [flow_factory.models.loader]: Successfully loaded Flux2KleinAdapter
+[2026-10-01 16:02:59] [Rank 5] [INFO] [flow_factory.models.abc]: Set 160 trainable parameters to torch.bfloat16; frozen component policy -> {'text_encoder': None, 'transformer': None, 'vae': None}
+[2026-10-01 16:02:59] [Rank 0] [INFO] [flow_factory.models.abc]: Precision inventory stage=initialize component=text_encoder {'trainable': {}, 'frozen': {'torch.bfloat16': 4022468096}}
+[2026-10-01 16:02:59] [Rank 5] [INFO] [flow_factory.models.loader]: Successfully loaded Flux2KleinAdapter
+[2026-10-01 16:02:59] [Rank 0] [INFO] [flow_factory.models.abc]: Precision inventory stage=initialize component=transformer {'trainable': {'torch.bfloat16': 76677120}, 'frozen': {'torch.float32': 3875544576}}
+[2026-10-01 16:02:59] [Rank 0] [INFO] [flow_factory.models.abc]: Precision inventory stage=initialize component=vae {'trainable': {}, 'frozen': {'torch.float32': 84046115}}
+[2026-10-01 16:02:59] [Rank 0] [INFO] [flow_factory.models.loader]: Successfully loaded Flux2KleinAdapter
+[2026-10-01 16:03:23] [Rank 4] [INFO] [flow_factory.data_utils.loader]: Multi-source: built DataLoader for 'vedai_rl_full' (dataset_size=872, unique_sample_num_per_epoch=48, num_batches_per_epoch=96)
+[2026-10-01 16:03:24] [Rank 0] [INFO] [flow_factory.data_utils.loader]: Loading train dataset from merged cache: /root/.cache/flow_factory/datasets/1e68492a08871f0f6ebe621929ff234a
+[2026-10-01 16:03:24] [Rank 1] [INFO] [flow_factory.data_utils.loader]: Multi-source: built DataLoader for 'vedai_rl_full' (dataset_size=872, unique_sample_num_per_epoch=48, num_batches_per_epoch=96)
+[2026-10-01 16:03:24] [Rank 2] [INFO] [flow_factory.data_utils.loader]: Multi-source: built DataLoader for 'vedai_rl_full' (dataset_size=872, unique_sample_num_per_epoch=48, num_batches_per_epoch=96)
+[2026-10-01 16:03:24] [Rank 5] [INFO] [flow_factory.data_utils.loader]: Multi-source: built DataLoader for 'vedai_rl_full' (dataset_size=872, unique_sample_num_per_epoch=48, num_batches_per_epoch=96)
+[2026-10-01 16:03:24] [Rank 7] [INFO] [flow_factory.data_utils.loader]: Multi-source: built DataLoader for 'vedai_rl_full' (dataset_size=872, unique_sample_num_per_epoch=48, num_batches_per_epoch=96)
+[2026-10-01 16:03:24] [Rank 0] [INFO] [flow_factory.data_utils.loader]: Multi-source: built DataLoader for 'vedai_rl_full' (dataset_size=872, unique_sample_num_per_epoch=48, num_batches_per_epoch=96)
+[2026-10-01 16:03:24] [Rank 6] [INFO] [flow_factory.data_utils.loader]: Multi-source: built DataLoader for 'vedai_rl_full' (dataset_size=872, unique_sample_num_per_epoch=48, num_batches_per_epoch=96)
+[2026-10-01 16:03:24] [Rank 0] [INFO] [flow_factory.data_utils.loader]: Loading test dataset from merged cache: /root/.cache/flow_factory/datasets/c624060e1ad3d97604e0883a86585d87
+[2026-10-01 16:03:24] [Rank 3] [INFO] [flow_factory.data_utils.loader]: Multi-source: built DataLoader for 'vedai_rl_full' (dataset_size=872, unique_sample_num_per_epoch=48, num_batches_per_epoch=96)
+/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.
+  warnings.warn(
+/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.
+  warnings.warn(
+/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=VGG16_Weights.IMAGENET1K_V1`. You can also use `weights=VGG16_Weights.DEFAULT` to get the most up-to-date weights.
+  warnings.warn(msg)
+/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.
+  warnings.warn(
+/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.
+  warnings.warn(
+/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=VGG16_Weights.IMAGENET1K_V1`. You can also use `weights=VGG16_Weights.DEFAULT` to get the most up-to-date weights.
+  warnings.warn(msg)
+/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.
+  warnings.warn(
+/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.
+  warnings.warn(
+/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.
+  warnings.warn(
+/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=VGG16_Weights.IMAGENET1K_V1`. You can also use `weights=VGG16_Weights.DEFAULT` to get the most up-to-date weights.
+  warnings.warn(msg)
+/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.
+  warnings.warn(
+/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=VGG16_Weights.IMAGENET1K_V1`. You can also use `weights=VGG16_Weights.DEFAULT` to get the most up-to-date weights.
+  warnings.warn(msg)
+/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=VGG16_Weights.IMAGENET1K_V1`. You can also use `weights=VGG16_Weights.DEFAULT` to get the most up-to-date weights.
+  warnings.warn(msg)
+/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=VGG16_Weights.IMAGENET1K_V1`. You can also use `weights=VGG16_Weights.DEFAULT` to get the most up-to-date weights.
+  warnings.warn(msg)
+/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=VGG16_Weights.IMAGENET1K_V1`. You can also use `weights=VGG16_Weights.DEFAULT` to get the most up-to-date weights.
+  warnings.warn(msg)
+/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=VGG16_Weights.IMAGENET1K_V1`. You can also use `weights=VGG16_Weights.DEFAULT` to get the most up-to-date weights.
+  warnings.warn(msg)
+wandb: [wandb.login()] Loaded credentials for https://api.wandb.ai from /root/.netrc.
+wandb: Currently logged in as: 315229706 (315229706-xi-an-jiaotong-university-) to https://api.wandb.ai. Use `wandb login --relogin` to force relogin
+wandb: setting up run netat1u8
+wandb: Tracking run with wandb version 0.28.2
+wandb: Run data is saved locally in /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/runs/klein4b_nft_vedai_full_ssim_dists_psnr/wandb/run-20261001_160342-netat1u8
+wandb: Run `wandb offline` to turn off syncing.
+wandb: Syncing run klein4b_nft_vedai_full_ssim_dists_psnr
+wandb: ⭐️ View project at https://wandb.ai/315229706-xi-an-jiaotong-university-/IR-Engine
+wandb: 🚀 View run at https://wandb.ai/315229706-xi-an-jiaotong-university-/IR-Engine/runs/netat1u8
+[2026-10-01 16:03:45] [Rank 0] [INFO] [flow_factory.models.abc]: ======================================================================
+[2026-10-01 16:03:45] [Rank 0] [INFO] [flow_factory.models.abc]: Transformer Trainable Parameters:
+[2026-10-01 16:03:45] [Rank 0] [INFO] [flow_factory.models.abc]:   Total:        3,952,221,696 (  7.36 GB)
+[2026-10-01 16:03:45] [Rank 0] [INFO] [flow_factory.models.abc]:   Trainable:       76,677,120 (  0.14 GB)
+[2026-10-01 16:03:45] [Rank 0] [INFO] [flow_factory.models.abc]:   Percentage:           1.94%
+[2026-10-01 16:03:45] [Rank 0] [INFO] [flow_factory.models.abc]: ======================================================================
+[2026-10-01 16:03:45] [Rank 0] [INFO] [flow_factory.models.abc]: Saving LoRA weights for transformer role=base to /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/runs/klein4b_nft_vedai_full_ssim_dists_psnr/saves/klein4b_nft_vedai_full_ssim_dists_psnr/checkpoints/checkpoint-0
+[2026-10-01 16:03:45] [Rank 0] [INFO] [flow_factory.models.abc]: Checkpoint saved successfully to /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/runs/klein4b_nft_vedai_full_ssim_dists_psnr/saves/klein4b_nft_vedai_full_ssim_dists_psnr/checkpoints/checkpoint-0
+
Eval/vedai_rl_full:   0%|          | 0/8 [00:00<?, ?it/s][2026-10-01 16:03:46] [Rank 5] [WARNING] [flow_factory.models.flux.flux2_klein]: FLUX.2 does not support batch inference with varying condition images per sample. Falling back to single-sample inference. This warning will only appear once.
+[2026-10-01 16:03:46] [Rank 0] [WARNING] [flow_factory.models.flux.flux2_klein]: FLUX.2 does not support batch inference with varying condition images per sample. Falling back to single-sample inference. This warning will only appear once.
+[2026-10-01 16:03:46] [Rank 6] [WARNING] [flow_factory.models.flux.flux2_klein]: FLUX.2 does not support batch inference with varying condition images per sample. Falling back to single-sample inference. This warning will only appear once.
+[2026-10-01 16:03:46] [Rank 4] [WARNING] [flow_factory.models.flux.flux2_klein]: FLUX.2 does not support batch inference with varying condition images per sample. Falling back to single-sample inference. This warning will only appear once.
+[2026-10-01 16:03:46] [Rank 3] [WARNING] [flow_factory.models.flux.flux2_klein]: FLUX.2 does not support batch inference with varying condition images per sample. Falling back to single-sample inference. This warning will only appear once.
+[2026-10-01 16:03:46] [Rank 7] [WARNING] [flow_factory.models.flux.flux2_klein]: FLUX.2 does not support batch inference with varying condition images per sample. Falling back to single-sample inference. This warning will only appear once.
+[2026-10-01 16:03:46] [Rank 1] [WARNING] [flow_factory.models.flux.flux2_klein]: FLUX.2 does not support batch inference with varying condition images per sample. Falling back to single-sample inference. This warning will only appear once.
+[2026-10-01 16:03:46] [Rank 2] [WARNING] [flow_factory.models.flux.flux2_klein]: FLUX.2 does not support batch inference with varying condition images per sample. Falling back to single-sample inference. This warning will only appear once.
+
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+
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Pointwise Rewards: ssim: 100%|██████████| 1/1 [00:00<00:00,  7.62it/s]
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+
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+
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+[2026-10-01 16:04:30] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0000 | Epoch 000] eval/vedai_rl_full/reward_dists_mean=0.9292 eval/vedai_rl_full/reward_dists_std=0.0183 eval/vedai_rl_full/reward_psnr_mean=29.0025 eval/vedai_rl_full/reward_psnr_std=2.6570 eval/vedai_rl_full/reward_ssim_mean=0.9194 eval/vedai_rl_full/reward_ssim_std=0.0282
+
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+
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Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:00<00:00,  6.21it/s]
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+
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Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.65it/s]
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+
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Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.76it/s]
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+[2026-10-01 16:07:01] [Rank 7] [WARNING] [flow_factory.models.flux.flux2_klein]: Flux.2: Ragged I2I detected (varying condition image sizes). Processing samples individually (warning shown once).
+[2026-10-01 16:07:01] [Rank 4] [WARNING] [flow_factory.models.flux.flux2_klein]: Flux.2: Ragged I2I detected (varying condition image sizes). Processing samples individually (warning shown once).
+[2026-10-01 16:07:01] [Rank 5] [WARNING] [flow_factory.models.flux.flux2_klein]: Flux.2: Ragged I2I detected (varying condition image sizes). Processing samples individually (warning shown once).
+[2026-10-01 16:07:01] [Rank 1] [WARNING] [flow_factory.models.flux.flux2_klein]: Flux.2: Ragged I2I detected (varying condition image sizes). Processing samples individually (warning shown once).
+[2026-10-01 16:07:01] [Rank 3] [WARNING] [flow_factory.models.flux.flux2_klein]: Flux.2: Ragged I2I detected (varying condition image sizes). Processing samples individually (warning shown once).
+[2026-10-01 16:07:01] [Rank 6] [WARNING] [flow_factory.models.flux.flux2_klein]: Flux.2: Ragged I2I detected (varying condition image sizes). Processing samples individually (warning shown once).
+[2026-10-01 16:07:01] [Rank 2] [WARNING] [flow_factory.models.flux.flux2_klein]: Flux.2: Ragged I2I detected (varying condition image sizes). Processing samples individually (warning shown once).
+[2026-10-01 16:07:04] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0000 | Epoch 000] train/reward_dists_mean=0.9192 train/reward_dists_std=0.0256 train/reward_psnr_mean=29.7356 train/reward_psnr_std=2.2339 train/reward_ssim_mean=0.9326 train/reward_ssim_std=0.0204 train/reward_dists_group_std_mean=0.0045 train/reward_dists_group_std_max=0.0096 train/reward_dists_group_std_min=0.0019 train/reward_dists_group_mean_std=0.0252 train/reward_psnr_group_std_mean=1.5639 train/reward_psnr_group_std_max=3.2470 train/reward_psnr_group_std_min=0.4404 train/reward_psnr_group_mean_std=1.4550 train/reward_ssim_group_std_mean=0.0021 train/reward_ssim_group_std_max=0.0179 train/reward_ssim_group_std_min=0.0006 train/reward_ssim_group_mean_std=0.0202 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-8.4142 train/adv_max=6.0486 train/adv_abs_mean=1.7681
+
Epoch 0 Training:   0%|          | 0/96 [00:00<?, ?it/s][2026-10-01 16:07:04] [Rank 0] [WARNING] [flow_factory.models.flux.flux2_klein]: Flux.2: Ragged I2I detected (varying condition image sizes). Processing samples individually (warning shown once).
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Epoch 0 Training:  99%|█████████▉| 95/96 [02:04<00:01,  1.31s/it]
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Epoch 0 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s][2026-10-01 16:09:10] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0000 | Epoch 000] train/policy_loss=13.9958 train/unweighted_policy_loss=2.7992 train/loss=13.9958 train/grad_norm=0.0810
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Epoch 0 Training: 100%|██████████| 96/96 [02:06<00:00,  1.31s/it]
+[2026-10-01 16:09:10] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0001 | Epoch 000] timing/rollout_seconds=147.6585 timing/feedback_seconds=7.7007 timing/optimization_seconds=129.0917 timing/cycle_seconds=280.2837
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+
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+
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Pointwise Rewards: dists:  83%|████████▎ | 5/6 [00:01<00:00,  3.39it/s]
Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.41it/s]
Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.42it/s]
+
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Pointwise Rewards: psnr:  33%|███▎      | 2/6 [00:00<00:00,  6.28it/s]
Pointwise Rewards: psnr:  50%|█████     | 3/6 [00:00<00:00,  6.15it/s]
Pointwise Rewards: psnr:  67%|██████▋   | 4/6 [00:00<00:00,  6.14it/s]
Pointwise Rewards: psnr:  83%|████████▎ | 5/6 [00:00<00:00,  6.05it/s]
Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.04it/s]
Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.10it/s]
+[2026-10-01 16:11:42] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0001 | Epoch 001] train/reward_dists_mean=0.9203 train/reward_dists_std=0.0229 train/reward_psnr_mean=25.8928 train/reward_psnr_std=2.8757 train/reward_ssim_mean=0.9139 train/reward_ssim_std=0.0297 train/reward_dists_group_std_mean=0.0054 train/reward_dists_group_std_max=0.0286 train/reward_dists_group_std_min=0.0020 train/reward_dists_group_mean_std=0.0219 train/reward_psnr_group_std_mean=1.4270 train/reward_psnr_group_std_max=5.1233 train/reward_psnr_group_std_min=0.4576 train/reward_psnr_group_mean_std=2.3602 train/reward_ssim_group_std_mean=0.0069 train/reward_ssim_group_std_max=0.0398 train/reward_ssim_group_std_min=0.0020 train/reward_ssim_group_mean_std=0.0279 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-7.2457 train/adv_max=7.2350 train/adv_abs_mean=1.8208
+
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Epoch 1 Training:  22%|██▏       | 21/96 [00:27<01:37,  1.31s/it]
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Epoch 1 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 1 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.37it/s]
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Epoch 1 Training:  23%|██▎       | 22/96 [00:28<01:36,  1.30s/it]
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Epoch 1 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 1 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.36it/s]
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Epoch 1 Training:  24%|██▍       | 23/96 [00:30<01:34,  1.30s/it]
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Epoch 1 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 1 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.35it/s]
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Epoch 1 Training:  25%|██▌       | 24/96 [00:31<01:33,  1.30s/it]
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Epoch 1 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 1 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.36it/s]
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Epoch 1 Training:  26%|██▌       | 25/96 [00:32<01:32,  1.30s/it]
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Epoch 1 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 1 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.33it/s]
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Epoch 1 Training:  27%|██▋       | 26/96 [00:33<01:30,  1.30s/it]
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Epoch 1 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 1 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.32it/s]
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Epoch 1 Training:  28%|██▊       | 27/96 [00:35<01:29,  1.30s/it]
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Epoch 1 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.44it/s]
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Epoch 1 Training:  29%|██▉       | 28/96 [00:36<01:28,  1.30s/it]
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Epoch 1 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 1 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 1 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.37it/s]
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Epoch 1 Training:  30%|███       | 29/96 [00:37<01:27,  1.30s/it]
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Epoch 1 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 1 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 1 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.35it/s]
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Epoch 1 Training:  31%|███▏      | 30/96 [00:39<01:25,  1.30s/it]
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Epoch 1 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 1 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 1 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.33it/s]
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Epoch 1 Training:  32%|███▏      | 31/96 [00:40<01:24,  1.30s/it]
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Epoch 1 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 1 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 1 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.32it/s]
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Epoch 1 Training:  33%|███▎      | 32/96 [00:41<01:23,  1.30s/it]
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Epoch 1 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 1 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 1 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.36it/s]
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Epoch 1 Training:  34%|███▍      | 33/96 [00:43<01:21,  1.30s/it]
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Epoch 1 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 1 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.37it/s]
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Epoch 1 Training:  35%|███▌      | 34/96 [00:44<01:20,  1.30s/it]
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Epoch 1 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 1 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.35it/s]
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Epoch 1 Training:  36%|███▋      | 35/96 [00:45<01:19,  1.30s/it]
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Epoch 1 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 1 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 1 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.33it/s]
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Epoch 1 Training:  38%|███▊      | 36/96 [00:46<01:17,  1.30s/it]
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Epoch 1 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 1 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.33it/s]
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Epoch 1 Training:  39%|███▊      | 37/96 [00:48<01:16,  1.30s/it]
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Epoch 1 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 1 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 1 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.44it/s]
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Epoch 1 Training:  40%|███▉      | 38/96 [00:49<01:15,  1.30s/it]
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Epoch 1 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 1 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 1 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.35it/s]
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Epoch 1 Training:  41%|████      | 39/96 [00:50<01:14,  1.30s/it]
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Epoch 1 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 1 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.62it/s]
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Epoch 1 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.35it/s]
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Epoch 1 Training:  42%|████▏     | 40/96 [00:52<01:12,  1.30s/it]
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Epoch 1 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 1 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 1 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.43it/s]
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Epoch 1 Training:  43%|████▎     | 41/96 [00:53<01:11,  1.30s/it]
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Epoch 1 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 1 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 1 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.39it/s]
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Epoch 1 Training:  44%|████▍     | 42/96 [00:54<01:10,  1.30s/it]
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Epoch 1 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 1 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 1 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.36it/s]
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Epoch 1 Training:  45%|████▍     | 43/96 [00:55<01:08,  1.30s/it]
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Epoch 1 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 1 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 1 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.33it/s]
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Epoch 1 Training:  46%|████▌     | 44/96 [00:57<01:07,  1.30s/it]
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Epoch 1 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 1 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 1 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.33it/s]
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Epoch 1 Training:  47%|████▋     | 45/96 [00:58<01:06,  1.30s/it]
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Epoch 1 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 1 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 1 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.34it/s]
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Epoch 1 Training:  48%|████▊     | 46/96 [00:59<01:04,  1.30s/it]
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Epoch 1 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 1 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.33it/s]
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Epoch 1 Training:  49%|████▉     | 47/96 [01:01<01:03,  1.30s/it]
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Epoch 1 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 1 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.37it/s]
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Epoch 1 Training:  50%|█████     | 48/96 [01:02<01:02,  1.29s/it]
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Epoch 1 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 1 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 1 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.49it/s]
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Epoch 1 Training:  51%|█████     | 49/96 [01:03<01:00,  1.30s/it]
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Epoch 1 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 1 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.50it/s]
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Epoch 1 Training:  52%|█████▏    | 50/96 [01:05<00:59,  1.30s/it]
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Epoch 1 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.43it/s]
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Epoch 1 Training:  53%|█████▎    | 51/96 [01:06<00:58,  1.30s/it]
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Epoch 1 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.36it/s]
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Epoch 1 Training:  54%|█████▍    | 52/96 [01:07<00:57,  1.30s/it]
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Epoch 1 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.38it/s]
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Epoch 1 Training:  55%|█████▌    | 53/96 [01:08<00:55,  1.30s/it]
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Epoch 1 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.48it/s]
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Epoch 1 Training:  56%|█████▋    | 54/96 [01:10<00:54,  1.30s/it]
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Epoch 1 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.48it/s]
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Epoch 1 Training:  57%|█████▋    | 55/96 [01:11<00:53,  1.29s/it]
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Epoch 1 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.36it/s]
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Epoch 1 Training:  58%|█████▊    | 56/96 [01:12<00:51,  1.29s/it]
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Epoch 1 Training:  59%|█████▉    | 57/96 [01:14<00:50,  1.30s/it]
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Epoch 1 Training:  60%|██████    | 58/96 [01:15<00:49,  1.30s/it]
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Epoch 1 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.38it/s]
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Epoch 1 Training:  61%|██████▏   | 59/96 [01:16<00:47,  1.29s/it]
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Epoch 1 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.37it/s]
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Epoch 1 Training:  62%|██████▎   | 60/96 [01:18<00:46,  1.30s/it]
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Epoch 1 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.36it/s]
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Epoch 1 Training:  64%|██████▎   | 61/96 [01:19<00:45,  1.30s/it]
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Epoch 1 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.44it/s]
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Epoch 1 Training:  65%|██████▍   | 62/96 [01:20<00:44,  1.30s/it]
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Epoch 1 Training:  66%|██████▌   | 63/96 [01:21<00:42,  1.30s/it]
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Epoch 1 Training:  67%|██████▋   | 64/96 [01:23<00:41,  1.30s/it]
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Epoch 1 Training:  68%|██████▊   | 65/96 [01:24<00:40,  1.31s/it]
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Epoch 1 Training:  69%|██████▉   | 66/96 [01:25<00:39,  1.30s/it]
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Epoch 1 Training:  70%|██████▉   | 67/96 [01:27<00:37,  1.30s/it]
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Epoch 1 Training:  71%|███████   | 68/96 [01:28<00:36,  1.30s/it]
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Epoch 1 Training:  72%|███████▏  | 69/96 [01:29<00:35,  1.30s/it]
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Epoch 1 Training:  73%|███████▎  | 70/96 [01:31<00:33,  1.30s/it]
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Epoch 1 Training:  74%|███████▍  | 71/96 [01:32<00:32,  1.30s/it]
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Epoch 1 Training:  75%|███████▌  | 72/96 [01:33<00:31,  1.30s/it]
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Epoch 1 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s][2026-10-01 16:13:47] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0001 | Epoch 001] train/policy_loss=13.2352 train/unweighted_policy_loss=2.6470 train/loss=13.2352 train/grad_norm=0.1099
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+[2026-10-01 16:13:47] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0002 | Epoch 001] timing/rollout_seconds=147.8034 timing/feedback_seconds=5.9814 timing/optimization_seconds=125.4661 timing/cycle_seconds=277.0744
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+
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+[2026-10-01 16:16:19] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0002 | Epoch 002] train/reward_dists_mean=0.9265 train/reward_dists_std=0.0269 train/reward_psnr_mean=24.9562 train/reward_psnr_std=2.6700 train/reward_ssim_mean=0.9297 train/reward_ssim_std=0.0256 train/reward_dists_group_std_mean=0.0045 train/reward_dists_group_std_max=0.0206 train/reward_dists_group_std_min=0.0021 train/reward_dists_group_mean_std=0.0264 train/reward_psnr_group_std_mean=1.5790 train/reward_psnr_group_std_max=5.7498 train/reward_psnr_group_std_min=0.7641 train/reward_psnr_group_mean_std=2.0177 train/reward_ssim_group_std_mean=0.0034 train/reward_ssim_group_std_max=0.0274 train/reward_ssim_group_std_min=0.0009 train/reward_ssim_group_mean_std=0.0249 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-8.0043 train/adv_max=7.3162 train/adv_abs_mean=1.8603
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Epoch 2 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s][2026-10-01 16:18:25] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0002 | Epoch 002] train/policy_loss=14.0935 train/unweighted_policy_loss=2.8187 train/loss=14.0935 train/grad_norm=0.1015
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+[2026-10-01 16:18:25] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0003 | Epoch 002] timing/rollout_seconds=147.7600 timing/feedback_seconds=5.6326 timing/optimization_seconds=126.2275 timing/cycle_seconds=277.4730
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+
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+[2026-10-01 16:20:56] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0003 | Epoch 003] train/reward_dists_mean=0.9323 train/reward_dists_std=0.0256 train/reward_psnr_mean=28.2646 train/reward_psnr_std=3.6656 train/reward_ssim_mean=0.9281 train/reward_ssim_std=0.0429 train/reward_dists_group_std_mean=0.0054 train/reward_dists_group_std_max=0.0736 train/reward_dists_group_std_min=0.0014 train/reward_dists_group_mean_std=0.0229 train/reward_psnr_group_std_mean=1.5975 train/reward_psnr_group_std_max=8.9929 train/reward_psnr_group_std_min=0.3453 train/reward_psnr_group_mean_std=3.0624 train/reward_ssim_group_std_mean=0.0058 train/reward_ssim_group_std_max=0.1512 train/reward_ssim_group_std_min=0.0006 train/reward_ssim_group_mean_std=0.0366 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-7.0891 train/adv_max=7.5472 train/adv_abs_mean=1.9187
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Epoch 3 Training:  21%|██        | 20/96 [00:25<01:37,  1.29s/it]
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Epoch 3 Training:  23%|██▎       | 22/96 [00:28<01:35,  1.29s/it]
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Epoch 3 Training:  24%|██▍       | 23/96 [00:29<01:34,  1.29s/it]
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Epoch 3 Training:  28%|██▊       | 27/96 [00:34<01:28,  1.28s/it]
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Epoch 3 Training:  29%|██▉       | 28/96 [00:35<01:26,  1.28s/it]
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Epoch 3 Training:  32%|███▏      | 31/96 [00:39<01:23,  1.28s/it]
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Epoch 3 Training:  33%|███▎      | 32/96 [00:41<01:22,  1.29s/it]
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Epoch 3 Training:  34%|███▍      | 33/96 [00:42<01:20,  1.28s/it]
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Epoch 3 Training:  35%|███▌      | 34/96 [00:43<01:19,  1.28s/it]
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Epoch 3 Training:  36%|███▋      | 35/96 [00:44<01:17,  1.28s/it]
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Epoch 3 Training:  38%|███▊      | 36/96 [00:46<01:16,  1.28s/it]
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Epoch 3 Training:  39%|███▊      | 37/96 [00:47<01:15,  1.28s/it]
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Epoch 3 Training:  40%|███▉      | 38/96 [00:48<01:14,  1.29s/it]
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Epoch 3 Training:  41%|████      | 39/96 [00:50<01:13,  1.29s/it]
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Epoch 3 Training:  42%|████▏     | 40/96 [00:51<01:11,  1.28s/it]
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Epoch 3 Training:  43%|████▎     | 41/96 [00:52<01:10,  1.29s/it]
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Epoch 3 Training:  44%|████▍     | 42/96 [00:53<01:09,  1.29s/it]
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Epoch 3 Training:  45%|████▍     | 43/96 [00:55<01:08,  1.29s/it]
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Epoch 3 Training:  46%|████▌     | 44/96 [00:56<01:07,  1.29s/it]
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Epoch 3 Training:  47%|████▋     | 45/96 [00:57<01:05,  1.28s/it]
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Epoch 3 Training:  48%|████▊     | 46/96 [00:59<01:04,  1.29s/it]
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Epoch 3 Training:  49%|████▉     | 47/96 [01:00<01:02,  1.28s/it]
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Epoch 3 Training:  51%|█████     | 49/96 [01:02<00:59,  1.28s/it]
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Epoch 3 Training:  53%|█████▎    | 51/96 [01:05<00:57,  1.28s/it]
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Epoch 3 Training:  54%|█████▍    | 52/96 [01:06<00:56,  1.28s/it]
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Epoch 3 Training:  55%|█████▌    | 53/96 [01:08<00:55,  1.28s/it]
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Epoch 3 Training:  56%|█████▋    | 54/96 [01:09<00:53,  1.28s/it]
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Epoch 3 Training:  57%|█████▋    | 55/96 [01:10<00:52,  1.28s/it]
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Epoch 3 Training:  64%|██████▎   | 61/96 [01:18<00:45,  1.29s/it]
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Epoch 3 Training:  65%|██████▍   | 62/96 [01:19<00:43,  1.28s/it]
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Epoch 3 Training:  67%|██████▋   | 64/96 [01:22<00:41,  1.28s/it]
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Epoch 3 Training:  68%|██████▊   | 65/96 [01:23<00:39,  1.29s/it]
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Epoch 3 Training:  69%|██████▉   | 66/96 [01:24<00:38,  1.29s/it]
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Epoch 3 Training:  71%|███████   | 68/96 [01:27<00:36,  1.29s/it]
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Epoch 3 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s][2026-10-01 16:23:00] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0003 | Epoch 003] train/policy_loss=13.6745 train/unweighted_policy_loss=2.7349 train/loss=13.6745 train/grad_norm=0.4330
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Epoch 3 Training: 100%|██████████| 96/96 [02:03<00:00,  1.29s/it]
+[2026-10-01 16:23:00] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0004 | Epoch 003] timing/rollout_seconds=147.4566 timing/feedback_seconds=5.5107 timing/optimization_seconds=124.0190 timing/cycle_seconds=275.0307
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+[2026-10-01 16:25:31] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0004 | Epoch 004] train/reward_dists_mean=0.9316 train/reward_dists_std=0.0151 train/reward_psnr_mean=29.6093 train/reward_psnr_std=1.8687 train/reward_ssim_mean=0.9294 train/reward_ssim_std=0.0218 train/reward_dists_group_std_mean=0.0037 train/reward_dists_group_std_max=0.0073 train/reward_dists_group_std_min=0.0017 train/reward_dists_group_mean_std=0.0146 train/reward_psnr_group_std_mean=1.2623 train/reward_psnr_group_std_max=2.7343 train/reward_psnr_group_std_min=0.4095 train/reward_psnr_group_mean_std=1.2703 train/reward_ssim_group_std_mean=0.0018 train/reward_ssim_group_std_max=0.0153 train/reward_ssim_group_std_min=0.0007 train/reward_ssim_group_mean_std=0.0216 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-7.8442 train/adv_max=5.0817 train/adv_abs_mean=1.7957
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Epoch 4 Training:  46%|████▌     | 44/96 [00:58<01:09,  1.33s/it]
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Epoch 4 Training:  47%|████▋     | 45/96 [00:59<01:07,  1.33s/it]
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Epoch 4 Training:  48%|████▊     | 46/96 [01:00<01:06,  1.32s/it]
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Epoch 4 Training:  49%|████▉     | 47/96 [01:01<01:04,  1.32s/it]
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Epoch 4 Training:  50%|█████     | 48/96 [01:03<01:03,  1.32s/it]
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Epoch 4 Training:  51%|█████     | 49/96 [01:04<01:01,  1.32s/it]
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Epoch 4 Training:  52%|█████▏    | 50/96 [01:05<01:00,  1.31s/it]
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Epoch 4 Training:  53%|█████▎    | 51/96 [01:07<00:59,  1.31s/it]
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Epoch 4 Training:  54%|█████▍    | 52/96 [01:08<00:57,  1.32s/it]
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Epoch 4 Training:  55%|█████▌    | 53/96 [01:09<00:56,  1.32s/it]
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Epoch 4 Training:  56%|█████▋    | 54/96 [01:11<00:55,  1.32s/it]
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Epoch 4 Training:  57%|█████▋    | 55/96 [01:12<00:53,  1.31s/it]
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Epoch 4 Training:  58%|█████▊    | 56/96 [01:13<00:52,  1.31s/it]
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Epoch 4 Training:  59%|█████▉    | 57/96 [01:15<00:51,  1.31s/it]
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Epoch 4 Training:  60%|██████    | 58/96 [01:16<00:49,  1.31s/it]
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Epoch 4 Training:  61%|██████▏   | 59/96 [01:17<00:48,  1.32s/it]
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Epoch 4 Training:  62%|██████▎   | 60/96 [01:19<00:47,  1.32s/it]
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Epoch 4 Training:  64%|██████▎   | 61/96 [01:20<00:46,  1.31s/it]
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Epoch 4 Training:  65%|██████▍   | 62/96 [01:21<00:44,  1.31s/it]
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Epoch 4 Training:  66%|██████▌   | 63/96 [01:23<00:45,  1.37s/it]
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Epoch 4 Training:  67%|██████▋   | 64/96 [01:24<00:43,  1.35s/it]
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Epoch 4 Training:  68%|██████▊   | 65/96 [01:25<00:41,  1.34s/it]
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Epoch 4 Training:  69%|██████▉   | 66/96 [01:27<00:40,  1.33s/it]
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Epoch 4 Training:  70%|██████▉   | 67/96 [01:28<00:38,  1.33s/it]
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Epoch 4 Training:  71%|███████   | 68/96 [01:29<00:37,  1.32s/it]
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Epoch 4 Training:  72%|███████▏  | 69/96 [01:31<00:35,  1.33s/it]
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Epoch 4 Training:  73%|███████▎  | 70/96 [01:32<00:34,  1.32s/it]
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Epoch 4 Training:  74%|███████▍  | 71/96 [01:33<00:33,  1.32s/it]
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Epoch 4 Training:  75%|███████▌  | 72/96 [01:35<00:31,  1.32s/it]
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Epoch 4 Training:  76%|███████▌  | 73/96 [01:36<00:30,  1.32s/it]
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Epoch 4 Training:  77%|███████▋  | 74/96 [01:37<00:29,  1.32s/it]
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Epoch 4 Training:  78%|███████▊  | 75/96 [01:38<00:27,  1.32s/it]
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Epoch 4 Training:  79%|███████▉  | 76/96 [01:40<00:26,  1.32s/it]
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Epoch 4 Training:  80%|████████  | 77/96 [01:41<00:25,  1.32s/it]
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Epoch 4 Training:  81%|████████▏ | 78/96 [01:42<00:23,  1.32s/it]
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Epoch 4 Training:  82%|████████▏ | 79/96 [01:44<00:22,  1.32s/it]
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Epoch 4 Training:  83%|████████▎ | 80/96 [01:45<00:21,  1.32s/it]
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Epoch 4 Training:  84%|████████▍ | 81/96 [01:46<00:19,  1.32s/it]
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Epoch 4 Training:  85%|████████▌ | 82/96 [01:48<00:18,  1.31s/it]
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Epoch 4 Training:  86%|████████▋ | 83/96 [01:49<00:17,  1.32s/it]
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Epoch 4 Training:  88%|████████▊ | 84/96 [01:50<00:15,  1.32s/it]
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Epoch 4 Training:  89%|████████▊ | 85/96 [01:52<00:14,  1.32s/it]
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Epoch 4 Training:  90%|████████▉ | 86/96 [01:53<00:13,  1.32s/it]
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Epoch 4 Training:  91%|█████████ | 87/96 [01:54<00:11,  1.32s/it]
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Epoch 4 Training:  92%|█████████▏| 88/96 [01:56<00:10,  1.31s/it]
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Epoch 4 Training:  93%|█████████▎| 89/96 [01:57<00:09,  1.32s/it]
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Epoch 4 Training:  94%|█████████▍| 90/96 [01:58<00:07,  1.32s/it]
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Epoch 4 Training:  95%|█████████▍| 91/96 [02:00<00:06,  1.32s/it]
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Epoch 4 Training:  96%|█████████▌| 92/96 [02:01<00:05,  1.32s/it]
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Epoch 4 Training:  97%|█████████▋| 93/96 [02:02<00:03,  1.32s/it]
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Epoch 4 Training:  98%|█████████▊| 94/96 [02:03<00:02,  1.31s/it]
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+[2026-10-01 16:27:38] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0005 | Epoch 004] timing/rollout_seconds=147.4960 timing/feedback_seconds=5.4572 timing/optimization_seconds=127.2539 timing/cycle_seconds=278.3287
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+
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+[2026-10-01 16:30:10] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0005 | Epoch 005] train/reward_dists_mean=0.9274 train/reward_dists_std=0.0179 train/reward_psnr_mean=29.3817 train/reward_psnr_std=2.2961 train/reward_ssim_mean=0.9280 train/reward_ssim_std=0.0222 train/reward_dists_group_std_mean=0.0039 train/reward_dists_group_std_max=0.0068 train/reward_dists_group_std_min=0.0019 train/reward_dists_group_mean_std=0.0174 train/reward_psnr_group_std_mean=1.3152 train/reward_psnr_group_std_max=2.7462 train/reward_psnr_group_std_min=0.4571 train/reward_psnr_group_mean_std=1.8066 train/reward_ssim_group_std_mean=0.0020 train/reward_ssim_group_std_max=0.0041 train/reward_ssim_group_std_min=0.0008 train/reward_ssim_group_mean_std=0.0220 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-7.4798 train/adv_max=6.3296 train/adv_abs_mean=1.8714
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Epoch 5 Training:  18%|█▊        | 17/96 [00:22<01:44,  1.33s/it]
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Epoch 5 Training:  19%|█▉        | 18/96 [00:23<01:43,  1.33s/it]
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Epoch 5 Training:  20%|█▉        | 19/96 [00:25<01:42,  1.33s/it]
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Epoch 5 Training:  21%|██        | 20/96 [00:26<01:40,  1.33s/it]
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Epoch 5 Training:  22%|██▏       | 21/96 [00:27<01:39,  1.33s/it]
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Epoch 5 Training:  23%|██▎       | 22/96 [00:29<01:38,  1.33s/it]
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Epoch 5 Training:  24%|██▍       | 23/96 [00:30<01:36,  1.33s/it]
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Epoch 5 Training:  25%|██▌       | 24/96 [00:31<01:35,  1.33s/it]
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Epoch 5 Training:  26%|██▌       | 25/96 [00:33<01:34,  1.33s/it]
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Epoch 5 Training:  27%|██▋       | 26/96 [00:34<01:32,  1.33s/it]
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Epoch 5 Training:  28%|██▊       | 27/96 [00:35<01:31,  1.33s/it]
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Epoch 5 Training:  29%|██▉       | 28/96 [00:37<01:30,  1.33s/it]
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Epoch 5 Training:  30%|███       | 29/96 [00:38<01:28,  1.33s/it]
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Epoch 5 Training:  31%|███▏      | 30/96 [00:39<01:27,  1.33s/it]
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Epoch 5 Training:  32%|███▏      | 31/96 [00:41<01:26,  1.33s/it]
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Epoch 5 Training:  33%|███▎      | 32/96 [00:42<01:25,  1.33s/it]
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Epoch 5 Training:  34%|███▍      | 33/96 [00:43<01:23,  1.33s/it]
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Epoch 5 Training:  35%|███▌      | 34/96 [00:45<01:22,  1.33s/it]
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Epoch 5 Training:  36%|███▋      | 35/96 [00:46<01:20,  1.33s/it]
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Epoch 5 Training:  38%|███▊      | 36/96 [00:47<01:19,  1.33s/it]
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Epoch 5 Training:  39%|███▊      | 37/96 [00:49<01:18,  1.33s/it]
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Epoch 5 Training:  40%|███▉      | 38/96 [00:50<01:16,  1.33s/it]
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Epoch 5 Training:  41%|████      | 39/96 [00:51<01:15,  1.33s/it]
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Epoch 5 Training:  42%|████▏     | 40/96 [00:53<01:14,  1.33s/it]
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Epoch 5 Training:  43%|████▎     | 41/96 [00:54<01:13,  1.33s/it]
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Epoch 5 Training:  44%|████▍     | 42/96 [00:55<01:11,  1.33s/it]
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Epoch 5 Training:  45%|████▍     | 43/96 [00:57<01:10,  1.33s/it]
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Epoch 5 Training:  46%|████▌     | 44/96 [00:58<01:09,  1.33s/it]
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Epoch 5 Training:  47%|████▋     | 45/96 [00:59<01:07,  1.33s/it]
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Epoch 5 Training:  48%|████▊     | 46/96 [01:00<01:06,  1.33s/it]
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Epoch 5 Training:  49%|████▉     | 47/96 [01:02<01:05,  1.33s/it]
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Epoch 5 Training:  50%|█████     | 48/96 [01:03<01:03,  1.33s/it]
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Epoch 5 Training:  51%|█████     | 49/96 [01:04<01:02,  1.33s/it]
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Epoch 5 Training:  52%|█████▏    | 50/96 [01:06<01:01,  1.33s/it]
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Epoch 5 Training:  53%|█████▎    | 51/96 [01:07<00:59,  1.33s/it]
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Epoch 5 Training:  54%|█████▍    | 52/96 [01:08<00:58,  1.33s/it]
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Epoch 5 Training:  55%|█████▌    | 53/96 [01:10<00:57,  1.33s/it]
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Epoch 5 Training:  56%|█████▋    | 54/96 [01:11<00:55,  1.33s/it]
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Epoch 5 Training:  57%|█████▋    | 55/96 [01:12<00:54,  1.33s/it]
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Epoch 5 Training:  58%|█████▊    | 56/96 [01:14<00:53,  1.33s/it]
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Epoch 5 Training:  59%|█████▉    | 57/96 [01:15<00:51,  1.33s/it]
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Epoch 5 Training:  60%|██████    | 58/96 [01:16<00:50,  1.33s/it]
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Epoch 5 Training:  61%|██████▏   | 59/96 [01:18<00:49,  1.33s/it]
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Epoch 5 Training:  62%|██████▎   | 60/96 [01:19<00:47,  1.33s/it]
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Epoch 5 Training:  64%|██████▎   | 61/96 [01:20<00:46,  1.33s/it]
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Epoch 5 Training:  65%|██████▍   | 62/96 [01:22<00:45,  1.33s/it]
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Epoch 5 Training:  66%|██████▌   | 63/96 [01:23<00:43,  1.33s/it]
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Epoch 5 Training:  67%|██████▋   | 64/96 [01:24<00:42,  1.33s/it]
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Epoch 5 Training:  68%|██████▊   | 65/96 [01:26<00:41,  1.33s/it]
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Epoch 5 Training:  69%|██████▉   | 66/96 [01:27<00:39,  1.33s/it]
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Epoch 5 Training:  70%|██████▉   | 67/96 [01:28<00:38,  1.33s/it]
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Epoch 5 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s][2026-10-01 16:32:17] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0005 | Epoch 005] train/policy_loss=14.3289 train/unweighted_policy_loss=2.8658 train/loss=14.3289 train/grad_norm=0.0941
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+[2026-10-01 16:32:17] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0006 | Epoch 005] timing/rollout_seconds=147.7034 timing/feedback_seconds=5.9160 timing/optimization_seconds=128.0414 timing/cycle_seconds=279.3285
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+
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+
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Pointwise Rewards: psnr:  83%|████████▎ | 5/6 [00:00<00:00,  6.72it/s]
Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.62it/s]
Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.60it/s]
+[2026-10-01 16:34:49] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0006 | Epoch 006] train/reward_dists_mean=0.9213 train/reward_dists_std=0.0192 train/reward_psnr_mean=29.6646 train/reward_psnr_std=2.6087 train/reward_ssim_mean=0.9305 train/reward_ssim_std=0.0209 train/reward_dists_group_std_mean=0.0045 train/reward_dists_group_std_max=0.0101 train/reward_dists_group_std_min=0.0020 train/reward_dists_group_mean_std=0.0186 train/reward_psnr_group_std_mean=1.3185 train/reward_psnr_group_std_max=3.0060 train/reward_psnr_group_std_min=0.3485 train/reward_psnr_group_mean_std=2.1338 train/reward_ssim_group_std_mean=0.0022 train/reward_ssim_group_std_max=0.0036 train/reward_ssim_group_std_min=0.0005 train/reward_ssim_group_mean_std=0.0207 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-8.5513 train/adv_max=6.4284 train/adv_abs_mean=1.9437
+
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Epoch 6 Training:  44%|████▍     | 42/96 [00:56<01:16,  1.41s/it]
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Epoch 6 Training:  45%|████▍     | 43/96 [00:57<01:13,  1.39s/it]
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Epoch 6 Training:  46%|████▌     | 44/96 [00:59<01:11,  1.38s/it]
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Epoch 6 Training:  47%|████▋     | 45/96 [01:00<01:09,  1.37s/it]
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Epoch 6 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.35it/s]
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Epoch 6 Training:  48%|████▊     | 46/96 [01:01<01:07,  1.36s/it]
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Epoch 6 Training:  49%|████▉     | 47/96 [01:03<01:06,  1.35s/it]
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Epoch 6 Training:  50%|█████     | 48/96 [01:04<01:04,  1.35s/it]
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Epoch 6 Training:  51%|█████     | 49/96 [01:05<01:03,  1.35s/it]
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Epoch 6 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.39it/s]
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Epoch 6 Training:  52%|█████▏    | 50/96 [01:07<01:01,  1.34s/it]
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Epoch 6 Training:  53%|█████▎    | 51/96 [01:08<01:00,  1.34s/it]
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Epoch 6 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.36it/s]
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Epoch 6 Training:  54%|█████▍    | 52/96 [01:09<00:58,  1.34s/it]
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Epoch 6 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.37it/s]
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Epoch 6 Training:  55%|█████▌    | 53/96 [01:11<00:57,  1.34s/it]
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Epoch 6 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.35it/s]
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Epoch 6 Training:  56%|█████▋    | 54/96 [01:12<00:56,  1.34s/it]
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Epoch 6 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 6 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.29it/s]
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Epoch 6 Training:  57%|█████▋    | 55/96 [01:14<00:58,  1.44s/it]
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Epoch 6 Training:  58%|█████▊    | 56/96 [01:15<00:56,  1.41s/it]
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Epoch 6 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 6 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.38it/s]
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Epoch 6 Training:  59%|█████▉    | 57/96 [01:16<00:54,  1.39s/it]
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Epoch 6 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 6 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.35it/s]
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Epoch 6 Training:  60%|██████    | 58/96 [01:18<00:52,  1.38s/it]
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Epoch 6 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 6 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.39it/s]
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Epoch 6 Training:  61%|██████▏   | 59/96 [01:19<00:50,  1.36s/it]
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Epoch 6 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 6 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.37it/s]
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Epoch 6 Training:  62%|██████▎   | 60/96 [01:20<00:48,  1.36s/it]
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Epoch 6 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 6 Training:  64%|██████▎   | 61/96 [01:22<00:49,  1.41s/it]
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Epoch 6 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.37it/s]
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Epoch 6 Training:  65%|██████▍   | 62/96 [01:23<00:47,  1.39s/it]
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Epoch 6 Training:  66%|██████▌   | 63/96 [01:25<00:45,  1.38s/it]
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Epoch 6 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.49it/s]
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Epoch 6 Training:  67%|██████▋   | 64/96 [01:26<00:43,  1.37s/it]
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Epoch 6 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.37it/s]
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Epoch 6 Training:  68%|██████▊   | 65/96 [01:27<00:42,  1.36s/it]
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Epoch 6 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.37it/s]
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Epoch 6 Training:  69%|██████▉   | 66/96 [01:29<00:40,  1.35s/it]
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Epoch 6 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.35it/s]
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Epoch 6 Training:  70%|██████▉   | 67/96 [01:30<00:39,  1.35s/it]
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Epoch 6 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.35it/s]
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Epoch 6 Training:  71%|███████   | 68/96 [01:31<00:37,  1.35s/it]
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Epoch 6 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.39it/s]
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Epoch 6 Training:  72%|███████▏  | 69/96 [01:33<00:36,  1.34s/it]
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Epoch 6 Training:  73%|███████▎  | 70/96 [01:34<00:34,  1.34s/it]
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Epoch 6 Training:  74%|███████▍  | 71/96 [01:35<00:33,  1.34s/it]
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Epoch 6 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.37it/s]
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Epoch 6 Training:  75%|███████▌  | 72/96 [01:37<00:32,  1.34s/it]
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Epoch 6 Training:  76%|███████▌  | 73/96 [01:38<00:31,  1.35s/it]
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Epoch 6 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.38it/s]
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Epoch 6 Training:  77%|███████▋  | 74/96 [01:39<00:29,  1.35s/it]
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Epoch 6 Training:  78%|███████▊  | 75/96 [01:41<00:28,  1.35s/it]
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Epoch 6 Training:  79%|███████▉  | 76/96 [01:42<00:26,  1.35s/it]
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Epoch 6 Training:  80%|████████  | 77/96 [01:43<00:25,  1.35s/it]
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Epoch 6 Training:  81%|████████▏ | 78/96 [01:45<00:24,  1.35s/it]
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Epoch 6 Training:  82%|████████▏ | 79/96 [01:46<00:22,  1.35s/it]
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Epoch 6 Training:  83%|████████▎ | 80/96 [01:47<00:21,  1.34s/it]
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Epoch 6 Training:  84%|████████▍ | 81/96 [01:49<00:20,  1.34s/it]
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Epoch 6 Training:  85%|████████▌ | 82/96 [01:50<00:18,  1.34s/it]
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Epoch 6 Training:  86%|████████▋ | 83/96 [01:51<00:17,  1.34s/it]
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Epoch 6 Training:  88%|████████▊ | 84/96 [01:53<00:16,  1.34s/it]
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Epoch 6 Training:  89%|████████▊ | 85/96 [01:54<00:14,  1.34s/it]
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Epoch 6 Training:  90%|████████▉ | 86/96 [01:55<00:13,  1.34s/it]
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Epoch 6 Training:  91%|█████████ | 87/96 [01:57<00:12,  1.34s/it]
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Epoch 6 Training:  92%|█████████▏| 88/96 [01:58<00:10,  1.34s/it]
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Epoch 6 Training:  93%|█████████▎| 89/96 [01:59<00:09,  1.34s/it]
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Epoch 6 Training:  94%|█████████▍| 90/96 [02:01<00:08,  1.34s/it]
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Epoch 6 Training:  95%|█████████▍| 91/96 [02:02<00:06,  1.34s/it]
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Epoch 6 Training:  96%|█████████▌| 92/96 [02:03<00:05,  1.34s/it]
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Epoch 6 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s][2026-10-01 16:36:59] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0006 | Epoch 006] train/policy_loss=14.4726 train/unweighted_policy_loss=2.8945 train/loss=14.4726 train/grad_norm=0.0991
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+[2026-10-01 16:36:59] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0007 | Epoch 006] timing/rollout_seconds=147.7040 timing/feedback_seconds=5.8348 timing/optimization_seconds=130.0517 timing/cycle_seconds=281.3390
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+[2026-10-01 16:39:31] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0007 | Epoch 007] train/reward_dists_mean=0.9308 train/reward_dists_std=0.0175 train/reward_psnr_mean=28.7925 train/reward_psnr_std=3.1407 train/reward_ssim_mean=0.9364 train/reward_ssim_std=0.0177 train/reward_dists_group_std_mean=0.0038 train/reward_dists_group_std_max=0.0170 train/reward_dists_group_std_min=0.0019 train/reward_dists_group_mean_std=0.0169 train/reward_psnr_group_std_mean=1.3489 train/reward_psnr_group_std_max=2.7656 train/reward_psnr_group_std_min=0.3974 train/reward_psnr_group_mean_std=2.7802 train/reward_ssim_group_std_mean=0.0023 train/reward_ssim_group_std_max=0.0339 train/reward_ssim_group_std_min=0.0007 train/reward_ssim_group_mean_std=0.0169 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-8.7288 train/adv_max=6.6378 train/adv_abs_mean=1.8444
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Epoch 7 Training:  32%|███▏      | 31/96 [00:41<01:27,  1.34s/it]
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Epoch 7 Training:  33%|███▎      | 32/96 [00:42<01:25,  1.34s/it]
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Epoch 7 Training:  34%|███▍      | 33/96 [00:44<01:24,  1.34s/it]
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Epoch 7 Training:  41%|████      | 39/96 [00:52<01:16,  1.34s/it]
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Epoch 7 Training:  42%|████▏     | 40/96 [00:53<01:15,  1.34s/it]
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Epoch 7 Training:  43%|████▎     | 41/96 [00:54<01:13,  1.34s/it]
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Epoch 7 Training:  44%|████▍     | 42/96 [00:56<01:12,  1.34s/it]
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Epoch 7 Training:  45%|████▍     | 43/96 [00:57<01:11,  1.34s/it]
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Epoch 7 Training:  46%|████▌     | 44/96 [00:58<01:09,  1.34s/it]
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Epoch 7 Training:  48%|████▊     | 46/96 [01:01<01:07,  1.34s/it]
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Epoch 7 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s][2026-10-01 16:41:39] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0007 | Epoch 007] train/policy_loss=14.2804 train/unweighted_policy_loss=2.8561 train/loss=14.2804 train/grad_norm=0.0679
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+[2026-10-01 16:41:39] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0008 | Epoch 007] timing/rollout_seconds=147.6508 timing/feedback_seconds=5.7085 timing/optimization_seconds=129.3323 timing/cycle_seconds=280.6718
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+
Pointwise Rewards: ssim:   0%|          | 0/6 [00:00<?, ?it/s]
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Pointwise Rewards: ssim:  83%|████████▎ | 5/6 [00:00<00:00,  6.19it/s]
Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:00<00:00,  6.26it/s]
Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:00<00:00,  6.12it/s]
+
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Pointwise Rewards: dists:  67%|██████▋   | 4/6 [00:01<00:00,  3.62it/s]
Pointwise Rewards: dists:  83%|████████▎ | 5/6 [00:01<00:00,  3.64it/s]
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Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.60it/s]
+
Pointwise Rewards: psnr:   0%|          | 0/6 [00:00<?, ?it/s]
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Pointwise Rewards: psnr:  33%|███▎      | 2/6 [00:00<00:00,  6.49it/s]
Pointwise Rewards: psnr:  50%|█████     | 3/6 [00:00<00:00,  6.64it/s]
Pointwise Rewards: psnr:  67%|██████▋   | 4/6 [00:00<00:00,  6.51it/s]
Pointwise Rewards: psnr:  83%|████████▎ | 5/6 [00:00<00:00,  6.67it/s]
Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.42it/s]
Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.50it/s]
+[2026-10-01 16:44:11] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0008 | Epoch 008] train/reward_dists_mean=0.9330 train/reward_dists_std=0.0173 train/reward_psnr_mean=28.6998 train/reward_psnr_std=2.4752 train/reward_ssim_mean=0.9361 train/reward_ssim_std=0.0216 train/reward_dists_group_std_mean=0.0034 train/reward_dists_group_std_max=0.0071 train/reward_dists_group_std_min=0.0016 train/reward_dists_group_mean_std=0.0169 train/reward_psnr_group_std_mean=1.4369 train/reward_psnr_group_std_max=3.3562 train/reward_psnr_group_std_min=0.5149 train/reward_psnr_group_mean_std=1.9374 train/reward_ssim_group_std_mean=0.0017 train/reward_ssim_group_std_max=0.0128 train/reward_ssim_group_std_min=0.0005 train/reward_ssim_group_mean_std=0.0215 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-9.0730 train/adv_max=6.2331 train/adv_abs_mean=1.8625
+
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Epoch 8 Training:   3%|▎         | 3/96 [00:03<02:03,  1.33s/it]
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Epoch 8 Training:   4%|▍         | 4/96 [00:05<02:02,  1.33s/it]
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Epoch 8 Training:   6%|▋         | 6/96 [00:07<01:59,  1.33s/it]
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Epoch 8 Training:   7%|▋         | 7/96 [00:09<01:58,  1.33s/it]
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Epoch 8 Training:   8%|▊         | 8/96 [00:10<01:57,  1.34s/it]
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Epoch 8 Training:  10%|█         | 10/96 [00:13<01:54,  1.34s/it]
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Epoch 8 Training:  11%|█▏        | 11/96 [00:14<01:53,  1.34s/it]
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Epoch 8 Training:  12%|█▎        | 12/96 [00:15<01:52,  1.34s/it]
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Epoch 8 Training:  14%|█▎        | 13/96 [00:17<01:50,  1.34s/it]
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Epoch 8 Training:  15%|█▍        | 14/96 [00:18<01:49,  1.34s/it]
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Epoch 8 Training:  16%|█▌        | 15/96 [00:19<01:48,  1.34s/it]
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Epoch 8 Training:  17%|█▋        | 16/96 [00:21<01:46,  1.34s/it]
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Epoch 8 Training:  18%|█▊        | 17/96 [00:22<01:45,  1.34s/it]
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Epoch 8 Training:  19%|█▉        | 18/96 [00:23<01:44,  1.34s/it]
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Epoch 8 Training:  21%|██        | 20/96 [00:26<01:41,  1.34s/it]
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Epoch 8 Training:  22%|██▏       | 21/96 [00:28<01:40,  1.34s/it]
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Epoch 8 Training:  23%|██▎       | 22/96 [00:29<01:38,  1.34s/it]
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Epoch 8 Training:  24%|██▍       | 23/96 [00:30<01:37,  1.34s/it]
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Epoch 8 Training:  25%|██▌       | 24/96 [00:32<01:36,  1.34s/it]
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Epoch 8 Training:  26%|██▌       | 25/96 [00:33<01:35,  1.34s/it]
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Epoch 8 Training:  27%|██▋       | 26/96 [00:34<01:34,  1.34s/it]
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Epoch 8 Training:  28%|██▊       | 27/96 [00:36<01:32,  1.34s/it]
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Epoch 8 Training:  29%|██▉       | 28/96 [00:37<01:31,  1.34s/it]
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Epoch 8 Training:  33%|███▎      | 32/96 [00:42<01:25,  1.34s/it]
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Epoch 8 Training:  41%|████      | 39/96 [00:52<01:16,  1.34s/it]
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Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.37it/s]
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Epoch 8 Training:  42%|████▏     | 40/96 [00:53<01:14,  1.34s/it]
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Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.40it/s]
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Epoch 8 Training:  43%|████▎     | 41/96 [00:54<01:13,  1.34s/it]
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Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.37it/s]
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Epoch 8 Training:  44%|████▍     | 42/96 [00:56<01:12,  1.34s/it]
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Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.41it/s]
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Epoch 8 Training:  45%|████▍     | 43/96 [00:57<01:11,  1.34s/it]
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Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.39it/s]
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Epoch 8 Training:  46%|████▌     | 44/96 [00:58<01:09,  1.34s/it]
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Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.43it/s]
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Epoch 8 Training:  47%|████▋     | 45/96 [01:00<01:08,  1.34s/it]
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Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.37it/s]
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Epoch 8 Training:  48%|████▊     | 46/96 [01:01<01:06,  1.34s/it]
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Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.39it/s]
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Epoch 8 Training:  49%|████▉     | 47/96 [01:02<01:05,  1.34s/it]
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Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.39it/s]
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Epoch 8 Training:  50%|█████     | 48/96 [01:04<01:04,  1.34s/it]
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Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.41it/s]
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Epoch 8 Training:  51%|█████     | 49/96 [01:05<01:02,  1.34s/it]
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Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.31it/s]
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Epoch 8 Training:  52%|█████▏    | 50/96 [01:06<01:01,  1.34s/it]
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Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.36it/s]
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Epoch 8 Training:  53%|█████▎    | 51/96 [01:08<01:00,  1.34s/it]
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Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.37it/s]
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Epoch 8 Training:  54%|█████▍    | 52/96 [01:09<00:58,  1.34s/it]
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Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.32it/s]
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Epoch 8 Training:  55%|█████▌    | 53/96 [01:10<00:57,  1.34s/it]
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Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.38it/s]
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Epoch 8 Training:  56%|█████▋    | 54/96 [01:12<00:56,  1.34s/it]
+
Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.33it/s]
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Epoch 8 Training:  57%|█████▋    | 55/96 [01:13<00:54,  1.34s/it]
+
Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.48it/s]
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Epoch 8 Training:  58%|█████▊    | 56/96 [01:14<00:53,  1.34s/it]
+
Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.49it/s]
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Epoch 8 Training:  59%|█████▉    | 57/96 [01:16<00:52,  1.34s/it]
+
Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.34it/s]
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Epoch 8 Training:  60%|██████    | 58/96 [01:17<00:50,  1.34s/it]
+
Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.40it/s]
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Epoch 8 Training:  61%|██████▏   | 59/96 [01:18<00:49,  1.34s/it]
+
Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.37it/s]
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Epoch 8 Training:  62%|██████▎   | 60/96 [01:20<00:48,  1.34s/it]
+
Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.31it/s]
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Epoch 8 Training:  64%|██████▎   | 61/96 [01:21<00:46,  1.34s/it]
+
Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.37it/s]
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Epoch 8 Training:  65%|██████▍   | 62/96 [01:22<00:45,  1.34s/it]
+
Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.37it/s]
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Epoch 8 Training:  66%|██████▌   | 63/96 [01:24<00:44,  1.34s/it]
+
Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.41it/s]
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Epoch 8 Training:  67%|██████▋   | 64/96 [01:25<00:42,  1.34s/it]
+
Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.43it/s]
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Epoch 8 Training:  68%|██████▊   | 65/96 [01:26<00:41,  1.33s/it]
+
Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.36it/s]
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Epoch 8 Training:  69%|██████▉   | 66/96 [01:28<00:40,  1.34s/it]
+
Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.30it/s]
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Epoch 8 Training:  70%|██████▉   | 67/96 [01:29<00:38,  1.34s/it]
+
Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.36it/s]
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Epoch 8 Training:  71%|███████   | 68/96 [01:30<00:37,  1.34s/it]
+
Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.41it/s]
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Epoch 8 Training:  72%|███████▏  | 69/96 [01:32<00:36,  1.34s/it]
+
Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.37it/s]
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Epoch 8 Training:  73%|███████▎  | 70/96 [01:33<00:34,  1.34s/it]
+
Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.34it/s]
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Epoch 8 Training:  74%|███████▍  | 71/96 [01:34<00:33,  1.34s/it]
+
Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.39it/s]
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Epoch 8 Training:  75%|███████▌  | 72/96 [01:36<00:32,  1.34s/it]
+
Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.37it/s]
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Epoch 8 Training:  76%|███████▌  | 73/96 [01:37<00:30,  1.34s/it]
+
Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.41it/s]
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Epoch 8 Training:  77%|███████▋  | 74/96 [01:38<00:29,  1.34s/it]
+
Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.40it/s]
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Epoch 8 Training:  78%|███████▊  | 75/96 [01:40<00:28,  1.34s/it]
+
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.35it/s]
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Epoch 8 Training:  79%|███████▉  | 76/96 [01:41<00:26,  1.34s/it]
+
Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.30it/s]
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Epoch 8 Training:  80%|████████  | 77/96 [01:42<00:25,  1.34s/it]
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Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.35it/s]
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Epoch 8 Training:  81%|████████▏ | 78/96 [01:44<00:24,  1.34s/it]
+
Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.30it/s]
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Epoch 8 Training:  82%|████████▏ | 79/96 [01:45<00:22,  1.34s/it]
+
Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.31it/s]
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Epoch 8 Training:  83%|████████▎ | 80/96 [01:46<00:21,  1.34s/it]
+
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.33it/s]
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Epoch 8 Training:  84%|████████▍ | 81/96 [01:48<00:20,  1.34s/it]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.36it/s]
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Epoch 8 Training:  85%|████████▌ | 82/96 [01:49<00:18,  1.34s/it]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.34it/s]
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Epoch 8 Training:  86%|████████▋ | 83/96 [01:51<00:17,  1.34s/it]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.33it/s]
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Epoch 8 Training:  88%|████████▊ | 84/96 [01:52<00:16,  1.34s/it]
+
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.37it/s]
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Epoch 8 Training:  89%|████████▊ | 85/96 [01:53<00:14,  1.34s/it]
+
Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.39it/s]
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Epoch 8 Training:  90%|████████▉ | 86/96 [01:55<00:13,  1.34s/it]
+
Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Training:  91%|█████████ | 87/96 [01:56<00:12,  1.34s/it]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.33it/s]
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Epoch 8 Training:  92%|█████████▏| 88/96 [01:57<00:10,  1.34s/it]
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Epoch 8 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 8 Training:  93%|█████████▎| 89/96 [01:59<00:09,  1.34s/it]
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Epoch 8 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.37it/s]
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Epoch 8 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s][2026-10-01 16:46:20] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0008 | Epoch 008] train/policy_loss=14.0626 train/unweighted_policy_loss=2.8125 train/loss=14.0626 train/grad_norm=0.0650
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+[2026-10-01 16:46:20] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0009 | Epoch 008] timing/rollout_seconds=147.6937 timing/feedback_seconds=5.6654 timing/optimization_seconds=129.1424 timing/cycle_seconds=280.4270
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+
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+[2026-10-01 16:48:52] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0009 | Epoch 009] train/reward_dists_mean=0.9355 train/reward_dists_std=0.0151 train/reward_psnr_mean=29.2578 train/reward_psnr_std=2.5562 train/reward_ssim_mean=0.9355 train/reward_ssim_std=0.0223 train/reward_dists_group_std_mean=0.0033 train/reward_dists_group_std_max=0.0072 train/reward_dists_group_std_min=0.0018 train/reward_dists_group_mean_std=0.0147 train/reward_psnr_group_std_mean=1.2667 train/reward_psnr_group_std_max=3.0549 train/reward_psnr_group_std_min=0.2576 train/reward_psnr_group_mean_std=2.1608 train/reward_ssim_group_std_mean=0.0017 train/reward_ssim_group_std_max=0.0115 train/reward_ssim_group_std_min=0.0005 train/reward_ssim_group_mean_std=0.0221 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-8.2453 train/adv_max=6.8463 train/adv_abs_mean=1.8532
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Epoch 9 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 9 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 9 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.32it/s]
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Epoch 9 Training:  14%|█▎        | 13/96 [00:17<01:51,  1.34s/it]
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Epoch 9 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 9 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 9 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.31it/s]
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Epoch 9 Training:  15%|█▍        | 14/96 [00:18<01:49,  1.34s/it]
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Epoch 9 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 9 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.67it/s]
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Epoch 9 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.30it/s]
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Epoch 9 Training:  16%|█▌        | 15/96 [00:20<01:48,  1.34s/it]
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Epoch 9 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 9 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 9 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.31it/s]
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Epoch 9 Training:  17%|█▋        | 16/96 [00:21<01:47,  1.34s/it]
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Epoch 9 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 9 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 9 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.29it/s]
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Epoch 9 Training:  18%|█▊        | 17/96 [00:22<01:45,  1.34s/it]
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Epoch 9 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 9 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 9 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.32it/s]
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Epoch 9 Training:  19%|█▉        | 18/96 [00:24<01:44,  1.34s/it]
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Epoch 9 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 9 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 9 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.31it/s]
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Epoch 9 Training:  20%|█▉        | 19/96 [00:25<01:43,  1.34s/it]
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Epoch 9 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 9 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 9 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.32it/s]
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Epoch 9 Training:  21%|██        | 20/96 [00:26<01:41,  1.34s/it]
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Epoch 9 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 9 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 9 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.32it/s]
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Epoch 9 Training:  22%|██▏       | 21/96 [00:28<01:40,  1.34s/it]
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Epoch 9 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 9 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 9 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 9 Training:  23%|██▎       | 22/96 [00:29<01:39,  1.34s/it]
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Epoch 9 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 9 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 9 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.32it/s]
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Epoch 9 Training:  24%|██▍       | 23/96 [00:30<01:38,  1.34s/it]
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Epoch 9 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 9 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.67it/s]
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Epoch 9 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.30it/s]
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Epoch 9 Training:  25%|██▌       | 24/96 [00:32<01:36,  1.34s/it]
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Epoch 9 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 9 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 9 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.35it/s]
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Epoch 9 Training:  26%|██▌       | 25/96 [00:33<01:35,  1.34s/it]
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Epoch 9 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 9 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 9 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.29it/s]
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Epoch 9 Training:  27%|██▋       | 26/96 [00:34<01:33,  1.34s/it]
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Epoch 9 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 9 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 9 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.33it/s]
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Epoch 9 Training:  28%|██▊       | 27/96 [00:36<01:32,  1.34s/it]
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Epoch 9 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 9 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 9 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.32it/s]
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Epoch 9 Training:  29%|██▉       | 28/96 [00:37<01:31,  1.34s/it]
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Epoch 9 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 9 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.67it/s]
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Epoch 9 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 9 Training:  30%|███       | 29/96 [00:38<01:29,  1.34s/it]
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Epoch 9 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 9 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 9 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.36it/s]
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Epoch 9 Training:  31%|███▏      | 30/96 [00:40<01:28,  1.34s/it]
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Epoch 9 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 9 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.67it/s]
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Epoch 9 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.30it/s]
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Epoch 9 Training:  32%|███▏      | 31/96 [00:41<01:27,  1.34s/it]
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Epoch 9 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 9 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 9 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.30it/s]
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Epoch 9 Training:  33%|███▎      | 32/96 [00:42<01:25,  1.34s/it]
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Epoch 9 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 9 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 9 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.32it/s]
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Epoch 9 Training:  34%|███▍      | 33/96 [00:44<01:24,  1.34s/it]
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Epoch 9 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 9 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 9 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 9 Training:  35%|███▌      | 34/96 [00:45<01:23,  1.34s/it]
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Epoch 9 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 9 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 9 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.37it/s]
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Epoch 9 Training:  36%|███▋      | 35/96 [00:46<01:21,  1.34s/it]
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Epoch 9 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 9 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 9 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.30it/s]
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Epoch 9 Training:  38%|███▊      | 36/96 [00:48<01:20,  1.34s/it]
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Epoch 9 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 9 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 9 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.34it/s]
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Epoch 9 Training:  39%|███▊      | 37/96 [00:49<01:19,  1.34s/it]
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Epoch 9 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 9 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 9 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.32it/s]
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Epoch 9 Training:  40%|███▉      | 38/96 [00:50<01:17,  1.34s/it]
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Epoch 9 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 9 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 9 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 9 Training:  41%|████      | 39/96 [00:52<01:16,  1.34s/it]
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Epoch 9 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 9 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.67it/s]
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Epoch 9 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.30it/s]
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Epoch 9 Training:  42%|████▏     | 40/96 [00:53<01:15,  1.34s/it]
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Epoch 9 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 9 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 9 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 9 Training:  43%|████▎     | 41/96 [00:54<01:14,  1.35s/it]
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Epoch 9 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 9 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.32it/s]
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Epoch 9 Training:  44%|████▍     | 42/96 [00:56<01:12,  1.35s/it]
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Epoch 9 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 9 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.39it/s]
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Epoch 9 Training:  45%|████▍     | 43/96 [00:57<01:11,  1.35s/it]
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Epoch 9 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 9 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.32it/s]
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Epoch 9 Training:  46%|████▌     | 44/96 [00:58<01:09,  1.34s/it]
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Epoch 9 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 9 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.38it/s]
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Epoch 9 Training:  47%|████▋     | 45/96 [01:00<01:08,  1.34s/it]
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Epoch 9 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 9 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.30it/s]
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Epoch 9 Training:  48%|████▊     | 46/96 [01:01<01:07,  1.34s/it]
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Epoch 9 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 9 Training:  49%|████▉     | 47/96 [01:02<01:05,  1.34s/it]
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Epoch 9 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 9 Training:  50%|█████     | 48/96 [01:04<01:04,  1.34s/it]
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Epoch 9 Training:  51%|█████     | 49/96 [01:05<01:03,  1.34s/it]
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Epoch 9 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.29it/s]
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Epoch 9 Training:  52%|█████▏    | 50/96 [01:07<01:01,  1.34s/it]
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Epoch 9 Training:  53%|█████▎    | 51/96 [01:08<01:00,  1.34s/it]
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Epoch 9 Training:  54%|█████▍    | 52/96 [01:09<00:59,  1.34s/it]
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Epoch 9 Training:  55%|█████▌    | 53/96 [01:11<00:57,  1.34s/it]
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Epoch 9 Training:  56%|█████▋    | 54/96 [01:12<00:56,  1.34s/it]
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Epoch 9 Training:  57%|█████▋    | 55/96 [01:13<00:55,  1.34s/it]
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Epoch 9 Training:  58%|█████▊    | 56/96 [01:15<00:53,  1.34s/it]
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Epoch 9 Training:  59%|█████▉    | 57/96 [01:16<00:52,  1.34s/it]
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Epoch 9 Training:  60%|██████    | 58/96 [01:17<00:51,  1.34s/it]
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Epoch 9 Training:  61%|██████▏   | 59/96 [01:19<00:49,  1.34s/it]
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Epoch 9 Training:  62%|██████▎   | 60/96 [01:20<00:48,  1.34s/it]
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Epoch 9 Training:  64%|██████▎   | 61/96 [01:21<00:46,  1.34s/it]
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Epoch 9 Training:  65%|██████▍   | 62/96 [01:23<00:45,  1.34s/it]
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Epoch 9 Training:  66%|██████▌   | 63/96 [01:24<00:44,  1.34s/it]
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Epoch 9 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s][2026-10-01 16:51:01] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0009 | Epoch 009] train/policy_loss=13.8117 train/unweighted_policy_loss=2.7623 train/loss=13.8117 train/grad_norm=0.0965
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+[2026-10-01 16:51:01] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0010 | Epoch 009] timing/rollout_seconds=147.8346 timing/feedback_seconds=5.8185 timing/optimization_seconds=129.4785 timing/cycle_seconds=280.8654
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+[2026-10-01 16:51:45] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0010 | Epoch 010] eval/vedai_rl_full/reward_dists_mean=0.9242 eval/vedai_rl_full/reward_dists_std=0.0420 eval/vedai_rl_full/reward_psnr_mean=26.8359 eval/vedai_rl_full/reward_psnr_std=4.1628 eval/vedai_rl_full/reward_ssim_mean=0.9098 eval/vedai_rl_full/reward_ssim_std=0.0506
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+
Pointwise Rewards: ssim:   0%|          | 0/6 [00:00<?, ?it/s]
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Pointwise Rewards: ssim:  67%|██████▋   | 4/6 [00:01<00:00,  3.40it/s]
Pointwise Rewards: ssim:  83%|████████▎ | 5/6 [00:01<00:00,  3.99it/s]
Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:01<00:00,  4.50it/s]
Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:01<00:00,  4.04it/s]
+
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Pointwise Rewards: dists:  83%|████████▎ | 5/6 [00:01<00:00,  3.53it/s]
Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.40it/s]
Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.45it/s]
+
Pointwise Rewards: psnr:   0%|          | 0/6 [00:00<?, ?it/s]
Pointwise Rewards: psnr:  17%|█▋        | 1/6 [00:00<00:00,  6.77it/s]
Pointwise Rewards: psnr:  33%|███▎      | 2/6 [00:00<00:00,  6.75it/s]
Pointwise Rewards: psnr:  50%|█████     | 3/6 [00:00<00:00,  6.66it/s]
Pointwise Rewards: psnr:  67%|██████▋   | 4/6 [00:00<00:00,  6.65it/s]
Pointwise Rewards: psnr:  83%|████████▎ | 5/6 [00:00<00:00,  6.60it/s]
Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.65it/s]
Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.65it/s]
+[2026-10-01 16:54:17] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0010 | Epoch 010] train/reward_dists_mean=0.9305 train/reward_dists_std=0.0252 train/reward_psnr_mean=29.8295 train/reward_psnr_std=3.1383 train/reward_ssim_mean=0.9359 train/reward_ssim_std=0.0232 train/reward_dists_group_std_mean=0.0039 train/reward_dists_group_std_max=0.0265 train/reward_dists_group_std_min=0.0008 train/reward_dists_group_mean_std=0.0247 train/reward_psnr_group_std_mean=1.2489 train/reward_psnr_group_std_max=4.6880 train/reward_psnr_group_std_min=0.3018 train/reward_psnr_group_mean_std=2.7877 train/reward_ssim_group_std_mean=0.0020 train/reward_ssim_group_std_max=0.0259 train/reward_ssim_group_std_min=0.0004 train/reward_ssim_group_mean_std=0.0229 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-7.5478 train/adv_max=4.8315 train/adv_abs_mean=1.7025
+
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Epoch 10 Training:   3%|▎         | 3/96 [00:03<02:02,  1.31s/it]
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Epoch 10 Training:   4%|▍         | 4/96 [00:05<02:02,  1.33s/it]
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Epoch 10 Training:   6%|▋         | 6/96 [00:07<01:59,  1.33s/it]
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Epoch 10 Training:   7%|▋         | 7/96 [00:09<01:58,  1.33s/it]
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Epoch 10 Training:   8%|▊         | 8/96 [00:10<01:57,  1.33s/it]
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Epoch 10 Training:   9%|▉         | 9/96 [00:11<01:56,  1.34s/it]
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Epoch 10 Training:  10%|█         | 10/96 [00:13<01:54,  1.34s/it]
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Epoch 10 Training:  11%|█▏        | 11/96 [00:14<01:53,  1.34s/it]
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Epoch 10 Training:  15%|█▍        | 14/96 [00:18<01:50,  1.34s/it]
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Epoch 10 Training:  16%|█▌        | 15/96 [00:19<01:48,  1.34s/it]
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Epoch 10 Training:  17%|█▋        | 16/96 [00:21<01:47,  1.34s/it]
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Epoch 10 Training:  18%|█▊        | 17/96 [00:22<01:45,  1.34s/it]
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Epoch 10 Training:  19%|█▉        | 18/96 [00:23<01:44,  1.34s/it]
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Epoch 10 Training:  21%|██        | 20/96 [00:26<01:41,  1.34s/it]
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Epoch 10 Training:  23%|██▎       | 22/96 [00:29<01:38,  1.34s/it]
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Epoch 10 Training:  26%|██▌       | 25/96 [00:33<01:35,  1.34s/it]
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Epoch 10 Training:  27%|██▋       | 26/96 [00:34<01:33,  1.34s/it]
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Epoch 10 Training:  28%|██▊       | 27/96 [00:36<01:32,  1.33s/it]
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Epoch 10 Training:  29%|██▉       | 28/96 [00:37<01:30,  1.34s/it]
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Epoch 10 Training:  32%|███▏      | 31/96 [00:41<01:29,  1.38s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Training:  33%|███▎      | 32/96 [00:43<01:27,  1.37s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 10 Training:  34%|███▍      | 33/96 [00:44<01:25,  1.36s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 10 Training:  35%|███▌      | 34/96 [00:45<01:24,  1.36s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 10 Training:  36%|███▋      | 35/96 [00:47<01:22,  1.35s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 10 Training:  38%|███▊      | 36/96 [00:48<01:20,  1.35s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.30it/s]
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Epoch 10 Training:  39%|███▊      | 37/96 [00:49<01:19,  1.34s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 10 Training:  40%|███▉      | 38/96 [00:51<01:17,  1.34s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 10 Training:  41%|████      | 39/96 [00:52<01:16,  1.34s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 10 Training:  42%|████▏     | 40/96 [00:53<01:15,  1.34s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.23it/s]
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Epoch 10 Training:  43%|████▎     | 41/96 [00:55<01:14,  1.35s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 10 Training:  44%|████▍     | 42/96 [00:56<01:12,  1.35s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.29it/s]
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Epoch 10 Training:  45%|████▍     | 43/96 [00:57<01:11,  1.34s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.31it/s]
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Epoch 10 Training:  46%|████▌     | 44/96 [00:59<01:09,  1.34s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 10 Training:  47%|████▋     | 45/96 [01:00<01:08,  1.35s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.40it/s]
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Epoch 10 Training:  48%|████▊     | 46/96 [01:01<01:06,  1.34s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 10 Training:  49%|████▉     | 47/96 [01:03<01:05,  1.34s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 10 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 10 Training:  50%|█████     | 48/96 [01:04<01:04,  1.34s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 10 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 10 Training:  51%|█████     | 49/96 [01:05<01:02,  1.34s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 10 Training:  52%|█████▏    | 50/96 [01:07<01:01,  1.34s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 10 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 10 Training:  53%|█████▎    | 51/96 [01:08<01:00,  1.34s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 10 Training:  54%|█████▍    | 52/96 [01:09<00:58,  1.34s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 10 Training:  55%|█████▌    | 53/96 [01:11<00:57,  1.34s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 10 Training:  56%|█████▋    | 54/96 [01:12<00:56,  1.34s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 10 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 10 Training:  57%|█████▋    | 55/96 [01:13<00:54,  1.34s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 10 Training:  58%|█████▊    | 56/96 [01:15<00:53,  1.34s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 10 Training:  59%|█████▉    | 57/96 [01:16<00:52,  1.34s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.23it/s]
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Epoch 10 Training:  60%|██████    | 58/96 [01:17<00:50,  1.34s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.23it/s]
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Epoch 10 Training:  61%|██████▏   | 59/96 [01:19<00:49,  1.34s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 10 Training:  62%|██████▎   | 60/96 [01:20<00:48,  1.34s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 10 Training:  64%|██████▎   | 61/96 [01:21<00:46,  1.34s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 10 Training:  65%|██████▍   | 62/96 [01:23<00:45,  1.34s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 10 Training:  66%|██████▌   | 63/96 [01:24<00:44,  1.34s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 10 Training:  67%|██████▋   | 64/96 [01:25<00:42,  1.34s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.23it/s]
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Epoch 10 Training:  68%|██████▊   | 65/96 [01:27<00:41,  1.34s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.22it/s]
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Epoch 10 Training:  69%|██████▉   | 66/96 [01:28<00:40,  1.34s/it]
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Epoch 10 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 10 Training:  70%|██████▉   | 67/96 [01:29<00:38,  1.34s/it]
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Epoch 10 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.19it/s]
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Epoch 10 Training:  71%|███████   | 68/96 [01:31<00:37,  1.35s/it]
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Epoch 10 Training:  72%|███████▏  | 69/96 [01:32<00:36,  1.34s/it]
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Epoch 10 Training:  73%|███████▎  | 70/96 [01:33<00:34,  1.34s/it]
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Epoch 10 Training:  74%|███████▍  | 71/96 [01:35<00:33,  1.34s/it]
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Epoch 10 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 10 Training:  75%|███████▌  | 72/96 [01:36<00:32,  1.34s/it]
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Epoch 10 Training:  76%|███████▌  | 73/96 [01:37<00:30,  1.34s/it]
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Epoch 10 Training:  77%|███████▋  | 74/96 [01:39<00:29,  1.34s/it]
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Epoch 10 Training:  78%|███████▊  | 75/96 [01:40<00:28,  1.34s/it]
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Epoch 10 Training:  79%|███████▉  | 76/96 [01:41<00:26,  1.34s/it]
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Epoch 10 Training:  80%|████████  | 77/96 [01:43<00:25,  1.34s/it]
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Epoch 10 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 10 Training:  81%|████████▏ | 78/96 [01:44<00:24,  1.34s/it]
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Epoch 10 Training:  82%|████████▏ | 79/96 [01:45<00:22,  1.34s/it]
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Epoch 10 Training:  83%|████████▎ | 80/96 [01:47<00:21,  1.34s/it]
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Epoch 10 Training:  84%|████████▍ | 81/96 [01:48<00:20,  1.34s/it]
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Epoch 10 Training:  85%|████████▌ | 82/96 [01:49<00:18,  1.34s/it]
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Epoch 10 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s][2026-10-01 16:56:26] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0010 | Epoch 010] train/policy_loss=14.0080 train/unweighted_policy_loss=2.8016 train/loss=14.0080 train/grad_norm=0.0686
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+[2026-10-01 16:56:26] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0011 | Epoch 010] timing/rollout_seconds=148.1030 timing/feedback_seconds=6.0751 timing/optimization_seconds=129.4618 timing/cycle_seconds=281.3501
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+
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+[2026-10-01 16:58:58] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0011 | Epoch 011] train/reward_dists_mean=0.9317 train/reward_dists_std=0.0236 train/reward_psnr_mean=27.3004 train/reward_psnr_std=3.3501 train/reward_ssim_mean=0.9362 train/reward_ssim_std=0.0325 train/reward_dists_group_std_mean=0.0045 train/reward_dists_group_std_max=0.0483 train/reward_dists_group_std_min=0.0012 train/reward_dists_group_mean_std=0.0222 train/reward_psnr_group_std_mean=1.7020 train/reward_psnr_group_std_max=4.3708 train/reward_psnr_group_std_min=0.6201 train/reward_psnr_group_mean_std=2.8077 train/reward_ssim_group_std_mean=0.0026 train/reward_ssim_group_std_max=0.0621 train/reward_ssim_group_std_min=0.0003 train/reward_ssim_group_mean_std=0.0312 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-7.7672 train/adv_max=7.2475 train/adv_abs_mean=1.9182
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Epoch 11 Training:   4%|▍         | 4/96 [00:05<02:03,  1.34s/it]
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Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.23it/s]
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Epoch 11 Training:   5%|▌         | 5/96 [00:06<02:02,  1.34s/it]
+
Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.22it/s]
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Epoch 11 Training:   6%|▋         | 6/96 [00:07<02:01,  1.34s/it]
+
Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.21it/s]
+
                                                                
Epoch 11 Training:   7%|▋         | 7/96 [00:09<01:59,  1.35s/it]
+
Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 11 Training:   8%|▊         | 8/96 [00:10<01:58,  1.34s/it]
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Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.22it/s]
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Epoch 11 Training:   9%|▉         | 9/96 [00:12<01:57,  1.35s/it]
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Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 11 Training:  10%|█         | 10/96 [00:13<01:55,  1.34s/it]
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Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.21it/s]
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Epoch 11 Training:  11%|█▏        | 11/96 [00:14<01:54,  1.35s/it]
+
Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.20it/s]
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Epoch 11 Training:  12%|█▎        | 12/96 [00:16<01:53,  1.35s/it]
+
Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.23it/s]
+
                                                                
Epoch 11 Training:  14%|█▎        | 13/96 [00:17<01:52,  1.35s/it]
+
Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 11 Training:  15%|█▍        | 14/96 [00:18<01:50,  1.35s/it]
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Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.21it/s]
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Epoch 11 Training:  16%|█▌        | 15/96 [00:20<01:49,  1.35s/it]
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Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 11 Training:  17%|█▋        | 16/96 [00:21<01:47,  1.35s/it]
+
Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.21it/s]
+
                                                                
Epoch 11 Training:  18%|█▊        | 17/96 [00:22<01:46,  1.35s/it]
+
Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.23it/s]
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Epoch 11 Training:  19%|█▉        | 18/96 [00:24<01:44,  1.35s/it]
+
Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.22it/s]
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Epoch 11 Training:  20%|█▉        | 19/96 [00:25<01:43,  1.35s/it]
+
Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.21it/s]
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Epoch 11 Training:  21%|██        | 20/96 [00:26<01:42,  1.35s/it]
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Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 11 Training:  22%|██▏       | 21/96 [00:28<01:40,  1.35s/it]
+
Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
+
                                                                
Epoch 11 Training:  23%|██▎       | 22/96 [00:29<01:39,  1.34s/it]
+
Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 11 Training:  24%|██▍       | 23/96 [00:30<01:38,  1.35s/it]
+
Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.21it/s]
+
                                                                
Epoch 11 Training:  25%|██▌       | 24/96 [00:32<01:36,  1.35s/it]
+
Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.63it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 11 Training:  26%|██▌       | 25/96 [00:33<01:35,  1.35s/it]
+
Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.23it/s]
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Epoch 11 Training:  27%|██▋       | 26/96 [00:34<01:34,  1.35s/it]
+
Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 11 Training:  28%|██▊       | 27/96 [00:36<01:32,  1.34s/it]
+
Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.23it/s]
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Epoch 11 Training:  29%|██▉       | 28/96 [00:37<01:31,  1.34s/it]
+
Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.23it/s]
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Epoch 11 Training:  30%|███       | 29/96 [00:38<01:30,  1.34s/it]
+
Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 11 Training:  31%|███▏      | 30/96 [00:40<01:28,  1.34s/it]
+
Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 11 Training:  32%|███▏      | 31/96 [00:41<01:27,  1.34s/it]
+
Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.23it/s]
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Epoch 11 Training:  33%|███▎      | 32/96 [00:42<01:26,  1.35s/it]
+
Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.22it/s]
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Epoch 11 Training:  34%|███▍      | 33/96 [00:44<01:24,  1.35s/it]
+
Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.22it/s]
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Epoch 11 Training:  35%|███▌      | 34/96 [00:45<01:23,  1.35s/it]
+
Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.21it/s]
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Epoch 11 Training:  36%|███▋      | 35/96 [00:47<01:22,  1.35s/it]
+
Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.20it/s]
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Epoch 11 Training:  38%|███▊      | 36/96 [00:48<01:21,  1.35s/it]
+
Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.22it/s]
+
                                                                
Epoch 11 Training:  39%|███▊      | 37/96 [00:49<01:19,  1.35s/it]
+
Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.23it/s]
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Epoch 11 Training:  40%|███▉      | 38/96 [00:51<01:18,  1.35s/it]
+
Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.21it/s]
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Epoch 11 Training:  41%|████      | 39/96 [00:52<01:16,  1.35s/it]
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Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 11 Training:  42%|████▏     | 40/96 [00:53<01:15,  1.35s/it]
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Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 11 Training:  43%|████▎     | 41/96 [00:55<01:14,  1.35s/it]
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Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 11 Training:  44%|████▍     | 42/96 [00:56<01:12,  1.34s/it]
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Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.38it/s]
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Epoch 11 Training:  45%|████▍     | 43/96 [00:57<01:11,  1.35s/it]
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Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 11 Training:  46%|████▌     | 44/96 [00:59<01:09,  1.34s/it]
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Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.22it/s]
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Epoch 11 Training:  47%|████▋     | 45/96 [01:00<01:08,  1.34s/it]
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Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.22it/s]
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Epoch 11 Training:  48%|████▊     | 46/96 [01:01<01:07,  1.34s/it]
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Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.21it/s]
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Epoch 11 Training:  49%|████▉     | 47/96 [01:03<01:05,  1.35s/it]
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Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.17it/s]
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Epoch 11 Training:  50%|█████     | 48/96 [01:04<01:04,  1.35s/it]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.19it/s]
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Epoch 11 Training:  51%|█████     | 49/96 [01:05<01:03,  1.35s/it]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.21it/s]
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Epoch 11 Training:  52%|█████▏    | 50/96 [01:07<01:02,  1.35s/it]
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Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.22it/s]
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Epoch 11 Training:  53%|█████▎    | 51/96 [01:08<01:00,  1.35s/it]
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Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.22it/s]
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Epoch 11 Training:  54%|█████▍    | 52/96 [01:09<00:59,  1.35s/it]
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Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 11 Training:  55%|█████▌    | 53/96 [01:11<00:57,  1.35s/it]
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Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.19it/s]
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Epoch 11 Training:  56%|█████▋    | 54/96 [01:12<00:56,  1.35s/it]
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Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.19it/s]
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Epoch 11 Training:  57%|█████▋    | 55/96 [01:14<00:55,  1.35s/it]
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Epoch 11 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 11 Training:  81%|████████▏ | 78/96 [01:44<00:24,  1.35s/it]
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Epoch 11 Training:  82%|████████▏ | 79/96 [01:46<00:22,  1.35s/it]
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Epoch 11 Training:  99%|█████████▉| 95/96 [02:07<00:01,  1.34s/it]
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Epoch 11 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s][2026-10-01 17:01:08] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0011 | Epoch 011] train/policy_loss=14.7820 train/unweighted_policy_loss=2.9564 train/loss=14.7820 train/grad_norm=0.1672
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Epoch 11 Training: 100%|██████████| 96/96 [02:09<00:00,  1.35s/it]
+[2026-10-01 17:01:08] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0012 | Epoch 011] timing/rollout_seconds=147.9505 timing/feedback_seconds=5.9206 timing/optimization_seconds=129.8839 timing/cycle_seconds=281.5262
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+
Pointwise Rewards: ssim:   0%|          | 0/6 [00:00<?, ?it/s]
Pointwise Rewards: ssim:  17%|█▋        | 1/6 [00:00<00:00,  5.58it/s]
Pointwise Rewards: ssim:  33%|███▎      | 2/6 [00:00<00:00,  5.66it/s]
Pointwise Rewards: ssim:  50%|█████     | 3/6 [00:00<00:00,  6.18it/s]
Pointwise Rewards: ssim:  67%|██████▋   | 4/6 [00:00<00:00,  6.22it/s]
Pointwise Rewards: ssim:  83%|████████▎ | 5/6 [00:00<00:00,  5.16it/s]
Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:01<00:00,  5.74it/s]
Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:01<00:00,  5.73it/s]
+
Pointwise Rewards: dists:   0%|          | 0/6 [00:00<?, ?it/s]
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Pointwise Rewards: dists:  33%|███▎      | 2/6 [00:00<00:01,  3.59it/s]
Pointwise Rewards: dists:  50%|█████     | 3/6 [00:00<00:00,  3.68it/s]
Pointwise Rewards: dists:  67%|██████▋   | 4/6 [00:01<00:00,  3.64it/s]
Pointwise Rewards: dists:  83%|████████▎ | 5/6 [00:01<00:00,  3.69it/s]
Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.74it/s]
Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.69it/s]
+
Pointwise Rewards: psnr:   0%|          | 0/6 [00:00<?, ?it/s]
Pointwise Rewards: psnr:  17%|█▋        | 1/6 [00:00<00:00,  6.70it/s]
Pointwise Rewards: psnr:  33%|███▎      | 2/6 [00:00<00:00,  6.66it/s]
Pointwise Rewards: psnr:  50%|█████     | 3/6 [00:00<00:00,  6.94it/s]
Pointwise Rewards: psnr:  67%|██████▋   | 4/6 [00:00<00:00,  6.83it/s]
Pointwise Rewards: psnr:  83%|████████▎ | 5/6 [00:00<00:00,  6.91it/s]
Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  7.16it/s]
Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.99it/s]
+[2026-10-01 17:03:40] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0012 | Epoch 012] train/reward_dists_mean=0.9307 train/reward_dists_std=0.0171 train/reward_psnr_mean=28.5467 train/reward_psnr_std=2.4568 train/reward_ssim_mean=0.9416 train/reward_ssim_std=0.0215 train/reward_dists_group_std_mean=0.0040 train/reward_dists_group_std_max=0.0248 train/reward_dists_group_std_min=0.0014 train/reward_dists_group_mean_std=0.0163 train/reward_psnr_group_std_mean=1.6576 train/reward_psnr_group_std_max=3.0725 train/reward_psnr_group_std_min=0.7323 train/reward_psnr_group_mean_std=1.7348 train/reward_ssim_group_std_mean=0.0014 train/reward_ssim_group_std_max=0.0086 train/reward_ssim_group_std_min=0.0004 train/reward_ssim_group_mean_std=0.0214 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-9.4610 train/adv_max=5.7640 train/adv_abs_mean=1.8163
+
Epoch 12 Training:   0%|          | 0/96 [00:00<?, ?it/s]
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Epoch 12 Training:   1%|          | 1/96 [00:01<01:56,  1.22s/it]
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Epoch 12 Training:   2%|▏         | 2/96 [00:02<02:01,  1.29s/it]
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Epoch 12 Training:   3%|▎         | 3/96 [00:03<02:02,  1.31s/it]
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Epoch 12 Training:   4%|▍         | 4/96 [00:05<02:01,  1.32s/it]
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Epoch 12 Training:   5%|▌         | 5/96 [00:06<02:01,  1.33s/it]
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Epoch 12 Training:   6%|▋         | 6/96 [00:07<02:00,  1.34s/it]
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Epoch 12 Training:   7%|▋         | 7/96 [00:09<01:59,  1.34s/it]
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Epoch 12 Training:   8%|▊         | 8/96 [00:10<01:58,  1.34s/it]
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Epoch 12 Training:   9%|▉         | 9/96 [00:11<01:56,  1.34s/it]
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Epoch 12 Training:  10%|█         | 10/96 [00:13<01:55,  1.34s/it]
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Epoch 12 Training:  11%|█▏        | 11/96 [00:14<01:53,  1.34s/it]
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Epoch 12 Training:  12%|█▎        | 12/96 [00:15<01:52,  1.34s/it]
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Epoch 12 Training:  14%|█▎        | 13/96 [00:17<01:50,  1.33s/it]
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Epoch 12 Training:  15%|█▍        | 14/96 [00:18<01:49,  1.34s/it]
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Epoch 12 Training:  16%|█▌        | 15/96 [00:19<01:48,  1.34s/it]
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Epoch 12 Training:  17%|█▋        | 16/96 [00:21<01:46,  1.33s/it]
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Epoch 12 Training:  18%|█▊        | 17/96 [00:22<01:45,  1.34s/it]
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Epoch 12 Training:  19%|█▉        | 18/96 [00:23<01:44,  1.33s/it]
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Epoch 12 Training:  20%|█▉        | 19/96 [00:25<01:42,  1.33s/it]
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Epoch 12 Training:  21%|██        | 20/96 [00:26<01:41,  1.34s/it]
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Epoch 12 Training:  22%|██▏       | 21/96 [00:27<01:40,  1.33s/it]
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Epoch 12 Training:  23%|██▎       | 22/96 [00:29<01:38,  1.33s/it]
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Epoch 12 Training:  24%|██▍       | 23/96 [00:30<01:37,  1.33s/it]
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Epoch 12 Training:  25%|██▌       | 24/96 [00:31<01:36,  1.33s/it]
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Epoch 12 Training:  26%|██▌       | 25/96 [00:33<01:34,  1.33s/it]
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Epoch 12 Training:  27%|██▋       | 26/96 [00:34<01:33,  1.33s/it]
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Epoch 12 Training:  28%|██▊       | 27/96 [00:35<01:32,  1.33s/it]
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Epoch 12 Training:  29%|██▉       | 28/96 [00:37<01:30,  1.33s/it]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
+
                                                                
Epoch 12 Training:  30%|███       | 29/96 [00:38<01:29,  1.34s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 12 Training:  31%|███▏      | 30/96 [00:40<01:34,  1.44s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 12 Training:  32%|███▏      | 31/96 [00:41<01:31,  1.41s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 12 Training:  33%|███▎      | 32/96 [00:42<01:28,  1.38s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 12 Training:  34%|███▍      | 33/96 [00:44<01:26,  1.37s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 12 Training:  35%|███▌      | 34/96 [00:45<01:24,  1.36s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
+
                                                                
Epoch 12 Training:  36%|███▋      | 35/96 [00:46<01:22,  1.35s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.29it/s]
+
                                                                
Epoch 12 Training:  38%|███▊      | 36/96 [00:48<01:20,  1.34s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
+
                                                                
Epoch 12 Training:  39%|███▊      | 37/96 [00:49<01:19,  1.34s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
+
                                                                
Epoch 12 Training:  40%|███▉      | 38/96 [00:51<01:17,  1.34s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.44it/s]
+
                                                                
Epoch 12 Training:  41%|████      | 39/96 [00:52<01:16,  1.34s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.23it/s]
+
                                                                
Epoch 12 Training:  42%|████▏     | 40/96 [00:53<01:14,  1.34s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
+
                                                                
Epoch 12 Training:  43%|████▎     | 41/96 [00:54<01:13,  1.34s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
+
                                                                
Epoch 12 Training:  44%|████▍     | 42/96 [00:56<01:12,  1.33s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.23it/s]
+
                                                                
Epoch 12 Training:  45%|████▍     | 43/96 [00:57<01:10,  1.34s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
+
                                                                
Epoch 12 Training:  46%|████▌     | 44/96 [00:58<01:09,  1.33s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.63it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
+
                                                                
Epoch 12 Training:  47%|████▋     | 45/96 [01:00<01:07,  1.33s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.22it/s]
+
                                                                
Epoch 12 Training:  48%|████▊     | 46/96 [01:01<01:06,  1.34s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
+
                                                                
Epoch 12 Training:  49%|████▉     | 47/96 [01:03<01:05,  1.34s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.29it/s]
+
                                                                
Epoch 12 Training:  50%|█████     | 48/96 [01:04<01:03,  1.33s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.23it/s]
+
                                                                
Epoch 12 Training:  51%|█████     | 49/96 [01:05<01:02,  1.33s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
+
                                                                
Epoch 12 Training:  52%|█████▏    | 50/96 [01:06<01:01,  1.33s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.22it/s]
+
                                                                
Epoch 12 Training:  53%|█████▎    | 51/96 [01:08<01:00,  1.34s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
+
                                                                
Epoch 12 Training:  54%|█████▍    | 52/96 [01:09<00:58,  1.34s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
+
                                                                
Epoch 12 Training:  55%|█████▌    | 53/96 [01:11<00:57,  1.34s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.84it/s]
+
                                                                
Epoch 12 Training:  56%|█████▋    | 54/96 [01:12<00:58,  1.39s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
+
                                                                
Epoch 12 Training:  57%|█████▋    | 55/96 [01:13<00:56,  1.37s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
+
                                                                
Epoch 12 Training:  58%|█████▊    | 56/96 [01:15<00:54,  1.36s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
+
                                                                
Epoch 12 Training:  59%|█████▉    | 57/96 [01:16<00:52,  1.35s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.23it/s]
+
                                                                
Epoch 12 Training:  60%|██████    | 58/96 [01:17<00:51,  1.35s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
+
                                                                
Epoch 12 Training:  61%|██████▏   | 59/96 [01:19<00:49,  1.34s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
+
                                                                
Epoch 12 Training:  62%|██████▎   | 60/96 [01:20<00:48,  1.34s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
+
                                                                
Epoch 12 Training:  64%|██████▎   | 61/96 [01:21<00:46,  1.34s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
+
                                                                
Epoch 12 Training:  65%|██████▍   | 62/96 [01:23<00:45,  1.34s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
+
                                                                
Epoch 12 Training:  66%|██████▌   | 63/96 [01:24<00:44,  1.34s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.23it/s]
+
                                                                
Epoch 12 Training:  67%|██████▋   | 64/96 [01:25<00:42,  1.34s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
+
                                                                
Epoch 12 Training:  68%|██████▊   | 65/96 [01:27<00:41,  1.34s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
+
                                                                
Epoch 12 Training:  69%|██████▉   | 66/96 [01:28<00:40,  1.33s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.21it/s]
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Epoch 12 Training:  70%|██████▉   | 67/96 [01:29<00:38,  1.34s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 12 Training:  71%|███████   | 68/96 [01:31<00:37,  1.34s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 12 Training:  72%|███████▏  | 69/96 [01:32<00:36,  1.34s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 12 Training:  73%|███████▎  | 70/96 [01:33<00:34,  1.34s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 12 Training:  74%|███████▍  | 71/96 [01:35<00:33,  1.33s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 12 Training:  75%|███████▌  | 72/96 [01:36<00:32,  1.33s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 12 Training:  76%|███████▌  | 73/96 [01:37<00:30,  1.33s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 12 Training:  77%|███████▋  | 74/96 [01:39<00:29,  1.33s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 12 Training:  78%|███████▊  | 75/96 [01:40<00:27,  1.33s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 12 Training:  79%|███████▉  | 76/96 [01:41<00:26,  1.33s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 12 Training:  80%|████████  | 77/96 [01:43<00:25,  1.33s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 12 Training:  81%|████████▏ | 78/96 [01:44<00:23,  1.33s/it]
+
Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 12 Training:  82%|████████▏ | 79/96 [01:45<00:22,  1.33s/it]
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Epoch 12 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 12 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 12 Training:  83%|████████▎ | 80/96 [01:47<00:21,  1.33s/it]
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Epoch 12 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s][2026-10-01 17:05:48] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0012 | Epoch 012] train/policy_loss=15.3230 train/unweighted_policy_loss=3.0646 train/loss=15.3230 train/grad_norm=0.1153
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+[2026-10-01 17:05:48] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0013 | Epoch 012] timing/rollout_seconds=148.0029 timing/feedback_seconds=6.0047 timing/optimization_seconds=129.1769 timing/cycle_seconds=280.7324
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+[2026-10-01 17:08:20] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0013 | Epoch 013] train/reward_dists_mean=0.9333 train/reward_dists_std=0.0166 train/reward_psnr_mean=30.0721 train/reward_psnr_std=2.2081 train/reward_ssim_mean=0.9404 train/reward_ssim_std=0.0198 train/reward_dists_group_std_mean=0.0034 train/reward_dists_group_std_max=0.0081 train/reward_dists_group_std_min=0.0015 train/reward_dists_group_mean_std=0.0162 train/reward_psnr_group_std_mean=1.0612 train/reward_psnr_group_std_max=2.1437 train/reward_psnr_group_std_min=0.2385 train/reward_psnr_group_mean_std=1.8809 train/reward_ssim_group_std_mean=0.0018 train/reward_ssim_group_std_max=0.0070 train/reward_ssim_group_std_min=0.0004 train/reward_ssim_group_mean_std=0.0197 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-7.1929 train/adv_max=6.3035 train/adv_abs_mean=1.7385
+
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Epoch 13 Training:   2%|▏         | 2/96 [00:02<02:01,  1.30s/it]
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Epoch 13 Training:   3%|▎         | 3/96 [00:03<02:02,  1.31s/it]
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Epoch 13 Training:   4%|▍         | 4/96 [00:05<02:01,  1.32s/it]
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Epoch 13 Training:   5%|▌         | 5/96 [00:06<02:10,  1.43s/it]
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Epoch 13 Training:   6%|▋         | 6/96 [00:08<02:05,  1.40s/it]
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Epoch 13 Training:   7%|▋         | 7/96 [00:09<02:02,  1.38s/it]
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Epoch 13 Training:   8%|▊         | 8/96 [00:10<01:59,  1.36s/it]
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Epoch 13 Training:   9%|▉         | 9/96 [00:12<01:57,  1.35s/it]
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Epoch 13 Training:  10%|█         | 10/96 [00:13<01:55,  1.35s/it]
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Epoch 13 Training:  11%|█▏        | 11/96 [00:14<01:54,  1.34s/it]
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Epoch 13 Training:  12%|█▎        | 12/96 [00:16<01:52,  1.34s/it]
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Epoch 13 Training:  14%|█▎        | 13/96 [00:17<01:51,  1.34s/it]
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Epoch 13 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 13 Training:  15%|█▍        | 14/96 [00:18<01:49,  1.34s/it]
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Epoch 13 Training:  16%|█▌        | 15/96 [00:20<01:48,  1.34s/it]
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Epoch 13 Training:  17%|█▋        | 16/96 [00:21<01:46,  1.34s/it]
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Epoch 13 Training:  18%|█▊        | 17/96 [00:22<01:45,  1.34s/it]
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Epoch 13 Training:  19%|█▉        | 18/96 [00:24<01:44,  1.34s/it]
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Epoch 13 Training:  20%|█▉        | 19/96 [00:25<01:42,  1.34s/it]
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Epoch 13 Training:  21%|██        | 20/96 [00:26<01:41,  1.34s/it]
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Epoch 13 Training:  22%|██▏       | 21/96 [00:28<01:40,  1.34s/it]
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Epoch 13 Training:  23%|██▎       | 22/96 [00:29<01:38,  1.34s/it]
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Epoch 13 Training:  24%|██▍       | 23/96 [00:30<01:37,  1.34s/it]
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Epoch 13 Training:  25%|██▌       | 24/96 [00:32<01:36,  1.34s/it]
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Epoch 13 Training:  26%|██▌       | 25/96 [00:33<01:35,  1.34s/it]
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Epoch 13 Training:  27%|██▋       | 26/96 [00:34<01:33,  1.34s/it]
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Epoch 13 Training:  28%|██▊       | 27/96 [00:36<01:32,  1.34s/it]
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Epoch 13 Training:  29%|██▉       | 28/96 [00:37<01:30,  1.33s/it]
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Epoch 13 Training:  30%|███       | 29/96 [00:38<01:29,  1.33s/it]
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Epoch 13 Training:  31%|███▏      | 30/96 [00:40<01:28,  1.33s/it]
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Epoch 13 Training:  32%|███▏      | 31/96 [00:41<01:26,  1.33s/it]
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Epoch 13 Training:  33%|███▎      | 32/96 [00:42<01:25,  1.34s/it]
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Epoch 13 Training:  34%|███▍      | 33/96 [00:44<01:24,  1.34s/it]
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Epoch 13 Training:  35%|███▌      | 34/96 [00:45<01:22,  1.33s/it]
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Epoch 13 Training:  36%|███▋      | 35/96 [00:46<01:21,  1.34s/it]
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Epoch 13 Training:  38%|███▊      | 36/96 [00:48<01:20,  1.33s/it]
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Epoch 13 Training:  39%|███▊      | 37/96 [00:49<01:18,  1.33s/it]
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Epoch 13 Training:  40%|███▉      | 38/96 [00:50<01:17,  1.33s/it]
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Epoch 13 Training:  41%|████      | 39/96 [00:52<01:15,  1.33s/it]
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Epoch 13 Training:  42%|████▏     | 40/96 [00:53<01:14,  1.33s/it]
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Epoch 13 Training:  43%|████▎     | 41/96 [00:54<01:13,  1.33s/it]
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Epoch 13 Training:  44%|████▍     | 42/96 [00:56<01:11,  1.33s/it]
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Epoch 13 Training:  45%|████▍     | 43/96 [00:57<01:10,  1.33s/it]
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Epoch 13 Training:  46%|████▌     | 44/96 [00:58<01:09,  1.33s/it]
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Epoch 13 Training:  47%|████▋     | 45/96 [01:00<01:08,  1.33s/it]
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Epoch 13 Training:  48%|████▊     | 46/96 [01:01<01:06,  1.33s/it]
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Epoch 13 Training:  49%|████▉     | 47/96 [01:02<01:05,  1.33s/it]
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Epoch 13 Training:  50%|█████     | 48/96 [01:04<01:03,  1.33s/it]
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Epoch 13 Training:  51%|█████     | 49/96 [01:05<01:02,  1.33s/it]
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Epoch 13 Training:  52%|█████▏    | 50/96 [01:06<01:01,  1.33s/it]
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Epoch 13 Training:  53%|█████▎    | 51/96 [01:08<00:59,  1.33s/it]
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Epoch 13 Training:  54%|█████▍    | 52/96 [01:09<00:58,  1.33s/it]
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Epoch 13 Training:  55%|█████▌    | 53/96 [01:10<00:57,  1.33s/it]
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Epoch 13 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s][2026-10-01 17:10:29] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0013 | Epoch 013] train/policy_loss=15.0348 train/unweighted_policy_loss=3.0070 train/loss=15.0348 train/grad_norm=0.0671
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+[2026-10-01 17:10:29] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0014 | Epoch 013] timing/rollout_seconds=147.8181 timing/feedback_seconds=5.6371 timing/optimization_seconds=128.8877 timing/cycle_seconds=280.2264
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+
Pointwise Rewards: ssim:   0%|          | 0/6 [00:00<?, ?it/s]
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Pointwise Rewards: ssim:  83%|████████▎ | 5/6 [00:00<00:00,  6.46it/s]
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Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:00<00:00,  6.35it/s]
+
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Pointwise Rewards: dists:  83%|████████▎ | 5/6 [00:01<00:00,  3.66it/s]
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Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.64it/s]
+
Pointwise Rewards: psnr:   0%|          | 0/6 [00:00<?, ?it/s]
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Pointwise Rewards: psnr:  33%|███▎      | 2/6 [00:00<00:00,  6.56it/s]
Pointwise Rewards: psnr:  50%|█████     | 3/6 [00:00<00:00,  6.72it/s]
Pointwise Rewards: psnr:  67%|██████▋   | 4/6 [00:00<00:00,  6.96it/s]
Pointwise Rewards: psnr:  83%|████████▎ | 5/6 [00:00<00:00,  6.77it/s]
Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.77it/s]
Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.75it/s]
+[2026-10-01 17:13:01] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0014 | Epoch 014] train/reward_dists_mean=0.9329 train/reward_dists_std=0.0192 train/reward_psnr_mean=29.8176 train/reward_psnr_std=2.5684 train/reward_ssim_mean=0.9376 train/reward_ssim_std=0.0210 train/reward_dists_group_std_mean=0.0035 train/reward_dists_group_std_max=0.0073 train/reward_dists_group_std_min=0.0015 train/reward_dists_group_mean_std=0.0189 train/reward_psnr_group_std_mean=1.3259 train/reward_psnr_group_std_max=2.2954 train/reward_psnr_group_std_min=0.4736 train/reward_psnr_group_mean_std=2.1578 train/reward_ssim_group_std_mean=0.0020 train/reward_ssim_group_std_max=0.0071 train/reward_ssim_group_std_min=0.0005 train/reward_ssim_group_mean_std=0.0208 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-9.0344 train/adv_max=7.2505 train/adv_abs_mean=1.9131
+
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Epoch 14 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.49it/s]
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Epoch 14 Training:  28%|██▊       | 27/96 [00:35<01:29,  1.30s/it]
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Epoch 14 Training:  29%|██▉       | 28/96 [00:36<01:28,  1.30s/it]
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Epoch 14 Training:  30%|███       | 29/96 [00:37<01:27,  1.30s/it]
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Epoch 14 Training:  31%|███▏      | 30/96 [00:38<01:25,  1.30s/it]
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Epoch 14 Training:  32%|███▏      | 31/96 [00:40<01:24,  1.30s/it]
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Epoch 14 Training:  33%|███▎      | 32/96 [00:41<01:23,  1.31s/it]
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Epoch 14 Training:  34%|███▍      | 33/96 [00:43<01:26,  1.37s/it]
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Epoch 14 Training:  35%|███▌      | 34/96 [00:44<01:23,  1.35s/it]
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Epoch 14 Training:  36%|███▋      | 35/96 [00:45<01:21,  1.33s/it]
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Epoch 14 Training:  38%|███▊      | 36/96 [00:47<01:19,  1.32s/it]
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Epoch 14 Training:  39%|███▊      | 37/96 [00:48<01:17,  1.32s/it]
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Epoch 14 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 14 Training:  40%|███▉      | 38/96 [00:49<01:15,  1.31s/it]
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Epoch 14 Training:  41%|████      | 39/96 [00:50<01:14,  1.30s/it]
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Epoch 14 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 14 Training:  42%|████▏     | 40/96 [00:52<01:12,  1.30s/it]
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Epoch 14 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 14 Training:  43%|████▎     | 41/96 [00:53<01:11,  1.30s/it]
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Epoch 14 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 14 Training:  44%|████▍     | 42/96 [00:54<01:10,  1.30s/it]
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Epoch 14 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 14 Training:  45%|████▍     | 43/96 [00:56<01:08,  1.30s/it]
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Epoch 14 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 14 Training:  46%|████▌     | 44/96 [00:57<01:07,  1.30s/it]
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Epoch 14 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 14 Training:  47%|████▋     | 45/96 [00:58<01:08,  1.35s/it]
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Epoch 14 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 14 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.49it/s]
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Epoch 14 Training:  48%|████▊     | 46/96 [01:00<01:06,  1.34s/it]
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Epoch 14 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.49it/s]
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Epoch 14 Training:  49%|████▉     | 47/96 [01:01<01:04,  1.32s/it]
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Epoch 14 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 14 Training:  50%|█████     | 48/96 [01:02<01:03,  1.32s/it]
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Epoch 14 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.49it/s]
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Epoch 14 Training:  51%|█████     | 49/96 [01:04<01:01,  1.31s/it]
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Epoch 14 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 14 Training:  52%|█████▏    | 50/96 [01:05<01:00,  1.31s/it]
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Epoch 14 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 14 Training:  53%|█████▎    | 51/96 [01:06<00:58,  1.31s/it]
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Epoch 14 Training:  54%|█████▍    | 52/96 [01:08<01:00,  1.37s/it]
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Epoch 14 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 14 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.50it/s]
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Epoch 14 Training:  55%|█████▌    | 53/96 [01:09<00:58,  1.35s/it]
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Epoch 14 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 14 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.48it/s]
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Epoch 14 Training:  56%|█████▋    | 54/96 [01:10<00:56,  1.34s/it]
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Epoch 14 Training:  57%|█████▋    | 55/96 [01:12<00:54,  1.32s/it]
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Epoch 14 Training:  58%|█████▊    | 56/96 [01:13<00:52,  1.32s/it]
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Epoch 14 Training:  59%|█████▉    | 57/96 [01:14<00:51,  1.31s/it]
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Epoch 14 Training:  60%|██████    | 58/96 [01:15<00:49,  1.30s/it]
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Epoch 14 Training:  61%|██████▏   | 59/96 [01:17<00:48,  1.30s/it]
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Epoch 14 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 14 Training:  62%|██████▎   | 60/96 [01:18<00:46,  1.30s/it]
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Epoch 14 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 14 Training:  64%|██████▎   | 61/96 [01:19<00:45,  1.30s/it]
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Epoch 14 Training:  65%|██████▍   | 62/96 [01:21<00:44,  1.30s/it]
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Epoch 14 Training:  66%|██████▌   | 63/96 [01:22<00:42,  1.30s/it]
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Epoch 14 Training:  67%|██████▋   | 64/96 [01:23<00:41,  1.30s/it]
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Epoch 14 Training:  68%|██████▊   | 65/96 [01:25<00:40,  1.30s/it]
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Epoch 14 Training:  69%|██████▉   | 66/96 [01:26<00:39,  1.30s/it]
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Epoch 14 Training:  70%|██████▉   | 67/96 [01:27<00:37,  1.30s/it]
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Epoch 14 Training:  71%|███████   | 68/96 [01:28<00:36,  1.30s/it]
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Epoch 14 Training:  72%|███████▏  | 69/96 [01:30<00:35,  1.30s/it]
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Epoch 14 Training:  73%|███████▎  | 70/96 [01:31<00:33,  1.30s/it]
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Epoch 14 Training:  74%|███████▍  | 71/96 [01:32<00:32,  1.30s/it]
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Epoch 14 Training:  75%|███████▌  | 72/96 [01:34<00:31,  1.30s/it]
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Epoch 14 Training:  76%|███████▌  | 73/96 [01:35<00:29,  1.30s/it]
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Epoch 14 Training:  77%|███████▋  | 74/96 [01:36<00:28,  1.30s/it]
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Epoch 14 Training:  78%|███████▊  | 75/96 [01:38<00:27,  1.30s/it]
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Epoch 14 Training:  79%|███████▉  | 76/96 [01:39<00:25,  1.30s/it]
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Epoch 14 Training:  80%|████████  | 77/96 [01:40<00:24,  1.30s/it]
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Epoch 14 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s][2026-10-01 17:15:06] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0014 | Epoch 014] train/policy_loss=14.4594 train/unweighted_policy_loss=2.8919 train/loss=14.4594 train/grad_norm=0.1117
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+[2026-10-01 17:15:06] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0015 | Epoch 014] timing/rollout_seconds=147.7814 timing/feedback_seconds=5.7656 timing/optimization_seconds=126.1209 timing/cycle_seconds=277.4080
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Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.88it/s]
+[2026-10-01 17:17:38] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0015 | Epoch 015] train/reward_dists_mean=0.9417 train/reward_dists_std=0.0187 train/reward_psnr_mean=30.0555 train/reward_psnr_std=2.4400 train/reward_ssim_mean=0.9428 train/reward_ssim_std=0.0211 train/reward_dists_group_std_mean=0.0031 train/reward_dists_group_std_max=0.0112 train/reward_dists_group_std_min=0.0012 train/reward_dists_group_mean_std=0.0184 train/reward_psnr_group_std_mean=1.1996 train/reward_psnr_group_std_max=2.3923 train/reward_psnr_group_std_min=0.3938 train/reward_psnr_group_mean_std=2.0663 train/reward_ssim_group_std_mean=0.0015 train/reward_ssim_group_std_max=0.0045 train/reward_ssim_group_std_min=0.0005 train/reward_ssim_group_mean_std=0.0211 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-7.2889 train/adv_max=6.3317 train/adv_abs_mean=1.8896
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Epoch 15 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s][2026-10-01 17:19:43] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0015 | Epoch 015] train/policy_loss=13.4565 train/unweighted_policy_loss=2.6913 train/loss=13.4565 train/grad_norm=0.0886
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Epoch 15 Training: 100%|██████████| 96/96 [02:04<00:00,  1.30s/it]
+[2026-10-01 17:19:43] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0016 | Epoch 015] timing/rollout_seconds=147.6900 timing/feedback_seconds=5.4899 timing/optimization_seconds=125.3505 timing/cycle_seconds=276.5563
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+
Pointwise Rewards: ssim:   0%|          | 0/6 [00:00<?, ?it/s]
Pointwise Rewards: ssim:  17%|█▋        | 1/6 [00:00<00:01,  2.84it/s]
Pointwise Rewards: ssim:  33%|███▎      | 2/6 [00:00<00:00,  4.52it/s]
Pointwise Rewards: ssim:  50%|█████     | 3/6 [00:00<00:00,  5.23it/s]
Pointwise Rewards: ssim:  67%|██████▋   | 4/6 [00:00<00:00,  5.54it/s]
Pointwise Rewards: ssim:  83%|████████▎ | 5/6 [00:00<00:00,  5.85it/s]
Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:01<00:00,  6.06it/s]
Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:01<00:00,  5.41it/s]
+
Pointwise Rewards: dists:   0%|          | 0/6 [00:00<?, ?it/s]
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Pointwise Rewards: dists:  33%|███▎      | 2/6 [00:00<00:01,  3.83it/s]
Pointwise Rewards: dists:  50%|█████     | 3/6 [00:00<00:00,  3.74it/s]
Pointwise Rewards: dists:  67%|██████▋   | 4/6 [00:01<00:00,  3.67it/s]
Pointwise Rewards: dists:  83%|████████▎ | 5/6 [00:01<00:00,  3.70it/s]
Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.68it/s]
Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.69it/s]
+
Pointwise Rewards: psnr:   0%|          | 0/6 [00:00<?, ?it/s]
Pointwise Rewards: psnr:  17%|█▋        | 1/6 [00:00<00:00,  6.72it/s]
Pointwise Rewards: psnr:  33%|███▎      | 2/6 [00:00<00:00,  7.54it/s]
Pointwise Rewards: psnr:  50%|█████     | 3/6 [00:00<00:00,  7.03it/s]
Pointwise Rewards: psnr:  67%|██████▋   | 4/6 [00:00<00:00,  6.84it/s]
Pointwise Rewards: psnr:  83%|████████▎ | 5/6 [00:00<00:00,  6.81it/s]
Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.77it/s]
Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.87it/s]
+[2026-10-01 17:22:16] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0016 | Epoch 016] train/reward_dists_mean=0.9342 train/reward_dists_std=0.0188 train/reward_psnr_mean=30.1092 train/reward_psnr_std=2.1515 train/reward_ssim_mean=0.9404 train/reward_ssim_std=0.0199 train/reward_dists_group_std_mean=0.0031 train/reward_dists_group_std_max=0.0065 train/reward_dists_group_std_min=0.0011 train/reward_dists_group_mean_std=0.0185 train/reward_psnr_group_std_mean=1.0477 train/reward_psnr_group_std_max=2.6550 train/reward_psnr_group_std_min=0.3751 train/reward_psnr_group_mean_std=1.8186 train/reward_ssim_group_std_mean=0.0013 train/reward_ssim_group_std_max=0.0086 train/reward_ssim_group_std_min=0.0004 train/reward_ssim_group_mean_std=0.0198 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-8.4470 train/adv_max=6.4905 train/adv_abs_mean=1.8106
+
Epoch 16 Training:   0%|          | 0/96 [00:00<?, ?it/s]
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Epoch 16 Training:   1%|          | 1/96 [00:01<01:57,  1.24s/it]
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Epoch 16 Training:   3%|▎         | 3/96 [00:03<01:59,  1.29s/it]
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Epoch 16 Training:   4%|▍         | 4/96 [00:05<01:59,  1.29s/it]
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Epoch 16 Training:   7%|▋         | 7/96 [00:09<01:55,  1.30s/it]
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Epoch 16 Training:   8%|▊         | 8/96 [00:10<01:54,  1.30s/it]
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Epoch 16 Training:   9%|▉         | 9/96 [00:11<01:53,  1.30s/it]
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Epoch 16 Training:  10%|█         | 10/96 [00:12<01:52,  1.30s/it]
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Epoch 16 Training:  11%|█▏        | 11/96 [00:14<01:50,  1.30s/it]
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Epoch 16 Training:  16%|█▌        | 15/96 [00:19<01:45,  1.30s/it]
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Epoch 16 Training:  18%|█▊        | 17/96 [00:22<01:42,  1.30s/it]
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Epoch 16 Training:  19%|█▉        | 18/96 [00:23<01:41,  1.30s/it]
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Epoch 16 Training:  20%|█▉        | 19/96 [00:24<01:40,  1.30s/it]
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Epoch 16 Training:  21%|██        | 20/96 [00:25<01:38,  1.30s/it]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.38it/s]
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Epoch 16 Training:  23%|██▎       | 22/96 [00:28<01:36,  1.30s/it]
+
Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.37it/s]
+
                                                                
Epoch 16 Training:  24%|██▍       | 23/96 [00:29<01:34,  1.30s/it]
+
Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.41it/s]
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Epoch 16 Training:  25%|██▌       | 24/96 [00:31<01:33,  1.30s/it]
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Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.49it/s]
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Epoch 16 Training:  26%|██▌       | 25/96 [00:32<01:32,  1.30s/it]
+
Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.36it/s]
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Epoch 16 Training:  27%|██▋       | 26/96 [00:33<01:31,  1.30s/it]
+
Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.37it/s]
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Epoch 16 Training:  28%|██▊       | 27/96 [00:35<01:29,  1.30s/it]
+
Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.35it/s]
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Epoch 16 Training:  29%|██▉       | 28/96 [00:36<01:28,  1.30s/it]
+
Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.37it/s]
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Epoch 16 Training:  30%|███       | 29/96 [00:37<01:27,  1.30s/it]
+
Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.37it/s]
+
                                                                
Epoch 16 Training:  31%|███▏      | 30/96 [00:38<01:25,  1.30s/it]
+
Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.47it/s]
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Epoch 16 Training:  32%|███▏      | 31/96 [00:40<01:24,  1.30s/it]
+
Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.38it/s]
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Epoch 16 Training:  33%|███▎      | 32/96 [00:41<01:23,  1.30s/it]
+
Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.36it/s]
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Epoch 16 Training:  34%|███▍      | 33/96 [00:42<01:21,  1.30s/it]
+
Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.34it/s]
+
                                                                
Epoch 16 Training:  35%|███▌      | 34/96 [00:44<01:20,  1.30s/it]
+
Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.38it/s]
+
                                                                
Epoch 16 Training:  36%|███▋      | 35/96 [00:45<01:19,  1.30s/it]
+
Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.63it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.37it/s]
+
                                                                
Epoch 16 Training:  38%|███▊      | 36/96 [00:46<01:18,  1.30s/it]
+
Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.37it/s]
+
                                                                
Epoch 16 Training:  39%|███▊      | 37/96 [00:48<01:16,  1.30s/it]
+
Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.36it/s]
+
                                                                
Epoch 16 Training:  40%|███▉      | 38/96 [00:49<01:15,  1.30s/it]
+
Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.45it/s]
+
                                                                
Epoch 16 Training:  41%|████      | 39/96 [00:50<01:14,  1.30s/it]
+
Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.37it/s]
+
                                                                
Epoch 16 Training:  42%|████▏     | 40/96 [00:52<01:12,  1.30s/it]
+
Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.45it/s]
+
                                                                
Epoch 16 Training:  43%|████▎     | 41/96 [00:53<01:11,  1.30s/it]
+
Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.42it/s]
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Epoch 16 Training:  44%|████▍     | 42/96 [00:54<01:10,  1.30s/it]
+
Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.47it/s]
+
                                                                
Epoch 16 Training:  45%|████▍     | 43/96 [00:55<01:08,  1.30s/it]
+
Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.40it/s]
+
                                                                
Epoch 16 Training:  46%|████▌     | 44/96 [00:57<01:07,  1.30s/it]
+
Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.45it/s]
+
                                                                
Epoch 16 Training:  47%|████▋     | 45/96 [00:58<01:06,  1.30s/it]
+
Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.36it/s]
+
                                                                
Epoch 16 Training:  48%|████▊     | 46/96 [00:59<01:05,  1.30s/it]
+
Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.45it/s]
+
                                                                
Epoch 16 Training:  49%|████▉     | 47/96 [01:01<01:03,  1.30s/it]
+
Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.39it/s]
+
                                                                
Epoch 16 Training:  50%|█████     | 48/96 [01:02<01:02,  1.30s/it]
+
Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.43it/s]
+
                                                                
Epoch 16 Training:  51%|█████     | 49/96 [01:03<01:01,  1.30s/it]
+
Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.39it/s]
+
                                                                
Epoch 16 Training:  52%|█████▏    | 50/96 [01:05<00:59,  1.30s/it]
+
Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.44it/s]
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Epoch 16 Training:  53%|█████▎    | 51/96 [01:06<00:58,  1.30s/it]
+
Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.40it/s]
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Epoch 16 Training:  54%|█████▍    | 52/96 [01:07<00:57,  1.30s/it]
+
Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.34it/s]
+
                                                                
Epoch 16 Training:  55%|█████▌    | 53/96 [01:08<00:56,  1.30s/it]
+
Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.36it/s]
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Epoch 16 Training:  56%|█████▋    | 54/96 [01:10<00:54,  1.30s/it]
+
Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.46it/s]
+
                                                                
Epoch 16 Training:  57%|█████▋    | 55/96 [01:11<00:53,  1.30s/it]
+
Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.49it/s]
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Epoch 16 Training:  58%|█████▊    | 56/96 [01:12<00:52,  1.30s/it]
+
Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.49it/s]
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Epoch 16 Training:  59%|█████▉    | 57/96 [01:14<00:50,  1.30s/it]
+
Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.39it/s]
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Epoch 16 Training:  60%|██████    | 58/96 [01:15<00:49,  1.30s/it]
+
Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.63it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.47it/s]
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Epoch 16 Training:  61%|██████▏   | 59/96 [01:16<00:48,  1.30s/it]
+
Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.63it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.49it/s]
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Epoch 16 Training:  62%|██████▎   | 60/96 [01:18<00:46,  1.30s/it]
+
Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.45it/s]
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Epoch 16 Training:  64%|██████▎   | 61/96 [01:19<00:45,  1.31s/it]
+
Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.39it/s]
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Epoch 16 Training:  65%|██████▍   | 62/96 [01:20<00:44,  1.31s/it]
+
Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.44it/s]
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Epoch 16 Training:  66%|██████▌   | 63/96 [01:21<00:43,  1.30s/it]
+
Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.39it/s]
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Epoch 16 Training:  67%|██████▋   | 64/96 [01:23<00:41,  1.30s/it]
+
Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.47it/s]
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Epoch 16 Training:  68%|██████▊   | 65/96 [01:24<00:40,  1.30s/it]
+
Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.49it/s]
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Epoch 16 Training:  69%|██████▉   | 66/96 [01:25<00:39,  1.30s/it]
+
Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.49it/s]
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Epoch 16 Training:  70%|██████▉   | 67/96 [01:27<00:37,  1.30s/it]
+
Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.47it/s]
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Epoch 16 Training:  71%|███████   | 68/96 [01:28<00:36,  1.30s/it]
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Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.48it/s]
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Epoch 16 Training:  72%|███████▏  | 69/96 [01:29<00:35,  1.31s/it]
+
Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.63it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.48it/s]
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Epoch 16 Training:  73%|███████▎  | 70/96 [01:31<00:33,  1.31s/it]
+
Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.49it/s]
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Epoch 16 Training:  74%|███████▍  | 71/96 [01:32<00:32,  1.30s/it]
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Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 16 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.49it/s]
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Epoch 16 Training:  75%|███████▌  | 72/96 [01:33<00:31,  1.30s/it]
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Epoch 16 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 16 Training:  99%|█████████▉| 95/96 [02:03<00:01,  1.30s/it]
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Epoch 16 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s][2026-10-01 17:24:21] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0016 | Epoch 016] train/policy_loss=13.3449 train/unweighted_policy_loss=2.6690 train/loss=13.3449 train/grad_norm=0.0520
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+[2026-10-01 17:24:21] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0017 | Epoch 016] timing/rollout_seconds=147.7901 timing/feedback_seconds=6.1689 timing/optimization_seconds=126.4076 timing/cycle_seconds=278.0775
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+
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Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.73it/s]
+[2026-10-01 17:26:53] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0017 | Epoch 017] train/reward_dists_mean=0.9318 train/reward_dists_std=0.0285 train/reward_psnr_mean=30.0566 train/reward_psnr_std=2.7380 train/reward_ssim_mean=0.9391 train/reward_ssim_std=0.0207 train/reward_dists_group_std_mean=0.0035 train/reward_dists_group_std_max=0.0212 train/reward_dists_group_std_min=0.0017 train/reward_dists_group_mean_std=0.0282 train/reward_psnr_group_std_mean=1.4480 train/reward_psnr_group_std_max=3.1310 train/reward_psnr_group_std_min=0.5033 train/reward_psnr_group_mean_std=2.2545 train/reward_ssim_group_std_mean=0.0019 train/reward_ssim_group_std_max=0.0183 train/reward_ssim_group_std_min=0.0003 train/reward_ssim_group_mean_std=0.0204 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-8.1457 train/adv_max=5.5815 train/adv_abs_mean=1.8899
+
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Epoch 17 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s][2026-10-01 17:28:58] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0017 | Epoch 017] train/policy_loss=13.3557 train/unweighted_policy_loss=2.6711 train/loss=13.3557 train/grad_norm=0.1559
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+[2026-10-01 17:28:58] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0018 | Epoch 017] timing/rollout_seconds=147.8891 timing/feedback_seconds=5.8625 timing/optimization_seconds=125.4747 timing/cycle_seconds=276.9177
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+
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+[2026-10-01 17:31:30] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0018 | Epoch 018] train/reward_dists_mean=0.9377 train/reward_dists_std=0.0167 train/reward_psnr_mean=30.3032 train/reward_psnr_std=2.2527 train/reward_ssim_mean=0.9377 train/reward_ssim_std=0.0209 train/reward_dists_group_std_mean=0.0033 train/reward_dists_group_std_max=0.0081 train/reward_dists_group_std_min=0.0013 train/reward_dists_group_mean_std=0.0163 train/reward_psnr_group_std_mean=1.1867 train/reward_psnr_group_std_max=2.6536 train/reward_psnr_group_std_min=0.3357 train/reward_psnr_group_mean_std=1.8521 train/reward_ssim_group_std_mean=0.0013 train/reward_ssim_group_std_max=0.0029 train/reward_ssim_group_std_min=0.0004 train/reward_ssim_group_mean_std=0.0208 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-7.3554 train/adv_max=4.9574 train/adv_abs_mean=1.8606
+
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Epoch 18 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.48it/s]
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Epoch 18 Training:  20%|█▉        | 19/96 [00:25<01:40,  1.31s/it]
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Epoch 18 Training:  21%|██        | 20/96 [00:26<01:39,  1.31s/it]
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Epoch 18 Training:  22%|██▏       | 21/96 [00:27<01:37,  1.31s/it]
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Epoch 18 Training:  23%|██▎       | 22/96 [00:28<01:36,  1.30s/it]
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Epoch 18 Training:  24%|██▍       | 23/96 [00:30<01:35,  1.30s/it]
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Epoch 18 Training:  25%|██▌       | 24/96 [00:31<01:33,  1.30s/it]
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Epoch 18 Training:  26%|██▌       | 25/96 [00:32<01:32,  1.30s/it]
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Epoch 18 Training:  27%|██▋       | 26/96 [00:34<01:31,  1.30s/it]
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Epoch 18 Training:  28%|██▊       | 27/96 [00:35<01:29,  1.30s/it]
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Epoch 18 Training:  29%|██▉       | 28/96 [00:36<01:28,  1.30s/it]
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Epoch 18 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 18 Training:  30%|███       | 29/96 [00:38<01:27,  1.30s/it]
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Epoch 18 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.46it/s]
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Epoch 18 Training:  31%|███▏      | 30/96 [00:39<01:26,  1.30s/it]
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Epoch 18 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 18 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.42it/s]
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Epoch 18 Training:  32%|███▏      | 31/96 [00:40<01:24,  1.30s/it]
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Epoch 18 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 18 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.43it/s]
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Epoch 18 Training:  33%|███▎      | 32/96 [00:41<01:23,  1.30s/it]
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Epoch 18 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 18 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.42it/s]
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Epoch 18 Training:  34%|███▍      | 33/96 [00:43<01:21,  1.30s/it]
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Epoch 18 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 18 Training:  35%|███▌      | 34/96 [00:44<01:20,  1.30s/it]
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Epoch 18 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 18 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.48it/s]
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Epoch 18 Training:  36%|███▋      | 35/96 [00:45<01:19,  1.30s/it]
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Epoch 18 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 18 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.43it/s]
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Epoch 18 Training:  38%|███▊      | 36/96 [00:47<01:17,  1.30s/it]
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Epoch 18 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 18 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.48it/s]
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Epoch 18 Training:  39%|███▊      | 37/96 [00:48<01:16,  1.30s/it]
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Epoch 18 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 18 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.48it/s]
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Epoch 18 Training:  40%|███▉      | 38/96 [00:49<01:15,  1.30s/it]
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Epoch 18 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 18 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.49it/s]
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Epoch 18 Training:  41%|████      | 39/96 [00:51<01:14,  1.30s/it]
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Epoch 18 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 18 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.42it/s]
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Epoch 18 Training:  42%|████▏     | 40/96 [00:52<01:12,  1.30s/it]
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Epoch 18 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 18 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.49it/s]
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Epoch 18 Training:  43%|████▎     | 41/96 [00:53<01:11,  1.30s/it]
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Epoch 18 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 18 Training:  44%|████▍     | 42/96 [00:54<01:10,  1.30s/it]
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Epoch 18 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 18 Training:  45%|████▍     | 43/96 [00:56<01:08,  1.30s/it]
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Epoch 18 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 18 Training:  46%|████▌     | 44/96 [00:57<01:07,  1.30s/it]
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Epoch 18 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 18 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.40it/s]
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Epoch 18 Training:  47%|████▋     | 45/96 [00:58<01:06,  1.30s/it]
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Epoch 18 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 18 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.43it/s]
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Epoch 18 Training:  48%|████▊     | 46/96 [01:00<01:05,  1.30s/it]
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Epoch 18 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 18 Training:  49%|████▉     | 47/96 [01:01<01:03,  1.30s/it]
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Epoch 18 Training:  50%|█████     | 48/96 [01:02<01:02,  1.30s/it]
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Epoch 18 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.42it/s]
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Epoch 18 Training:  51%|█████     | 49/96 [01:04<01:01,  1.30s/it]
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Epoch 18 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.42it/s]
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Epoch 18 Training:  52%|█████▏    | 50/96 [01:05<00:59,  1.30s/it]
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Epoch 18 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 18 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.41it/s]
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Epoch 18 Training:  53%|█████▎    | 51/96 [01:06<00:58,  1.30s/it]
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Epoch 18 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 18 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.42it/s]
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Epoch 18 Training:  54%|█████▍    | 52/96 [01:07<00:57,  1.30s/it]
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Epoch 18 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.43it/s]
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Epoch 18 Training:  55%|█████▌    | 53/96 [01:09<00:55,  1.30s/it]
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Epoch 18 Training:  56%|█████▋    | 54/96 [01:10<00:54,  1.30s/it]
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Epoch 18 Training:  57%|█████▋    | 55/96 [01:11<00:53,  1.30s/it]
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Epoch 18 Training:  58%|█████▊    | 56/96 [01:13<00:51,  1.30s/it]
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Epoch 18 Training:  59%|█████▉    | 57/96 [01:14<00:50,  1.30s/it]
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Epoch 18 Training:  60%|██████    | 58/96 [01:15<00:49,  1.30s/it]
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Epoch 18 Training:  61%|██████▏   | 59/96 [01:17<00:47,  1.30s/it]
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Epoch 18 Training:  62%|██████▎   | 60/96 [01:18<00:46,  1.30s/it]
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Epoch 18 Training:  64%|██████▎   | 61/96 [01:19<00:45,  1.30s/it]
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Epoch 18 Training:  65%|██████▍   | 62/96 [01:20<00:44,  1.30s/it]
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Epoch 18 Training:  66%|██████▌   | 63/96 [01:22<00:42,  1.30s/it]
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Epoch 18 Training:  67%|██████▋   | 64/96 [01:23<00:41,  1.30s/it]
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Epoch 18 Training:  68%|██████▊   | 65/96 [01:24<00:40,  1.30s/it]
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Epoch 18 Training:  69%|██████▉   | 66/96 [01:26<00:38,  1.30s/it]
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Epoch 18 Training:  70%|██████▉   | 67/96 [01:27<00:37,  1.30s/it]
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Epoch 18 Training:  71%|███████   | 68/96 [01:28<00:36,  1.30s/it]
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Epoch 18 Training:  72%|███████▏  | 69/96 [01:30<00:35,  1.30s/it]
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Epoch 18 Training:  76%|███████▌  | 73/96 [01:35<00:29,  1.30s/it]
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Epoch 18 Training:  77%|███████▋  | 74/96 [01:36<00:28,  1.30s/it]
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Epoch 18 Training:  81%|████████▏ | 78/96 [01:41<00:23,  1.30s/it]
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Epoch 18 Training:  82%|████████▏ | 79/96 [01:43<00:22,  1.30s/it]
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Epoch 18 Training:  97%|█████████▋| 93/96 [02:01<00:03,  1.30s/it]
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Epoch 18 Training:  98%|█████████▊| 94/96 [02:02<00:02,  1.30s/it]
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Epoch 18 Training:  99%|█████████▉| 95/96 [02:03<00:01,  1.30s/it]
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Epoch 18 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s][2026-10-01 17:33:35] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0018 | Epoch 018] train/policy_loss=12.4999 train/unweighted_policy_loss=2.5000 train/loss=12.4999 train/grad_norm=0.0652
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Epoch 18 Training: 100%|██████████| 96/96 [02:05<00:00,  1.32s/it]
Epoch 18 Training: 100%|██████████| 96/96 [02:05<00:00,  1.30s/it]
+[2026-10-01 17:33:35] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0019 | Epoch 018] timing/rollout_seconds=147.9181 timing/feedback_seconds=5.8488 timing/optimization_seconds=125.7633 timing/cycle_seconds=277.1462
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+[2026-10-01 17:36:08] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0019 | Epoch 019] train/reward_dists_mean=0.9387 train/reward_dists_std=0.0209 train/reward_psnr_mean=30.2148 train/reward_psnr_std=2.1587 train/reward_ssim_mean=0.9424 train/reward_ssim_std=0.0173 train/reward_dists_group_std_mean=0.0026 train/reward_dists_group_std_max=0.0050 train/reward_dists_group_std_min=0.0012 train/reward_dists_group_mean_std=0.0207 train/reward_psnr_group_std_mean=0.9056 train/reward_psnr_group_std_max=2.4359 train/reward_psnr_group_std_min=0.2080 train/reward_psnr_group_mean_std=1.9062 train/reward_ssim_group_std_mean=0.0014 train/reward_ssim_group_std_max=0.0083 train/reward_ssim_group_std_min=0.0003 train/reward_ssim_group_mean_std=0.0172 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-7.0878 train/adv_max=5.5745 train/adv_abs_mean=1.8688
+
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Epoch 19 Training:  18%|█▊        | 17/96 [00:21<01:41,  1.28s/it]
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Epoch 19 Training:  19%|█▉        | 18/96 [00:23<01:40,  1.28s/it]
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Epoch 19 Training:  46%|████▌     | 44/96 [00:56<01:06,  1.28s/it]
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Epoch 19 Training:  47%|████▋     | 45/96 [00:57<01:05,  1.28s/it]
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Epoch 19 Training:  48%|████▊     | 46/96 [00:58<01:04,  1.29s/it]
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Epoch 19 Training:  49%|████▉     | 47/96 [01:00<01:02,  1.28s/it]
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Epoch 19 Training:  50%|█████     | 48/96 [01:01<01:01,  1.28s/it]
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Epoch 19 Training:  51%|█████     | 49/96 [01:02<01:00,  1.28s/it]
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Epoch 19 Training:  52%|█████▏    | 50/96 [01:04<00:58,  1.28s/it]
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Epoch 19 Training:  53%|█████▎    | 51/96 [01:05<00:57,  1.28s/it]
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Epoch 19 Training:  54%|█████▍    | 52/96 [01:06<00:56,  1.28s/it]
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Epoch 19 Training:  55%|█████▌    | 53/96 [01:07<00:55,  1.28s/it]
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Epoch 19 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.48it/s]
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Epoch 19 Training:  56%|█████▋    | 54/96 [01:09<00:53,  1.28s/it]
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Epoch 19 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 19 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.49it/s]
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Epoch 19 Training:  57%|█████▋    | 55/96 [01:10<00:52,  1.28s/it]
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Epoch 19 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 19 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.39it/s]
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Epoch 19 Training:  58%|█████▊    | 56/96 [01:11<00:51,  1.28s/it]
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Epoch 19 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 19 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.36it/s]
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Epoch 19 Training:  59%|█████▉    | 57/96 [01:13<00:50,  1.28s/it]
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Epoch 19 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 19 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.44it/s]
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Epoch 19 Training:  60%|██████    | 58/96 [01:14<00:48,  1.28s/it]
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Epoch 19 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 19 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.47it/s]
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Epoch 19 Training:  61%|██████▏   | 59/96 [01:15<00:47,  1.28s/it]
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Epoch 19 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 19 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.47it/s]
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Epoch 19 Training:  62%|██████▎   | 60/96 [01:16<00:46,  1.28s/it]
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Epoch 19 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 19 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.48it/s]
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Epoch 19 Training:  64%|██████▎   | 61/96 [01:18<00:44,  1.28s/it]
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Epoch 19 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 19 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.47it/s]
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Epoch 19 Training:  65%|██████▍   | 62/96 [01:19<00:43,  1.28s/it]
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Epoch 19 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 19 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.47it/s]
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Epoch 19 Training:  66%|██████▌   | 63/96 [01:20<00:42,  1.28s/it]
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Epoch 19 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 19 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.48it/s]
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Epoch 19 Training:  67%|██████▋   | 64/96 [01:22<00:40,  1.28s/it]
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Epoch 19 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 19 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.46it/s]
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Epoch 19 Training:  68%|██████▊   | 65/96 [01:23<00:39,  1.28s/it]
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Epoch 19 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 19 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.45it/s]
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Epoch 19 Training:  69%|██████▉   | 66/96 [01:24<00:38,  1.28s/it]
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Epoch 19 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 19 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.47it/s]
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Epoch 19 Training:  70%|██████▉   | 67/96 [01:25<00:37,  1.28s/it]
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Epoch 19 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 19 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.49it/s]
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Epoch 19 Training:  71%|███████   | 68/96 [01:27<00:35,  1.28s/it]
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Epoch 19 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 19 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.46it/s]
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Epoch 19 Training:  72%|███████▏  | 69/96 [01:28<00:34,  1.28s/it]
+
Epoch 19 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 19 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.46it/s]
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Epoch 19 Training:  73%|███████▎  | 70/96 [01:29<00:33,  1.28s/it]
+
Epoch 19 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 19 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.45it/s]
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Epoch 19 Training:  74%|███████▍  | 71/96 [01:30<00:32,  1.28s/it]
+
Epoch 19 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 19 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.41it/s]
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Epoch 19 Training:  75%|███████▌  | 72/96 [01:32<00:30,  1.28s/it]
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Epoch 19 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.47it/s]
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Epoch 19 Training:  76%|███████▌  | 73/96 [01:33<00:29,  1.28s/it]
+
Epoch 19 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 19 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.48it/s]
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Epoch 19 Training:  77%|███████▋  | 74/96 [01:34<00:28,  1.28s/it]
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Epoch 19 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 19 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.48it/s]
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Epoch 19 Training:  78%|███████▊  | 75/96 [01:36<00:26,  1.28s/it]
+
Epoch 19 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 19 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.50it/s]
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Epoch 19 Training:  79%|███████▉  | 76/96 [01:37<00:25,  1.28s/it]
+
Epoch 19 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 19 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.46it/s]
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Epoch 19 Training:  80%|████████  | 77/96 [01:38<00:24,  1.28s/it]
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Epoch 19 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 19 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.41it/s]
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Epoch 19 Training:  81%|████████▏ | 78/96 [01:39<00:23,  1.28s/it]
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Epoch 19 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 19 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.48it/s]
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Epoch 19 Training:  82%|████████▏ | 79/96 [01:41<00:21,  1.28s/it]
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Epoch 19 Training:  83%|████████▎ | 80/96 [01:42<00:20,  1.28s/it]
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Epoch 19 Training:  84%|████████▍ | 81/96 [01:43<00:19,  1.28s/it]
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Epoch 19 Training:  85%|████████▌ | 82/96 [01:45<00:17,  1.28s/it]
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Epoch 19 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.49it/s]
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Epoch 19 Training:  86%|████████▋ | 83/96 [01:46<00:16,  1.28s/it]
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Epoch 19 Training:  88%|████████▊ | 84/96 [01:47<00:15,  1.28s/it]
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Epoch 19 Training:  89%|████████▊ | 85/96 [01:48<00:14,  1.28s/it]
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Epoch 19 Training:  90%|████████▉ | 86/96 [01:50<00:12,  1.28s/it]
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Epoch 19 Training:  91%|█████████ | 87/96 [01:51<00:11,  1.28s/it]
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Epoch 19 Training:  92%|█████████▏| 88/96 [01:52<00:10,  1.28s/it]
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Epoch 19 Training:  93%|█████████▎| 89/96 [01:54<00:08,  1.28s/it]
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Epoch 19 Training:  94%|█████████▍| 90/96 [01:55<00:07,  1.28s/it]
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Epoch 19 Training:  95%|█████████▍| 91/96 [01:56<00:06,  1.28s/it]
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Epoch 19 Training:  96%|█████████▌| 92/96 [01:57<00:05,  1.28s/it]
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Epoch 19 Training:  97%|█████████▋| 93/96 [01:59<00:03,  1.28s/it]
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Epoch 19 Training:  98%|█████████▊| 94/96 [02:00<00:02,  1.28s/it]
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+[2026-10-01 17:38:11] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0020 | Epoch 019] timing/rollout_seconds=147.7762 timing/feedback_seconds=6.6402 timing/optimization_seconds=123.6147 timing/cycle_seconds=275.8908
+[2026-10-01 17:38:11] [Rank 0] [INFO] [flow_factory.models.abc]: Saving LoRA weights for transformer role=base to /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/runs/klein4b_nft_vedai_full_ssim_dists_psnr/saves/klein4b_nft_vedai_full_ssim_dists_psnr/checkpoints/checkpoint-20
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+[2026-10-01 17:38:55] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0020 | Epoch 020] eval/vedai_rl_full/reward_dists_mean=0.9329 eval/vedai_rl_full/reward_dists_std=0.0189 eval/vedai_rl_full/reward_psnr_mean=28.2355 eval/vedai_rl_full/reward_psnr_std=2.6656 eval/vedai_rl_full/reward_ssim_mean=0.9242 eval/vedai_rl_full/reward_ssim_std=0.0253
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+[2026-10-01 17:41:27] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0020 | Epoch 020] train/reward_dists_mean=0.9377 train/reward_dists_std=0.0134 train/reward_psnr_mean=29.0885 train/reward_psnr_std=2.2375 train/reward_ssim_mean=0.9375 train/reward_ssim_std=0.0145 train/reward_dists_group_std_mean=0.0029 train/reward_dists_group_std_max=0.0073 train/reward_dists_group_std_min=0.0013 train/reward_dists_group_mean_std=0.0131 train/reward_psnr_group_std_mean=1.2217 train/reward_psnr_group_std_max=2.8856 train/reward_psnr_group_std_min=0.5104 train/reward_psnr_group_mean_std=1.8078 train/reward_ssim_group_std_mean=0.0013 train/reward_ssim_group_std_max=0.0072 train/reward_ssim_group_std_min=0.0005 train/reward_ssim_group_mean_std=0.0144 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-7.7355 train/adv_max=7.7635 train/adv_abs_mean=1.8468
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Epoch 20 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.38it/s]
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Epoch 20 Training:  10%|█         | 10/96 [00:13<01:56,  1.36s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.43it/s]
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Epoch 20 Training:  11%|█▏        | 11/96 [00:14<01:53,  1.34s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.36it/s]
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Epoch 20 Training:  12%|█▎        | 12/96 [00:15<01:51,  1.33s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.36it/s]
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Epoch 20 Training:  14%|█▎        | 13/96 [00:17<01:49,  1.32s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.35it/s]
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Epoch 20 Training:  15%|█▍        | 14/96 [00:18<01:47,  1.31s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.43it/s]
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Epoch 20 Training:  16%|█▌        | 15/96 [00:19<01:45,  1.31s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.42it/s]
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Epoch 20 Training:  17%|█▋        | 16/96 [00:20<01:44,  1.30s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.41it/s]
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Epoch 20 Training:  18%|█▊        | 17/96 [00:22<01:42,  1.29s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.40it/s]
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Epoch 20 Training:  19%|█▉        | 18/96 [00:23<01:40,  1.29s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.41it/s]
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Epoch 20 Training:  20%|█▉        | 19/96 [00:24<01:39,  1.29s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.41it/s]
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Epoch 20 Training:  21%|██        | 20/96 [00:26<01:37,  1.29s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.42it/s]
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Epoch 20 Training:  22%|██▏       | 21/96 [00:27<01:36,  1.29s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.42it/s]
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Epoch 20 Training:  23%|██▎       | 22/96 [00:28<01:35,  1.29s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.39it/s]
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Epoch 20 Training:  24%|██▍       | 23/96 [00:29<01:33,  1.29s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.42it/s]
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Epoch 20 Training:  25%|██▌       | 24/96 [00:31<01:32,  1.28s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.43it/s]
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Epoch 20 Training:  26%|██▌       | 25/96 [00:32<01:31,  1.28s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.43it/s]
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Epoch 20 Training:  27%|██▋       | 26/96 [00:33<01:29,  1.28s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.44it/s]
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Epoch 20 Training:  28%|██▊       | 27/96 [00:35<01:28,  1.28s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.41it/s]
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Epoch 20 Training:  29%|██▉       | 28/96 [00:36<01:27,  1.28s/it]
+
Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.34it/s]
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Epoch 20 Training:  30%|███       | 29/96 [00:37<01:26,  1.29s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.34it/s]
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Epoch 20 Training:  31%|███▏      | 30/96 [00:38<01:25,  1.29s/it]
+
Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.41it/s]
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Epoch 20 Training:  32%|███▏      | 31/96 [00:40<01:23,  1.29s/it]
+
Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.45it/s]
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Epoch 20 Training:  33%|███▎      | 32/96 [00:41<01:22,  1.29s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.43it/s]
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Epoch 20 Training:  34%|███▍      | 33/96 [00:42<01:21,  1.29s/it]
+
Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.35it/s]
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Epoch 20 Training:  35%|███▌      | 34/96 [00:44<01:20,  1.29s/it]
+
Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.41it/s]
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Epoch 20 Training:  36%|███▋      | 35/96 [00:45<01:18,  1.29s/it]
+
Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.40it/s]
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Epoch 20 Training:  38%|███▊      | 36/96 [00:46<01:17,  1.29s/it]
+
Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.37it/s]
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Epoch 20 Training:  39%|███▊      | 37/96 [00:47<01:16,  1.29s/it]
+
Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.40it/s]
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Epoch 20 Training:  40%|███▉      | 38/96 [00:49<01:14,  1.29s/it]
+
Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.39it/s]
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Epoch 20 Training:  41%|████      | 39/96 [00:50<01:13,  1.29s/it]
+
Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.35it/s]
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Epoch 20 Training:  42%|████▏     | 40/96 [00:51<01:12,  1.29s/it]
+
Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.44it/s]
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Epoch 20 Training:  43%|████▎     | 41/96 [00:53<01:11,  1.29s/it]
+
Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.29it/s]
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Epoch 20 Training:  44%|████▍     | 42/96 [00:54<01:10,  1.30s/it]
+
Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.34it/s]
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Epoch 20 Training:  45%|████▍     | 43/96 [00:55<01:09,  1.30s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.35it/s]
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Epoch 20 Training:  46%|████▌     | 44/96 [00:57<01:07,  1.30s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.40it/s]
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Epoch 20 Training:  47%|████▋     | 45/96 [00:58<01:06,  1.30s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Training:  48%|████▊     | 46/96 [00:59<01:04,  1.30s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.38it/s]
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Epoch 20 Training:  49%|████▉     | 47/96 [01:00<01:03,  1.29s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Training:  50%|█████     | 48/96 [01:02<01:02,  1.29s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.41it/s]
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Epoch 20 Training:  51%|█████     | 49/96 [01:03<01:00,  1.29s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.42it/s]
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Epoch 20 Training:  52%|█████▏    | 50/96 [01:04<00:59,  1.29s/it]
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Epoch 20 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 20 Training:  53%|█████▎    | 51/96 [01:06<00:57,  1.29s/it]
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Epoch 20 Training:  54%|█████▍    | 52/96 [01:07<00:56,  1.29s/it]
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Epoch 20 Training:  55%|█████▌    | 53/96 [01:08<00:55,  1.29s/it]
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Epoch 20 Training:  56%|█████▋    | 54/96 [01:09<00:53,  1.28s/it]
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Epoch 20 Training:  57%|█████▋    | 55/96 [01:11<00:52,  1.28s/it]
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Epoch 20 Training:  58%|█████▊    | 56/96 [01:12<00:54,  1.36s/it]
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Epoch 20 Training:  59%|█████▉    | 57/96 [01:14<00:52,  1.34s/it]
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Epoch 20 Training:  60%|██████    | 58/96 [01:15<00:50,  1.32s/it]
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Epoch 20 Training:  61%|██████▏   | 59/96 [01:16<00:48,  1.31s/it]
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Epoch 20 Training:  62%|██████▎   | 60/96 [01:17<00:46,  1.30s/it]
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Epoch 20 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s][2026-10-01 17:43:31] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0020 | Epoch 020] train/policy_loss=12.2416 train/unweighted_policy_loss=2.4483 train/loss=12.2416 train/grad_norm=0.0879
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+[2026-10-01 17:43:31] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0021 | Epoch 020] timing/rollout_seconds=147.7496 timing/feedback_seconds=5.4534 timing/optimization_seconds=124.8215 timing/cycle_seconds=276.2429
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+
Pointwise Rewards: ssim:   0%|          | 0/6 [00:00<?, ?it/s]
Pointwise Rewards: ssim:  17%|█▋        | 1/6 [00:00<00:00,  5.53it/s]
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Pointwise Rewards: ssim:  50%|█████     | 3/6 [00:00<00:00,  6.19it/s]
Pointwise Rewards: ssim:  67%|██████▋   | 4/6 [00:00<00:00,  6.15it/s]
Pointwise Rewards: ssim:  83%|████████▎ | 5/6 [00:00<00:00,  6.32it/s]
Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:00<00:00,  6.42it/s]
Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:00<00:00,  6.25it/s]
+
Pointwise Rewards: dists:   0%|          | 0/6 [00:00<?, ?it/s]
Pointwise Rewards: dists:  17%|█▋        | 1/6 [00:00<00:01,  3.63it/s]
Pointwise Rewards: dists:  33%|███▎      | 2/6 [00:00<00:01,  3.66it/s]
Pointwise Rewards: dists:  50%|█████     | 3/6 [00:00<00:00,  3.63it/s]
Pointwise Rewards: dists:  67%|██████▋   | 4/6 [00:01<00:00,  3.62it/s]
Pointwise Rewards: dists:  83%|████████▎ | 5/6 [00:01<00:00,  3.65it/s]
Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.63it/s]
Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.63it/s]
+
Pointwise Rewards: psnr:   0%|          | 0/6 [00:00<?, ?it/s]
Pointwise Rewards: psnr:  17%|█▋        | 1/6 [00:00<00:00,  6.26it/s]
Pointwise Rewards: psnr:  33%|███▎      | 2/6 [00:00<00:00,  6.43it/s]
Pointwise Rewards: psnr:  50%|█████     | 3/6 [00:00<00:00,  6.53it/s]
Pointwise Rewards: psnr:  67%|██████▋   | 4/6 [00:00<00:00,  6.48it/s]
Pointwise Rewards: psnr:  83%|████████▎ | 5/6 [00:00<00:00,  6.64it/s]
Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.51it/s]
Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.50it/s]
+[2026-10-01 17:46:03] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0021 | Epoch 021] train/reward_dists_mean=0.9433 train/reward_dists_std=0.0155 train/reward_psnr_mean=30.6663 train/reward_psnr_std=2.1707 train/reward_ssim_mean=0.9441 train/reward_ssim_std=0.0195 train/reward_dists_group_std_mean=0.0026 train/reward_dists_group_std_max=0.0053 train/reward_dists_group_std_min=0.0010 train/reward_dists_group_mean_std=0.0153 train/reward_psnr_group_std_mean=1.0944 train/reward_psnr_group_std_max=2.4225 train/reward_psnr_group_std_min=0.3152 train/reward_psnr_group_mean_std=1.8066 train/reward_ssim_group_std_mean=0.0009 train/reward_ssim_group_std_max=0.0027 train/reward_ssim_group_std_min=0.0002 train/reward_ssim_group_mean_std=0.0195 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-8.8202 train/adv_max=5.0024 train/adv_abs_mean=1.7462
+
Epoch 21 Training:   0%|          | 0/96 [00:00<?, ?it/s]
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Epoch 21 Training:   1%|          | 1/96 [00:01<01:57,  1.23s/it]
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Epoch 21 Training:   2%|▏         | 2/96 [00:02<02:11,  1.40s/it]
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Epoch 21 Training:   3%|▎         | 3/96 [00:04<02:06,  1.36s/it]
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Epoch 21 Training:   4%|▍         | 4/96 [00:05<02:03,  1.34s/it]
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Epoch 21 Training:   5%|▌         | 5/96 [00:06<02:01,  1.34s/it]
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Epoch 21 Training:   6%|▋         | 6/96 [00:08<01:59,  1.33s/it]
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Epoch 21 Training:   7%|▋         | 7/96 [00:09<01:57,  1.33s/it]
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Epoch 21 Training:   8%|▊         | 8/96 [00:10<01:56,  1.32s/it]
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Epoch 21 Training:   9%|▉         | 9/96 [00:11<01:55,  1.32s/it]
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Epoch 21 Training:  10%|█         | 10/96 [00:13<01:53,  1.32s/it]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.31it/s]
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Epoch 21 Training:  11%|█▏        | 11/96 [00:14<01:52,  1.32s/it]
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Epoch 21 Training:  12%|█▎        | 12/96 [00:15<01:50,  1.32s/it]
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Epoch 21 Training:  14%|█▎        | 13/96 [00:17<01:49,  1.32s/it]
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Epoch 21 Training:  15%|█▍        | 14/96 [00:18<01:48,  1.32s/it]
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Epoch 21 Training:  16%|█▌        | 15/96 [00:19<01:46,  1.32s/it]
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Epoch 21 Training:  17%|█▋        | 16/96 [00:21<01:45,  1.32s/it]
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Epoch 21 Training:  18%|█▊        | 17/96 [00:22<01:44,  1.32s/it]
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Epoch 21 Training:  19%|█▉        | 18/96 [00:23<01:42,  1.32s/it]
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Epoch 21 Training:  20%|█▉        | 19/96 [00:25<01:41,  1.32s/it]
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Epoch 21 Training:  21%|██        | 20/96 [00:26<01:40,  1.32s/it]
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Epoch 21 Training:  22%|██▏       | 21/96 [00:27<01:38,  1.32s/it]
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Epoch 21 Training:  23%|██▎       | 22/96 [00:29<01:37,  1.32s/it]
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Epoch 21 Training:  24%|██▍       | 23/96 [00:30<01:36,  1.32s/it]
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Epoch 21 Training:  25%|██▌       | 24/96 [00:31<01:34,  1.31s/it]
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Epoch 21 Training:  26%|██▌       | 25/96 [00:33<01:33,  1.31s/it]
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Epoch 21 Training:  27%|██▋       | 26/96 [00:34<01:32,  1.32s/it]
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Epoch 21 Training:  28%|██▊       | 27/96 [00:35<01:30,  1.32s/it]
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Epoch 21 Training:  29%|██▉       | 28/96 [00:36<01:29,  1.32s/it]
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Epoch 21 Training:  30%|███       | 29/96 [00:38<01:28,  1.32s/it]
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Epoch 21 Training:  31%|███▏      | 30/96 [00:39<01:26,  1.32s/it]
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Epoch 21 Training:  32%|███▏      | 31/96 [00:40<01:25,  1.32s/it]
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Epoch 21 Training:  33%|███▎      | 32/96 [00:42<01:24,  1.32s/it]
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Epoch 21 Training:  34%|███▍      | 33/96 [00:43<01:22,  1.32s/it]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.30it/s]
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Epoch 21 Training:  35%|███▌      | 34/96 [00:44<01:21,  1.32s/it]
+
Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 21 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.30it/s]
+
                                                                
Epoch 21 Training:  36%|███▋      | 35/96 [00:46<01:20,  1.32s/it]
+
Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 21 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.29it/s]
+
                                                                
Epoch 21 Training:  38%|███▊      | 36/96 [00:47<01:19,  1.32s/it]
+
Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 21 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.31it/s]
+
                                                                
Epoch 21 Training:  39%|███▊      | 37/96 [00:48<01:17,  1.32s/it]
+
Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 21 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.34it/s]
+
                                                                
Epoch 21 Training:  40%|███▉      | 38/96 [00:50<01:16,  1.31s/it]
+
Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 21 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.32it/s]
+
                                                                
Epoch 21 Training:  41%|████      | 39/96 [00:51<01:14,  1.31s/it]
+
Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 21 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.32it/s]
+
                                                                
Epoch 21 Training:  42%|████▏     | 40/96 [00:52<01:13,  1.31s/it]
+
Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 21 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.31it/s]
+
                                                                
Epoch 21 Training:  43%|████▎     | 41/96 [00:54<01:12,  1.31s/it]
+
Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 21 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.32it/s]
+
                                                                
Epoch 21 Training:  44%|████▍     | 42/96 [00:55<01:10,  1.31s/it]
+
Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 21 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.31it/s]
+
                                                                
Epoch 21 Training:  45%|████▍     | 43/96 [00:56<01:09,  1.31s/it]
+
Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 21 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.33it/s]
+
                                                                
Epoch 21 Training:  46%|████▌     | 44/96 [00:58<01:08,  1.31s/it]
+
Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 21 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.33it/s]
+
                                                                
Epoch 21 Training:  47%|████▋     | 45/96 [00:59<01:06,  1.31s/it]
+
Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 21 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
+
                                                                
Epoch 21 Training:  48%|████▊     | 46/96 [01:00<01:05,  1.32s/it]
+
Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 21 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.29it/s]
+
                                                                
Epoch 21 Training:  49%|████▉     | 47/96 [01:01<01:04,  1.32s/it]
+
Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 21 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.31it/s]
+
                                                                
Epoch 21 Training:  50%|█████     | 48/96 [01:03<01:03,  1.32s/it]
+
Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 21 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.30it/s]
+
                                                                
Epoch 21 Training:  51%|█████     | 49/96 [01:04<01:01,  1.32s/it]
+
Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 21 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.30it/s]
+
                                                                
Epoch 21 Training:  52%|█████▏    | 50/96 [01:05<01:00,  1.32s/it]
+
Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 21 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
+
                                                                
Epoch 21 Training:  53%|█████▎    | 51/96 [01:07<00:59,  1.32s/it]
+
Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 21 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
+
Epoch 21 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
+
                                                                
Epoch 21 Training:  54%|█████▍    | 52/96 [01:08<00:58,  1.32s/it]
+
Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 21 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
+
Epoch 21 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.30it/s]
+
                                                                
Epoch 21 Training:  55%|█████▌    | 53/96 [01:09<00:56,  1.32s/it]
+
Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 21 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
+
Epoch 21 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.31it/s]
+
                                                                
Epoch 21 Training:  56%|█████▋    | 54/96 [01:11<00:55,  1.32s/it]
+
Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 21 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
+
Epoch 21 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.29it/s]
+
                                                                
Epoch 21 Training:  57%|█████▋    | 55/96 [01:12<00:54,  1.32s/it]
+
Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 21 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
+
Epoch 21 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
+
                                                                
Epoch 21 Training:  58%|█████▊    | 56/96 [01:13<00:52,  1.32s/it]
+
Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 21 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
+
Epoch 21 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.29it/s]
+
                                                                
Epoch 21 Training:  59%|█████▉    | 57/96 [01:15<00:51,  1.32s/it]
+
Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 21 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
+
Epoch 21 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.29it/s]
+
                                                                
Epoch 21 Training:  60%|██████    | 58/96 [01:16<00:50,  1.32s/it]
+
Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 21 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
+
Epoch 21 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.29it/s]
+
                                                                
Epoch 21 Training:  61%|██████▏   | 59/96 [01:17<00:48,  1.32s/it]
+
Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 21 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
+
Epoch 21 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.31it/s]
+
                                                                
Epoch 21 Training:  62%|██████▎   | 60/96 [01:19<00:47,  1.32s/it]
+
Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 21 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
+
Epoch 21 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.29it/s]
+
                                                                
Epoch 21 Training:  64%|██████▎   | 61/96 [01:20<00:46,  1.32s/it]
+
Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 21 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
+
Epoch 21 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.29it/s]
+
                                                                
Epoch 21 Training:  65%|██████▍   | 62/96 [01:21<00:44,  1.32s/it]
+
Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 21 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
+
Epoch 21 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.29it/s]
+
                                                                
Epoch 21 Training:  66%|██████▌   | 63/96 [01:23<00:43,  1.32s/it]
+
Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 21 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
+
Epoch 21 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.32it/s]
+
                                                                
Epoch 21 Training:  67%|██████▋   | 64/96 [01:24<00:42,  1.32s/it]
+
Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 21 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
+
Epoch 21 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.32it/s]
+
                                                                
Epoch 21 Training:  68%|██████▊   | 65/96 [01:25<00:40,  1.32s/it]
+
Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 21 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.30it/s]
+
                                                                
Epoch 21 Training:  69%|██████▉   | 66/96 [01:27<00:39,  1.32s/it]
+
Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 21 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
+
Epoch 21 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.29it/s]
+
                                                                
Epoch 21 Training:  70%|██████▉   | 67/96 [01:28<00:38,  1.32s/it]
+
Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 21 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
+
Epoch 21 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
+
                                                                
Epoch 21 Training:  71%|███████   | 68/96 [01:29<00:36,  1.32s/it]
+
Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 21 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.35it/s]
+
                                                                
Epoch 21 Training:  72%|███████▏  | 69/96 [01:30<00:35,  1.32s/it]
+
Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 21 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.31it/s]
+
                                                                
Epoch 21 Training:  73%|███████▎  | 70/96 [01:32<00:34,  1.32s/it]
+
Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 21 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.32it/s]
+
                                                                
Epoch 21 Training:  74%|███████▍  | 71/96 [01:33<00:32,  1.32s/it]
+
Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 21 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.30it/s]
+
                                                                
Epoch 21 Training:  75%|███████▌  | 72/96 [01:34<00:31,  1.32s/it]
+
Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 21 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
+
                                                                
Epoch 21 Training:  76%|███████▌  | 73/96 [01:36<00:30,  1.32s/it]
+
Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 21 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.32it/s]
+
                                                                
Epoch 21 Training:  77%|███████▋  | 74/96 [01:37<00:28,  1.31s/it]
+
Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 21 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.30it/s]
+
                                                                
Epoch 21 Training:  78%|███████▊  | 75/96 [01:38<00:27,  1.32s/it]
+
Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 21 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.63it/s]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.33it/s]
+
                                                                
Epoch 21 Training:  79%|███████▉  | 76/96 [01:40<00:26,  1.31s/it]
+
Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 21 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.35it/s]
+
                                                                
Epoch 21 Training:  80%|████████  | 77/96 [01:41<00:24,  1.31s/it]
+
Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 21 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.33it/s]
+
                                                                
Epoch 21 Training:  81%|████████▏ | 78/96 [01:42<00:23,  1.31s/it]
+
Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 21 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.34it/s]
+
                                                                
Epoch 21 Training:  82%|████████▏ | 79/96 [01:44<00:22,  1.31s/it]
+
Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 21 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.33it/s]
+
                                                                
Epoch 21 Training:  83%|████████▎ | 80/96 [01:45<00:20,  1.31s/it]
+
Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 21 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.34it/s]
+
                                                                
Epoch 21 Training:  84%|████████▍ | 81/96 [01:46<00:19,  1.31s/it]
+
Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 21 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 21 Training:  85%|████████▌ | 82/96 [01:48<00:18,  1.31s/it]
+
Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 21 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.63it/s]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.31it/s]
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Epoch 21 Training:  86%|████████▋ | 83/96 [01:49<00:17,  1.31s/it]
+
Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 21 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.30it/s]
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Epoch 21 Training:  88%|████████▊ | 84/96 [01:50<00:15,  1.31s/it]
+
Epoch 21 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 21 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 21 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.33it/s]
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Epoch 21 Training:  89%|████████▊ | 85/96 [01:51<00:14,  1.31s/it]
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Epoch 21 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s][2026-10-01 17:48:10] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0021 | Epoch 021] train/policy_loss=12.0292 train/unweighted_policy_loss=2.4058 train/loss=12.0292 train/grad_norm=0.0488
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+[2026-10-01 17:48:10] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0022 | Epoch 021] timing/rollout_seconds=148.1294 timing/feedback_seconds=5.8868 timing/optimization_seconds=127.0908 timing/cycle_seconds=278.6565
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+
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+
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+
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+[2026-10-01 17:50:42] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0022 | Epoch 022] train/reward_dists_mean=0.9425 train/reward_dists_std=0.0139 train/reward_psnr_mean=30.5078 train/reward_psnr_std=2.3526 train/reward_ssim_mean=0.9459 train/reward_ssim_std=0.0161 train/reward_dists_group_std_mean=0.0028 train/reward_dists_group_std_max=0.0070 train/reward_dists_group_std_min=0.0012 train/reward_dists_group_mean_std=0.0136 train/reward_psnr_group_std_mean=1.1374 train/reward_psnr_group_std_max=2.8400 train/reward_psnr_group_std_min=0.2527 train/reward_psnr_group_mean_std=1.9955 train/reward_ssim_group_std_mean=0.0012 train/reward_ssim_group_std_max=0.0073 train/reward_ssim_group_std_min=0.0003 train/reward_ssim_group_mean_std=0.0160 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-7.7220 train/adv_max=4.8997 train/adv_abs_mean=1.8278
+
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Epoch 22 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.29it/s]
+
                                                                
Epoch 22 Training:   7%|▋         | 7/96 [00:09<01:57,  1.31s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
+
Epoch 22 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.30it/s]
+
                                                                
Epoch 22 Training:   8%|▊         | 8/96 [00:10<01:55,  1.32s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
+
Epoch 22 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.30it/s]
+
                                                                
Epoch 22 Training:   9%|▉         | 9/96 [00:11<01:54,  1.32s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
+
Epoch 22 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.32it/s]
+
                                                                
Epoch 22 Training:  10%|█         | 10/96 [00:13<01:53,  1.32s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
+
Epoch 22 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.30it/s]
+
                                                                
Epoch 22 Training:  11%|█▏        | 11/96 [00:14<01:51,  1.32s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
+
Epoch 22 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
+
                                                                
Epoch 22 Training:  12%|█▎        | 12/96 [00:15<01:50,  1.32s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
+
Epoch 22 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.31it/s]
+
                                                                
Epoch 22 Training:  14%|█▎        | 13/96 [00:17<01:49,  1.32s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
+
Epoch 22 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.29it/s]
+
                                                                
Epoch 22 Training:  15%|█▍        | 14/96 [00:18<01:48,  1.32s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
+
Epoch 22 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.30it/s]
+
                                                                
Epoch 22 Training:  16%|█▌        | 15/96 [00:19<01:46,  1.32s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
+
Epoch 22 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
+
                                                                
Epoch 22 Training:  17%|█▋        | 16/96 [00:21<01:45,  1.32s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
+
Epoch 22 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
+
                                                                
Epoch 22 Training:  18%|█▊        | 17/96 [00:22<01:51,  1.41s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
+
Epoch 22 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
+
                                                                
Epoch 22 Training:  19%|█▉        | 18/96 [00:23<01:48,  1.39s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
+
Epoch 22 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.31it/s]
+
                                                                
Epoch 22 Training:  20%|█▉        | 19/96 [00:25<01:45,  1.37s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
+
Epoch 22 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.30it/s]
+
                                                                
Epoch 22 Training:  21%|██        | 20/96 [00:26<01:42,  1.35s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
+
Epoch 22 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.30it/s]
+
                                                                
Epoch 22 Training:  22%|██▏       | 21/96 [00:27<01:40,  1.34s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
+
Epoch 22 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.30it/s]
+
                                                                
Epoch 22 Training:  23%|██▎       | 22/96 [00:29<01:38,  1.33s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
+
Epoch 22 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
+
                                                                
Epoch 22 Training:  24%|██▍       | 23/96 [00:30<01:37,  1.33s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
+
Epoch 22 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.31it/s]
+
                                                                
Epoch 22 Training:  25%|██▌       | 24/96 [00:31<01:35,  1.33s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
+
Epoch 22 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.32it/s]
+
                                                                
Epoch 22 Training:  26%|██▌       | 25/96 [00:33<01:33,  1.32s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
+
Epoch 22 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.31it/s]
+
                                                                
Epoch 22 Training:  27%|██▋       | 26/96 [00:34<01:32,  1.32s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
+
Epoch 22 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.29it/s]
+
                                                                
Epoch 22 Training:  28%|██▊       | 27/96 [00:35<01:31,  1.32s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
+
Epoch 22 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.29it/s]
+
                                                                
Epoch 22 Training:  29%|██▉       | 28/96 [00:37<01:29,  1.32s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
+
Epoch 22 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.31it/s]
+
                                                                
Epoch 22 Training:  30%|███       | 29/96 [00:38<01:28,  1.32s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
+
Epoch 22 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.31it/s]
+
                                                                
Epoch 22 Training:  31%|███▏      | 30/96 [00:39<01:26,  1.32s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
+
Epoch 22 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.32it/s]
+
                                                                
Epoch 22 Training:  32%|███▏      | 31/96 [00:41<01:25,  1.32s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
+
Epoch 22 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.32it/s]
+
                                                                
Epoch 22 Training:  33%|███▎      | 32/96 [00:42<01:24,  1.31s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
+
Epoch 22 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.29it/s]
+
                                                                
Epoch 22 Training:  34%|███▍      | 33/96 [00:43<01:22,  1.32s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
+
Epoch 22 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.29it/s]
+
                                                                
Epoch 22 Training:  35%|███▌      | 34/96 [00:45<01:21,  1.32s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
+
Epoch 22 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.30it/s]
+
                                                                
Epoch 22 Training:  36%|███▋      | 35/96 [00:46<01:20,  1.32s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
+
Epoch 22 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.29it/s]
+
                                                                
Epoch 22 Training:  38%|███▊      | 36/96 [00:47<01:19,  1.32s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
+
Epoch 22 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.31it/s]
+
                                                                
Epoch 22 Training:  39%|███▊      | 37/96 [00:48<01:17,  1.32s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
+
Epoch 22 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.31it/s]
+
                                                                
Epoch 22 Training:  40%|███▉      | 38/96 [00:50<01:16,  1.32s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
+
Epoch 22 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.30it/s]
+
                                                                
Epoch 22 Training:  41%|████      | 39/96 [00:51<01:15,  1.32s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
+
Epoch 22 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
+
                                                                
Epoch 22 Training:  42%|████▏     | 40/96 [00:52<01:13,  1.32s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
+
Epoch 22 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.31it/s]
+
                                                                
Epoch 22 Training:  43%|████▎     | 41/96 [00:54<01:12,  1.32s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
+
Epoch 22 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.72it/s]
+
                                                                
Epoch 22 Training:  44%|████▍     | 42/96 [00:55<01:15,  1.39s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
+
Epoch 22 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.29it/s]
+
                                                                
Epoch 22 Training:  45%|████▍     | 43/96 [00:57<01:12,  1.37s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
+
Epoch 22 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
+
                                                                
Epoch 22 Training:  46%|████▌     | 44/96 [00:58<01:10,  1.36s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
+
Epoch 22 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.30it/s]
+
                                                                
Epoch 22 Training:  47%|████▋     | 45/96 [00:59<01:08,  1.35s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
+
Epoch 22 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
+
                                                                
Epoch 22 Training:  48%|████▊     | 46/96 [01:01<01:06,  1.34s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
+
Epoch 22 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.29it/s]
+
                                                                
Epoch 22 Training:  49%|████▉     | 47/96 [01:02<01:05,  1.33s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
+
Epoch 22 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.29it/s]
+
                                                                
Epoch 22 Training:  50%|█████     | 48/96 [01:03<01:03,  1.33s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
+
Epoch 22 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.29it/s]
+
                                                                
Epoch 22 Training:  51%|█████     | 49/96 [01:05<01:05,  1.40s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
+
Epoch 22 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
+
                                                                
Epoch 22 Training:  52%|█████▏    | 50/96 [01:06<01:03,  1.37s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
+
Epoch 22 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.30it/s]
+
                                                                
Epoch 22 Training:  53%|█████▎    | 51/96 [01:07<01:01,  1.36s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
+
Epoch 22 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.31it/s]
+
                                                                
Epoch 22 Training:  54%|█████▍    | 52/96 [01:09<00:59,  1.34s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
+
Epoch 22 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.32it/s]
+
                                                                
Epoch 22 Training:  55%|█████▌    | 53/96 [01:10<00:57,  1.34s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
+
Epoch 22 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.32it/s]
+
                                                                
Epoch 22 Training:  56%|█████▋    | 54/96 [01:11<00:55,  1.33s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
+
Epoch 22 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.31it/s]
+
                                                                
Epoch 22 Training:  57%|█████▋    | 55/96 [01:13<00:54,  1.33s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
+
Epoch 22 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
+
                                                                
Epoch 22 Training:  58%|█████▊    | 56/96 [01:14<00:52,  1.32s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
+
Epoch 22 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
+
                                                                
Epoch 22 Training:  59%|█████▉    | 57/96 [01:15<00:51,  1.32s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 22 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.35it/s]
+
                                                                
Epoch 22 Training:  60%|██████    | 58/96 [01:17<00:50,  1.32s/it]
+
Epoch 22 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 22 Training:  61%|██████▏   | 59/96 [01:18<00:48,  1.32s/it]
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Epoch 22 Training:  62%|██████▎   | 60/96 [01:19<00:47,  1.32s/it]
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Epoch 22 Training:  64%|██████▎   | 61/96 [01:21<00:46,  1.32s/it]
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Epoch 22 Training:  65%|██████▍   | 62/96 [01:22<00:45,  1.32s/it]
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Epoch 22 Training:  66%|██████▌   | 63/96 [01:23<00:43,  1.32s/it]
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Epoch 22 Training:  67%|██████▋   | 64/96 [01:25<00:42,  1.32s/it]
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Epoch 22 Training:  77%|███████▋  | 74/96 [01:38<00:28,  1.31s/it]
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Epoch 22 Training:  79%|███████▉  | 76/96 [01:40<00:26,  1.31s/it]
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Epoch 22 Training:  81%|████████▏ | 78/96 [01:43<00:23,  1.32s/it]
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Epoch 22 Training:  82%|████████▏ | 79/96 [01:44<00:22,  1.32s/it]
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Epoch 22 Training:  99%|█████████▉| 95/96 [02:05<00:01,  1.32s/it]
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Epoch 22 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s][2026-10-01 17:52:49] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0022 | Epoch 022] train/policy_loss=11.7571 train/unweighted_policy_loss=2.3514 train/loss=11.7571 train/grad_norm=0.0606
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Epoch 22 Training: 100%|██████████| 96/96 [02:07<00:00,  1.33s/it]
+[2026-10-01 17:52:49] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0023 | Epoch 022] timing/rollout_seconds=147.6597 timing/feedback_seconds=5.5600 timing/optimization_seconds=127.9044 timing/cycle_seconds=279.0834
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+
Pointwise Rewards: ssim:   0%|          | 0/6 [00:00<?, ?it/s]
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Pointwise Rewards: ssim:  83%|████████▎ | 5/6 [00:00<00:00,  6.65it/s]
Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:00<00:00,  6.62it/s]
Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:00<00:00,  6.55it/s]
+
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Pointwise Rewards: dists:  50%|█████     | 3/6 [00:00<00:00,  3.66it/s]
Pointwise Rewards: dists:  67%|██████▋   | 4/6 [00:01<00:00,  3.66it/s]
Pointwise Rewards: dists:  83%|████████▎ | 5/6 [00:01<00:00,  3.69it/s]
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Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.68it/s]
+
Pointwise Rewards: psnr:   0%|          | 0/6 [00:00<?, ?it/s]
Pointwise Rewards: psnr:  17%|█▋        | 1/6 [00:00<00:00,  6.99it/s]
Pointwise Rewards: psnr:  33%|███▎      | 2/6 [00:00<00:00,  7.05it/s]
Pointwise Rewards: psnr:  50%|█████     | 3/6 [00:00<00:00,  6.78it/s]
Pointwise Rewards: psnr:  67%|██████▋   | 4/6 [00:00<00:00,  6.97it/s]
Pointwise Rewards: psnr:  83%|████████▎ | 5/6 [00:00<00:00,  6.87it/s]
Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.87it/s]
Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.89it/s]
+[2026-10-01 17:55:21] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0023 | Epoch 023] train/reward_dists_mean=0.9418 train/reward_dists_std=0.0144 train/reward_psnr_mean=29.2591 train/reward_psnr_std=2.9806 train/reward_ssim_mean=0.9410 train/reward_ssim_std=0.0189 train/reward_dists_group_std_mean=0.0033 train/reward_dists_group_std_max=0.0068 train/reward_dists_group_std_min=0.0013 train/reward_dists_group_mean_std=0.0139 train/reward_psnr_group_std_mean=1.3951 train/reward_psnr_group_std_max=2.5082 train/reward_psnr_group_std_min=0.4616 train/reward_psnr_group_mean_std=2.5979 train/reward_ssim_group_std_mean=0.0017 train/reward_ssim_group_std_max=0.0115 train/reward_ssim_group_std_min=0.0002 train/reward_ssim_group_mean_std=0.0187 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-8.8799 train/adv_max=6.6605 train/adv_abs_mean=1.9863
+
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Epoch 23 Training:   1%|          | 1/96 [00:01<01:57,  1.24s/it]
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Epoch 23 Training:   2%|▏         | 2/96 [00:02<02:00,  1.29s/it]
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Epoch 23 Training:   3%|▎         | 3/96 [00:03<02:00,  1.30s/it]
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Epoch 23 Training:   4%|▍         | 4/96 [00:05<02:00,  1.31s/it]
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Epoch 23 Training:   5%|▌         | 5/96 [00:06<01:59,  1.31s/it]
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Epoch 23 Training:   6%|▋         | 6/96 [00:07<01:58,  1.31s/it]
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Epoch 23 Training:   7%|▋         | 7/96 [00:09<01:56,  1.31s/it]
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Epoch 23 Training:   8%|▊         | 8/96 [00:10<01:55,  1.32s/it]
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Epoch 23 Training:   9%|▉         | 9/96 [00:11<01:54,  1.32s/it]
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Epoch 23 Training:  10%|█         | 10/96 [00:13<01:53,  1.32s/it]
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Epoch 23 Training:  11%|█▏        | 11/96 [00:14<01:51,  1.32s/it]
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Epoch 23 Training:  12%|█▎        | 12/96 [00:15<01:50,  1.32s/it]
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Epoch 23 Training:  14%|█▎        | 13/96 [00:17<01:49,  1.32s/it]
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Epoch 23 Training:  15%|█▍        | 14/96 [00:18<01:48,  1.32s/it]
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Epoch 23 Training:  16%|█▌        | 15/96 [00:19<01:46,  1.32s/it]
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Epoch 23 Training:  17%|█▋        | 16/96 [00:20<01:45,  1.32s/it]
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Epoch 23 Training:  18%|█▊        | 17/96 [00:22<01:44,  1.32s/it]
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Epoch 23 Training:  19%|█▉        | 18/96 [00:23<01:42,  1.32s/it]
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Epoch 23 Training:  20%|█▉        | 19/96 [00:24<01:41,  1.32s/it]
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Epoch 23 Training:  21%|██        | 20/96 [00:26<01:40,  1.32s/it]
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Epoch 23 Training:  22%|██▏       | 21/96 [00:27<01:38,  1.32s/it]
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Epoch 23 Training:  23%|██▎       | 22/96 [00:28<01:37,  1.32s/it]
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Epoch 23 Training:  24%|██▍       | 23/96 [00:30<01:36,  1.32s/it]
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Epoch 23 Training:  25%|██▌       | 24/96 [00:31<01:34,  1.32s/it]
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Epoch 23 Training:  26%|██▌       | 25/96 [00:32<01:33,  1.32s/it]
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Epoch 23 Training:  27%|██▋       | 26/96 [00:34<01:32,  1.32s/it]
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Epoch 23 Training:  28%|██▊       | 27/96 [00:35<01:30,  1.32s/it]
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Epoch 23 Training:  29%|██▉       | 28/96 [00:36<01:29,  1.32s/it]
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Epoch 23 Training:  30%|███       | 29/96 [00:38<01:28,  1.32s/it]
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Epoch 23 Training:  31%|███▏      | 30/96 [00:39<01:27,  1.32s/it]
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Epoch 23 Training:  32%|███▏      | 31/96 [00:40<01:25,  1.32s/it]
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Epoch 23 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 23 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.38it/s]
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Epoch 23 Training:  33%|███▎      | 32/96 [00:42<01:24,  1.32s/it]
+
Epoch 23 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 23 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 23 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.36it/s]
+
                                                                
Epoch 23 Training:  34%|███▍      | 33/96 [00:43<01:23,  1.32s/it]
+
Epoch 23 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 23 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 23 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.38it/s]
+
                                                                
Epoch 23 Training:  35%|███▌      | 34/96 [00:44<01:21,  1.32s/it]
+
Epoch 23 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 23 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 23 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.35it/s]
+
                                                                
Epoch 23 Training:  36%|███▋      | 35/96 [00:46<01:20,  1.32s/it]
+
Epoch 23 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 23 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 23 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.35it/s]
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Epoch 23 Training:  38%|███▊      | 36/96 [00:47<01:19,  1.32s/it]
+
Epoch 23 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 23 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 23 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.35it/s]
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Epoch 23 Training:  39%|███▊      | 37/96 [00:48<01:17,  1.32s/it]
+
Epoch 23 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 23 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 23 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.40it/s]
+
                                                                
Epoch 23 Training:  40%|███▉      | 38/96 [00:49<01:16,  1.31s/it]
+
Epoch 23 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 23 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 23 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.38it/s]
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Epoch 23 Training:  41%|████      | 39/96 [00:51<01:14,  1.31s/it]
+
Epoch 23 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 23 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 23 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.38it/s]
+
                                                                
Epoch 23 Training:  42%|████▏     | 40/96 [00:52<01:13,  1.31s/it]
+
Epoch 23 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 23 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 23 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.36it/s]
+
                                                                
Epoch 23 Training:  43%|████▎     | 41/96 [00:53<01:12,  1.31s/it]
+
Epoch 23 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 23 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 23 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.38it/s]
+
                                                                
Epoch 23 Training:  44%|████▍     | 42/96 [00:55<01:10,  1.31s/it]
+
Epoch 23 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 23 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 23 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.35it/s]
+
                                                                
Epoch 23 Training:  45%|████▍     | 43/96 [00:56<01:09,  1.32s/it]
+
Epoch 23 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 23 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 23 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.37it/s]
+
                                                                
Epoch 23 Training:  46%|████▌     | 44/96 [00:57<01:08,  1.32s/it]
+
Epoch 23 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 23 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 23 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.38it/s]
+
                                                                
Epoch 23 Training:  47%|████▋     | 45/96 [00:59<01:07,  1.32s/it]
+
Epoch 23 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 23 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 23 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.39it/s]
+
                                                                
Epoch 23 Training:  48%|████▊     | 46/96 [01:00<01:05,  1.31s/it]
+
Epoch 23 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 23 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 23 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.36it/s]
+
                                                                
Epoch 23 Training:  49%|████▉     | 47/96 [01:01<01:04,  1.32s/it]
+
Epoch 23 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 23 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 23 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.37it/s]
+
                                                                
Epoch 23 Training:  50%|█████     | 48/96 [01:03<01:03,  1.31s/it]
+
Epoch 23 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 23 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 23 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.34it/s]
+
                                                                
Epoch 23 Training:  51%|█████     | 49/96 [01:04<01:01,  1.32s/it]
+
Epoch 23 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 23 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 23 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.35it/s]
+
                                                                
Epoch 23 Training:  52%|█████▏    | 50/96 [01:05<01:00,  1.32s/it]
+
Epoch 23 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 23 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 23 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.38it/s]
+
                                                                
Epoch 23 Training:  53%|█████▎    | 51/96 [01:07<00:59,  1.32s/it]
+
Epoch 23 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 23 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 23 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.38it/s]
+
                                                                
Epoch 23 Training:  54%|█████▍    | 52/96 [01:08<00:57,  1.32s/it]
+
Epoch 23 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 23 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
+
Epoch 23 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.37it/s]
+
                                                                
Epoch 23 Training:  55%|█████▌    | 53/96 [01:09<00:56,  1.31s/it]
+
Epoch 23 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 23 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 23 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.38it/s]
+
                                                                
Epoch 23 Training:  56%|█████▋    | 54/96 [01:11<00:55,  1.31s/it]
+
Epoch 23 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 23 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 23 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.38it/s]
+
                                                                
Epoch 23 Training:  57%|█████▋    | 55/96 [01:12<00:53,  1.31s/it]
+
Epoch 23 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 23 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 23 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.38it/s]
+
                                                                
Epoch 23 Training:  58%|█████▊    | 56/96 [01:13<00:52,  1.31s/it]
+
Epoch 23 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 23 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 23 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.38it/s]
+
                                                                
Epoch 23 Training:  59%|█████▉    | 57/96 [01:14<00:51,  1.31s/it]
+
Epoch 23 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 23 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
+
Epoch 23 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.38it/s]
+
                                                                
Epoch 23 Training:  60%|██████    | 58/96 [01:16<00:49,  1.31s/it]
+
Epoch 23 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 23 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
+
Epoch 23 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.38it/s]
+
                                                                
Epoch 23 Training:  61%|██████▏   | 59/96 [01:17<00:48,  1.31s/it]
+
Epoch 23 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 23 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
+
Epoch 23 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.38it/s]
+
                                                                
Epoch 23 Training:  62%|██████▎   | 60/96 [01:18<00:47,  1.31s/it]
+
Epoch 23 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 23 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
+
Epoch 23 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.32it/s]
+
                                                                
Epoch 23 Training:  64%|██████▎   | 61/96 [01:20<00:46,  1.32s/it]
+
Epoch 23 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 23 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 23 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.37it/s]
+
                                                                
Epoch 23 Training:  65%|██████▍   | 62/96 [01:21<00:44,  1.32s/it]
+
Epoch 23 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 23 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 23 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.34it/s]
+
                                                                
Epoch 23 Training:  66%|██████▌   | 63/96 [01:22<00:43,  1.32s/it]
+
Epoch 23 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 23 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 23 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.35it/s]
+
                                                                
Epoch 23 Training:  67%|██████▋   | 64/96 [01:24<00:42,  1.32s/it]
+
Epoch 23 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 23 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 23 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.39it/s]
+
                                                                
Epoch 23 Training:  68%|██████▊   | 65/96 [01:25<00:40,  1.31s/it]
+
Epoch 23 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 23 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 23 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.41it/s]
+
                                                                
Epoch 23 Training:  69%|██████▉   | 66/96 [01:26<00:39,  1.31s/it]
+
Epoch 23 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 23 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 23 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.40it/s]
+
                                                                
Epoch 23 Training:  70%|██████▉   | 67/96 [01:28<00:38,  1.31s/it]
+
Epoch 23 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 23 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 23 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.39it/s]
+
                                                                
Epoch 23 Training:  71%|███████   | 68/96 [01:29<00:36,  1.31s/it]
+
Epoch 23 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 23 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 23 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.39it/s]
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Epoch 23 Training:  72%|███████▏  | 69/96 [01:30<00:35,  1.31s/it]
+
Epoch 23 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 23 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 23 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.38it/s]
+
                                                                
Epoch 23 Training:  73%|███████▎  | 70/96 [01:32<00:34,  1.31s/it]
+
Epoch 23 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 23 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 23 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.39it/s]
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Epoch 23 Training:  74%|███████▍  | 71/96 [01:33<00:32,  1.31s/it]
+
Epoch 23 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 23 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 23 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.39it/s]
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Epoch 23 Training:  75%|███████▌  | 72/96 [01:34<00:31,  1.31s/it]
+
Epoch 23 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 23 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 23 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.36it/s]
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Epoch 23 Training:  76%|███████▌  | 73/96 [01:35<00:30,  1.31s/it]
+
Epoch 23 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 23 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 23 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.33it/s]
+
                                                                
Epoch 23 Training:  77%|███████▋  | 74/96 [01:37<00:28,  1.31s/it]
+
Epoch 23 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 23 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 23 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.36it/s]
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Epoch 23 Training:  78%|███████▊  | 75/96 [01:38<00:27,  1.31s/it]
+
Epoch 23 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 23 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 23 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.38it/s]
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Epoch 23 Training:  79%|███████▉  | 76/96 [01:39<00:26,  1.32s/it]
+
Epoch 23 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 23 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 23 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.37it/s]
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Epoch 23 Training:  80%|████████  | 77/96 [01:41<00:25,  1.32s/it]
+
Epoch 23 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 23 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 23 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.36it/s]
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Epoch 23 Training:  81%|████████▏ | 78/96 [01:42<00:23,  1.32s/it]
+
Epoch 23 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 23 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 23 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.33it/s]
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Epoch 23 Training:  82%|████████▏ | 79/96 [01:43<00:22,  1.32s/it]
+
Epoch 23 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 23 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 23 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.34it/s]
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Epoch 23 Training:  83%|████████▎ | 80/96 [01:45<00:21,  1.32s/it]
+
Epoch 23 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 23 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 23 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.38it/s]
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Epoch 23 Training:  84%|████████▍ | 81/96 [01:46<00:19,  1.32s/it]
+
Epoch 23 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 23 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 23 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.37it/s]
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Epoch 23 Training:  85%|████████▌ | 82/96 [01:47<00:18,  1.32s/it]
+
Epoch 23 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 23 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 23 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s][2026-10-01 17:57:27] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0023 | Epoch 023] train/policy_loss=11.8562 train/unweighted_policy_loss=2.3712 train/loss=11.8562 train/grad_norm=0.0984
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+[2026-10-01 17:57:27] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0024 | Epoch 023] timing/rollout_seconds=147.7913 timing/feedback_seconds=5.6152 timing/optimization_seconds=126.9184 timing/cycle_seconds=278.2052
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+
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+[2026-10-01 17:59:59] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0024 | Epoch 024] train/reward_dists_mean=0.9407 train/reward_dists_std=0.0139 train/reward_psnr_mean=30.6643 train/reward_psnr_std=2.0947 train/reward_ssim_mean=0.9406 train/reward_ssim_std=0.0179 train/reward_dists_group_std_mean=0.0029 train/reward_dists_group_std_max=0.0067 train/reward_dists_group_std_min=0.0014 train/reward_dists_group_mean_std=0.0136 train/reward_psnr_group_std_mean=1.1910 train/reward_psnr_group_std_max=2.5819 train/reward_psnr_group_std_min=0.4290 train/reward_psnr_group_mean_std=1.6587 train/reward_ssim_group_std_mean=0.0011 train/reward_ssim_group_std_max=0.0040 train/reward_ssim_group_std_min=0.0003 train/reward_ssim_group_mean_std=0.0178 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-9.7424 train/adv_max=5.2890 train/adv_abs_mean=1.7958
+
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Epoch 24 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.37it/s]
+
                                                                
Epoch 24 Training:   5%|▌         | 5/96 [00:06<01:59,  1.31s/it]
+
Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 24 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
+
Epoch 24 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.38it/s]
+
                                                                
Epoch 24 Training:   6%|▋         | 6/96 [00:07<01:57,  1.31s/it]
+
Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 24 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
+
Epoch 24 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.38it/s]
+
                                                                
Epoch 24 Training:   7%|▋         | 7/96 [00:09<01:56,  1.31s/it]
+
Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 24 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
+
Epoch 24 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.39it/s]
+
                                                                
Epoch 24 Training:   8%|▊         | 8/96 [00:10<01:54,  1.31s/it]
+
Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 24 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
+
Epoch 24 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.38it/s]
+
                                                                
Epoch 24 Training:   9%|▉         | 9/96 [00:11<01:53,  1.31s/it]
+
Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 24 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 24 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.35it/s]
+
                                                                
Epoch 24 Training:  10%|█         | 10/96 [00:13<01:52,  1.31s/it]
+
Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 24 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
+
Epoch 24 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.34it/s]
+
                                                                
Epoch 24 Training:  11%|█▏        | 11/96 [00:14<01:51,  1.31s/it]
+
Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 24 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 24 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.39it/s]
+
                                                                
Epoch 24 Training:  12%|█▎        | 12/96 [00:15<01:50,  1.31s/it]
+
Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 24 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 24 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.38it/s]
+
                                                                
Epoch 24 Training:  14%|█▎        | 13/96 [00:16<01:49,  1.31s/it]
+
Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 24 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
+
Epoch 24 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.37it/s]
+
                                                                
Epoch 24 Training:  15%|█▍        | 14/96 [00:18<01:47,  1.31s/it]
+
Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 24 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
+
Epoch 24 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.38it/s]
+
                                                                
Epoch 24 Training:  16%|█▌        | 15/96 [00:19<01:46,  1.31s/it]
+
Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 24 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 24 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.38it/s]
+
                                                                
Epoch 24 Training:  17%|█▋        | 16/96 [00:20<01:44,  1.31s/it]
+
Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 24 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 24 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.35it/s]
+
                                                                
Epoch 24 Training:  18%|█▊        | 17/96 [00:22<01:43,  1.31s/it]
+
Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 24 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 24 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.35it/s]
+
                                                                
Epoch 24 Training:  19%|█▉        | 18/96 [00:23<01:42,  1.32s/it]
+
Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 24 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
+
Epoch 24 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.37it/s]
+
                                                                
Epoch 24 Training:  20%|█▉        | 19/96 [00:24<01:41,  1.32s/it]
+
Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 24 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
+
Epoch 24 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.37it/s]
+
                                                                
Epoch 24 Training:  21%|██        | 20/96 [00:26<01:40,  1.32s/it]
+
Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 24 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
+
Epoch 24 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.33it/s]
+
                                                                
Epoch 24 Training:  22%|██▏       | 21/96 [00:27<01:38,  1.32s/it]
+
Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 24 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 24 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.36it/s]
+
                                                                
Epoch 24 Training:  23%|██▎       | 22/96 [00:28<01:37,  1.32s/it]
+
Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 24 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 24 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.37it/s]
+
                                                                
Epoch 24 Training:  24%|██▍       | 23/96 [00:30<01:36,  1.32s/it]
+
Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 24 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
+
Epoch 24 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.37it/s]
+
                                                                
Epoch 24 Training:  25%|██▌       | 24/96 [00:31<01:34,  1.32s/it]
+
Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 24 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
+
Epoch 24 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.36it/s]
+
                                                                
Epoch 24 Training:  26%|██▌       | 25/96 [00:32<01:33,  1.32s/it]
+
Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 24 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
+
Epoch 24 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.34it/s]
+
                                                                
Epoch 24 Training:  27%|██▋       | 26/96 [00:34<01:32,  1.32s/it]
+
Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 24 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
+
Epoch 24 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.33it/s]
+
                                                                
Epoch 24 Training:  28%|██▊       | 27/96 [00:35<01:31,  1.32s/it]
+
Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 24 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 24 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.38it/s]
+
                                                                
Epoch 24 Training:  29%|██▉       | 28/96 [00:36<01:29,  1.32s/it]
+
Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 24 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
+
Epoch 24 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.40it/s]
+
                                                                
Epoch 24 Training:  30%|███       | 29/96 [00:38<01:28,  1.32s/it]
+
Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 24 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
+
Epoch 24 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.37it/s]
+
                                                                
Epoch 24 Training:  31%|███▏      | 30/96 [00:39<01:26,  1.32s/it]
+
Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 24 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
+
Epoch 24 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.38it/s]
+
                                                                
Epoch 24 Training:  32%|███▏      | 31/96 [00:40<01:25,  1.32s/it]
+
Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 24 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
+
Epoch 24 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.34it/s]
+
                                                                
Epoch 24 Training:  33%|███▎      | 32/96 [00:42<01:24,  1.32s/it]
+
Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 24 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 24 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.32it/s]
+
                                                                
Epoch 24 Training:  34%|███▍      | 33/96 [00:43<01:23,  1.32s/it]
+
Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 24 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 24 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.39it/s]
+
                                                                
Epoch 24 Training:  35%|███▌      | 34/96 [00:44<01:21,  1.32s/it]
+
Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 24 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 24 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.36it/s]
+
                                                                
Epoch 24 Training:  36%|███▋      | 35/96 [00:45<01:20,  1.32s/it]
+
Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 24 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 24 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.37it/s]
+
                                                                
Epoch 24 Training:  38%|███▊      | 36/96 [00:47<01:19,  1.32s/it]
+
Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 24 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 24 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.38it/s]
+
                                                                
Epoch 24 Training:  39%|███▊      | 37/96 [00:48<01:17,  1.32s/it]
+
Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 24 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 24 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.34it/s]
+
                                                                
Epoch 24 Training:  40%|███▉      | 38/96 [00:49<01:16,  1.32s/it]
+
Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 24 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 24 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.34it/s]
+
                                                                
Epoch 24 Training:  41%|████      | 39/96 [00:51<01:15,  1.32s/it]
+
Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 24 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 24 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.37it/s]
+
                                                                
Epoch 24 Training:  42%|████▏     | 40/96 [00:52<01:13,  1.32s/it]
+
Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 24 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 24 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.35it/s]
+
                                                                
Epoch 24 Training:  43%|████▎     | 41/96 [00:53<01:12,  1.32s/it]
+
Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 24 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 24 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.37it/s]
+
                                                                
Epoch 24 Training:  44%|████▍     | 42/96 [00:55<01:11,  1.32s/it]
+
Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 24 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 24 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.38it/s]
+
                                                                
Epoch 24 Training:  45%|████▍     | 43/96 [00:56<01:09,  1.32s/it]
+
Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 24 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 24 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.34it/s]
+
                                                                
Epoch 24 Training:  46%|████▌     | 44/96 [00:57<01:08,  1.32s/it]
+
Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 24 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 24 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.36it/s]
+
                                                                
Epoch 24 Training:  47%|████▋     | 45/96 [00:59<01:07,  1.32s/it]
+
Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 24 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 24 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.37it/s]
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Epoch 24 Training:  48%|████▊     | 46/96 [01:00<01:05,  1.32s/it]
+
Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 24 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 24 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.37it/s]
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Epoch 24 Training:  49%|████▉     | 47/96 [01:01<01:04,  1.32s/it]
+
Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 24 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 24 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.36it/s]
+
                                                                
Epoch 24 Training:  50%|█████     | 48/96 [01:03<01:03,  1.32s/it]
+
Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 24 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 24 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.34it/s]
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Epoch 24 Training:  51%|█████     | 49/96 [01:04<01:01,  1.32s/it]
+
Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 24 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 24 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.34it/s]
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Epoch 24 Training:  52%|█████▏    | 50/96 [01:05<01:00,  1.32s/it]
+
Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 24 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 24 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.36it/s]
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Epoch 24 Training:  53%|█████▎    | 51/96 [01:07<00:59,  1.32s/it]
+
Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 24 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 24 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.36it/s]
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Epoch 24 Training:  54%|█████▍    | 52/96 [01:08<00:58,  1.32s/it]
+
Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 24 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 24 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.37it/s]
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Epoch 24 Training:  55%|█████▌    | 53/96 [01:09<00:56,  1.32s/it]
+
Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 24 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 24 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.38it/s]
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Epoch 24 Training:  56%|█████▋    | 54/96 [01:11<00:55,  1.32s/it]
+
Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 24 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.35it/s]
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Epoch 24 Training:  57%|█████▋    | 55/96 [01:12<00:54,  1.32s/it]
+
Epoch 24 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 24 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 24 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.37it/s]
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Epoch 24 Training:  58%|█████▊    | 56/96 [01:13<00:52,  1.32s/it]
+
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Epoch 24 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s][2026-10-01 18:02:06] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0024 | Epoch 024] train/policy_loss=11.9539 train/unweighted_policy_loss=2.3908 train/loss=11.9539 train/grad_norm=0.0480
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+[2026-10-01 18:02:06] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0025 | Epoch 024] timing/rollout_seconds=147.7957 timing/feedback_seconds=5.1917 timing/optimization_seconds=127.0417 timing/cycle_seconds=278.3451
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+
Pointwise Rewards: ssim:   0%|          | 0/6 [00:00<?, ?it/s]
Pointwise Rewards: ssim:  17%|█▋        | 1/6 [00:00<00:00,  6.66it/s]
Pointwise Rewards: ssim:  33%|███▎      | 2/6 [00:00<00:00,  6.41it/s]
Pointwise Rewards: ssim:  50%|█████     | 3/6 [00:00<00:00,  6.47it/s]
Pointwise Rewards: ssim:  67%|██████▋   | 4/6 [00:00<00:00,  6.40it/s]
Pointwise Rewards: ssim:  83%|████████▎ | 5/6 [00:00<00:00,  6.64it/s]
Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:00<00:00,  6.57it/s]
Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:00<00:00,  6.53it/s]
+
Pointwise Rewards: dists:   0%|          | 0/6 [00:00<?, ?it/s]
Pointwise Rewards: dists:  17%|█▋        | 1/6 [00:00<00:01,  3.80it/s]
Pointwise Rewards: dists:  33%|███▎      | 2/6 [00:00<00:01,  3.72it/s]
Pointwise Rewards: dists:  50%|█████     | 3/6 [00:00<00:00,  3.67it/s]
Pointwise Rewards: dists:  67%|██████▋   | 4/6 [00:01<00:00,  3.61it/s]
Pointwise Rewards: dists:  83%|████████▎ | 5/6 [00:01<00:00,  3.69it/s]
Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.71it/s]
Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.69it/s]
+
Pointwise Rewards: psnr:   0%|          | 0/6 [00:00<?, ?it/s]
Pointwise Rewards: psnr:  17%|█▋        | 1/6 [00:00<00:00,  6.65it/s]
Pointwise Rewards: psnr:  33%|███▎      | 2/6 [00:00<00:00,  6.73it/s]
Pointwise Rewards: psnr:  50%|█████     | 3/6 [00:00<00:00,  6.59it/s]
Pointwise Rewards: psnr:  67%|██████▋   | 4/6 [00:00<00:00,  6.62it/s]
Pointwise Rewards: psnr:  83%|████████▎ | 5/6 [00:00<00:00,  6.78it/s]
Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.84it/s]
Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.75it/s]
+[2026-10-01 18:04:37] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0025 | Epoch 025] train/reward_dists_mean=0.9391 train/reward_dists_std=0.0218 train/reward_psnr_mean=30.3677 train/reward_psnr_std=2.2722 train/reward_ssim_mean=0.9407 train/reward_ssim_std=0.0199 train/reward_dists_group_std_mean=0.0030 train/reward_dists_group_std_max=0.0193 train/reward_dists_group_std_min=0.0010 train/reward_dists_group_mean_std=0.0214 train/reward_psnr_group_std_mean=1.2200 train/reward_psnr_group_std_max=6.2095 train/reward_psnr_group_std_min=0.3763 train/reward_psnr_group_mean_std=1.6929 train/reward_ssim_group_std_mean=0.0021 train/reward_ssim_group_std_max=0.0314 train/reward_ssim_group_std_min=0.0003 train/reward_ssim_group_mean_std=0.0192 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-6.7689 train/adv_max=5.6921 train/adv_abs_mean=1.8146
+
Epoch 25 Training:   0%|          | 0/96 [00:00<?, ?it/s]
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Epoch 25 Training:   1%|          | 1/96 [00:01<01:59,  1.25s/it]
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Epoch 25 Training:   2%|▏         | 2/96 [00:02<02:01,  1.29s/it]
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Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.35it/s]
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Epoch 25 Training:   3%|▎         | 3/96 [00:03<02:01,  1.30s/it]
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Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.33it/s]
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Epoch 25 Training:   4%|▍         | 4/96 [00:05<02:00,  1.31s/it]
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Epoch 25 Training:   5%|▌         | 5/96 [00:06<01:59,  1.32s/it]
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Epoch 25 Training:   6%|▋         | 6/96 [00:07<01:58,  1.32s/it]
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Epoch 25 Training:   7%|▋         | 7/96 [00:09<01:57,  1.32s/it]
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Epoch 25 Training:   8%|▊         | 8/96 [00:10<01:56,  1.32s/it]
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Epoch 25 Training:   9%|▉         | 9/96 [00:11<01:54,  1.32s/it]
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Epoch 25 Training:  10%|█         | 10/96 [00:13<01:53,  1.32s/it]
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Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.34it/s]
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Epoch 25 Training:  11%|█▏        | 11/96 [00:14<01:52,  1.32s/it]
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Epoch 25 Training:  12%|█▎        | 12/96 [00:15<01:51,  1.32s/it]
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Epoch 25 Training:  14%|█▎        | 13/96 [00:17<01:49,  1.32s/it]
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Epoch 25 Training:  15%|█▍        | 14/96 [00:18<01:48,  1.32s/it]
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Epoch 25 Training:  16%|█▌        | 15/96 [00:19<01:47,  1.32s/it]
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Epoch 25 Training:  17%|█▋        | 16/96 [00:21<01:45,  1.32s/it]
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Epoch 25 Training:  18%|█▊        | 17/96 [00:22<01:44,  1.32s/it]
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Epoch 25 Training:  19%|█▉        | 18/96 [00:23<01:43,  1.32s/it]
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Epoch 25 Training:  20%|█▉        | 19/96 [00:25<01:42,  1.33s/it]
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Epoch 25 Training:  21%|██        | 20/96 [00:26<01:40,  1.33s/it]
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Epoch 25 Training:  22%|██▏       | 21/96 [00:27<01:39,  1.32s/it]
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Epoch 25 Training:  23%|██▎       | 22/96 [00:29<01:37,  1.32s/it]
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Epoch 25 Training:  24%|██▍       | 23/96 [00:30<01:36,  1.32s/it]
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Epoch 25 Training:  25%|██▌       | 24/96 [00:31<01:35,  1.32s/it]
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Epoch 25 Training:  26%|██▌       | 25/96 [00:32<01:33,  1.32s/it]
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Epoch 25 Training:  27%|██▋       | 26/96 [00:34<01:32,  1.32s/it]
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Epoch 25 Training:  28%|██▊       | 27/96 [00:35<01:31,  1.32s/it]
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Epoch 25 Training:  29%|██▉       | 28/96 [00:36<01:29,  1.32s/it]
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Epoch 25 Training:  30%|███       | 29/96 [00:38<01:28,  1.32s/it]
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Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
+
Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.35it/s]
+
                                                                
Epoch 25 Training:  31%|███▏      | 30/96 [00:39<01:27,  1.32s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
+
Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.38it/s]
+
                                                                
Epoch 25 Training:  32%|███▏      | 31/96 [00:40<01:25,  1.32s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
+
Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.36it/s]
+
                                                                
Epoch 25 Training:  33%|███▎      | 32/96 [00:42<01:24,  1.32s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.36it/s]
+
                                                                
Epoch 25 Training:  34%|███▍      | 33/96 [00:43<01:23,  1.32s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.35it/s]
+
                                                                
Epoch 25 Training:  35%|███▌      | 34/96 [00:44<01:21,  1.32s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
+
Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.34it/s]
+
                                                                
Epoch 25 Training:  36%|███▋      | 35/96 [00:46<01:20,  1.32s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.34it/s]
+
                                                                
Epoch 25 Training:  38%|███▊      | 36/96 [00:47<01:19,  1.32s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.36it/s]
+
                                                                
Epoch 25 Training:  39%|███▊      | 37/96 [00:48<01:17,  1.32s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.35it/s]
+
                                                                
Epoch 25 Training:  40%|███▉      | 38/96 [00:50<01:16,  1.32s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.35it/s]
+
                                                                
Epoch 25 Training:  41%|████      | 39/96 [00:51<01:15,  1.32s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.34it/s]
+
                                                                
Epoch 25 Training:  42%|████▏     | 40/96 [00:52<01:13,  1.32s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.36it/s]
+
                                                                
Epoch 25 Training:  43%|████▎     | 41/96 [00:54<01:12,  1.32s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.36it/s]
+
                                                                
Epoch 25 Training:  44%|████▍     | 42/96 [00:55<01:11,  1.32s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
+
Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.37it/s]
+
                                                                
Epoch 25 Training:  45%|████▍     | 43/96 [00:56<01:09,  1.32s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
+
Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.37it/s]
+
                                                                
Epoch 25 Training:  46%|████▌     | 44/96 [00:58<01:08,  1.32s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
+
Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.33it/s]
+
                                                                
Epoch 25 Training:  47%|████▋     | 45/96 [00:59<01:07,  1.32s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
+
Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.36it/s]
+
                                                                
Epoch 25 Training:  48%|████▊     | 46/96 [01:00<01:05,  1.32s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
+
Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.40it/s]
+
                                                                
Epoch 25 Training:  49%|████▉     | 47/96 [01:02<01:04,  1.32s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
+
Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.36it/s]
+
                                                                
Epoch 25 Training:  50%|█████     | 48/96 [01:03<01:03,  1.32s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
+
Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.38it/s]
+
                                                                
Epoch 25 Training:  51%|█████     | 49/96 [01:04<01:01,  1.32s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
+
Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.31it/s]
+
                                                                
Epoch 25 Training:  52%|█████▏    | 50/96 [01:05<01:00,  1.32s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
+
Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.35it/s]
+
                                                                
Epoch 25 Training:  53%|█████▎    | 51/96 [01:07<00:59,  1.32s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
+
Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.35it/s]
+
                                                                
Epoch 25 Training:  54%|█████▍    | 52/96 [01:08<00:58,  1.32s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
+
Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.31it/s]
+
                                                                
Epoch 25 Training:  55%|█████▌    | 53/96 [01:09<00:56,  1.32s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
+
Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.36it/s]
+
                                                                
Epoch 25 Training:  56%|█████▋    | 54/96 [01:11<00:55,  1.32s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
+
Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.36it/s]
+
                                                                
Epoch 25 Training:  57%|█████▋    | 55/96 [01:12<00:54,  1.32s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
+
Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.37it/s]
+
                                                                
Epoch 25 Training:  58%|█████▊    | 56/96 [01:13<00:52,  1.32s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
+
Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.33it/s]
+
                                                                
Epoch 25 Training:  59%|█████▉    | 57/96 [01:15<00:51,  1.32s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
+
Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.35it/s]
+
                                                                
Epoch 25 Training:  60%|██████    | 58/96 [01:16<00:50,  1.32s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
+
Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.36it/s]
+
                                                                
Epoch 25 Training:  61%|██████▏   | 59/96 [01:17<00:48,  1.32s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.63it/s]
+
Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.38it/s]
+
                                                                
Epoch 25 Training:  62%|██████▎   | 60/96 [01:19<00:47,  1.32s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
+
Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.34it/s]
+
                                                                
Epoch 25 Training:  64%|██████▎   | 61/96 [01:20<00:46,  1.32s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
+
Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.36it/s]
+
                                                                
Epoch 25 Training:  65%|██████▍   | 62/96 [01:21<00:44,  1.32s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
+
Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.38it/s]
+
                                                                
Epoch 25 Training:  66%|██████▌   | 63/96 [01:23<00:43,  1.32s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
+
Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.38it/s]
+
                                                                
Epoch 25 Training:  67%|██████▋   | 64/96 [01:24<00:42,  1.31s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
+
Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.36it/s]
+
                                                                
Epoch 25 Training:  68%|██████▊   | 65/96 [01:25<00:40,  1.32s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
+
Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.34it/s]
+
                                                                
Epoch 25 Training:  69%|██████▉   | 66/96 [01:27<00:39,  1.32s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
+
Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.32it/s]
+
                                                                
Epoch 25 Training:  70%|██████▉   | 67/96 [01:28<00:38,  1.32s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
+
Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.35it/s]
+
                                                                
Epoch 25 Training:  71%|███████   | 68/96 [01:29<00:37,  1.32s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
+
Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.35it/s]
+
                                                                
Epoch 25 Training:  72%|███████▏  | 69/96 [01:31<00:35,  1.32s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.35it/s]
+
                                                                
Epoch 25 Training:  73%|███████▎  | 70/96 [01:32<00:34,  1.32s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.35it/s]
+
                                                                
Epoch 25 Training:  74%|███████▍  | 71/96 [01:33<00:33,  1.32s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
+
Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.35it/s]
+
                                                                
Epoch 25 Training:  75%|███████▌  | 72/96 [01:34<00:31,  1.32s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
+
Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.35it/s]
+
                                                                
Epoch 25 Training:  76%|███████▌  | 73/96 [01:36<00:30,  1.32s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
+
Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.34it/s]
+
                                                                
Epoch 25 Training:  77%|███████▋  | 74/96 [01:37<00:29,  1.32s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
+
Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.36it/s]
+
                                                                
Epoch 25 Training:  78%|███████▊  | 75/96 [01:38<00:27,  1.32s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
+
Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.33it/s]
+
                                                                
Epoch 25 Training:  79%|███████▉  | 76/96 [01:40<00:26,  1.32s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.36it/s]
+
                                                                
Epoch 25 Training:  80%|████████  | 77/96 [01:41<00:25,  1.32s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.36it/s]
+
                                                                
Epoch 25 Training:  81%|████████▏ | 78/96 [01:42<00:23,  1.32s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.34it/s]
+
                                                                
Epoch 25 Training:  82%|████████▏ | 79/96 [01:44<00:22,  1.32s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.37it/s]
+
                                                                
Epoch 25 Training:  83%|████████▎ | 80/96 [01:45<00:21,  1.32s/it]
+
Epoch 25 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 25 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.32it/s]
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Epoch 25 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s][2026-10-01 18:06:44] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0025 | Epoch 025] train/policy_loss=11.9315 train/unweighted_policy_loss=2.3863 train/loss=11.9315 train/grad_norm=0.1196
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+[2026-10-01 18:06:44] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0026 | Epoch 025] timing/rollout_seconds=147.7828 timing/feedback_seconds=5.1221 timing/optimization_seconds=127.3140 timing/cycle_seconds=278.5582
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+
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+
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+
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+[2026-10-01 18:09:16] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0026 | Epoch 026] train/reward_dists_mean=0.9426 train/reward_dists_std=0.0137 train/reward_psnr_mean=30.4150 train/reward_psnr_std=2.1696 train/reward_ssim_mean=0.9389 train/reward_ssim_std=0.0207 train/reward_dists_group_std_mean=0.0027 train/reward_dists_group_std_max=0.0052 train/reward_dists_group_std_min=0.0015 train/reward_dists_group_mean_std=0.0134 train/reward_psnr_group_std_mean=1.2146 train/reward_psnr_group_std_max=2.0666 train/reward_psnr_group_std_min=0.3568 train/reward_psnr_group_mean_std=1.7314 train/reward_ssim_group_std_mean=0.0010 train/reward_ssim_group_std_max=0.0027 train/reward_ssim_group_std_min=0.0004 train/reward_ssim_group_mean_std=0.0207 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-8.8148 train/adv_max=5.1856 train/adv_abs_mean=1.7604
+
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Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
+
Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.35it/s]
+
                                                                
Epoch 26 Training:   3%|▎         | 3/96 [00:03<02:00,  1.30s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
+
Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.34it/s]
+
                                                                
Epoch 26 Training:   4%|▍         | 4/96 [00:05<02:00,  1.31s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
+
Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.36it/s]
+
                                                                
Epoch 26 Training:   5%|▌         | 5/96 [00:06<01:59,  1.31s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
+
Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.39it/s]
+
                                                                
Epoch 26 Training:   6%|▋         | 6/96 [00:07<01:58,  1.31s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
+
Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.36it/s]
+
                                                                
Epoch 26 Training:   7%|▋         | 7/96 [00:09<01:57,  1.31s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
+
Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.38it/s]
+
                                                                
Epoch 26 Training:   8%|▊         | 8/96 [00:10<01:55,  1.32s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
+
Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.33it/s]
+
                                                                
Epoch 26 Training:   9%|▉         | 9/96 [00:11<01:54,  1.32s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
+
Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.33it/s]
+
                                                                
Epoch 26 Training:  10%|█         | 10/96 [00:13<01:53,  1.32s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
+
Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.35it/s]
+
                                                                
Epoch 26 Training:  11%|█▏        | 11/96 [00:14<01:52,  1.32s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
+
Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.36it/s]
+
                                                                
Epoch 26 Training:  12%|█▎        | 12/96 [00:15<01:51,  1.32s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
+
Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.34it/s]
+
                                                                
Epoch 26 Training:  14%|█▎        | 13/96 [00:17<01:49,  1.32s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
+
Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.32it/s]
+
                                                                
Epoch 26 Training:  15%|█▍        | 14/96 [00:18<01:48,  1.32s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
+
Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.35it/s]
+
                                                                
Epoch 26 Training:  16%|█▌        | 15/96 [00:19<01:47,  1.32s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
+
Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.37it/s]
+
                                                                
Epoch 26 Training:  17%|█▋        | 16/96 [00:21<01:45,  1.32s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
+
Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.36it/s]
+
                                                                
Epoch 26 Training:  18%|█▊        | 17/96 [00:22<01:44,  1.32s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
+
Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.35it/s]
+
                                                                
Epoch 26 Training:  19%|█▉        | 18/96 [00:23<01:43,  1.32s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
+
Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.35it/s]
+
                                                                
Epoch 26 Training:  20%|█▉        | 19/96 [00:25<01:41,  1.32s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
+
Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.36it/s]
+
                                                                
Epoch 26 Training:  21%|██        | 20/96 [00:26<01:40,  1.32s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
+
Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.38it/s]
+
                                                                
Epoch 26 Training:  22%|██▏       | 21/96 [00:27<01:39,  1.32s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
+
Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.35it/s]
+
                                                                
Epoch 26 Training:  23%|██▎       | 22/96 [00:28<01:37,  1.32s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
+
Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.35it/s]
+
                                                                
Epoch 26 Training:  24%|██▍       | 23/96 [00:30<01:36,  1.32s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
+
Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.35it/s]
+
                                                                
Epoch 26 Training:  25%|██▌       | 24/96 [00:31<01:35,  1.32s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
+
Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.35it/s]
+
                                                                
Epoch 26 Training:  26%|██▌       | 25/96 [00:32<01:33,  1.32s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
+
Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.36it/s]
+
                                                                
Epoch 26 Training:  27%|██▋       | 26/96 [00:34<01:32,  1.32s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
+
Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.36it/s]
+
                                                                
Epoch 26 Training:  28%|██▊       | 27/96 [00:35<01:31,  1.32s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
+
Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.35it/s]
+
                                                                
Epoch 26 Training:  29%|██▉       | 28/96 [00:36<01:29,  1.32s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
+
Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.36it/s]
+
                                                                
Epoch 26 Training:  30%|███       | 29/96 [00:38<01:28,  1.32s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
+
Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.38it/s]
+
                                                                
Epoch 26 Training:  31%|███▏      | 30/96 [00:39<01:27,  1.32s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
+
Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.36it/s]
+
                                                                
Epoch 26 Training:  32%|███▏      | 31/96 [00:40<01:25,  1.32s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
+
Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.35it/s]
+
                                                                
Epoch 26 Training:  33%|███▎      | 32/96 [00:42<01:24,  1.32s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
+
Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.34it/s]
+
                                                                
Epoch 26 Training:  34%|███▍      | 33/96 [00:43<01:23,  1.32s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
+
Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.35it/s]
+
                                                                
Epoch 26 Training:  35%|███▌      | 34/96 [00:44<01:21,  1.32s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
+
Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.38it/s]
+
                                                                
Epoch 26 Training:  36%|███▋      | 35/96 [00:46<01:20,  1.32s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
+
Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.35it/s]
+
                                                                
Epoch 26 Training:  38%|███▊      | 36/96 [00:47<01:19,  1.32s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
+
Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.35it/s]
+
                                                                
Epoch 26 Training:  39%|███▊      | 37/96 [00:48<01:17,  1.32s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
+
Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.34it/s]
+
                                                                
Epoch 26 Training:  40%|███▉      | 38/96 [00:50<01:16,  1.32s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
+
Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.37it/s]
+
                                                                
Epoch 26 Training:  41%|████      | 39/96 [00:51<01:15,  1.32s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
+
Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.35it/s]
+
                                                                
Epoch 26 Training:  42%|████▏     | 40/96 [00:52<01:13,  1.32s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
+
Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.35it/s]
+
                                                                
Epoch 26 Training:  43%|████▎     | 41/96 [00:54<01:12,  1.32s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
+
Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.35it/s]
+
                                                                
Epoch 26 Training:  44%|████▍     | 42/96 [00:55<01:11,  1.32s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
+
Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.34it/s]
+
                                                                
Epoch 26 Training:  45%|████▍     | 43/96 [00:56<01:10,  1.32s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
+
Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.37it/s]
+
                                                                
Epoch 26 Training:  46%|████▌     | 44/96 [00:58<01:08,  1.32s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
+
Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.37it/s]
+
                                                                
Epoch 26 Training:  47%|████▋     | 45/96 [00:59<01:07,  1.32s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
+
Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.36it/s]
+
                                                                
Epoch 26 Training:  48%|████▊     | 46/96 [01:00<01:05,  1.32s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
+
Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.36it/s]
+
                                                                
Epoch 26 Training:  49%|████▉     | 47/96 [01:01<01:04,  1.32s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
+
Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.35it/s]
+
                                                                
Epoch 26 Training:  50%|█████     | 48/96 [01:03<01:03,  1.32s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
+
Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.37it/s]
+
                                                                
Epoch 26 Training:  51%|█████     | 49/96 [01:04<01:01,  1.32s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
+
Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.38it/s]
+
                                                                
Epoch 26 Training:  52%|█████▏    | 50/96 [01:05<01:00,  1.32s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
+
Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.36it/s]
+
                                                                
Epoch 26 Training:  53%|█████▎    | 51/96 [01:07<00:59,  1.32s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
+
Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.37it/s]
+
                                                                
Epoch 26 Training:  54%|█████▍    | 52/96 [01:08<00:57,  1.32s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
+
Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.36it/s]
+
                                                                
Epoch 26 Training:  55%|█████▌    | 53/96 [01:09<00:56,  1.32s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 26 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.34it/s]
+
                                                                
Epoch 26 Training:  56%|█████▋    | 54/96 [01:11<00:55,  1.32s/it]
+
Epoch 26 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 26 Training:  57%|█████▋    | 55/96 [01:12<00:54,  1.32s/it]
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Epoch 26 Training:  58%|█████▊    | 56/96 [01:13<00:52,  1.32s/it]
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Epoch 26 Training:  59%|█████▉    | 57/96 [01:15<00:51,  1.32s/it]
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Epoch 26 Training:  60%|██████    | 58/96 [01:16<00:50,  1.32s/it]
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Epoch 26 Training:  61%|██████▏   | 59/96 [01:17<00:48,  1.32s/it]
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Epoch 26 Training:  64%|██████▎   | 61/96 [01:20<00:46,  1.32s/it]
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Epoch 26 Training:  65%|██████▍   | 62/96 [01:21<00:44,  1.32s/it]
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Epoch 26 Training:  66%|██████▌   | 63/96 [01:23<00:43,  1.32s/it]
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Epoch 26 Training:  67%|██████▋   | 64/96 [01:24<00:42,  1.32s/it]
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Epoch 26 Training:  75%|███████▌  | 72/96 [01:34<00:31,  1.32s/it]
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Epoch 26 Training:  76%|███████▌  | 73/96 [01:36<00:30,  1.32s/it]
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Epoch 26 Training:  77%|███████▋  | 74/96 [01:37<00:29,  1.32s/it]
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Epoch 26 Training:  81%|████████▏ | 78/96 [01:42<00:23,  1.32s/it]
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Epoch 26 Training:  82%|████████▏ | 79/96 [01:44<00:22,  1.32s/it]
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Epoch 26 Training:  83%|████████▎ | 80/96 [01:45<00:21,  1.32s/it]
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Epoch 26 Training:  84%|████████▍ | 81/96 [01:46<00:19,  1.32s/it]
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Epoch 26 Training:  85%|████████▌ | 82/96 [01:48<00:18,  1.32s/it]
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Epoch 26 Training:  86%|████████▋ | 83/96 [01:49<00:17,  1.32s/it]
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Epoch 26 Training:  88%|████████▊ | 84/96 [01:50<00:15,  1.32s/it]
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Epoch 26 Training:  89%|████████▊ | 85/96 [01:52<00:14,  1.32s/it]
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Epoch 26 Training:  90%|████████▉ | 86/96 [01:53<00:13,  1.32s/it]
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Epoch 26 Training:  91%|█████████ | 87/96 [01:54<00:11,  1.32s/it]
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Epoch 26 Training:  92%|█████████▏| 88/96 [01:56<00:10,  1.32s/it]
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Epoch 26 Training:  93%|█████████▎| 89/96 [01:57<00:09,  1.32s/it]
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Epoch 26 Training:  94%|█████████▍| 90/96 [01:58<00:07,  1.32s/it]
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Epoch 26 Training:  96%|█████████▌| 92/96 [02:01<00:05,  1.32s/it]
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Epoch 26 Training:  97%|█████████▋| 93/96 [02:02<00:03,  1.32s/it]
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Epoch 26 Training:  98%|█████████▊| 94/96 [02:04<00:02,  1.32s/it]
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Epoch 26 Training:  99%|█████████▉| 95/96 [02:05<00:01,  1.32s/it]
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Epoch 26 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s][2026-10-01 18:11:23] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0026 | Epoch 026] train/policy_loss=11.6645 train/unweighted_policy_loss=2.3329 train/loss=11.6645 train/grad_norm=0.0601
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Epoch 26 Training: 100%|██████████| 96/96 [02:06<00:00,  1.34s/it]
Epoch 26 Training: 100%|██████████| 96/96 [02:06<00:00,  1.32s/it]
+[2026-10-01 18:11:23] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0027 | Epoch 026] timing/rollout_seconds=147.8544 timing/feedback_seconds=5.2275 timing/optimization_seconds=127.2718 timing/cycle_seconds=278.6570
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+
Pointwise Rewards: ssim:   0%|          | 0/6 [00:00<?, ?it/s]
Pointwise Rewards: ssim:  17%|█▋        | 1/6 [00:00<00:00,  6.09it/s]
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Pointwise Rewards: ssim:  50%|█████     | 3/6 [00:00<00:00,  6.15it/s]
Pointwise Rewards: ssim:  67%|██████▋   | 4/6 [00:00<00:00,  6.22it/s]
Pointwise Rewards: ssim:  83%|████████▎ | 5/6 [00:00<00:00,  6.25it/s]
Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:00<00:00,  6.28it/s]
Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:00<00:00,  6.24it/s]
+
Pointwise Rewards: dists:   0%|          | 0/6 [00:00<?, ?it/s]
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Pointwise Rewards: dists:  33%|███▎      | 2/6 [00:00<00:01,  3.63it/s]
Pointwise Rewards: dists:  50%|█████     | 3/6 [00:00<00:00,  3.58it/s]
Pointwise Rewards: dists:  67%|██████▋   | 4/6 [00:01<00:00,  3.61it/s]
Pointwise Rewards: dists:  83%|████████▎ | 5/6 [00:01<00:00,  3.60it/s]
Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.59it/s]
Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.60it/s]
+
Pointwise Rewards: psnr:   0%|          | 0/6 [00:00<?, ?it/s]
Pointwise Rewards: psnr:  17%|█▋        | 1/6 [00:00<00:00,  6.88it/s]
Pointwise Rewards: psnr:  33%|███▎      | 2/6 [00:00<00:00,  6.92it/s]
Pointwise Rewards: psnr:  50%|█████     | 3/6 [00:00<00:00,  6.71it/s]
Pointwise Rewards: psnr:  67%|██████▋   | 4/6 [00:00<00:00,  6.85it/s]
Pointwise Rewards: psnr:  83%|████████▎ | 5/6 [00:00<00:00,  6.77it/s]
Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.68it/s]
Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.74it/s]
+[2026-10-01 18:13:55] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0027 | Epoch 027] train/reward_dists_mean=0.9407 train/reward_dists_std=0.0169 train/reward_psnr_mean=30.5683 train/reward_psnr_std=2.3313 train/reward_ssim_mean=0.9402 train/reward_ssim_std=0.0228 train/reward_dists_group_std_mean=0.0028 train/reward_dists_group_std_max=0.0065 train/reward_dists_group_std_min=0.0012 train/reward_dists_group_mean_std=0.0166 train/reward_psnr_group_std_mean=1.2655 train/reward_psnr_group_std_max=2.3615 train/reward_psnr_group_std_min=0.4316 train/reward_psnr_group_mean_std=1.8904 train/reward_ssim_group_std_mean=0.0010 train/reward_ssim_group_std_max=0.0024 train/reward_ssim_group_std_min=0.0003 train/reward_ssim_group_mean_std=0.0228 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-7.9000 train/adv_max=5.0801 train/adv_abs_mean=1.7785
+
Epoch 27 Training:   0%|          | 0/96 [00:00<?, ?it/s]
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Epoch 27 Training:   1%|          | 1/96 [00:01<01:58,  1.25s/it]
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Epoch 27 Training:   2%|▏         | 2/96 [00:02<02:01,  1.29s/it]
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Epoch 27 Training:   3%|▎         | 3/96 [00:03<02:01,  1.31s/it]
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Epoch 27 Training:   4%|▍         | 4/96 [00:05<02:01,  1.32s/it]
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Epoch 27 Training:   5%|▌         | 5/96 [00:06<02:00,  1.32s/it]
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Epoch 27 Training:   6%|▋         | 6/96 [00:07<01:59,  1.32s/it]
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Epoch 27 Training:   7%|▋         | 7/96 [00:09<01:57,  1.33s/it]
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Epoch 27 Training:   8%|▊         | 8/96 [00:10<01:56,  1.32s/it]
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Epoch 27 Training:   9%|▉         | 9/96 [00:11<01:55,  1.32s/it]
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Epoch 27 Training:  10%|█         | 10/96 [00:13<01:53,  1.32s/it]
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Epoch 27 Training:  11%|█▏        | 11/96 [00:14<01:52,  1.32s/it]
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Epoch 27 Training:  12%|█▎        | 12/96 [00:15<01:51,  1.32s/it]
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Epoch 27 Training:  14%|█▎        | 13/96 [00:17<01:49,  1.32s/it]
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Epoch 27 Training:  15%|█▍        | 14/96 [00:18<01:48,  1.32s/it]
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Epoch 27 Training:  16%|█▌        | 15/96 [00:19<01:47,  1.32s/it]
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Epoch 27 Training:  17%|█▋        | 16/96 [00:21<01:45,  1.32s/it]
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Epoch 27 Training:  18%|█▊        | 17/96 [00:22<01:44,  1.32s/it]
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Epoch 27 Training:  19%|█▉        | 18/96 [00:23<01:42,  1.32s/it]
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Epoch 27 Training:  20%|█▉        | 19/96 [00:25<01:41,  1.32s/it]
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Epoch 27 Training:  21%|██        | 20/96 [00:26<01:40,  1.32s/it]
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Epoch 27 Training:  22%|██▏       | 21/96 [00:27<01:39,  1.32s/it]
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Epoch 27 Training:  23%|██▎       | 22/96 [00:29<01:37,  1.32s/it]
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Epoch 27 Training:  24%|██▍       | 23/96 [00:30<01:36,  1.32s/it]
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Epoch 27 Training:  25%|██▌       | 24/96 [00:31<01:34,  1.32s/it]
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Epoch 27 Training:  26%|██▌       | 25/96 [00:32<01:33,  1.32s/it]
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Epoch 27 Training:  27%|██▋       | 26/96 [00:34<01:32,  1.32s/it]
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Epoch 27 Training:  28%|██▊       | 27/96 [00:35<01:31,  1.32s/it]
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Epoch 27 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 27 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.43it/s]
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Epoch 27 Training:  29%|██▉       | 28/96 [00:36<01:29,  1.32s/it]
+
Epoch 27 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 27 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 27 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.41it/s]
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Epoch 27 Training:  30%|███       | 29/96 [00:38<01:28,  1.32s/it]
+
Epoch 27 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 27 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 27 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.44it/s]
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Epoch 27 Training:  31%|███▏      | 30/96 [00:39<01:27,  1.32s/it]
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Epoch 27 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 27 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 27 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.44it/s]
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Epoch 27 Training:  32%|███▏      | 31/96 [00:40<01:25,  1.32s/it]
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Epoch 27 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 27 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 27 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.44it/s]
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Epoch 27 Training:  33%|███▎      | 32/96 [00:42<01:24,  1.32s/it]
+
Epoch 27 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 27 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 27 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.43it/s]
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Epoch 27 Training:  34%|███▍      | 33/96 [00:43<01:23,  1.32s/it]
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Epoch 27 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 27 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 27 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.43it/s]
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Epoch 27 Training:  35%|███▌      | 34/96 [00:44<01:21,  1.32s/it]
+
Epoch 27 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 27 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 27 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.43it/s]
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Epoch 27 Training:  36%|███▋      | 35/96 [00:46<01:20,  1.32s/it]
+
Epoch 27 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 27 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 27 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.44it/s]
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Epoch 27 Training:  38%|███▊      | 36/96 [00:47<01:19,  1.32s/it]
+
Epoch 27 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 27 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 27 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.44it/s]
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Epoch 27 Training:  39%|███▊      | 37/96 [00:48<01:17,  1.32s/it]
+
Epoch 27 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 27 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 27 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.42it/s]
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Epoch 27 Training:  40%|███▉      | 38/96 [00:50<01:16,  1.32s/it]
+
Epoch 27 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 27 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 27 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.42it/s]
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Epoch 27 Training:  41%|████      | 39/96 [00:51<01:15,  1.32s/it]
+
Epoch 27 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 27 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 27 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.44it/s]
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Epoch 27 Training:  42%|████▏     | 40/96 [00:52<01:13,  1.32s/it]
+
Epoch 27 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 27 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 27 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.40it/s]
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Epoch 27 Training:  43%|████▎     | 41/96 [00:54<01:12,  1.32s/it]
+
Epoch 27 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 27 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 27 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.45it/s]
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Epoch 27 Training:  44%|████▍     | 42/96 [00:55<01:11,  1.32s/it]
+
Epoch 27 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 27 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 27 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.43it/s]
+
                                                                
Epoch 27 Training:  45%|████▍     | 43/96 [00:56<01:09,  1.32s/it]
+
Epoch 27 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 27 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 27 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.41it/s]
+
                                                                
Epoch 27 Training:  46%|████▌     | 44/96 [00:58<01:08,  1.32s/it]
+
Epoch 27 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 27 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 27 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.47it/s]
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Epoch 27 Training:  47%|████▋     | 45/96 [00:59<01:07,  1.32s/it]
+
Epoch 27 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 27 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 27 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.41it/s]
+
                                                                
Epoch 27 Training:  48%|████▊     | 46/96 [01:00<01:06,  1.32s/it]
+
Epoch 27 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 27 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 27 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.44it/s]
+
                                                                
Epoch 27 Training:  49%|████▉     | 47/96 [01:02<01:04,  1.32s/it]
+
Epoch 27 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 27 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 27 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.37it/s]
+
                                                                
Epoch 27 Training:  50%|█████     | 48/96 [01:03<01:03,  1.32s/it]
+
Epoch 27 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 27 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 27 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.43it/s]
+
                                                                
Epoch 27 Training:  51%|█████     | 49/96 [01:04<01:02,  1.32s/it]
+
Epoch 27 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 27 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 27 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.44it/s]
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Epoch 27 Training:  52%|█████▏    | 50/96 [01:05<01:00,  1.32s/it]
+
Epoch 27 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 27 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 27 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.40it/s]
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Epoch 27 Training:  53%|█████▎    | 51/96 [01:07<00:59,  1.32s/it]
+
Epoch 27 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 27 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 27 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.42it/s]
+
                                                                
Epoch 27 Training:  54%|█████▍    | 52/96 [01:08<00:58,  1.32s/it]
+
Epoch 27 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 27 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 27 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.39it/s]
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Epoch 27 Training:  55%|█████▌    | 53/96 [01:09<00:56,  1.32s/it]
+
Epoch 27 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 27 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 27 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.44it/s]
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Epoch 27 Training:  56%|█████▋    | 54/96 [01:11<00:55,  1.32s/it]
+
Epoch 27 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 27 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.63it/s]
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Epoch 27 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.43it/s]
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Epoch 27 Training:  57%|█████▋    | 55/96 [01:12<00:54,  1.32s/it]
+
Epoch 27 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 27 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 27 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.43it/s]
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Epoch 27 Training:  58%|█████▊    | 56/96 [01:13<00:52,  1.32s/it]
+
Epoch 27 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 27 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 27 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.44it/s]
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Epoch 27 Training:  59%|█████▉    | 57/96 [01:15<00:51,  1.32s/it]
+
Epoch 27 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 27 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 27 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.41it/s]
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Epoch 27 Training:  60%|██████    | 58/96 [01:16<00:50,  1.32s/it]
+
Epoch 27 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 27 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 27 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.44it/s]
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Epoch 27 Training:  61%|██████▏   | 59/96 [01:17<00:48,  1.32s/it]
+
Epoch 27 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 27 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 27 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.43it/s]
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Epoch 27 Training:  62%|██████▎   | 60/96 [01:19<00:47,  1.32s/it]
+
Epoch 27 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 27 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 27 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.43it/s]
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Epoch 27 Training:  64%|██████▎   | 61/96 [01:20<00:46,  1.32s/it]
+
Epoch 27 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 27 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 27 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.42it/s]
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Epoch 27 Training:  65%|██████▍   | 62/96 [01:21<00:44,  1.32s/it]
+
Epoch 27 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 27 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 27 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.41it/s]
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Epoch 27 Training:  66%|██████▌   | 63/96 [01:23<00:43,  1.32s/it]
+
Epoch 27 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 27 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 27 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.44it/s]
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Epoch 27 Training:  67%|██████▋   | 64/96 [01:24<00:42,  1.32s/it]
+
Epoch 27 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 27 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 27 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.44it/s]
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Epoch 27 Training:  68%|██████▊   | 65/96 [01:25<00:40,  1.32s/it]
+
Epoch 27 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 27 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 27 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.43it/s]
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Epoch 27 Training:  69%|██████▉   | 66/96 [01:27<00:39,  1.32s/it]
+
Epoch 27 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 27 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 27 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.40it/s]
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Epoch 27 Training:  70%|██████▉   | 67/96 [01:28<00:38,  1.32s/it]
+
Epoch 27 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 27 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 27 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.44it/s]
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Epoch 27 Training:  71%|███████   | 68/96 [01:29<00:36,  1.32s/it]
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Epoch 27 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 27 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 27 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.45it/s]
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Epoch 27 Training:  72%|███████▏  | 69/96 [01:31<00:35,  1.32s/it]
+
Epoch 27 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 27 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 27 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.43it/s]
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Epoch 27 Training:  73%|███████▎  | 70/96 [01:32<00:34,  1.32s/it]
+
Epoch 27 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 27 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 27 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.41it/s]
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Epoch 27 Training:  74%|███████▍  | 71/96 [01:33<00:33,  1.32s/it]
+
Epoch 27 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 27 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 27 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.41it/s]
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Epoch 27 Training:  75%|███████▌  | 72/96 [01:35<00:31,  1.32s/it]
+
Epoch 27 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 27 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 27 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.43it/s]
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Epoch 27 Training:  76%|███████▌  | 73/96 [01:36<00:30,  1.32s/it]
+
Epoch 27 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 27 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 27 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.45it/s]
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Epoch 27 Training:  77%|███████▋  | 74/96 [01:37<00:29,  1.32s/it]
+
Epoch 27 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 27 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 27 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.44it/s]
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Epoch 27 Training:  78%|███████▊  | 75/96 [01:39<00:27,  1.32s/it]
+
Epoch 27 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 27 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 27 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.44it/s]
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Epoch 27 Training:  79%|███████▉  | 76/96 [01:40<00:26,  1.32s/it]
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Epoch 27 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 27 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 27 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.42it/s]
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Epoch 27 Training:  80%|████████  | 77/96 [01:41<00:25,  1.32s/it]
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Epoch 27 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 27 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 27 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.42it/s]
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Epoch 27 Training:  81%|████████▏ | 78/96 [01:42<00:23,  1.32s/it]
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Epoch 27 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 27 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s][2026-10-01 18:16:01] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0027 | Epoch 027] train/policy_loss=11.7953 train/unweighted_policy_loss=2.3591 train/loss=11.7953 train/grad_norm=0.0472
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+[2026-10-01 18:16:01] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0028 | Epoch 027] timing/rollout_seconds=147.6532 timing/feedback_seconds=4.9688 timing/optimization_seconds=127.4147 timing/cycle_seconds=278.5284
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+
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+
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+[2026-10-01 18:18:33] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0028 | Epoch 028] train/reward_dists_mean=0.9426 train/reward_dists_std=0.0167 train/reward_psnr_mean=30.9014 train/reward_psnr_std=2.2877 train/reward_ssim_mean=0.9406 train/reward_ssim_std=0.0207 train/reward_dists_group_std_mean=0.0032 train/reward_dists_group_std_max=0.0068 train/reward_dists_group_std_min=0.0015 train/reward_dists_group_mean_std=0.0164 train/reward_psnr_group_std_mean=1.2087 train/reward_psnr_group_std_max=3.0115 train/reward_psnr_group_std_min=0.1755 train/reward_psnr_group_mean_std=1.8722 train/reward_ssim_group_std_mean=0.0012 train/reward_ssim_group_std_max=0.0028 train/reward_ssim_group_std_min=0.0004 train/reward_ssim_group_mean_std=0.0206 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-8.3231 train/adv_max=4.9656 train/adv_abs_mean=1.7958
+
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Epoch 28 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.69it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.49it/s]
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Epoch 28 Training:   1%|          | 1/96 [00:01<01:59,  1.25s/it]
+
Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.42it/s]
+
                                                                
Epoch 28 Training:   2%|▏         | 2/96 [00:02<02:01,  1.29s/it]
+
Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.45it/s]
+
                                                                
Epoch 28 Training:   3%|▎         | 3/96 [00:03<02:01,  1.30s/it]
+
Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.43it/s]
+
                                                                
Epoch 28 Training:   4%|▍         | 4/96 [00:05<02:00,  1.31s/it]
+
Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.44it/s]
+
                                                                
Epoch 28 Training:   5%|▌         | 5/96 [00:06<01:59,  1.31s/it]
+
Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.44it/s]
+
                                                                
Epoch 28 Training:   6%|▋         | 6/96 [00:07<01:58,  1.31s/it]
+
Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 28 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.41it/s]
+
                                                                
Epoch 28 Training:   7%|▋         | 7/96 [00:09<01:57,  1.32s/it]
+
Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 28 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.43it/s]
+
                                                                
Epoch 28 Training:   8%|▊         | 8/96 [00:10<01:56,  1.32s/it]
+
Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 28 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.43it/s]
+
                                                                
Epoch 28 Training:   9%|▉         | 9/96 [00:12<02:03,  1.42s/it]
+
Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.44it/s]
+
                                                                
Epoch 28 Training:  10%|█         | 10/96 [00:13<01:59,  1.39s/it]
+
Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.43it/s]
+
                                                                
Epoch 28 Training:  11%|█▏        | 11/96 [00:14<01:56,  1.37s/it]
+
Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.39it/s]
+
                                                                
Epoch 28 Training:  12%|█▎        | 12/96 [00:16<01:54,  1.36s/it]
+
Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.44it/s]
+
                                                                
Epoch 28 Training:  14%|█▎        | 13/96 [00:17<01:51,  1.35s/it]
+
Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.44it/s]
+
                                                                
Epoch 28 Training:  15%|█▍        | 14/96 [00:18<01:49,  1.34s/it]
+
Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.41it/s]
+
                                                                
Epoch 28 Training:  16%|█▌        | 15/96 [00:20<01:48,  1.33s/it]
+
Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 28 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.49it/s]
+
                                                                
Epoch 28 Training:  17%|█▋        | 16/96 [00:21<01:46,  1.33s/it]
+
Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 28 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.44it/s]
+
                                                                
Epoch 28 Training:  18%|█▊        | 17/96 [00:22<01:44,  1.33s/it]
+
Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 28 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.45it/s]
+
                                                                
Epoch 28 Training:  19%|█▉        | 18/96 [00:24<01:43,  1.32s/it]
+
Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 28 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.42it/s]
+
                                                                
Epoch 28 Training:  20%|█▉        | 19/96 [00:25<01:41,  1.32s/it]
+
Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 28 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.37it/s]
+
                                                                
Epoch 28 Training:  21%|██        | 20/96 [00:26<01:40,  1.33s/it]
+
Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.44it/s]
+
                                                                
Epoch 28 Training:  22%|██▏       | 21/96 [00:28<01:39,  1.33s/it]
+
Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 28 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.40it/s]
+
                                                                
Epoch 28 Training:  23%|██▎       | 22/96 [00:29<01:38,  1.33s/it]
+
Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.44it/s]
+
                                                                
Epoch 28 Training:  24%|██▍       | 23/96 [00:30<01:36,  1.32s/it]
+
Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.42it/s]
+
                                                                
Epoch 28 Training:  25%|██▌       | 24/96 [00:31<01:35,  1.32s/it]
+
Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 28 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.35it/s]
+
                                                                
Epoch 28 Training:  26%|██▌       | 25/96 [00:33<01:34,  1.33s/it]
+
Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 28 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.41it/s]
+
                                                                
Epoch 28 Training:  27%|██▋       | 26/96 [00:34<01:32,  1.33s/it]
+
Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 28 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.46it/s]
+
                                                                
Epoch 28 Training:  28%|██▊       | 27/96 [00:35<01:31,  1.32s/it]
+
Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 28 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.42it/s]
+
                                                                
Epoch 28 Training:  29%|██▉       | 28/96 [00:37<01:30,  1.32s/it]
+
Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.45it/s]
+
                                                                
Epoch 28 Training:  30%|███       | 29/96 [00:38<01:28,  1.32s/it]
+
Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.40it/s]
+
                                                                
Epoch 28 Training:  31%|███▏      | 30/96 [00:39<01:27,  1.32s/it]
+
Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.44it/s]
+
                                                                
Epoch 28 Training:  32%|███▏      | 31/96 [00:41<01:25,  1.32s/it]
+
Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.44it/s]
+
                                                                
Epoch 28 Training:  33%|███▎      | 32/96 [00:42<01:24,  1.32s/it]
+
Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.41it/s]
+
                                                                
Epoch 28 Training:  34%|███▍      | 33/96 [00:43<01:23,  1.32s/it]
+
Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.45it/s]
+
                                                                
Epoch 28 Training:  35%|███▌      | 34/96 [00:45<01:21,  1.32s/it]
+
Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.45it/s]
+
                                                                
Epoch 28 Training:  36%|███▋      | 35/96 [00:46<01:20,  1.32s/it]
+
Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.43it/s]
+
                                                                
Epoch 28 Training:  38%|███▊      | 36/96 [00:47<01:19,  1.32s/it]
+
Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.46it/s]
+
                                                                
Epoch 28 Training:  39%|███▊      | 37/96 [00:49<01:17,  1.32s/it]
+
Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.45it/s]
+
                                                                
Epoch 28 Training:  40%|███▉      | 38/96 [00:50<01:16,  1.32s/it]
+
Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.43it/s]
+
                                                                
Epoch 28 Training:  41%|████      | 39/96 [00:51<01:15,  1.32s/it]
+
Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.43it/s]
+
                                                                
Epoch 28 Training:  42%|████▏     | 40/96 [00:53<01:13,  1.32s/it]
+
Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.43it/s]
+
                                                                
Epoch 28 Training:  43%|████▎     | 41/96 [00:54<01:12,  1.32s/it]
+
Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.42it/s]
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Epoch 28 Training:  44%|████▍     | 42/96 [00:55<01:11,  1.32s/it]
+
Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.42it/s]
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Epoch 28 Training:  45%|████▍     | 43/96 [00:57<01:09,  1.32s/it]
+
Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.42it/s]
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Epoch 28 Training:  46%|████▌     | 44/96 [00:58<01:08,  1.32s/it]
+
Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.45it/s]
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Epoch 28 Training:  47%|████▋     | 45/96 [00:59<01:07,  1.32s/it]
+
Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.43it/s]
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Epoch 28 Training:  48%|████▊     | 46/96 [01:01<01:06,  1.32s/it]
+
Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.42it/s]
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Epoch 28 Training:  49%|████▉     | 47/96 [01:02<01:04,  1.32s/it]
+
Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.44it/s]
+
                                                                
Epoch 28 Training:  50%|█████     | 48/96 [01:03<01:03,  1.32s/it]
+
Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.42it/s]
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Epoch 28 Training:  51%|█████     | 49/96 [01:04<01:02,  1.32s/it]
+
Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.44it/s]
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Epoch 28 Training:  52%|█████▏    | 50/96 [01:06<01:00,  1.32s/it]
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Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.44it/s]
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Epoch 28 Training:  53%|█████▎    | 51/96 [01:07<00:59,  1.32s/it]
+
Epoch 28 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 28 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.44it/s]
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Epoch 28 Training:  54%|█████▍    | 52/96 [01:08<00:58,  1.32s/it]
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Epoch 28 Training:  58%|█████▊    | 56/96 [01:14<00:53,  1.35s/it]
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Epoch 28 Training:  81%|████████▏ | 78/96 [01:43<00:23,  1.32s/it]
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Epoch 28 Training:  82%|████████▏ | 79/96 [01:44<00:22,  1.32s/it]
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Epoch 28 Training:  99%|█████████▉| 95/96 [02:05<00:01,  1.32s/it]
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Epoch 28 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s][2026-10-01 18:20:41] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0028 | Epoch 028] train/policy_loss=12.0792 train/unweighted_policy_loss=2.4158 train/loss=12.0792 train/grad_norm=0.0421
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Epoch 28 Training: 100%|██████████| 96/96 [02:07<00:00,  1.33s/it]
+[2026-10-01 18:20:41] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0029 | Epoch 028] timing/rollout_seconds=147.7182 timing/feedback_seconds=5.4834 timing/optimization_seconds=127.8691 timing/cycle_seconds=279.1593
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+
Pointwise Rewards: ssim:   0%|          | 0/6 [00:00<?, ?it/s]
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Pointwise Rewards: ssim:  83%|████████▎ | 5/6 [00:00<00:00,  6.53it/s]
Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:00<00:00,  6.66it/s]
Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:00<00:00,  6.47it/s]
+
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Pointwise Rewards: dists:  83%|████████▎ | 5/6 [00:01<00:00,  3.72it/s]
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Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.69it/s]
+
Pointwise Rewards: psnr:   0%|          | 0/6 [00:00<?, ?it/s]
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Pointwise Rewards: psnr:  33%|███▎      | 2/6 [00:00<00:00,  6.83it/s]
Pointwise Rewards: psnr:  50%|█████     | 3/6 [00:00<00:00,  6.63it/s]
Pointwise Rewards: psnr:  67%|██████▋   | 4/6 [00:00<00:00,  6.57it/s]
Pointwise Rewards: psnr:  83%|████████▎ | 5/6 [00:00<00:00,  6.98it/s]
Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  7.02it/s]
Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.86it/s]
+[2026-10-01 18:23:12] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0029 | Epoch 029] train/reward_dists_mean=0.9375 train/reward_dists_std=0.0160 train/reward_psnr_mean=30.5085 train/reward_psnr_std=2.0296 train/reward_ssim_mean=0.9380 train/reward_ssim_std=0.0201 train/reward_dists_group_std_mean=0.0031 train/reward_dists_group_std_max=0.0075 train/reward_dists_group_std_min=0.0016 train/reward_dists_group_mean_std=0.0157 train/reward_psnr_group_std_mean=1.1388 train/reward_psnr_group_std_max=4.1082 train/reward_psnr_group_std_min=0.2761 train/reward_psnr_group_mean_std=1.5605 train/reward_ssim_group_std_mean=0.0012 train/reward_ssim_group_std_max=0.0026 train/reward_ssim_group_std_min=0.0004 train/reward_ssim_group_mean_std=0.0201 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-7.7802 train/adv_max=5.8623 train/adv_abs_mean=1.8210
+
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Epoch 29 Training:  10%|█         | 10/96 [00:13<01:55,  1.34s/it]
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Epoch 29 Training:  11%|█▏        | 11/96 [00:14<01:53,  1.33s/it]
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Epoch 29 Training:  18%|█▊        | 17/96 [00:22<01:44,  1.32s/it]
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Epoch 29 Training:  19%|█▉        | 18/96 [00:24<01:43,  1.32s/it]
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Epoch 29 Training:  22%|██▏       | 21/96 [00:27<01:38,  1.32s/it]
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Epoch 29 Training:  26%|██▌       | 25/96 [00:33<01:33,  1.32s/it]
+
Epoch 29 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 29 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 29 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.43it/s]
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Epoch 29 Training:  27%|██▋       | 26/96 [00:34<01:32,  1.32s/it]
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Epoch 29 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 29 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 29 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.44it/s]
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Epoch 29 Training:  28%|██▊       | 27/96 [00:35<01:30,  1.32s/it]
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Epoch 29 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 29 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 29 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.43it/s]
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Epoch 29 Training:  29%|██▉       | 28/96 [00:37<01:29,  1.32s/it]
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Epoch 29 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 29 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 29 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.40it/s]
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Epoch 29 Training:  30%|███       | 29/96 [00:38<01:28,  1.32s/it]
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Epoch 29 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 29 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 29 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.46it/s]
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Epoch 29 Training:  31%|███▏      | 30/96 [00:39<01:26,  1.32s/it]
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Epoch 29 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 29 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 29 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.44it/s]
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Epoch 29 Training:  32%|███▏      | 31/96 [00:41<01:25,  1.32s/it]
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Epoch 29 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 29 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 29 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.43it/s]
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Epoch 29 Training:  33%|███▎      | 32/96 [00:42<01:24,  1.32s/it]
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Epoch 29 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 29 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 29 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.42it/s]
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Epoch 29 Training:  34%|███▍      | 33/96 [00:43<01:23,  1.32s/it]
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Epoch 29 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 29 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 29 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.43it/s]
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Epoch 29 Training:  35%|███▌      | 34/96 [00:45<01:21,  1.32s/it]
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Epoch 29 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 29 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 29 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.44it/s]
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Epoch 29 Training:  36%|███▋      | 35/96 [00:46<01:20,  1.32s/it]
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Epoch 29 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 29 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 29 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.43it/s]
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Epoch 29 Training:  38%|███▊      | 36/96 [00:47<01:19,  1.32s/it]
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Epoch 29 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 29 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 29 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.39it/s]
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Epoch 29 Training:  39%|███▊      | 37/96 [00:49<01:17,  1.32s/it]
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Epoch 29 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 29 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 29 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.43it/s]
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Epoch 29 Training:  40%|███▉      | 38/96 [00:50<01:16,  1.32s/it]
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Epoch 29 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 29 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 29 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.43it/s]
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Epoch 29 Training:  41%|████      | 39/96 [00:51<01:15,  1.32s/it]
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Epoch 29 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 29 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 29 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.42it/s]
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Epoch 29 Training:  42%|████▏     | 40/96 [00:53<01:14,  1.32s/it]
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Epoch 29 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 29 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 29 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.43it/s]
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Epoch 29 Training:  43%|████▎     | 41/96 [00:54<01:12,  1.32s/it]
+
Epoch 29 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 29 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 29 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.43it/s]
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Epoch 29 Training:  44%|████▍     | 42/96 [00:55<01:11,  1.32s/it]
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Epoch 29 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 29 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 29 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.41it/s]
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Epoch 29 Training:  45%|████▍     | 43/96 [00:56<01:10,  1.32s/it]
+
Epoch 29 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 29 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 29 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.42it/s]
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Epoch 29 Training:  46%|████▌     | 44/96 [00:58<01:08,  1.32s/it]
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Epoch 29 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 29 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 29 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.45it/s]
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Epoch 29 Training:  47%|████▋     | 45/96 [00:59<01:07,  1.32s/it]
+
Epoch 29 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 29 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 29 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.43it/s]
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Epoch 29 Training:  48%|████▊     | 46/96 [01:00<01:05,  1.32s/it]
+
Epoch 29 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 29 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 29 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.43it/s]
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Epoch 29 Training:  49%|████▉     | 47/96 [01:02<01:04,  1.32s/it]
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Epoch 29 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 29 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 29 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.42it/s]
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Epoch 29 Training:  50%|█████     | 48/96 [01:03<01:03,  1.32s/it]
+
Epoch 29 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 29 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 29 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.40it/s]
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Epoch 29 Training:  51%|█████     | 49/96 [01:04<01:02,  1.32s/it]
+
Epoch 29 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 29 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 29 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.44it/s]
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Epoch 29 Training:  52%|█████▏    | 50/96 [01:06<01:00,  1.32s/it]
+
Epoch 29 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 29 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 29 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.45it/s]
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Epoch 29 Training:  53%|█████▎    | 51/96 [01:07<00:59,  1.32s/it]
+
Epoch 29 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 29 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 29 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.43it/s]
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Epoch 29 Training:  54%|█████▍    | 52/96 [01:08<00:58,  1.32s/it]
+
Epoch 29 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 29 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 29 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.43it/s]
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Epoch 29 Training:  55%|█████▌    | 53/96 [01:10<00:56,  1.32s/it]
+
Epoch 29 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 29 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 29 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.41it/s]
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Epoch 29 Training:  56%|█████▋    | 54/96 [01:11<00:55,  1.32s/it]
+
Epoch 29 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 29 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 29 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.43it/s]
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Epoch 29 Training:  57%|█████▋    | 55/96 [01:12<00:54,  1.32s/it]
+
Epoch 29 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 29 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 29 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.44it/s]
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Epoch 29 Training:  58%|█████▊    | 56/96 [01:14<00:52,  1.32s/it]
+
Epoch 29 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 29 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 29 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.43it/s]
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Epoch 29 Training:  59%|█████▉    | 57/96 [01:15<00:51,  1.32s/it]
+
Epoch 29 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 29 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 29 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.43it/s]
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Epoch 29 Training:  60%|██████    | 58/96 [01:16<00:50,  1.32s/it]
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Epoch 29 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 29 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 29 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.44it/s]
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Epoch 29 Training:  61%|██████▏   | 59/96 [01:18<00:48,  1.32s/it]
+
Epoch 29 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 29 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 29 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.39it/s]
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Epoch 29 Training:  62%|██████▎   | 60/96 [01:19<00:47,  1.32s/it]
+
Epoch 29 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 29 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 29 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.43it/s]
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Epoch 29 Training:  64%|██████▎   | 61/96 [01:20<00:46,  1.32s/it]
+
Epoch 29 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 29 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 29 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.44it/s]
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Epoch 29 Training:  65%|██████▍   | 62/96 [01:22<00:44,  1.32s/it]
+
Epoch 29 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 29 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 29 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.42it/s]
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Epoch 29 Training:  66%|██████▌   | 63/96 [01:23<00:43,  1.32s/it]
+
Epoch 29 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 29 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.42it/s]
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Epoch 29 Training:  67%|██████▋   | 64/96 [01:24<00:42,  1.32s/it]
+
Epoch 29 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 29 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.40it/s]
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Epoch 29 Training:  68%|██████▊   | 65/96 [01:25<00:40,  1.32s/it]
+
Epoch 29 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 29 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.45it/s]
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Epoch 29 Training:  69%|██████▉   | 66/96 [01:27<00:39,  1.32s/it]
+
Epoch 29 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 29 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 29 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.43it/s]
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Epoch 29 Training:  70%|██████▉   | 67/96 [01:28<00:38,  1.32s/it]
+
Epoch 29 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 29 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 29 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.44it/s]
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Epoch 29 Training:  71%|███████   | 68/96 [01:29<00:36,  1.32s/it]
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Epoch 29 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 29 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.43it/s]
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Epoch 29 Training:  72%|███████▏  | 69/96 [01:31<00:35,  1.32s/it]
+
Epoch 29 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 29 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.43it/s]
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Epoch 29 Training:  73%|███████▎  | 70/96 [01:32<00:34,  1.32s/it]
+
Epoch 29 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 29 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.39it/s]
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Epoch 29 Training:  74%|███████▍  | 71/96 [01:33<00:32,  1.32s/it]
+
Epoch 29 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 29 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.44it/s]
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Epoch 29 Training:  75%|███████▌  | 72/96 [01:35<00:31,  1.32s/it]
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Epoch 29 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.44it/s]
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Epoch 29 Training:  76%|███████▌  | 73/96 [01:36<00:30,  1.32s/it]
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Epoch 29 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.44it/s]
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Epoch 29 Training:  77%|███████▋  | 74/96 [01:37<00:28,  1.32s/it]
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Epoch 29 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.44it/s]
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Epoch 29 Training:  78%|███████▊  | 75/96 [01:39<00:27,  1.32s/it]
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Epoch 29 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.42it/s]
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Epoch 29 Training:  79%|███████▉  | 76/96 [01:40<00:26,  1.32s/it]
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Epoch 29 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 29 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s][2026-10-01 18:25:19] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0029 | Epoch 029] train/policy_loss=12.4566 train/unweighted_policy_loss=2.4913 train/loss=12.4566 train/grad_norm=0.0571
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+[2026-10-01 18:25:19] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0030 | Epoch 029] timing/rollout_seconds=147.6957 timing/feedback_seconds=5.4035 timing/optimization_seconds=127.4901 timing/cycle_seconds=278.4781
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+[2026-10-01 18:26:04] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0030 | Epoch 030] eval/vedai_rl_full/reward_dists_mean=0.9310 eval/vedai_rl_full/reward_dists_std=0.0179 eval/vedai_rl_full/reward_psnr_mean=29.2355 eval/vedai_rl_full/reward_psnr_std=2.4635 eval/vedai_rl_full/reward_ssim_mean=0.9221 eval/vedai_rl_full/reward_ssim_std=0.0266
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Pointwise Rewards: ssim:  67%|██████▋   | 4/6 [00:00<00:00,  6.34it/s]
Pointwise Rewards: ssim:  83%|████████▎ | 5/6 [00:00<00:00,  6.50it/s]
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Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:00<00:00,  6.35it/s]
+
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Pointwise Rewards: dists:  83%|████████▎ | 5/6 [00:01<00:00,  3.67it/s]
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Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.62it/s]
+
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Pointwise Rewards: psnr:  33%|███▎      | 2/6 [00:00<00:00,  6.75it/s]
Pointwise Rewards: psnr:  50%|█████     | 3/6 [00:00<00:00,  6.71it/s]
Pointwise Rewards: psnr:  67%|██████▋   | 4/6 [00:00<00:00,  6.71it/s]
Pointwise Rewards: psnr:  83%|████████▎ | 5/6 [00:00<00:00,  6.97it/s]
Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.73it/s]
Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.76it/s]
+[2026-10-01 18:28:36] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0030 | Epoch 030] train/reward_dists_mean=0.9410 train/reward_dists_std=0.0164 train/reward_psnr_mean=30.5993 train/reward_psnr_std=1.8520 train/reward_ssim_mean=0.9399 train/reward_ssim_std=0.0215 train/reward_dists_group_std_mean=0.0029 train/reward_dists_group_std_max=0.0080 train/reward_dists_group_std_min=0.0012 train/reward_dists_group_mean_std=0.0161 train/reward_psnr_group_std_mean=1.0097 train/reward_psnr_group_std_max=2.2295 train/reward_psnr_group_std_min=0.4315 train/reward_psnr_group_mean_std=1.4878 train/reward_ssim_group_std_mean=0.0012 train/reward_ssim_group_std_max=0.0027 train/reward_ssim_group_std_min=0.0004 train/reward_ssim_group_mean_std=0.0215 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-8.0952 train/adv_max=5.4806 train/adv_abs_mean=1.7842
+
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Epoch 30 Training:  11%|█▏        | 11/96 [00:14<01:52,  1.33s/it]
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Epoch 30 Training:  15%|█▍        | 14/96 [00:18<01:47,  1.31s/it]
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Epoch 30 Training:  16%|█▌        | 15/96 [00:19<01:45,  1.30s/it]
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Epoch 30 Training:  17%|█▋        | 16/96 [00:20<01:44,  1.30s/it]
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Epoch 30 Training:  18%|█▊        | 17/96 [00:22<01:42,  1.30s/it]
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Epoch 30 Training:  22%|██▏       | 21/96 [00:27<01:37,  1.30s/it]
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Epoch 30 Training:  23%|██▎       | 22/96 [00:28<01:36,  1.30s/it]
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Epoch 30 Training:  25%|██▌       | 24/96 [00:31<01:33,  1.30s/it]
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Epoch 30 Training:  26%|██▌       | 25/96 [00:32<01:32,  1.30s/it]
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Epoch 30 Training:  27%|██▋       | 26/96 [00:33<01:30,  1.30s/it]
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Epoch 30 Training:  28%|██▊       | 27/96 [00:35<01:29,  1.30s/it]
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Epoch 30 Training:  29%|██▉       | 28/96 [00:36<01:28,  1.30s/it]
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Epoch 30 Training:  33%|███▎      | 32/96 [00:41<01:23,  1.30s/it]
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Epoch 30 Training:  34%|███▍      | 33/96 [00:43<01:21,  1.30s/it]
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Epoch 30 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 30 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.36it/s]
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Epoch 30 Training:  47%|████▋     | 45/96 [00:59<01:08,  1.35s/it]
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Epoch 30 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 30 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 30 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.35it/s]
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Epoch 30 Training:  48%|████▊     | 46/96 [01:00<01:06,  1.34s/it]
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Epoch 30 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 30 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.36it/s]
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Epoch 30 Training:  49%|████▉     | 47/96 [01:01<01:04,  1.33s/it]
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Epoch 30 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 30 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.37it/s]
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Epoch 30 Training:  50%|█████     | 48/96 [01:02<01:03,  1.32s/it]
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Epoch 30 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 30 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 30 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.37it/s]
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Epoch 30 Training:  51%|█████     | 49/96 [01:04<01:01,  1.31s/it]
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Epoch 30 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 30 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 30 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.37it/s]
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Epoch 30 Training:  52%|█████▏    | 50/96 [01:05<01:00,  1.31s/it]
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Epoch 30 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 30 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 30 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.39it/s]
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Epoch 30 Training:  53%|█████▎    | 51/96 [01:06<00:58,  1.31s/it]
+
Epoch 30 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 30 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 30 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.37it/s]
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Epoch 30 Training:  54%|█████▍    | 52/96 [01:08<00:57,  1.31s/it]
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Epoch 30 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 30 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 30 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.37it/s]
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Epoch 30 Training:  55%|█████▌    | 53/96 [01:09<00:56,  1.31s/it]
+
Epoch 30 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 30 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 30 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.38it/s]
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Epoch 30 Training:  56%|█████▋    | 54/96 [01:10<00:54,  1.30s/it]
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Epoch 30 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 30 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 30 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.39it/s]
+
                                                                
Epoch 30 Training:  57%|█████▋    | 55/96 [01:12<00:53,  1.30s/it]
+
Epoch 30 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 30 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 30 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.38it/s]
+
                                                                
Epoch 30 Training:  58%|█████▊    | 56/96 [01:13<00:52,  1.30s/it]
+
Epoch 30 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 30 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 30 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.39it/s]
+
                                                                
Epoch 30 Training:  59%|█████▉    | 57/96 [01:14<00:50,  1.30s/it]
+
Epoch 30 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 30 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 30 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.39it/s]
+
                                                                
Epoch 30 Training:  60%|██████    | 58/96 [01:15<00:49,  1.30s/it]
+
Epoch 30 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 30 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 30 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.40it/s]
+
                                                                
Epoch 30 Training:  61%|██████▏   | 59/96 [01:17<00:48,  1.30s/it]
+
Epoch 30 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 30 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 30 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.37it/s]
+
                                                                
Epoch 30 Training:  62%|██████▎   | 60/96 [01:18<00:46,  1.30s/it]
+
Epoch 30 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 30 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 30 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.35it/s]
+
                                                                
Epoch 30 Training:  64%|██████▎   | 61/96 [01:19<00:45,  1.30s/it]
+
Epoch 30 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 30 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 30 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.38it/s]
+
                                                                
Epoch 30 Training:  65%|██████▍   | 62/96 [01:21<00:44,  1.30s/it]
+
Epoch 30 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 30 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 30 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.38it/s]
+
                                                                
Epoch 30 Training:  66%|██████▌   | 63/96 [01:22<00:42,  1.30s/it]
+
Epoch 30 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 30 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 30 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.37it/s]
+
                                                                
Epoch 30 Training:  67%|██████▋   | 64/96 [01:23<00:41,  1.30s/it]
+
Epoch 30 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 30 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 30 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.34it/s]
+
                                                                
Epoch 30 Training:  68%|██████▊   | 65/96 [01:25<00:40,  1.30s/it]
+
Epoch 30 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 30 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 30 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.36it/s]
+
                                                                
Epoch 30 Training:  69%|██████▉   | 66/96 [01:26<00:39,  1.30s/it]
+
Epoch 30 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 30 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 30 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.40it/s]
+
                                                                
Epoch 30 Training:  70%|██████▉   | 67/96 [01:27<00:37,  1.30s/it]
+
Epoch 30 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 30 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 30 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.39it/s]
+
                                                                
Epoch 30 Training:  71%|███████   | 68/96 [01:28<00:36,  1.30s/it]
+
Epoch 30 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 30 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 30 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.40it/s]
+
                                                                
Epoch 30 Training:  72%|███████▏  | 69/96 [01:30<00:35,  1.30s/it]
+
Epoch 30 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 30 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 30 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.39it/s]
+
                                                                
Epoch 30 Training:  73%|███████▎  | 70/96 [01:31<00:33,  1.30s/it]
+
Epoch 30 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 30 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 30 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.38it/s]
+
                                                                
Epoch 30 Training:  74%|███████▍  | 71/96 [01:32<00:32,  1.30s/it]
+
Epoch 30 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 30 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 30 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.37it/s]
+
                                                                
Epoch 30 Training:  75%|███████▌  | 72/96 [01:34<00:31,  1.30s/it]
+
Epoch 30 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 30 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 30 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.39it/s]
+
                                                                
Epoch 30 Training:  76%|███████▌  | 73/96 [01:35<00:29,  1.30s/it]
+
Epoch 30 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 30 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 30 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.37it/s]
+
                                                                
Epoch 30 Training:  77%|███████▋  | 74/96 [01:36<00:28,  1.31s/it]
+
Epoch 30 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 30 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 30 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.49it/s]
+
                                                                
Epoch 30 Training:  78%|███████▊  | 75/96 [01:38<00:27,  1.30s/it]
+
Epoch 30 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 30 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 30 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.34it/s]
+
                                                                
Epoch 30 Training:  79%|███████▉  | 76/96 [01:39<00:25,  1.30s/it]
+
Epoch 30 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 30 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 30 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.34it/s]
+
                                                                
Epoch 30 Training:  80%|████████  | 77/96 [01:40<00:24,  1.30s/it]
+
Epoch 30 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 30 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 30 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.36it/s]
+
                                                                
Epoch 30 Training:  81%|████████▏ | 78/96 [01:41<00:23,  1.29s/it]
+
Epoch 30 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 30 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 30 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.36it/s]
+
                                                                
Epoch 30 Training:  82%|████████▏ | 79/96 [01:43<00:21,  1.29s/it]
+
Epoch 30 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 30 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 30 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.36it/s]
+
                                                                
Epoch 30 Training:  83%|████████▎ | 80/96 [01:44<00:20,  1.29s/it]
+
Epoch 30 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 30 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 30 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.35it/s]
+
                                                                
Epoch 30 Training:  84%|████████▍ | 81/96 [01:45<00:19,  1.29s/it]
+
Epoch 30 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 30 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 30 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.37it/s]
+
                                                                
Epoch 30 Training:  85%|████████▌ | 82/96 [01:47<00:18,  1.29s/it]
+
Epoch 30 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 30 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 30 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.37it/s]
+
                                                                
Epoch 30 Training:  86%|████████▋ | 83/96 [01:48<00:16,  1.29s/it]
+
Epoch 30 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 30 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 30 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.36it/s]
+
                                                                
Epoch 30 Training:  88%|████████▊ | 84/96 [01:49<00:15,  1.29s/it]
+
Epoch 30 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 30 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 30 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.36it/s]
+
                                                                
Epoch 30 Training:  89%|████████▊ | 85/96 [01:50<00:14,  1.29s/it]
+
Epoch 30 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 30 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 30 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.36it/s]
+
                                                                
Epoch 30 Training:  90%|████████▉ | 86/96 [01:52<00:12,  1.29s/it]
+
Epoch 30 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 30 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 30 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.34it/s]
+
                                                                
Epoch 30 Training:  91%|█████████ | 87/96 [01:53<00:11,  1.29s/it]
+
Epoch 30 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 30 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 30 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.34it/s]
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Epoch 30 Training:  92%|█████████▏| 88/96 [01:54<00:10,  1.29s/it]
+
Epoch 30 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 30 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 30 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.35it/s]
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Epoch 30 Training:  93%|█████████▎| 89/96 [01:56<00:09,  1.29s/it]
+
Epoch 30 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 30 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 30 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.33it/s]
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Epoch 30 Training:  94%|█████████▍| 90/96 [01:57<00:07,  1.29s/it]
+
Epoch 30 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 30 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 30 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.37it/s]
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Epoch 30 Training:  95%|█████████▍| 91/96 [01:58<00:06,  1.29s/it]
+
Epoch 30 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 30 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 30 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.36it/s]
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Epoch 30 Training:  96%|█████████▌| 92/96 [02:00<00:05,  1.29s/it]
+
Epoch 30 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 30 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 30 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.37it/s]
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Epoch 30 Training:  97%|█████████▋| 93/96 [02:01<00:03,  1.29s/it]
+
Epoch 30 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 30 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 30 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.38it/s]
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Epoch 30 Training:  98%|█████████▊| 94/96 [02:02<00:02,  1.29s/it]
+
Epoch 30 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 30 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.37it/s]
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Epoch 30 Training:  99%|█████████▉| 95/96 [02:03<00:01,  1.29s/it]
+
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+[2026-10-01 18:30:41] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0031 | Epoch 030] timing/rollout_seconds=147.8988 timing/feedback_seconds=5.9946 timing/optimization_seconds=126.0274 timing/cycle_seconds=277.5215
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+
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+[2026-10-01 18:33:13] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0031 | Epoch 031] train/reward_dists_mean=0.9394 train/reward_dists_std=0.0125 train/reward_psnr_mean=30.4044 train/reward_psnr_std=1.9592 train/reward_ssim_mean=0.9373 train/reward_ssim_std=0.0158 train/reward_dists_group_std_mean=0.0030 train/reward_dists_group_std_max=0.0071 train/reward_dists_group_std_min=0.0013 train/reward_dists_group_mean_std=0.0120 train/reward_psnr_group_std_mean=1.1548 train/reward_psnr_group_std_max=2.9687 train/reward_psnr_group_std_min=0.4050 train/reward_psnr_group_mean_std=1.4884 train/reward_ssim_group_std_mean=0.0012 train/reward_ssim_group_std_max=0.0029 train/reward_ssim_group_std_min=0.0005 train/reward_ssim_group_mean_std=0.0157 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-8.3563 train/adv_max=5.8522 train/adv_abs_mean=1.8037
+
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Epoch 31 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 31 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.43it/s]
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Epoch 31 Training:  19%|█▉        | 18/96 [00:23<01:41,  1.30s/it]
+
Epoch 31 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 31 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 31 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.40it/s]
+
                                                                
Epoch 31 Training:  20%|█▉        | 19/96 [00:24<01:40,  1.30s/it]
+
Epoch 31 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 31 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 31 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.36it/s]
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Epoch 31 Training:  21%|██        | 20/96 [00:25<01:38,  1.30s/it]
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Epoch 31 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 31 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 31 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.43it/s]
+
                                                                
Epoch 31 Training:  22%|██▏       | 21/96 [00:27<01:37,  1.30s/it]
+
Epoch 31 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 31 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 31 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.49it/s]
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Epoch 31 Training:  23%|██▎       | 22/96 [00:28<01:35,  1.30s/it]
+
Epoch 31 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 31 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 31 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.42it/s]
+
                                                                
Epoch 31 Training:  24%|██▍       | 23/96 [00:29<01:34,  1.30s/it]
+
Epoch 31 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 31 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 31 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.41it/s]
+
                                                                
Epoch 31 Training:  25%|██▌       | 24/96 [00:31<01:33,  1.30s/it]
+
Epoch 31 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 31 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 31 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.34it/s]
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Epoch 31 Training:  26%|██▌       | 25/96 [00:32<01:32,  1.30s/it]
+
Epoch 31 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 31 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 31 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.38it/s]
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Epoch 31 Training:  27%|██▋       | 26/96 [00:33<01:31,  1.31s/it]
+
Epoch 31 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 31 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 31 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.40it/s]
+
                                                                
Epoch 31 Training:  28%|██▊       | 27/96 [00:35<01:30,  1.31s/it]
+
Epoch 31 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 31 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 31 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.49it/s]
+
                                                                
Epoch 31 Training:  29%|██▉       | 28/96 [00:36<01:28,  1.30s/it]
+
Epoch 31 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 31 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 31 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.41it/s]
+
                                                                
Epoch 31 Training:  30%|███       | 29/96 [00:37<01:27,  1.30s/it]
+
Epoch 31 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 31 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 31 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.30it/s]
+
                                                                
Epoch 31 Training:  31%|███▏      | 30/96 [00:38<01:26,  1.30s/it]
+
Epoch 31 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 31 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 31 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.36it/s]
+
                                                                
Epoch 31 Training:  32%|███▏      | 31/96 [00:40<01:24,  1.30s/it]
+
Epoch 31 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 31 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 31 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.38it/s]
+
                                                                
Epoch 31 Training:  33%|███▎      | 32/96 [00:41<01:23,  1.30s/it]
+
Epoch 31 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 31 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 31 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.32it/s]
+
                                                                
Epoch 31 Training:  34%|███▍      | 33/96 [00:42<01:22,  1.31s/it]
+
Epoch 31 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 31 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 31 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.36it/s]
+
                                                                
Epoch 31 Training:  35%|███▌      | 34/96 [00:44<01:21,  1.31s/it]
+
Epoch 31 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 31 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 31 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.37it/s]
+
                                                                
Epoch 31 Training:  36%|███▋      | 35/96 [00:45<01:19,  1.31s/it]
+
Epoch 31 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 31 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 31 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.42it/s]
+
                                                                
Epoch 31 Training:  38%|███▊      | 36/96 [00:46<01:18,  1.31s/it]
+
Epoch 31 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 31 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 31 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.44it/s]
+
                                                                
Epoch 31 Training:  39%|███▊      | 37/96 [00:48<01:17,  1.31s/it]
+
Epoch 31 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 31 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 31 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.35it/s]
+
                                                                
Epoch 31 Training:  40%|███▉      | 38/96 [00:49<01:16,  1.32s/it]
+
Epoch 31 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 31 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 31 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.40it/s]
+
                                                                
Epoch 31 Training:  41%|████      | 39/96 [00:50<01:14,  1.31s/it]
+
Epoch 31 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 31 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 31 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.39it/s]
+
                                                                
Epoch 31 Training:  42%|████▏     | 40/96 [00:52<01:13,  1.31s/it]
+
Epoch 31 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 31 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 31 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.40it/s]
+
                                                                
Epoch 31 Training:  43%|████▎     | 41/96 [00:53<01:12,  1.31s/it]
+
Epoch 31 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 31 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 31 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.47it/s]
+
                                                                
Epoch 31 Training:  44%|████▍     | 42/96 [00:54<01:10,  1.31s/it]
+
Epoch 31 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 31 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 31 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.33it/s]
+
                                                                
Epoch 31 Training:  45%|████▍     | 43/96 [00:55<01:09,  1.31s/it]
+
Epoch 31 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 31 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 31 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.43it/s]
+
                                                                
Epoch 31 Training:  46%|████▌     | 44/96 [00:57<01:08,  1.31s/it]
+
Epoch 31 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 31 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 31 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.43it/s]
+
                                                                
Epoch 31 Training:  47%|████▋     | 45/96 [00:58<01:06,  1.31s/it]
+
Epoch 31 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 31 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 31 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.40it/s]
+
                                                                
Epoch 31 Training:  48%|████▊     | 46/96 [00:59<01:05,  1.31s/it]
+
Epoch 31 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 31 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 31 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.38it/s]
+
                                                                
Epoch 31 Training:  49%|████▉     | 47/96 [01:01<01:03,  1.30s/it]
+
Epoch 31 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 31 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 31 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.41it/s]
+
                                                                
Epoch 31 Training:  50%|█████     | 48/96 [01:02<01:02,  1.31s/it]
+
Epoch 31 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 31 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 31 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.37it/s]
+
                                                                
Epoch 31 Training:  51%|█████     | 49/96 [01:03<01:01,  1.31s/it]
+
Epoch 31 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 31 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 31 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.42it/s]
+
                                                                
Epoch 31 Training:  52%|█████▏    | 50/96 [01:05<01:00,  1.31s/it]
+
Epoch 31 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 31 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 31 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.42it/s]
+
                                                                
Epoch 31 Training:  53%|█████▎    | 51/96 [01:06<00:58,  1.31s/it]
+
Epoch 31 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 31 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 31 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.39it/s]
+
                                                                
Epoch 31 Training:  54%|█████▍    | 52/96 [01:07<00:57,  1.30s/it]
+
Epoch 31 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 31 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 31 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.32it/s]
+
                                                                
Epoch 31 Training:  55%|█████▌    | 53/96 [01:09<00:56,  1.31s/it]
+
Epoch 31 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 31 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 31 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.33it/s]
+
                                                                
Epoch 31 Training:  56%|█████▋    | 54/96 [01:10<00:55,  1.31s/it]
+
Epoch 31 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 31 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 31 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.36it/s]
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Epoch 31 Training:  57%|█████▋    | 55/96 [01:11<00:53,  1.31s/it]
+
Epoch 31 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 31 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 31 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.46it/s]
+
                                                                
Epoch 31 Training:  58%|█████▊    | 56/96 [01:12<00:52,  1.31s/it]
+
Epoch 31 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 31 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 31 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.42it/s]
+
                                                                
Epoch 31 Training:  59%|█████▉    | 57/96 [01:14<00:50,  1.31s/it]
+
Epoch 31 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 31 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 31 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.46it/s]
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Epoch 31 Training:  60%|██████    | 58/96 [01:15<00:49,  1.31s/it]
+
Epoch 31 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 31 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 31 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.37it/s]
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Epoch 31 Training:  61%|██████▏   | 59/96 [01:16<00:48,  1.31s/it]
+
Epoch 31 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 31 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 31 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.41it/s]
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Epoch 31 Training:  62%|██████▎   | 60/96 [01:18<00:47,  1.31s/it]
+
Epoch 31 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 31 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 31 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.42it/s]
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Epoch 31 Training:  64%|██████▎   | 61/96 [01:19<00:45,  1.31s/it]
+
Epoch 31 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 31 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 31 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.48it/s]
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Epoch 31 Training:  65%|██████▍   | 62/96 [01:20<00:44,  1.31s/it]
+
Epoch 31 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 31 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 31 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.43it/s]
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Epoch 31 Training:  66%|██████▌   | 63/96 [01:22<00:42,  1.30s/it]
+
Epoch 31 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 31 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 31 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.41it/s]
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Epoch 31 Training:  67%|██████▋   | 64/96 [01:23<00:41,  1.31s/it]
+
Epoch 31 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 31 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 31 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.36it/s]
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Epoch 31 Training:  68%|██████▊   | 65/96 [01:24<00:40,  1.31s/it]
+
Epoch 31 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 31 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 31 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.45it/s]
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Epoch 31 Training:  69%|██████▉   | 66/96 [01:26<00:39,  1.31s/it]
+
Epoch 31 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 31 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.48it/s]
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Epoch 31 Training:  70%|██████▉   | 67/96 [01:27<00:37,  1.30s/it]
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Epoch 31 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 31 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.34it/s]
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Epoch 31 Training:  71%|███████   | 68/96 [01:28<00:36,  1.31s/it]
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Epoch 31 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 31 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 31 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s][2026-10-01 18:35:18] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0031 | Epoch 031] train/policy_loss=12.4970 train/unweighted_policy_loss=2.4994 train/loss=12.4970 train/grad_norm=0.0559
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+[2026-10-01 18:35:18] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0032 | Epoch 031] timing/rollout_seconds=147.6747 timing/feedback_seconds=5.5850 timing/optimization_seconds=125.9605 timing/cycle_seconds=277.2315
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+
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+
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Pointwise Rewards: psnr:  83%|████████▎ | 5/6 [00:00<00:00,  6.69it/s]
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Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.79it/s]
+[2026-10-01 18:37:50] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0032 | Epoch 032] train/reward_dists_mean=0.9355 train/reward_dists_std=0.0142 train/reward_psnr_mean=29.5122 train/reward_psnr_std=2.3481 train/reward_ssim_mean=0.9376 train/reward_ssim_std=0.0176 train/reward_dists_group_std_mean=0.0034 train/reward_dists_group_std_max=0.0093 train/reward_dists_group_std_min=0.0010 train/reward_dists_group_mean_std=0.0137 train/reward_psnr_group_std_mean=1.4482 train/reward_psnr_group_std_max=3.3792 train/reward_psnr_group_std_min=0.3085 train/reward_psnr_group_mean_std=1.7432 train/reward_ssim_group_std_mean=0.0012 train/reward_ssim_group_std_max=0.0032 train/reward_ssim_group_std_min=0.0005 train/reward_ssim_group_mean_std=0.0176 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-8.2860 train/adv_max=5.8324 train/adv_abs_mean=1.8561
+
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Epoch 32 Training:   7%|▋         | 7/96 [00:08<01:54,  1.28s/it]
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Epoch 32 Training:   8%|▊         | 8/96 [00:10<01:52,  1.28s/it]
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Epoch 32 Training:   9%|▉         | 9/96 [00:11<01:51,  1.28s/it]
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Epoch 32 Training:  10%|█         | 10/96 [00:12<01:50,  1.28s/it]
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Epoch 32 Training:  11%|█▏        | 11/96 [00:14<01:49,  1.29s/it]
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Epoch 32 Training:  14%|█▎        | 13/96 [00:16<01:46,  1.29s/it]
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Epoch 32 Training:  15%|█▍        | 14/96 [00:17<01:46,  1.29s/it]
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Epoch 32 Training:  16%|█▌        | 15/96 [00:19<01:45,  1.30s/it]
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Epoch 32 Training:  17%|█▋        | 16/96 [00:20<01:43,  1.30s/it]
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Epoch 32 Training:  18%|█▊        | 17/96 [00:21<01:42,  1.30s/it]
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Epoch 32 Training:  19%|█▉        | 18/96 [00:23<01:40,  1.29s/it]
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Epoch 32 Training:  20%|█▉        | 19/96 [00:24<01:39,  1.29s/it]
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Epoch 32 Training:  21%|██        | 20/96 [00:25<01:38,  1.29s/it]
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Epoch 32 Training:  22%|██▏       | 21/96 [00:27<01:37,  1.30s/it]
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Epoch 32 Training:  23%|██▎       | 22/96 [00:28<01:35,  1.29s/it]
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Epoch 32 Training:  24%|██▍       | 23/96 [00:29<01:34,  1.29s/it]
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Epoch 32 Training:  26%|██▌       | 25/96 [00:32<01:31,  1.28s/it]
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Epoch 32 Training:  27%|██▋       | 26/96 [00:33<01:30,  1.29s/it]
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Epoch 32 Training:  28%|██▊       | 27/96 [00:34<01:29,  1.29s/it]
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Epoch 32 Training:  29%|██▉       | 28/96 [00:36<01:28,  1.29s/it]
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Epoch 32 Training:  44%|████▍     | 42/96 [00:54<01:09,  1.29s/it]
+
Epoch 32 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 32 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 32 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.33it/s]
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Epoch 32 Training:  45%|████▍     | 43/96 [00:55<01:08,  1.30s/it]
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Epoch 32 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 32 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 32 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.43it/s]
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Epoch 32 Training:  46%|████▌     | 44/96 [00:56<01:07,  1.29s/it]
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Epoch 32 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 32 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 32 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.33it/s]
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Epoch 32 Training:  47%|████▋     | 45/96 [00:57<01:06,  1.30s/it]
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Epoch 32 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 32 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 32 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.44it/s]
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Epoch 32 Training:  48%|████▊     | 46/96 [00:59<01:04,  1.29s/it]
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Epoch 32 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 32 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 32 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.41it/s]
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Epoch 32 Training:  49%|████▉     | 47/96 [01:00<01:03,  1.29s/it]
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Epoch 32 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 32 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 32 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.35it/s]
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Epoch 32 Training:  50%|█████     | 48/96 [01:01<01:01,  1.29s/it]
+
Epoch 32 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 32 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 32 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.41it/s]
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Epoch 32 Training:  51%|█████     | 49/96 [01:03<01:00,  1.29s/it]
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Epoch 32 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 32 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 32 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.42it/s]
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Epoch 32 Training:  52%|█████▏    | 50/96 [01:04<00:59,  1.28s/it]
+
Epoch 32 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 32 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 32 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.40it/s]
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Epoch 32 Training:  53%|█████▎    | 51/96 [01:05<00:57,  1.28s/it]
+
Epoch 32 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 32 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 32 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.44it/s]
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Epoch 32 Training:  54%|█████▍    | 52/96 [01:06<00:56,  1.28s/it]
+
Epoch 32 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 32 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 32 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.43it/s]
+
                                                                
Epoch 32 Training:  55%|█████▌    | 53/96 [01:08<00:55,  1.28s/it]
+
Epoch 32 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 32 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 32 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.42it/s]
+
                                                                
Epoch 32 Training:  56%|█████▋    | 54/96 [01:09<00:53,  1.28s/it]
+
Epoch 32 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 32 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 32 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.40it/s]
+
                                                                
Epoch 32 Training:  57%|█████▋    | 55/96 [01:10<00:52,  1.28s/it]
+
Epoch 32 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 32 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 32 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.36it/s]
+
                                                                
Epoch 32 Training:  58%|█████▊    | 56/96 [01:12<00:51,  1.29s/it]
+
Epoch 32 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 32 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 32 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.31it/s]
+
                                                                
Epoch 32 Training:  59%|█████▉    | 57/96 [01:13<00:50,  1.29s/it]
+
Epoch 32 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 32 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 32 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.30it/s]
+
                                                                
Epoch 32 Training:  60%|██████    | 58/96 [01:14<00:49,  1.30s/it]
+
Epoch 32 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 32 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 32 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.38it/s]
+
                                                                
Epoch 32 Training:  61%|██████▏   | 59/96 [01:15<00:47,  1.30s/it]
+
Epoch 32 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 32 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 32 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.40it/s]
+
                                                                
Epoch 32 Training:  62%|██████▎   | 60/96 [01:17<00:46,  1.29s/it]
+
Epoch 32 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 32 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 32 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.47it/s]
+
                                                                
Epoch 32 Training:  64%|██████▎   | 61/96 [01:18<00:45,  1.29s/it]
+
Epoch 32 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 32 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 32 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.45it/s]
+
                                                                
Epoch 32 Training:  65%|██████▍   | 62/96 [01:19<00:43,  1.28s/it]
+
Epoch 32 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 32 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 32 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.42it/s]
+
                                                                
Epoch 32 Training:  66%|██████▌   | 63/96 [01:21<00:42,  1.28s/it]
+
Epoch 32 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 32 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 32 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.40it/s]
+
                                                                
Epoch 32 Training:  67%|██████▋   | 64/96 [01:22<00:41,  1.28s/it]
+
Epoch 32 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 32 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 32 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.41it/s]
+
                                                                
Epoch 32 Training:  68%|██████▊   | 65/96 [01:23<00:39,  1.28s/it]
+
Epoch 32 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 32 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.63it/s]
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Epoch 32 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.41it/s]
+
                                                                
Epoch 32 Training:  69%|██████▉   | 66/96 [01:24<00:38,  1.28s/it]
+
Epoch 32 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 32 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 32 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.43it/s]
+
                                                                
Epoch 32 Training:  70%|██████▉   | 67/96 [01:26<00:37,  1.28s/it]
+
Epoch 32 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 32 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
+
Epoch 32 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.49it/s]
+
                                                                
Epoch 32 Training:  71%|███████   | 68/96 [01:27<00:35,  1.28s/it]
+
Epoch 32 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 32 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 32 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.43it/s]
+
                                                                
Epoch 32 Training:  72%|███████▏  | 69/96 [01:28<00:34,  1.28s/it]
+
Epoch 32 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 32 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
+
Epoch 32 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.38it/s]
+
                                                                
Epoch 32 Training:  73%|███████▎  | 70/96 [01:30<00:33,  1.29s/it]
+
Epoch 32 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 32 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 32 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.39it/s]
+
                                                                
Epoch 32 Training:  74%|███████▍  | 71/96 [01:31<00:32,  1.29s/it]
+
Epoch 32 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 32 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 32 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.32it/s]
+
                                                                
Epoch 32 Training:  75%|███████▌  | 72/96 [01:32<00:30,  1.29s/it]
+
Epoch 32 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 32 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 32 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.39it/s]
+
                                                                
Epoch 32 Training:  76%|███████▌  | 73/96 [01:33<00:29,  1.29s/it]
+
Epoch 32 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 32 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
+
Epoch 32 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.42it/s]
+
                                                                
Epoch 32 Training:  77%|███████▋  | 74/96 [01:35<00:28,  1.28s/it]
+
Epoch 32 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 32 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
+
Epoch 32 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.35it/s]
+
                                                                
Epoch 32 Training:  78%|███████▊  | 75/96 [01:36<00:27,  1.29s/it]
+
Epoch 32 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 32 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
+
Epoch 32 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.34it/s]
+
                                                                
Epoch 32 Training:  79%|███████▉  | 76/96 [01:37<00:25,  1.29s/it]
+
Epoch 32 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 32 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 32 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.43it/s]
+
                                                                
Epoch 32 Training:  80%|████████  | 77/96 [01:39<00:24,  1.28s/it]
+
Epoch 32 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 32 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 32 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.40it/s]
+
                                                                
Epoch 32 Training:  81%|████████▏ | 78/96 [01:40<00:23,  1.28s/it]
+
Epoch 32 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 32 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 32 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.33it/s]
+
                                                                
Epoch 32 Training:  82%|████████▏ | 79/96 [01:41<00:21,  1.29s/it]
+
Epoch 32 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 32 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 32 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.43it/s]
+
                                                                
Epoch 32 Training:  83%|████████▎ | 80/96 [01:42<00:20,  1.28s/it]
+
Epoch 32 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 32 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 32 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.33it/s]
+
                                                                
Epoch 32 Training:  84%|████████▍ | 81/96 [01:44<00:19,  1.29s/it]
+
Epoch 32 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 32 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 32 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.44it/s]
+
                                                                
Epoch 32 Training:  85%|████████▌ | 82/96 [01:45<00:17,  1.28s/it]
+
Epoch 32 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 32 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 32 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.41it/s]
+
                                                                
Epoch 32 Training:  86%|████████▋ | 83/96 [01:46<00:16,  1.28s/it]
+
Epoch 32 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 32 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 32 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.40it/s]
+
                                                                
Epoch 32 Training:  88%|████████▊ | 84/96 [01:48<00:15,  1.28s/it]
+
Epoch 32 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 32 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 32 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.40it/s]
+
                                                                
Epoch 32 Training:  89%|████████▊ | 85/96 [01:49<00:14,  1.28s/it]
+
Epoch 32 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 32 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 32 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.43it/s]
+
                                                                
Epoch 32 Training:  90%|████████▉ | 86/96 [01:50<00:12,  1.28s/it]
+
Epoch 32 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 32 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 32 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.42it/s]
+
                                                                
Epoch 32 Training:  91%|█████████ | 87/96 [01:51<00:11,  1.28s/it]
+
Epoch 32 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 32 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 32 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.34it/s]
+
                                                                
Epoch 32 Training:  92%|█████████▏| 88/96 [01:53<00:10,  1.29s/it]
+
Epoch 32 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 32 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 32 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.35it/s]
+
                                                                
Epoch 32 Training:  93%|█████████▎| 89/96 [01:54<00:09,  1.29s/it]
+
Epoch 32 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 32 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 32 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.34it/s]
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Epoch 32 Training:  94%|█████████▍| 90/96 [01:55<00:07,  1.29s/it]
+
Epoch 32 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 32 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 32 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.40it/s]
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Epoch 32 Training:  95%|█████████▍| 91/96 [01:57<00:06,  1.29s/it]
+
Epoch 32 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 32 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 32 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.39it/s]
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Epoch 32 Training:  96%|█████████▌| 92/96 [01:58<00:05,  1.29s/it]
+
Epoch 32 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 32 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.63it/s]
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Epoch 32 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s][2026-10-01 18:39:54] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0032 | Epoch 032] train/policy_loss=12.4145 train/unweighted_policy_loss=2.4829 train/loss=12.4145 train/grad_norm=0.0742
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+[2026-10-01 18:39:54] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0033 | Epoch 032] timing/rollout_seconds=147.8493 timing/feedback_seconds=5.1141 timing/optimization_seconds=124.1308 timing/cycle_seconds=275.5053
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+
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+[2026-10-01 18:42:26] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0033 | Epoch 033] train/reward_dists_mean=0.9396 train/reward_dists_std=0.0148 train/reward_psnr_mean=30.3758 train/reward_psnr_std=2.1428 train/reward_ssim_mean=0.9347 train/reward_ssim_std=0.0187 train/reward_dists_group_std_mean=0.0031 train/reward_dists_group_std_max=0.0072 train/reward_dists_group_std_min=0.0015 train/reward_dists_group_mean_std=0.0145 train/reward_psnr_group_std_mean=1.2267 train/reward_psnr_group_std_max=2.6973 train/reward_psnr_group_std_min=0.4398 train/reward_psnr_group_mean_std=1.6912 train/reward_ssim_group_std_mean=0.0011 train/reward_ssim_group_std_max=0.0027 train/reward_ssim_group_std_min=0.0004 train/reward_ssim_group_mean_std=0.0186 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-7.0117 train/adv_max=6.2049 train/adv_abs_mean=1.8368
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Epoch 33 Training:  16%|█▌        | 15/96 [00:19<01:46,  1.32s/it]
+
Epoch 33 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 33 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 33 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.33it/s]
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Epoch 33 Training:  17%|█▋        | 16/96 [00:21<01:45,  1.32s/it]
+
Epoch 33 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 33 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 33 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.32it/s]
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Epoch 33 Training:  18%|█▊        | 17/96 [00:22<01:44,  1.32s/it]
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Epoch 33 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 33 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 33 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.33it/s]
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Epoch 33 Training:  19%|█▉        | 18/96 [00:23<01:42,  1.32s/it]
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Epoch 33 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 33 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 33 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.37it/s]
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Epoch 33 Training:  20%|█▉        | 19/96 [00:24<01:41,  1.32s/it]
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Epoch 33 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 33 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.37it/s]
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Epoch 33 Training:  21%|██        | 20/96 [00:26<01:40,  1.32s/it]
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Epoch 33 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 33 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 33 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.37it/s]
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Epoch 33 Training:  22%|██▏       | 21/96 [00:27<01:38,  1.32s/it]
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Epoch 33 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 33 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 33 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.37it/s]
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Epoch 33 Training:  23%|██▎       | 22/96 [00:28<01:37,  1.32s/it]
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Epoch 33 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 33 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 33 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.38it/s]
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Epoch 33 Training:  24%|██▍       | 23/96 [00:30<01:36,  1.32s/it]
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Epoch 33 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 33 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 33 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.36it/s]
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Epoch 33 Training:  25%|██▌       | 24/96 [00:31<01:34,  1.32s/it]
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Epoch 33 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 33 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 33 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.37it/s]
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Epoch 33 Training:  26%|██▌       | 25/96 [00:32<01:33,  1.32s/it]
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Epoch 33 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 33 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.37it/s]
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Epoch 33 Training:  27%|██▋       | 26/96 [00:34<01:32,  1.32s/it]
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Epoch 33 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 33 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 33 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.34it/s]
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Epoch 33 Training:  28%|██▊       | 27/96 [00:35<01:31,  1.32s/it]
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Epoch 33 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 33 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 33 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.37it/s]
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Epoch 33 Training:  29%|██▉       | 28/96 [00:36<01:29,  1.32s/it]
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Epoch 33 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 33 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.63it/s]
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Epoch 33 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.36it/s]
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Epoch 33 Training:  30%|███       | 29/96 [00:38<01:28,  1.32s/it]
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Epoch 33 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 33 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 33 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.36it/s]
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Epoch 33 Training:  31%|███▏      | 30/96 [00:39<01:27,  1.32s/it]
+
Epoch 33 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 33 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 33 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.36it/s]
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Epoch 33 Training:  32%|███▏      | 31/96 [00:40<01:25,  1.32s/it]
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Epoch 33 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 33 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 33 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.35it/s]
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Epoch 33 Training:  33%|███▎      | 32/96 [00:42<01:24,  1.32s/it]
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Epoch 33 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 33 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 33 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.36it/s]
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Epoch 33 Training:  34%|███▍      | 33/96 [00:43<01:22,  1.32s/it]
+
Epoch 33 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 33 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 33 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.31it/s]
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Epoch 33 Training:  35%|███▌      | 34/96 [00:44<01:21,  1.32s/it]
+
Epoch 33 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 33 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 33 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.33it/s]
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Epoch 33 Training:  36%|███▋      | 35/96 [00:46<01:20,  1.32s/it]
+
Epoch 33 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 33 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 33 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.32it/s]
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Epoch 33 Training:  38%|███▊      | 36/96 [00:47<01:19,  1.32s/it]
+
Epoch 33 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 33 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 33 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.36it/s]
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Epoch 33 Training:  39%|███▊      | 37/96 [00:48<01:17,  1.32s/it]
+
Epoch 33 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 33 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 33 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.31it/s]
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Epoch 33 Training:  40%|███▉      | 38/96 [00:50<01:16,  1.32s/it]
+
Epoch 33 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 33 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 33 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.34it/s]
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Epoch 33 Training:  41%|████      | 39/96 [00:51<01:15,  1.32s/it]
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Epoch 33 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 33 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 33 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.30it/s]
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Epoch 33 Training:  42%|████▏     | 40/96 [00:52<01:13,  1.32s/it]
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Epoch 33 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 33 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 33 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.37it/s]
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Epoch 33 Training:  43%|████▎     | 41/96 [00:54<01:12,  1.32s/it]
+
Epoch 33 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 33 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 33 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.35it/s]
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Epoch 33 Training:  44%|████▍     | 42/96 [00:55<01:11,  1.32s/it]
+
Epoch 33 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 33 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 33 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.37it/s]
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Epoch 33 Training:  45%|████▍     | 43/96 [00:56<01:10,  1.32s/it]
+
Epoch 33 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 33 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 33 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.37it/s]
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Epoch 33 Training:  46%|████▌     | 44/96 [00:57<01:08,  1.32s/it]
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Epoch 33 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 33 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 33 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.38it/s]
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Epoch 33 Training:  47%|████▋     | 45/96 [00:59<01:07,  1.32s/it]
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Epoch 33 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 33 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 33 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.38it/s]
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Epoch 33 Training:  48%|████▊     | 46/96 [01:00<01:05,  1.32s/it]
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Epoch 33 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 33 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 33 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.39it/s]
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Epoch 33 Training:  49%|████▉     | 47/96 [01:01<01:04,  1.32s/it]
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Epoch 33 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 33 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 33 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.39it/s]
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Epoch 33 Training:  50%|█████     | 48/96 [01:03<01:03,  1.32s/it]
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Epoch 33 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 33 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.31it/s]
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Epoch 33 Training:  51%|█████     | 49/96 [01:04<01:01,  1.32s/it]
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Epoch 33 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 33 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.34it/s]
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Epoch 33 Training:  52%|█████▏    | 50/96 [01:05<01:00,  1.32s/it]
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Epoch 33 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 33 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.38it/s]
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Epoch 33 Training:  53%|█████▎    | 51/96 [01:07<00:59,  1.32s/it]
+
Epoch 33 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 33 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.34it/s]
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Epoch 33 Training:  54%|█████▍    | 52/96 [01:08<00:57,  1.32s/it]
+
Epoch 33 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 33 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.32it/s]
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Epoch 33 Training:  55%|█████▌    | 53/96 [01:09<00:56,  1.31s/it]
+
Epoch 33 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 33 Training:  56%|█████▋    | 54/96 [01:11<00:55,  1.31s/it]
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Epoch 33 Training:  57%|█████▋    | 55/96 [01:12<00:53,  1.31s/it]
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Epoch 33 Training:  58%|█████▊    | 56/96 [01:13<00:52,  1.31s/it]
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Epoch 33 Training:  59%|█████▉    | 57/96 [01:15<00:51,  1.31s/it]
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Epoch 33 Training:  60%|██████    | 58/96 [01:16<00:49,  1.32s/it]
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Epoch 33 Training:  61%|██████▏   | 59/96 [01:17<00:48,  1.32s/it]
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Epoch 33 Training:  62%|██████▎   | 60/96 [01:19<00:47,  1.32s/it]
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Epoch 33 Training:  64%|██████▎   | 61/96 [01:20<00:46,  1.32s/it]
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Epoch 33 Training:  65%|██████▍   | 62/96 [01:21<00:44,  1.32s/it]
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Epoch 33 Training:  66%|██████▌   | 63/96 [01:22<00:43,  1.32s/it]
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Epoch 33 Training:  67%|██████▋   | 64/96 [01:24<00:42,  1.32s/it]
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Epoch 33 Training:  68%|██████▊   | 65/96 [01:25<00:40,  1.32s/it]
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Epoch 33 Training:  69%|██████▉   | 66/96 [01:26<00:39,  1.32s/it]
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Epoch 33 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s][2026-10-01 18:44:33] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0033 | Epoch 033] train/policy_loss=12.0643 train/unweighted_policy_loss=2.4129 train/loss=12.0643 train/grad_norm=0.0567
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+[2026-10-01 18:44:33] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0034 | Epoch 033] timing/rollout_seconds=147.9804 timing/feedback_seconds=5.8516 timing/optimization_seconds=127.3529 timing/cycle_seconds=278.9356
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+
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+
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+
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Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.66it/s]
+[2026-10-01 18:47:06] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0034 | Epoch 034] train/reward_dists_mean=0.9371 train/reward_dists_std=0.0161 train/reward_psnr_mean=29.8399 train/reward_psnr_std=2.4407 train/reward_ssim_mean=0.9376 train/reward_ssim_std=0.0214 train/reward_dists_group_std_mean=0.0033 train/reward_dists_group_std_max=0.0110 train/reward_dists_group_std_min=0.0015 train/reward_dists_group_mean_std=0.0157 train/reward_psnr_group_std_mean=1.5258 train/reward_psnr_group_std_max=4.1935 train/reward_psnr_group_std_min=0.5513 train/reward_psnr_group_mean_std=1.7732 train/reward_ssim_group_std_mean=0.0013 train/reward_ssim_group_std_max=0.0113 train/reward_ssim_group_std_min=0.0004 train/reward_ssim_group_mean_std=0.0214 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-9.5765 train/adv_max=5.3463 train/adv_abs_mean=1.9073
+
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Epoch 34 Training:   7%|▋         | 7/96 [00:09<01:56,  1.31s/it]
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Epoch 34 Training:   8%|▊         | 8/96 [00:10<01:55,  1.31s/it]
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Epoch 34 Training:   9%|▉         | 9/96 [00:11<01:54,  1.31s/it]
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Epoch 34 Training:  10%|█         | 10/96 [00:13<01:52,  1.31s/it]
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Epoch 34 Training:  11%|█▏        | 11/96 [00:14<01:51,  1.31s/it]
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Epoch 34 Training:  14%|█▎        | 13/96 [00:17<01:49,  1.31s/it]
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Epoch 34 Training:  15%|█▍        | 14/96 [00:18<01:47,  1.31s/it]
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Epoch 34 Training:  16%|█▌        | 15/96 [00:19<01:46,  1.31s/it]
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Epoch 34 Training:  17%|█▋        | 16/96 [00:20<01:45,  1.31s/it]
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Epoch 34 Training:  18%|█▊        | 17/96 [00:22<01:43,  1.31s/it]
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Epoch 34 Training:  19%|█▉        | 18/96 [00:23<01:42,  1.31s/it]
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Epoch 34 Training:  20%|█▉        | 19/96 [00:24<01:41,  1.31s/it]
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Epoch 34 Training:  21%|██        | 20/96 [00:26<01:39,  1.31s/it]
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Epoch 34 Training:  22%|██▏       | 21/96 [00:27<01:38,  1.32s/it]
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Epoch 34 Training:  23%|██▎       | 22/96 [00:28<01:37,  1.32s/it]
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Epoch 34 Training:  26%|██▌       | 25/96 [00:32<01:33,  1.32s/it]
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Epoch 34 Training:  29%|██▉       | 28/96 [00:36<01:29,  1.32s/it]
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Epoch 34 Training:  42%|████▏     | 40/96 [00:52<01:13,  1.32s/it]
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Epoch 34 Training:  43%|████▎     | 41/96 [00:53<01:12,  1.32s/it]
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Epoch 34 Training:  44%|████▍     | 42/96 [00:55<01:11,  1.32s/it]
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Epoch 34 Training:  45%|████▍     | 43/96 [00:56<01:09,  1.32s/it]
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Epoch 34 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 34 Training:  46%|████▌     | 44/96 [00:57<01:08,  1.32s/it]
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Epoch 34 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 34 Training:  47%|████▋     | 45/96 [00:59<01:07,  1.32s/it]
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Epoch 34 Training:  48%|████▊     | 46/96 [01:00<01:05,  1.32s/it]
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Epoch 34 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 34 Training:  49%|████▉     | 47/96 [01:01<01:04,  1.32s/it]
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Epoch 34 Training:  50%|█████     | 48/96 [01:03<01:03,  1.32s/it]
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Epoch 34 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 34 Training:  51%|█████     | 49/96 [01:04<01:01,  1.32s/it]
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Epoch 34 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 34 Training:  52%|█████▏    | 50/96 [01:05<01:00,  1.32s/it]
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Epoch 34 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 34 Training:  53%|█████▎    | 51/96 [01:07<00:59,  1.32s/it]
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Epoch 34 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 34 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.29it/s]
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Epoch 34 Training:  54%|█████▍    | 52/96 [01:08<00:57,  1.32s/it]
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Epoch 34 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 34 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.30it/s]
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Epoch 34 Training:  55%|█████▌    | 53/96 [01:09<00:56,  1.32s/it]
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Epoch 34 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 34 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.30it/s]
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Epoch 34 Training:  56%|█████▋    | 54/96 [01:11<00:55,  1.32s/it]
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Epoch 34 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 34 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 34 Training:  57%|█████▋    | 55/96 [01:12<00:54,  1.32s/it]
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Epoch 34 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 34 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 34 Training:  58%|█████▊    | 56/96 [01:13<00:52,  1.32s/it]
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Epoch 34 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 34 Training:  59%|█████▉    | 57/96 [01:14<00:51,  1.32s/it]
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Epoch 34 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 34 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 34 Training:  60%|██████    | 58/96 [01:16<00:50,  1.32s/it]
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Epoch 34 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 34 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 34 Training:  61%|██████▏   | 59/96 [01:17<00:48,  1.32s/it]
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Epoch 34 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 34 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.29it/s]
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Epoch 34 Training:  62%|██████▎   | 60/96 [01:18<00:47,  1.32s/it]
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Epoch 34 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 34 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.42it/s]
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Epoch 34 Training:  64%|██████▎   | 61/96 [01:20<00:46,  1.32s/it]
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Epoch 34 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 34 Training:  65%|██████▍   | 62/96 [01:21<00:44,  1.32s/it]
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Epoch 34 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 34 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.31it/s]
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Epoch 34 Training:  66%|██████▌   | 63/96 [01:22<00:43,  1.32s/it]
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Epoch 34 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.29it/s]
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Epoch 34 Training:  67%|██████▋   | 64/96 [01:24<00:42,  1.32s/it]
+
Epoch 34 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 34 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 34 Training:  68%|██████▊   | 65/96 [01:25<00:40,  1.32s/it]
+
Epoch 34 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 34 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.29it/s]
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Epoch 34 Training:  69%|██████▉   | 66/96 [01:26<00:39,  1.32s/it]
+
Epoch 34 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 34 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.23it/s]
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Epoch 34 Training:  70%|██████▉   | 67/96 [01:28<00:38,  1.32s/it]
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Epoch 34 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 34 Training:  71%|███████   | 68/96 [01:29<00:36,  1.32s/it]
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Epoch 34 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 34 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.29it/s]
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Epoch 34 Training:  72%|███████▏  | 69/96 [01:30<00:35,  1.32s/it]
+
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Epoch 34 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 34 Training:  73%|███████▎  | 70/96 [01:32<00:34,  1.32s/it]
+
Epoch 34 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 34 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 34 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 34 Training:  74%|███████▍  | 71/96 [01:33<00:33,  1.32s/it]
+
Epoch 34 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 34 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 34 Training:  75%|███████▌  | 72/96 [01:34<00:31,  1.32s/it]
+
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Epoch 34 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.33it/s]
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Epoch 34 Training:  76%|███████▌  | 73/96 [01:36<00:30,  1.32s/it]
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Epoch 34 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 34 Training:  77%|███████▋  | 74/96 [01:37<00:29,  1.32s/it]
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Epoch 34 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 34 Training:  78%|███████▊  | 75/96 [01:38<00:27,  1.32s/it]
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Epoch 34 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.29it/s]
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Epoch 34 Training:  79%|███████▉  | 76/96 [01:40<00:26,  1.32s/it]
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Epoch 34 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 34 Training:  80%|████████  | 77/96 [01:41<00:25,  1.32s/it]
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Epoch 34 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 34 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 34 Training:  81%|████████▏ | 78/96 [01:42<00:23,  1.32s/it]
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Epoch 34 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.29it/s]
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Epoch 34 Training:  82%|████████▏ | 79/96 [01:43<00:22,  1.32s/it]
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Epoch 34 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.29it/s]
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Epoch 34 Training:  83%|████████▎ | 80/96 [01:45<00:21,  1.32s/it]
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Epoch 34 Training:  84%|████████▍ | 81/96 [01:46<00:19,  1.32s/it]
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Epoch 34 Training:  85%|████████▌ | 82/96 [01:47<00:18,  1.32s/it]
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Epoch 34 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 34 Training:  86%|████████▋ | 83/96 [01:49<00:17,  1.32s/it]
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Epoch 34 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 34 Training:  88%|████████▊ | 84/96 [01:50<00:15,  1.32s/it]
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Epoch 34 Training:  89%|████████▊ | 85/96 [01:51<00:14,  1.32s/it]
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Epoch 34 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 34 Training:  90%|████████▉ | 86/96 [01:53<00:13,  1.32s/it]
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Epoch 34 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 34 Training:  91%|█████████ | 87/96 [01:54<00:11,  1.32s/it]
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Epoch 34 Training:  92%|█████████▏| 88/96 [01:55<00:10,  1.32s/it]
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Epoch 34 Training:  93%|█████████▎| 89/96 [01:57<00:09,  1.32s/it]
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Epoch 34 Training:  94%|█████████▍| 90/96 [01:58<00:07,  1.32s/it]
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Epoch 34 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s][2026-10-01 18:49:13] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0034 | Epoch 034] train/policy_loss=12.3747 train/unweighted_policy_loss=2.4749 train/loss=12.3747 train/grad_norm=0.0990
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+[2026-10-01 18:49:13] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0035 | Epoch 034] timing/rollout_seconds=147.7781 timing/feedback_seconds=7.3377 timing/optimization_seconds=127.0126 timing/cycle_seconds=279.8538
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+
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+[2026-10-01 18:51:45] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0035 | Epoch 035] train/reward_dists_mean=0.9367 train/reward_dists_std=0.0210 train/reward_psnr_mean=29.8214 train/reward_psnr_std=2.2204 train/reward_ssim_mean=0.9379 train/reward_ssim_std=0.0174 train/reward_dists_group_std_mean=0.0028 train/reward_dists_group_std_max=0.0064 train/reward_dists_group_std_min=0.0014 train/reward_dists_group_mean_std=0.0208 train/reward_psnr_group_std_mean=1.2457 train/reward_psnr_group_std_max=2.8361 train/reward_psnr_group_std_min=0.3416 train/reward_psnr_group_mean_std=1.7593 train/reward_ssim_group_std_mean=0.0015 train/reward_ssim_group_std_max=0.0270 train/reward_ssim_group_std_min=0.0004 train/reward_ssim_group_mean_std=0.0170 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-6.9338 train/adv_max=6.7606 train/adv_abs_mean=1.7962
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Epoch 35 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.41it/s]
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Epoch 35 Training:  14%|█▎        | 13/96 [00:17<01:49,  1.32s/it]
+
Epoch 35 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 35 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.63it/s]
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Epoch 35 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.42it/s]
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Epoch 35 Training:  15%|█▍        | 14/96 [00:18<01:48,  1.32s/it]
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Epoch 35 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 35 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 35 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.43it/s]
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Epoch 35 Training:  16%|█▌        | 15/96 [00:19<01:46,  1.32s/it]
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Epoch 35 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 35 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 35 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.43it/s]
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Epoch 35 Training:  17%|█▋        | 16/96 [00:21<01:45,  1.32s/it]
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Epoch 35 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 35 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 35 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.44it/s]
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Epoch 35 Training:  18%|█▊        | 17/96 [00:22<01:43,  1.31s/it]
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Epoch 35 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 35 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.42it/s]
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Epoch 35 Training:  19%|█▉        | 18/96 [00:23<01:42,  1.31s/it]
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Epoch 35 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 35 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.40it/s]
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Epoch 35 Training:  20%|█▉        | 19/96 [00:24<01:41,  1.32s/it]
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Epoch 35 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 35 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.44it/s]
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Epoch 35 Training:  21%|██        | 20/96 [00:26<01:39,  1.32s/it]
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Epoch 35 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 35 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.44it/s]
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Epoch 35 Training:  22%|██▏       | 21/96 [00:27<01:38,  1.31s/it]
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Epoch 35 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 35 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.43it/s]
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Epoch 35 Training:  23%|██▎       | 22/96 [00:28<01:37,  1.31s/it]
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Epoch 35 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 35 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.41it/s]
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Epoch 35 Training:  24%|██▍       | 23/96 [00:30<01:36,  1.32s/it]
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Epoch 35 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 35 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.42it/s]
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Epoch 35 Training:  25%|██▌       | 24/96 [00:31<01:34,  1.32s/it]
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Epoch 35 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 35 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.40it/s]
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Epoch 35 Training:  26%|██▌       | 25/96 [00:32<01:33,  1.32s/it]
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Epoch 35 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 35 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.63it/s]
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Epoch 35 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.43it/s]
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Epoch 35 Training:  27%|██▋       | 26/96 [00:34<01:32,  1.32s/it]
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Epoch 35 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 35 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.63it/s]
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Epoch 35 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.44it/s]
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Epoch 35 Training:  28%|██▊       | 27/96 [00:35<01:30,  1.32s/it]
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Epoch 35 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 35 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.42it/s]
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Epoch 35 Training:  29%|██▉       | 28/96 [00:36<01:29,  1.32s/it]
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Epoch 35 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 35 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.44it/s]
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Epoch 35 Training:  30%|███       | 29/96 [00:38<01:28,  1.31s/it]
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Epoch 35 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 35 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.43it/s]
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Epoch 35 Training:  31%|███▏      | 30/96 [00:39<01:26,  1.31s/it]
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Epoch 35 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 35 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 35 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.43it/s]
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Epoch 35 Training:  32%|███▏      | 31/96 [00:40<01:25,  1.31s/it]
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Epoch 35 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 35 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 35 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.40it/s]
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Epoch 35 Training:  33%|███▎      | 32/96 [00:42<01:24,  1.32s/it]
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Epoch 35 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 35 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.63it/s]
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Epoch 35 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.44it/s]
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Epoch 35 Training:  34%|███▍      | 33/96 [00:43<01:22,  1.31s/it]
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Epoch 35 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 35 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 35 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.44it/s]
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Epoch 35 Training:  35%|███▌      | 34/96 [00:44<01:21,  1.31s/it]
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Epoch 35 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 35 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.44it/s]
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Epoch 35 Training:  36%|███▋      | 35/96 [00:45<01:20,  1.31s/it]
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Epoch 35 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 35 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 35 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.45it/s]
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Epoch 35 Training:  38%|███▊      | 36/96 [00:47<01:18,  1.31s/it]
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Epoch 35 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 35 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.44it/s]
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Epoch 35 Training:  39%|███▊      | 37/96 [00:48<01:17,  1.31s/it]
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Epoch 35 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 35 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 35 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.43it/s]
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Epoch 35 Training:  40%|███▉      | 38/96 [00:49<01:16,  1.31s/it]
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Epoch 35 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 35 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 35 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.39it/s]
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Epoch 35 Training:  41%|████      | 39/96 [00:51<01:14,  1.31s/it]
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Epoch 35 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 35 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 35 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.42it/s]
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Epoch 35 Training:  42%|████▏     | 40/96 [00:52<01:13,  1.32s/it]
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Epoch 35 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 35 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.63it/s]
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Epoch 35 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.43it/s]
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Epoch 35 Training:  43%|████▎     | 41/96 [00:53<01:12,  1.32s/it]
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Epoch 35 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 35 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.45it/s]
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Epoch 35 Training:  44%|████▍     | 42/96 [00:55<01:11,  1.32s/it]
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Epoch 35 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 35 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.45it/s]
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Epoch 35 Training:  45%|████▍     | 43/96 [00:56<01:09,  1.31s/it]
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Epoch 35 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 35 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.44it/s]
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Epoch 35 Training:  46%|████▌     | 44/96 [00:57<01:08,  1.31s/it]
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Epoch 35 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 35 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.38it/s]
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Epoch 35 Training:  47%|████▋     | 45/96 [00:59<01:06,  1.31s/it]
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Epoch 35 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 35 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.43it/s]
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Epoch 35 Training:  48%|████▊     | 46/96 [01:00<01:05,  1.31s/it]
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Epoch 35 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 35 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.42it/s]
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Epoch 35 Training:  49%|████▉     | 47/96 [01:01<01:04,  1.31s/it]
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Epoch 35 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 35 Training:  50%|█████     | 48/96 [01:03<01:03,  1.31s/it]
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Epoch 35 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 35 Training:  51%|█████     | 49/96 [01:04<01:01,  1.31s/it]
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Epoch 35 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 35 Training:  52%|█████▏    | 50/96 [01:05<01:00,  1.32s/it]
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Epoch 35 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 35 Training:  53%|█████▎    | 51/96 [01:07<00:59,  1.32s/it]
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Epoch 35 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 35 Training:  54%|█████▍    | 52/96 [01:08<00:57,  1.31s/it]
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Epoch 35 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 35 Training:  55%|█████▌    | 53/96 [01:09<00:56,  1.31s/it]
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Epoch 35 Training:  56%|█████▋    | 54/96 [01:10<00:55,  1.32s/it]
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Epoch 35 Training:  57%|█████▋    | 55/96 [01:12<00:53,  1.31s/it]
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Epoch 35 Training:  58%|█████▊    | 56/96 [01:13<00:52,  1.32s/it]
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Epoch 35 Training:  59%|█████▉    | 57/96 [01:14<00:51,  1.32s/it]
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Epoch 35 Training:  60%|██████    | 58/96 [01:16<00:50,  1.32s/it]
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Epoch 35 Training:  61%|██████▏   | 59/96 [01:17<00:48,  1.32s/it]
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Epoch 35 Training:  62%|██████▎   | 60/96 [01:18<00:47,  1.32s/it]
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Epoch 35 Training:  64%|██████▎   | 61/96 [01:20<00:46,  1.32s/it]
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Epoch 35 Training:  65%|██████▍   | 62/96 [01:21<00:44,  1.32s/it]
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Epoch 35 Training:  66%|██████▌   | 63/96 [01:22<00:43,  1.31s/it]
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Epoch 35 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s][2026-10-01 18:53:51] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0035 | Epoch 035] train/policy_loss=12.5083 train/unweighted_policy_loss=2.5017 train/loss=12.5083 train/grad_norm=0.0663
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+[2026-10-01 18:53:51] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0036 | Epoch 035] timing/rollout_seconds=147.8124 timing/feedback_seconds=5.6140 timing/optimization_seconds=126.9584 timing/cycle_seconds=278.2416
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+
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+
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+
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Pointwise Rewards: psnr:  50%|█████     | 3/6 [00:00<00:00,  6.65it/s]
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Pointwise Rewards: psnr:  83%|████████▎ | 5/6 [00:00<00:00,  6.51it/s]
Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.49it/s]
Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.52it/s]
+[2026-10-01 18:56:23] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0036 | Epoch 036] train/reward_dists_mean=0.9381 train/reward_dists_std=0.0151 train/reward_psnr_mean=29.7007 train/reward_psnr_std=2.0188 train/reward_ssim_mean=0.9364 train/reward_ssim_std=0.0188 train/reward_dists_group_std_mean=0.0028 train/reward_dists_group_std_max=0.0067 train/reward_dists_group_std_min=0.0013 train/reward_dists_group_mean_std=0.0148 train/reward_psnr_group_std_mean=0.9226 train/reward_psnr_group_std_max=1.9287 train/reward_psnr_group_std_min=0.4151 train/reward_psnr_group_mean_std=1.7588 train/reward_ssim_group_std_mean=0.0012 train/reward_ssim_group_std_max=0.0064 train/reward_ssim_group_std_min=0.0006 train/reward_ssim_group_mean_std=0.0188 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-8.4592 train/adv_max=5.8087 train/adv_abs_mean=1.7671
+
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Epoch 36 Training:   8%|▊         | 8/96 [00:10<01:55,  1.31s/it]
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Epoch 36 Training:   9%|▉         | 9/96 [00:11<01:54,  1.31s/it]
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Epoch 36 Training:  10%|█         | 10/96 [00:13<01:53,  1.31s/it]
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Epoch 36 Training:  11%|█▏        | 11/96 [00:14<01:52,  1.32s/it]
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Epoch 36 Training:  18%|█▊        | 17/96 [00:22<01:45,  1.34s/it]
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Epoch 36 Training:  19%|█▉        | 18/96 [00:23<01:43,  1.33s/it]
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Epoch 36 Training:  23%|██▎       | 22/96 [00:29<01:37,  1.32s/it]
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Epoch 36 Training:  39%|███▊      | 37/96 [00:48<01:17,  1.31s/it]
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Epoch 36 Training:  40%|███▉      | 38/96 [00:50<01:16,  1.32s/it]
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Epoch 36 Training:  41%|████      | 39/96 [00:51<01:15,  1.32s/it]
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Epoch 36 Training:  42%|████▏     | 40/96 [00:52<01:13,  1.32s/it]
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Epoch 36 Training:  43%|████▎     | 41/96 [00:54<01:12,  1.31s/it]
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Epoch 36 Training:  44%|████▍     | 42/96 [00:55<01:11,  1.32s/it]
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Epoch 36 Training:  45%|████▍     | 43/96 [00:56<01:09,  1.32s/it]
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Epoch 36 Training:  46%|████▌     | 44/96 [00:58<01:08,  1.32s/it]
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Epoch 36 Training:  47%|████▋     | 45/96 [00:59<01:07,  1.32s/it]
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Epoch 36 Training:  48%|████▊     | 46/96 [01:00<01:05,  1.32s/it]
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Epoch 36 Training:  49%|████▉     | 47/96 [01:02<01:04,  1.32s/it]
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Epoch 36 Training:  50%|█████     | 48/96 [01:03<01:03,  1.32s/it]
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Epoch 36 Training:  51%|█████     | 49/96 [01:04<01:01,  1.31s/it]
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Epoch 36 Training:  52%|█████▏    | 50/96 [01:06<01:00,  1.32s/it]
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Epoch 36 Training:  53%|█████▎    | 51/96 [01:07<00:59,  1.32s/it]
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Epoch 36 Training:  54%|█████▍    | 52/96 [01:08<00:57,  1.32s/it]
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Epoch 36 Training:  55%|█████▌    | 53/96 [01:09<00:56,  1.32s/it]
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Epoch 36 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 36 Training:  56%|█████▋    | 54/96 [01:11<00:55,  1.32s/it]
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Epoch 36 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 36 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.38it/s]
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Epoch 36 Training:  57%|█████▋    | 55/96 [01:12<00:53,  1.31s/it]
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Epoch 36 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 36 Training:  58%|█████▊    | 56/96 [01:14<00:55,  1.38s/it]
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Epoch 36 Training:  59%|█████▉    | 57/96 [01:15<00:53,  1.36s/it]
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Epoch 36 Training:  60%|██████    | 58/96 [01:16<00:51,  1.35s/it]
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Epoch 36 Training:  61%|██████▏   | 59/96 [01:18<00:49,  1.34s/it]
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Epoch 36 Training:  62%|██████▎   | 60/96 [01:19<00:47,  1.33s/it]
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Epoch 36 Training:  64%|██████▎   | 61/96 [01:20<00:46,  1.33s/it]
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Epoch 36 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.34it/s]
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Epoch 36 Training:  65%|██████▍   | 62/96 [01:22<00:44,  1.32s/it]
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Epoch 36 Training:  66%|██████▌   | 63/96 [01:23<00:43,  1.32s/it]
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Epoch 36 Training:  67%|██████▋   | 64/96 [01:24<00:42,  1.32s/it]
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Epoch 36 Training:  68%|██████▊   | 65/96 [01:25<00:40,  1.32s/it]
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Epoch 36 Training:  69%|██████▉   | 66/96 [01:27<00:39,  1.32s/it]
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Epoch 36 Training:  70%|██████▉   | 67/96 [01:28<00:38,  1.32s/it]
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Epoch 36 Training:  71%|███████   | 68/96 [01:29<00:36,  1.32s/it]
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Epoch 36 Training:  72%|███████▏  | 69/96 [01:31<00:35,  1.32s/it]
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Epoch 36 Training:  73%|███████▎  | 70/96 [01:32<00:34,  1.32s/it]
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Epoch 36 Training:  74%|███████▍  | 71/96 [01:33<00:32,  1.32s/it]
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Epoch 36 Training:  75%|███████▌  | 72/96 [01:35<00:31,  1.32s/it]
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Epoch 36 Training:  76%|███████▌  | 73/96 [01:36<00:30,  1.32s/it]
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Epoch 36 Training:  77%|███████▋  | 74/96 [01:37<00:29,  1.32s/it]
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Epoch 36 Training:  78%|███████▊  | 75/96 [01:39<00:27,  1.32s/it]
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Epoch 36 Training:  79%|███████▉  | 76/96 [01:40<00:26,  1.32s/it]
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Epoch 36 Training:  80%|████████  | 77/96 [01:41<00:25,  1.32s/it]
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Epoch 36 Training:  81%|████████▏ | 78/96 [01:43<00:23,  1.32s/it]
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Epoch 36 Training:  82%|████████▏ | 79/96 [01:44<00:22,  1.32s/it]
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Epoch 36 Training:  83%|████████▎ | 80/96 [01:45<00:21,  1.32s/it]
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Epoch 36 Training:  84%|████████▍ | 81/96 [01:47<00:19,  1.32s/it]
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Epoch 36 Training:  85%|████████▌ | 82/96 [01:48<00:18,  1.32s/it]
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Epoch 36 Training:  86%|████████▋ | 83/96 [01:49<00:17,  1.32s/it]
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Epoch 36 Training:  88%|████████▊ | 84/96 [01:50<00:15,  1.32s/it]
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Epoch 36 Training:  89%|████████▊ | 85/96 [01:52<00:14,  1.31s/it]
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Epoch 36 Training:  90%|████████▉ | 86/96 [01:53<00:13,  1.31s/it]
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Epoch 36 Training:  91%|█████████ | 87/96 [01:54<00:11,  1.32s/it]
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Epoch 36 Training:  92%|█████████▏| 88/96 [01:56<00:10,  1.31s/it]
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Epoch 36 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s][2026-10-01 18:58:29] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0036 | Epoch 036] train/policy_loss=12.0472 train/unweighted_policy_loss=2.4094 train/loss=12.0472 train/grad_norm=0.0633
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+[2026-10-01 18:58:30] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0037 | Epoch 036] timing/rollout_seconds=147.6818 timing/feedback_seconds=4.6671 timing/optimization_seconds=127.4192 timing/cycle_seconds=278.5664
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+[2026-10-01 19:01:03] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0037 | Epoch 037] train/reward_dists_mean=0.9400 train/reward_dists_std=0.0137 train/reward_psnr_mean=30.2767 train/reward_psnr_std=2.4126 train/reward_ssim_mean=0.9378 train/reward_ssim_std=0.0210 train/reward_dists_group_std_mean=0.0026 train/reward_dists_group_std_max=0.0060 train/reward_dists_group_std_min=0.0014 train/reward_dists_group_mean_std=0.0134 train/reward_psnr_group_std_mean=1.0515 train/reward_psnr_group_std_max=1.9347 train/reward_psnr_group_std_min=0.3391 train/reward_psnr_group_mean_std=2.1276 train/reward_ssim_group_std_mean=0.0012 train/reward_ssim_group_std_max=0.0086 train/reward_ssim_group_std_min=0.0002 train/reward_ssim_group_mean_std=0.0209 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-7.6388 train/adv_max=6.1804 train/adv_abs_mean=1.8261
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Epoch 37 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 37 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.40it/s]
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Epoch 37 Training:  11%|█▏        | 11/96 [00:14<01:52,  1.32s/it]
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Epoch 37 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 37 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 37 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.38it/s]
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Epoch 37 Training:  12%|█▎        | 12/96 [00:15<01:50,  1.32s/it]
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Epoch 37 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 37 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 37 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.37it/s]
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Epoch 37 Training:  14%|█▎        | 13/96 [00:17<01:49,  1.32s/it]
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Epoch 37 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 37 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.38it/s]
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Epoch 37 Training:  15%|█▍        | 14/96 [00:18<01:47,  1.32s/it]
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Epoch 37 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 37 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 37 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.38it/s]
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Epoch 37 Training:  16%|█▌        | 15/96 [00:19<01:46,  1.32s/it]
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Epoch 37 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 37 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 37 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.39it/s]
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Epoch 37 Training:  17%|█▋        | 16/96 [00:21<01:45,  1.31s/it]
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Epoch 37 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 37 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 37 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.37it/s]
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Epoch 37 Training:  18%|█▊        | 17/96 [00:22<01:43,  1.32s/it]
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Epoch 37 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 37 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.38it/s]
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Epoch 37 Training:  19%|█▉        | 18/96 [00:23<01:42,  1.31s/it]
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Epoch 37 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 37 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 37 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.37it/s]
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Epoch 37 Training:  20%|█▉        | 19/96 [00:25<01:41,  1.31s/it]
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Epoch 37 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 37 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 37 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.36it/s]
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Epoch 37 Training:  21%|██        | 20/96 [00:26<01:39,  1.31s/it]
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Epoch 37 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 37 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 37 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.38it/s]
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Epoch 37 Training:  22%|██▏       | 21/96 [00:27<01:38,  1.31s/it]
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Epoch 37 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 37 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 37 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.37it/s]
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Epoch 37 Training:  23%|██▎       | 22/96 [00:29<01:37,  1.31s/it]
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Epoch 37 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 37 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.63it/s]
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Epoch 37 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.40it/s]
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Epoch 37 Training:  24%|██▍       | 23/96 [00:30<01:35,  1.31s/it]
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Epoch 37 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 37 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 37 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.36it/s]
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Epoch 37 Training:  25%|██▌       | 24/96 [00:31<01:34,  1.32s/it]
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Epoch 37 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 37 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 37 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.36it/s]
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Epoch 37 Training:  26%|██▌       | 25/96 [00:32<01:33,  1.32s/it]
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Epoch 37 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 37 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 37 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.37it/s]
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Epoch 37 Training:  27%|██▋       | 26/96 [00:34<01:32,  1.32s/it]
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Epoch 37 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 37 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 37 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.36it/s]
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Epoch 37 Training:  28%|██▊       | 27/96 [00:35<01:30,  1.32s/it]
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Epoch 37 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 37 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 37 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.39it/s]
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Epoch 37 Training:  29%|██▉       | 28/96 [00:36<01:29,  1.32s/it]
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Epoch 37 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 37 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 37 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.33it/s]
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Epoch 37 Training:  30%|███       | 29/96 [00:38<01:28,  1.32s/it]
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Epoch 37 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 37 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 37 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.37it/s]
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Epoch 37 Training:  31%|███▏      | 30/96 [00:39<01:27,  1.32s/it]
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Epoch 37 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 37 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 37 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.37it/s]
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Epoch 37 Training:  32%|███▏      | 31/96 [00:40<01:25,  1.32s/it]
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Epoch 37 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 37 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 37 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.36it/s]
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Epoch 37 Training:  33%|███▎      | 32/96 [00:42<01:24,  1.32s/it]
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Epoch 37 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 37 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 37 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.39it/s]
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Epoch 37 Training:  34%|███▍      | 33/96 [00:43<01:23,  1.32s/it]
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Epoch 37 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 37 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 37 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.37it/s]
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Epoch 37 Training:  35%|███▌      | 34/96 [00:44<01:21,  1.32s/it]
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Epoch 37 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 37 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 37 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.40it/s]
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Epoch 37 Training:  36%|███▋      | 35/96 [00:46<01:20,  1.32s/it]
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Epoch 37 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 37 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 37 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.38it/s]
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Epoch 37 Training:  38%|███▊      | 36/96 [00:47<01:18,  1.32s/it]
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Epoch 37 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 37 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 37 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.37it/s]
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Epoch 37 Training:  39%|███▊      | 37/96 [00:48<01:17,  1.32s/it]
+
Epoch 37 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 37 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 37 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.36it/s]
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Epoch 37 Training:  40%|███▉      | 38/96 [00:50<01:16,  1.32s/it]
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Epoch 37 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 37 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 37 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.36it/s]
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Epoch 37 Training:  41%|████      | 39/96 [00:51<01:15,  1.32s/it]
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Epoch 37 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 37 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 37 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.38it/s]
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Epoch 37 Training:  42%|████▏     | 40/96 [00:52<01:13,  1.32s/it]
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Epoch 37 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 37 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 37 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.39it/s]
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Epoch 37 Training:  43%|████▎     | 41/96 [00:54<01:12,  1.32s/it]
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Epoch 37 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 37 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 37 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.39it/s]
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Epoch 37 Training:  44%|████▍     | 42/96 [00:55<01:10,  1.31s/it]
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Epoch 37 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 37 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.39it/s]
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Epoch 37 Training:  45%|████▍     | 43/96 [00:56<01:09,  1.31s/it]
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Epoch 37 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 37 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 37 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.37it/s]
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Epoch 37 Training:  46%|████▌     | 44/96 [00:57<01:08,  1.31s/it]
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Epoch 37 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 37 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.36it/s]
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Epoch 37 Training:  47%|████▋     | 45/96 [00:59<01:07,  1.32s/it]
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Epoch 37 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 37 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.39it/s]
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Epoch 37 Training:  48%|████▊     | 46/96 [01:00<01:05,  1.32s/it]
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Epoch 37 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 37 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 37 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.39it/s]
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Epoch 37 Training:  49%|████▉     | 47/96 [01:01<01:04,  1.32s/it]
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Epoch 37 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 37 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.39it/s]
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Epoch 37 Training:  50%|█████     | 48/96 [01:03<01:03,  1.32s/it]
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Epoch 37 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 37 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.38it/s]
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Epoch 37 Training:  51%|█████     | 49/96 [01:04<01:01,  1.32s/it]
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Epoch 37 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 37 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.49it/s]
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Epoch 37 Training:  52%|█████▏    | 50/96 [01:05<01:00,  1.32s/it]
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Epoch 37 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 37 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.36it/s]
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Epoch 37 Training:  53%|█████▎    | 51/96 [01:07<00:59,  1.32s/it]
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Epoch 37 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 37 Training:  54%|█████▍    | 52/96 [01:08<00:57,  1.32s/it]
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Epoch 37 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 37 Training:  55%|█████▌    | 53/96 [01:09<00:56,  1.32s/it]
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Epoch 37 Training:  56%|█████▋    | 54/96 [01:11<00:55,  1.32s/it]
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Epoch 37 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.37it/s]
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Epoch 37 Training:  57%|█████▋    | 55/96 [01:12<00:53,  1.32s/it]
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Epoch 37 Training:  58%|█████▊    | 56/96 [01:13<00:52,  1.32s/it]
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Epoch 37 Training:  59%|█████▉    | 57/96 [01:15<00:51,  1.32s/it]
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Epoch 37 Training:  60%|██████    | 58/96 [01:16<00:49,  1.32s/it]
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Epoch 37 Training:  61%|██████▏   | 59/96 [01:17<00:48,  1.32s/it]
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Epoch 37 Training:  62%|██████▎   | 60/96 [01:19<00:47,  1.31s/it]
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Epoch 37 Training:  64%|██████▎   | 61/96 [01:20<00:46,  1.32s/it]
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Epoch 37 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s][2026-10-01 19:03:10] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0037 | Epoch 037] train/policy_loss=12.1490 train/unweighted_policy_loss=2.4298 train/loss=12.1490 train/grad_norm=0.0699
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+[2026-10-01 19:03:10] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0038 | Epoch 037] timing/rollout_seconds=147.5314 timing/feedback_seconds=6.6328 timing/optimization_seconds=129.0278 timing/cycle_seconds=280.0133
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+
Pointwise Rewards: ssim:   0%|          | 0/6 [00:00<?, ?it/s]
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+
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+
Pointwise Rewards: psnr:   0%|          | 0/6 [00:00<?, ?it/s]
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Pointwise Rewards: psnr:  50%|█████     | 3/6 [00:00<00:00,  6.73it/s]
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Pointwise Rewards: psnr:  83%|████████▎ | 5/6 [00:00<00:00,  6.77it/s]
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Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.78it/s]
+[2026-10-01 19:05:41] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0038 | Epoch 038] train/reward_dists_mean=0.9394 train/reward_dists_std=0.0168 train/reward_psnr_mean=30.7584 train/reward_psnr_std=2.2049 train/reward_ssim_mean=0.9394 train/reward_ssim_std=0.0196 train/reward_dists_group_std_mean=0.0029 train/reward_dists_group_std_max=0.0059 train/reward_dists_group_std_min=0.0010 train/reward_dists_group_mean_std=0.0165 train/reward_psnr_group_std_mean=1.0721 train/reward_psnr_group_std_max=2.5377 train/reward_psnr_group_std_min=0.4297 train/reward_psnr_group_mean_std=1.8690 train/reward_ssim_group_std_mean=0.0010 train/reward_ssim_group_std_max=0.0054 train/reward_ssim_group_std_min=0.0003 train/reward_ssim_group_mean_std=0.0196 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-8.2465 train/adv_max=5.5436 train/adv_abs_mean=1.7247
+
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Epoch 38 Training:   7%|▋         | 7/96 [00:09<01:56,  1.31s/it]
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Epoch 38 Training:   8%|▊         | 8/96 [00:10<02:04,  1.42s/it]
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Epoch 38 Training:   9%|▉         | 9/96 [00:12<02:00,  1.39s/it]
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Epoch 38 Training:  10%|█         | 10/96 [00:13<01:57,  1.36s/it]
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Epoch 38 Training:  11%|█▏        | 11/96 [00:14<01:54,  1.35s/it]
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Epoch 38 Training:  16%|█▌        | 15/96 [00:20<01:47,  1.33s/it]
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Epoch 38 Training:  18%|█▊        | 17/96 [00:22<01:44,  1.32s/it]
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Epoch 38 Training:  19%|█▉        | 18/96 [00:23<01:42,  1.32s/it]
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Epoch 38 Training:  36%|███▋      | 35/96 [00:46<01:20,  1.32s/it]
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Epoch 38 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 38 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.38it/s]
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Epoch 38 Training:  38%|███▊      | 36/96 [00:47<01:18,  1.32s/it]
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Epoch 38 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 38 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.39it/s]
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Epoch 38 Training:  39%|███▊      | 37/96 [00:48<01:17,  1.32s/it]
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Epoch 38 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 38 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.36it/s]
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Epoch 38 Training:  40%|███▉      | 38/96 [00:50<01:16,  1.32s/it]
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Epoch 38 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 38 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.38it/s]
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Epoch 38 Training:  41%|████      | 39/96 [00:51<01:15,  1.32s/it]
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Epoch 38 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 38 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.42it/s]
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Epoch 38 Training:  42%|████▏     | 40/96 [00:52<01:13,  1.32s/it]
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Epoch 38 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 38 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.73it/s]
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Epoch 38 Training:  43%|████▎     | 41/96 [00:54<01:16,  1.40s/it]
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Epoch 38 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 38 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.39it/s]
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Epoch 38 Training:  44%|████▍     | 42/96 [00:55<01:14,  1.37s/it]
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Epoch 38 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 38 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.82it/s]
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Epoch 38 Training:  45%|████▍     | 43/96 [00:57<01:15,  1.42s/it]
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Epoch 38 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 38 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.33it/s]
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Epoch 38 Training:  46%|████▌     | 44/96 [00:58<01:12,  1.39s/it]
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Epoch 38 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 38 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.38it/s]
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Epoch 38 Training:  47%|████▋     | 45/96 [01:00<01:09,  1.37s/it]
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Epoch 38 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 38 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 38 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.37it/s]
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Epoch 38 Training:  48%|████▊     | 46/96 [01:01<01:07,  1.36s/it]
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Epoch 38 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 38 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.48it/s]
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Epoch 38 Training:  49%|████▉     | 47/96 [01:02<01:06,  1.35s/it]
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Epoch 38 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 38 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.37it/s]
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Epoch 38 Training:  50%|█████     | 48/96 [01:04<01:04,  1.34s/it]
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Epoch 38 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 38 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.35it/s]
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Epoch 38 Training:  51%|█████     | 49/96 [01:05<01:02,  1.33s/it]
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Epoch 38 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 38 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 38 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.36it/s]
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Epoch 38 Training:  52%|█████▏    | 50/96 [01:06<01:01,  1.33s/it]
+
Epoch 38 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 38 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 38 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.39it/s]
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Epoch 38 Training:  53%|█████▎    | 51/96 [01:07<00:59,  1.32s/it]
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Epoch 38 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 38 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 38 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.38it/s]
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Epoch 38 Training:  54%|█████▍    | 52/96 [01:09<00:58,  1.32s/it]
+
Epoch 38 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 38 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 38 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.36it/s]
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Epoch 38 Training:  55%|█████▌    | 53/96 [01:10<00:56,  1.32s/it]
+
Epoch 38 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 38 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.36it/s]
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Epoch 38 Training:  56%|█████▋    | 54/96 [01:11<00:55,  1.32s/it]
+
Epoch 38 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 38 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.36it/s]
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Epoch 38 Training:  57%|█████▋    | 55/96 [01:13<00:54,  1.32s/it]
+
Epoch 38 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 38 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 38 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.35it/s]
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Epoch 38 Training:  58%|█████▊    | 56/96 [01:14<00:52,  1.32s/it]
+
Epoch 38 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 38 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.38it/s]
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Epoch 38 Training:  59%|█████▉    | 57/96 [01:15<00:51,  1.32s/it]
+
Epoch 38 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 38 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 38 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.37it/s]
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Epoch 38 Training:  60%|██████    | 58/96 [01:17<00:50,  1.32s/it]
+
Epoch 38 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 38 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 38 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.36it/s]
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Epoch 38 Training:  61%|██████▏   | 59/96 [01:18<00:48,  1.32s/it]
+
Epoch 38 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 38 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.37it/s]
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Epoch 38 Training:  62%|██████▎   | 60/96 [01:19<00:47,  1.32s/it]
+
Epoch 38 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 38 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 38 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.37it/s]
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Epoch 38 Training:  64%|██████▎   | 61/96 [01:21<00:46,  1.32s/it]
+
Epoch 38 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 38 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 38 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.37it/s]
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Epoch 38 Training:  65%|██████▍   | 62/96 [01:22<00:44,  1.32s/it]
+
Epoch 38 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 38 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.36it/s]
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Epoch 38 Training:  66%|██████▌   | 63/96 [01:23<00:43,  1.32s/it]
+
Epoch 38 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 38 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 38 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.36it/s]
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Epoch 38 Training:  67%|██████▋   | 64/96 [01:25<00:42,  1.32s/it]
+
Epoch 38 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 38 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.32it/s]
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Epoch 38 Training:  68%|██████▊   | 65/96 [01:26<00:41,  1.32s/it]
+
Epoch 38 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 38 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.37it/s]
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Epoch 38 Training:  69%|██████▉   | 66/96 [01:27<00:39,  1.32s/it]
+
Epoch 38 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 38 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 38 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.39it/s]
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Epoch 38 Training:  70%|██████▉   | 67/96 [01:29<00:38,  1.32s/it]
+
Epoch 38 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 38 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 38 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.37it/s]
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Epoch 38 Training:  71%|███████   | 68/96 [01:30<00:36,  1.32s/it]
+
Epoch 38 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 38 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.35it/s]
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Epoch 38 Training:  72%|███████▏  | 69/96 [01:31<00:35,  1.32s/it]
+
Epoch 38 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 38 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.34it/s]
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Epoch 38 Training:  73%|███████▎  | 70/96 [01:33<00:34,  1.32s/it]
+
Epoch 38 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 38 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.37it/s]
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Epoch 38 Training:  74%|███████▍  | 71/96 [01:34<00:32,  1.32s/it]
+
Epoch 38 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 38 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.37it/s]
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Epoch 38 Training:  75%|███████▌  | 72/96 [01:35<00:31,  1.32s/it]
+
Epoch 38 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 38 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.38it/s]
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Epoch 38 Training:  76%|███████▌  | 73/96 [01:36<00:30,  1.32s/it]
+
Epoch 38 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 38 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.37it/s]
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Epoch 38 Training:  77%|███████▋  | 74/96 [01:38<00:28,  1.32s/it]
+
Epoch 38 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 38 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.37it/s]
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Epoch 38 Training:  78%|███████▊  | 75/96 [01:39<00:27,  1.32s/it]
+
Epoch 38 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 38 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.34it/s]
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Epoch 38 Training:  79%|███████▉  | 76/96 [01:40<00:26,  1.32s/it]
+
Epoch 38 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 38 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.36it/s]
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Epoch 38 Training:  80%|████████  | 77/96 [01:42<00:25,  1.32s/it]
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Epoch 38 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 38 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.38it/s]
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Epoch 38 Training:  81%|████████▏ | 78/96 [01:43<00:23,  1.32s/it]
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Epoch 38 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 38 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.37it/s]
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Epoch 38 Training:  82%|████████▏ | 79/96 [01:44<00:22,  1.32s/it]
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Epoch 38 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 38 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.38it/s]
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Epoch 38 Training:  83%|████████▎ | 80/96 [01:46<00:21,  1.32s/it]
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Epoch 38 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 38 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.37it/s]
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Epoch 38 Training:  84%|████████▍ | 81/96 [01:47<00:19,  1.32s/it]
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Epoch 38 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 38 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.35it/s]
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Epoch 38 Training:  85%|████████▌ | 82/96 [01:48<00:18,  1.32s/it]
+
Epoch 38 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 38 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.37it/s]
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Epoch 38 Training:  86%|████████▋ | 83/96 [01:50<00:17,  1.32s/it]
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Epoch 38 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 38 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.38it/s]
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Epoch 38 Training:  88%|████████▊ | 84/96 [01:51<00:15,  1.32s/it]
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Epoch 38 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 38 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.35it/s]
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Epoch 38 Training:  89%|████████▊ | 85/96 [01:52<00:14,  1.32s/it]
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Epoch 38 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 38 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.42it/s]
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Epoch 38 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.63it/s][2026-10-01 19:07:49] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0038 | Epoch 038] train/policy_loss=12.3191 train/unweighted_policy_loss=2.4638 train/loss=12.3191 train/grad_norm=0.0534
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+[2026-10-01 19:07:49] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0039 | Epoch 038] timing/rollout_seconds=147.6630 timing/feedback_seconds=5.6593 timing/optimization_seconds=128.0683 timing/cycle_seconds=279.2600
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+
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+[2026-10-01 19:10:21] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0039 | Epoch 039] train/reward_dists_mean=0.9382 train/reward_dists_std=0.0137 train/reward_psnr_mean=30.2460 train/reward_psnr_std=2.1728 train/reward_ssim_mean=0.9323 train/reward_ssim_std=0.0236 train/reward_dists_group_std_mean=0.0029 train/reward_dists_group_std_max=0.0055 train/reward_dists_group_std_min=0.0013 train/reward_dists_group_mean_std=0.0134 train/reward_psnr_group_std_mean=1.1678 train/reward_psnr_group_std_max=2.2266 train/reward_psnr_group_std_min=0.2643 train/reward_psnr_group_mean_std=1.7799 train/reward_ssim_group_std_mean=0.0013 train/reward_ssim_group_std_max=0.0102 train/reward_ssim_group_std_min=0.0005 train/reward_ssim_group_mean_std=0.0235 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-8.5473 train/adv_max=5.7883 train/adv_abs_mean=1.8413
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Epoch 39 Training:   8%|▊         | 8/96 [00:10<01:54,  1.30s/it]
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Epoch 39 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 39 Training:   9%|▉         | 9/96 [00:11<01:53,  1.30s/it]
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Epoch 39 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 39 Training:  10%|█         | 10/96 [00:12<01:51,  1.30s/it]
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Epoch 39 Training:  11%|█▏        | 11/96 [00:14<01:50,  1.30s/it]
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Epoch 39 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 39 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.46it/s]
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Epoch 39 Training:  12%|█▎        | 12/96 [00:15<01:49,  1.30s/it]
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Epoch 39 Training:  14%|█▎        | 13/96 [00:16<01:47,  1.30s/it]
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Epoch 39 Training:  15%|█▍        | 14/96 [00:18<01:46,  1.30s/it]
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Epoch 39 Training:  16%|█▌        | 15/96 [00:19<01:45,  1.30s/it]
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Epoch 39 Training:  17%|█▋        | 16/96 [00:20<01:43,  1.30s/it]
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Epoch 39 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.44it/s]
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Epoch 39 Training:  18%|█▊        | 17/96 [00:22<01:42,  1.30s/it]
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Epoch 39 Training:  19%|█▉        | 18/96 [00:23<01:41,  1.30s/it]
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Epoch 39 Training:  20%|█▉        | 19/96 [00:24<01:39,  1.30s/it]
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Epoch 39 Training:  21%|██        | 20/96 [00:25<01:38,  1.30s/it]
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Epoch 39 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.42it/s]
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Epoch 39 Training:  22%|██▏       | 21/96 [00:27<01:37,  1.30s/it]
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Epoch 39 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 39 Training:  23%|██▎       | 22/96 [00:28<01:36,  1.30s/it]
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Epoch 39 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 39 Training:  24%|██▍       | 23/96 [00:29<01:34,  1.30s/it]
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Epoch 39 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 39 Training:  25%|██▌       | 24/96 [00:31<01:33,  1.30s/it]
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Epoch 39 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 39 Training:  26%|██▌       | 25/96 [00:32<01:32,  1.30s/it]
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Epoch 39 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 39 Training:  27%|██▋       | 26/96 [00:33<01:30,  1.30s/it]
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Epoch 39 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.45it/s]
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Epoch 39 Training:  28%|██▊       | 27/96 [00:35<01:29,  1.30s/it]
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Epoch 39 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.44it/s]
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Epoch 39 Training:  29%|██▉       | 28/96 [00:36<01:28,  1.30s/it]
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Epoch 39 Training:  30%|███       | 29/96 [00:37<01:26,  1.30s/it]
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Epoch 39 Training:  31%|███▏      | 30/96 [00:38<01:25,  1.30s/it]
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Epoch 39 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 39 Training:  32%|███▏      | 31/96 [00:40<01:24,  1.30s/it]
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Epoch 39 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 39 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.46it/s]
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Epoch 39 Training:  33%|███▎      | 32/96 [00:41<01:23,  1.30s/it]
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Epoch 39 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 39 Training:  34%|███▍      | 33/96 [00:42<01:21,  1.30s/it]
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Epoch 39 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 39 Training:  35%|███▌      | 34/96 [00:44<01:20,  1.30s/it]
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Epoch 39 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 39 Training:  36%|███▋      | 35/96 [00:45<01:19,  1.30s/it]
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Epoch 39 Training:  38%|███▊      | 36/96 [00:46<01:18,  1.30s/it]
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Epoch 39 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 39 Training:  39%|███▊      | 37/96 [00:48<01:16,  1.30s/it]
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Epoch 39 Training:  40%|███▉      | 38/96 [00:49<01:15,  1.30s/it]
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Epoch 39 Training:  41%|████      | 39/96 [00:50<01:14,  1.30s/it]
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Epoch 39 Training:  42%|████▏     | 40/96 [00:51<01:12,  1.30s/it]
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Epoch 39 Training:  43%|████▎     | 41/96 [00:53<01:11,  1.30s/it]
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Epoch 39 Training:  44%|████▍     | 42/96 [00:54<01:10,  1.30s/it]
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Epoch 39 Training:  45%|████▍     | 43/96 [00:55<01:09,  1.30s/it]
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Epoch 39 Training:  46%|████▌     | 44/96 [00:57<01:07,  1.30s/it]
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Epoch 39 Training:  47%|████▋     | 45/96 [00:58<01:06,  1.30s/it]
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Epoch 39 Training:  48%|████▊     | 46/96 [00:59<01:04,  1.30s/it]
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Epoch 39 Training:  49%|████▉     | 47/96 [01:01<01:03,  1.30s/it]
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Epoch 39 Training:  50%|█████     | 48/96 [01:02<01:02,  1.30s/it]
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Epoch 39 Training:  51%|█████     | 49/96 [01:03<01:01,  1.30s/it]
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Epoch 39 Training:  52%|█████▏    | 50/96 [01:04<00:59,  1.30s/it]
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Epoch 39 Training:  53%|█████▎    | 51/96 [01:06<00:58,  1.30s/it]
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Epoch 39 Training:  54%|█████▍    | 52/96 [01:07<00:57,  1.30s/it]
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Epoch 39 Training:  55%|█████▌    | 53/96 [01:08<00:55,  1.30s/it]
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Epoch 39 Training:  56%|█████▋    | 54/96 [01:10<00:54,  1.30s/it]
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Epoch 39 Training:  57%|█████▋    | 55/96 [01:11<00:53,  1.30s/it]
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Epoch 39 Training:  58%|█████▊    | 56/96 [01:12<00:51,  1.30s/it]
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Epoch 39 Training:  59%|█████▉    | 57/96 [01:13<00:50,  1.30s/it]
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Epoch 39 Training:  60%|██████    | 58/96 [01:15<00:49,  1.30s/it]
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Epoch 39 Training:  61%|██████▏   | 59/96 [01:16<00:48,  1.30s/it]
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+[2026-10-01 19:12:25] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0040 | Epoch 039] timing/rollout_seconds=147.7174 timing/feedback_seconds=5.6854 timing/optimization_seconds=125.2698 timing/cycle_seconds=276.5233
+[2026-10-01 19:12:25] [Rank 0] [INFO] [flow_factory.models.abc]: Saving LoRA weights for transformer role=base to /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/runs/klein4b_nft_vedai_full_ssim_dists_psnr/saves/klein4b_nft_vedai_full_ssim_dists_psnr/checkpoints/checkpoint-40
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+[2026-10-01 19:13:09] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0040 | Epoch 040] eval/vedai_rl_full/reward_dists_mean=0.9286 eval/vedai_rl_full/reward_dists_std=0.0194 eval/vedai_rl_full/reward_psnr_mean=29.1190 eval/vedai_rl_full/reward_psnr_std=2.4640 eval/vedai_rl_full/reward_ssim_mean=0.9201 eval/vedai_rl_full/reward_ssim_std=0.0275
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+
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Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:00<00:00,  6.60it/s]
+
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+
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Pointwise Rewards: psnr:  50%|█████     | 3/6 [00:00<00:00,  6.87it/s]
Pointwise Rewards: psnr:  67%|██████▋   | 4/6 [00:00<00:00,  7.00it/s]
Pointwise Rewards: psnr:  83%|████████▎ | 5/6 [00:00<00:00,  6.98it/s]
Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.89it/s]
Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.87it/s]
+[2026-10-01 19:15:41] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0040 | Epoch 040] train/reward_dists_mean=0.9410 train/reward_dists_std=0.0129 train/reward_psnr_mean=31.3392 train/reward_psnr_std=2.1809 train/reward_ssim_mean=0.9413 train/reward_ssim_std=0.0199 train/reward_dists_group_std_mean=0.0029 train/reward_dists_group_std_max=0.0055 train/reward_dists_group_std_min=0.0010 train/reward_dists_group_mean_std=0.0125 train/reward_psnr_group_std_mean=1.0923 train/reward_psnr_group_std_max=2.7226 train/reward_psnr_group_std_min=0.2104 train/reward_psnr_group_mean_std=1.8102 train/reward_ssim_group_std_mean=0.0010 train/reward_ssim_group_std_max=0.0033 train/reward_ssim_group_std_min=0.0003 train/reward_ssim_group_mean_std=0.0199 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-7.7276 train/adv_max=4.7513 train/adv_abs_mean=1.7540
+
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Epoch 40 Training:  27%|██▋       | 26/96 [00:33<01:30,  1.30s/it]
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Epoch 40 Training:  28%|██▊       | 27/96 [00:34<01:29,  1.30s/it]
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Epoch 40 Training:  29%|██▉       | 28/96 [00:36<01:28,  1.30s/it]
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Epoch 40 Training:  30%|███       | 29/96 [00:37<01:26,  1.30s/it]
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Epoch 40 Training:  31%|███▏      | 30/96 [00:38<01:25,  1.30s/it]
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Epoch 40 Training:  32%|███▏      | 31/96 [00:40<01:24,  1.30s/it]
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Epoch 40 Training:  33%|███▎      | 32/96 [00:41<01:23,  1.30s/it]
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Epoch 40 Training:  34%|███▍      | 33/96 [00:42<01:21,  1.30s/it]
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Epoch 40 Training:  35%|███▌      | 34/96 [00:44<01:20,  1.30s/it]
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Epoch 40 Training:  36%|███▋      | 35/96 [00:45<01:19,  1.30s/it]
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Epoch 40 Training:  38%|███▊      | 36/96 [00:46<01:18,  1.30s/it]
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Epoch 40 Training:  39%|███▊      | 37/96 [00:47<01:16,  1.30s/it]
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Epoch 40 Training:  40%|███▉      | 38/96 [00:49<01:15,  1.30s/it]
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Epoch 40 Training:  41%|████      | 39/96 [00:50<01:14,  1.30s/it]
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Epoch 40 Training:  42%|████▏     | 40/96 [00:51<01:12,  1.30s/it]
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Epoch 40 Training:  43%|████▎     | 41/96 [00:53<01:11,  1.30s/it]
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Epoch 40 Training:  44%|████▍     | 42/96 [00:54<01:10,  1.30s/it]
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Epoch 40 Training:  45%|████▍     | 43/96 [00:55<01:08,  1.30s/it]
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Epoch 40 Training:  46%|████▌     | 44/96 [00:57<01:07,  1.30s/it]
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Epoch 40 Training:  47%|████▋     | 45/96 [00:58<01:06,  1.30s/it]
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Epoch 40 Training:  48%|████▊     | 46/96 [00:59<01:04,  1.29s/it]
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Epoch 40 Training:  49%|████▉     | 47/96 [01:00<01:03,  1.30s/it]
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Epoch 40 Training:  50%|█████     | 48/96 [01:02<01:02,  1.30s/it]
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Epoch 40 Training:  51%|█████     | 49/96 [01:03<01:00,  1.30s/it]
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Epoch 40 Training:  52%|█████▏    | 50/96 [01:04<00:59,  1.30s/it]
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Epoch 40 Training:  53%|█████▎    | 51/96 [01:06<00:58,  1.29s/it]
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Epoch 40 Training:  54%|█████▍    | 52/96 [01:07<00:56,  1.29s/it]
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Epoch 40 Training:  55%|█████▌    | 53/96 [01:08<00:55,  1.30s/it]
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Epoch 40 Training:  56%|█████▋    | 54/96 [01:10<00:54,  1.30s/it]
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Epoch 40 Training:  57%|█████▋    | 55/96 [01:11<00:53,  1.30s/it]
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Epoch 40 Training:  58%|█████▊    | 56/96 [01:12<00:52,  1.30s/it]
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Epoch 40 Training:  59%|█████▉    | 57/96 [01:13<00:50,  1.30s/it]
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Epoch 40 Training:  60%|██████    | 58/96 [01:15<00:49,  1.30s/it]
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Epoch 40 Training:  61%|██████▏   | 59/96 [01:16<00:48,  1.30s/it]
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Epoch 40 Training:  62%|██████▎   | 60/96 [01:17<00:46,  1.30s/it]
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Epoch 40 Training:  64%|██████▎   | 61/96 [01:19<00:45,  1.30s/it]
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Epoch 40 Training:  65%|██████▍   | 62/96 [01:20<00:44,  1.30s/it]
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Epoch 40 Training:  66%|██████▌   | 63/96 [01:21<00:42,  1.30s/it]
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Epoch 40 Training:  67%|██████▋   | 64/96 [01:23<00:41,  1.30s/it]
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Epoch 40 Training:  68%|██████▊   | 65/96 [01:24<00:40,  1.30s/it]
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Epoch 40 Training:  69%|██████▉   | 66/96 [01:25<00:38,  1.30s/it]
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Epoch 40 Training:  70%|██████▉   | 67/96 [01:26<00:37,  1.30s/it]
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Epoch 40 Training:  71%|███████   | 68/96 [01:28<00:36,  1.30s/it]
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Epoch 40 Training:  72%|███████▏  | 69/96 [01:29<00:35,  1.30s/it]
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Epoch 40 Training:  73%|███████▎  | 70/96 [01:30<00:33,  1.30s/it]
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Epoch 40 Training:  74%|███████▍  | 71/96 [01:32<00:32,  1.30s/it]
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Epoch 40 Training:  75%|███████▌  | 72/96 [01:33<00:31,  1.30s/it]
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Epoch 40 Training:  76%|███████▌  | 73/96 [01:34<00:29,  1.30s/it]
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Epoch 40 Training:  77%|███████▋  | 74/96 [01:36<00:28,  1.30s/it]
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Epoch 40 Training:  78%|███████▊  | 75/96 [01:37<00:27,  1.30s/it]
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Epoch 40 Training:  79%|███████▉  | 76/96 [01:38<00:26,  1.30s/it]
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Epoch 40 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s][2026-10-01 19:17:46] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0040 | Epoch 040] train/policy_loss=12.3352 train/unweighted_policy_loss=2.4670 train/loss=12.3352 train/grad_norm=0.0564
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+[2026-10-01 19:17:46] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0041 | Epoch 040] timing/rollout_seconds=147.8351 timing/feedback_seconds=5.7871 timing/optimization_seconds=125.2450 timing/cycle_seconds=276.5711
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+[2026-10-01 19:20:18] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0041 | Epoch 041] train/reward_dists_mean=0.9315 train/reward_dists_std=0.0343 train/reward_psnr_mean=29.7312 train/reward_psnr_std=2.2710 train/reward_ssim_mean=0.9305 train/reward_ssim_std=0.0186 train/reward_dists_group_std_mean=0.0030 train/reward_dists_group_std_max=0.0113 train/reward_dists_group_std_min=0.0012 train/reward_dists_group_mean_std=0.0342 train/reward_psnr_group_std_mean=1.2821 train/reward_psnr_group_std_max=6.1463 train/reward_psnr_group_std_min=0.2401 train/reward_psnr_group_mean_std=1.6617 train/reward_ssim_group_std_mean=0.0019 train/reward_ssim_group_std_max=0.0335 train/reward_ssim_group_std_min=0.0005 train/reward_ssim_group_mean_std=0.0179 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-6.8272 train/adv_max=5.9958 train/adv_abs_mean=1.8202
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Epoch 41 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s][2026-10-01 19:22:23] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0041 | Epoch 041] train/policy_loss=12.4838 train/unweighted_policy_loss=2.4968 train/loss=12.4838 train/grad_norm=0.0778
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+[2026-10-01 19:22:23] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0042 | Epoch 041] timing/rollout_seconds=147.7806 timing/feedback_seconds=5.6537 timing/optimization_seconds=125.4317 timing/cycle_seconds=276.7051
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+
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+
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Pointwise Rewards: psnr:  83%|████████▎ | 5/6 [00:00<00:00,  6.55it/s]
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+[2026-10-01 19:24:55] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0042 | Epoch 042] train/reward_dists_mean=0.9413 train/reward_dists_std=0.0132 train/reward_psnr_mean=30.6957 train/reward_psnr_std=1.8915 train/reward_ssim_mean=0.9404 train/reward_ssim_std=0.0176 train/reward_dists_group_std_mean=0.0027 train/reward_dists_group_std_max=0.0046 train/reward_dists_group_std_min=0.0010 train/reward_dists_group_mean_std=0.0129 train/reward_psnr_group_std_mean=1.0470 train/reward_psnr_group_std_max=1.9474 train/reward_psnr_group_std_min=0.2525 train/reward_psnr_group_mean_std=1.5246 train/reward_ssim_group_std_mean=0.0011 train/reward_ssim_group_std_max=0.0070 train/reward_ssim_group_std_min=0.0002 train/reward_ssim_group_mean_std=0.0175 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-8.6800 train/adv_max=6.9400 train/adv_abs_mean=1.8257
+
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Epoch 42 Training:  23%|██▎       | 22/96 [00:28<01:36,  1.30s/it]
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Epoch 42 Training:  24%|██▍       | 23/96 [00:29<01:34,  1.30s/it]
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Epoch 42 Training:  25%|██▌       | 24/96 [00:31<01:33,  1.30s/it]
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Epoch 42 Training:  26%|██▌       | 25/96 [00:32<01:32,  1.30s/it]
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Epoch 42 Training:  27%|██▋       | 26/96 [00:33<01:30,  1.30s/it]
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Epoch 42 Training:  28%|██▊       | 27/96 [00:35<01:29,  1.30s/it]
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Epoch 42 Training:  29%|██▉       | 28/96 [00:36<01:28,  1.30s/it]
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Epoch 42 Training:  30%|███       | 29/96 [00:37<01:27,  1.30s/it]
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Epoch 42 Training:  31%|███▏      | 30/96 [00:38<01:25,  1.30s/it]
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Epoch 42 Training:  32%|███▏      | 31/96 [00:40<01:24,  1.30s/it]
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Epoch 42 Training:  33%|███▎      | 32/96 [00:41<01:23,  1.30s/it]
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Epoch 42 Training:  34%|███▍      | 33/96 [00:42<01:21,  1.30s/it]
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Epoch 42 Training:  35%|███▌      | 34/96 [00:44<01:20,  1.30s/it]
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Epoch 42 Training:  36%|███▋      | 35/96 [00:45<01:19,  1.30s/it]
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Epoch 42 Training:  38%|███▊      | 36/96 [00:46<01:18,  1.30s/it]
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Epoch 42 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 42 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.36it/s]
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Epoch 42 Training:  39%|███▊      | 37/96 [00:48<01:16,  1.30s/it]
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Epoch 42 Training:  40%|███▉      | 38/96 [00:49<01:15,  1.30s/it]
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Epoch 42 Training:  41%|████      | 39/96 [00:50<01:14,  1.30s/it]
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Epoch 42 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.34it/s]
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Epoch 42 Training:  42%|████▏     | 40/96 [00:51<01:12,  1.30s/it]
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Epoch 42 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 42 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.37it/s]
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Epoch 42 Training:  43%|████▎     | 41/96 [00:53<01:11,  1.30s/it]
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Epoch 42 Training:  44%|████▍     | 42/96 [00:54<01:10,  1.30s/it]
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Epoch 42 Training:  45%|████▍     | 43/96 [00:55<01:08,  1.30s/it]
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Epoch 42 Training:  46%|████▌     | 44/96 [00:57<01:07,  1.30s/it]
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Epoch 42 Training:  47%|████▋     | 45/96 [00:58<01:06,  1.30s/it]
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Epoch 42 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.37it/s]
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Epoch 42 Training:  48%|████▊     | 46/96 [00:59<01:05,  1.30s/it]
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Epoch 42 Training:  49%|████▉     | 47/96 [01:01<01:03,  1.30s/it]
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Epoch 42 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.36it/s]
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Epoch 42 Training:  50%|█████     | 48/96 [01:02<01:02,  1.30s/it]
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Epoch 42 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.34it/s]
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Epoch 42 Training:  51%|█████     | 49/96 [01:03<01:01,  1.30s/it]
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Epoch 42 Training:  52%|█████▏    | 50/96 [01:04<00:59,  1.30s/it]
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Epoch 42 Training:  53%|█████▎    | 51/96 [01:06<00:58,  1.30s/it]
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Epoch 42 Training:  54%|█████▍    | 52/96 [01:07<00:57,  1.30s/it]
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Epoch 42 Training:  55%|█████▌    | 53/96 [01:08<00:55,  1.30s/it]
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Epoch 42 Training:  56%|█████▋    | 54/96 [01:10<00:54,  1.30s/it]
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Epoch 42 Training:  57%|█████▋    | 55/96 [01:11<00:53,  1.30s/it]
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Epoch 42 Training:  58%|█████▊    | 56/96 [01:12<00:51,  1.30s/it]
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Epoch 42 Training:  59%|█████▉    | 57/96 [01:14<00:50,  1.30s/it]
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Epoch 42 Training:  60%|██████    | 58/96 [01:15<00:49,  1.30s/it]
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Epoch 42 Training:  61%|██████▏   | 59/96 [01:16<00:48,  1.30s/it]
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Epoch 42 Training:  62%|██████▎   | 60/96 [01:17<00:46,  1.30s/it]
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Epoch 42 Training:  64%|██████▎   | 61/96 [01:19<00:45,  1.30s/it]
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Epoch 42 Training:  65%|██████▍   | 62/96 [01:20<00:44,  1.30s/it]
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Epoch 42 Training:  66%|██████▌   | 63/96 [01:21<00:42,  1.30s/it]
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Epoch 42 Training:  67%|██████▋   | 64/96 [01:23<00:41,  1.30s/it]
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Epoch 42 Training:  68%|██████▊   | 65/96 [01:24<00:40,  1.30s/it]
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Epoch 42 Training:  69%|██████▉   | 66/96 [01:25<00:38,  1.30s/it]
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Epoch 42 Training:  70%|██████▉   | 67/96 [01:27<00:37,  1.30s/it]
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Epoch 42 Training:  71%|███████   | 68/96 [01:28<00:36,  1.30s/it]
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Epoch 42 Training:  72%|███████▏  | 69/96 [01:29<00:34,  1.29s/it]
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Epoch 42 Training:  73%|███████▎  | 70/96 [01:30<00:33,  1.29s/it]
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Epoch 42 Training:  74%|███████▍  | 71/96 [01:32<00:32,  1.29s/it]
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Epoch 42 Training:  75%|███████▌  | 72/96 [01:33<00:30,  1.29s/it]
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Epoch 42 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s][2026-10-01 19:26:59] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0042 | Epoch 042] train/policy_loss=11.8264 train/unweighted_policy_loss=2.3653 train/loss=11.8264 train/grad_norm=0.0611
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+[2026-10-01 19:26:59] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0043 | Epoch 042] timing/rollout_seconds=147.6774 timing/feedback_seconds=5.7066 timing/optimization_seconds=125.2392 timing/cycle_seconds=276.4935
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+
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Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.74it/s]
+[2026-10-01 19:29:31] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0043 | Epoch 043] train/reward_dists_mean=0.9432 train/reward_dists_std=0.0147 train/reward_psnr_mean=30.8053 train/reward_psnr_std=2.3140 train/reward_ssim_mean=0.9418 train/reward_ssim_std=0.0217 train/reward_dists_group_std_mean=0.0028 train/reward_dists_group_std_max=0.0061 train/reward_dists_group_std_min=0.0015 train/reward_dists_group_mean_std=0.0144 train/reward_psnr_group_std_mean=1.0298 train/reward_psnr_group_std_max=2.1437 train/reward_psnr_group_std_min=0.2748 train/reward_psnr_group_mean_std=2.0229 train/reward_ssim_group_std_mean=0.0009 train/reward_ssim_group_std_max=0.0024 train/reward_ssim_group_std_min=0.0005 train/reward_ssim_group_mean_std=0.0217 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-9.3111 train/adv_max=5.3653 train/adv_abs_mean=1.7909
+
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Epoch 43 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s][2026-10-01 19:31:36] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0043 | Epoch 043] train/policy_loss=11.6620 train/unweighted_policy_loss=2.3324 train/loss=11.6620 train/grad_norm=0.0467
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+[2026-10-01 19:31:36] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0044 | Epoch 043] timing/rollout_seconds=147.6963 timing/feedback_seconds=5.5847 timing/optimization_seconds=125.4177 timing/cycle_seconds=276.6062
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+[2026-10-01 19:34:08] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0044 | Epoch 044] train/reward_dists_mean=0.9400 train/reward_dists_std=0.0144 train/reward_psnr_mean=30.4491 train/reward_psnr_std=2.1773 train/reward_ssim_mean=0.9406 train/reward_ssim_std=0.0182 train/reward_dists_group_std_mean=0.0028 train/reward_dists_group_std_max=0.0055 train/reward_dists_group_std_min=0.0010 train/reward_dists_group_mean_std=0.0141 train/reward_psnr_group_std_mean=1.1186 train/reward_psnr_group_std_max=2.5804 train/reward_psnr_group_std_min=0.2427 train/reward_psnr_group_mean_std=1.7710 train/reward_ssim_group_std_mean=0.0009 train/reward_ssim_group_std_max=0.0018 train/reward_ssim_group_std_min=0.0003 train/reward_ssim_group_mean_std=0.0182 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-7.5658 train/adv_max=5.4674 train/adv_abs_mean=1.7880
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Epoch 44 Training:  20%|█▉        | 19/96 [00:24<01:40,  1.30s/it]
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Epoch 44 Training:  21%|██        | 20/96 [00:26<01:38,  1.30s/it]
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Epoch 44 Training:  22%|██▏       | 21/96 [00:27<01:37,  1.30s/it]
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Epoch 44 Training:  23%|██▎       | 22/96 [00:28<01:36,  1.30s/it]
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Epoch 44 Training:  24%|██▍       | 23/96 [00:30<01:34,  1.30s/it]
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Epoch 44 Training:  25%|██▌       | 24/96 [00:31<01:33,  1.30s/it]
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Epoch 44 Training:  26%|██▌       | 25/96 [00:32<01:32,  1.30s/it]
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Epoch 44 Training:  27%|██▋       | 26/96 [00:34<01:30,  1.30s/it]
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Epoch 44 Training:  28%|██▊       | 27/96 [00:35<01:29,  1.30s/it]
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Epoch 44 Training:  29%|██▉       | 28/96 [00:36<01:28,  1.30s/it]
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Epoch 44 Training:  30%|███       | 29/96 [00:37<01:27,  1.30s/it]
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Epoch 44 Training:  31%|███▏      | 30/96 [00:39<01:25,  1.30s/it]
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Epoch 44 Training:  32%|███▏      | 31/96 [00:40<01:24,  1.30s/it]
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Epoch 44 Training:  33%|███▎      | 32/96 [00:41<01:23,  1.30s/it]
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Epoch 44 Training:  34%|███▍      | 33/96 [00:43<01:21,  1.30s/it]
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Epoch 44 Training:  35%|███▌      | 34/96 [00:44<01:20,  1.30s/it]
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Epoch 44 Training:  36%|███▋      | 35/96 [00:45<01:19,  1.30s/it]
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Epoch 44 Training:  38%|███▊      | 36/96 [00:47<01:18,  1.30s/it]
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Epoch 44 Training:  39%|███▊      | 37/96 [00:48<01:16,  1.30s/it]
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Epoch 44 Training:  40%|███▉      | 38/96 [00:49<01:15,  1.30s/it]
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Epoch 44 Training:  41%|████      | 39/96 [00:50<01:14,  1.30s/it]
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Epoch 44 Training:  42%|████▏     | 40/96 [00:52<01:12,  1.30s/it]
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Epoch 44 Training:  43%|████▎     | 41/96 [00:53<01:11,  1.30s/it]
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Epoch 44 Training:  44%|████▍     | 42/96 [00:54<01:10,  1.30s/it]
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Epoch 44 Training:  45%|████▍     | 43/96 [00:56<01:08,  1.30s/it]
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Epoch 44 Training:  46%|████▌     | 44/96 [00:57<01:07,  1.30s/it]
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Epoch 44 Training:  47%|████▋     | 45/96 [00:58<01:06,  1.30s/it]
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Epoch 44 Training:  48%|████▊     | 46/96 [01:00<01:04,  1.30s/it]
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Epoch 44 Training:  49%|████▉     | 47/96 [01:01<01:03,  1.30s/it]
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Epoch 44 Training:  50%|█████     | 48/96 [01:02<01:02,  1.30s/it]
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Epoch 44 Training:  51%|█████     | 49/96 [01:03<01:01,  1.30s/it]
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Epoch 44 Training:  52%|█████▏    | 50/96 [01:05<00:59,  1.30s/it]
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Epoch 44 Training:  53%|█████▎    | 51/96 [01:06<00:58,  1.30s/it]
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Epoch 44 Training:  54%|█████▍    | 52/96 [01:07<00:57,  1.30s/it]
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Epoch 44 Training:  55%|█████▌    | 53/96 [01:09<00:55,  1.30s/it]
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Epoch 44 Training:  56%|█████▋    | 54/96 [01:10<00:54,  1.30s/it]
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Epoch 44 Training:  57%|█████▋    | 55/96 [01:11<00:53,  1.30s/it]
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Epoch 44 Training:  58%|█████▊    | 56/96 [01:13<00:51,  1.30s/it]
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Epoch 44 Training:  59%|█████▉    | 57/96 [01:14<00:50,  1.30s/it]
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Epoch 44 Training:  60%|██████    | 58/96 [01:15<00:49,  1.30s/it]
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Epoch 44 Training:  61%|██████▏   | 59/96 [01:16<00:48,  1.30s/it]
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Epoch 44 Training:  62%|██████▎   | 60/96 [01:18<00:49,  1.38s/it]
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Epoch 44 Training:  64%|██████▎   | 61/96 [01:19<00:47,  1.35s/it]
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Epoch 44 Training:  65%|██████▍   | 62/96 [01:21<00:45,  1.33s/it]
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Epoch 44 Training:  66%|██████▌   | 63/96 [01:22<00:43,  1.32s/it]
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Epoch 44 Training:  67%|██████▋   | 64/96 [01:23<00:42,  1.31s/it]
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Epoch 44 Training:  68%|██████▊   | 65/96 [01:24<00:40,  1.31s/it]
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Epoch 44 Training:  69%|██████▉   | 66/96 [01:26<00:39,  1.30s/it]
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Epoch 44 Training:  70%|██████▉   | 67/96 [01:27<00:37,  1.30s/it]
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Epoch 44 Training:  71%|███████   | 68/96 [01:28<00:36,  1.30s/it]
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Epoch 44 Training:  72%|███████▏  | 69/96 [01:30<00:35,  1.30s/it]
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Epoch 44 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s][2026-10-01 19:36:13] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0044 | Epoch 044] train/policy_loss=11.6268 train/unweighted_policy_loss=2.3254 train/loss=11.6268 train/grad_norm=0.0872
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Epoch 44 Training: 100%|██████████| 96/96 [02:05<00:00,  1.31s/it]
+[2026-10-01 19:36:13] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0045 | Epoch 044] timing/rollout_seconds=147.7090 timing/feedback_seconds=5.7006 timing/optimization_seconds=125.9372 timing/cycle_seconds=277.2908
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+[2026-10-01 19:38:45] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0045 | Epoch 045] train/reward_dists_mean=0.9341 train/reward_dists_std=0.0352 train/reward_psnr_mean=30.8739 train/reward_psnr_std=2.3385 train/reward_ssim_mean=0.9431 train/reward_ssim_std=0.0180 train/reward_dists_group_std_mean=0.0030 train/reward_dists_group_std_max=0.0090 train/reward_dists_group_std_min=0.0010 train/reward_dists_group_mean_std=0.0350 train/reward_psnr_group_std_mean=1.1193 train/reward_psnr_group_std_max=4.2085 train/reward_psnr_group_std_min=0.2148 train/reward_psnr_group_mean_std=1.9230 train/reward_ssim_group_std_mean=0.0014 train/reward_ssim_group_std_max=0.0159 train/reward_ssim_group_std_min=0.0003 train/reward_ssim_group_mean_std=0.0178 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-8.2482 train/adv_max=6.3019 train/adv_abs_mean=1.7667
+
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Epoch 45 Training:  46%|████▌     | 44/96 [00:57<01:07,  1.30s/it]
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Epoch 45 Training:  47%|████▋     | 45/96 [00:58<01:06,  1.30s/it]
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Epoch 45 Training:  48%|████▊     | 46/96 [00:59<01:04,  1.30s/it]
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Epoch 45 Training:  49%|████▉     | 47/96 [01:01<01:03,  1.30s/it]
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Epoch 45 Training:  50%|█████     | 48/96 [01:02<01:02,  1.30s/it]
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Epoch 45 Training:  51%|█████     | 49/96 [01:03<01:00,  1.30s/it]
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Epoch 45 Training:  52%|█████▏    | 50/96 [01:05<00:59,  1.30s/it]
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Epoch 45 Training:  53%|█████▎    | 51/96 [01:06<00:58,  1.30s/it]
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Epoch 45 Training:  54%|█████▍    | 52/96 [01:07<00:56,  1.29s/it]
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Epoch 45 Training:  55%|█████▌    | 53/96 [01:09<00:55,  1.29s/it]
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Epoch 45 Training:  56%|█████▋    | 54/96 [01:10<00:54,  1.29s/it]
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Epoch 45 Training:  57%|█████▋    | 55/96 [01:11<00:53,  1.30s/it]
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Epoch 45 Training:  58%|█████▊    | 56/96 [01:12<00:51,  1.30s/it]
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Epoch 45 Training:  59%|█████▉    | 57/96 [01:14<00:50,  1.30s/it]
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Epoch 45 Training:  60%|██████    | 58/96 [01:15<00:49,  1.30s/it]
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Epoch 45 Training:  61%|██████▏   | 59/96 [01:16<00:47,  1.30s/it]
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Epoch 45 Training:  62%|██████▎   | 60/96 [01:18<00:46,  1.30s/it]
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Epoch 45 Training:  64%|██████▎   | 61/96 [01:19<00:45,  1.30s/it]
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Epoch 45 Training:  65%|██████▍   | 62/96 [01:20<00:44,  1.30s/it]
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Epoch 45 Training:  66%|██████▌   | 63/96 [01:22<00:42,  1.30s/it]
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Epoch 45 Training:  67%|██████▋   | 64/96 [01:23<00:41,  1.30s/it]
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Epoch 45 Training:  68%|██████▊   | 65/96 [01:24<00:40,  1.30s/it]
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Epoch 45 Training:  69%|██████▉   | 66/96 [01:25<00:38,  1.30s/it]
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Epoch 45 Training:  70%|██████▉   | 67/96 [01:27<00:37,  1.30s/it]
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Epoch 45 Training:  71%|███████   | 68/96 [01:28<00:36,  1.29s/it]
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Epoch 45 Training:  72%|███████▏  | 69/96 [01:29<00:34,  1.29s/it]
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Epoch 45 Training:  73%|███████▎  | 70/96 [01:31<00:33,  1.29s/it]
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Epoch 45 Training:  74%|███████▍  | 71/96 [01:32<00:32,  1.29s/it]
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Epoch 45 Training:  75%|███████▌  | 72/96 [01:33<00:31,  1.29s/it]
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Epoch 45 Training:  76%|███████▌  | 73/96 [01:34<00:29,  1.29s/it]
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Epoch 45 Training:  77%|███████▋  | 74/96 [01:36<00:28,  1.29s/it]
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Epoch 45 Training:  78%|███████▊  | 75/96 [01:37<00:27,  1.29s/it]
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Epoch 45 Training:  79%|███████▉  | 76/96 [01:38<00:25,  1.29s/it]
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Epoch 45 Training:  80%|████████  | 77/96 [01:40<00:24,  1.29s/it]
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Epoch 45 Training:  81%|████████▏ | 78/96 [01:41<00:23,  1.29s/it]
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Epoch 45 Training:  82%|████████▏ | 79/96 [01:42<00:22,  1.30s/it]
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Epoch 45 Training:  83%|████████▎ | 80/96 [01:44<00:20,  1.30s/it]
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Epoch 45 Training:  84%|████████▍ | 81/96 [01:45<00:19,  1.30s/it]
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Epoch 45 Training:  85%|████████▌ | 82/96 [01:46<00:18,  1.30s/it]
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Epoch 45 Training:  86%|████████▋ | 83/96 [01:47<00:16,  1.30s/it]
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Epoch 45 Training:  88%|████████▊ | 84/96 [01:49<00:15,  1.29s/it]
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Epoch 45 Training:  89%|████████▊ | 85/96 [01:50<00:14,  1.29s/it]
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Epoch 45 Training:  90%|████████▉ | 86/96 [01:51<00:12,  1.29s/it]
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Epoch 45 Training:  91%|█████████ | 87/96 [01:53<00:11,  1.29s/it]
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Epoch 45 Training:  92%|█████████▏| 88/96 [01:54<00:10,  1.29s/it]
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Epoch 45 Training:  93%|█████████▎| 89/96 [01:55<00:09,  1.30s/it]
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Epoch 45 Training:  94%|█████████▍| 90/96 [01:56<00:07,  1.30s/it]
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Epoch 45 Training:  95%|█████████▍| 91/96 [01:58<00:06,  1.30s/it]
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Epoch 45 Training:  96%|█████████▌| 92/96 [01:59<00:05,  1.30s/it]
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Epoch 45 Training:  97%|█████████▋| 93/96 [02:00<00:03,  1.30s/it]
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Epoch 45 Training:  98%|█████████▊| 94/96 [02:02<00:02,  1.30s/it]
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+[2026-10-01 19:40:50] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0046 | Epoch 045] timing/rollout_seconds=147.8462 timing/feedback_seconds=5.3599 timing/optimization_seconds=125.4376 timing/cycle_seconds=276.7848
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+[2026-10-01 19:43:22] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0046 | Epoch 046] train/reward_dists_mean=0.9407 train/reward_dists_std=0.0181 train/reward_psnr_mean=30.6432 train/reward_psnr_std=2.5471 train/reward_ssim_mean=0.9423 train/reward_ssim_std=0.0209 train/reward_dists_group_std_mean=0.0027 train/reward_dists_group_std_max=0.0067 train/reward_dists_group_std_min=0.0013 train/reward_dists_group_mean_std=0.0179 train/reward_psnr_group_std_mean=1.0374 train/reward_psnr_group_std_max=2.6260 train/reward_psnr_group_std_min=0.2492 train/reward_psnr_group_mean_std=2.2604 train/reward_ssim_group_std_mean=0.0015 train/reward_ssim_group_std_max=0.0196 train/reward_ssim_group_std_min=0.0003 train/reward_ssim_group_mean_std=0.0207 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-7.6389 train/adv_max=6.2538 train/adv_abs_mean=1.8469
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Epoch 46 Training:  18%|█▊        | 17/96 [00:22<01:42,  1.30s/it]
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Epoch 46 Training:  19%|█▉        | 18/96 [00:23<01:41,  1.30s/it]
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Epoch 46 Training:  20%|█▉        | 19/96 [00:24<01:40,  1.30s/it]
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Epoch 46 Training:  21%|██        | 20/96 [00:26<01:38,  1.30s/it]
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Epoch 46 Training:  22%|██▏       | 21/96 [00:27<01:43,  1.38s/it]
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Epoch 46 Training:  23%|██▎       | 22/96 [00:29<01:40,  1.35s/it]
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Epoch 46 Training:  24%|██▍       | 23/96 [00:30<01:37,  1.34s/it]
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Epoch 46 Training:  25%|██▌       | 24/96 [00:31<01:35,  1.33s/it]
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Epoch 46 Training:  26%|██▌       | 25/96 [00:32<01:33,  1.32s/it]
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Epoch 46 Training:  27%|██▋       | 26/96 [00:34<01:31,  1.31s/it]
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Epoch 46 Training:  28%|██▊       | 27/96 [00:35<01:30,  1.31s/it]
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Epoch 46 Training:  29%|██▉       | 28/96 [00:36<01:28,  1.31s/it]
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Epoch 46 Training:  30%|███       | 29/96 [00:38<01:27,  1.31s/it]
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Epoch 46 Training:  31%|███▏      | 30/96 [00:39<01:26,  1.31s/it]
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Epoch 46 Training:  32%|███▏      | 31/96 [00:40<01:25,  1.31s/it]
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Epoch 46 Training:  33%|███▎      | 32/96 [00:42<01:23,  1.31s/it]
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Epoch 46 Training:  34%|███▍      | 33/96 [00:43<01:22,  1.31s/it]
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Epoch 46 Training:  35%|███▌      | 34/96 [00:44<01:20,  1.30s/it]
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Epoch 46 Training:  36%|███▋      | 35/96 [00:45<01:19,  1.30s/it]
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Epoch 46 Training:  38%|███▊      | 36/96 [00:47<01:18,  1.30s/it]
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Epoch 46 Training:  39%|███▊      | 37/96 [00:48<01:16,  1.30s/it]
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Epoch 46 Training:  40%|███▉      | 38/96 [00:49<01:15,  1.30s/it]
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Epoch 46 Training:  41%|████      | 39/96 [00:51<01:14,  1.30s/it]
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Epoch 46 Training:  42%|████▏     | 40/96 [00:52<01:12,  1.30s/it]
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Epoch 46 Training:  43%|████▎     | 41/96 [00:53<01:11,  1.30s/it]
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Epoch 46 Training:  44%|████▍     | 42/96 [00:55<01:10,  1.30s/it]
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Epoch 46 Training:  45%|████▍     | 43/96 [00:56<01:08,  1.30s/it]
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Epoch 46 Training:  46%|████▌     | 44/96 [00:57<01:07,  1.30s/it]
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Epoch 46 Training:  47%|████▋     | 45/96 [00:58<01:06,  1.30s/it]
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Epoch 46 Training:  48%|████▊     | 46/96 [01:00<01:04,  1.30s/it]
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Epoch 46 Training:  49%|████▉     | 47/96 [01:01<01:03,  1.30s/it]
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Epoch 46 Training:  50%|█████     | 48/96 [01:02<01:02,  1.30s/it]
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Epoch 46 Training:  51%|█████     | 49/96 [01:04<01:00,  1.30s/it]
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Epoch 46 Training:  52%|█████▏    | 50/96 [01:05<00:59,  1.30s/it]
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Epoch 46 Training:  53%|█████▎    | 51/96 [01:06<00:58,  1.30s/it]
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Epoch 46 Training:  54%|█████▍    | 52/96 [01:08<00:57,  1.30s/it]
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Epoch 46 Training:  55%|█████▌    | 53/96 [01:09<00:58,  1.36s/it]
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Epoch 46 Training:  56%|█████▋    | 54/96 [01:10<00:56,  1.34s/it]
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Epoch 46 Training:  57%|█████▋    | 55/96 [01:12<00:54,  1.33s/it]
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Epoch 46 Training:  58%|█████▊    | 56/96 [01:13<00:52,  1.32s/it]
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Epoch 46 Training:  59%|█████▉    | 57/96 [01:14<00:51,  1.32s/it]
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Epoch 46 Training:  60%|██████    | 58/96 [01:16<00:49,  1.31s/it]
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Epoch 46 Training:  61%|██████▏   | 59/96 [01:17<00:48,  1.31s/it]
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Epoch 46 Training:  62%|██████▎   | 60/96 [01:18<00:46,  1.30s/it]
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Epoch 46 Training:  64%|██████▎   | 61/96 [01:19<00:45,  1.30s/it]
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Epoch 46 Training:  65%|██████▍   | 62/96 [01:21<00:44,  1.30s/it]
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Epoch 46 Training:  66%|██████▌   | 63/96 [01:22<00:42,  1.30s/it]
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Epoch 46 Training:  67%|██████▋   | 64/96 [01:23<00:41,  1.30s/it]
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Epoch 46 Training:  68%|██████▊   | 65/96 [01:25<00:40,  1.30s/it]
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Epoch 46 Training:  69%|██████▉   | 66/96 [01:26<00:39,  1.30s/it]
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Epoch 46 Training:  70%|██████▉   | 67/96 [01:27<00:37,  1.30s/it]
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Epoch 46 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s][2026-10-01 19:45:28] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0046 | Epoch 046] train/policy_loss=11.7887 train/unweighted_policy_loss=2.3577 train/loss=11.7887 train/grad_norm=0.0537
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+[2026-10-01 19:45:28] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0047 | Epoch 046] timing/rollout_seconds=147.7088 timing/feedback_seconds=5.1893 timing/optimization_seconds=126.4189 timing/cycle_seconds=277.6099
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+
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Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:00<00:00,  6.65it/s]
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Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.80it/s]
+[2026-10-01 19:48:00] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0047 | Epoch 047] train/reward_dists_mean=0.9379 train/reward_dists_std=0.0192 train/reward_psnr_mean=30.6422 train/reward_psnr_std=2.0189 train/reward_ssim_mean=0.9409 train/reward_ssim_std=0.0242 train/reward_dists_group_std_mean=0.0027 train/reward_dists_group_std_max=0.0063 train/reward_dists_group_std_min=0.0012 train/reward_dists_group_mean_std=0.0190 train/reward_psnr_group_std_mean=1.1171 train/reward_psnr_group_std_max=2.6813 train/reward_psnr_group_std_min=0.2101 train/reward_psnr_group_mean_std=1.5763 train/reward_ssim_group_std_mean=0.0010 train/reward_ssim_group_std_max=0.0030 train/reward_ssim_group_std_min=0.0003 train/reward_ssim_group_mean_std=0.0241 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-7.3870 train/adv_max=5.2170 train/adv_abs_mean=1.7360
+
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Epoch 47 Training:  44%|████▍     | 42/96 [00:54<01:10,  1.30s/it]
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Epoch 47 Training:  45%|████▍     | 43/96 [00:55<01:08,  1.30s/it]
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Epoch 47 Training:  46%|████▌     | 44/96 [00:57<01:07,  1.30s/it]
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Epoch 47 Training:  47%|████▋     | 45/96 [00:58<01:06,  1.30s/it]
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Epoch 47 Training:  48%|████▊     | 46/96 [00:59<01:05,  1.30s/it]
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Epoch 47 Training:  49%|████▉     | 47/96 [01:01<01:03,  1.30s/it]
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Epoch 47 Training:  50%|█████     | 48/96 [01:02<01:02,  1.30s/it]
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Epoch 47 Training:  51%|█████     | 49/96 [01:03<01:01,  1.30s/it]
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Epoch 47 Training:  52%|█████▏    | 50/96 [01:05<00:59,  1.30s/it]
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Epoch 47 Training:  53%|█████▎    | 51/96 [01:06<00:58,  1.30s/it]
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Epoch 47 Training:  54%|█████▍    | 52/96 [01:07<00:57,  1.30s/it]
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Epoch 47 Training:  55%|█████▌    | 53/96 [01:08<00:55,  1.30s/it]
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Epoch 47 Training:  56%|█████▋    | 54/96 [01:10<00:54,  1.30s/it]
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Epoch 47 Training:  57%|█████▋    | 55/96 [01:11<00:53,  1.30s/it]
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Epoch 47 Training:  58%|█████▊    | 56/96 [01:12<00:52,  1.30s/it]
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Epoch 47 Training:  59%|█████▉    | 57/96 [01:14<00:50,  1.31s/it]
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Epoch 47 Training:  60%|██████    | 58/96 [01:15<00:49,  1.30s/it]
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Epoch 47 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.40it/s]
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Epoch 47 Training:  61%|██████▏   | 59/96 [01:16<00:48,  1.30s/it]
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Epoch 47 Training:  62%|██████▎   | 60/96 [01:18<00:46,  1.30s/it]
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Epoch 47 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.41it/s]
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Epoch 47 Training:  64%|██████▎   | 61/96 [01:19<00:45,  1.30s/it]
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Epoch 47 Training:  65%|██████▍   | 62/96 [01:20<00:44,  1.30s/it]
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Epoch 47 Training:  66%|██████▌   | 63/96 [01:21<00:42,  1.30s/it]
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Epoch 47 Training:  67%|██████▋   | 64/96 [01:23<00:41,  1.30s/it]
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Epoch 47 Training:  68%|██████▊   | 65/96 [01:24<00:40,  1.30s/it]
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Epoch 47 Training:  69%|██████▉   | 66/96 [01:25<00:38,  1.30s/it]
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Epoch 47 Training:  70%|██████▉   | 67/96 [01:27<00:37,  1.30s/it]
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Epoch 47 Training:  71%|███████   | 68/96 [01:28<00:36,  1.30s/it]
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Epoch 47 Training:  72%|███████▏  | 69/96 [01:29<00:35,  1.30s/it]
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Epoch 47 Training:  73%|███████▎  | 70/96 [01:31<00:33,  1.30s/it]
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Epoch 47 Training:  74%|███████▍  | 71/96 [01:32<00:32,  1.30s/it]
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Epoch 47 Training:  75%|███████▌  | 72/96 [01:33<00:31,  1.30s/it]
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Epoch 47 Training:  76%|███████▌  | 73/96 [01:34<00:29,  1.30s/it]
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Epoch 47 Training:  77%|███████▋  | 74/96 [01:36<00:28,  1.30s/it]
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Epoch 47 Training:  78%|███████▊  | 75/96 [01:37<00:27,  1.30s/it]
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Epoch 47 Training:  79%|███████▉  | 76/96 [01:38<00:26,  1.30s/it]
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Epoch 47 Training:  80%|████████  | 77/96 [01:40<00:24,  1.30s/it]
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Epoch 47 Training:  81%|████████▏ | 78/96 [01:41<00:23,  1.30s/it]
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Epoch 47 Training:  82%|████████▏ | 79/96 [01:42<00:22,  1.30s/it]
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Epoch 47 Training:  83%|████████▎ | 80/96 [01:44<00:20,  1.31s/it]
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Epoch 47 Training:  84%|████████▍ | 81/96 [01:45<00:19,  1.30s/it]
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Epoch 47 Training:  85%|████████▌ | 82/96 [01:46<00:18,  1.30s/it]
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Epoch 47 Training:  86%|████████▋ | 83/96 [01:47<00:16,  1.30s/it]
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Epoch 47 Training:  88%|████████▊ | 84/96 [01:49<00:15,  1.30s/it]
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Epoch 47 Training:  89%|████████▊ | 85/96 [01:50<00:14,  1.30s/it]
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Epoch 47 Training:  90%|████████▉ | 86/96 [01:51<00:12,  1.30s/it]
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Epoch 47 Training:  91%|█████████ | 87/96 [01:53<00:11,  1.30s/it]
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Epoch 47 Training:  92%|█████████▏| 88/96 [01:54<00:10,  1.30s/it]
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Epoch 47 Training:  93%|█████████▎| 89/96 [01:55<00:09,  1.30s/it]
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Epoch 47 Training:  94%|█████████▍| 90/96 [01:57<00:07,  1.30s/it]
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Epoch 47 Training:  95%|█████████▍| 91/96 [01:58<00:06,  1.30s/it]
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Epoch 47 Training:  96%|█████████▌| 92/96 [01:59<00:05,  1.30s/it]
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Epoch 47 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s][2026-10-01 19:50:04] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0047 | Epoch 047] train/policy_loss=11.9247 train/unweighted_policy_loss=2.3849 train/loss=11.9247 train/grad_norm=0.0518
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+[2026-10-01 19:50:04] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0048 | Epoch 047] timing/rollout_seconds=147.7398 timing/feedback_seconds=5.2955 timing/optimization_seconds=125.5825 timing/cycle_seconds=276.8316
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+
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+[2026-10-01 19:52:36] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0048 | Epoch 048] train/reward_dists_mean=0.9384 train/reward_dists_std=0.0146 train/reward_psnr_mean=30.3590 train/reward_psnr_std=2.3025 train/reward_ssim_mean=0.9381 train/reward_ssim_std=0.0191 train/reward_dists_group_std_mean=0.0030 train/reward_dists_group_std_max=0.0061 train/reward_dists_group_std_min=0.0014 train/reward_dists_group_mean_std=0.0142 train/reward_psnr_group_std_mean=1.0639 train/reward_psnr_group_std_max=2.2478 train/reward_psnr_group_std_min=0.3638 train/reward_psnr_group_mean_std=2.0008 train/reward_ssim_group_std_mean=0.0010 train/reward_ssim_group_std_max=0.0027 train/reward_ssim_group_std_min=0.0004 train/reward_ssim_group_mean_std=0.0190 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-8.0901 train/adv_max=5.7847 train/adv_abs_mean=1.7827
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Epoch 48 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 48 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.34it/s]
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Epoch 48 Training:  16%|█▌        | 15/96 [00:19<01:45,  1.30s/it]
+
Epoch 48 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 48 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 48 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.35it/s]
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Epoch 48 Training:  17%|█▋        | 16/96 [00:20<01:43,  1.30s/it]
+
Epoch 48 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 48 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 48 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.37it/s]
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Epoch 48 Training:  18%|█▊        | 17/96 [00:21<01:42,  1.30s/it]
+
Epoch 48 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 48 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 48 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.38it/s]
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Epoch 48 Training:  19%|█▉        | 18/96 [00:23<01:41,  1.30s/it]
+
Epoch 48 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 48 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 48 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.37it/s]
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Epoch 48 Training:  20%|█▉        | 19/96 [00:24<01:40,  1.30s/it]
+
Epoch 48 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 48 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 48 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.43it/s]
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Epoch 48 Training:  21%|██        | 20/96 [00:25<01:38,  1.30s/it]
+
Epoch 48 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 48 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 48 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.43it/s]
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Epoch 48 Training:  22%|██▏       | 21/96 [00:27<01:37,  1.30s/it]
+
Epoch 48 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 48 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 48 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.43it/s]
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Epoch 48 Training:  23%|██▎       | 22/96 [00:28<01:36,  1.30s/it]
+
Epoch 48 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 48 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 48 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.42it/s]
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Epoch 48 Training:  24%|██▍       | 23/96 [00:29<01:34,  1.30s/it]
+
Epoch 48 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 48 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 48 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.44it/s]
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Epoch 48 Training:  25%|██▌       | 24/96 [00:31<01:33,  1.30s/it]
+
Epoch 48 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 48 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 48 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.41it/s]
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Epoch 48 Training:  26%|██▌       | 25/96 [00:32<01:32,  1.30s/it]
+
Epoch 48 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 48 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 48 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.42it/s]
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Epoch 48 Training:  27%|██▋       | 26/96 [00:33<01:30,  1.30s/it]
+
Epoch 48 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 48 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 48 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.44it/s]
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Epoch 48 Training:  28%|██▊       | 27/96 [00:34<01:29,  1.30s/it]
+
Epoch 48 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 48 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 48 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.39it/s]
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Epoch 48 Training:  29%|██▉       | 28/96 [00:36<01:28,  1.30s/it]
+
Epoch 48 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 48 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.63it/s]
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Epoch 48 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.37it/s]
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Epoch 48 Training:  30%|███       | 29/96 [00:37<01:26,  1.30s/it]
+
Epoch 48 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 48 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 48 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.38it/s]
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Epoch 48 Training:  31%|███▏      | 30/96 [00:38<01:25,  1.30s/it]
+
Epoch 48 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 48 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 48 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.33it/s]
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Epoch 48 Training:  32%|███▏      | 31/96 [00:40<01:24,  1.30s/it]
+
Epoch 48 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 48 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 48 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.32it/s]
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Epoch 48 Training:  33%|███▎      | 32/96 [00:41<01:23,  1.30s/it]
+
Epoch 48 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 48 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 48 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.29it/s]
+
                                                                
Epoch 48 Training:  34%|███▍      | 33/96 [00:42<01:22,  1.30s/it]
+
Epoch 48 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 48 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 48 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.33it/s]
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Epoch 48 Training:  35%|███▌      | 34/96 [00:44<01:20,  1.30s/it]
+
Epoch 48 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 48 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 48 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.34it/s]
+
                                                                
Epoch 48 Training:  36%|███▋      | 35/96 [00:45<01:19,  1.30s/it]
+
Epoch 48 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 48 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 48 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.37it/s]
+
                                                                
Epoch 48 Training:  38%|███▊      | 36/96 [00:46<01:17,  1.30s/it]
+
Epoch 48 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 48 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 48 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.35it/s]
+
                                                                
Epoch 48 Training:  39%|███▊      | 37/96 [00:47<01:16,  1.30s/it]
+
Epoch 48 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 48 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 48 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.36it/s]
+
                                                                
Epoch 48 Training:  40%|███▉      | 38/96 [00:49<01:15,  1.30s/it]
+
Epoch 48 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 48 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 48 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.36it/s]
+
                                                                
Epoch 48 Training:  41%|████      | 39/96 [00:50<01:14,  1.30s/it]
+
Epoch 48 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 48 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 48 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.39it/s]
+
                                                                
Epoch 48 Training:  42%|████▏     | 40/96 [00:51<01:12,  1.30s/it]
+
Epoch 48 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 48 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 48 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.36it/s]
+
                                                                
Epoch 48 Training:  43%|████▎     | 41/96 [00:53<01:11,  1.30s/it]
+
Epoch 48 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 48 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 48 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.36it/s]
+
                                                                
Epoch 48 Training:  44%|████▍     | 42/96 [00:54<01:10,  1.30s/it]
+
Epoch 48 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 48 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 48 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.37it/s]
+
                                                                
Epoch 48 Training:  45%|████▍     | 43/96 [00:55<01:08,  1.30s/it]
+
Epoch 48 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 48 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 48 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.39it/s]
+
                                                                
Epoch 48 Training:  46%|████▌     | 44/96 [00:57<01:07,  1.30s/it]
+
Epoch 48 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 48 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 48 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.37it/s]
+
                                                                
Epoch 48 Training:  47%|████▋     | 45/96 [00:58<01:06,  1.30s/it]
+
Epoch 48 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 48 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 48 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.39it/s]
+
                                                                
Epoch 48 Training:  48%|████▊     | 46/96 [00:59<01:04,  1.30s/it]
+
Epoch 48 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 48 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 48 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.37it/s]
+
                                                                
Epoch 48 Training:  49%|████▉     | 47/96 [01:00<01:03,  1.30s/it]
+
Epoch 48 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 48 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 48 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.37it/s]
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Epoch 48 Training:  50%|█████     | 48/96 [01:02<01:02,  1.30s/it]
+
Epoch 48 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 48 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 48 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.38it/s]
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Epoch 48 Training:  51%|█████     | 49/96 [01:03<01:00,  1.30s/it]
+
Epoch 48 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 48 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 48 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.39it/s]
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Epoch 48 Training:  52%|█████▏    | 50/96 [01:04<00:59,  1.30s/it]
+
Epoch 48 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 48 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 48 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.38it/s]
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Epoch 48 Training:  53%|█████▎    | 51/96 [01:06<00:58,  1.30s/it]
+
Epoch 48 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 48 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 48 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.38it/s]
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Epoch 48 Training:  54%|█████▍    | 52/96 [01:07<00:57,  1.30s/it]
+
Epoch 48 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 48 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 48 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.39it/s]
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Epoch 48 Training:  55%|█████▌    | 53/96 [01:08<00:55,  1.30s/it]
+
Epoch 48 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 48 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 48 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.39it/s]
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Epoch 48 Training:  56%|█████▋    | 54/96 [01:10<00:54,  1.29s/it]
+
Epoch 48 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 48 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 48 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.38it/s]
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Epoch 48 Training:  57%|█████▋    | 55/96 [01:11<00:53,  1.29s/it]
+
Epoch 48 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 48 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 48 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.41it/s]
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Epoch 48 Training:  58%|█████▊    | 56/96 [01:12<00:51,  1.29s/it]
+
Epoch 48 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 48 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.41it/s]
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Epoch 48 Training:  59%|█████▉    | 57/96 [01:13<00:50,  1.29s/it]
+
Epoch 48 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 48 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.40it/s]
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Epoch 48 Training:  60%|██████    | 58/96 [01:15<00:49,  1.29s/it]
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Epoch 48 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 48 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.41it/s]
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Epoch 48 Training:  61%|██████▏   | 59/96 [01:16<00:47,  1.29s/it]
+
Epoch 48 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 48 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.39it/s]
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Epoch 48 Training:  62%|██████▎   | 60/96 [01:17<00:46,  1.29s/it]
+
Epoch 48 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 48 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.42it/s]
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Epoch 48 Training:  64%|██████▎   | 61/96 [01:19<00:45,  1.29s/it]
+
Epoch 48 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 48 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.38it/s]
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Epoch 48 Training:  65%|██████▍   | 62/96 [01:20<00:44,  1.29s/it]
+
Epoch 48 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 48 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.40it/s]
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Epoch 48 Training:  66%|██████▌   | 63/96 [01:21<00:42,  1.29s/it]
+
Epoch 48 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 48 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.38it/s]
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Epoch 48 Training:  67%|██████▋   | 64/96 [01:22<00:41,  1.29s/it]
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Epoch 48 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 48 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.38it/s]
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Epoch 48 Training:  68%|██████▊   | 65/96 [01:24<00:40,  1.30s/it]
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Epoch 48 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 48 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s][2026-10-01 19:54:41] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0048 | Epoch 048] train/policy_loss=11.5860 train/unweighted_policy_loss=2.3172 train/loss=11.5860 train/grad_norm=0.0525
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+[2026-10-01 19:54:41] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0049 | Epoch 048] timing/rollout_seconds=147.6777 timing/feedback_seconds=5.2794 timing/optimization_seconds=125.1925 timing/cycle_seconds=276.4030
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+
Pointwise Rewards: ssim:   0%|          | 0/6 [00:00<?, ?it/s]
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Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:00<00:00,  6.58it/s]
+
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Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.74it/s]
+
Pointwise Rewards: psnr:   0%|          | 0/6 [00:00<?, ?it/s]
Pointwise Rewards: psnr:  17%|█▋        | 1/6 [00:00<00:00,  6.21it/s]
Pointwise Rewards: psnr:  33%|███▎      | 2/6 [00:00<00:00,  6.50it/s]
Pointwise Rewards: psnr:  50%|█████     | 3/6 [00:00<00:00,  6.74it/s]
Pointwise Rewards: psnr:  67%|██████▋   | 4/6 [00:00<00:00,  6.88it/s]
Pointwise Rewards: psnr:  83%|████████▎ | 5/6 [00:00<00:00,  7.17it/s]
Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.91it/s]
Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.84it/s]
+[2026-10-01 19:57:13] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0049 | Epoch 049] train/reward_dists_mean=0.9396 train/reward_dists_std=0.0113 train/reward_psnr_mean=30.5407 train/reward_psnr_std=2.2150 train/reward_ssim_mean=0.9380 train/reward_ssim_std=0.0190 train/reward_dists_group_std_mean=0.0027 train/reward_dists_group_std_max=0.0053 train/reward_dists_group_std_min=0.0015 train/reward_dists_group_mean_std=0.0110 train/reward_psnr_group_std_mean=1.0365 train/reward_psnr_group_std_max=2.4563 train/reward_psnr_group_std_min=0.2857 train/reward_psnr_group_mean_std=1.8786 train/reward_ssim_group_std_mean=0.0009 train/reward_ssim_group_std_max=0.0020 train/reward_ssim_group_std_min=0.0005 train/reward_ssim_group_mean_std=0.0190 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-6.4892 train/adv_max=5.2113 train/adv_abs_mean=1.8516
+
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Epoch 49 Training:   3%|▎         | 3/96 [00:03<01:59,  1.29s/it]
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Epoch 49 Training:   4%|▍         | 4/96 [00:05<01:58,  1.29s/it]
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Epoch 49 Training:   5%|▌         | 5/96 [00:06<01:57,  1.29s/it]
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Epoch 49 Training:   6%|▋         | 6/96 [00:07<01:56,  1.29s/it]
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Epoch 49 Training:   7%|▋         | 7/96 [00:09<01:54,  1.29s/it]
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Epoch 49 Training:   8%|▊         | 8/96 [00:10<01:53,  1.29s/it]
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Epoch 49 Training:   9%|▉         | 9/96 [00:11<01:52,  1.30s/it]
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Epoch 49 Training:  10%|█         | 10/96 [00:12<01:51,  1.29s/it]
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Epoch 49 Training:  11%|█▏        | 11/96 [00:14<01:50,  1.30s/it]
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Epoch 49 Training:  12%|█▎        | 12/96 [00:15<01:49,  1.30s/it]
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Epoch 49 Training:  14%|█▎        | 13/96 [00:16<01:47,  1.30s/it]
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Epoch 49 Training:  15%|█▍        | 14/96 [00:18<01:46,  1.30s/it]
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Epoch 49 Training:  16%|█▌        | 15/96 [00:19<01:45,  1.30s/it]
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Epoch 49 Training:  17%|█▋        | 16/96 [00:20<01:43,  1.30s/it]
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Epoch 49 Training:  18%|█▊        | 17/96 [00:21<01:42,  1.30s/it]
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Epoch 49 Training:  19%|█▉        | 18/96 [00:23<01:41,  1.30s/it]
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Epoch 49 Training:  20%|█▉        | 19/96 [00:24<01:39,  1.29s/it]
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Epoch 49 Training:  21%|██        | 20/96 [00:25<01:38,  1.29s/it]
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Epoch 49 Training:  22%|██▏       | 21/96 [00:27<01:36,  1.29s/it]
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Epoch 49 Training:  23%|██▎       | 22/96 [00:28<01:35,  1.29s/it]
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Epoch 49 Training:  24%|██▍       | 23/96 [00:29<01:34,  1.29s/it]
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Epoch 49 Training:  25%|██▌       | 24/96 [00:31<01:33,  1.29s/it]
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Epoch 49 Training:  26%|██▌       | 25/96 [00:32<01:31,  1.29s/it]
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Epoch 49 Training:  27%|██▋       | 26/96 [00:33<01:30,  1.29s/it]
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Epoch 49 Training:  28%|██▊       | 27/96 [00:34<01:29,  1.30s/it]
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Epoch 49 Training:  29%|██▉       | 28/96 [00:36<01:28,  1.29s/it]
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Epoch 49 Training:  33%|███▎      | 32/96 [00:41<01:22,  1.30s/it]
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Epoch 49 Training:  41%|████      | 39/96 [00:50<01:13,  1.29s/it]
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Epoch 49 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 49 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 49 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.36it/s]
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Epoch 49 Training:  42%|████▏     | 40/96 [00:51<01:12,  1.30s/it]
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Epoch 49 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 49 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 49 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.38it/s]
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Epoch 49 Training:  43%|████▎     | 41/96 [00:53<01:11,  1.29s/it]
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Epoch 49 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 49 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 49 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.40it/s]
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Epoch 49 Training:  44%|████▍     | 42/96 [00:54<01:09,  1.29s/it]
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Epoch 49 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 49 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 49 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.45it/s]
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Epoch 49 Training:  45%|████▍     | 43/96 [00:55<01:08,  1.29s/it]
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Epoch 49 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 49 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 49 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.43it/s]
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Epoch 49 Training:  46%|████▌     | 44/96 [00:56<01:07,  1.29s/it]
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Epoch 49 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 49 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 49 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.44it/s]
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Epoch 49 Training:  47%|████▋     | 45/96 [00:58<01:05,  1.29s/it]
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Epoch 49 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 49 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 49 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.44it/s]
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Epoch 49 Training:  48%|████▊     | 46/96 [00:59<01:04,  1.29s/it]
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Epoch 49 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 49 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 49 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.43it/s]
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Epoch 49 Training:  49%|████▉     | 47/96 [01:00<01:03,  1.29s/it]
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Epoch 49 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 49 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 49 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.41it/s]
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Epoch 49 Training:  50%|█████     | 48/96 [01:02<01:02,  1.30s/it]
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Epoch 49 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 49 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 49 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.41it/s]
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Epoch 49 Training:  51%|█████     | 49/96 [01:03<01:00,  1.30s/it]
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Epoch 49 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 49 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 49 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.62it/s]
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Epoch 49 Training:  52%|█████▏    | 50/96 [01:05<01:04,  1.40s/it]
+
Epoch 49 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 49 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 49 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.39it/s]
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Epoch 49 Training:  53%|█████▎    | 51/96 [01:06<01:01,  1.37s/it]
+
Epoch 49 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 49 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 49 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.41it/s]
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Epoch 49 Training:  54%|█████▍    | 52/96 [01:07<00:59,  1.35s/it]
+
Epoch 49 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 49 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 49 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.43it/s]
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Epoch 49 Training:  55%|█████▌    | 53/96 [01:08<00:57,  1.33s/it]
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Epoch 49 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 49 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 49 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.43it/s]
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Epoch 49 Training:  56%|█████▋    | 54/96 [01:10<00:55,  1.32s/it]
+
Epoch 49 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 49 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 49 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.40it/s]
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Epoch 49 Training:  57%|█████▋    | 55/96 [01:11<00:53,  1.31s/it]
+
Epoch 49 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 49 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 49 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.40it/s]
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Epoch 49 Training:  58%|█████▊    | 56/96 [01:12<00:52,  1.30s/it]
+
Epoch 49 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 49 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 49 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.33it/s]
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Epoch 49 Training:  59%|█████▉    | 57/96 [01:14<00:50,  1.31s/it]
+
Epoch 49 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 49 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 49 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.30it/s]
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Epoch 49 Training:  60%|██████    | 58/96 [01:15<00:49,  1.31s/it]
+
Epoch 49 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 49 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 49 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.42it/s]
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Epoch 49 Training:  61%|██████▏   | 59/96 [01:16<00:48,  1.31s/it]
+
Epoch 49 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 49 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 49 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.43it/s]
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Epoch 49 Training:  62%|██████▎   | 60/96 [01:18<00:46,  1.30s/it]
+
Epoch 49 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 49 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 49 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.43it/s]
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Epoch 49 Training:  64%|██████▎   | 61/96 [01:19<00:45,  1.30s/it]
+
Epoch 49 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 49 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 49 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.43it/s]
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Epoch 49 Training:  65%|██████▍   | 62/96 [01:20<00:44,  1.30s/it]
+
Epoch 49 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 49 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 49 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.37it/s]
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Epoch 49 Training:  66%|██████▌   | 63/96 [01:21<00:42,  1.30s/it]
+
Epoch 49 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 49 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 49 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.38it/s]
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Epoch 49 Training:  67%|██████▋   | 64/96 [01:23<00:41,  1.30s/it]
+
Epoch 49 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 49 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 49 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.41it/s]
+
                                                                
Epoch 49 Training:  68%|██████▊   | 65/96 [01:24<00:40,  1.30s/it]
+
Epoch 49 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 49 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 49 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.44it/s]
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Epoch 49 Training:  69%|██████▉   | 66/96 [01:25<00:38,  1.30s/it]
+
Epoch 49 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 49 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 49 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.41it/s]
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Epoch 49 Training:  70%|██████▉   | 67/96 [01:27<00:37,  1.30s/it]
+
Epoch 49 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 49 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 49 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.44it/s]
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Epoch 49 Training:  71%|███████   | 68/96 [01:28<00:36,  1.30s/it]
+
Epoch 49 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 49 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 49 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.42it/s]
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Epoch 49 Training:  72%|███████▏  | 69/96 [01:29<00:35,  1.30s/it]
+
Epoch 49 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 49 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 49 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.41it/s]
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Epoch 49 Training:  73%|███████▎  | 70/96 [01:30<00:33,  1.30s/it]
+
Epoch 49 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 49 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 49 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.41it/s]
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Epoch 49 Training:  74%|███████▍  | 71/96 [01:32<00:32,  1.30s/it]
+
Epoch 49 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 49 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 49 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.42it/s]
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Epoch 49 Training:  75%|███████▌  | 72/96 [01:33<00:31,  1.30s/it]
+
Epoch 49 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 49 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 49 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.47it/s]
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Epoch 49 Training:  76%|███████▌  | 73/96 [01:34<00:29,  1.30s/it]
+
Epoch 49 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 49 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.43it/s]
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Epoch 49 Training:  77%|███████▋  | 74/96 [01:36<00:28,  1.29s/it]
+
Epoch 49 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 49 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 49 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.43it/s]
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Epoch 49 Training:  78%|███████▊  | 75/96 [01:37<00:27,  1.29s/it]
+
Epoch 49 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 49 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 49 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.43it/s]
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Epoch 49 Training:  79%|███████▉  | 76/96 [01:38<00:25,  1.29s/it]
+
Epoch 49 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 49 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 49 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.44it/s]
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Epoch 49 Training:  80%|████████  | 77/96 [01:40<00:24,  1.29s/it]
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Epoch 49 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 49 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.44it/s]
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Epoch 49 Training:  81%|████████▏ | 78/96 [01:41<00:23,  1.29s/it]
+
Epoch 49 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 49 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 49 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.40it/s]
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Epoch 49 Training:  82%|████████▏ | 79/96 [01:42<00:22,  1.29s/it]
+
Epoch 49 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 49 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 49 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.43it/s]
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Epoch 49 Training:  83%|████████▎ | 80/96 [01:43<00:20,  1.29s/it]
+
Epoch 49 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 49 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 49 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.42it/s]
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Epoch 49 Training:  84%|████████▍ | 81/96 [01:45<00:19,  1.30s/it]
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Epoch 49 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 49 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.44it/s]
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Epoch 49 Training:  85%|████████▌ | 82/96 [01:46<00:18,  1.29s/it]
+
Epoch 49 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 49 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.42it/s]
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Epoch 49 Training:  86%|████████▋ | 83/96 [01:47<00:16,  1.29s/it]
+
Epoch 49 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 49 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 49 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.40it/s]
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Epoch 49 Training:  88%|████████▊ | 84/96 [01:49<00:15,  1.30s/it]
+
Epoch 49 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 49 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.42it/s]
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Epoch 49 Training:  89%|████████▊ | 85/96 [01:50<00:14,  1.30s/it]
+
Epoch 49 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 49 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 49 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.41it/s]
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Epoch 49 Training:  90%|████████▉ | 86/96 [01:51<00:12,  1.30s/it]
+
Epoch 49 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 49 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 49 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.42it/s]
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Epoch 49 Training:  91%|█████████ | 87/96 [01:53<00:11,  1.30s/it]
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Epoch 49 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 49 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.43it/s]
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Epoch 49 Training:  92%|█████████▏| 88/96 [01:54<00:10,  1.30s/it]
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Epoch 49 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 49 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.44it/s]
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Epoch 49 Training:  93%|█████████▎| 89/96 [01:55<00:09,  1.30s/it]
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Epoch 49 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 49 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 49 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.41it/s]
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+[2026-10-01 19:59:17] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0050 | Epoch 049] timing/rollout_seconds=147.7750 timing/feedback_seconds=5.1625 timing/optimization_seconds=125.2887 timing/cycle_seconds=276.5874
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+[2026-10-01 20:00:01] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0050 | Epoch 050] eval/vedai_rl_full/reward_dists_mean=0.9340 eval/vedai_rl_full/reward_dists_std=0.0178 eval/vedai_rl_full/reward_psnr_mean=29.2285 eval/vedai_rl_full/reward_psnr_std=2.5143 eval/vedai_rl_full/reward_ssim_mean=0.9230 eval/vedai_rl_full/reward_ssim_std=0.0261
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+[2026-10-01 20:02:33] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0050 | Epoch 050] train/reward_dists_mean=0.9365 train/reward_dists_std=0.0139 train/reward_psnr_mean=30.6238 train/reward_psnr_std=2.2143 train/reward_ssim_mean=0.9367 train/reward_ssim_std=0.0182 train/reward_dists_group_std_mean=0.0029 train/reward_dists_group_std_max=0.0061 train/reward_dists_group_std_min=0.0013 train/reward_dists_group_mean_std=0.0135 train/reward_psnr_group_std_mean=1.0334 train/reward_psnr_group_std_max=2.1829 train/reward_psnr_group_std_min=0.2304 train/reward_psnr_group_mean_std=1.8977 train/reward_ssim_group_std_mean=0.0011 train/reward_ssim_group_std_max=0.0058 train/reward_ssim_group_std_min=0.0003 train/reward_ssim_group_mean_std=0.0182 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-9.1353 train/adv_max=5.2412 train/adv_abs_mean=1.7868
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Epoch 50 Training:   7%|▋         | 7/96 [00:08<01:54,  1.29s/it]
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Epoch 50 Training:   8%|▊         | 8/96 [00:10<01:53,  1.29s/it]
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Epoch 50 Training:   9%|▉         | 9/96 [00:11<01:52,  1.29s/it]
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Epoch 50 Training:  10%|█         | 10/96 [00:12<01:50,  1.29s/it]
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Epoch 50 Training:  11%|█▏        | 11/96 [00:14<01:49,  1.29s/it]
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Epoch 50 Training:  12%|█▎        | 12/96 [00:15<01:48,  1.29s/it]
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Epoch 50 Training:  14%|█▎        | 13/96 [00:16<01:46,  1.29s/it]
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Epoch 50 Training:  15%|█▍        | 14/96 [00:17<01:45,  1.29s/it]
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Epoch 50 Training:  16%|█▌        | 15/96 [00:19<01:44,  1.29s/it]
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Epoch 50 Training:  17%|█▋        | 16/96 [00:20<01:43,  1.29s/it]
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Epoch 50 Training:  18%|█▊        | 17/96 [00:21<01:42,  1.29s/it]
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Epoch 50 Training:  19%|█▉        | 18/96 [00:23<01:40,  1.29s/it]
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Epoch 50 Training:  20%|█▉        | 19/96 [00:24<01:39,  1.29s/it]
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Epoch 50 Training:  21%|██        | 20/96 [00:25<01:38,  1.29s/it]
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Epoch 50 Training:  22%|██▏       | 21/96 [00:27<01:36,  1.29s/it]
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Epoch 50 Training:  23%|██▎       | 22/96 [00:28<01:35,  1.29s/it]
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Epoch 50 Training:  24%|██▍       | 23/96 [00:29<01:34,  1.30s/it]
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Epoch 50 Training:  25%|██▌       | 24/96 [00:30<01:32,  1.29s/it]
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Epoch 50 Training:  26%|██▌       | 25/96 [00:32<01:31,  1.29s/it]
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Epoch 50 Training:  27%|██▋       | 26/96 [00:33<01:30,  1.29s/it]
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Epoch 50 Training:  28%|██▊       | 27/96 [00:34<01:29,  1.29s/it]
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Epoch 50 Training:  29%|██▉       | 28/96 [00:36<01:27,  1.29s/it]
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Epoch 50 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.39it/s]
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Epoch 50 Training:  30%|███       | 29/96 [00:37<01:26,  1.29s/it]
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Epoch 50 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.38it/s]
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Epoch 50 Training:  31%|███▏      | 30/96 [00:38<01:25,  1.29s/it]
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Epoch 50 Training:  32%|███▏      | 31/96 [00:39<01:24,  1.29s/it]
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Epoch 50 Training:  33%|███▎      | 32/96 [00:41<01:22,  1.29s/it]
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Epoch 50 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.36it/s]
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Epoch 50 Training:  34%|███▍      | 33/96 [00:42<01:21,  1.29s/it]
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Epoch 50 Training:  35%|███▌      | 34/96 [00:43<01:20,  1.30s/it]
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Epoch 50 Training:  36%|███▋      | 35/96 [00:45<01:18,  1.29s/it]
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Epoch 50 Training:  38%|███▊      | 36/96 [00:46<01:17,  1.29s/it]
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Epoch 50 Training:  39%|███▊      | 37/96 [00:47<01:16,  1.29s/it]
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Epoch 50 Training:  40%|███▉      | 38/96 [00:49<01:14,  1.29s/it]
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Epoch 50 Training:  41%|████      | 39/96 [00:50<01:13,  1.29s/it]
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Epoch 50 Training:  42%|████▏     | 40/96 [00:51<01:12,  1.29s/it]
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Epoch 50 Training:  43%|████▎     | 41/96 [00:52<01:10,  1.29s/it]
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Epoch 50 Training:  44%|████▍     | 42/96 [00:54<01:09,  1.29s/it]
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Epoch 50 Training:  45%|████▍     | 43/96 [00:55<01:08,  1.29s/it]
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Epoch 50 Training:  46%|████▌     | 44/96 [00:56<01:07,  1.29s/it]
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Epoch 50 Training:  47%|████▋     | 45/96 [00:58<01:05,  1.29s/it]
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Epoch 50 Training:  48%|████▊     | 46/96 [00:59<01:04,  1.29s/it]
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Epoch 50 Training:  49%|████▉     | 47/96 [01:00<01:03,  1.29s/it]
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Epoch 50 Training:  50%|█████     | 48/96 [01:01<01:02,  1.29s/it]
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Epoch 50 Training:  51%|█████     | 49/96 [01:03<01:00,  1.29s/it]
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Epoch 50 Training:  52%|█████▏    | 50/96 [01:04<00:59,  1.29s/it]
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Epoch 50 Training:  53%|█████▎    | 51/96 [01:05<00:58,  1.29s/it]
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Epoch 50 Training:  54%|█████▍    | 52/96 [01:07<00:56,  1.29s/it]
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Epoch 50 Training:  55%|█████▌    | 53/96 [01:08<00:55,  1.29s/it]
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Epoch 50 Training:  56%|█████▋    | 54/96 [01:09<00:54,  1.29s/it]
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Epoch 50 Training:  57%|█████▋    | 55/96 [01:10<00:52,  1.29s/it]
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Epoch 50 Training:  58%|█████▊    | 56/96 [01:12<00:51,  1.29s/it]
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Epoch 50 Training:  59%|█████▉    | 57/96 [01:13<00:50,  1.29s/it]
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Epoch 50 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s][2026-10-01 20:04:37] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0050 | Epoch 050] train/policy_loss=11.8852 train/unweighted_policy_loss=2.3770 train/loss=11.8852 train/grad_norm=0.0544
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+[2026-10-01 20:04:37] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0051 | Epoch 050] timing/rollout_seconds=147.6708 timing/feedback_seconds=5.4680 timing/optimization_seconds=124.5550 timing/cycle_seconds=275.7345
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+
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+
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Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.65it/s]
+
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Pointwise Rewards: psnr:  33%|███▎      | 2/6 [00:00<00:00,  6.80it/s]
Pointwise Rewards: psnr:  50%|█████     | 3/6 [00:00<00:00,  6.73it/s]
Pointwise Rewards: psnr:  67%|██████▋   | 4/6 [00:00<00:00,  6.71it/s]
Pointwise Rewards: psnr:  83%|████████▎ | 5/6 [00:00<00:00,  6.63it/s]
Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.68it/s]
Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.68it/s]
+[2026-10-01 20:07:09] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0051 | Epoch 051] train/reward_dists_mean=0.9437 train/reward_dists_std=0.0134 train/reward_psnr_mean=30.4754 train/reward_psnr_std=2.3228 train/reward_ssim_mean=0.9405 train/reward_ssim_std=0.0181 train/reward_dists_group_std_mean=0.0026 train/reward_dists_group_std_max=0.0072 train/reward_dists_group_std_min=0.0012 train/reward_dists_group_mean_std=0.0131 train/reward_psnr_group_std_mean=1.1004 train/reward_psnr_group_std_max=2.7515 train/reward_psnr_group_std_min=0.2290 train/reward_psnr_group_mean_std=1.9918 train/reward_ssim_group_std_mean=0.0010 train/reward_ssim_group_std_max=0.0047 train/reward_ssim_group_std_min=0.0002 train/reward_ssim_group_mean_std=0.0180 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-7.7778 train/adv_max=5.7489 train/adv_abs_mean=1.7790
+
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Epoch 51 Training:   7%|▋         | 7/96 [00:09<01:56,  1.31s/it]
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Epoch 51 Training:  11%|█▏        | 11/96 [00:14<01:51,  1.31s/it]
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Epoch 51 Training:  32%|███▏      | 31/96 [00:40<01:25,  1.31s/it]
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Epoch 51 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 51 Training:  33%|███▎      | 32/96 [00:41<01:24,  1.31s/it]
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Epoch 51 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 51 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.31it/s]
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Epoch 51 Training:  34%|███▍      | 33/96 [00:43<01:22,  1.31s/it]
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Epoch 51 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 51 Training:  35%|███▌      | 34/96 [00:44<01:21,  1.31s/it]
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Epoch 51 Training:  36%|███▋      | 35/96 [00:45<01:20,  1.31s/it]
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Epoch 51 Training:  38%|███▊      | 36/96 [00:47<01:18,  1.31s/it]
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Epoch 51 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 51 Training:  39%|███▊      | 37/96 [00:48<01:17,  1.31s/it]
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Epoch 51 Training:  40%|███▉      | 38/96 [00:49<01:16,  1.31s/it]
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Epoch 51 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 51 Training:  41%|████      | 39/96 [00:51<01:14,  1.31s/it]
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Epoch 51 Training:  42%|████▏     | 40/96 [00:52<01:13,  1.31s/it]
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Epoch 51 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 51 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.29it/s]
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Epoch 51 Training:  43%|████▎     | 41/96 [00:53<01:12,  1.31s/it]
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Epoch 51 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 51 Training:  44%|████▍     | 42/96 [00:55<01:10,  1.31s/it]
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Epoch 51 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 51 Training:  45%|████▍     | 43/96 [00:56<01:09,  1.31s/it]
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Epoch 51 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 51 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 51 Training:  46%|████▌     | 44/96 [00:57<01:08,  1.32s/it]
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Epoch 51 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 51 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 51 Training:  47%|████▋     | 45/96 [00:59<01:07,  1.32s/it]
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Epoch 51 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 51 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 51 Training:  48%|████▊     | 46/96 [01:00<01:05,  1.31s/it]
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Epoch 51 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 51 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 51 Training:  49%|████▉     | 47/96 [01:01<01:04,  1.31s/it]
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Epoch 51 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 51 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.30it/s]
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Epoch 51 Training:  50%|█████     | 48/96 [01:02<01:03,  1.31s/it]
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Epoch 51 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 51 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.29it/s]
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Epoch 51 Training:  51%|█████     | 49/96 [01:04<01:01,  1.31s/it]
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Epoch 51 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 51 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.30it/s]
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Epoch 51 Training:  52%|█████▏    | 50/96 [01:05<01:00,  1.31s/it]
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Epoch 51 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 51 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.30it/s]
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Epoch 51 Training:  53%|█████▎    | 51/96 [01:06<00:59,  1.31s/it]
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Epoch 51 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 51 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 51 Training:  54%|█████▍    | 52/96 [01:08<00:57,  1.31s/it]
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Epoch 51 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 51 Training:  55%|█████▌    | 53/96 [01:09<00:56,  1.31s/it]
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Epoch 51 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 51 Training:  56%|█████▋    | 54/96 [01:10<00:55,  1.31s/it]
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Epoch 51 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 51 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 51 Training:  57%|█████▋    | 55/96 [01:12<00:53,  1.31s/it]
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Epoch 51 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 51 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.32it/s]
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Epoch 51 Training:  58%|█████▊    | 56/96 [01:13<00:52,  1.31s/it]
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Epoch 51 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 51 Training:  59%|█████▉    | 57/96 [01:14<00:51,  1.31s/it]
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Epoch 51 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 51 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.31it/s]
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Epoch 51 Training:  60%|██████    | 58/96 [01:16<00:49,  1.31s/it]
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Epoch 51 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 51 Training:  61%|██████▏   | 59/96 [01:17<00:48,  1.31s/it]
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Epoch 51 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.29it/s]
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Epoch 51 Training:  62%|██████▎   | 60/96 [01:18<00:47,  1.31s/it]
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Epoch 51 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 51 Training:  64%|██████▎   | 61/96 [01:20<00:45,  1.31s/it]
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Epoch 51 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 51 Training:  65%|██████▍   | 62/96 [01:21<00:44,  1.31s/it]
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Epoch 51 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.29it/s]
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Epoch 51 Training:  66%|██████▌   | 63/96 [01:22<00:43,  1.31s/it]
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Epoch 51 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 51 Training:  67%|██████▋   | 64/96 [01:23<00:42,  1.31s/it]
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Epoch 51 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 51 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.31it/s]
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Epoch 51 Training:  68%|██████▊   | 65/96 [01:25<00:40,  1.31s/it]
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Epoch 51 Training:  69%|██████▉   | 66/96 [01:26<00:39,  1.31s/it]
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Epoch 51 Training:  70%|██████▉   | 67/96 [01:27<00:38,  1.31s/it]
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Epoch 51 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 51 Training:  71%|███████   | 68/96 [01:29<00:36,  1.31s/it]
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Epoch 51 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 51 Training:  72%|███████▏  | 69/96 [01:30<00:35,  1.31s/it]
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Epoch 51 Training:  73%|███████▎  | 70/96 [01:31<00:34,  1.31s/it]
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Epoch 51 Training:  74%|███████▍  | 71/96 [01:33<00:32,  1.31s/it]
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Epoch 51 Training:  75%|███████▌  | 72/96 [01:34<00:31,  1.31s/it]
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Epoch 51 Training:  76%|███████▌  | 73/96 [01:35<00:30,  1.31s/it]
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Epoch 51 Training:  77%|███████▋  | 74/96 [01:37<00:28,  1.31s/it]
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Epoch 51 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 51 Training:  78%|███████▊  | 75/96 [01:38<00:27,  1.32s/it]
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Epoch 51 Training:  79%|███████▉  | 76/96 [01:39<00:26,  1.31s/it]
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Epoch 51 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 51 Training:  80%|████████  | 77/96 [01:41<00:24,  1.31s/it]
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Epoch 51 Training:  81%|████████▏ | 78/96 [01:42<00:23,  1.31s/it]
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Epoch 51 Training:  82%|████████▏ | 79/96 [01:43<00:22,  1.31s/it]
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Epoch 51 Training:  83%|████████▎ | 80/96 [01:44<00:21,  1.32s/it]
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Epoch 51 Training:  84%|████████▍ | 81/96 [01:46<00:19,  1.31s/it]
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Epoch 51 Training:  85%|████████▌ | 82/96 [01:47<00:18,  1.32s/it]
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+[2026-10-01 20:09:15] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0052 | Epoch 051] timing/rollout_seconds=147.5614 timing/feedback_seconds=5.4486 timing/optimization_seconds=126.7130 timing/cycle_seconds=277.7589
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+[2026-10-01 20:11:47] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0052 | Epoch 052] train/reward_dists_mean=0.9390 train/reward_dists_std=0.0140 train/reward_psnr_mean=30.7848 train/reward_psnr_std=2.1991 train/reward_ssim_mean=0.9399 train/reward_ssim_std=0.0167 train/reward_dists_group_std_mean=0.0030 train/reward_dists_group_std_max=0.0066 train/reward_dists_group_std_min=0.0014 train/reward_dists_group_mean_std=0.0136 train/reward_psnr_group_std_mean=1.1600 train/reward_psnr_group_std_max=2.3753 train/reward_psnr_group_std_min=0.3983 train/reward_psnr_group_mean_std=1.8159 train/reward_ssim_group_std_mean=0.0010 train/reward_ssim_group_std_max=0.0055 train/reward_ssim_group_std_min=0.0002 train/reward_ssim_group_mean_std=0.0167 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-7.3425 train/adv_max=5.4118 train/adv_abs_mean=1.7544
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Epoch 52 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 52 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 52 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.32it/s]
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Epoch 52 Training:   5%|▌         | 5/96 [00:06<02:00,  1.32s/it]
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Epoch 52 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 52 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.31it/s]
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Epoch 52 Training:   6%|▋         | 6/96 [00:07<01:58,  1.32s/it]
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Epoch 52 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 52 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.35it/s]
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Epoch 52 Training:   7%|▋         | 7/96 [00:09<01:56,  1.31s/it]
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Epoch 52 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 52 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.31it/s]
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Epoch 52 Training:   8%|▊         | 8/96 [00:10<01:55,  1.31s/it]
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Epoch 52 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 52 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.32it/s]
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Epoch 52 Training:   9%|▉         | 9/96 [00:11<01:54,  1.31s/it]
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Epoch 52 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 52 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.31it/s]
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Epoch 52 Training:  10%|█         | 10/96 [00:13<01:52,  1.31s/it]
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Epoch 52 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 52 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.29it/s]
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Epoch 52 Training:  11%|█▏        | 11/96 [00:14<01:51,  1.31s/it]
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Epoch 52 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 52 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 52 Training:  12%|█▎        | 12/96 [00:15<01:50,  1.32s/it]
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Epoch 52 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 52 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.31it/s]
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Epoch 52 Training:  14%|█▎        | 13/96 [00:17<01:49,  1.32s/it]
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Epoch 52 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 52 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.30it/s]
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Epoch 52 Training:  15%|█▍        | 14/96 [00:18<01:47,  1.32s/it]
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Epoch 52 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 52 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.31it/s]
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Epoch 52 Training:  16%|█▌        | 15/96 [00:19<01:46,  1.32s/it]
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Epoch 52 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 52 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.31it/s]
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Epoch 52 Training:  17%|█▋        | 16/96 [00:21<01:45,  1.32s/it]
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Epoch 52 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 52 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.29it/s]
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Epoch 52 Training:  18%|█▊        | 17/96 [00:22<01:44,  1.32s/it]
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Epoch 52 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 52 Training:  19%|█▉        | 18/96 [00:23<01:42,  1.32s/it]
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Epoch 52 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 52 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.33it/s]
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Epoch 52 Training:  20%|█▉        | 19/96 [00:24<01:41,  1.32s/it]
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Epoch 52 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 52 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.32it/s]
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Epoch 52 Training:  21%|██        | 20/96 [00:26<01:40,  1.32s/it]
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Epoch 52 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 52 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.33it/s]
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Epoch 52 Training:  22%|██▏       | 21/96 [00:27<01:38,  1.31s/it]
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Epoch 52 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 52 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.30it/s]
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Epoch 52 Training:  23%|██▎       | 22/96 [00:28<01:37,  1.31s/it]
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Epoch 52 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 52 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 52 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.30it/s]
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Epoch 52 Training:  24%|██▍       | 23/96 [00:30<01:36,  1.32s/it]
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Epoch 52 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 52 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.30it/s]
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Epoch 52 Training:  25%|██▌       | 24/96 [00:31<01:34,  1.32s/it]
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Epoch 52 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 52 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.31it/s]
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Epoch 52 Training:  26%|██▌       | 25/96 [00:32<01:33,  1.31s/it]
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Epoch 52 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 52 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.32it/s]
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Epoch 52 Training:  27%|██▋       | 26/96 [00:34<01:31,  1.31s/it]
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Epoch 52 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 52 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.32it/s]
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Epoch 52 Training:  28%|██▊       | 27/96 [00:35<01:30,  1.31s/it]
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Epoch 52 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 52 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.31it/s]
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Epoch 52 Training:  29%|██▉       | 28/96 [00:36<01:29,  1.31s/it]
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Epoch 52 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 52 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.33it/s]
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Epoch 52 Training:  30%|███       | 29/96 [00:38<01:27,  1.31s/it]
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Epoch 52 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 52 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 52 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.30it/s]
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Epoch 52 Training:  31%|███▏      | 30/96 [00:39<01:26,  1.31s/it]
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Epoch 52 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 52 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 52 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.30it/s]
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Epoch 52 Training:  32%|███▏      | 31/96 [00:40<01:25,  1.32s/it]
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Epoch 52 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 52 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 52 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.30it/s]
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Epoch 52 Training:  33%|███▎      | 32/96 [00:42<01:24,  1.32s/it]
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Epoch 52 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 52 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 52 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.31it/s]
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Epoch 52 Training:  34%|███▍      | 33/96 [00:43<01:22,  1.31s/it]
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Epoch 52 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 52 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.31it/s]
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Epoch 52 Training:  35%|███▌      | 34/96 [00:44<01:21,  1.32s/it]
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Epoch 52 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 52 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.33it/s]
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Epoch 52 Training:  36%|███▋      | 35/96 [00:45<01:20,  1.31s/it]
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Epoch 52 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 52 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.32it/s]
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Epoch 52 Training:  38%|███▊      | 36/96 [00:47<01:18,  1.31s/it]
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Epoch 52 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 52 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.32it/s]
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Epoch 52 Training:  39%|███▊      | 37/96 [00:48<01:17,  1.31s/it]
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Epoch 52 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 52 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.29it/s]
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Epoch 52 Training:  40%|███▉      | 38/96 [00:49<01:16,  1.31s/it]
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Epoch 52 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 52 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.29it/s]
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Epoch 52 Training:  41%|████      | 39/96 [00:51<01:14,  1.31s/it]
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Epoch 52 Training:  42%|████▏     | 40/96 [00:52<01:13,  1.32s/it]
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Epoch 52 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 52 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.35it/s]
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Epoch 52 Training:  43%|████▎     | 41/96 [00:53<01:12,  1.32s/it]
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Epoch 52 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.33it/s]
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Epoch 52 Training:  44%|████▍     | 42/96 [00:55<01:11,  1.31s/it]
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Epoch 52 Training:  45%|████▍     | 43/96 [00:56<01:09,  1.31s/it]
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Epoch 52 Training:  46%|████▌     | 44/96 [00:57<01:08,  1.32s/it]
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Epoch 52 Training:  47%|████▋     | 45/96 [00:59<01:07,  1.32s/it]
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Epoch 52 Training:  48%|████▊     | 46/96 [01:00<01:05,  1.32s/it]
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Epoch 52 Training:  49%|████▉     | 47/96 [01:01<01:04,  1.32s/it]
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Epoch 52 Training:  50%|█████     | 48/96 [01:03<01:03,  1.32s/it]
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Epoch 52 Training:  51%|█████     | 49/96 [01:04<01:01,  1.32s/it]
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Epoch 52 Training:  52%|█████▏    | 50/96 [01:05<01:00,  1.32s/it]
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Epoch 52 Training:  53%|█████▎    | 51/96 [01:07<00:59,  1.32s/it]
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Epoch 52 Training:  54%|█████▍    | 52/96 [01:08<00:57,  1.32s/it]
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Epoch 52 Training:  55%|█████▌    | 53/96 [01:09<00:56,  1.31s/it]
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Epoch 52 Training:  56%|█████▋    | 54/96 [01:10<00:55,  1.31s/it]
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Epoch 52 Training:  57%|█████▋    | 55/96 [01:12<00:56,  1.37s/it]
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Epoch 52 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s][2026-10-01 20:13:53] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0052 | Epoch 052] train/policy_loss=11.9274 train/unweighted_policy_loss=2.3855 train/loss=11.9274 train/grad_norm=0.0554
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+[2026-10-01 20:13:53] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0053 | Epoch 052] timing/rollout_seconds=147.8050 timing/feedback_seconds=5.2955 timing/optimization_seconds=127.0550 timing/cycle_seconds=278.3693
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+
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+
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+
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+[2026-10-01 20:16:25] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0053 | Epoch 053] train/reward_dists_mean=0.9396 train/reward_dists_std=0.0135 train/reward_psnr_mean=30.7076 train/reward_psnr_std=2.0653 train/reward_ssim_mean=0.9390 train/reward_ssim_std=0.0205 train/reward_dists_group_std_mean=0.0028 train/reward_dists_group_std_max=0.0060 train/reward_dists_group_std_min=0.0014 train/reward_dists_group_mean_std=0.0132 train/reward_psnr_group_std_mean=1.1896 train/reward_psnr_group_std_max=2.3245 train/reward_psnr_group_std_min=0.2659 train/reward_psnr_group_mean_std=1.6060 train/reward_ssim_group_std_mean=0.0009 train/reward_ssim_group_std_max=0.0021 train/reward_ssim_group_std_min=0.0004 train/reward_ssim_group_mean_std=0.0204 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-8.9866 train/adv_max=6.0380 train/adv_abs_mean=1.8215
+
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Epoch 53 Training:  30%|███       | 29/96 [00:38<01:27,  1.31s/it]
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Epoch 53 Training:  31%|███▏      | 30/96 [00:39<01:26,  1.31s/it]
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Epoch 53 Training:  32%|███▏      | 31/96 [00:40<01:25,  1.32s/it]
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Epoch 53 Training:  33%|███▎      | 32/96 [00:42<01:23,  1.31s/it]
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Epoch 53 Training:  34%|███▍      | 33/96 [00:43<01:22,  1.32s/it]
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Epoch 53 Training:  35%|███▌      | 34/96 [00:44<01:21,  1.31s/it]
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Epoch 53 Training:  36%|███▋      | 35/96 [00:45<01:20,  1.31s/it]
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Epoch 53 Training:  38%|███▊      | 36/96 [00:47<01:18,  1.31s/it]
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Epoch 53 Training:  39%|███▊      | 37/96 [00:48<01:17,  1.31s/it]
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Epoch 53 Training:  40%|███▉      | 38/96 [00:49<01:16,  1.31s/it]
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Epoch 53 Training:  41%|████      | 39/96 [00:51<01:14,  1.31s/it]
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Epoch 53 Training:  42%|████▏     | 40/96 [00:52<01:13,  1.31s/it]
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Epoch 53 Training:  43%|████▎     | 41/96 [00:53<01:12,  1.31s/it]
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Epoch 53 Training:  44%|████▍     | 42/96 [00:55<01:10,  1.31s/it]
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Epoch 53 Training:  45%|████▍     | 43/96 [00:56<01:09,  1.31s/it]
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Epoch 53 Training:  46%|████▌     | 44/96 [00:57<01:08,  1.31s/it]
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Epoch 53 Training:  47%|████▋     | 45/96 [00:59<01:06,  1.31s/it]
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Epoch 53 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.39it/s]
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Epoch 53 Training:  48%|████▊     | 46/96 [01:00<01:05,  1.31s/it]
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Epoch 53 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.37it/s]
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Epoch 53 Training:  49%|████▉     | 47/96 [01:01<01:04,  1.31s/it]
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Epoch 53 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.33it/s]
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Epoch 53 Training:  50%|█████     | 48/96 [01:03<01:02,  1.31s/it]
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Epoch 53 Training:  51%|█████     | 49/96 [01:04<01:01,  1.31s/it]
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Epoch 53 Training:  52%|█████▏    | 50/96 [01:05<01:00,  1.31s/it]
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Epoch 53 Training:  53%|█████▎    | 51/96 [01:06<00:58,  1.31s/it]
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Epoch 53 Training:  54%|█████▍    | 52/96 [01:08<00:57,  1.31s/it]
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Epoch 53 Training:  55%|█████▌    | 53/96 [01:09<00:56,  1.31s/it]
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Epoch 53 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.35it/s]
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Epoch 53 Training:  56%|█████▋    | 54/96 [01:10<00:55,  1.31s/it]
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Epoch 53 Training:  57%|█████▋    | 55/96 [01:12<00:53,  1.31s/it]
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Epoch 53 Training:  58%|█████▊    | 56/96 [01:13<00:52,  1.31s/it]
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Epoch 53 Training:  59%|█████▉    | 57/96 [01:14<00:51,  1.31s/it]
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Epoch 53 Training:  60%|██████    | 58/96 [01:16<00:49,  1.31s/it]
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Epoch 53 Training:  61%|██████▏   | 59/96 [01:17<00:48,  1.31s/it]
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Epoch 53 Training:  62%|██████▎   | 60/96 [01:18<00:47,  1.31s/it]
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Epoch 53 Training:  64%|██████▎   | 61/96 [01:20<00:45,  1.31s/it]
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Epoch 53 Training:  65%|██████▍   | 62/96 [01:21<00:44,  1.32s/it]
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Epoch 53 Training:  66%|██████▌   | 63/96 [01:22<00:43,  1.31s/it]
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Epoch 53 Training:  67%|██████▋   | 64/96 [01:23<00:42,  1.31s/it]
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Epoch 53 Training:  68%|██████▊   | 65/96 [01:25<00:40,  1.31s/it]
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Epoch 53 Training:  69%|██████▉   | 66/96 [01:26<00:39,  1.31s/it]
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Epoch 53 Training:  70%|██████▉   | 67/96 [01:27<00:38,  1.31s/it]
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Epoch 53 Training:  71%|███████   | 68/96 [01:29<00:36,  1.31s/it]
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Epoch 53 Training:  72%|███████▏  | 69/96 [01:30<00:35,  1.31s/it]
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Epoch 53 Training:  73%|███████▎  | 70/96 [01:31<00:34,  1.31s/it]
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Epoch 53 Training:  74%|███████▍  | 71/96 [01:33<00:32,  1.31s/it]
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Epoch 53 Training:  75%|███████▌  | 72/96 [01:34<00:31,  1.31s/it]
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Epoch 53 Training:  76%|███████▌  | 73/96 [01:35<00:30,  1.31s/it]
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Epoch 53 Training:  77%|███████▋  | 74/96 [01:37<00:28,  1.31s/it]
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Epoch 53 Training:  78%|███████▊  | 75/96 [01:38<00:27,  1.31s/it]
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Epoch 53 Training:  79%|███████▉  | 76/96 [01:39<00:26,  1.31s/it]
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Epoch 53 Training:  80%|████████  | 77/96 [01:41<00:24,  1.31s/it]
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Epoch 53 Training:  81%|████████▏ | 78/96 [01:42<00:23,  1.31s/it]
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Epoch 53 Training:  82%|████████▏ | 79/96 [01:43<00:22,  1.31s/it]
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Epoch 53 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s][2026-10-01 20:18:31] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0053 | Epoch 053] train/policy_loss=11.4822 train/unweighted_policy_loss=2.2964 train/loss=11.4822 train/grad_norm=0.0773
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+[2026-10-01 20:18:31] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0054 | Epoch 053] timing/rollout_seconds=147.7754 timing/feedback_seconds=5.1600 timing/optimization_seconds=126.6660 timing/cycle_seconds=277.9336
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+
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+[2026-10-01 20:21:03] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0054 | Epoch 054] train/reward_dists_mean=0.9393 train/reward_dists_std=0.0204 train/reward_psnr_mean=30.9152 train/reward_psnr_std=2.1217 train/reward_ssim_mean=0.9407 train/reward_ssim_std=0.0198 train/reward_dists_group_std_mean=0.0027 train/reward_dists_group_std_max=0.0052 train/reward_dists_group_std_min=0.0014 train/reward_dists_group_mean_std=0.0201 train/reward_psnr_group_std_mean=1.0528 train/reward_psnr_group_std_max=2.2902 train/reward_psnr_group_std_min=0.2220 train/reward_psnr_group_mean_std=1.7815 train/reward_ssim_group_std_mean=0.0011 train/reward_ssim_group_std_max=0.0096 train/reward_ssim_group_std_min=0.0004 train/reward_ssim_group_mean_std=0.0197 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-9.2024 train/adv_max=5.1732 train/adv_abs_mean=1.7932
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Epoch 54 Training:   2%|▏         | 2/96 [00:02<02:19,  1.48s/it]
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Epoch 54 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 54 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.34it/s]
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Epoch 54 Training:   3%|▎         | 3/96 [00:04<02:10,  1.40s/it]
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Epoch 54 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 54 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.35it/s]
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Epoch 54 Training:   4%|▍         | 4/96 [00:05<02:05,  1.37s/it]
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Epoch 54 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 54 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.35it/s]
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Epoch 54 Training:   5%|▌         | 5/96 [00:06<02:02,  1.35s/it]
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Epoch 54 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 54 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.35it/s]
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Epoch 54 Training:   6%|▋         | 6/96 [00:08<02:00,  1.34s/it]
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Epoch 54 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 54 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.35it/s]
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Epoch 54 Training:   7%|▋         | 7/96 [00:09<01:58,  1.33s/it]
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Epoch 54 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 54 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.37it/s]
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Epoch 54 Training:   8%|▊         | 8/96 [00:10<01:56,  1.33s/it]
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Epoch 54 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 54 Training:   9%|▉         | 9/96 [00:12<01:55,  1.32s/it]
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Epoch 54 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 54 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.37it/s]
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Epoch 54 Training:  10%|█         | 10/96 [00:13<01:53,  1.32s/it]
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Epoch 54 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.37it/s]
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Epoch 54 Training:  11%|█▏        | 11/96 [00:14<01:52,  1.32s/it]
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Epoch 54 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 54 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.29it/s]
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Epoch 54 Training:  12%|█▎        | 12/96 [00:16<01:50,  1.32s/it]
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Epoch 54 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 54 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.37it/s]
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Epoch 54 Training:  14%|█▎        | 13/96 [00:17<01:49,  1.32s/it]
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Epoch 54 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 54 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.34it/s]
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Epoch 54 Training:  15%|█▍        | 14/96 [00:18<01:48,  1.32s/it]
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Epoch 54 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 54 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.34it/s]
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Epoch 54 Training:  16%|█▌        | 15/96 [00:19<01:46,  1.32s/it]
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Epoch 54 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 54 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.33it/s]
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Epoch 54 Training:  17%|█▋        | 16/96 [00:21<01:45,  1.32s/it]
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Epoch 54 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 54 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.32it/s]
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Epoch 54 Training:  18%|█▊        | 17/96 [00:22<01:44,  1.32s/it]
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Epoch 54 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 54 Timestep: 100%|██████████| 2/2 [00:01<00:00,  1.77it/s]
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Epoch 54 Training:  19%|█▉        | 18/96 [00:24<01:48,  1.39s/it]
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Epoch 54 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 54 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.33it/s]
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Epoch 54 Training:  20%|█▉        | 19/96 [00:25<01:45,  1.37s/it]
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Epoch 54 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 54 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.34it/s]
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Epoch 54 Training:  21%|██        | 20/96 [00:26<01:42,  1.35s/it]
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Epoch 54 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 54 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.38it/s]
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Epoch 54 Training:  22%|██▏       | 21/96 [00:28<01:40,  1.34s/it]
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Epoch 54 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 54 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.36it/s]
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Epoch 54 Training:  23%|██▎       | 22/96 [00:29<01:38,  1.33s/it]
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Epoch 54 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 54 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.35it/s]
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Epoch 54 Training:  24%|██▍       | 23/96 [00:30<01:37,  1.33s/it]
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Epoch 54 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 54 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.38it/s]
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Epoch 54 Training:  25%|██▌       | 24/96 [00:32<01:35,  1.32s/it]
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Epoch 54 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 54 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.35it/s]
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Epoch 54 Training:  26%|██▌       | 25/96 [00:33<01:33,  1.32s/it]
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Epoch 54 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 54 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.34it/s]
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Epoch 54 Training:  27%|██▋       | 26/96 [00:34<01:32,  1.32s/it]
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Epoch 54 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 54 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.33it/s]
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Epoch 54 Training:  28%|██▊       | 27/96 [00:36<01:31,  1.32s/it]
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Epoch 54 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 54 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.35it/s]
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Epoch 54 Training:  29%|██▉       | 28/96 [00:37<01:29,  1.32s/it]
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Epoch 54 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 54 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.37it/s]
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Epoch 54 Training:  30%|███       | 29/96 [00:38<01:28,  1.32s/it]
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Epoch 54 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 54 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.38it/s]
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Epoch 54 Training:  31%|███▏      | 30/96 [00:39<01:26,  1.32s/it]
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Epoch 54 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 54 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.35it/s]
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Epoch 54 Training:  32%|███▏      | 31/96 [00:41<01:25,  1.32s/it]
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Epoch 54 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 54 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.35it/s]
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Epoch 54 Training:  33%|███▎      | 32/96 [00:42<01:24,  1.32s/it]
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Epoch 54 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 54 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.33it/s]
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Epoch 54 Training:  34%|███▍      | 33/96 [00:43<01:22,  1.32s/it]
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Epoch 54 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 54 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.29it/s]
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Epoch 54 Training:  35%|███▌      | 34/96 [00:45<01:21,  1.32s/it]
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Epoch 54 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 54 Training:  36%|███▋      | 35/96 [00:46<01:20,  1.32s/it]
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Epoch 54 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 54 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.33it/s]
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Epoch 54 Training:  38%|███▊      | 36/96 [00:47<01:19,  1.32s/it]
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Epoch 54 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 54 Training:  39%|███▊      | 37/96 [00:49<01:17,  1.32s/it]
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Epoch 54 Training:  40%|███▉      | 38/96 [00:50<01:20,  1.39s/it]
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Epoch 54 Training:  41%|████      | 39/96 [00:52<01:17,  1.37s/it]
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Epoch 54 Training:  42%|████▏     | 40/96 [00:53<01:15,  1.35s/it]
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Epoch 54 Training:  43%|████▎     | 41/96 [00:54<01:13,  1.34s/it]
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Epoch 54 Training:  44%|████▍     | 42/96 [00:56<01:12,  1.34s/it]
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Epoch 54 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 54 Training:  45%|████▍     | 43/96 [00:57<01:10,  1.33s/it]
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Epoch 54 Training:  46%|████▌     | 44/96 [00:58<01:08,  1.33s/it]
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Epoch 54 Training:  47%|████▋     | 45/96 [00:59<01:07,  1.32s/it]
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Epoch 54 Training:  48%|████▊     | 46/96 [01:01<01:05,  1.32s/it]
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Epoch 54 Training:  49%|████▉     | 47/96 [01:02<01:04,  1.32s/it]
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Epoch 54 Training:  50%|█████     | 48/96 [01:03<01:03,  1.32s/it]
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Epoch 54 Training:  51%|█████     | 49/96 [01:05<01:01,  1.32s/it]
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Epoch 54 Training:  52%|█████▏    | 50/96 [01:06<01:00,  1.32s/it]
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Epoch 54 Training:  53%|█████▎    | 51/96 [01:07<00:59,  1.32s/it]
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Epoch 54 Training:  54%|█████▍    | 52/96 [01:09<00:57,  1.32s/it]
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Epoch 54 Training:  55%|█████▌    | 53/96 [01:10<00:56,  1.32s/it]
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Epoch 54 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s][2026-10-01 20:23:10] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0054 | Epoch 054] train/policy_loss=11.9121 train/unweighted_policy_loss=2.3824 train/loss=11.9121 train/grad_norm=0.0634
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+[2026-10-01 20:23:10] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0055 | Epoch 054] timing/rollout_seconds=147.6544 timing/feedback_seconds=5.2000 timing/optimization_seconds=128.1444 timing/cycle_seconds=279.2857
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+
Pointwise Rewards: ssim:   0%|          | 0/6 [00:00<?, ?it/s]
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Pointwise Rewards: ssim:  83%|████████▎ | 5/6 [00:00<00:00,  6.16it/s]
Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:00<00:00,  6.15it/s]
Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:00<00:00,  6.19it/s]
+
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Pointwise Rewards: dists:  83%|████████▎ | 5/6 [00:01<00:00,  3.56it/s]
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+
Pointwise Rewards: psnr:   0%|          | 0/6 [00:00<?, ?it/s]
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Pointwise Rewards: psnr:  33%|███▎      | 2/6 [00:00<00:00,  6.46it/s]
Pointwise Rewards: psnr:  50%|█████     | 3/6 [00:00<00:00,  6.59it/s]
Pointwise Rewards: psnr:  67%|██████▋   | 4/6 [00:00<00:00,  6.51it/s]
Pointwise Rewards: psnr:  83%|████████▎ | 5/6 [00:00<00:00,  6.48it/s]
Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.49it/s]
Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.47it/s]
+[2026-10-01 20:25:42] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0055 | Epoch 055] train/reward_dists_mean=0.9392 train/reward_dists_std=0.0095 train/reward_psnr_mean=30.1143 train/reward_psnr_std=2.1954 train/reward_ssim_mean=0.9365 train/reward_ssim_std=0.0175 train/reward_dists_group_std_mean=0.0027 train/reward_dists_group_std_max=0.0051 train/reward_dists_group_std_min=0.0015 train/reward_dists_group_mean_std=0.0091 train/reward_psnr_group_std_mean=1.0742 train/reward_psnr_group_std_max=2.2137 train/reward_psnr_group_std_min=0.2263 train/reward_psnr_group_mean_std=1.8643 train/reward_ssim_group_std_mean=0.0009 train/reward_ssim_group_std_max=0.0030 train/reward_ssim_group_std_min=0.0004 train/reward_ssim_group_mean_std=0.0175 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-9.1662 train/adv_max=5.5167 train/adv_abs_mean=1.7756
+
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Epoch 55 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.31it/s]
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Epoch 55 Training:  28%|██▊       | 27/96 [00:36<01:32,  1.34s/it]
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Epoch 55 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 55 Training:  29%|██▉       | 28/96 [00:37<01:31,  1.34s/it]
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Epoch 55 Training:  30%|███       | 29/96 [00:38<01:29,  1.34s/it]
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Epoch 55 Training:  31%|███▏      | 30/96 [00:40<01:28,  1.34s/it]
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Epoch 55 Training:  32%|███▏      | 31/96 [00:41<01:27,  1.34s/it]
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Epoch 55 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 55 Training:  33%|███▎      | 32/96 [00:42<01:25,  1.34s/it]
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Epoch 55 Training:  34%|███▍      | 33/96 [00:44<01:24,  1.34s/it]
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Epoch 55 Training:  35%|███▌      | 34/96 [00:45<01:23,  1.34s/it]
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Epoch 55 Training:  36%|███▋      | 35/96 [00:46<01:21,  1.34s/it]
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Epoch 55 Training:  38%|███▊      | 36/96 [00:48<01:20,  1.34s/it]
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Epoch 55 Training:  39%|███▊      | 37/96 [00:49<01:19,  1.34s/it]
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Epoch 55 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 55 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 55 Training:  40%|███▉      | 38/96 [00:50<01:17,  1.34s/it]
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Epoch 55 Training:  41%|████      | 39/96 [00:52<01:16,  1.34s/it]
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Epoch 55 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 55 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 55 Training:  42%|████▏     | 40/96 [00:53<01:15,  1.34s/it]
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Epoch 55 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 55 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.31it/s]
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Epoch 55 Training:  43%|████▎     | 41/96 [00:54<01:13,  1.34s/it]
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Epoch 55 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 55 Training:  44%|████▍     | 42/96 [00:56<01:12,  1.34s/it]
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Epoch 55 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 55 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 55 Training:  45%|████▍     | 43/96 [00:57<01:10,  1.34s/it]
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Epoch 55 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 55 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.29it/s]
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Epoch 55 Training:  46%|████▌     | 44/96 [00:58<01:09,  1.34s/it]
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Epoch 55 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 55 Training:  47%|████▋     | 45/96 [01:00<01:08,  1.34s/it]
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Epoch 55 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 55 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 55 Training:  48%|████▊     | 46/96 [01:01<01:06,  1.34s/it]
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Epoch 55 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 55 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.34it/s]
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Epoch 55 Training:  49%|████▉     | 47/96 [01:02<01:05,  1.34s/it]
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Epoch 55 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 55 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 55 Training:  50%|█████     | 48/96 [01:04<01:04,  1.34s/it]
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Epoch 55 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 55 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 55 Training:  51%|█████     | 49/96 [01:05<01:03,  1.34s/it]
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Epoch 55 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 55 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 55 Training:  52%|█████▏    | 50/96 [01:06<01:01,  1.35s/it]
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Epoch 55 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 55 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.30it/s]
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Epoch 55 Training:  53%|█████▎    | 51/96 [01:08<01:00,  1.34s/it]
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Epoch 55 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 55 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.20it/s]
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Epoch 55 Training:  54%|█████▍    | 52/96 [01:09<00:59,  1.35s/it]
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Epoch 55 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 55 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 55 Training:  55%|█████▌    | 53/96 [01:10<00:58,  1.35s/it]
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Epoch 55 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 55 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 55 Training:  56%|█████▋    | 54/96 [01:12<00:56,  1.35s/it]
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Epoch 55 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 55 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.29it/s]
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Epoch 55 Training:  57%|█████▋    | 55/96 [01:13<00:55,  1.34s/it]
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Epoch 55 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 55 Training:  58%|█████▊    | 56/96 [01:14<00:53,  1.34s/it]
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Epoch 55 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 55 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 55 Training:  59%|█████▉    | 57/96 [01:16<00:52,  1.34s/it]
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Epoch 55 Training:  60%|██████    | 58/96 [01:17<00:50,  1.34s/it]
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Epoch 55 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.31it/s]
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Epoch 55 Training:  61%|██████▏   | 59/96 [01:18<00:49,  1.34s/it]
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Epoch 55 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 55 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 55 Training:  62%|██████▎   | 60/96 [01:20<00:48,  1.34s/it]
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Epoch 55 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 55 Training:  64%|██████▎   | 61/96 [01:21<00:46,  1.34s/it]
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Epoch 55 Training:  65%|██████▍   | 62/96 [01:22<00:45,  1.34s/it]
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Epoch 55 Training:  66%|██████▌   | 63/96 [01:24<00:44,  1.34s/it]
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Epoch 55 Training:  67%|██████▋   | 64/96 [01:25<00:42,  1.34s/it]
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Epoch 55 Training:  68%|██████▊   | 65/96 [01:26<00:41,  1.34s/it]
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Epoch 55 Training:  69%|██████▉   | 66/96 [01:28<00:40,  1.34s/it]
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Epoch 55 Training:  70%|██████▉   | 67/96 [01:29<00:38,  1.34s/it]
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Epoch 55 Training:  71%|███████   | 68/96 [01:30<00:37,  1.34s/it]
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Epoch 55 Training:  72%|███████▏  | 69/96 [01:32<00:36,  1.34s/it]
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Epoch 55 Training:  73%|███████▎  | 70/96 [01:33<00:34,  1.34s/it]
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Epoch 55 Training:  74%|███████▍  | 71/96 [01:34<00:33,  1.34s/it]
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Epoch 55 Training:  75%|███████▌  | 72/96 [01:36<00:32,  1.34s/it]
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Epoch 55 Training:  76%|███████▌  | 73/96 [01:37<00:30,  1.34s/it]
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Epoch 55 Training:  77%|███████▋  | 74/96 [01:39<00:29,  1.34s/it]
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Epoch 55 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 55 Training:  78%|███████▊  | 75/96 [01:40<00:28,  1.34s/it]
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Epoch 55 Training:  79%|███████▉  | 76/96 [01:41<00:26,  1.34s/it]
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Epoch 55 Training:  80%|████████  | 77/96 [01:43<00:25,  1.34s/it]
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Epoch 55 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s][2026-10-01 20:27:51] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0055 | Epoch 055] train/policy_loss=11.4286 train/unweighted_policy_loss=2.2857 train/loss=11.4286 train/grad_norm=0.0931
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+[2026-10-01 20:27:51] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0056 | Epoch 055] timing/rollout_seconds=147.7571 timing/feedback_seconds=5.3192 timing/optimization_seconds=129.0729 timing/cycle_seconds=280.3389
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Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.74it/s]
+[2026-10-01 20:30:23] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0056 | Epoch 056] train/reward_dists_mean=0.9404 train/reward_dists_std=0.0155 train/reward_psnr_mean=30.4356 train/reward_psnr_std=2.0765 train/reward_ssim_mean=0.9363 train/reward_ssim_std=0.0197 train/reward_dists_group_std_mean=0.0027 train/reward_dists_group_std_max=0.0048 train/reward_dists_group_std_min=0.0012 train/reward_dists_group_mean_std=0.0152 train/reward_psnr_group_std_mean=1.1887 train/reward_psnr_group_std_max=3.1493 train/reward_psnr_group_std_min=0.4392 train/reward_psnr_group_mean_std=1.6047 train/reward_ssim_group_std_mean=0.0011 train/reward_ssim_group_std_max=0.0057 train/reward_ssim_group_std_min=0.0003 train/reward_ssim_group_mean_std=0.0197 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-8.0787 train/adv_max=6.5255 train/adv_abs_mean=1.8787
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Epoch 56 Training:  99%|█████████▉| 95/96 [02:03<00:01,  1.30s/it]
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Epoch 56 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s][2026-10-01 20:32:28] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0056 | Epoch 056] train/policy_loss=11.3416 train/unweighted_policy_loss=2.2683 train/loss=11.3416 train/grad_norm=0.0841
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Epoch 56 Training: 100%|██████████| 96/96 [02:05<00:00,  1.30s/it]
+[2026-10-01 20:32:28] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0057 | Epoch 056] timing/rollout_seconds=148.1281 timing/feedback_seconds=5.3734 timing/optimization_seconds=125.8617 timing/cycle_seconds=277.5379
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+
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+[2026-10-01 20:35:00] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0057 | Epoch 057] train/reward_dists_mean=0.9389 train/reward_dists_std=0.0161 train/reward_psnr_mean=30.6987 train/reward_psnr_std=2.1094 train/reward_ssim_mean=0.9395 train/reward_ssim_std=0.0191 train/reward_dists_group_std_mean=0.0032 train/reward_dists_group_std_max=0.0095 train/reward_dists_group_std_min=0.0015 train/reward_dists_group_mean_std=0.0157 train/reward_psnr_group_std_mean=1.1922 train/reward_psnr_group_std_max=2.9421 train/reward_psnr_group_std_min=0.2468 train/reward_psnr_group_mean_std=1.6536 train/reward_ssim_group_std_mean=0.0009 train/reward_ssim_group_std_max=0.0025 train/reward_ssim_group_std_min=0.0004 train/reward_ssim_group_mean_std=0.0190 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-7.3665 train/adv_max=4.7549 train/adv_abs_mean=1.8799
+
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Epoch 57 Training:  23%|██▎       | 22/96 [00:29<01:38,  1.34s/it]
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Epoch 57 Training:  24%|██▍       | 23/96 [00:30<01:37,  1.34s/it]
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Epoch 57 Training:  25%|██▌       | 24/96 [00:32<01:36,  1.34s/it]
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Epoch 57 Training:  26%|██▌       | 25/96 [00:33<01:34,  1.34s/it]
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Epoch 57 Training:  27%|██▋       | 26/96 [00:34<01:33,  1.34s/it]
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Epoch 57 Training:  28%|██▊       | 27/96 [00:36<01:32,  1.34s/it]
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Epoch 57 Training:  29%|██▉       | 28/96 [00:37<01:30,  1.34s/it]
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Epoch 57 Training:  30%|███       | 29/96 [00:38<01:29,  1.34s/it]
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Epoch 57 Training:  31%|███▏      | 30/96 [00:40<01:28,  1.34s/it]
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Epoch 57 Training:  32%|███▏      | 31/96 [00:41<01:26,  1.34s/it]
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Epoch 57 Training:  33%|███▎      | 32/96 [00:42<01:25,  1.34s/it]
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Epoch 57 Training:  34%|███▍      | 33/96 [00:44<01:24,  1.34s/it]
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Epoch 57 Training:  35%|███▌      | 34/96 [00:45<01:23,  1.34s/it]
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Epoch 57 Training:  36%|███▋      | 35/96 [00:46<01:21,  1.34s/it]
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Epoch 57 Training:  38%|███▊      | 36/96 [00:48<01:20,  1.34s/it]
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Epoch 57 Training:  39%|███▊      | 37/96 [00:49<01:19,  1.34s/it]
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Epoch 57 Training:  40%|███▉      | 38/96 [00:50<01:17,  1.34s/it]
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Epoch 57 Training:  41%|████      | 39/96 [00:52<01:16,  1.34s/it]
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Epoch 57 Training:  42%|████▏     | 40/96 [00:53<01:15,  1.34s/it]
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Epoch 57 Training:  43%|████▎     | 41/96 [00:55<01:18,  1.42s/it]
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Epoch 57 Training:  44%|████▍     | 42/96 [00:56<01:15,  1.40s/it]
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Epoch 57 Training:  45%|████▍     | 43/96 [00:57<01:13,  1.38s/it]
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Epoch 57 Training:  46%|████▌     | 44/96 [00:59<01:11,  1.37s/it]
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Epoch 57 Training:  47%|████▋     | 45/96 [01:00<01:09,  1.36s/it]
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Epoch 57 Training:  48%|████▊     | 46/96 [01:01<01:07,  1.35s/it]
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Epoch 57 Training:  49%|████▉     | 47/96 [01:03<01:06,  1.35s/it]
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Epoch 57 Training:  50%|█████     | 48/96 [01:04<01:04,  1.35s/it]
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Epoch 57 Training:  51%|█████     | 49/96 [01:05<01:03,  1.34s/it]
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Epoch 57 Training:  52%|█████▏    | 50/96 [01:07<01:01,  1.34s/it]
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Epoch 57 Training:  53%|█████▎    | 51/96 [01:08<01:00,  1.34s/it]
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Epoch 57 Training:  54%|█████▍    | 52/96 [01:09<00:59,  1.34s/it]
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Epoch 57 Training:  55%|█████▌    | 53/96 [01:11<00:57,  1.34s/it]
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Epoch 57 Training:  56%|█████▋    | 54/96 [01:12<00:56,  1.34s/it]
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Epoch 57 Training:  57%|█████▋    | 55/96 [01:13<00:54,  1.34s/it]
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Epoch 57 Training:  58%|█████▊    | 56/96 [01:15<00:53,  1.34s/it]
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Epoch 57 Training:  59%|█████▉    | 57/96 [01:16<00:52,  1.34s/it]
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Epoch 57 Training:  60%|██████    | 58/96 [01:17<00:50,  1.34s/it]
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Epoch 57 Training:  61%|██████▏   | 59/96 [01:19<00:49,  1.34s/it]
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Epoch 57 Training:  62%|██████▎   | 60/96 [01:20<00:48,  1.34s/it]
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Epoch 57 Training:  64%|██████▎   | 61/96 [01:21<00:46,  1.34s/it]
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Epoch 57 Training:  65%|██████▍   | 62/96 [01:23<00:45,  1.34s/it]
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Epoch 57 Training:  66%|██████▌   | 63/96 [01:24<00:44,  1.34s/it]
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Epoch 57 Training:  67%|██████▋   | 64/96 [01:25<00:42,  1.34s/it]
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Epoch 57 Training:  68%|██████▊   | 65/96 [01:27<00:41,  1.34s/it]
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Epoch 57 Training:  69%|██████▉   | 66/96 [01:28<00:40,  1.34s/it]
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Epoch 57 Training:  70%|██████▉   | 67/96 [01:29<00:38,  1.34s/it]
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Epoch 57 Training:  71%|███████   | 68/96 [01:31<00:37,  1.34s/it]
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Epoch 57 Training:  72%|███████▏  | 69/96 [01:32<00:36,  1.34s/it]
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Epoch 57 Training:  73%|███████▎  | 70/96 [01:33<00:34,  1.34s/it]
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Epoch 57 Training:  74%|███████▍  | 71/96 [01:35<00:33,  1.34s/it]
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Epoch 57 Training:  75%|███████▌  | 72/96 [01:36<00:32,  1.34s/it]
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Epoch 57 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s][2026-10-01 20:37:09] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0057 | Epoch 057] train/policy_loss=11.3885 train/unweighted_policy_loss=2.2777 train/loss=11.3885 train/grad_norm=0.0611
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+[2026-10-01 20:37:09] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0058 | Epoch 057] timing/rollout_seconds=147.8941 timing/feedback_seconds=5.5536 timing/optimization_seconds=129.4160 timing/cycle_seconds=280.7875
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Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.66it/s]
+
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Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.67it/s]
+[2026-10-01 20:39:41] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0058 | Epoch 058] train/reward_dists_mean=0.9407 train/reward_dists_std=0.0146 train/reward_psnr_mean=30.1207 train/reward_psnr_std=2.4138 train/reward_ssim_mean=0.9382 train/reward_ssim_std=0.0209 train/reward_dists_group_std_mean=0.0028 train/reward_dists_group_std_max=0.0076 train/reward_dists_group_std_min=0.0013 train/reward_dists_group_mean_std=0.0143 train/reward_psnr_group_std_mean=1.3053 train/reward_psnr_group_std_max=3.4598 train/reward_psnr_group_std_min=0.1887 train/reward_psnr_group_mean_std=1.9210 train/reward_ssim_group_std_mean=0.0010 train/reward_ssim_group_std_max=0.0033 train/reward_ssim_group_std_min=0.0004 train/reward_ssim_group_mean_std=0.0209 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-8.4295 train/adv_max=5.2235 train/adv_abs_mean=1.8348
+
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Epoch 58 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s][2026-10-01 20:41:49] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0058 | Epoch 058] train/policy_loss=11.3220 train/unweighted_policy_loss=2.2644 train/loss=11.3220 train/grad_norm=0.0985
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+[2026-10-01 20:41:49] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0059 | Epoch 058] timing/rollout_seconds=147.8914 timing/feedback_seconds=5.4476 timing/optimization_seconds=128.3911 timing/cycle_seconds=279.8188
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+
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+[2026-10-01 20:44:21] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0059 | Epoch 059] train/reward_dists_mean=0.9377 train/reward_dists_std=0.0153 train/reward_psnr_mean=30.6617 train/reward_psnr_std=2.2190 train/reward_ssim_mean=0.9393 train/reward_ssim_std=0.0168 train/reward_dists_group_std_mean=0.0029 train/reward_dists_group_std_max=0.0064 train/reward_dists_group_std_min=0.0013 train/reward_dists_group_mean_std=0.0150 train/reward_psnr_group_std_mean=1.0583 train/reward_psnr_group_std_max=1.9065 train/reward_psnr_group_std_min=0.2011 train/reward_psnr_group_mean_std=1.9032 train/reward_ssim_group_std_mean=0.0009 train/reward_ssim_group_std_max=0.0022 train/reward_ssim_group_std_min=0.0003 train/reward_ssim_group_mean_std=0.0168 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-9.2786 train/adv_max=6.0087 train/adv_abs_mean=1.8014
+
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Epoch 59 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 59 Training:  20%|█▉        | 19/96 [00:25<01:42,  1.34s/it]
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Epoch 59 Training:  21%|██        | 20/96 [00:26<01:41,  1.34s/it]
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Epoch 59 Training:  22%|██▏       | 21/96 [00:27<01:40,  1.33s/it]
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Epoch 59 Training:  23%|██▎       | 22/96 [00:29<01:38,  1.33s/it]
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Epoch 59 Training:  24%|██▍       | 23/96 [00:30<01:37,  1.33s/it]
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Epoch 59 Training:  25%|██▌       | 24/96 [00:31<01:35,  1.33s/it]
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Epoch 59 Training:  26%|██▌       | 25/96 [00:33<01:34,  1.34s/it]
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Epoch 59 Training:  27%|██▋       | 26/96 [00:34<01:33,  1.34s/it]
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Epoch 59 Training:  28%|██▊       | 27/96 [00:35<01:32,  1.33s/it]
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Epoch 59 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 59 Training:  29%|██▉       | 28/96 [00:37<01:30,  1.33s/it]
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Epoch 59 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.31it/s]
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Epoch 59 Training:  30%|███       | 29/96 [00:38<01:29,  1.33s/it]
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Epoch 59 Training:  31%|███▏      | 30/96 [00:39<01:27,  1.33s/it]
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Epoch 59 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 59 Training:  32%|███▏      | 31/96 [00:41<01:26,  1.33s/it]
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Epoch 59 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 59 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.33it/s]
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Epoch 59 Training:  33%|███▎      | 32/96 [00:42<01:25,  1.33s/it]
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Epoch 59 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 59 Training:  34%|███▍      | 33/96 [00:43<01:23,  1.33s/it]
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Epoch 59 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 59 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 59 Training:  35%|███▌      | 34/96 [00:45<01:22,  1.33s/it]
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Epoch 59 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 59 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.34it/s]
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Epoch 59 Training:  36%|███▋      | 35/96 [00:46<01:21,  1.33s/it]
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Epoch 59 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 59 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.31it/s]
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Epoch 59 Training:  38%|███▊      | 36/96 [00:47<01:19,  1.33s/it]
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Epoch 59 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 59 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.29it/s]
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Epoch 59 Training:  39%|███▊      | 37/96 [00:49<01:18,  1.33s/it]
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Epoch 59 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 59 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.31it/s]
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Epoch 59 Training:  40%|███▉      | 38/96 [00:50<01:17,  1.33s/it]
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Epoch 59 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 59 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.31it/s]
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Epoch 59 Training:  41%|████      | 39/96 [00:51<01:15,  1.33s/it]
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Epoch 59 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 59 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.29it/s]
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Epoch 59 Training:  42%|████▏     | 40/96 [00:53<01:14,  1.33s/it]
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Epoch 59 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 59 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.31it/s]
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Epoch 59 Training:  43%|████▎     | 41/96 [00:54<01:13,  1.33s/it]
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Epoch 59 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 59 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.33it/s]
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Epoch 59 Training:  44%|████▍     | 42/96 [00:55<01:11,  1.33s/it]
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Epoch 59 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 59 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.32it/s]
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Epoch 59 Training:  45%|████▍     | 43/96 [00:57<01:10,  1.33s/it]
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Epoch 59 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 59 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 59 Training:  46%|████▌     | 44/96 [00:58<01:09,  1.33s/it]
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Epoch 59 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 59 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.35it/s]
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Epoch 59 Training:  47%|████▋     | 45/96 [00:59<01:07,  1.33s/it]
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Epoch 59 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 59 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.31it/s]
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Epoch 59 Training:  48%|████▊     | 46/96 [01:01<01:06,  1.33s/it]
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Epoch 59 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 59 Training:  49%|████▉     | 47/96 [01:02<01:05,  1.33s/it]
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Epoch 59 Training:  50%|█████     | 48/96 [01:03<01:03,  1.33s/it]
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Epoch 59 Training:  51%|█████     | 49/96 [01:05<01:02,  1.33s/it]
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Epoch 59 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 59 Training:  52%|█████▏    | 50/96 [01:06<01:01,  1.33s/it]
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Epoch 59 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 59 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.21it/s]
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Epoch 59 Training:  53%|█████▎    | 51/96 [01:07<01:00,  1.34s/it]
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Epoch 59 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 59 Training:  54%|█████▍    | 52/96 [01:09<00:59,  1.34s/it]
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Epoch 59 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 59 Training:  55%|█████▌    | 53/96 [01:10<00:57,  1.34s/it]
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Epoch 59 Training:  56%|█████▋    | 54/96 [01:11<00:56,  1.34s/it]
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Epoch 59 Training:  57%|█████▋    | 55/96 [01:13<00:54,  1.34s/it]
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Epoch 59 Training:  58%|█████▊    | 56/96 [01:14<00:53,  1.33s/it]
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Epoch 59 Training:  59%|█████▉    | 57/96 [01:15<00:51,  1.33s/it]
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Epoch 59 Training:  60%|██████    | 58/96 [01:17<00:50,  1.33s/it]
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Epoch 59 Training:  61%|██████▏   | 59/96 [01:18<00:49,  1.33s/it]
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Epoch 59 Training:  62%|██████▎   | 60/96 [01:19<00:47,  1.33s/it]
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Epoch 59 Training:  64%|██████▎   | 61/96 [01:21<00:46,  1.33s/it]
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Epoch 59 Training:  65%|██████▍   | 62/96 [01:22<00:45,  1.33s/it]
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Epoch 59 Training:  66%|██████▌   | 63/96 [01:23<00:43,  1.33s/it]
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Epoch 59 Training:  67%|██████▋   | 64/96 [01:25<00:42,  1.33s/it]
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Epoch 59 Training:  68%|██████▊   | 65/96 [01:26<00:41,  1.33s/it]
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Epoch 59 Training:  69%|██████▉   | 66/96 [01:27<00:39,  1.33s/it]
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Epoch 59 Training:  70%|██████▉   | 67/96 [01:29<00:38,  1.33s/it]
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Epoch 59 Training:  71%|███████   | 68/96 [01:30<00:37,  1.33s/it]
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Epoch 59 Training:  72%|███████▏  | 69/96 [01:31<00:35,  1.33s/it]
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Epoch 59 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s][2026-10-01 20:46:29] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0059 | Epoch 059] train/policy_loss=11.3502 train/unweighted_policy_loss=2.2700 train/loss=11.3502 train/grad_norm=0.0514
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+[2026-10-01 20:46:29] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0060 | Epoch 059] timing/rollout_seconds=147.8656 timing/feedback_seconds=5.5026 timing/optimization_seconds=128.5166 timing/cycle_seconds=279.9145
+[2026-10-01 20:46:29] [Rank 0] [INFO] [flow_factory.models.abc]: Saving LoRA weights for transformer role=base to /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/runs/klein4b_nft_vedai_full_ssim_dists_psnr/saves/klein4b_nft_vedai_full_ssim_dists_psnr/checkpoints/checkpoint-60
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+[2026-10-01 20:47:13] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0060 | Epoch 060] eval/vedai_rl_full/reward_dists_mean=0.9322 eval/vedai_rl_full/reward_dists_std=0.0175 eval/vedai_rl_full/reward_psnr_mean=28.8474 eval/vedai_rl_full/reward_psnr_std=2.0269 eval/vedai_rl_full/reward_ssim_mean=0.9231 eval/vedai_rl_full/reward_ssim_std=0.0256
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+
Pointwise Rewards: ssim:   0%|          | 0/6 [00:00<?, ?it/s]
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Pointwise Rewards: ssim:  67%|██████▋   | 4/6 [00:00<00:00,  6.34it/s]
Pointwise Rewards: ssim:  83%|████████▎ | 5/6 [00:00<00:00,  6.45it/s]
Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:00<00:00,  6.50it/s]
Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:00<00:00,  6.37it/s]
+
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Pointwise Rewards: dists:  50%|█████     | 3/6 [00:00<00:00,  3.66it/s]
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Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.67it/s]
Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.66it/s]
+
Pointwise Rewards: psnr:   0%|          | 0/6 [00:00<?, ?it/s]
Pointwise Rewards: psnr:  17%|█▋        | 1/6 [00:00<00:00,  6.54it/s]
Pointwise Rewards: psnr:  33%|███▎      | 2/6 [00:00<00:00,  6.64it/s]
Pointwise Rewards: psnr:  50%|█████     | 3/6 [00:00<00:00,  6.71it/s]
Pointwise Rewards: psnr:  67%|██████▋   | 4/6 [00:00<00:00,  6.72it/s]
Pointwise Rewards: psnr:  83%|████████▎ | 5/6 [00:00<00:00,  6.74it/s]
Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.72it/s]
Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.70it/s]
+[2026-10-01 20:49:45] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0060 | Epoch 060] train/reward_dists_mean=0.9376 train/reward_dists_std=0.0195 train/reward_psnr_mean=30.8374 train/reward_psnr_std=2.2965 train/reward_ssim_mean=0.9374 train/reward_ssim_std=0.0227 train/reward_dists_group_std_mean=0.0029 train/reward_dists_group_std_max=0.0054 train/reward_dists_group_std_min=0.0015 train/reward_dists_group_mean_std=0.0192 train/reward_psnr_group_std_mean=1.1875 train/reward_psnr_group_std_max=3.2598 train/reward_psnr_group_std_min=0.2975 train/reward_psnr_group_mean_std=1.8667 train/reward_ssim_group_std_mean=0.0009 train/reward_ssim_group_std_max=0.0022 train/reward_ssim_group_std_min=0.0004 train/reward_ssim_group_mean_std=0.0227 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-10.1046 train/adv_max=4.8975 train/adv_abs_mean=1.8081
+
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Epoch 60 Training:   3%|▎         | 3/96 [00:04<02:09,  1.40s/it]
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Epoch 60 Training:   4%|▍         | 4/96 [00:05<02:06,  1.37s/it]
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Epoch 60 Training:   6%|▋         | 6/96 [00:08<02:00,  1.34s/it]
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Epoch 60 Training:   7%|▋         | 7/96 [00:09<01:58,  1.34s/it]
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Epoch 60 Training:   8%|▊         | 8/96 [00:10<01:57,  1.34s/it]
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Epoch 60 Training:   9%|▉         | 9/96 [00:12<01:56,  1.34s/it]
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Epoch 60 Training:  10%|█         | 10/96 [00:13<01:54,  1.33s/it]
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Epoch 60 Training:  11%|█▏        | 11/96 [00:14<01:53,  1.33s/it]
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Epoch 60 Training:  12%|█▎        | 12/96 [00:16<01:51,  1.33s/it]
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Epoch 60 Training:  14%|█▎        | 13/96 [00:17<01:50,  1.33s/it]
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Epoch 60 Training:  15%|█▍        | 14/96 [00:18<01:48,  1.33s/it]
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Epoch 60 Training:  16%|█▌        | 15/96 [00:20<01:47,  1.33s/it]
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Epoch 60 Training:  17%|█▋        | 16/96 [00:21<01:46,  1.33s/it]
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Epoch 60 Training:  18%|█▊        | 17/96 [00:22<01:44,  1.33s/it]
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Epoch 60 Training:  19%|█▉        | 18/96 [00:24<01:43,  1.33s/it]
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Epoch 60 Training:  20%|█▉        | 19/96 [00:25<01:42,  1.33s/it]
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Epoch 60 Training:  21%|██        | 20/96 [00:26<01:40,  1.33s/it]
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Epoch 60 Training:  22%|██▏       | 21/96 [00:28<01:39,  1.32s/it]
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Epoch 60 Training:  23%|██▎       | 22/96 [00:29<01:37,  1.32s/it]
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Epoch 60 Training:  24%|██▍       | 23/96 [00:30<01:36,  1.32s/it]
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Epoch 60 Training:  25%|██▌       | 24/96 [00:32<01:35,  1.32s/it]
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Epoch 60 Training:  26%|██▌       | 25/96 [00:33<01:34,  1.32s/it]
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Epoch 60 Training:  27%|██▋       | 26/96 [00:34<01:32,  1.32s/it]
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Epoch 60 Training:  28%|██▊       | 27/96 [00:36<01:31,  1.33s/it]
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Epoch 60 Training:  29%|██▉       | 28/96 [00:37<01:30,  1.33s/it]
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Epoch 60 Training:  32%|███▏      | 31/96 [00:41<01:26,  1.33s/it]
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Epoch 60 Training:  33%|███▎      | 32/96 [00:42<01:24,  1.32s/it]
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Epoch 60 Training:  38%|███▊      | 36/96 [00:47<01:19,  1.33s/it]
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Epoch 60 Training:  39%|███▊      | 37/96 [00:49<01:18,  1.32s/it]
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Epoch 60 Training:  40%|███▉      | 38/96 [00:50<01:16,  1.32s/it]
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Epoch 60 Training:  41%|████      | 39/96 [00:51<01:15,  1.33s/it]
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Epoch 60 Training:  42%|████▏     | 40/96 [00:53<01:14,  1.33s/it]
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Epoch 60 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 60 Training:  43%|████▎     | 41/96 [00:54<01:12,  1.32s/it]
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Epoch 60 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 60 Training:  44%|████▍     | 42/96 [00:55<01:11,  1.33s/it]
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Epoch 60 Training:  45%|████▍     | 43/96 [00:57<01:10,  1.33s/it]
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Epoch 60 Training:  46%|████▌     | 44/96 [00:58<01:08,  1.33s/it]
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Epoch 60 Training:  47%|████▋     | 45/96 [00:59<01:07,  1.32s/it]
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Epoch 60 Training:  48%|████▊     | 46/96 [01:01<01:06,  1.32s/it]
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Epoch 60 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 60 Training:  49%|████▉     | 47/96 [01:02<01:04,  1.32s/it]
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Epoch 60 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 60 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 60 Training:  50%|█████     | 48/96 [01:03<01:03,  1.32s/it]
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Epoch 60 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 60 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 60 Training:  51%|█████     | 49/96 [01:05<01:02,  1.33s/it]
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Epoch 60 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 60 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 60 Training:  52%|█████▏    | 50/96 [01:06<01:01,  1.33s/it]
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Epoch 60 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.30it/s]
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Epoch 60 Training:  53%|█████▎    | 51/96 [01:08<01:03,  1.40s/it]
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Epoch 60 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 60 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.18it/s]
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Epoch 60 Training:  54%|█████▍    | 52/96 [01:09<01:01,  1.39s/it]
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Epoch 60 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 60 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.22it/s]
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Epoch 60 Training:  55%|█████▌    | 53/96 [01:10<00:59,  1.38s/it]
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Epoch 60 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 60 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.21it/s]
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Epoch 60 Training:  56%|█████▋    | 54/96 [01:12<00:57,  1.37s/it]
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Epoch 60 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 60 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 60 Training:  57%|█████▋    | 55/96 [01:13<00:55,  1.36s/it]
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Epoch 60 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 60 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 60 Training:  58%|█████▊    | 56/96 [01:14<00:53,  1.35s/it]
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Epoch 60 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 60 Training:  59%|█████▉    | 57/96 [01:16<00:52,  1.34s/it]
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Epoch 60 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 60 Training:  60%|██████    | 58/96 [01:17<00:50,  1.34s/it]
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Epoch 60 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 60 Training:  61%|██████▏   | 59/96 [01:18<00:49,  1.34s/it]
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Epoch 60 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 60 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 60 Training:  62%|██████▎   | 60/96 [01:20<00:48,  1.34s/it]
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Epoch 60 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 60 Training:  64%|██████▎   | 61/96 [01:21<00:46,  1.33s/it]
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Epoch 60 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 60 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 60 Training:  65%|██████▍   | 62/96 [01:22<00:45,  1.33s/it]
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Epoch 60 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.30it/s]
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Epoch 60 Training:  66%|██████▌   | 63/96 [01:24<00:43,  1.33s/it]
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Epoch 60 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 60 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 60 Training:  67%|██████▋   | 64/96 [01:25<00:42,  1.33s/it]
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Epoch 60 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.44it/s]
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Epoch 60 Training:  68%|██████▊   | 65/96 [01:26<00:41,  1.33s/it]
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Epoch 60 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.29it/s]
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Epoch 60 Training:  69%|██████▉   | 66/96 [01:28<00:39,  1.33s/it]
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Epoch 60 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.29it/s]
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Epoch 60 Training:  70%|██████▉   | 67/96 [01:29<00:38,  1.33s/it]
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Epoch 60 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 60 Training:  71%|███████   | 68/96 [01:30<00:37,  1.33s/it]
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Epoch 60 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 60 Training:  72%|███████▏  | 69/96 [01:32<00:35,  1.33s/it]
+
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Epoch 60 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.29it/s]
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Epoch 60 Training:  73%|███████▎  | 70/96 [01:33<00:34,  1.33s/it]
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Epoch 60 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 60 Training:  74%|███████▍  | 71/96 [01:34<00:33,  1.33s/it]
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Epoch 60 Training:  75%|███████▌  | 72/96 [01:36<00:31,  1.33s/it]
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Epoch 60 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 60 Training:  76%|███████▌  | 73/96 [01:37<00:30,  1.33s/it]
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Epoch 60 Training:  77%|███████▋  | 74/96 [01:38<00:29,  1.33s/it]
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Epoch 60 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 60 Training:  78%|███████▊  | 75/96 [01:39<00:27,  1.33s/it]
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Epoch 60 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 60 Training:  79%|███████▉  | 76/96 [01:41<00:26,  1.33s/it]
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Epoch 60 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 60 Training:  80%|████████  | 77/96 [01:42<00:25,  1.33s/it]
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Epoch 60 Training:  81%|████████▏ | 78/96 [01:43<00:23,  1.33s/it]
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Epoch 60 Training:  82%|████████▏ | 79/96 [01:45<00:22,  1.33s/it]
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Epoch 60 Training:  83%|████████▎ | 80/96 [01:46<00:21,  1.33s/it]
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Epoch 60 Training:  84%|████████▍ | 81/96 [01:47<00:19,  1.33s/it]
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Epoch 60 Training:  85%|████████▌ | 82/96 [01:49<00:18,  1.33s/it]
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Epoch 60 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 60 Training:  86%|████████▋ | 83/96 [01:50<00:17,  1.33s/it]
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Epoch 60 Training:  88%|████████▊ | 84/96 [01:51<00:15,  1.33s/it]
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Epoch 60 Training:  89%|████████▊ | 85/96 [01:53<00:14,  1.32s/it]
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Epoch 60 Training:  90%|████████▉ | 86/96 [01:54<00:13,  1.33s/it]
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Epoch 60 Training:  91%|█████████ | 87/96 [01:55<00:11,  1.33s/it]
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+[2026-10-01 20:51:53] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0061 | Epoch 060] timing/rollout_seconds=148.0627 timing/feedback_seconds=5.5861 timing/optimization_seconds=128.5452 timing/cycle_seconds=280.0917
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+[2026-10-01 20:54:25] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0061 | Epoch 061] train/reward_dists_mean=0.9434 train/reward_dists_std=0.0170 train/reward_psnr_mean=31.0128 train/reward_psnr_std=2.1270 train/reward_ssim_mean=0.9432 train/reward_ssim_std=0.0203 train/reward_dists_group_std_mean=0.0026 train/reward_dists_group_std_max=0.0065 train/reward_dists_group_std_min=0.0009 train/reward_dists_group_mean_std=0.0168 train/reward_psnr_group_std_mean=1.1078 train/reward_psnr_group_std_max=2.9030 train/reward_psnr_group_std_min=0.4162 train/reward_psnr_group_mean_std=1.7502 train/reward_ssim_group_std_mean=0.0009 train/reward_ssim_group_std_max=0.0027 train/reward_ssim_group_std_min=0.0003 train/reward_ssim_group_mean_std=0.0202 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-8.0417 train/adv_max=5.6173 train/adv_abs_mean=1.7879
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Epoch 61 Training:  10%|█         | 10/96 [00:13<01:54,  1.33s/it]
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Epoch 61 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 61 Training:  11%|█▏        | 11/96 [00:14<01:53,  1.33s/it]
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Epoch 61 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 61 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 61 Training:  12%|█▎        | 12/96 [00:16<01:51,  1.33s/it]
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Epoch 61 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.23it/s]
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Epoch 61 Training:  14%|█▎        | 13/96 [00:17<01:50,  1.33s/it]
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Epoch 61 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 61 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 61 Training:  15%|█▍        | 14/96 [00:18<01:48,  1.33s/it]
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Epoch 61 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 61 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 61 Training:  16%|█▌        | 15/96 [00:20<01:47,  1.33s/it]
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Epoch 61 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 61 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 61 Training:  17%|█▋        | 16/96 [00:21<01:46,  1.33s/it]
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Epoch 61 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 61 Training:  18%|█▊        | 17/96 [00:22<01:44,  1.33s/it]
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Epoch 61 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 61 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 61 Training:  19%|█▉        | 18/96 [00:24<01:43,  1.33s/it]
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Epoch 61 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 61 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 61 Training:  20%|█▉        | 19/96 [00:25<01:42,  1.33s/it]
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Epoch 61 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 61 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 61 Training:  21%|██        | 20/96 [00:26<01:40,  1.32s/it]
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Epoch 61 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 61 Training:  22%|██▏       | 21/96 [00:28<01:39,  1.33s/it]
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Epoch 61 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 61 Training:  23%|██▎       | 22/96 [00:29<01:38,  1.33s/it]
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Epoch 61 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 61 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 61 Training:  24%|██▍       | 23/96 [00:30<01:36,  1.33s/it]
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Epoch 61 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 61 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 61 Training:  25%|██▌       | 24/96 [00:32<01:35,  1.33s/it]
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Epoch 61 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 61 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.21it/s]
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Epoch 61 Training:  26%|██▌       | 25/96 [00:33<01:34,  1.33s/it]
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Epoch 61 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 61 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 61 Training:  27%|██▋       | 26/96 [00:34<01:32,  1.33s/it]
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Epoch 61 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 61 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 61 Training:  28%|██▊       | 27/96 [00:36<01:31,  1.33s/it]
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Epoch 61 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 61 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.17it/s]
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Epoch 61 Training:  29%|██▉       | 28/96 [00:37<01:30,  1.33s/it]
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Epoch 61 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 61 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.23it/s]
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Epoch 61 Training:  30%|███       | 29/96 [00:38<01:29,  1.33s/it]
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Epoch 61 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 61 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 61 Training:  31%|███▏      | 30/96 [00:40<01:27,  1.33s/it]
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Epoch 61 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 61 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 61 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.23it/s]
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Epoch 61 Training:  32%|███▏      | 31/96 [00:41<01:26,  1.33s/it]
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Epoch 61 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 61 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 61 Training:  33%|███▎      | 32/96 [00:42<01:25,  1.33s/it]
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Epoch 61 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 61 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 61 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 61 Training:  34%|███▍      | 33/96 [00:43<01:23,  1.33s/it]
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Epoch 61 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 61 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 61 Training:  35%|███▌      | 34/96 [00:45<01:22,  1.33s/it]
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Epoch 61 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 61 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.23it/s]
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Epoch 61 Training:  36%|███▋      | 35/96 [00:46<01:21,  1.33s/it]
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Epoch 61 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 61 Training:  38%|███▊      | 36/96 [00:47<01:19,  1.33s/it]
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Epoch 61 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 61 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 61 Training:  39%|███▊      | 37/96 [00:49<01:18,  1.33s/it]
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Epoch 61 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 61 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.22it/s]
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Epoch 61 Training:  40%|███▉      | 38/96 [00:50<01:17,  1.33s/it]
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Epoch 61 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 61 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 61 Training:  41%|████      | 39/96 [00:51<01:15,  1.33s/it]
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Epoch 61 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 61 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 61 Training:  42%|████▏     | 40/96 [00:53<01:14,  1.33s/it]
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Epoch 61 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 61 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 61 Training:  43%|████▎     | 41/96 [00:54<01:12,  1.33s/it]
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Epoch 61 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 61 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 61 Training:  44%|████▍     | 42/96 [00:55<01:11,  1.33s/it]
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Epoch 61 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 61 Training:  45%|████▍     | 43/96 [00:57<01:10,  1.33s/it]
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Epoch 61 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 61 Training:  46%|████▌     | 44/96 [00:58<01:08,  1.33s/it]
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Epoch 61 Training:  47%|████▋     | 45/96 [00:59<01:07,  1.32s/it]
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Epoch 61 Training:  48%|████▊     | 46/96 [01:01<01:06,  1.33s/it]
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Epoch 61 Training:  49%|████▉     | 47/96 [01:02<01:04,  1.32s/it]
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Epoch 61 Training:  50%|█████     | 48/96 [01:03<01:03,  1.32s/it]
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Epoch 61 Training:  51%|█████     | 49/96 [01:05<01:02,  1.32s/it]
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Epoch 61 Training:  52%|█████▏    | 50/96 [01:06<01:01,  1.33s/it]
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Epoch 61 Training:  53%|█████▎    | 51/96 [01:07<00:59,  1.33s/it]
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Epoch 61 Training:  54%|█████▍    | 52/96 [01:09<00:58,  1.32s/it]
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Epoch 61 Training:  55%|█████▌    | 53/96 [01:10<00:56,  1.32s/it]
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Epoch 61 Training:  56%|█████▋    | 54/96 [01:11<00:55,  1.33s/it]
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Epoch 61 Training:  57%|█████▋    | 55/96 [01:13<00:54,  1.33s/it]
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Epoch 61 Training:  58%|█████▊    | 56/96 [01:14<00:52,  1.32s/it]
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Epoch 61 Training:  59%|█████▉    | 57/96 [01:15<00:51,  1.33s/it]
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Epoch 61 Training:  60%|██████    | 58/96 [01:17<00:50,  1.33s/it]
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Epoch 61 Training:  61%|██████▏   | 59/96 [01:18<00:49,  1.33s/it]
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Epoch 61 Training:  62%|██████▎   | 60/96 [01:19<00:47,  1.33s/it]
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Epoch 61 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s][2026-10-01 20:56:33] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0061 | Epoch 061] train/policy_loss=10.8589 train/unweighted_policy_loss=2.1718 train/loss=10.8589 train/grad_norm=0.0517
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+[2026-10-01 20:56:33] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0062 | Epoch 061] timing/rollout_seconds=147.9623 timing/feedback_seconds=5.4967 timing/optimization_seconds=128.2772 timing/cycle_seconds=279.8085
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+
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+
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+
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+[2026-10-01 20:59:06] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0062 | Epoch 062] train/reward_dists_mean=0.9404 train/reward_dists_std=0.0181 train/reward_psnr_mean=30.5722 train/reward_psnr_std=2.4676 train/reward_ssim_mean=0.9431 train/reward_ssim_std=0.0201 train/reward_dists_group_std_mean=0.0027 train/reward_dists_group_std_max=0.0070 train/reward_dists_group_std_min=0.0012 train/reward_dists_group_mean_std=0.0179 train/reward_psnr_group_std_mean=1.2410 train/reward_psnr_group_std_max=2.6391 train/reward_psnr_group_std_min=0.5082 train/reward_psnr_group_mean_std=2.0725 train/reward_ssim_group_std_mean=0.0010 train/reward_ssim_group_std_max=0.0027 train/reward_ssim_group_std_min=0.0004 train/reward_ssim_group_mean_std=0.0200 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-8.5452 train/adv_max=5.5607 train/adv_abs_mean=1.8176
+
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Epoch 62 Training:  35%|███▌      | 34/96 [00:45<01:22,  1.34s/it]
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Epoch 62 Training:  36%|███▋      | 35/96 [00:46<01:21,  1.33s/it]
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Epoch 62 Training:  38%|███▊      | 36/96 [00:48<01:19,  1.33s/it]
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Epoch 62 Training:  39%|███▊      | 37/96 [00:49<01:18,  1.32s/it]
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Epoch 62 Training:  40%|███▉      | 38/96 [00:50<01:16,  1.32s/it]
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Epoch 62 Training:  41%|████      | 39/96 [00:52<01:15,  1.32s/it]
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Epoch 62 Training:  42%|████▏     | 40/96 [00:53<01:14,  1.32s/it]
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Epoch 62 Training:  43%|████▎     | 41/96 [00:54<01:12,  1.32s/it]
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Epoch 62 Training:  44%|████▍     | 42/96 [00:56<01:11,  1.33s/it]
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Epoch 62 Training:  45%|████▍     | 43/96 [00:57<01:10,  1.32s/it]
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Epoch 62 Training:  46%|████▌     | 44/96 [00:58<01:08,  1.32s/it]
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Epoch 62 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.33it/s]
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Epoch 62 Training:  47%|████▋     | 45/96 [01:00<01:07,  1.32s/it]
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Epoch 62 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.34it/s]
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Epoch 62 Training:  48%|████▊     | 46/96 [01:01<01:05,  1.32s/it]
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Epoch 62 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 62 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.34it/s]
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Epoch 62 Training:  49%|████▉     | 47/96 [01:02<01:04,  1.32s/it]
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Epoch 62 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 62 Training:  50%|█████     | 48/96 [01:03<01:03,  1.32s/it]
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Epoch 62 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 62 Training:  51%|█████     | 49/96 [01:05<01:01,  1.32s/it]
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Epoch 62 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 62 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.32it/s]
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Epoch 62 Training:  52%|█████▏    | 50/96 [01:06<01:00,  1.32s/it]
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Epoch 62 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 62 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.33it/s]
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Epoch 62 Training:  53%|█████▎    | 51/96 [01:07<00:59,  1.32s/it]
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Epoch 62 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 62 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.34it/s]
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Epoch 62 Training:  54%|█████▍    | 52/96 [01:09<00:57,  1.32s/it]
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Epoch 62 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 62 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.31it/s]
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Epoch 62 Training:  55%|█████▌    | 53/96 [01:10<00:56,  1.32s/it]
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Epoch 62 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 62 Training:  56%|█████▋    | 54/96 [01:11<00:55,  1.33s/it]
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Epoch 62 Training:  57%|█████▋    | 55/96 [01:13<00:54,  1.33s/it]
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Epoch 62 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 62 Training:  58%|█████▊    | 56/96 [01:14<00:53,  1.33s/it]
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Epoch 62 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 62 Training:  59%|█████▉    | 57/96 [01:15<00:51,  1.33s/it]
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Epoch 62 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 62 Training:  60%|██████    | 58/96 [01:17<00:50,  1.33s/it]
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Epoch 62 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 62 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.31it/s]
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Epoch 62 Training:  61%|██████▏   | 59/96 [01:18<00:48,  1.32s/it]
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Epoch 62 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 62 Training:  62%|██████▎   | 60/96 [01:20<00:49,  1.37s/it]
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Epoch 62 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.32it/s]
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Epoch 62 Training:  64%|██████▎   | 61/96 [01:21<00:47,  1.36s/it]
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Epoch 62 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.32it/s]
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Epoch 62 Training:  65%|██████▍   | 62/96 [01:22<00:45,  1.34s/it]
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Epoch 62 Training:  66%|██████▌   | 63/96 [01:23<00:44,  1.34s/it]
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Epoch 62 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 62 Training:  67%|██████▋   | 64/96 [01:25<00:42,  1.33s/it]
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Epoch 62 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.29it/s]
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Epoch 62 Training:  68%|██████▊   | 65/96 [01:26<00:41,  1.33s/it]
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Epoch 62 Training:  69%|██████▉   | 66/96 [01:27<00:39,  1.32s/it]
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Epoch 62 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 62 Training:  70%|██████▉   | 67/96 [01:29<00:38,  1.32s/it]
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Epoch 62 Training:  71%|███████   | 68/96 [01:30<00:36,  1.32s/it]
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Epoch 62 Training:  72%|███████▏  | 69/96 [01:31<00:35,  1.32s/it]
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Epoch 62 Training:  73%|███████▎  | 70/96 [01:33<00:34,  1.32s/it]
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Epoch 62 Training:  74%|███████▍  | 71/96 [01:34<00:33,  1.32s/it]
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Epoch 62 Training:  75%|███████▌  | 72/96 [01:35<00:31,  1.32s/it]
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Epoch 62 Training:  76%|███████▌  | 73/96 [01:37<00:30,  1.32s/it]
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Epoch 62 Training:  77%|███████▋  | 74/96 [01:38<00:29,  1.32s/it]
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Epoch 62 Training:  78%|███████▊  | 75/96 [01:39<00:27,  1.32s/it]
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Epoch 62 Training:  79%|███████▉  | 76/96 [01:41<00:26,  1.32s/it]
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Epoch 62 Training:  80%|████████  | 77/96 [01:42<00:25,  1.32s/it]
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Epoch 62 Training:  81%|████████▏ | 78/96 [01:43<00:23,  1.32s/it]
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Epoch 62 Training:  82%|████████▏ | 79/96 [01:45<00:22,  1.32s/it]
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Epoch 62 Training:  83%|████████▎ | 80/96 [01:46<00:21,  1.32s/it]
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Epoch 62 Training:  84%|████████▍ | 81/96 [01:47<00:19,  1.32s/it]
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Epoch 62 Training:  85%|████████▌ | 82/96 [01:49<00:18,  1.32s/it]
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Epoch 62 Training:  86%|████████▋ | 83/96 [01:50<00:17,  1.32s/it]
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Epoch 62 Training:  88%|████████▊ | 84/96 [01:51<00:15,  1.32s/it]
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Epoch 62 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s][2026-10-01 21:01:14] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0062 | Epoch 062] train/policy_loss=11.0569 train/unweighted_policy_loss=2.2114 train/loss=11.0569 train/grad_norm=0.0715
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+[2026-10-01 21:01:14] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0063 | Epoch 062] timing/rollout_seconds=147.8647 timing/feedback_seconds=6.0233 timing/optimization_seconds=128.2247 timing/cycle_seconds=280.5753
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+
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+[2026-10-01 21:03:46] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0063 | Epoch 063] train/reward_dists_mean=0.9362 train/reward_dists_std=0.0142 train/reward_psnr_mean=30.3459 train/reward_psnr_std=1.8131 train/reward_ssim_mean=0.9344 train/reward_ssim_std=0.0173 train/reward_dists_group_std_mean=0.0027 train/reward_dists_group_std_max=0.0059 train/reward_dists_group_std_min=0.0011 train/reward_dists_group_mean_std=0.0139 train/reward_psnr_group_std_mean=1.0841 train/reward_psnr_group_std_max=2.2488 train/reward_psnr_group_std_min=0.1892 train/reward_psnr_group_mean_std=1.3626 train/reward_ssim_group_std_mean=0.0010 train/reward_ssim_group_std_max=0.0038 train/reward_ssim_group_std_min=0.0003 train/reward_ssim_group_mean_std=0.0173 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-8.7962 train/adv_max=5.9122 train/adv_abs_mean=1.7594
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Epoch 63 Training:   7%|▋         | 7/96 [00:09<01:56,  1.31s/it]
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Epoch 63 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 63 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.29it/s]
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Epoch 63 Training:   8%|▊         | 8/96 [00:10<01:55,  1.31s/it]
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Epoch 63 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 63 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.31it/s]
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Epoch 63 Training:   9%|▉         | 9/96 [00:11<01:54,  1.32s/it]
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Epoch 63 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 63 Training:  10%|█         | 10/96 [00:13<01:53,  1.32s/it]
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Epoch 63 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 63 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.32it/s]
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Epoch 63 Training:  11%|█▏        | 11/96 [00:14<01:51,  1.32s/it]
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Epoch 63 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 63 Training:  12%|█▎        | 12/96 [00:15<01:50,  1.32s/it]
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Epoch 63 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 63 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 63 Training:  14%|█▎        | 13/96 [00:17<01:49,  1.32s/it]
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Epoch 63 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 63 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.29it/s]
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Epoch 63 Training:  15%|█▍        | 14/96 [00:18<01:48,  1.32s/it]
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Epoch 63 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 63 Training:  16%|█▌        | 15/96 [00:19<01:46,  1.32s/it]
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Epoch 63 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 63 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.31it/s]
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Epoch 63 Training:  17%|█▋        | 16/96 [00:21<01:45,  1.32s/it]
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Epoch 63 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 63 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.31it/s]
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Epoch 63 Training:  18%|█▊        | 17/96 [00:22<01:44,  1.32s/it]
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Epoch 63 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 63 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.29it/s]
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Epoch 63 Training:  19%|█▉        | 18/96 [00:23<01:42,  1.32s/it]
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Epoch 63 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 63 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.31it/s]
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Epoch 63 Training:  20%|█▉        | 19/96 [00:24<01:41,  1.32s/it]
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Epoch 63 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 63 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 63 Training:  21%|██        | 20/96 [00:26<01:40,  1.32s/it]
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Epoch 63 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 63 Training:  22%|██▏       | 21/96 [00:27<01:38,  1.31s/it]
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Epoch 63 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 63 Training:  23%|██▎       | 22/96 [00:28<01:37,  1.32s/it]
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Epoch 63 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 63 Training:  24%|██▍       | 23/96 [00:30<01:35,  1.31s/it]
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Epoch 63 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 63 Training:  25%|██▌       | 24/96 [00:31<01:34,  1.31s/it]
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Epoch 63 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 63 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.29it/s]
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Epoch 63 Training:  26%|██▌       | 25/96 [00:32<01:33,  1.31s/it]
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Epoch 63 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 63 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.32it/s]
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Epoch 63 Training:  27%|██▋       | 26/96 [00:34<01:32,  1.31s/it]
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Epoch 63 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 63 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 63 Training:  28%|██▊       | 27/96 [00:35<01:30,  1.32s/it]
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Epoch 63 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 63 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.31it/s]
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Epoch 63 Training:  29%|██▉       | 28/96 [00:36<01:29,  1.32s/it]
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Epoch 63 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 63 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.31it/s]
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Epoch 63 Training:  30%|███       | 29/96 [00:38<01:28,  1.32s/it]
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Epoch 63 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 63 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.31it/s]
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Epoch 63 Training:  31%|███▏      | 30/96 [00:39<01:26,  1.31s/it]
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Epoch 63 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 63 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 63 Training:  32%|███▏      | 31/96 [00:40<01:25,  1.32s/it]
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Epoch 63 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 63 Training:  33%|███▎      | 32/96 [00:42<01:24,  1.32s/it]
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Epoch 63 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 63 Training:  34%|███▍      | 33/96 [00:43<01:22,  1.32s/it]
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Epoch 63 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 63 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.29it/s]
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Epoch 63 Training:  35%|███▌      | 34/96 [00:44<01:21,  1.32s/it]
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Epoch 63 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 63 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.31it/s]
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Epoch 63 Training:  36%|███▋      | 35/96 [00:45<01:20,  1.32s/it]
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Epoch 63 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 63 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.31it/s]
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Epoch 63 Training:  38%|███▊      | 36/96 [00:47<01:18,  1.32s/it]
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Epoch 63 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 63 Training:  39%|███▊      | 37/96 [00:48<01:17,  1.32s/it]
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Epoch 63 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 63 Training:  40%|███▉      | 38/96 [00:49<01:16,  1.32s/it]
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Epoch 63 Training:  41%|████      | 39/96 [00:51<01:14,  1.32s/it]
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Epoch 63 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.31it/s]
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Epoch 63 Training:  42%|████▏     | 40/96 [00:52<01:13,  1.32s/it]
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Epoch 63 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 63 Training:  43%|████▎     | 41/96 [00:53<01:12,  1.32s/it]
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Epoch 63 Training:  44%|████▍     | 42/96 [00:55<01:10,  1.31s/it]
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Epoch 63 Training:  45%|████▍     | 43/96 [00:56<01:09,  1.31s/it]
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Epoch 63 Training:  46%|████▌     | 44/96 [00:57<01:08,  1.31s/it]
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Epoch 63 Training:  47%|████▋     | 45/96 [00:59<01:07,  1.32s/it]
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Epoch 63 Training:  48%|████▊     | 46/96 [01:00<01:05,  1.31s/it]
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Epoch 63 Training:  49%|████▉     | 47/96 [01:01<01:04,  1.31s/it]
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Epoch 63 Training:  50%|█████     | 48/96 [01:03<01:03,  1.31s/it]
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Epoch 63 Training:  51%|█████     | 49/96 [01:04<01:01,  1.31s/it]
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Epoch 63 Training:  52%|█████▏    | 50/96 [01:05<01:00,  1.31s/it]
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Epoch 63 Training:  53%|█████▎    | 51/96 [01:07<00:59,  1.31s/it]
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Epoch 63 Training:  54%|█████▍    | 52/96 [01:08<00:57,  1.31s/it]
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Epoch 63 Training:  55%|█████▌    | 53/96 [01:09<00:56,  1.32s/it]
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Epoch 63 Training:  56%|█████▋    | 54/96 [01:10<00:55,  1.31s/it]
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Epoch 63 Training:  57%|█████▋    | 55/96 [01:12<00:53,  1.32s/it]
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Epoch 63 Training:  58%|█████▊    | 56/96 [01:13<00:52,  1.32s/it]
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Epoch 63 Training:  59%|█████▉    | 57/96 [01:14<00:51,  1.32s/it]
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Epoch 63 Training:  60%|██████    | 58/96 [01:16<00:50,  1.32s/it]
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Epoch 63 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s][2026-10-01 21:05:52] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0063 | Epoch 063] train/policy_loss=11.1468 train/unweighted_policy_loss=2.2294 train/loss=11.1468 train/grad_norm=0.0584
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+[2026-10-01 21:05:52] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0064 | Epoch 063] timing/rollout_seconds=147.8954 timing/feedback_seconds=5.7045 timing/optimization_seconds=126.9671 timing/cycle_seconds=278.4064
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+
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+
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+
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Pointwise Rewards: psnr:  33%|███▎      | 2/6 [00:00<00:00,  6.49it/s]
Pointwise Rewards: psnr:  50%|█████     | 3/6 [00:00<00:00,  6.50it/s]
Pointwise Rewards: psnr:  67%|██████▋   | 4/6 [00:00<00:00,  6.64it/s]
Pointwise Rewards: psnr:  83%|████████▎ | 5/6 [00:00<00:00,  6.77it/s]
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Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.67it/s]
+[2026-10-01 21:08:24] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0064 | Epoch 064] train/reward_dists_mean=0.9422 train/reward_dists_std=0.0138 train/reward_psnr_mean=30.6551 train/reward_psnr_std=1.9956 train/reward_ssim_mean=0.9418 train/reward_ssim_std=0.0189 train/reward_dists_group_std_mean=0.0026 train/reward_dists_group_std_max=0.0054 train/reward_dists_group_std_min=0.0009 train/reward_dists_group_mean_std=0.0136 train/reward_psnr_group_std_mean=1.1767 train/reward_psnr_group_std_max=3.2579 train/reward_psnr_group_std_min=0.3121 train/reward_psnr_group_mean_std=1.4980 train/reward_ssim_group_std_mean=0.0010 train/reward_ssim_group_std_max=0.0074 train/reward_ssim_group_std_min=0.0004 train/reward_ssim_group_mean_std=0.0189 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-7.5047 train/adv_max=5.8834 train/adv_abs_mean=1.8572
+
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Epoch 64 Training:  19%|█▉        | 18/96 [00:23<01:42,  1.32s/it]
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Epoch 64 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.29it/s]
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Epoch 64 Training:  33%|███▎      | 32/96 [00:41<01:24,  1.32s/it]
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Epoch 64 Training:  34%|███▍      | 33/96 [00:43<01:23,  1.32s/it]
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Epoch 64 Training:  35%|███▌      | 34/96 [00:44<01:21,  1.32s/it]
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Epoch 64 Training:  36%|███▋      | 35/96 [00:45<01:20,  1.32s/it]
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Epoch 64 Training:  38%|███▊      | 36/96 [00:47<01:19,  1.32s/it]
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Epoch 64 Training:  39%|███▊      | 37/96 [00:48<01:17,  1.32s/it]
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Epoch 64 Training:  40%|███▉      | 38/96 [00:49<01:16,  1.32s/it]
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Epoch 64 Training:  41%|████      | 39/96 [00:51<01:15,  1.32s/it]
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Epoch 64 Training:  42%|████▏     | 40/96 [00:52<01:13,  1.32s/it]
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Epoch 64 Training:  43%|████▎     | 41/96 [00:53<01:12,  1.32s/it]
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Epoch 64 Training:  44%|████▍     | 42/96 [00:55<01:11,  1.32s/it]
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Epoch 64 Training:  45%|████▍     | 43/96 [00:56<01:09,  1.32s/it]
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Epoch 64 Training:  46%|████▌     | 44/96 [00:57<01:08,  1.32s/it]
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Epoch 64 Training:  47%|████▋     | 45/96 [00:59<01:07,  1.32s/it]
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Epoch 64 Training:  48%|████▊     | 46/96 [01:00<01:05,  1.32s/it]
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Epoch 64 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.29it/s]
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Epoch 64 Training:  49%|████▉     | 47/96 [01:01<01:04,  1.32s/it]
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Epoch 64 Training:  50%|█████     | 48/96 [01:03<01:03,  1.32s/it]
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Epoch 64 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 64 Training:  51%|█████     | 49/96 [01:04<01:01,  1.32s/it]
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Epoch 64 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 64 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.49it/s]
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Epoch 64 Training:  52%|█████▏    | 50/96 [01:05<01:00,  1.32s/it]
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Epoch 64 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.31it/s]
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Epoch 64 Training:  53%|█████▎    | 51/96 [01:07<00:59,  1.32s/it]
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Epoch 64 Training:  54%|█████▍    | 52/96 [01:08<00:58,  1.32s/it]
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Epoch 64 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.31it/s]
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Epoch 64 Training:  55%|█████▌    | 53/96 [01:09<00:56,  1.32s/it]
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Epoch 64 Training:  56%|█████▋    | 54/96 [01:10<00:55,  1.32s/it]
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Epoch 64 Training:  57%|█████▋    | 55/96 [01:12<00:53,  1.32s/it]
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Epoch 64 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 64 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.31it/s]
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Epoch 64 Training:  58%|█████▊    | 56/96 [01:13<00:52,  1.31s/it]
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Epoch 64 Training:  59%|█████▉    | 57/96 [01:14<00:51,  1.31s/it]
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Epoch 64 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 64 Training:  60%|██████    | 58/96 [01:16<00:49,  1.31s/it]
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Epoch 64 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 64 Training:  61%|██████▏   | 59/96 [01:17<00:48,  1.32s/it]
+
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Epoch 64 Training:  62%|██████▎   | 60/96 [01:18<00:47,  1.32s/it]
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Epoch 64 Training:  64%|██████▎   | 61/96 [01:20<00:46,  1.32s/it]
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Epoch 64 Training:  65%|██████▍   | 62/96 [01:21<00:44,  1.32s/it]
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Epoch 64 Training:  66%|██████▌   | 63/96 [01:22<00:43,  1.32s/it]
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Epoch 64 Training:  67%|██████▋   | 64/96 [01:24<00:42,  1.31s/it]
+
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Epoch 64 Training:  68%|██████▊   | 65/96 [01:25<00:40,  1.32s/it]
+
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Epoch 64 Training:  69%|██████▉   | 66/96 [01:26<00:39,  1.32s/it]
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Epoch 64 Training:  70%|██████▉   | 67/96 [01:28<00:38,  1.32s/it]
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Epoch 64 Training:  71%|███████   | 68/96 [01:29<00:36,  1.32s/it]
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Epoch 64 Training:  72%|███████▏  | 69/96 [01:30<00:35,  1.31s/it]
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Epoch 64 Training:  73%|███████▎  | 70/96 [01:32<00:34,  1.31s/it]
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Epoch 64 Training:  74%|███████▍  | 71/96 [01:33<00:32,  1.31s/it]
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Epoch 64 Training:  75%|███████▌  | 72/96 [01:34<00:31,  1.31s/it]
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Epoch 64 Training:  76%|███████▌  | 73/96 [01:35<00:30,  1.31s/it]
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Epoch 64 Training:  77%|███████▋  | 74/96 [01:37<00:28,  1.31s/it]
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Epoch 64 Training:  78%|███████▊  | 75/96 [01:38<00:27,  1.32s/it]
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Epoch 64 Training:  79%|███████▉  | 76/96 [01:39<00:26,  1.32s/it]
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Epoch 64 Training:  80%|████████  | 77/96 [01:41<00:25,  1.32s/it]
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Epoch 64 Training:  81%|████████▏ | 78/96 [01:42<00:23,  1.32s/it]
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Epoch 64 Training:  82%|████████▏ | 79/96 [01:43<00:22,  1.33s/it]
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Epoch 64 Training:  83%|████████▎ | 80/96 [01:45<00:21,  1.32s/it]
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Epoch 64 Training:  84%|████████▍ | 81/96 [01:46<00:19,  1.32s/it]
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Epoch 64 Training:  85%|████████▌ | 82/96 [01:47<00:18,  1.32s/it]
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Epoch 64 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s][2026-10-01 21:10:30] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0064 | Epoch 064] train/policy_loss=11.0448 train/unweighted_policy_loss=2.2090 train/loss=11.0448 train/grad_norm=0.0759
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+[2026-10-01 21:10:30] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0065 | Epoch 064] timing/rollout_seconds=147.6657 timing/feedback_seconds=5.4352 timing/optimization_seconds=126.9002 timing/cycle_seconds=278.1520
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+[2026-10-01 21:13:03] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0065 | Epoch 065] train/reward_dists_mean=0.9381 train/reward_dists_std=0.0160 train/reward_psnr_mean=30.7431 train/reward_psnr_std=1.9088 train/reward_ssim_mean=0.9360 train/reward_ssim_std=0.0220 train/reward_dists_group_std_mean=0.0026 train/reward_dists_group_std_max=0.0051 train/reward_dists_group_std_min=0.0014 train/reward_dists_group_mean_std=0.0158 train/reward_psnr_group_std_mean=1.0467 train/reward_psnr_group_std_max=2.5729 train/reward_psnr_group_std_min=0.2953 train/reward_psnr_group_mean_std=1.5251 train/reward_ssim_group_std_mean=0.0009 train/reward_ssim_group_std_max=0.0030 train/reward_ssim_group_std_min=0.0003 train/reward_ssim_group_mean_std=0.0220 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-8.2573 train/adv_max=4.8840 train/adv_abs_mean=1.7562
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Epoch 65 Training:   5%|▌         | 5/96 [00:06<01:56,  1.29s/it]
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Epoch 65 Training:   6%|▋         | 6/96 [00:07<01:55,  1.29s/it]
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Epoch 65 Training:   7%|▋         | 7/96 [00:08<01:54,  1.29s/it]
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Epoch 65 Training:   8%|▊         | 8/96 [00:10<01:53,  1.29s/it]
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Epoch 65 Training:   9%|▉         | 9/96 [00:11<01:52,  1.29s/it]
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Epoch 65 Training:  10%|█         | 10/96 [00:12<01:51,  1.29s/it]
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Epoch 65 Training:  11%|█▏        | 11/96 [00:14<01:49,  1.29s/it]
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Epoch 65 Training:  12%|█▎        | 12/96 [00:15<01:48,  1.29s/it]
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Epoch 65 Training:  14%|█▎        | 13/96 [00:16<01:47,  1.29s/it]
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Epoch 65 Training:  15%|█▍        | 14/96 [00:18<01:45,  1.29s/it]
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Epoch 65 Training:  16%|█▌        | 15/96 [00:19<01:44,  1.29s/it]
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Epoch 65 Training:  17%|█▋        | 16/96 [00:20<01:42,  1.29s/it]
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Epoch 65 Training:  18%|█▊        | 17/96 [00:21<01:41,  1.28s/it]
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Epoch 65 Training:  19%|█▉        | 18/96 [00:23<01:40,  1.28s/it]
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Epoch 65 Training:  20%|█▉        | 19/96 [00:24<01:39,  1.29s/it]
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Epoch 65 Training:  21%|██        | 20/96 [00:25<01:37,  1.29s/it]
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Epoch 65 Training:  22%|██▏       | 21/96 [00:27<01:36,  1.29s/it]
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Epoch 65 Training:  23%|██▎       | 22/96 [00:28<01:35,  1.29s/it]
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Epoch 65 Training:  24%|██▍       | 23/96 [00:29<01:34,  1.29s/it]
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Epoch 65 Training:  25%|██▌       | 24/96 [00:30<01:32,  1.29s/it]
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Epoch 65 Training:  26%|██▌       | 25/96 [00:32<01:31,  1.29s/it]
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Epoch 65 Training:  27%|██▋       | 26/96 [00:33<01:29,  1.29s/it]
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Epoch 65 Training:  28%|██▊       | 27/96 [00:34<01:28,  1.29s/it]
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Epoch 65 Training:  29%|██▉       | 28/96 [00:36<01:32,  1.35s/it]
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Epoch 65 Training:  30%|███       | 29/96 [00:37<01:29,  1.33s/it]
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Epoch 65 Training:  31%|███▏      | 30/96 [00:38<01:27,  1.32s/it]
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Epoch 65 Training:  32%|███▏      | 31/96 [00:40<01:25,  1.31s/it]
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Epoch 65 Training:  33%|███▎      | 32/96 [00:41<01:23,  1.30s/it]
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Epoch 65 Training:  34%|███▍      | 33/96 [00:42<01:21,  1.30s/it]
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Epoch 65 Training:  35%|███▌      | 34/96 [00:43<01:20,  1.30s/it]
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Epoch 65 Training:  36%|███▋      | 35/96 [00:45<01:18,  1.29s/it]
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Epoch 65 Training:  38%|███▊      | 36/96 [00:46<01:17,  1.29s/it]
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Epoch 65 Training:  39%|███▊      | 37/96 [00:47<01:16,  1.29s/it]
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Epoch 65 Training:  40%|███▉      | 38/96 [00:49<01:14,  1.29s/it]
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Epoch 65 Training:  41%|████      | 39/96 [00:50<01:13,  1.29s/it]
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Epoch 65 Training:  42%|████▏     | 40/96 [00:51<01:12,  1.29s/it]
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Epoch 65 Training:  43%|████▎     | 41/96 [00:52<01:10,  1.29s/it]
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Epoch 65 Training:  44%|████▍     | 42/96 [00:54<01:09,  1.29s/it]
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Epoch 65 Training:  45%|████▍     | 43/96 [00:55<01:08,  1.28s/it]
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Epoch 65 Training:  46%|████▌     | 44/96 [00:56<01:06,  1.29s/it]
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Epoch 65 Training:  47%|████▋     | 45/96 [00:58<01:05,  1.29s/it]
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Epoch 65 Training:  48%|████▊     | 46/96 [00:59<01:04,  1.29s/it]
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Epoch 65 Training:  49%|████▉     | 47/96 [01:00<01:03,  1.30s/it]
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Epoch 65 Training:  50%|█████     | 48/96 [01:02<01:02,  1.30s/it]
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Epoch 65 Training:  51%|█████     | 49/96 [01:03<01:00,  1.30s/it]
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Epoch 65 Training:  52%|█████▏    | 50/96 [01:04<00:59,  1.30s/it]
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Epoch 65 Training:  53%|█████▎    | 51/96 [01:05<00:58,  1.30s/it]
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Epoch 65 Training:  54%|█████▍    | 52/96 [01:07<00:57,  1.30s/it]
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Epoch 65 Training:  55%|█████▌    | 53/96 [01:08<00:55,  1.30s/it]
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Epoch 65 Training:  56%|█████▋    | 54/96 [01:09<00:54,  1.30s/it]
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Epoch 65 Training:  57%|█████▋    | 55/96 [01:11<00:53,  1.30s/it]
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Epoch 65 Training:  58%|█████▊    | 56/96 [01:12<00:52,  1.30s/it]
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Epoch 65 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s][2026-10-01 21:15:08] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0065 | Epoch 065] train/policy_loss=11.0396 train/unweighted_policy_loss=2.2079 train/loss=11.0396 train/grad_norm=0.0640
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+[2026-10-01 21:15:08] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0066 | Epoch 065] timing/rollout_seconds=147.6651 timing/feedback_seconds=6.2048 timing/optimization_seconds=126.3400 timing/cycle_seconds=277.4919
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+
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Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:00<00:00,  6.18it/s]
+
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Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.66it/s]
+
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Pointwise Rewards: psnr:  50%|█████     | 3/6 [00:00<00:00,  6.30it/s]
Pointwise Rewards: psnr:  67%|██████▋   | 4/6 [00:00<00:00,  6.72it/s]
Pointwise Rewards: psnr:  83%|████████▎ | 5/6 [00:00<00:00,  6.75it/s]
Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.71it/s]
Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.65it/s]
+[2026-10-01 21:17:39] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0066 | Epoch 066] train/reward_dists_mean=0.9378 train/reward_dists_std=0.0149 train/reward_psnr_mean=30.3192 train/reward_psnr_std=1.9510 train/reward_ssim_mean=0.9355 train/reward_ssim_std=0.0155 train/reward_dists_group_std_mean=0.0027 train/reward_dists_group_std_max=0.0063 train/reward_dists_group_std_min=0.0012 train/reward_dists_group_mean_std=0.0146 train/reward_psnr_group_std_mean=1.0423 train/reward_psnr_group_std_max=2.6865 train/reward_psnr_group_std_min=0.3073 train/reward_psnr_group_mean_std=1.5766 train/reward_ssim_group_std_mean=0.0010 train/reward_ssim_group_std_max=0.0057 train/reward_ssim_group_std_min=0.0003 train/reward_ssim_group_mean_std=0.0155 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-7.1273 train/adv_max=5.6324 train/adv_abs_mean=1.8760
+
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Epoch 66 Training:  11%|█▏        | 11/96 [00:14<01:52,  1.32s/it]
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Epoch 66 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.30it/s]
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Epoch 66 Training:  31%|███▏      | 30/96 [00:39<01:27,  1.32s/it]
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Epoch 66 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 66 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.31it/s]
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Epoch 66 Training:  32%|███▏      | 31/96 [00:40<01:25,  1.32s/it]
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Epoch 66 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 66 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 66 Training:  33%|███▎      | 32/96 [00:42<01:24,  1.32s/it]
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Epoch 66 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 66 Training:  34%|███▍      | 33/96 [00:43<01:23,  1.32s/it]
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Epoch 66 Training:  35%|███▌      | 34/96 [00:44<01:21,  1.32s/it]
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Epoch 66 Training:  36%|███▋      | 35/96 [00:46<01:20,  1.32s/it]
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Epoch 66 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.29it/s]
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Epoch 66 Training:  38%|███▊      | 36/96 [00:47<01:19,  1.32s/it]
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Epoch 66 Training:  39%|███▊      | 37/96 [00:48<01:17,  1.32s/it]
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Epoch 66 Training:  40%|███▉      | 38/96 [00:50<01:16,  1.32s/it]
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Epoch 66 Training:  41%|████      | 39/96 [00:51<01:15,  1.32s/it]
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Epoch 66 Training:  42%|████▏     | 40/96 [00:52<01:13,  1.32s/it]
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Epoch 66 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 66 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 66 Training:  43%|████▎     | 41/96 [00:53<01:12,  1.32s/it]
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Epoch 66 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 66 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.29it/s]
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Epoch 66 Training:  44%|████▍     | 42/96 [00:55<01:11,  1.32s/it]
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Epoch 66 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 66 Training:  45%|████▍     | 43/96 [00:56<01:09,  1.32s/it]
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Epoch 66 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 66 Training:  46%|████▌     | 44/96 [00:57<01:08,  1.32s/it]
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Epoch 66 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 66 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.31it/s]
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Epoch 66 Training:  47%|████▋     | 45/96 [00:59<01:07,  1.32s/it]
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Epoch 66 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 66 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.31it/s]
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Epoch 66 Training:  48%|████▊     | 46/96 [01:00<01:05,  1.32s/it]
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Epoch 66 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 66 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 66 Training:  49%|████▉     | 47/96 [01:01<01:04,  1.32s/it]
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Epoch 66 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 66 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.29it/s]
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Epoch 66 Training:  50%|█████     | 48/96 [01:03<01:03,  1.32s/it]
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Epoch 66 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 66 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 66 Training:  51%|█████     | 49/96 [01:04<01:02,  1.32s/it]
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Epoch 66 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 66 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.31it/s]
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Epoch 66 Training:  52%|█████▏    | 50/96 [01:05<01:00,  1.32s/it]
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Epoch 66 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 66 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.31it/s]
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Epoch 66 Training:  53%|█████▎    | 51/96 [01:07<00:59,  1.32s/it]
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Epoch 66 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 66 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.29it/s]
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Epoch 66 Training:  54%|█████▍    | 52/96 [01:08<00:58,  1.32s/it]
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Epoch 66 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 66 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.30it/s]
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Epoch 66 Training:  55%|█████▌    | 53/96 [01:09<00:56,  1.32s/it]
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Epoch 66 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 66 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.31it/s]
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Epoch 66 Training:  56%|█████▋    | 54/96 [01:11<00:55,  1.32s/it]
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Epoch 66 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 66 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.31it/s]
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Epoch 66 Training:  57%|█████▋    | 55/96 [01:12<00:54,  1.32s/it]
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Epoch 66 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 66 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.32it/s]
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Epoch 66 Training:  58%|█████▊    | 56/96 [01:13<00:52,  1.32s/it]
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Epoch 66 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 66 Training:  59%|█████▉    | 57/96 [01:15<00:51,  1.32s/it]
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Epoch 66 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 66 Training:  60%|██████    | 58/96 [01:16<00:50,  1.32s/it]
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Epoch 66 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.29it/s]
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Epoch 66 Training:  61%|██████▏   | 59/96 [01:17<00:48,  1.32s/it]
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Epoch 66 Training:  62%|██████▎   | 60/96 [01:19<00:47,  1.32s/it]
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Epoch 66 Training:  64%|██████▎   | 61/96 [01:20<00:46,  1.32s/it]
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Epoch 66 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.31it/s]
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Epoch 66 Training:  65%|██████▍   | 62/96 [01:21<00:44,  1.32s/it]
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Epoch 66 Training:  66%|██████▌   | 63/96 [01:22<00:43,  1.32s/it]
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Epoch 66 Training:  67%|██████▋   | 64/96 [01:24<00:42,  1.32s/it]
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Epoch 66 Training:  68%|██████▊   | 65/96 [01:25<00:40,  1.32s/it]
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Epoch 66 Training:  69%|██████▉   | 66/96 [01:26<00:39,  1.32s/it]
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Epoch 66 Training:  70%|██████▉   | 67/96 [01:28<00:38,  1.32s/it]
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Epoch 66 Training:  71%|███████   | 68/96 [01:29<00:37,  1.32s/it]
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Epoch 66 Training:  72%|███████▏  | 69/96 [01:30<00:35,  1.32s/it]
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Epoch 66 Training:  73%|███████▎  | 70/96 [01:32<00:34,  1.32s/it]
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Epoch 66 Training:  74%|███████▍  | 71/96 [01:33<00:32,  1.32s/it]
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Epoch 66 Training:  75%|███████▌  | 72/96 [01:34<00:31,  1.32s/it]
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Epoch 66 Training:  76%|███████▌  | 73/96 [01:36<00:30,  1.32s/it]
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Epoch 66 Training:  77%|███████▋  | 74/96 [01:37<00:29,  1.32s/it]
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Epoch 66 Training:  78%|███████▊  | 75/96 [01:38<00:27,  1.32s/it]
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Epoch 66 Training:  79%|███████▉  | 76/96 [01:40<00:26,  1.32s/it]
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Epoch 66 Training:  80%|████████  | 77/96 [01:41<00:25,  1.32s/it]
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Epoch 66 Training:  81%|████████▏ | 78/96 [01:42<00:23,  1.32s/it]
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Epoch 66 Training:  82%|████████▏ | 79/96 [01:44<00:22,  1.32s/it]
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Epoch 66 Training:  83%|████████▎ | 80/96 [01:45<00:21,  1.32s/it]
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Epoch 66 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s][2026-10-01 21:19:46] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0066 | Epoch 066] train/policy_loss=10.9354 train/unweighted_policy_loss=2.1871 train/loss=10.9354 train/grad_norm=0.0904
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+[2026-10-01 21:19:46] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0067 | Epoch 066] timing/rollout_seconds=147.6730 timing/feedback_seconds=5.5414 timing/optimization_seconds=127.1338 timing/cycle_seconds=278.2869
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+[2026-10-01 21:22:18] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0067 | Epoch 067] train/reward_dists_mean=0.9397 train/reward_dists_std=0.0136 train/reward_psnr_mean=30.4688 train/reward_psnr_std=1.7721 train/reward_ssim_mean=0.9352 train/reward_ssim_std=0.0179 train/reward_dists_group_std_mean=0.0028 train/reward_dists_group_std_max=0.0066 train/reward_dists_group_std_min=0.0012 train/reward_dists_group_mean_std=0.0133 train/reward_psnr_group_std_mean=1.0207 train/reward_psnr_group_std_max=2.1433 train/reward_psnr_group_std_min=0.3013 train/reward_psnr_group_mean_std=1.3875 train/reward_ssim_group_std_mean=0.0009 train/reward_ssim_group_std_max=0.0030 train/reward_ssim_group_std_min=0.0004 train/reward_ssim_group_mean_std=0.0179 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-9.0520 train/adv_max=5.6302 train/adv_abs_mean=1.8194
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Epoch 67 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.30it/s]
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Epoch 67 Training:   3%|▎         | 3/96 [00:03<02:00,  1.29s/it]
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Epoch 67 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 67 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 67 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.31it/s]
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Epoch 67 Training:   4%|▍         | 4/96 [00:05<01:59,  1.30s/it]
+
Epoch 67 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 67 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 67 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 67 Training:   5%|▌         | 5/96 [00:06<01:59,  1.31s/it]
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Epoch 67 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 67 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 67 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 67 Training:   6%|▋         | 6/96 [00:07<01:58,  1.31s/it]
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Epoch 67 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 67 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 67 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.30it/s]
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Epoch 67 Training:   7%|▋         | 7/96 [00:09<01:56,  1.31s/it]
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Epoch 67 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 67 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 67 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.30it/s]
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Epoch 67 Training:   8%|▊         | 8/96 [00:10<01:55,  1.31s/it]
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Epoch 67 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 67 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 67 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.30it/s]
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Epoch 67 Training:   9%|▉         | 9/96 [00:11<01:54,  1.32s/it]
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Epoch 67 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 67 Training:  10%|█         | 10/96 [00:13<01:53,  1.31s/it]
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Epoch 67 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 67 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 67 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 67 Training:  11%|█▏        | 11/96 [00:14<01:51,  1.32s/it]
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Epoch 67 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 67 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.30it/s]
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Epoch 67 Training:  12%|█▎        | 12/96 [00:15<01:50,  1.32s/it]
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Epoch 67 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 67 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.29it/s]
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Epoch 67 Training:  14%|█▎        | 13/96 [00:17<01:49,  1.32s/it]
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Epoch 67 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 67 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.31it/s]
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Epoch 67 Training:  15%|█▍        | 14/96 [00:18<01:48,  1.32s/it]
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Epoch 67 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 67 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.31it/s]
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Epoch 67 Training:  16%|█▌        | 15/96 [00:19<01:46,  1.32s/it]
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Epoch 67 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 67 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 67 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.30it/s]
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Epoch 67 Training:  17%|█▋        | 16/96 [00:20<01:45,  1.32s/it]
+
Epoch 67 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 67 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 67 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 67 Training:  18%|█▊        | 17/96 [00:22<01:43,  1.32s/it]
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Epoch 67 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 67 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 67 Training:  19%|█▉        | 18/96 [00:23<01:42,  1.32s/it]
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Epoch 67 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 67 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.30it/s]
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Epoch 67 Training:  20%|█▉        | 19/96 [00:24<01:41,  1.32s/it]
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Epoch 67 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 67 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.31it/s]
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Epoch 67 Training:  21%|██        | 20/96 [00:26<01:40,  1.32s/it]
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Epoch 67 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 67 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.31it/s]
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Epoch 67 Training:  22%|██▏       | 21/96 [00:27<01:38,  1.32s/it]
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Epoch 67 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 67 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 67 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.30it/s]
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Epoch 67 Training:  23%|██▎       | 22/96 [00:28<01:37,  1.32s/it]
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Epoch 67 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 67 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 67 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 67 Training:  24%|██▍       | 23/96 [00:30<01:36,  1.32s/it]
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Epoch 67 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 67 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 67 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 67 Training:  25%|██▌       | 24/96 [00:31<01:34,  1.32s/it]
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Epoch 67 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 67 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.29it/s]
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Epoch 67 Training:  26%|██▌       | 25/96 [00:32<01:33,  1.32s/it]
+
Epoch 67 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 67 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.31it/s]
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Epoch 67 Training:  27%|██▋       | 26/96 [00:34<01:32,  1.32s/it]
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Epoch 67 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 67 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 67 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.30it/s]
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Epoch 67 Training:  28%|██▊       | 27/96 [00:35<01:30,  1.32s/it]
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Epoch 67 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 67 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.30it/s]
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Epoch 67 Training:  29%|██▉       | 28/96 [00:36<01:29,  1.32s/it]
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Epoch 67 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 67 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 67 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.29it/s]
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Epoch 67 Training:  30%|███       | 29/96 [00:38<01:28,  1.32s/it]
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Epoch 67 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 67 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 67 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 67 Training:  31%|███▏      | 30/96 [00:39<01:26,  1.32s/it]
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Epoch 67 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 67 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.29it/s]
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Epoch 67 Training:  32%|███▏      | 31/96 [00:40<01:25,  1.32s/it]
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Epoch 67 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 67 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.30it/s]
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Epoch 67 Training:  33%|███▎      | 32/96 [00:42<01:24,  1.32s/it]
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Epoch 67 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 67 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.31it/s]
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Epoch 67 Training:  34%|███▍      | 33/96 [00:43<01:22,  1.32s/it]
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Epoch 67 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 67 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.29it/s]
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Epoch 67 Training:  35%|███▌      | 34/96 [00:44<01:21,  1.32s/it]
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Epoch 67 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 67 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 67 Training:  36%|███▋      | 35/96 [00:46<01:20,  1.32s/it]
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Epoch 67 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 67 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 67 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 67 Training:  38%|███▊      | 36/96 [00:47<01:19,  1.32s/it]
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Epoch 67 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 67 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.30it/s]
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Epoch 67 Training:  39%|███▊      | 37/96 [00:48<01:17,  1.32s/it]
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Epoch 67 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 67 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.31it/s]
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Epoch 67 Training:  40%|███▉      | 38/96 [00:49<01:16,  1.32s/it]
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Epoch 67 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 67 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.35it/s]
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Epoch 67 Training:  41%|████      | 39/96 [00:51<01:15,  1.32s/it]
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Epoch 67 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 67 Training:  42%|████▏     | 40/96 [00:52<01:13,  1.32s/it]
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Epoch 67 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 67 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 67 Training:  43%|████▎     | 41/96 [00:53<01:12,  1.32s/it]
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Epoch 67 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 67 Training:  44%|████▍     | 42/96 [00:55<01:11,  1.32s/it]
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Epoch 67 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 67 Training:  45%|████▍     | 43/96 [00:56<01:09,  1.32s/it]
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Epoch 67 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 67 Training:  46%|████▌     | 44/96 [00:57<01:08,  1.32s/it]
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Epoch 67 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 67 Training:  47%|████▋     | 45/96 [00:59<01:07,  1.32s/it]
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Epoch 67 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 67 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 67 Training:  48%|████▊     | 46/96 [01:00<01:05,  1.32s/it]
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Epoch 67 Training:  49%|████▉     | 47/96 [01:01<01:04,  1.32s/it]
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Epoch 67 Training:  50%|█████     | 48/96 [01:03<01:03,  1.32s/it]
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Epoch 67 Training:  51%|█████     | 49/96 [01:04<01:02,  1.32s/it]
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Epoch 67 Training:  52%|█████▏    | 50/96 [01:05<01:00,  1.32s/it]
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Epoch 67 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 67 Training:  53%|█████▎    | 51/96 [01:07<00:59,  1.32s/it]
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Epoch 67 Training:  54%|█████▍    | 52/96 [01:08<00:57,  1.32s/it]
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Epoch 67 Training:  55%|█████▌    | 53/96 [01:09<00:56,  1.32s/it]
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Epoch 67 Training:  56%|█████▋    | 54/96 [01:11<00:55,  1.32s/it]
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Epoch 67 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s][2026-10-01 21:24:24] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0067 | Epoch 067] train/policy_loss=10.8978 train/unweighted_policy_loss=2.1796 train/loss=10.8978 train/grad_norm=0.1277
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+[2026-10-01 21:24:24] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0068 | Epoch 067] timing/rollout_seconds=147.6482 timing/feedback_seconds=5.5135 timing/optimization_seconds=127.2084 timing/cycle_seconds=278.3601
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+
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Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:00<00:00,  6.40it/s]
+
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Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.66it/s]
+
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Pointwise Rewards: psnr:  83%|████████▎ | 5/6 [00:00<00:00,  6.91it/s]
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Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.78it/s]
+[2026-10-01 21:26:56] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0068 | Epoch 068] train/reward_dists_mean=0.9396 train/reward_dists_std=0.0147 train/reward_psnr_mean=31.2613 train/reward_psnr_std=2.1909 train/reward_ssim_mean=0.9404 train/reward_ssim_std=0.0197 train/reward_dists_group_std_mean=0.0025 train/reward_dists_group_std_max=0.0051 train/reward_dists_group_std_min=0.0009 train/reward_dists_group_mean_std=0.0144 train/reward_psnr_group_std_mean=1.0437 train/reward_psnr_group_std_max=2.3432 train/reward_psnr_group_std_min=0.4110 train/reward_psnr_group_mean_std=1.8492 train/reward_ssim_group_std_mean=0.0008 train/reward_ssim_group_std_max=0.0016 train/reward_ssim_group_std_min=0.0004 train/reward_ssim_group_mean_std=0.0196 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-8.3181 train/adv_max=5.5303 train/adv_abs_mean=1.7670
+
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Epoch 68 Training:  29%|██▉       | 28/96 [00:37<01:31,  1.34s/it]
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Epoch 68 Training:  30%|███       | 29/96 [00:38<01:29,  1.34s/it]
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Epoch 68 Training:  31%|███▏      | 30/96 [00:40<01:27,  1.33s/it]
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Epoch 68 Training:  32%|███▏      | 31/96 [00:41<01:31,  1.41s/it]
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Epoch 68 Training:  33%|███▎      | 32/96 [00:42<01:28,  1.38s/it]
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Epoch 68 Training:  34%|███▍      | 33/96 [00:44<01:25,  1.36s/it]
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Epoch 68 Training:  35%|███▌      | 34/96 [00:45<01:23,  1.35s/it]
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Epoch 68 Training:  36%|███▋      | 35/96 [00:46<01:22,  1.35s/it]
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Epoch 68 Training:  38%|███▊      | 36/96 [00:48<01:20,  1.34s/it]
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Epoch 68 Training:  39%|███▊      | 37/96 [00:49<01:18,  1.34s/it]
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Epoch 68 Training:  40%|███▉      | 38/96 [00:50<01:17,  1.33s/it]
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Epoch 68 Training:  41%|████      | 39/96 [00:52<01:15,  1.33s/it]
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Epoch 68 Training:  42%|████▏     | 40/96 [00:53<01:14,  1.33s/it]
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Epoch 68 Training:  43%|████▎     | 41/96 [00:54<01:12,  1.33s/it]
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Epoch 68 Training:  44%|████▍     | 42/96 [00:56<01:11,  1.33s/it]
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Epoch 68 Training:  45%|████▍     | 43/96 [00:57<01:10,  1.33s/it]
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Epoch 68 Training:  46%|████▌     | 44/96 [00:58<01:08,  1.32s/it]
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Epoch 68 Training:  47%|████▋     | 45/96 [01:00<01:07,  1.32s/it]
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Epoch 68 Training:  48%|████▊     | 46/96 [01:01<01:06,  1.33s/it]
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Epoch 68 Training:  49%|████▉     | 47/96 [01:02<01:04,  1.32s/it]
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Epoch 68 Training:  50%|█████     | 48/96 [01:04<01:03,  1.32s/it]
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Epoch 68 Training:  51%|█████     | 49/96 [01:05<01:02,  1.32s/it]
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Epoch 68 Training:  52%|█████▏    | 50/96 [01:06<01:01,  1.33s/it]
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Epoch 68 Training:  53%|█████▎    | 51/96 [01:08<00:59,  1.32s/it]
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Epoch 68 Training:  54%|█████▍    | 52/96 [01:09<00:58,  1.33s/it]
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Epoch 68 Training:  55%|█████▌    | 53/96 [01:10<00:56,  1.33s/it]
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Epoch 68 Training:  56%|█████▋    | 54/96 [01:12<00:55,  1.33s/it]
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Epoch 68 Training:  57%|█████▋    | 55/96 [01:13<00:54,  1.33s/it]
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Epoch 68 Training:  58%|█████▊    | 56/96 [01:14<00:53,  1.33s/it]
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Epoch 68 Training:  59%|█████▉    | 57/96 [01:16<00:51,  1.33s/it]
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Epoch 68 Training:  60%|██████    | 58/96 [01:17<00:50,  1.33s/it]
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Epoch 68 Training:  61%|██████▏   | 59/96 [01:18<00:49,  1.33s/it]
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Epoch 68 Training:  62%|██████▎   | 60/96 [01:20<00:47,  1.32s/it]
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Epoch 68 Training:  64%|██████▎   | 61/96 [01:21<00:46,  1.33s/it]
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Epoch 68 Training:  65%|██████▍   | 62/96 [01:22<00:45,  1.33s/it]
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Epoch 68 Training:  66%|██████▌   | 63/96 [01:24<00:43,  1.33s/it]
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Epoch 68 Training:  67%|██████▋   | 64/96 [01:25<00:42,  1.33s/it]
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Epoch 68 Training:  68%|██████▊   | 65/96 [01:26<00:41,  1.33s/it]
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Epoch 68 Training:  69%|██████▉   | 66/96 [01:27<00:39,  1.33s/it]
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Epoch 68 Training:  70%|██████▉   | 67/96 [01:29<00:38,  1.33s/it]
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Epoch 68 Training:  71%|███████   | 68/96 [01:30<00:37,  1.33s/it]
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Epoch 68 Training:  72%|███████▏  | 69/96 [01:31<00:35,  1.33s/it]
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Epoch 68 Training:  73%|███████▎  | 70/96 [01:33<00:34,  1.33s/it]
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Epoch 68 Training:  74%|███████▍  | 71/96 [01:34<00:33,  1.32s/it]
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Epoch 68 Training:  75%|███████▌  | 72/96 [01:35<00:31,  1.32s/it]
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Epoch 68 Training:  76%|███████▌  | 73/96 [01:37<00:30,  1.33s/it]
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Epoch 68 Training:  77%|███████▋  | 74/96 [01:38<00:29,  1.33s/it]
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Epoch 68 Training:  78%|███████▊  | 75/96 [01:39<00:27,  1.33s/it]
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Epoch 68 Training:  79%|███████▉  | 76/96 [01:41<00:26,  1.33s/it]
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Epoch 68 Training:  80%|████████  | 77/96 [01:42<00:25,  1.33s/it]
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Epoch 68 Training:  81%|████████▏ | 78/96 [01:43<00:23,  1.33s/it]
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Epoch 68 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s][2026-10-01 21:29:04] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0068 | Epoch 068] train/policy_loss=10.9348 train/unweighted_policy_loss=2.1870 train/loss=10.9348 train/grad_norm=0.0649
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+[2026-10-01 21:29:04] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0069 | Epoch 068] timing/rollout_seconds=147.8917 timing/feedback_seconds=5.7991 timing/optimization_seconds=128.4106 timing/cycle_seconds=279.8634
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+[2026-10-01 21:31:36] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0069 | Epoch 069] train/reward_dists_mean=0.9390 train/reward_dists_std=0.0143 train/reward_psnr_mean=30.6480 train/reward_psnr_std=1.8441 train/reward_ssim_mean=0.9383 train/reward_ssim_std=0.0178 train/reward_dists_group_std_mean=0.0024 train/reward_dists_group_std_max=0.0053 train/reward_dists_group_std_min=0.0013 train/reward_dists_group_mean_std=0.0141 train/reward_psnr_group_std_mean=0.9472 train/reward_psnr_group_std_max=2.1849 train/reward_psnr_group_std_min=0.1978 train/reward_psnr_group_mean_std=1.4993 train/reward_ssim_group_std_mean=0.0009 train/reward_ssim_group_std_max=0.0034 train/reward_ssim_group_std_min=0.0003 train/reward_ssim_group_mean_std=0.0178 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-8.8339 train/adv_max=5.5965 train/adv_abs_mean=1.7695
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Epoch 69 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.68it/s]
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Epoch 69 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.50it/s]
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Epoch 69 Training:   1%|          | 1/96 [00:01<01:55,  1.22s/it]
+
Epoch 69 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 69 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 69 Timestep: 100%|██████████| 2/2 [00:00<00:00,  1.97it/s]
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Epoch 69 Training:   2%|▏         | 2/96 [00:02<02:06,  1.35s/it]
+
Epoch 69 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 69 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 69 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.21it/s]
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Epoch 69 Training:   3%|▎         | 3/96 [00:03<02:05,  1.35s/it]
+
Epoch 69 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 69 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 69 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
+
                                                                
Epoch 69 Training:   4%|▍         | 4/96 [00:05<02:03,  1.34s/it]
+
Epoch 69 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 69 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 69 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 69 Training:   5%|▌         | 5/96 [00:06<02:01,  1.33s/it]
+
Epoch 69 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 69 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 69 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 69 Training:   6%|▋         | 6/96 [00:07<01:59,  1.33s/it]
+
Epoch 69 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 69 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 69 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
+
                                                                
Epoch 69 Training:   7%|▋         | 7/96 [00:09<01:58,  1.33s/it]
+
Epoch 69 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 69 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 69 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
+
                                                                
Epoch 69 Training:   8%|▊         | 8/96 [00:10<01:56,  1.33s/it]
+
Epoch 69 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 69 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 69 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 69 Training:   9%|▉         | 9/96 [00:11<01:55,  1.33s/it]
+
Epoch 69 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 69 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 69 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 69 Training:  10%|█         | 10/96 [00:13<01:54,  1.33s/it]
+
Epoch 69 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 69 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 69 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
+
                                                                
Epoch 69 Training:  11%|█▏        | 11/96 [00:14<01:52,  1.33s/it]
+
Epoch 69 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 69 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 69 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
+
                                                                
Epoch 69 Training:  12%|█▎        | 12/96 [00:15<01:51,  1.33s/it]
+
Epoch 69 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 69 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 69 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
+
                                                                
Epoch 69 Training:  14%|█▎        | 13/96 [00:17<01:50,  1.33s/it]
+
Epoch 69 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 69 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 69 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.23it/s]
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Epoch 69 Training:  15%|█▍        | 14/96 [00:18<01:48,  1.33s/it]
+
Epoch 69 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 69 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 69 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
+
                                                                
Epoch 69 Training:  16%|█▌        | 15/96 [00:19<01:47,  1.33s/it]
+
Epoch 69 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 69 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 69 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
+
                                                                
Epoch 69 Training:  17%|█▋        | 16/96 [00:21<01:46,  1.33s/it]
+
Epoch 69 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 69 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 69 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
+
                                                                
Epoch 69 Training:  18%|█▊        | 17/96 [00:22<01:44,  1.33s/it]
+
Epoch 69 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 69 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 69 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
+
                                                                
Epoch 69 Training:  19%|█▉        | 18/96 [00:23<01:43,  1.33s/it]
+
Epoch 69 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 69 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 69 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
+
                                                                
Epoch 69 Training:  20%|█▉        | 19/96 [00:25<01:42,  1.33s/it]
+
Epoch 69 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 69 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 69 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.22it/s]
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Epoch 69 Training:  21%|██        | 20/96 [00:26<01:41,  1.33s/it]
+
Epoch 69 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 69 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 69 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
+
                                                                
Epoch 69 Training:  22%|██▏       | 21/96 [00:27<01:39,  1.33s/it]
+
Epoch 69 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 69 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 69 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
+
                                                                
Epoch 69 Training:  23%|██▎       | 22/96 [00:29<01:38,  1.33s/it]
+
Epoch 69 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 69 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 69 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 69 Training:  24%|██▍       | 23/96 [00:30<01:37,  1.33s/it]
+
Epoch 69 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 69 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 69 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 69 Training:  25%|██▌       | 24/96 [00:31<01:35,  1.33s/it]
+
Epoch 69 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 69 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 69 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
+
                                                                
Epoch 69 Training:  26%|██▌       | 25/96 [00:33<01:34,  1.33s/it]
+
Epoch 69 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 69 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 69 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.29it/s]
+
                                                                
Epoch 69 Training:  27%|██▋       | 26/96 [00:34<01:32,  1.33s/it]
+
Epoch 69 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 69 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 69 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 69 Training:  28%|██▊       | 27/96 [00:35<01:31,  1.33s/it]
+
Epoch 69 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 69 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 69 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 69 Training:  29%|██▉       | 28/96 [00:37<01:30,  1.33s/it]
+
Epoch 69 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 69 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 69 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 69 Training:  30%|███       | 29/96 [00:38<01:28,  1.33s/it]
+
Epoch 69 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 69 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 69 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.29it/s]
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Epoch 69 Training:  31%|███▏      | 30/96 [00:39<01:27,  1.33s/it]
+
Epoch 69 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 69 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 69 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 69 Training:  32%|███▏      | 31/96 [00:41<01:26,  1.33s/it]
+
Epoch 69 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 69 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 69 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.43it/s]
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Epoch 69 Training:  33%|███▎      | 32/96 [00:42<01:25,  1.33s/it]
+
Epoch 69 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 69 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 69 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 69 Training:  34%|███▍      | 33/96 [00:43<01:23,  1.33s/it]
+
Epoch 69 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 69 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 69 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.23it/s]
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Epoch 69 Training:  35%|███▌      | 34/96 [00:45<01:22,  1.33s/it]
+
Epoch 69 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 69 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 69 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 69 Training:  36%|███▋      | 35/96 [00:46<01:21,  1.33s/it]
+
Epoch 69 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 69 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 69 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.23it/s]
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Epoch 69 Training:  38%|███▊      | 36/96 [00:47<01:19,  1.33s/it]
+
Epoch 69 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 69 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 69 Training:  39%|███▊      | 37/96 [00:49<01:18,  1.33s/it]
+
Epoch 69 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 69 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 69 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 69 Training:  40%|███▉      | 38/96 [00:50<01:17,  1.33s/it]
+
Epoch 69 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 69 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 69 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 69 Training:  41%|████      | 39/96 [00:51<01:15,  1.33s/it]
+
Epoch 69 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 69 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 69 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.29it/s]
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Epoch 69 Training:  42%|████▏     | 40/96 [00:53<01:14,  1.33s/it]
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Epoch 69 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 69 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 69 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 69 Training:  43%|████▎     | 41/96 [00:54<01:12,  1.33s/it]
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Epoch 69 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 69 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 69 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 69 Training:  44%|████▍     | 42/96 [00:55<01:11,  1.33s/it]
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Epoch 69 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 69 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 69 Training:  45%|████▍     | 43/96 [00:57<01:10,  1.33s/it]
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Epoch 69 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 69 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 69 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 69 Training:  46%|████▌     | 44/96 [00:58<01:09,  1.33s/it]
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Epoch 69 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 69 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.22it/s]
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Epoch 69 Training:  47%|████▋     | 45/96 [00:59<01:08,  1.33s/it]
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Epoch 69 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 69 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 69 Training:  48%|████▊     | 46/96 [01:01<01:06,  1.33s/it]
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Epoch 69 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 69 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 69 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 69 Training:  49%|████▉     | 47/96 [01:02<01:05,  1.33s/it]
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Epoch 69 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 69 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 69 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 69 Training:  50%|█████     | 48/96 [01:03<01:03,  1.33s/it]
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Epoch 69 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 69 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 69 Training:  51%|█████     | 49/96 [01:05<01:02,  1.33s/it]
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Epoch 69 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 69 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 69 Training:  52%|█████▏    | 50/96 [01:06<01:00,  1.33s/it]
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Epoch 69 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 69 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 69 Training:  53%|█████▎    | 51/96 [01:07<00:59,  1.32s/it]
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Epoch 69 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 69 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 69 Training:  54%|█████▍    | 52/96 [01:09<00:58,  1.33s/it]
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Epoch 69 Training:  99%|█████████▉| 95/96 [02:06<00:01,  1.33s/it]
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Epoch 69 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s][2026-10-01 21:33:44] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0069 | Epoch 069] train/policy_loss=11.1694 train/unweighted_policy_loss=2.2339 train/loss=11.1694 train/grad_norm=0.0819
+
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Epoch 69 Training: 100%|██████████| 96/96 [02:07<00:00,  1.34s/it]
Epoch 69 Training: 100%|██████████| 96/96 [02:07<00:00,  1.33s/it]
+[2026-10-01 21:33:44] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0070 | Epoch 069] timing/rollout_seconds=147.8634 timing/feedback_seconds=5.5804 timing/optimization_seconds=128.0988 timing/cycle_seconds=279.5289
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+
Pointwise Rewards: ssim:   0%|          | 0/1 [00:00<?, ?it/s]
Pointwise Rewards: ssim: 100%|██████████| 1/1 [00:00<00:00,  8.59it/s]
Pointwise Rewards: ssim: 100%|██████████| 1/1 [00:00<00:00,  8.54it/s]
+
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Pointwise Rewards: psnr: 100%|██████████| 1/1 [00:00<00:00, 13.41it/s]
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Pointwise Rewards: dists: 100%|██████████| 1/1 [00:00<00:00,  8.58it/s]
+[2026-10-01 21:34:28] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0070 | Epoch 070] eval/vedai_rl_full/reward_dists_mean=0.9299 eval/vedai_rl_full/reward_dists_std=0.0182 eval/vedai_rl_full/reward_psnr_mean=29.2309 eval/vedai_rl_full/reward_psnr_std=2.3838 eval/vedai_rl_full/reward_ssim_mean=0.9235 eval/vedai_rl_full/reward_ssim_std=0.0259
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+
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Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:00<00:00,  6.50it/s]
+
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+
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Pointwise Rewards: psnr:  50%|█████     | 3/6 [00:00<00:00,  6.84it/s]
Pointwise Rewards: psnr:  67%|██████▋   | 4/6 [00:00<00:00,  6.93it/s]
Pointwise Rewards: psnr:  83%|████████▎ | 5/6 [00:00<00:00,  7.12it/s]
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Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.92it/s]
+[2026-10-01 21:37:00] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0070 | Epoch 070] train/reward_dists_mean=0.9418 train/reward_dists_std=0.0137 train/reward_psnr_mean=31.0166 train/reward_psnr_std=2.0351 train/reward_ssim_mean=0.9437 train/reward_ssim_std=0.0181 train/reward_dists_group_std_mean=0.0025 train/reward_dists_group_std_max=0.0051 train/reward_dists_group_std_min=0.0010 train/reward_dists_group_mean_std=0.0135 train/reward_psnr_group_std_mean=1.0308 train/reward_psnr_group_std_max=2.6638 train/reward_psnr_group_std_min=0.2812 train/reward_psnr_group_mean_std=1.6776 train/reward_ssim_group_std_mean=0.0008 train/reward_ssim_group_std_max=0.0020 train/reward_ssim_group_std_min=0.0003 train/reward_ssim_group_mean_std=0.0181 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-7.5593 train/adv_max=4.7214 train/adv_abs_mean=1.7998
+
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Epoch 70 Training:  21%|██        | 20/96 [00:26<01:41,  1.33s/it]
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Epoch 70 Training:  22%|██▏       | 21/96 [00:28<01:45,  1.41s/it]
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Epoch 70 Training:  23%|██▎       | 22/96 [00:29<01:42,  1.39s/it]
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Epoch 70 Training:  24%|██▍       | 23/96 [00:31<01:40,  1.37s/it]
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Epoch 70 Training:  25%|██▌       | 24/96 [00:32<01:37,  1.36s/it]
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Epoch 70 Training:  26%|██▌       | 25/96 [00:33<01:36,  1.35s/it]
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Epoch 70 Training:  27%|██▋       | 26/96 [00:35<01:34,  1.35s/it]
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Epoch 70 Training:  28%|██▊       | 27/96 [00:36<01:32,  1.35s/it]
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Epoch 70 Training:  29%|██▉       | 28/96 [00:37<01:31,  1.34s/it]
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Epoch 70 Training:  30%|███       | 29/96 [00:39<01:29,  1.34s/it]
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Epoch 70 Training:  31%|███▏      | 30/96 [00:40<01:28,  1.34s/it]
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Epoch 70 Training:  32%|███▏      | 31/96 [00:41<01:26,  1.34s/it]
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Epoch 70 Training:  33%|███▎      | 32/96 [00:43<01:25,  1.33s/it]
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Epoch 70 Training:  34%|███▍      | 33/96 [00:44<01:23,  1.33s/it]
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Epoch 70 Training:  35%|███▌      | 34/96 [00:45<01:22,  1.33s/it]
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Epoch 70 Training:  36%|███▋      | 35/96 [00:47<01:21,  1.33s/it]
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Epoch 70 Training:  38%|███▊      | 36/96 [00:48<01:19,  1.33s/it]
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Epoch 70 Training:  39%|███▊      | 37/96 [00:49<01:18,  1.33s/it]
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Epoch 70 Training:  40%|███▉      | 38/96 [00:51<01:17,  1.33s/it]
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Epoch 70 Training:  41%|████      | 39/96 [00:52<01:15,  1.33s/it]
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Epoch 70 Training:  42%|████▏     | 40/96 [00:53<01:14,  1.33s/it]
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Epoch 70 Training:  43%|████▎     | 41/96 [00:54<01:12,  1.32s/it]
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Epoch 70 Training:  44%|████▍     | 42/96 [00:56<01:11,  1.33s/it]
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Epoch 70 Training:  45%|████▍     | 43/96 [00:57<01:10,  1.33s/it]
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Epoch 70 Training:  46%|████▌     | 44/96 [00:58<01:09,  1.33s/it]
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Epoch 70 Training:  47%|████▋     | 45/96 [01:00<01:07,  1.33s/it]
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Epoch 70 Training:  48%|████▊     | 46/96 [01:01<01:06,  1.33s/it]
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Epoch 70 Training:  49%|████▉     | 47/96 [01:02<01:05,  1.33s/it]
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Epoch 70 Training:  50%|█████     | 48/96 [01:04<01:03,  1.33s/it]
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Epoch 70 Training:  51%|█████     | 49/96 [01:05<01:02,  1.33s/it]
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Epoch 70 Training:  52%|█████▏    | 50/96 [01:07<01:02,  1.35s/it]
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Epoch 70 Training:  53%|█████▎    | 51/96 [01:08<01:00,  1.35s/it]
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Epoch 70 Training:  54%|█████▍    | 52/96 [01:09<00:58,  1.34s/it]
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Epoch 70 Training:  55%|█████▌    | 53/96 [01:10<00:57,  1.34s/it]
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Epoch 70 Training:  56%|█████▋    | 54/96 [01:12<00:55,  1.33s/it]
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Epoch 70 Training:  57%|█████▋    | 55/96 [01:13<00:54,  1.33s/it]
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Epoch 70 Training:  58%|█████▊    | 56/96 [01:14<00:53,  1.33s/it]
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Epoch 70 Training:  59%|█████▉    | 57/96 [01:16<00:51,  1.33s/it]
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Epoch 70 Training:  60%|██████    | 58/96 [01:17<00:50,  1.33s/it]
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Epoch 70 Training:  61%|██████▏   | 59/96 [01:18<00:49,  1.33s/it]
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Epoch 70 Training:  62%|██████▎   | 60/96 [01:20<00:47,  1.33s/it]
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Epoch 70 Training:  64%|██████▎   | 61/96 [01:21<00:46,  1.34s/it]
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Epoch 70 Training:  65%|██████▍   | 62/96 [01:23<00:45,  1.34s/it]
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Epoch 70 Training:  66%|██████▌   | 63/96 [01:24<00:44,  1.34s/it]
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Epoch 70 Training:  67%|██████▋   | 64/96 [01:25<00:42,  1.34s/it]
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Epoch 70 Training:  68%|██████▊   | 65/96 [01:26<00:41,  1.34s/it]
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Epoch 70 Training:  69%|██████▉   | 66/96 [01:28<00:39,  1.33s/it]
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Epoch 70 Training:  70%|██████▉   | 67/96 [01:29<00:38,  1.33s/it]
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Epoch 70 Training:  71%|███████   | 68/96 [01:30<00:37,  1.33s/it]
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Epoch 70 Training:  72%|███████▏  | 69/96 [01:32<00:35,  1.33s/it]
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Epoch 70 Training:  73%|███████▎  | 70/96 [01:33<00:34,  1.33s/it]
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Epoch 70 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s][2026-10-01 21:39:08] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0070 | Epoch 070] train/policy_loss=10.5527 train/unweighted_policy_loss=2.1105 train/loss=10.5527 train/grad_norm=0.1037
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Epoch 70 Training: 100%|██████████| 96/96 [02:08<00:00,  1.34s/it]
+[2026-10-01 21:39:08] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0071 | Epoch 070] timing/rollout_seconds=147.8598 timing/feedback_seconds=5.7610 timing/optimization_seconds=128.8175 timing/cycle_seconds=280.1571
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Pointwise Rewards: ssim:  83%|████████▎ | 5/6 [00:00<00:00,  6.50it/s]
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+
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Pointwise Rewards: psnr:  83%|████████▎ | 5/6 [00:00<00:00,  6.55it/s]
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Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.56it/s]
+[2026-10-01 21:41:40] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0071 | Epoch 071] train/reward_dists_mean=0.9371 train/reward_dists_std=0.0187 train/reward_psnr_mean=30.8495 train/reward_psnr_std=2.5000 train/reward_ssim_mean=0.9397 train/reward_ssim_std=0.0191 train/reward_dists_group_std_mean=0.0027 train/reward_dists_group_std_max=0.0093 train/reward_dists_group_std_min=0.0010 train/reward_dists_group_mean_std=0.0184 train/reward_psnr_group_std_mean=1.1525 train/reward_psnr_group_std_max=3.7075 train/reward_psnr_group_std_min=0.3817 train/reward_psnr_group_mean_std=2.1062 train/reward_ssim_group_std_mean=0.0011 train/reward_ssim_group_std_max=0.0122 train/reward_ssim_group_std_min=0.0003 train/reward_ssim_group_mean_std=0.0190 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-8.0086 train/adv_max=7.3768 train/adv_abs_mean=1.7888
+
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Epoch 71 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 71 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.29it/s]
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Epoch 71 Training:  47%|████▋     | 45/96 [00:59<01:07,  1.33s/it]
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Epoch 71 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 71 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 71 Training:  48%|████▊     | 46/96 [01:01<01:06,  1.33s/it]
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Epoch 71 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 71 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 71 Training:  49%|████▉     | 47/96 [01:02<01:05,  1.33s/it]
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Epoch 71 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 71 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.29it/s]
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Epoch 71 Training:  50%|█████     | 48/96 [01:03<01:03,  1.33s/it]
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Epoch 71 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 71 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.18it/s]
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Epoch 71 Training:  51%|█████     | 49/96 [01:05<01:02,  1.34s/it]
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Epoch 71 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 71 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 71 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 71 Training:  52%|█████▏    | 50/96 [01:06<01:01,  1.34s/it]
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Epoch 71 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 71 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 71 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.29it/s]
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Epoch 71 Training:  53%|█████▎    | 51/96 [01:07<00:59,  1.33s/it]
+
Epoch 71 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 71 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 71 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 71 Training:  54%|█████▍    | 52/96 [01:09<00:58,  1.33s/it]
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Epoch 71 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 71 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 71 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 71 Training:  55%|█████▌    | 53/96 [01:10<00:57,  1.33s/it]
+
Epoch 71 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 71 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 71 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 71 Training:  56%|█████▋    | 54/96 [01:11<00:55,  1.33s/it]
+
Epoch 71 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 71 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 71 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 71 Training:  57%|█████▋    | 55/96 [01:13<00:54,  1.33s/it]
+
Epoch 71 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 71 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 71 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 71 Training:  58%|█████▊    | 56/96 [01:14<00:53,  1.33s/it]
+
Epoch 71 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 71 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 71 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 71 Training:  59%|█████▉    | 57/96 [01:15<00:51,  1.33s/it]
+
Epoch 71 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 71 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 71 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
+
                                                                
Epoch 71 Training:  60%|██████    | 58/96 [01:17<00:50,  1.33s/it]
+
Epoch 71 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 71 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 71 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
+
                                                                
Epoch 71 Training:  61%|██████▏   | 59/96 [01:18<00:49,  1.33s/it]
+
Epoch 71 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 71 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 71 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
+
                                                                
Epoch 71 Training:  62%|██████▎   | 60/96 [01:19<00:47,  1.33s/it]
+
Epoch 71 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 71 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 71 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
+
                                                                
Epoch 71 Training:  64%|██████▎   | 61/96 [01:21<00:46,  1.34s/it]
+
Epoch 71 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 71 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 71 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
+
                                                                
Epoch 71 Training:  65%|██████▍   | 62/96 [01:22<00:45,  1.33s/it]
+
Epoch 71 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 71 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 71 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
+
                                                                
Epoch 71 Training:  66%|██████▌   | 63/96 [01:23<00:44,  1.33s/it]
+
Epoch 71 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 71 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 71 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.23it/s]
+
                                                                
Epoch 71 Training:  67%|██████▋   | 64/96 [01:25<00:42,  1.34s/it]
+
Epoch 71 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 71 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 71 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
+
                                                                
Epoch 71 Training:  68%|██████▊   | 65/96 [01:26<00:41,  1.33s/it]
+
Epoch 71 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 71 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 71 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
+
                                                                
Epoch 71 Training:  69%|██████▉   | 66/96 [01:27<00:39,  1.33s/it]
+
Epoch 71 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 71 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 71 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
+
                                                                
Epoch 71 Training:  70%|██████▉   | 67/96 [01:29<00:38,  1.33s/it]
+
Epoch 71 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 71 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 71 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
+
                                                                
Epoch 71 Training:  71%|███████   | 68/96 [01:30<00:37,  1.33s/it]
+
Epoch 71 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 71 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 71 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
+
                                                                
Epoch 71 Training:  72%|███████▏  | 69/96 [01:31<00:35,  1.33s/it]
+
Epoch 71 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 71 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 71 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.23it/s]
+
                                                                
Epoch 71 Training:  73%|███████▎  | 70/96 [01:33<00:34,  1.33s/it]
+
Epoch 71 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 71 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 71 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
+
                                                                
Epoch 71 Training:  74%|███████▍  | 71/96 [01:34<00:33,  1.33s/it]
+
Epoch 71 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 71 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 71 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
+
                                                                
Epoch 71 Training:  75%|███████▌  | 72/96 [01:35<00:31,  1.33s/it]
+
Epoch 71 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 71 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 71 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 71 Training:  76%|███████▌  | 73/96 [01:37<00:30,  1.33s/it]
+
Epoch 71 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 71 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 71 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
+
                                                                
Epoch 71 Training:  77%|███████▋  | 74/96 [01:38<00:29,  1.33s/it]
+
Epoch 71 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 71 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 71 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.29it/s]
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Epoch 71 Training:  78%|███████▊  | 75/96 [01:39<00:27,  1.33s/it]
+
Epoch 71 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 71 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 71 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 71 Training:  79%|███████▉  | 76/96 [01:41<00:26,  1.33s/it]
+
Epoch 71 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 71 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 71 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 71 Training:  80%|████████  | 77/96 [01:42<00:25,  1.33s/it]
+
Epoch 71 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 71 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 71 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.19it/s]
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Epoch 71 Training:  81%|████████▏ | 78/96 [01:43<00:24,  1.34s/it]
+
Epoch 71 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 71 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 71 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 71 Training:  82%|████████▏ | 79/96 [01:45<00:22,  1.34s/it]
+
Epoch 71 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 71 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 71 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 71 Training:  83%|████████▎ | 80/96 [01:46<00:21,  1.34s/it]
+
Epoch 71 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 71 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 71 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.21it/s]
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Epoch 71 Training:  84%|████████▍ | 81/96 [01:47<00:20,  1.34s/it]
+
Epoch 71 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 71 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 71 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 71 Training:  85%|████████▌ | 82/96 [01:49<00:18,  1.34s/it]
+
Epoch 71 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 71 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 71 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 71 Training:  86%|████████▋ | 83/96 [01:50<00:17,  1.33s/it]
+
Epoch 71 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 71 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 71 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 71 Training:  88%|████████▊ | 84/96 [01:51<00:15,  1.33s/it]
+
Epoch 71 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 71 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 71 Training:  89%|████████▊ | 85/96 [01:53<00:14,  1.33s/it]
+
Epoch 71 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 71 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 71 Training:  90%|████████▉ | 86/96 [01:54<00:13,  1.33s/it]
+
Epoch 71 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 71 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 71 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 71 Training:  91%|█████████ | 87/96 [01:55<00:11,  1.33s/it]
+
Epoch 71 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 71 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 71 Training:  92%|█████████▏| 88/96 [01:57<00:10,  1.33s/it]
+
Epoch 71 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 71 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 71 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 71 Training:  93%|█████████▎| 89/96 [01:58<00:09,  1.33s/it]
+
Epoch 71 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 71 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 71 Training:  94%|█████████▍| 90/96 [01:59<00:07,  1.33s/it]
+
Epoch 71 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 71 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 71 Training:  95%|█████████▍| 91/96 [02:01<00:06,  1.33s/it]
+
Epoch 71 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 71 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 71 Training:  96%|█████████▌| 92/96 [02:02<00:05,  1.33s/it]
+
Epoch 71 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 71 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 71 Training:  97%|█████████▋| 93/96 [02:03<00:03,  1.33s/it]
+
Epoch 71 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 71 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 71 Training:  98%|█████████▊| 94/96 [02:05<00:02,  1.33s/it]
+
Epoch 71 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 71 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 71 Training:  99%|█████████▉| 95/96 [02:06<00:01,  1.33s/it]
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+[2026-10-01 21:43:48] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0072 | Epoch 071] timing/rollout_seconds=147.9923 timing/feedback_seconds=5.9423 timing/optimization_seconds=128.4721 timing/cycle_seconds=280.0052
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+[2026-10-01 21:46:20] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0072 | Epoch 072] train/reward_dists_mean=0.9350 train/reward_dists_std=0.0214 train/reward_psnr_mean=30.3890 train/reward_psnr_std=2.3412 train/reward_ssim_mean=0.9393 train/reward_ssim_std=0.0178 train/reward_dists_group_std_mean=0.0029 train/reward_dists_group_std_max=0.0239 train/reward_dists_group_std_min=0.0013 train/reward_dists_group_mean_std=0.0209 train/reward_psnr_group_std_mean=1.1631 train/reward_psnr_group_std_max=8.4944 train/reward_psnr_group_std_min=0.3595 train/reward_psnr_group_mean_std=1.6641 train/reward_ssim_group_std_mean=0.0012 train/reward_ssim_group_std_max=0.0256 train/reward_ssim_group_std_min=0.0003 train/reward_ssim_group_mean_std=0.0174 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-8.5130 train/adv_max=5.3706 train/adv_abs_mean=1.7819
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Epoch 72 Training:  19%|█▉        | 18/96 [00:23<01:43,  1.33s/it]
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Epoch 72 Training:  20%|█▉        | 19/96 [00:25<01:42,  1.33s/it]
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Epoch 72 Training:  21%|██        | 20/96 [00:26<01:40,  1.33s/it]
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Epoch 72 Training:  22%|██▏       | 21/96 [00:27<01:39,  1.33s/it]
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Epoch 72 Training:  23%|██▎       | 22/96 [00:29<01:38,  1.33s/it]
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Epoch 72 Training:  24%|██▍       | 23/96 [00:30<01:36,  1.33s/it]
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Epoch 72 Training:  25%|██▌       | 24/96 [00:31<01:35,  1.33s/it]
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Epoch 72 Training:  26%|██▌       | 25/96 [00:33<01:34,  1.33s/it]
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Epoch 72 Training:  27%|██▋       | 26/96 [00:34<01:32,  1.33s/it]
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Epoch 72 Training:  28%|██▊       | 27/96 [00:35<01:31,  1.33s/it]
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Epoch 72 Training:  29%|██▉       | 28/96 [00:37<01:30,  1.33s/it]
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Epoch 72 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 72 Training:  30%|███       | 29/96 [00:38<01:28,  1.33s/it]
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Epoch 72 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 72 Training:  31%|███▏      | 30/96 [00:39<01:27,  1.33s/it]
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Epoch 72 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 72 Training:  32%|███▏      | 31/96 [00:41<01:26,  1.33s/it]
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Epoch 72 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 72 Training:  33%|███▎      | 32/96 [00:42<01:25,  1.33s/it]
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Epoch 72 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 72 Training:  34%|███▍      | 33/96 [00:43<01:23,  1.33s/it]
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Epoch 72 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 72 Training:  35%|███▌      | 34/96 [00:45<01:22,  1.33s/it]
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Epoch 72 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 72 Training:  36%|███▋      | 35/96 [00:46<01:21,  1.33s/it]
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Epoch 72 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 72 Training:  38%|███▊      | 36/96 [00:47<01:19,  1.33s/it]
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Epoch 72 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 72 Training:  39%|███▊      | 37/96 [00:49<01:18,  1.33s/it]
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Epoch 72 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 72 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 72 Training:  40%|███▉      | 38/96 [00:50<01:17,  1.33s/it]
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Epoch 72 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 72 Training:  41%|████      | 39/96 [00:51<01:15,  1.33s/it]
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Epoch 72 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 72 Training:  42%|████▏     | 40/96 [00:53<01:14,  1.33s/it]
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Epoch 72 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 72 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 72 Training:  43%|████▎     | 41/96 [00:54<01:13,  1.33s/it]
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Epoch 72 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 72 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 72 Training:  44%|████▍     | 42/96 [00:55<01:11,  1.33s/it]
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Epoch 72 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 72 Training:  45%|████▍     | 43/96 [00:57<01:10,  1.33s/it]
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Epoch 72 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 72 Training:  46%|████▌     | 44/96 [00:58<01:09,  1.33s/it]
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Epoch 72 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 72 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 72 Training:  47%|████▋     | 45/96 [00:59<01:07,  1.33s/it]
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Epoch 72 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 72 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 72 Training:  48%|████▊     | 46/96 [01:01<01:06,  1.33s/it]
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Epoch 72 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 72 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 72 Training:  49%|████▉     | 47/96 [01:02<01:05,  1.33s/it]
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Epoch 72 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 72 Training:  50%|█████     | 48/96 [01:03<01:03,  1.33s/it]
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Epoch 72 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 72 Training:  51%|█████     | 49/96 [01:05<01:02,  1.33s/it]
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Epoch 72 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 72 Training:  52%|█████▏    | 50/96 [01:06<01:01,  1.33s/it]
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Epoch 72 Training:  53%|█████▎    | 51/96 [01:07<00:59,  1.33s/it]
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Epoch 72 Training:  54%|█████▍    | 52/96 [01:09<00:58,  1.33s/it]
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Epoch 72 Training:  55%|█████▌    | 53/96 [01:10<00:57,  1.33s/it]
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Epoch 72 Training:  56%|█████▋    | 54/96 [01:11<00:55,  1.33s/it]
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Epoch 72 Training:  57%|█████▋    | 55/96 [01:13<00:54,  1.33s/it]
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Epoch 72 Training:  58%|█████▊    | 56/96 [01:14<00:53,  1.34s/it]
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Epoch 72 Training:  59%|█████▉    | 57/96 [01:15<00:52,  1.34s/it]
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Epoch 72 Training:  60%|██████    | 58/96 [01:17<00:50,  1.33s/it]
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Epoch 72 Training:  61%|██████▏   | 59/96 [01:18<00:49,  1.33s/it]
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Epoch 72 Training:  62%|██████▎   | 60/96 [01:19<00:47,  1.33s/it]
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Epoch 72 Training:  64%|██████▎   | 61/96 [01:21<00:46,  1.33s/it]
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Epoch 72 Training:  65%|██████▍   | 62/96 [01:22<00:45,  1.33s/it]
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Epoch 72 Training:  66%|██████▌   | 63/96 [01:23<00:43,  1.33s/it]
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Epoch 72 Training:  67%|██████▋   | 64/96 [01:25<00:42,  1.33s/it]
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Epoch 72 Training:  68%|██████▊   | 65/96 [01:26<00:41,  1.33s/it]
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Epoch 72 Training:  69%|██████▉   | 66/96 [01:27<00:39,  1.33s/it]
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Epoch 72 Training:  70%|██████▉   | 67/96 [01:29<00:38,  1.33s/it]
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Epoch 72 Training:  71%|███████   | 68/96 [01:30<00:37,  1.33s/it]
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Epoch 72 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s][2026-10-01 21:48:28] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0072 | Epoch 072] train/policy_loss=11.3393 train/unweighted_policy_loss=2.2679 train/loss=11.3393 train/grad_norm=0.0812
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+[2026-10-01 21:48:28] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0073 | Epoch 072] timing/rollout_seconds=148.0228 timing/feedback_seconds=5.7844 timing/optimization_seconds=128.1547 timing/cycle_seconds=279.6798
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+
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+
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+[2026-10-01 21:51:00] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0073 | Epoch 073] train/reward_dists_mean=0.9410 train/reward_dists_std=0.0136 train/reward_psnr_mean=30.2331 train/reward_psnr_std=2.3250 train/reward_ssim_mean=0.9389 train/reward_ssim_std=0.0193 train/reward_dists_group_std_mean=0.0025 train/reward_dists_group_std_max=0.0089 train/reward_dists_group_std_min=0.0011 train/reward_dists_group_mean_std=0.0133 train/reward_psnr_group_std_mean=1.2360 train/reward_psnr_group_std_max=3.5004 train/reward_psnr_group_std_min=0.3978 train/reward_psnr_group_mean_std=1.8733 train/reward_ssim_group_std_mean=0.0011 train/reward_ssim_group_std_max=0.0041 train/reward_ssim_group_std_min=0.0003 train/reward_ssim_group_mean_std=0.0192 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-7.8519 train/adv_max=6.4058 train/adv_abs_mean=1.9084
+
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Epoch 73 Training:  44%|████▍     | 42/96 [00:55<01:11,  1.33s/it]
+
Epoch 73 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 73 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 73 Training:  45%|████▍     | 43/96 [00:57<01:10,  1.33s/it]
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Epoch 73 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 73 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 73 Training:  46%|████▌     | 44/96 [00:58<01:09,  1.33s/it]
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Epoch 73 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 73 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 73 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 73 Training:  47%|████▋     | 45/96 [00:59<01:07,  1.33s/it]
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Epoch 73 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 73 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 73 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 73 Training:  48%|████▊     | 46/96 [01:01<01:06,  1.33s/it]
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Epoch 73 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 73 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 73 Training:  49%|████▉     | 47/96 [01:02<01:05,  1.33s/it]
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Epoch 73 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 73 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 73 Training:  50%|█████     | 48/96 [01:03<01:03,  1.33s/it]
+
Epoch 73 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 73 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 73 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 73 Training:  51%|█████     | 49/96 [01:05<01:02,  1.33s/it]
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Epoch 73 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 73 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 73 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 73 Training:  52%|█████▏    | 50/96 [01:06<01:01,  1.33s/it]
+
Epoch 73 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 73 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 73 Training:  53%|█████▎    | 51/96 [01:07<00:59,  1.33s/it]
+
Epoch 73 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 73 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 73 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 73 Training:  54%|█████▍    | 52/96 [01:09<00:58,  1.33s/it]
+
Epoch 73 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 73 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 73 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.29it/s]
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Epoch 73 Training:  55%|█████▌    | 53/96 [01:10<00:57,  1.33s/it]
+
Epoch 73 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 73 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.63it/s]
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Epoch 73 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 73 Training:  56%|█████▋    | 54/96 [01:11<00:55,  1.33s/it]
+
Epoch 73 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 73 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 73 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 73 Training:  57%|█████▋    | 55/96 [01:13<00:54,  1.33s/it]
+
Epoch 73 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 73 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 73 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 73 Training:  58%|█████▊    | 56/96 [01:14<00:53,  1.33s/it]
+
Epoch 73 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 73 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 73 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 73 Training:  59%|█████▉    | 57/96 [01:15<00:51,  1.33s/it]
+
Epoch 73 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 73 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 73 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
+
                                                                
Epoch 73 Training:  60%|██████    | 58/96 [01:17<00:50,  1.33s/it]
+
Epoch 73 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 73 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 73 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 73 Training:  61%|██████▏   | 59/96 [01:18<00:49,  1.33s/it]
+
Epoch 73 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 73 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 73 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
+
                                                                
Epoch 73 Training:  62%|██████▎   | 60/96 [01:19<00:48,  1.33s/it]
+
Epoch 73 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 73 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 73 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
+
                                                                
Epoch 73 Training:  64%|██████▎   | 61/96 [01:21<00:46,  1.33s/it]
+
Epoch 73 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 73 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 73 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
+
                                                                
Epoch 73 Training:  65%|██████▍   | 62/96 [01:22<00:45,  1.33s/it]
+
Epoch 73 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 73 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 73 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.23it/s]
+
                                                                
Epoch 73 Training:  66%|██████▌   | 63/96 [01:23<00:44,  1.33s/it]
+
Epoch 73 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 73 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 73 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
+
                                                                
Epoch 73 Training:  67%|██████▋   | 64/96 [01:25<00:42,  1.33s/it]
+
Epoch 73 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 73 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 73 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
+
                                                                
Epoch 73 Training:  68%|██████▊   | 65/96 [01:26<00:41,  1.33s/it]
+
Epoch 73 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 73 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 73 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
+
                                                                
Epoch 73 Training:  69%|██████▉   | 66/96 [01:27<00:39,  1.33s/it]
+
Epoch 73 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 73 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 73 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
+
                                                                
Epoch 73 Training:  70%|██████▉   | 67/96 [01:29<00:38,  1.33s/it]
+
Epoch 73 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 73 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 73 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
+
                                                                
Epoch 73 Training:  71%|███████   | 68/96 [01:30<00:37,  1.33s/it]
+
Epoch 73 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 73 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 73 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
+
                                                                
Epoch 73 Training:  72%|███████▏  | 69/96 [01:31<00:35,  1.33s/it]
+
Epoch 73 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 73 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 73 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
+
                                                                
Epoch 73 Training:  73%|███████▎  | 70/96 [01:33<00:34,  1.33s/it]
+
Epoch 73 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 73 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 73 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 73 Training:  74%|███████▍  | 71/96 [01:34<00:33,  1.33s/it]
+
Epoch 73 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 73 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 73 Training:  75%|███████▌  | 72/96 [01:35<00:31,  1.33s/it]
+
Epoch 73 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 73 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 73 Training:  76%|███████▌  | 73/96 [01:37<00:30,  1.33s/it]
+
Epoch 73 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 73 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 73 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.22it/s]
+
                                                                
Epoch 73 Training:  77%|███████▋  | 74/96 [01:38<00:29,  1.33s/it]
+
Epoch 73 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 73 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 73 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 73 Training:  78%|███████▊  | 75/96 [01:39<00:27,  1.33s/it]
+
Epoch 73 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 73 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 73 Training:  79%|███████▉  | 76/96 [01:41<00:26,  1.33s/it]
+
Epoch 73 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 73 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 73 Training:  80%|████████  | 77/96 [01:42<00:25,  1.33s/it]
+
Epoch 73 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 73 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 73 Training:  81%|████████▏ | 78/96 [01:43<00:24,  1.33s/it]
+
Epoch 73 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 73 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 73 Training:  82%|████████▏ | 79/96 [01:45<00:22,  1.33s/it]
+
Epoch 73 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 73 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 73 Training:  83%|████████▎ | 80/96 [01:46<00:21,  1.33s/it]
+
Epoch 73 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 73 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 73 Training:  84%|████████▍ | 81/96 [01:47<00:19,  1.33s/it]
+
Epoch 73 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 73 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 73 Training:  85%|████████▌ | 82/96 [01:49<00:18,  1.33s/it]
+
Epoch 73 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 73 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 73 Training:  86%|████████▋ | 83/96 [01:50<00:17,  1.33s/it]
+
Epoch 73 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 73 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 73 Training:  88%|████████▊ | 84/96 [01:51<00:15,  1.33s/it]
+
Epoch 73 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 73 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 73 Training:  89%|████████▊ | 85/96 [01:53<00:14,  1.33s/it]
+
Epoch 73 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 73 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 73 Training:  90%|████████▉ | 86/96 [01:54<00:13,  1.33s/it]
+
Epoch 73 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 73 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 73 Training:  91%|█████████ | 87/96 [01:55<00:11,  1.33s/it]
+
Epoch 73 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 73 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 73 Training:  92%|█████████▏| 88/96 [01:57<00:10,  1.33s/it]
+
Epoch 73 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 73 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 73 Training:  93%|█████████▎| 89/96 [01:58<00:09,  1.33s/it]
+
Epoch 73 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 73 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 73 Training:  94%|█████████▍| 90/96 [01:59<00:07,  1.33s/it]
+
Epoch 73 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 73 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 73 Training:  95%|█████████▍| 91/96 [02:01<00:06,  1.33s/it]
+
Epoch 73 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 73 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 73 Training:  96%|█████████▌| 92/96 [02:02<00:05,  1.33s/it]
+
Epoch 73 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 73 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s][2026-10-01 21:53:08] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0073 | Epoch 073] train/policy_loss=11.4206 train/unweighted_policy_loss=2.2841 train/loss=11.4206 train/grad_norm=0.1449
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+[2026-10-01 21:53:08] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0074 | Epoch 073] timing/rollout_seconds=148.0132 timing/feedback_seconds=6.0289 timing/optimization_seconds=128.4407 timing/cycle_seconds=280.0409
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+
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+[2026-10-01 21:55:40] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0074 | Epoch 074] train/reward_dists_mean=0.9400 train/reward_dists_std=0.0145 train/reward_psnr_mean=30.2788 train/reward_psnr_std=1.7508 train/reward_ssim_mean=0.9373 train/reward_ssim_std=0.0203 train/reward_dists_group_std_mean=0.0024 train/reward_dists_group_std_max=0.0043 train/reward_dists_group_std_min=0.0011 train/reward_dists_group_mean_std=0.0142 train/reward_psnr_group_std_mean=0.8270 train/reward_psnr_group_std_max=2.1175 train/reward_psnr_group_std_min=0.2445 train/reward_psnr_group_mean_std=1.4783 train/reward_ssim_group_std_mean=0.0010 train/reward_ssim_group_std_max=0.0087 train/reward_ssim_group_std_min=0.0003 train/reward_ssim_group_mean_std=0.0203 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-8.9075 train/adv_max=5.9943 train/adv_abs_mean=1.7262
+
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Epoch 74 Training:  16%|█▌        | 15/96 [00:19<01:47,  1.33s/it]
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Epoch 74 Training:  17%|█▋        | 16/96 [00:21<01:46,  1.33s/it]
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Epoch 74 Training:  18%|█▊        | 17/96 [00:22<01:45,  1.33s/it]
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Epoch 74 Training:  19%|█▉        | 18/96 [00:23<01:43,  1.33s/it]
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Epoch 74 Training:  20%|█▉        | 19/96 [00:25<01:42,  1.34s/it]
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Epoch 74 Training:  21%|██        | 20/96 [00:26<01:41,  1.33s/it]
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Epoch 74 Training:  22%|██▏       | 21/96 [00:27<01:39,  1.33s/it]
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Epoch 74 Training:  23%|██▎       | 22/96 [00:29<01:38,  1.33s/it]
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Epoch 74 Training:  24%|██▍       | 23/96 [00:30<01:37,  1.33s/it]
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Epoch 74 Training:  25%|██▌       | 24/96 [00:31<01:35,  1.33s/it]
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Epoch 74 Training:  26%|██▌       | 25/96 [00:33<01:34,  1.33s/it]
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Epoch 74 Training:  27%|██▋       | 26/96 [00:34<01:33,  1.33s/it]
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Epoch 74 Training:  28%|██▊       | 27/96 [00:35<01:31,  1.33s/it]
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Epoch 74 Training:  29%|██▉       | 28/96 [00:37<01:30,  1.33s/it]
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Epoch 74 Training:  30%|███       | 29/96 [00:38<01:28,  1.33s/it]
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Epoch 74 Training:  31%|███▏      | 30/96 [00:39<01:27,  1.33s/it]
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Epoch 74 Training:  32%|███▏      | 31/96 [00:41<01:26,  1.33s/it]
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Epoch 74 Training:  33%|███▎      | 32/96 [00:42<01:25,  1.33s/it]
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Epoch 74 Training:  34%|███▍      | 33/96 [00:43<01:23,  1.33s/it]
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Epoch 74 Training:  35%|███▌      | 34/96 [00:45<01:22,  1.33s/it]
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Epoch 74 Training:  36%|███▋      | 35/96 [00:46<01:21,  1.33s/it]
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Epoch 74 Training:  38%|███▊      | 36/96 [00:47<01:19,  1.33s/it]
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Epoch 74 Training:  39%|███▊      | 37/96 [00:49<01:18,  1.33s/it]
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Epoch 74 Training:  40%|███▉      | 38/96 [00:50<01:17,  1.33s/it]
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Epoch 74 Training:  41%|████      | 39/96 [00:51<01:15,  1.33s/it]
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Epoch 74 Training:  42%|████▏     | 40/96 [00:53<01:14,  1.33s/it]
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Epoch 74 Training:  43%|████▎     | 41/96 [00:54<01:13,  1.33s/it]
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Epoch 74 Training:  44%|████▍     | 42/96 [00:55<01:11,  1.33s/it]
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Epoch 74 Training:  45%|████▍     | 43/96 [00:57<01:10,  1.33s/it]
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Epoch 74 Training:  46%|████▌     | 44/96 [00:58<01:09,  1.33s/it]
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Epoch 74 Training:  47%|████▋     | 45/96 [00:59<01:07,  1.33s/it]
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Epoch 74 Training:  48%|████▊     | 46/96 [01:01<01:06,  1.33s/it]
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Epoch 74 Training:  49%|████▉     | 47/96 [01:02<01:05,  1.33s/it]
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Epoch 74 Training:  50%|█████     | 48/96 [01:03<01:03,  1.33s/it]
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Epoch 74 Training:  51%|█████     | 49/96 [01:05<01:02,  1.34s/it]
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Epoch 74 Training:  52%|█████▏    | 50/96 [01:06<01:01,  1.33s/it]
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Epoch 74 Training:  53%|█████▎    | 51/96 [01:07<00:59,  1.33s/it]
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Epoch 74 Training:  54%|█████▍    | 52/96 [01:09<00:58,  1.33s/it]
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Epoch 74 Training:  55%|█████▌    | 53/96 [01:10<00:57,  1.33s/it]
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Epoch 74 Training:  56%|█████▋    | 54/96 [01:11<00:55,  1.33s/it]
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Epoch 74 Training:  57%|█████▋    | 55/96 [01:13<00:54,  1.33s/it]
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Epoch 74 Training:  58%|█████▊    | 56/96 [01:14<00:53,  1.33s/it]
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Epoch 74 Training:  59%|█████▉    | 57/96 [01:15<00:51,  1.33s/it]
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Epoch 74 Training:  60%|██████    | 58/96 [01:17<00:50,  1.33s/it]
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Epoch 74 Training:  61%|██████▏   | 59/96 [01:18<00:49,  1.33s/it]
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Epoch 74 Training:  62%|██████▎   | 60/96 [01:19<00:47,  1.33s/it]
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Epoch 74 Training:  64%|██████▎   | 61/96 [01:21<00:46,  1.33s/it]
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Epoch 74 Training:  65%|██████▍   | 62/96 [01:22<00:45,  1.33s/it]
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Epoch 74 Training:  66%|██████▌   | 63/96 [01:23<00:43,  1.33s/it]
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Epoch 74 Training:  67%|██████▋   | 64/96 [01:25<00:42,  1.33s/it]
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Epoch 74 Training:  68%|██████▊   | 65/96 [01:26<00:41,  1.33s/it]
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Epoch 74 Training:  69%|██████▉   | 66/96 [01:27<00:39,  1.33s/it]
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Epoch 74 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s][2026-10-01 21:57:48] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0074 | Epoch 074] train/policy_loss=11.8898 train/unweighted_policy_loss=2.3780 train/loss=11.8898 train/grad_norm=0.0628
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+[2026-10-01 21:57:48] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0075 | Epoch 074] timing/rollout_seconds=147.8380 timing/feedback_seconds=5.8472 timing/optimization_seconds=128.3750 timing/cycle_seconds=279.7853
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+
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Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:01<00:00,  5.61it/s]
+
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+
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Pointwise Rewards: psnr:  83%|████████▎ | 5/6 [00:00<00:00,  6.64it/s]
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Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.69it/s]
+[2026-10-01 22:00:20] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0075 | Epoch 075] train/reward_dists_mean=0.9373 train/reward_dists_std=0.0209 train/reward_psnr_mean=29.7922 train/reward_psnr_std=2.2891 train/reward_ssim_mean=0.9376 train/reward_ssim_std=0.0204 train/reward_dists_group_std_mean=0.0026 train/reward_dists_group_std_max=0.0062 train/reward_dists_group_std_min=0.0011 train/reward_dists_group_mean_std=0.0207 train/reward_psnr_group_std_mean=0.9166 train/reward_psnr_group_std_max=1.8906 train/reward_psnr_group_std_min=0.2016 train/reward_psnr_group_mean_std=2.0556 train/reward_ssim_group_std_mean=0.0012 train/reward_ssim_group_std_max=0.0127 train/reward_ssim_group_std_min=0.0003 train/reward_ssim_group_mean_std=0.0203 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-7.7214 train/adv_max=5.7191 train/adv_abs_mean=1.8593
+
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Epoch 75 Training:  19%|█▉        | 18/96 [00:23<01:43,  1.33s/it]
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Epoch 75 Training:  26%|██▌       | 25/96 [00:33<01:34,  1.33s/it]
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Epoch 75 Training:  42%|████▏     | 40/96 [00:53<01:14,  1.33s/it]
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Epoch 75 Training:  43%|████▎     | 41/96 [00:54<01:13,  1.33s/it]
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Epoch 75 Training:  44%|████▍     | 42/96 [00:55<01:11,  1.33s/it]
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Epoch 75 Training:  45%|████▍     | 43/96 [00:57<01:10,  1.33s/it]
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Epoch 75 Training:  46%|████▌     | 44/96 [00:58<01:09,  1.33s/it]
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Epoch 75 Training:  47%|████▋     | 45/96 [00:59<01:07,  1.33s/it]
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Epoch 75 Training:  48%|████▊     | 46/96 [01:01<01:06,  1.33s/it]
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Epoch 75 Training:  49%|████▉     | 47/96 [01:02<01:05,  1.33s/it]
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Epoch 75 Training:  50%|█████     | 48/96 [01:03<01:03,  1.33s/it]
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Epoch 75 Training:  51%|█████     | 49/96 [01:05<01:02,  1.33s/it]
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Epoch 75 Training:  52%|█████▏    | 50/96 [01:06<01:01,  1.33s/it]
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Epoch 75 Training:  53%|█████▎    | 51/96 [01:07<00:59,  1.33s/it]
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Epoch 75 Training:  54%|█████▍    | 52/96 [01:09<00:58,  1.33s/it]
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Epoch 75 Training:  55%|█████▌    | 53/96 [01:10<00:57,  1.33s/it]
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Epoch 75 Training:  56%|█████▋    | 54/96 [01:11<00:55,  1.33s/it]
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Epoch 75 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 75 Training:  57%|█████▋    | 55/96 [01:13<00:54,  1.33s/it]
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Epoch 75 Training:  58%|█████▊    | 56/96 [01:14<00:53,  1.33s/it]
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Epoch 75 Training:  59%|█████▉    | 57/96 [01:15<00:51,  1.33s/it]
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Epoch 75 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 75 Training:  60%|██████    | 58/96 [01:17<00:50,  1.33s/it]
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Epoch 75 Training:  61%|██████▏   | 59/96 [01:18<00:49,  1.33s/it]
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Epoch 75 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 75 Training:  62%|██████▎   | 60/96 [01:19<00:47,  1.33s/it]
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Epoch 75 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.23it/s]
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Epoch 75 Training:  64%|██████▎   | 61/96 [01:21<00:46,  1.33s/it]
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Epoch 75 Training:  65%|██████▍   | 62/96 [01:22<00:45,  1.33s/it]
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Epoch 75 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.19it/s]
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Epoch 75 Training:  66%|██████▌   | 63/96 [01:23<00:43,  1.33s/it]
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Epoch 75 Training:  67%|██████▋   | 64/96 [01:25<00:42,  1.33s/it]
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Epoch 75 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 75 Training:  68%|██████▊   | 65/96 [01:26<00:41,  1.33s/it]
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Epoch 75 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.21it/s]
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Epoch 75 Training:  69%|██████▉   | 66/96 [01:27<00:40,  1.33s/it]
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Epoch 75 Training:  70%|██████▉   | 67/96 [01:29<00:38,  1.33s/it]
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Epoch 75 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 75 Training:  71%|███████   | 68/96 [01:30<00:37,  1.33s/it]
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Epoch 75 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.23it/s]
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Epoch 75 Training:  72%|███████▏  | 69/96 [01:31<00:35,  1.33s/it]
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Epoch 75 Training:  73%|███████▎  | 70/96 [01:32<00:34,  1.33s/it]
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Epoch 75 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 75 Training:  74%|███████▍  | 71/96 [01:34<00:33,  1.33s/it]
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Epoch 75 Training:  75%|███████▌  | 72/96 [01:35<00:31,  1.33s/it]
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Epoch 75 Training:  76%|███████▌  | 73/96 [01:36<00:30,  1.33s/it]
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Epoch 75 Training:  77%|███████▋  | 74/96 [01:38<00:29,  1.33s/it]
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Epoch 75 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 75 Training:  78%|███████▊  | 75/96 [01:39<00:27,  1.33s/it]
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Epoch 75 Training:  79%|███████▉  | 76/96 [01:40<00:26,  1.33s/it]
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Epoch 75 Training:  80%|████████  | 77/96 [01:42<00:25,  1.33s/it]
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Epoch 75 Training:  81%|████████▏ | 78/96 [01:43<00:23,  1.33s/it]
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Epoch 75 Training:  82%|████████▏ | 79/96 [01:44<00:22,  1.33s/it]
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Epoch 75 Training:  83%|████████▎ | 80/96 [01:46<00:21,  1.33s/it]
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Epoch 75 Training:  84%|████████▍ | 81/96 [01:47<00:19,  1.33s/it]
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Epoch 75 Training:  85%|████████▌ | 82/96 [01:48<00:18,  1.33s/it]
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Epoch 75 Training:  86%|████████▋ | 83/96 [01:50<00:17,  1.33s/it]
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Epoch 75 Training:  88%|████████▊ | 84/96 [01:51<00:15,  1.33s/it]
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Epoch 75 Training:  89%|████████▊ | 85/96 [01:52<00:14,  1.33s/it]
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Epoch 75 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.22it/s]
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Epoch 75 Training:  90%|████████▉ | 86/96 [01:54<00:13,  1.33s/it]
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Epoch 75 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.21it/s]
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Epoch 75 Training:  91%|█████████ | 87/96 [01:55<00:11,  1.33s/it]
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Epoch 75 Training:  92%|█████████▏| 88/96 [01:56<00:10,  1.33s/it]
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Epoch 75 Training:  93%|█████████▎| 89/96 [01:58<00:09,  1.33s/it]
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Epoch 75 Training:  94%|█████████▍| 90/96 [01:59<00:07,  1.33s/it]
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Epoch 75 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s][2026-10-01 22:02:28] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0075 | Epoch 075] train/policy_loss=12.0188 train/unweighted_policy_loss=2.4038 train/loss=12.0188 train/grad_norm=0.1230
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+[2026-10-01 22:02:28] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0076 | Epoch 075] timing/rollout_seconds=148.0965 timing/feedback_seconds=5.6213 timing/optimization_seconds=128.2351 timing/cycle_seconds=279.8432
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+[2026-10-01 22:05:00] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0076 | Epoch 076] train/reward_dists_mean=0.9353 train/reward_dists_std=0.0156 train/reward_psnr_mean=30.1453 train/reward_psnr_std=2.2843 train/reward_ssim_mean=0.9352 train/reward_ssim_std=0.0173 train/reward_dists_group_std_mean=0.0025 train/reward_dists_group_std_max=0.0046 train/reward_dists_group_std_min=0.0012 train/reward_dists_group_mean_std=0.0154 train/reward_psnr_group_std_mean=0.9114 train/reward_psnr_group_std_max=1.9679 train/reward_psnr_group_std_min=0.2976 train/reward_psnr_group_mean_std=2.0532 train/reward_ssim_group_std_mean=0.0008 train/reward_ssim_group_std_max=0.0029 train/reward_ssim_group_std_min=0.0004 train/reward_ssim_group_mean_std=0.0173 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-8.5332 train/adv_max=5.5166 train/adv_abs_mean=1.7622
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+
Epoch 76 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.31it/s]
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Epoch 76 Training:  14%|█▎        | 13/96 [00:17<01:50,  1.33s/it]
+
Epoch 76 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 76 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 76 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 76 Training:  15%|█▍        | 14/96 [00:18<01:49,  1.33s/it]
+
Epoch 76 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 76 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 76 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 76 Training:  16%|█▌        | 15/96 [00:20<01:47,  1.33s/it]
+
Epoch 76 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 76 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 76 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 76 Training:  17%|█▋        | 16/96 [00:21<01:46,  1.33s/it]
+
Epoch 76 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 76 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 76 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.29it/s]
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Epoch 76 Training:  18%|█▊        | 17/96 [00:22<01:45,  1.33s/it]
+
Epoch 76 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 76 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 76 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 76 Training:  19%|█▉        | 18/96 [00:24<01:43,  1.33s/it]
+
Epoch 76 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 76 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 76 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 76 Training:  20%|█▉        | 19/96 [00:25<01:42,  1.33s/it]
+
Epoch 76 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 76 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 76 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 76 Training:  21%|██        | 20/96 [00:26<01:41,  1.33s/it]
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Epoch 76 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 76 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 76 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 76 Training:  22%|██▏       | 21/96 [00:28<01:40,  1.33s/it]
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Epoch 76 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 76 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 76 Training:  23%|██▎       | 22/96 [00:29<01:38,  1.33s/it]
+
Epoch 76 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 76 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 76 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 76 Training:  24%|██▍       | 23/96 [00:31<01:42,  1.41s/it]
+
Epoch 76 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 76 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 76 Training:  25%|██▌       | 24/96 [00:32<01:39,  1.39s/it]
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Epoch 76 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 76 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 76 Training:  26%|██▌       | 25/96 [00:33<01:37,  1.37s/it]
+
Epoch 76 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 76 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 76 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 76 Training:  27%|██▋       | 26/96 [00:35<01:35,  1.36s/it]
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Epoch 76 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 76 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 76 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 76 Training:  28%|██▊       | 27/96 [00:36<01:33,  1.35s/it]
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Epoch 76 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 76 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 76 Training:  29%|██▉       | 28/96 [00:37<01:31,  1.34s/it]
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Epoch 76 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 76 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 76 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 76 Training:  30%|███       | 29/96 [00:39<01:29,  1.34s/it]
+
Epoch 76 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 76 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 76 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 76 Training:  31%|███▏      | 30/96 [00:40<01:28,  1.34s/it]
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Epoch 76 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 76 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 76 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 76 Training:  32%|███▏      | 31/96 [00:41<01:26,  1.34s/it]
+
Epoch 76 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 76 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 76 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 76 Training:  33%|███▎      | 32/96 [00:43<01:25,  1.34s/it]
+
Epoch 76 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 76 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 76 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 76 Training:  34%|███▍      | 33/96 [00:44<01:24,  1.34s/it]
+
Epoch 76 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 76 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 76 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.23it/s]
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Epoch 76 Training:  35%|███▌      | 34/96 [00:45<01:23,  1.34s/it]
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Epoch 76 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 76 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 76 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 76 Training:  36%|███▋      | 35/96 [00:47<01:21,  1.34s/it]
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Epoch 76 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 76 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 76 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 76 Training:  38%|███▊      | 36/96 [00:48<01:20,  1.34s/it]
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Epoch 76 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 76 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 76 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 76 Training:  39%|███▊      | 37/96 [00:49<01:18,  1.34s/it]
+
Epoch 76 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 76 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 76 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 76 Training:  40%|███▉      | 38/96 [00:51<01:17,  1.34s/it]
+
Epoch 76 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 76 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 76 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.29it/s]
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Epoch 76 Training:  41%|████      | 39/96 [00:52<01:16,  1.34s/it]
+
Epoch 76 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 76 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 76 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 76 Training:  42%|████▏     | 40/96 [00:53<01:14,  1.34s/it]
+
Epoch 76 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 76 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 76 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 76 Training:  43%|████▎     | 41/96 [00:55<01:13,  1.34s/it]
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Epoch 76 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 76 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 76 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.17it/s]
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Epoch 76 Training:  44%|████▍     | 42/96 [00:56<01:12,  1.35s/it]
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Epoch 76 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 76 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 76 Training:  45%|████▍     | 43/96 [00:57<01:11,  1.34s/it]
+
Epoch 76 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 76 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 76 Training:  46%|████▌     | 44/96 [00:59<01:09,  1.34s/it]
+
Epoch 76 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 76 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 76 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 76 Training:  47%|████▋     | 45/96 [01:00<01:08,  1.34s/it]
+
Epoch 76 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 76 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 76 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 76 Training:  48%|████▊     | 46/96 [01:01<01:06,  1.34s/it]
+
Epoch 76 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 76 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 76 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 76 Training:  49%|████▉     | 47/96 [01:03<01:05,  1.33s/it]
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Epoch 76 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 76 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.23it/s]
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Epoch 76 Training:  50%|█████     | 48/96 [01:04<01:04,  1.34s/it]
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Epoch 76 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 76 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.32it/s]
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Epoch 76 Training:  51%|█████     | 49/96 [01:05<01:02,  1.33s/it]
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Epoch 76 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 76 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 76 Training:  52%|█████▏    | 50/96 [01:07<01:01,  1.34s/it]
+
Epoch 76 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 76 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 76 Training:  53%|█████▎    | 51/96 [01:08<01:00,  1.33s/it]
+
Epoch 76 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 76 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.29it/s]
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Epoch 76 Training:  54%|█████▍    | 52/96 [01:09<00:58,  1.33s/it]
+
Epoch 76 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 76 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 76 Training:  55%|█████▌    | 53/96 [01:11<00:57,  1.33s/it]
+
Epoch 76 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 76 Training:  56%|█████▋    | 54/96 [01:12<00:55,  1.33s/it]
+
Epoch 76 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 76 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 76 Training:  57%|█████▋    | 55/96 [01:13<00:54,  1.33s/it]
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Epoch 76 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 76 Training:  58%|█████▊    | 56/96 [01:15<00:53,  1.33s/it]
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Epoch 76 Training:  59%|█████▉    | 57/96 [01:16<00:51,  1.33s/it]
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Epoch 76 Training:  60%|██████    | 58/96 [01:17<00:50,  1.33s/it]
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Epoch 76 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 76 Training:  61%|██████▏   | 59/96 [01:19<00:49,  1.33s/it]
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Epoch 76 Training:  62%|██████▎   | 60/96 [01:20<00:47,  1.33s/it]
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Epoch 76 Training:  64%|██████▎   | 61/96 [01:21<00:46,  1.33s/it]
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Epoch 76 Training:  65%|██████▍   | 62/96 [01:23<00:45,  1.33s/it]
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Epoch 76 Training:  66%|██████▌   | 63/96 [01:24<00:44,  1.33s/it]
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Epoch 76 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 76 Training:  67%|██████▋   | 64/96 [01:25<00:42,  1.33s/it]
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Epoch 76 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s][2026-10-01 22:07:08] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0076 | Epoch 076] train/policy_loss=11.7500 train/unweighted_policy_loss=2.3500 train/loss=11.7500 train/grad_norm=0.1137
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+[2026-10-01 22:07:08] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0077 | Epoch 076] timing/rollout_seconds=147.8014 timing/feedback_seconds=5.4998 timing/optimization_seconds=129.2339 timing/cycle_seconds=280.6109
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+
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+
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Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.67it/s]
+[2026-10-01 22:09:41] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0077 | Epoch 077] train/reward_dists_mean=0.9349 train/reward_dists_std=0.0157 train/reward_psnr_mean=29.6403 train/reward_psnr_std=2.2589 train/reward_ssim_mean=0.9364 train/reward_ssim_std=0.0208 train/reward_dists_group_std_mean=0.0025 train/reward_dists_group_std_max=0.0065 train/reward_dists_group_std_min=0.0010 train/reward_dists_group_mean_std=0.0155 train/reward_psnr_group_std_mean=0.9822 train/reward_psnr_group_std_max=3.3078 train/reward_psnr_group_std_min=0.2896 train/reward_psnr_group_mean_std=1.9395 train/reward_ssim_group_std_mean=0.0010 train/reward_ssim_group_std_max=0.0029 train/reward_ssim_group_std_min=0.0003 train/reward_ssim_group_mean_std=0.0208 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-6.9272 train/adv_max=5.6833 train/adv_abs_mean=1.8598
+
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Epoch 77 Training:  11%|█▏        | 11/96 [00:14<01:53,  1.33s/it]
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Epoch 77 Training:  19%|█▉        | 18/96 [00:23<01:44,  1.34s/it]
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Epoch 77 Training:  40%|███▉      | 38/96 [00:50<01:17,  1.33s/it]
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Epoch 77 Training:  41%|████      | 39/96 [00:52<01:16,  1.33s/it]
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Epoch 77 Training:  42%|████▏     | 40/96 [00:53<01:14,  1.33s/it]
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Epoch 77 Training:  43%|████▎     | 41/96 [00:54<01:13,  1.33s/it]
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Epoch 77 Training:  44%|████▍     | 42/96 [00:56<01:12,  1.33s/it]
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Epoch 77 Training:  45%|████▍     | 43/96 [00:57<01:10,  1.33s/it]
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Epoch 77 Training:  46%|████▌     | 44/96 [00:58<01:09,  1.33s/it]
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Epoch 77 Training:  47%|████▋     | 45/96 [01:00<01:08,  1.33s/it]
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Epoch 77 Training:  48%|████▊     | 46/96 [01:01<01:06,  1.33s/it]
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Epoch 77 Training:  49%|████▉     | 47/96 [01:02<01:05,  1.33s/it]
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Epoch 77 Training:  50%|█████     | 48/96 [01:04<01:03,  1.33s/it]
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Epoch 77 Training:  51%|█████     | 49/96 [01:05<01:02,  1.33s/it]
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Epoch 77 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.22it/s]
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Epoch 77 Training:  52%|█████▏    | 50/96 [01:06<01:01,  1.34s/it]
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Epoch 77 Training:  53%|█████▎    | 51/96 [01:08<00:59,  1.33s/it]
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Epoch 77 Training:  54%|█████▍    | 52/96 [01:09<00:58,  1.34s/it]
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Epoch 77 Training:  55%|█████▌    | 53/96 [01:10<00:57,  1.34s/it]
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Epoch 77 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 77 Training:  56%|█████▋    | 54/96 [01:12<00:56,  1.33s/it]
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Epoch 77 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 77 Training:  57%|█████▋    | 55/96 [01:13<00:54,  1.33s/it]
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Epoch 77 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.22it/s]
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Epoch 77 Training:  58%|█████▊    | 56/96 [01:14<00:53,  1.33s/it]
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Epoch 77 Training:  59%|█████▉    | 57/96 [01:16<00:51,  1.33s/it]
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Epoch 77 Training:  60%|██████    | 58/96 [01:17<00:50,  1.33s/it]
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Epoch 77 Training:  61%|██████▏   | 59/96 [01:18<00:49,  1.33s/it]
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Epoch 77 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 77 Training:  62%|██████▎   | 60/96 [01:20<00:47,  1.33s/it]
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Epoch 77 Training:  64%|██████▎   | 61/96 [01:21<00:46,  1.34s/it]
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Epoch 77 Training:  65%|██████▍   | 62/96 [01:22<00:45,  1.34s/it]
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Epoch 77 Training:  66%|██████▌   | 63/96 [01:24<00:44,  1.34s/it]
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Epoch 77 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 77 Training:  67%|██████▋   | 64/96 [01:25<00:42,  1.33s/it]
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Epoch 77 Training:  68%|██████▊   | 65/96 [01:26<00:41,  1.34s/it]
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Epoch 77 Training:  69%|██████▉   | 66/96 [01:28<00:40,  1.33s/it]
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Epoch 77 Training:  70%|██████▉   | 67/96 [01:29<00:38,  1.33s/it]
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Epoch 77 Training:  71%|███████   | 68/96 [01:30<00:37,  1.34s/it]
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Epoch 77 Training:  72%|███████▏  | 69/96 [01:32<00:36,  1.33s/it]
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Epoch 77 Training:  73%|███████▎  | 70/96 [01:33<00:34,  1.33s/it]
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Epoch 77 Training:  74%|███████▍  | 71/96 [01:34<00:33,  1.33s/it]
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Epoch 77 Training:  75%|███████▌  | 72/96 [01:36<00:31,  1.33s/it]
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Epoch 77 Training:  76%|███████▌  | 73/96 [01:37<00:30,  1.33s/it]
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Epoch 77 Training:  77%|███████▋  | 74/96 [01:38<00:29,  1.33s/it]
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Epoch 77 Training:  78%|███████▊  | 75/96 [01:40<00:27,  1.33s/it]
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Epoch 77 Training:  79%|███████▉  | 76/96 [01:41<00:26,  1.33s/it]
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Epoch 77 Training:  80%|████████  | 77/96 [01:42<00:25,  1.33s/it]
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Epoch 77 Training:  81%|████████▏ | 78/96 [01:44<00:23,  1.33s/it]
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Epoch 77 Training:  82%|████████▏ | 79/96 [01:45<00:22,  1.33s/it]
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Epoch 77 Training:  83%|████████▎ | 80/96 [01:46<00:21,  1.33s/it]
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Epoch 77 Training:  84%|████████▍ | 81/96 [01:48<00:19,  1.33s/it]
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Epoch 77 Training:  85%|████████▌ | 82/96 [01:49<00:18,  1.33s/it]
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Epoch 77 Training:  86%|████████▋ | 83/96 [01:50<00:17,  1.33s/it]
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Epoch 77 Training:  88%|████████▊ | 84/96 [01:52<00:15,  1.33s/it]
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Epoch 77 Training:  89%|████████▊ | 85/96 [01:53<00:14,  1.33s/it]
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Epoch 77 Training:  90%|████████▉ | 86/96 [01:54<00:13,  1.33s/it]
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Epoch 77 Training:  91%|█████████ | 87/96 [01:56<00:11,  1.33s/it]
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Epoch 77 Training:  92%|█████████▏| 88/96 [01:57<00:10,  1.33s/it]
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Epoch 77 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s][2026-10-01 22:11:49] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0077 | Epoch 077] train/policy_loss=11.2516 train/unweighted_policy_loss=2.2503 train/loss=11.2516 train/grad_norm=0.1124
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+[2026-10-01 22:11:49] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0078 | Epoch 077] timing/rollout_seconds=148.0913 timing/feedback_seconds=5.5076 timing/optimization_seconds=128.8655 timing/cycle_seconds=280.5654
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+[2026-10-01 22:14:21] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0078 | Epoch 078] train/reward_dists_mean=0.9380 train/reward_dists_std=0.0135 train/reward_psnr_mean=30.0293 train/reward_psnr_std=2.1302 train/reward_ssim_mean=0.9353 train/reward_ssim_std=0.0216 train/reward_dists_group_std_mean=0.0024 train/reward_dists_group_std_max=0.0061 train/reward_dists_group_std_min=0.0010 train/reward_dists_group_mean_std=0.0132 train/reward_psnr_group_std_mean=0.8930 train/reward_psnr_group_std_max=2.1671 train/reward_psnr_group_std_min=0.1723 train/reward_psnr_group_mean_std=1.8777 train/reward_ssim_group_std_mean=0.0010 train/reward_ssim_group_std_max=0.0048 train/reward_ssim_group_std_min=0.0003 train/reward_ssim_group_mean_std=0.0215 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-8.2567 train/adv_max=6.3339 train/adv_abs_mean=1.7763
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Epoch 78 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 78 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 78 Training:  11%|█▏        | 11/96 [00:14<01:53,  1.33s/it]
+
Epoch 78 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 78 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 78 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.30it/s]
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Epoch 78 Training:  12%|█▎        | 12/96 [00:15<01:51,  1.33s/it]
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Epoch 78 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 78 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.29it/s]
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Epoch 78 Training:  14%|█▎        | 13/96 [00:17<01:50,  1.33s/it]
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Epoch 78 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 78 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 78 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 78 Training:  15%|█▍        | 14/96 [00:18<01:54,  1.40s/it]
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Epoch 78 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 78 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.30it/s]
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Epoch 78 Training:  16%|█▌        | 15/96 [00:20<01:51,  1.38s/it]
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Epoch 78 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 78 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 78 Training:  17%|█▋        | 16/96 [00:21<01:49,  1.36s/it]
+
Epoch 78 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 78 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 78 Training:  18%|█▊        | 17/96 [00:22<01:47,  1.36s/it]
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Epoch 78 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 78 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.29it/s]
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Epoch 78 Training:  19%|█▉        | 18/96 [00:24<01:45,  1.35s/it]
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Epoch 78 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 78 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 78 Training:  20%|█▉        | 19/96 [00:25<01:43,  1.35s/it]
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Epoch 78 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 78 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 78 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 78 Training:  21%|██        | 20/96 [00:26<01:41,  1.34s/it]
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Epoch 78 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 78 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.29it/s]
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Epoch 78 Training:  22%|██▏       | 21/96 [00:28<01:40,  1.34s/it]
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Epoch 78 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 78 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 78 Training:  23%|██▎       | 22/96 [00:29<01:38,  1.34s/it]
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Epoch 78 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 78 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 78 Training:  24%|██▍       | 23/96 [00:30<01:37,  1.34s/it]
+
Epoch 78 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 78 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.29it/s]
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Epoch 78 Training:  25%|██▌       | 24/96 [00:32<01:35,  1.33s/it]
+
Epoch 78 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 78 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.29it/s]
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Epoch 78 Training:  26%|██▌       | 25/96 [00:33<01:34,  1.33s/it]
+
Epoch 78 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 78 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 78 Training:  27%|██▋       | 26/96 [00:34<01:33,  1.33s/it]
+
Epoch 78 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 78 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 78 Training:  28%|██▊       | 27/96 [00:36<01:31,  1.33s/it]
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Epoch 78 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 78 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 78 Training:  29%|██▉       | 28/96 [00:37<01:30,  1.33s/it]
+
Epoch 78 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 78 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 78 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 78 Training:  30%|███       | 29/96 [00:38<01:29,  1.33s/it]
+
Epoch 78 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 78 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 78 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.31it/s]
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Epoch 78 Training:  31%|███▏      | 30/96 [00:40<01:27,  1.33s/it]
+
Epoch 78 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 78 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 78 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.22it/s]
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Epoch 78 Training:  32%|███▏      | 31/96 [00:41<01:26,  1.34s/it]
+
Epoch 78 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 78 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 78 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 78 Training:  33%|███▎      | 32/96 [00:42<01:25,  1.33s/it]
+
Epoch 78 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 78 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 78 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 78 Training:  34%|███▍      | 33/96 [00:44<01:24,  1.33s/it]
+
Epoch 78 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 78 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 78 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 78 Training:  35%|███▌      | 34/96 [00:45<01:22,  1.33s/it]
+
Epoch 78 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 78 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 78 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
+
                                                                
Epoch 78 Training:  36%|███▋      | 35/96 [00:46<01:21,  1.33s/it]
+
Epoch 78 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 78 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 78 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
+
                                                                
Epoch 78 Training:  38%|███▊      | 36/96 [00:48<01:19,  1.33s/it]
+
Epoch 78 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 78 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 78 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
+
                                                                
Epoch 78 Training:  39%|███▊      | 37/96 [00:49<01:18,  1.33s/it]
+
Epoch 78 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 78 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 78 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 78 Training:  40%|███▉      | 38/96 [00:50<01:17,  1.33s/it]
+
Epoch 78 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 78 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 78 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
+
                                                                
Epoch 78 Training:  41%|████      | 39/96 [00:52<01:15,  1.33s/it]
+
Epoch 78 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 78 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 78 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 78 Training:  42%|████▏     | 40/96 [00:53<01:14,  1.33s/it]
+
Epoch 78 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 78 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 78 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 78 Training:  43%|████▎     | 41/96 [00:54<01:13,  1.33s/it]
+
Epoch 78 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 78 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 78 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 78 Training:  44%|████▍     | 42/96 [00:56<01:11,  1.33s/it]
+
Epoch 78 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 78 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 78 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 78 Training:  45%|████▍     | 43/96 [00:57<01:10,  1.33s/it]
+
Epoch 78 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 78 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 78 Training:  46%|████▌     | 44/96 [00:58<01:09,  1.33s/it]
+
Epoch 78 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 78 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 78 Training:  47%|████▋     | 45/96 [01:00<01:07,  1.33s/it]
+
Epoch 78 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 78 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 78 Training:  48%|████▊     | 46/96 [01:01<01:06,  1.33s/it]
+
Epoch 78 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 78 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 78 Training:  49%|████▉     | 47/96 [01:03<01:09,  1.43s/it]
+
Epoch 78 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 78 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 78 Training:  50%|█████     | 48/96 [01:04<01:07,  1.40s/it]
+
Epoch 78 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 78 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 78 Training:  51%|█████     | 49/96 [01:05<01:04,  1.38s/it]
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Epoch 78 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 78 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 78 Training:  52%|█████▏    | 50/96 [01:07<01:02,  1.36s/it]
+
Epoch 78 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 78 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 78 Training:  53%|█████▎    | 51/96 [01:08<01:00,  1.35s/it]
+
Epoch 78 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 78 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 78 Training:  54%|█████▍    | 52/96 [01:09<00:59,  1.35s/it]
+
Epoch 78 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 78 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.29it/s]
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Epoch 78 Training:  55%|█████▌    | 53/96 [01:10<00:57,  1.34s/it]
+
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Epoch 78 Training:  56%|█████▋    | 54/96 [01:12<00:56,  1.34s/it]
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Epoch 78 Training:  57%|█████▋    | 55/96 [01:13<00:54,  1.34s/it]
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Epoch 78 Training:  58%|█████▊    | 56/96 [01:14<00:53,  1.33s/it]
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Epoch 78 Training:  59%|█████▉    | 57/96 [01:16<00:52,  1.33s/it]
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Epoch 78 Training:  60%|██████    | 58/96 [01:17<00:50,  1.33s/it]
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Epoch 78 Training:  61%|██████▏   | 59/96 [01:18<00:49,  1.33s/it]
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Epoch 78 Training:  62%|██████▎   | 60/96 [01:20<00:47,  1.33s/it]
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Epoch 78 Training:  64%|██████▎   | 61/96 [01:21<00:46,  1.34s/it]
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Epoch 78 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s][2026-10-01 22:16:29] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0078 | Epoch 078] train/policy_loss=11.4404 train/unweighted_policy_loss=2.2881 train/loss=11.4404 train/grad_norm=0.1382
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+[2026-10-01 22:16:29] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0079 | Epoch 078] timing/rollout_seconds=148.1098 timing/feedback_seconds=5.2312 timing/optimization_seconds=128.9716 timing/cycle_seconds=280.5975
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+
Pointwise Rewards: ssim:   0%|          | 0/6 [00:00<?, ?it/s]
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Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:00<00:00,  6.50it/s]
+
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Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.71it/s]
+
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Pointwise Rewards: psnr:  33%|███▎      | 2/6 [00:00<00:00,  7.45it/s]
Pointwise Rewards: psnr:  50%|█████     | 3/6 [00:00<00:00,  7.07it/s]
Pointwise Rewards: psnr:  67%|██████▋   | 4/6 [00:00<00:00,  6.95it/s]
Pointwise Rewards: psnr:  83%|████████▎ | 5/6 [00:00<00:00,  6.86it/s]
Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.93it/s]
Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.97it/s]
+[2026-10-01 22:19:01] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0079 | Epoch 079] train/reward_dists_mean=0.9360 train/reward_dists_std=0.0144 train/reward_psnr_mean=30.0473 train/reward_psnr_std=2.1717 train/reward_ssim_mean=0.9326 train/reward_ssim_std=0.0191 train/reward_dists_group_std_mean=0.0028 train/reward_dists_group_std_max=0.0061 train/reward_dists_group_std_min=0.0009 train/reward_dists_group_mean_std=0.0141 train/reward_psnr_group_std_mean=0.9145 train/reward_psnr_group_std_max=2.4376 train/reward_psnr_group_std_min=0.2574 train/reward_psnr_group_mean_std=1.9148 train/reward_ssim_group_std_mean=0.0009 train/reward_ssim_group_std_max=0.0032 train/reward_ssim_group_std_min=0.0004 train/reward_ssim_group_mean_std=0.0191 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-8.2786 train/adv_max=5.6132 train/adv_abs_mean=1.7697
+
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Epoch 79 Training:   4%|▍         | 4/96 [00:05<02:01,  1.32s/it]
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Epoch 79 Training:   6%|▋         | 6/96 [00:07<01:59,  1.33s/it]
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Epoch 79 Training:   7%|▋         | 7/96 [00:09<01:58,  1.33s/it]
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Epoch 79 Training:   8%|▊         | 8/96 [00:10<01:57,  1.33s/it]
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Epoch 79 Training:   9%|▉         | 9/96 [00:11<01:55,  1.33s/it]
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Epoch 79 Training:  10%|█         | 10/96 [00:13<01:54,  1.33s/it]
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Epoch 79 Training:  11%|█▏        | 11/96 [00:14<01:53,  1.33s/it]
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Epoch 79 Training:  15%|█▍        | 14/96 [00:18<01:49,  1.33s/it]
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Epoch 79 Training:  16%|█▌        | 15/96 [00:19<01:47,  1.33s/it]
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Epoch 79 Training:  17%|█▋        | 16/96 [00:21<01:46,  1.33s/it]
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Epoch 79 Training:  18%|█▊        | 17/96 [00:22<01:45,  1.33s/it]
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Epoch 79 Training:  19%|█▉        | 18/96 [00:23<01:43,  1.33s/it]
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Epoch 79 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 79 Training:  36%|███▋      | 35/96 [00:46<01:21,  1.34s/it]
+
Epoch 79 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 79 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 79 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 79 Training:  38%|███▊      | 36/96 [00:47<01:20,  1.34s/it]
+
Epoch 79 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 79 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 79 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.23it/s]
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Epoch 79 Training:  39%|███▊      | 37/96 [00:49<01:18,  1.34s/it]
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Epoch 79 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 79 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 79 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 79 Training:  40%|███▉      | 38/96 [00:50<01:17,  1.34s/it]
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Epoch 79 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 79 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 79 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 79 Training:  41%|████      | 39/96 [00:52<01:16,  1.34s/it]
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Epoch 79 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 79 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 79 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.22it/s]
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Epoch 79 Training:  42%|████▏     | 40/96 [00:53<01:14,  1.34s/it]
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Epoch 79 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 79 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 79 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 79 Training:  43%|████▎     | 41/96 [00:54<01:13,  1.34s/it]
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Epoch 79 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 79 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.20it/s]
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Epoch 79 Training:  44%|████▍     | 42/96 [00:56<01:12,  1.34s/it]
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Epoch 79 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 79 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 79 Training:  45%|████▍     | 43/96 [00:57<01:10,  1.34s/it]
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Epoch 79 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 79 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 79 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 79 Training:  46%|████▌     | 44/96 [00:58<01:09,  1.34s/it]
+
Epoch 79 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 79 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.19it/s]
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Epoch 79 Training:  47%|████▋     | 45/96 [01:00<01:08,  1.34s/it]
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Epoch 79 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 79 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 79 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
+
                                                                
Epoch 79 Training:  48%|████▊     | 46/96 [01:01<01:06,  1.34s/it]
+
Epoch 79 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 79 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 79 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.23it/s]
+
                                                                
Epoch 79 Training:  49%|████▉     | 47/96 [01:02<01:05,  1.34s/it]
+
Epoch 79 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 79 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 79 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
+
                                                                
Epoch 79 Training:  50%|█████     | 48/96 [01:04<01:04,  1.34s/it]
+
Epoch 79 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 79 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 79 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
+
                                                                
Epoch 79 Training:  51%|█████     | 49/96 [01:05<01:02,  1.34s/it]
+
Epoch 79 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 79 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 79 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.23it/s]
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Epoch 79 Training:  52%|█████▏    | 50/96 [01:06<01:01,  1.34s/it]
+
Epoch 79 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 79 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 79 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
+
                                                                
Epoch 79 Training:  53%|█████▎    | 51/96 [01:08<01:00,  1.34s/it]
+
Epoch 79 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 79 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 79 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 79 Training:  54%|█████▍    | 52/96 [01:09<00:58,  1.34s/it]
+
Epoch 79 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 79 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 79 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.22it/s]
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Epoch 79 Training:  55%|█████▌    | 53/96 [01:10<00:57,  1.34s/it]
+
Epoch 79 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 79 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 79 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
+
                                                                
Epoch 79 Training:  56%|█████▋    | 54/96 [01:12<00:56,  1.34s/it]
+
Epoch 79 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 79 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 79 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
+
                                                                
Epoch 79 Training:  57%|█████▋    | 55/96 [01:13<00:54,  1.34s/it]
+
Epoch 79 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 79 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 79 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
+
                                                                
Epoch 79 Training:  58%|█████▊    | 56/96 [01:14<00:53,  1.34s/it]
+
Epoch 79 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 79 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 79 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
+
                                                                
Epoch 79 Training:  59%|█████▉    | 57/96 [01:16<00:52,  1.34s/it]
+
Epoch 79 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 79 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 79 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.22it/s]
+
                                                                
Epoch 79 Training:  60%|██████    | 58/96 [01:17<00:50,  1.34s/it]
+
Epoch 79 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 79 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 79 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
+
                                                                
Epoch 79 Training:  61%|██████▏   | 59/96 [01:18<00:49,  1.34s/it]
+
Epoch 79 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 79 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 79 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.23it/s]
+
                                                                
Epoch 79 Training:  62%|██████▎   | 60/96 [01:20<00:48,  1.34s/it]
+
Epoch 79 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 79 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 79 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
+
                                                                
Epoch 79 Training:  64%|██████▎   | 61/96 [01:21<00:46,  1.34s/it]
+
Epoch 79 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 79 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 79 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
+
                                                                
Epoch 79 Training:  65%|██████▍   | 62/96 [01:22<00:45,  1.34s/it]
+
Epoch 79 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
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Epoch 79 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
+
                                                                
Epoch 79 Training:  66%|██████▌   | 63/96 [01:24<00:44,  1.34s/it]
+
Epoch 79 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 79 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
+
                                                                
Epoch 79 Training:  67%|██████▋   | 64/96 [01:25<00:42,  1.34s/it]
+
Epoch 79 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
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Epoch 79 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
+
                                                                
Epoch 79 Training:  68%|██████▊   | 65/96 [01:26<00:41,  1.34s/it]
+
Epoch 79 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 79 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
+
                                                                
Epoch 79 Training:  69%|██████▉   | 66/96 [01:28<00:40,  1.34s/it]
+
Epoch 79 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 79 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 79 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.23it/s]
+
                                                                
Epoch 79 Training:  70%|██████▉   | 67/96 [01:29<00:38,  1.34s/it]
+
Epoch 79 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 79 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 79 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
+
                                                                
Epoch 79 Training:  71%|███████   | 68/96 [01:30<00:37,  1.34s/it]
+
Epoch 79 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 79 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 79 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 79 Training:  72%|███████▏  | 69/96 [01:32<00:36,  1.34s/it]
+
Epoch 79 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 79 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 79 Training:  73%|███████▎  | 70/96 [01:33<00:34,  1.33s/it]
+
Epoch 79 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 79 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
+
                                                                
Epoch 79 Training:  74%|███████▍  | 71/96 [01:34<00:33,  1.33s/it]
+
Epoch 79 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 79 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 79 Training:  75%|███████▌  | 72/96 [01:36<00:32,  1.33s/it]
+
Epoch 79 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 79 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 79 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 79 Training:  76%|███████▌  | 73/96 [01:37<00:30,  1.34s/it]
+
Epoch 79 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 79 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 79 Training:  77%|███████▋  | 74/96 [01:38<00:29,  1.34s/it]
+
Epoch 79 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 79 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 79 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.23it/s]
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Epoch 79 Training:  78%|███████▊  | 75/96 [01:40<00:28,  1.34s/it]
+
Epoch 79 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 79 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 79 Training:  79%|███████▉  | 76/96 [01:41<00:26,  1.34s/it]
+
Epoch 79 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 79 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.23it/s]
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Epoch 79 Training:  80%|████████  | 77/96 [01:42<00:25,  1.34s/it]
+
Epoch 79 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 79 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.23it/s]
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Epoch 79 Training:  81%|████████▏ | 78/96 [01:44<00:24,  1.34s/it]
+
Epoch 79 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 79 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 79 Training:  82%|████████▏ | 79/96 [01:45<00:22,  1.34s/it]
+
Epoch 79 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 79 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 79 Training:  83%|████████▎ | 80/96 [01:46<00:21,  1.34s/it]
+
Epoch 79 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 79 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 79 Training:  84%|████████▍ | 81/96 [01:48<00:20,  1.34s/it]
+
Epoch 79 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 79 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 79 Training:  85%|████████▌ | 82/96 [01:49<00:18,  1.34s/it]
+
Epoch 79 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 79 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 79 Training:  86%|████████▋ | 83/96 [01:50<00:17,  1.34s/it]
+
Epoch 79 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 79 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 79 Training:  88%|████████▊ | 84/96 [01:52<00:16,  1.34s/it]
+
Epoch 79 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 79 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.23it/s]
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Epoch 79 Training:  89%|████████▊ | 85/96 [01:53<00:14,  1.34s/it]
+
Epoch 79 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 79 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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+[2026-10-01 22:21:10] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0080 | Epoch 079] timing/rollout_seconds=147.8800 timing/feedback_seconds=5.2827 timing/optimization_seconds=128.9223 timing/cycle_seconds=280.3240
+[2026-10-01 22:21:10] [Rank 0] [INFO] [flow_factory.models.abc]: Saving LoRA weights for transformer role=base to /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/runs/klein4b_nft_vedai_full_ssim_dists_psnr/saves/klein4b_nft_vedai_full_ssim_dists_psnr/checkpoints/checkpoint-80
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+[2026-10-01 22:21:54] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0080 | Epoch 080] eval/vedai_rl_full/reward_dists_mean=0.9206 eval/vedai_rl_full/reward_dists_std=0.0200 eval/vedai_rl_full/reward_psnr_mean=28.3011 eval/vedai_rl_full/reward_psnr_std=2.4144 eval/vedai_rl_full/reward_ssim_mean=0.9160 eval/vedai_rl_full/reward_ssim_std=0.0275
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+[2026-10-01 22:24:26] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0080 | Epoch 080] train/reward_dists_mean=0.9312 train/reward_dists_std=0.0137 train/reward_psnr_mean=29.3815 train/reward_psnr_std=2.3562 train/reward_ssim_mean=0.9272 train/reward_ssim_std=0.0207 train/reward_dists_group_std_mean=0.0028 train/reward_dists_group_std_max=0.0067 train/reward_dists_group_std_min=0.0014 train/reward_dists_group_mean_std=0.0133 train/reward_psnr_group_std_mean=1.0343 train/reward_psnr_group_std_max=2.2161 train/reward_psnr_group_std_min=0.3664 train/reward_psnr_group_mean_std=2.0706 train/reward_ssim_group_std_mean=0.0010 train/reward_ssim_group_std_max=0.0040 train/reward_ssim_group_std_min=0.0003 train/reward_ssim_group_mean_std=0.0207 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-7.3886 train/adv_max=5.5015 train/adv_abs_mean=1.8404
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+
                                                                
Epoch 80 Training:   1%|          | 1/96 [00:01<01:55,  1.22s/it]
+
Epoch 80 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 80 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
+
                                                                
Epoch 80 Training:   2%|▏         | 2/96 [00:02<02:00,  1.29s/it]
+
Epoch 80 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 80 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 80 Training:   3%|▎         | 3/96 [00:03<02:01,  1.31s/it]
+
Epoch 80 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 80 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 80 Training:   4%|▍         | 4/96 [00:05<02:01,  1.32s/it]
+
Epoch 80 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 80 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 80 Training:   5%|▌         | 5/96 [00:06<02:00,  1.32s/it]
+
Epoch 80 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 80 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 80 Training:   6%|▋         | 6/96 [00:07<01:59,  1.33s/it]
+
Epoch 80 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 80 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 80 Training:   7%|▋         | 7/96 [00:09<01:58,  1.33s/it]
+
Epoch 80 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 80 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 80 Training:   8%|▊         | 8/96 [00:10<01:57,  1.33s/it]
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Epoch 80 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 80 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 80 Training:   9%|▉         | 9/96 [00:11<01:55,  1.33s/it]
+
Epoch 80 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 80 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 80 Training:  10%|█         | 10/96 [00:13<01:54,  1.33s/it]
+
Epoch 80 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 80 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.23it/s]
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Epoch 80 Training:  11%|█▏        | 11/96 [00:14<01:53,  1.33s/it]
+
Epoch 80 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 80 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 80 Training:  12%|█▎        | 12/96 [00:15<01:52,  1.33s/it]
+
Epoch 80 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 80 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
+
                                                                
Epoch 80 Training:  14%|█▎        | 13/96 [00:17<01:50,  1.34s/it]
+
Epoch 80 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 80 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 80 Training:  15%|█▍        | 14/96 [00:18<01:49,  1.33s/it]
+
Epoch 80 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 80 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 80 Training:  16%|█▌        | 15/96 [00:19<01:48,  1.33s/it]
+
Epoch 80 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 80 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 80 Training:  17%|█▋        | 16/96 [00:21<01:46,  1.33s/it]
+
Epoch 80 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 80 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 80 Training:  18%|█▊        | 17/96 [00:22<01:45,  1.34s/it]
+
Epoch 80 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 80 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
+
                                                                
Epoch 80 Training:  19%|█▉        | 18/96 [00:23<01:44,  1.34s/it]
+
Epoch 80 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 80 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
+
                                                                
Epoch 80 Training:  20%|█▉        | 19/96 [00:25<01:42,  1.34s/it]
+
Epoch 80 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 80 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.63it/s]
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Epoch 80 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.29it/s]
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Epoch 80 Training:  21%|██        | 20/96 [00:26<01:41,  1.34s/it]
+
Epoch 80 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 80 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 80 Training:  22%|██▏       | 21/96 [00:27<01:40,  1.34s/it]
+
Epoch 80 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 80 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 80 Training:  23%|██▎       | 22/96 [00:29<01:38,  1.34s/it]
+
Epoch 80 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 80 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.29it/s]
+
                                                                
Epoch 80 Training:  24%|██▍       | 23/96 [00:30<01:37,  1.33s/it]
+
Epoch 80 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 80 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.29it/s]
+
                                                                
Epoch 80 Training:  25%|██▌       | 24/96 [00:31<01:36,  1.34s/it]
+
Epoch 80 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 80 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
+
                                                                
Epoch 80 Training:  26%|██▌       | 25/96 [00:33<01:34,  1.34s/it]
+
Epoch 80 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 80 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.30it/s]
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Epoch 80 Training:  27%|██▋       | 26/96 [00:34<01:33,  1.33s/it]
+
Epoch 80 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 80 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 80 Training:  28%|██▊       | 27/96 [00:35<01:32,  1.34s/it]
+
Epoch 80 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 80 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 80 Training:  29%|██▉       | 28/96 [00:37<01:31,  1.34s/it]
+
Epoch 80 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 80 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 80 Training:  30%|███       | 29/96 [00:38<01:29,  1.34s/it]
+
Epoch 80 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 80 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.21it/s]
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Epoch 80 Training:  31%|███▏      | 30/96 [00:39<01:28,  1.34s/it]
+
Epoch 80 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 80 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 80 Training:  32%|███▏      | 31/96 [00:41<01:27,  1.34s/it]
+
Epoch 80 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 80 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 80 Training:  33%|███▎      | 32/96 [00:42<01:25,  1.34s/it]
+
Epoch 80 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 80 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 80 Training:  34%|███▍      | 33/96 [00:43<01:24,  1.34s/it]
+
Epoch 80 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 80 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 80 Training:  35%|███▌      | 34/96 [00:45<01:22,  1.34s/it]
+
Epoch 80 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 80 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 80 Training:  36%|███▋      | 35/96 [00:46<01:21,  1.34s/it]
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Epoch 80 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 80 Training:  38%|███▊      | 36/96 [00:47<01:20,  1.34s/it]
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Epoch 80 Training:  39%|███▊      | 37/96 [00:49<01:18,  1.34s/it]
+
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Epoch 80 Training:  40%|███▉      | 38/96 [00:50<01:17,  1.34s/it]
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Epoch 80 Training:  41%|████      | 39/96 [00:51<01:16,  1.34s/it]
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Epoch 80 Training:  42%|████▏     | 40/96 [00:53<01:14,  1.34s/it]
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Epoch 80 Training:  43%|████▎     | 41/96 [00:54<01:13,  1.34s/it]
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Epoch 80 Training:  44%|████▍     | 42/96 [00:55<01:12,  1.33s/it]
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Epoch 80 Training:  45%|████▍     | 43/96 [00:57<01:10,  1.34s/it]
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Epoch 80 Training:  46%|████▌     | 44/96 [00:58<01:09,  1.33s/it]
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Epoch 80 Training:  47%|████▋     | 45/96 [00:59<01:07,  1.33s/it]
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Epoch 80 Training:  48%|████▊     | 46/96 [01:01<01:06,  1.33s/it]
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Epoch 80 Training:  49%|████▉     | 47/96 [01:02<01:05,  1.33s/it]
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Epoch 80 Training:  50%|█████     | 48/96 [01:03<01:04,  1.33s/it]
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Epoch 80 Training:  51%|█████     | 49/96 [01:05<01:02,  1.34s/it]
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Epoch 80 Training:  52%|█████▏    | 50/96 [01:06<01:01,  1.34s/it]
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Epoch 80 Training:  53%|█████▎    | 51/96 [01:08<01:00,  1.34s/it]
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Epoch 80 Training:  54%|█████▍    | 52/96 [01:09<00:58,  1.34s/it]
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Epoch 80 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s][2026-10-01 22:26:34] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0080 | Epoch 080] train/policy_loss=12.0293 train/unweighted_policy_loss=2.4059 train/loss=12.0293 train/grad_norm=0.1176
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+[2026-10-01 22:26:34] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0081 | Epoch 080] timing/rollout_seconds=147.8767 timing/feedback_seconds=5.1532 timing/optimization_seconds=128.8297 timing/cycle_seconds=280.2363
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+
Pointwise Rewards: ssim:   0%|          | 0/6 [00:00<?, ?it/s]
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Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:00<00:00,  6.33it/s]
+
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+
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Pointwise Rewards: psnr:  33%|███▎      | 2/6 [00:00<00:00,  6.70it/s]
Pointwise Rewards: psnr:  50%|█████     | 3/6 [00:00<00:00,  6.67it/s]
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Pointwise Rewards: psnr:  83%|████████▎ | 5/6 [00:00<00:00,  6.71it/s]
Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.69it/s]
Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.69it/s]
+[2026-10-01 22:29:07] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0081 | Epoch 081] train/reward_dists_mean=0.9327 train/reward_dists_std=0.0143 train/reward_psnr_mean=29.5438 train/reward_psnr_std=2.1811 train/reward_ssim_mean=0.9284 train/reward_ssim_std=0.0249 train/reward_dists_group_std_mean=0.0027 train/reward_dists_group_std_max=0.0062 train/reward_dists_group_std_min=0.0013 train/reward_dists_group_mean_std=0.0140 train/reward_psnr_group_std_mean=0.9409 train/reward_psnr_group_std_max=2.6084 train/reward_psnr_group_std_min=0.2017 train/reward_psnr_group_mean_std=1.8959 train/reward_ssim_group_std_mean=0.0012 train/reward_ssim_group_std_max=0.0029 train/reward_ssim_group_std_min=0.0003 train/reward_ssim_group_mean_std=0.0249 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-6.6212 train/adv_max=5.7688 train/adv_abs_mean=1.7872
+
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Epoch 81 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 81 Training:  27%|██▋       | 26/96 [00:34<01:32,  1.33s/it]
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Epoch 81 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 81 Training:  28%|██▊       | 27/96 [00:36<01:31,  1.33s/it]
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Epoch 81 Training:  29%|██▉       | 28/96 [00:37<01:30,  1.33s/it]
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Epoch 81 Training:  30%|███       | 29/96 [00:38<01:28,  1.33s/it]
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Epoch 81 Training:  31%|███▏      | 30/96 [00:40<01:28,  1.34s/it]
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Epoch 81 Training:  32%|███▏      | 31/96 [00:41<01:26,  1.33s/it]
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Epoch 81 Training:  33%|███▎      | 32/96 [00:42<01:25,  1.34s/it]
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Epoch 81 Training:  34%|███▍      | 33/96 [00:44<01:24,  1.34s/it]
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Epoch 81 Training:  35%|███▌      | 34/96 [00:45<01:22,  1.33s/it]
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Epoch 81 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 81 Training:  36%|███▋      | 35/96 [00:46<01:21,  1.33s/it]
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Epoch 81 Training:  38%|███▊      | 36/96 [00:48<01:19,  1.33s/it]
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Epoch 81 Training:  39%|███▊      | 37/96 [00:49<01:18,  1.33s/it]
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Epoch 81 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 81 Training:  40%|███▉      | 38/96 [00:50<01:17,  1.33s/it]
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Epoch 81 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.19it/s]
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Epoch 81 Training:  41%|████      | 39/96 [00:52<01:16,  1.34s/it]
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Epoch 81 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 81 Training:  42%|████▏     | 40/96 [00:53<01:14,  1.33s/it]
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Epoch 81 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 81 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 81 Training:  43%|████▎     | 41/96 [00:54<01:13,  1.33s/it]
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Epoch 81 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 81 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.22it/s]
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Epoch 81 Training:  44%|████▍     | 42/96 [00:56<01:12,  1.34s/it]
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Epoch 81 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 81 Training:  45%|████▍     | 43/96 [00:57<01:10,  1.33s/it]
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Epoch 81 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 81 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.23it/s]
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Epoch 81 Training:  46%|████▌     | 44/96 [00:58<01:09,  1.33s/it]
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Epoch 81 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 81 Training:  47%|████▋     | 45/96 [01:00<01:07,  1.33s/it]
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Epoch 81 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 81 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.22it/s]
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Epoch 81 Training:  48%|████▊     | 46/96 [01:01<01:06,  1.33s/it]
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Epoch 81 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 81 Training:  49%|████▉     | 47/96 [01:02<01:05,  1.33s/it]
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Epoch 81 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 81 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 81 Training:  50%|█████     | 48/96 [01:04<01:03,  1.33s/it]
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Epoch 81 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 81 Training:  51%|█████     | 49/96 [01:05<01:02,  1.33s/it]
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Epoch 81 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 81 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 81 Training:  52%|█████▏    | 50/96 [01:06<01:01,  1.33s/it]
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Epoch 81 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 81 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 81 Training:  53%|█████▎    | 51/96 [01:08<00:59,  1.33s/it]
+
Epoch 81 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 81 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 81 Training:  54%|█████▍    | 52/96 [01:09<00:58,  1.33s/it]
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Epoch 81 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 81 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 81 Training:  55%|█████▌    | 53/96 [01:10<00:57,  1.33s/it]
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Epoch 81 Training:  56%|█████▋    | 54/96 [01:12<00:55,  1.33s/it]
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Epoch 81 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 81 Training:  57%|█████▋    | 55/96 [01:13<00:54,  1.33s/it]
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Epoch 81 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 81 Training:  58%|█████▊    | 56/96 [01:14<00:53,  1.33s/it]
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Epoch 81 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 81 Training:  59%|█████▉    | 57/96 [01:16<00:51,  1.33s/it]
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Epoch 81 Training:  60%|██████    | 58/96 [01:17<00:50,  1.33s/it]
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Epoch 81 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 81 Training:  61%|██████▏   | 59/96 [01:18<00:49,  1.33s/it]
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Epoch 81 Training:  62%|██████▎   | 60/96 [01:20<00:47,  1.33s/it]
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Epoch 81 Training:  64%|██████▎   | 61/96 [01:21<00:46,  1.33s/it]
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Epoch 81 Training:  65%|██████▍   | 62/96 [01:22<00:45,  1.33s/it]
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Epoch 81 Training:  66%|██████▌   | 63/96 [01:24<00:43,  1.33s/it]
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Epoch 81 Training:  67%|██████▋   | 64/96 [01:25<00:42,  1.33s/it]
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Epoch 81 Training:  68%|██████▊   | 65/96 [01:26<00:41,  1.33s/it]
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Epoch 81 Training:  69%|██████▉   | 66/96 [01:28<00:39,  1.33s/it]
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Epoch 81 Training:  70%|██████▉   | 67/96 [01:29<00:38,  1.33s/it]
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Epoch 81 Training:  71%|███████   | 68/96 [01:30<00:37,  1.33s/it]
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Epoch 81 Training:  72%|███████▏  | 69/96 [01:32<00:35,  1.33s/it]
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Epoch 81 Training:  73%|███████▎  | 70/96 [01:33<00:34,  1.33s/it]
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Epoch 81 Training:  74%|███████▍  | 71/96 [01:34<00:33,  1.33s/it]
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Epoch 81 Training:  75%|███████▌  | 72/96 [01:36<00:31,  1.33s/it]
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Epoch 81 Training:  76%|███████▌  | 73/96 [01:37<00:30,  1.32s/it]
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Epoch 81 Training:  77%|███████▋  | 74/96 [01:38<00:29,  1.33s/it]
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Epoch 81 Training:  78%|███████▊  | 75/96 [01:40<00:27,  1.33s/it]
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Epoch 81 Training:  79%|███████▉  | 76/96 [01:41<00:26,  1.33s/it]
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Epoch 81 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s][2026-10-01 22:31:14] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0081 | Epoch 081] train/policy_loss=12.0746 train/unweighted_policy_loss=2.4149 train/loss=12.0746 train/grad_norm=0.2470
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+[2026-10-01 22:31:14] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0082 | Epoch 081] timing/rollout_seconds=148.0347 timing/feedback_seconds=5.4741 timing/optimization_seconds=128.5451 timing/cycle_seconds=280.0592
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+[2026-10-01 22:33:47] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0082 | Epoch 082] train/reward_dists_mean=0.9367 train/reward_dists_std=0.0154 train/reward_psnr_mean=30.2881 train/reward_psnr_std=1.7556 train/reward_ssim_mean=0.9325 train/reward_ssim_std=0.0209 train/reward_dists_group_std_mean=0.0026 train/reward_dists_group_std_max=0.0055 train/reward_dists_group_std_min=0.0011 train/reward_dists_group_mean_std=0.0152 train/reward_psnr_group_std_mean=0.8265 train/reward_psnr_group_std_max=2.1329 train/reward_psnr_group_std_min=0.1574 train/reward_psnr_group_mean_std=1.4844 train/reward_ssim_group_std_mean=0.0010 train/reward_ssim_group_std_max=0.0055 train/reward_ssim_group_std_min=0.0004 train/reward_ssim_group_mean_std=0.0208 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-6.9608 train/adv_max=5.9173 train/adv_abs_mean=1.7904
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Epoch 82 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s][2026-10-01 22:35:54] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0082 | Epoch 082] train/policy_loss=12.7766 train/unweighted_policy_loss=2.5553 train/loss=12.7766 train/grad_norm=0.0817
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+[2026-10-01 22:35:54] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0083 | Epoch 082] timing/rollout_seconds=147.9432 timing/feedback_seconds=5.4761 timing/optimization_seconds=127.9227 timing/cycle_seconds=279.3561
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+
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+
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+[2026-10-01 22:38:26] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0083 | Epoch 083] train/reward_dists_mean=0.9332 train/reward_dists_std=0.0155 train/reward_psnr_mean=30.1142 train/reward_psnr_std=1.8437 train/reward_ssim_mean=0.9319 train/reward_ssim_std=0.0210 train/reward_dists_group_std_mean=0.0027 train/reward_dists_group_std_max=0.0064 train/reward_dists_group_std_min=0.0013 train/reward_dists_group_mean_std=0.0152 train/reward_psnr_group_std_mean=0.9256 train/reward_psnr_group_std_max=2.8288 train/reward_psnr_group_std_min=0.1655 train/reward_psnr_group_mean_std=1.5214 train/reward_ssim_group_std_mean=0.0010 train/reward_ssim_group_std_max=0.0087 train/reward_ssim_group_std_min=0.0003 train/reward_ssim_group_mean_std=0.0209 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-8.0961 train/adv_max=5.4991 train/adv_abs_mean=1.8053
+
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Epoch 83 Training:  23%|██▎       | 22/96 [00:29<01:38,  1.33s/it]
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Epoch 83 Training:  24%|██▍       | 23/96 [00:30<01:36,  1.32s/it]
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Epoch 83 Training:  25%|██▌       | 24/96 [00:31<01:35,  1.32s/it]
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Epoch 83 Training:  26%|██▌       | 25/96 [00:33<01:34,  1.32s/it]
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Epoch 83 Training:  27%|██▋       | 26/96 [00:34<01:32,  1.33s/it]
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Epoch 83 Training:  28%|██▊       | 27/96 [00:35<01:31,  1.32s/it]
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Epoch 83 Training:  29%|██▉       | 28/96 [00:36<01:30,  1.32s/it]
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Epoch 83 Training:  30%|███       | 29/96 [00:38<01:28,  1.33s/it]
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Epoch 83 Training:  31%|███▏      | 30/96 [00:39<01:27,  1.33s/it]
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Epoch 83 Training:  32%|███▏      | 31/96 [00:40<01:26,  1.33s/it]
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Epoch 83 Training:  33%|███▎      | 32/96 [00:42<01:25,  1.33s/it]
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Epoch 83 Training:  34%|███▍      | 33/96 [00:43<01:23,  1.33s/it]
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Epoch 83 Training:  35%|███▌      | 34/96 [00:44<01:22,  1.33s/it]
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Epoch 83 Training:  36%|███▋      | 35/96 [00:46<01:21,  1.33s/it]
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Epoch 83 Training:  38%|███▊      | 36/96 [00:47<01:19,  1.33s/it]
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Epoch 83 Training:  39%|███▊      | 37/96 [00:48<01:18,  1.33s/it]
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Epoch 83 Training:  40%|███▉      | 38/96 [00:50<01:17,  1.33s/it]
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Epoch 83 Training:  41%|████      | 39/96 [00:51<01:15,  1.33s/it]
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Epoch 83 Training:  42%|████▏     | 40/96 [00:52<01:14,  1.33s/it]
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Epoch 83 Training:  43%|████▎     | 41/96 [00:54<01:12,  1.33s/it]
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Epoch 83 Training:  44%|████▍     | 42/96 [00:55<01:11,  1.33s/it]
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Epoch 83 Training:  45%|████▍     | 43/96 [00:56<01:10,  1.33s/it]
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Epoch 83 Training:  46%|████▌     | 44/96 [00:58<01:08,  1.33s/it]
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Epoch 83 Training:  47%|████▋     | 45/96 [00:59<01:07,  1.33s/it]
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Epoch 83 Training:  48%|████▊     | 46/96 [01:00<01:06,  1.33s/it]
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Epoch 83 Training:  49%|████▉     | 47/96 [01:02<01:04,  1.33s/it]
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Epoch 83 Training:  50%|█████     | 48/96 [01:03<01:03,  1.33s/it]
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Epoch 83 Training:  51%|█████     | 49/96 [01:04<01:02,  1.32s/it]
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Epoch 83 Training:  52%|█████▏    | 50/96 [01:06<01:01,  1.33s/it]
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Epoch 83 Training:  53%|█████▎    | 51/96 [01:07<00:59,  1.33s/it]
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Epoch 83 Training:  54%|█████▍    | 52/96 [01:08<00:58,  1.33s/it]
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Epoch 83 Training:  55%|█████▌    | 53/96 [01:10<00:57,  1.33s/it]
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Epoch 83 Training:  56%|█████▋    | 54/96 [01:11<00:55,  1.33s/it]
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Epoch 83 Training:  57%|█████▋    | 55/96 [01:12<00:54,  1.33s/it]
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Epoch 83 Training:  58%|█████▊    | 56/96 [01:14<00:53,  1.33s/it]
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Epoch 83 Training:  59%|█████▉    | 57/96 [01:15<00:51,  1.33s/it]
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Epoch 83 Training:  60%|██████    | 58/96 [01:16<00:50,  1.33s/it]
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Epoch 83 Training:  61%|██████▏   | 59/96 [01:18<00:49,  1.33s/it]
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Epoch 83 Training:  62%|██████▎   | 60/96 [01:19<00:47,  1.32s/it]
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Epoch 83 Training:  64%|██████▎   | 61/96 [01:20<00:46,  1.33s/it]
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Epoch 83 Training:  65%|██████▍   | 62/96 [01:22<00:45,  1.33s/it]
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Epoch 83 Training:  66%|██████▌   | 63/96 [01:23<00:43,  1.33s/it]
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Epoch 83 Training:  67%|██████▋   | 64/96 [01:24<00:42,  1.32s/it]
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Epoch 83 Training:  68%|██████▊   | 65/96 [01:26<00:41,  1.33s/it]
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Epoch 83 Training:  69%|██████▉   | 66/96 [01:27<00:39,  1.33s/it]
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Epoch 83 Training:  70%|██████▉   | 67/96 [01:28<00:38,  1.33s/it]
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Epoch 83 Training:  71%|███████   | 68/96 [01:30<00:37,  1.33s/it]
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Epoch 83 Training:  72%|███████▏  | 69/96 [01:31<00:35,  1.33s/it]
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Epoch 83 Training:  73%|███████▎  | 70/96 [01:32<00:34,  1.33s/it]
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Epoch 83 Training:  74%|███████▍  | 71/96 [01:34<00:33,  1.33s/it]
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Epoch 83 Training:  75%|███████▌  | 72/96 [01:35<00:31,  1.33s/it]
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Epoch 83 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s][2026-10-01 22:40:33] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0083 | Epoch 083] train/policy_loss=13.3419 train/unweighted_policy_loss=2.6684 train/loss=13.3419 train/grad_norm=0.1194
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+[2026-10-01 22:40:33] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0084 | Epoch 083] timing/rollout_seconds=147.6398 timing/feedback_seconds=5.4154 timing/optimization_seconds=127.9391 timing/cycle_seconds=279.1065
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Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.61it/s]
+
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Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.78it/s]
+[2026-10-01 22:43:05] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0084 | Epoch 084] train/reward_dists_mean=0.9287 train/reward_dists_std=0.0175 train/reward_psnr_mean=30.6834 train/reward_psnr_std=2.1409 train/reward_ssim_mean=0.9280 train/reward_ssim_std=0.0220 train/reward_dists_group_std_mean=0.0030 train/reward_dists_group_std_max=0.0071 train/reward_dists_group_std_min=0.0014 train/reward_dists_group_mean_std=0.0172 train/reward_psnr_group_std_mean=0.8945 train/reward_psnr_group_std_max=2.1376 train/reward_psnr_group_std_min=0.2590 train/reward_psnr_group_mean_std=1.8780 train/reward_ssim_group_std_mean=0.0012 train/reward_ssim_group_std_max=0.0077 train/reward_ssim_group_std_min=0.0004 train/reward_ssim_group_mean_std=0.0219 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-9.2672 train/adv_max=5.6962 train/adv_abs_mean=1.7960
+
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Epoch 84 Training:  53%|█████▎    | 51/96 [01:07<00:59,  1.33s/it]
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Epoch 84 Training:  54%|█████▍    | 52/96 [01:09<00:58,  1.33s/it]
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Epoch 84 Training:  55%|█████▌    | 53/96 [01:10<00:57,  1.33s/it]
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Epoch 84 Training:  56%|█████▋    | 54/96 [01:11<00:55,  1.32s/it]
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Epoch 84 Training:  57%|█████▋    | 55/96 [01:13<00:54,  1.33s/it]
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Epoch 84 Training:  58%|█████▊    | 56/96 [01:14<00:53,  1.33s/it]
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Epoch 84 Training:  59%|█████▉    | 57/96 [01:15<00:51,  1.32s/it]
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Epoch 84 Training:  60%|██████    | 58/96 [01:17<00:50,  1.32s/it]
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Epoch 84 Training:  61%|██████▏   | 59/96 [01:18<00:49,  1.33s/it]
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Epoch 84 Training:  62%|██████▎   | 60/96 [01:19<00:47,  1.32s/it]
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Epoch 84 Training:  64%|██████▎   | 61/96 [01:21<00:46,  1.32s/it]
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Epoch 84 Training:  65%|██████▍   | 62/96 [01:22<00:44,  1.32s/it]
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Epoch 84 Training:  66%|██████▌   | 63/96 [01:23<00:43,  1.32s/it]
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Epoch 84 Training:  67%|██████▋   | 64/96 [01:24<00:42,  1.32s/it]
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Epoch 84 Training:  68%|██████▊   | 65/96 [01:26<00:41,  1.32s/it]
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Epoch 84 Training:  69%|██████▉   | 66/96 [01:27<00:39,  1.32s/it]
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Epoch 84 Training:  70%|██████▉   | 67/96 [01:28<00:38,  1.32s/it]
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Epoch 84 Training:  71%|███████   | 68/96 [01:30<00:37,  1.32s/it]
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Epoch 84 Training:  72%|███████▏  | 69/96 [01:31<00:35,  1.32s/it]
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Epoch 84 Training:  73%|███████▎  | 70/96 [01:32<00:34,  1.32s/it]
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Epoch 84 Training:  74%|███████▍  | 71/96 [01:34<00:33,  1.32s/it]
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Epoch 84 Training:  75%|███████▌  | 72/96 [01:35<00:31,  1.32s/it]
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Epoch 84 Training:  76%|███████▌  | 73/96 [01:36<00:30,  1.32s/it]
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Epoch 84 Training:  77%|███████▋  | 74/96 [01:38<00:29,  1.32s/it]
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Epoch 84 Training:  78%|███████▊  | 75/96 [01:39<00:27,  1.32s/it]
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Epoch 84 Training:  79%|███████▉  | 76/96 [01:40<00:26,  1.32s/it]
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Epoch 84 Training:  80%|████████  | 77/96 [01:42<00:25,  1.32s/it]
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Epoch 84 Training:  81%|████████▏ | 78/96 [01:43<00:23,  1.33s/it]
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Epoch 84 Training:  82%|████████▏ | 79/96 [01:44<00:22,  1.32s/it]
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Epoch 84 Training:  83%|████████▎ | 80/96 [01:46<00:21,  1.32s/it]
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Epoch 84 Training:  84%|████████▍ | 81/96 [01:47<00:19,  1.32s/it]
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Epoch 84 Training:  85%|████████▌ | 82/96 [01:48<00:18,  1.32s/it]
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Epoch 84 Training:  86%|████████▋ | 83/96 [01:50<00:17,  1.32s/it]
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Epoch 84 Training:  88%|████████▊ | 84/96 [01:51<00:16,  1.33s/it]
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Epoch 84 Training:  89%|████████▊ | 85/96 [01:52<00:14,  1.33s/it]
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Epoch 84 Training:  90%|████████▉ | 86/96 [01:54<00:13,  1.33s/it]
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Epoch 84 Training:  91%|█████████ | 87/96 [01:55<00:11,  1.33s/it]
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Epoch 84 Training:  92%|█████████▏| 88/96 [01:56<00:10,  1.33s/it]
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Epoch 84 Training:  93%|█████████▎| 89/96 [01:58<00:09,  1.33s/it]
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Epoch 84 Training:  94%|█████████▍| 90/96 [01:59<00:07,  1.33s/it]
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Epoch 84 Training:  97%|█████████▋| 93/96 [02:03<00:03,  1.33s/it]
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Epoch 84 Training:  98%|█████████▊| 94/96 [02:04<00:02,  1.33s/it]
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Epoch 84 Training:  99%|█████████▉| 95/96 [02:06<00:01,  1.33s/it]
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Epoch 84 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s][2026-10-01 22:45:12] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0084 | Epoch 084] train/policy_loss=13.9187 train/unweighted_policy_loss=2.7837 train/loss=13.9187 train/grad_norm=0.1981
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+[2026-10-01 22:45:12] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0085 | Epoch 084] timing/rollout_seconds=147.6979 timing/feedback_seconds=5.4424 timing/optimization_seconds=128.0839 timing/cycle_seconds=279.3337
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+[2026-10-01 22:47:44] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0085 | Epoch 085] train/reward_dists_mean=0.9347 train/reward_dists_std=0.0162 train/reward_psnr_mean=30.3031 train/reward_psnr_std=2.6489 train/reward_ssim_mean=0.9347 train/reward_ssim_std=0.0230 train/reward_dists_group_std_mean=0.0028 train/reward_dists_group_std_max=0.0071 train/reward_dists_group_std_min=0.0012 train/reward_dists_group_mean_std=0.0160 train/reward_psnr_group_std_mean=0.9471 train/reward_psnr_group_std_max=1.9089 train/reward_psnr_group_std_min=0.1873 train/reward_psnr_group_mean_std=2.4428 train/reward_ssim_group_std_mean=0.0011 train/reward_ssim_group_std_max=0.0030 train/reward_ssim_group_std_min=0.0002 train/reward_ssim_group_mean_std=0.0230 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-7.3580 train/adv_max=6.0540 train/adv_abs_mean=1.7911
+
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Epoch 85 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 85 Training:  20%|█▉        | 19/96 [00:25<01:42,  1.33s/it]
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Epoch 85 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 85 Training:  21%|██        | 20/96 [00:26<01:40,  1.33s/it]
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Epoch 85 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 85 Training:  22%|██▏       | 21/96 [00:27<01:39,  1.32s/it]
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Epoch 85 Training:  23%|██▎       | 22/96 [00:29<01:38,  1.33s/it]
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Epoch 85 Training:  24%|██▍       | 23/96 [00:30<01:36,  1.32s/it]
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Epoch 85 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 85 Training:  25%|██▌       | 24/96 [00:31<01:35,  1.32s/it]
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Epoch 85 Training:  26%|██▌       | 25/96 [00:33<01:34,  1.32s/it]
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Epoch 85 Training:  27%|██▋       | 26/96 [00:34<01:32,  1.33s/it]
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Epoch 85 Training:  28%|██▊       | 27/96 [00:35<01:31,  1.33s/it]
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Epoch 85 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 85 Training:  29%|██▉       | 28/96 [00:37<01:30,  1.32s/it]
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Epoch 85 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 85 Training:  30%|███       | 29/96 [00:38<01:28,  1.32s/it]
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Epoch 85 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 85 Training:  31%|███▏      | 30/96 [00:39<01:27,  1.33s/it]
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Epoch 85 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 85 Training:  32%|███▏      | 31/96 [00:41<01:26,  1.32s/it]
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Epoch 85 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 85 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 85 Training:  33%|███▎      | 32/96 [00:42<01:24,  1.32s/it]
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Epoch 85 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 85 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 85 Training:  34%|███▍      | 33/96 [00:43<01:23,  1.32s/it]
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Epoch 85 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 85 Training:  35%|███▌      | 34/96 [00:45<01:22,  1.33s/it]
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Epoch 85 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 85 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 85 Training:  36%|███▋      | 35/96 [00:46<01:20,  1.33s/it]
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Epoch 85 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 85 Training:  38%|███▊      | 36/96 [00:47<01:19,  1.33s/it]
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Epoch 85 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 85 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 85 Training:  39%|███▊      | 37/96 [00:49<01:18,  1.33s/it]
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Epoch 85 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 85 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 85 Training:  40%|███▉      | 38/96 [00:50<01:16,  1.33s/it]
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Epoch 85 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 85 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 85 Training:  41%|████      | 39/96 [00:51<01:15,  1.33s/it]
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Epoch 85 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 85 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 85 Training:  42%|████▏     | 40/96 [00:53<01:14,  1.32s/it]
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Epoch 85 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 85 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 85 Training:  43%|████▎     | 41/96 [00:54<01:12,  1.32s/it]
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Epoch 85 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 85 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 85 Training:  44%|████▍     | 42/96 [00:55<01:11,  1.32s/it]
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Epoch 85 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 85 Training:  45%|████▍     | 43/96 [00:56<01:10,  1.32s/it]
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Epoch 85 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 85 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 85 Training:  46%|████▌     | 44/96 [00:58<01:08,  1.32s/it]
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Epoch 85 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 85 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 85 Training:  47%|████▋     | 45/96 [00:59<01:07,  1.32s/it]
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Epoch 85 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 85 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 85 Training:  48%|████▊     | 46/96 [01:00<01:06,  1.32s/it]
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Epoch 85 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 85 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 85 Training:  49%|████▉     | 47/96 [01:02<01:04,  1.32s/it]
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Epoch 85 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 85 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 85 Training:  50%|█████     | 48/96 [01:03<01:03,  1.32s/it]
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Epoch 85 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 85 Training:  51%|█████     | 49/96 [01:04<01:02,  1.32s/it]
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Epoch 85 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 85 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 85 Training:  52%|█████▏    | 50/96 [01:06<01:00,  1.32s/it]
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Epoch 85 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 85 Training:  53%|█████▎    | 51/96 [01:07<00:59,  1.32s/it]
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Epoch 85 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 85 Training:  54%|█████▍    | 52/96 [01:08<00:58,  1.32s/it]
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Epoch 85 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 85 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 85 Training:  55%|█████▌    | 53/96 [01:10<00:56,  1.32s/it]
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Epoch 85 Training:  56%|█████▋    | 54/96 [01:11<00:55,  1.32s/it]
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Epoch 85 Training:  57%|█████▋    | 55/96 [01:12<00:54,  1.32s/it]
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Epoch 85 Training:  58%|█████▊    | 56/96 [01:14<00:52,  1.32s/it]
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Epoch 85 Training:  59%|█████▉    | 57/96 [01:15<00:51,  1.32s/it]
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Epoch 85 Training:  60%|██████    | 58/96 [01:16<00:50,  1.32s/it]
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Epoch 85 Training:  61%|██████▏   | 59/96 [01:18<00:49,  1.33s/it]
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Epoch 85 Training:  62%|██████▎   | 60/96 [01:19<00:47,  1.33s/it]
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Epoch 85 Training:  64%|██████▎   | 61/96 [01:20<00:46,  1.33s/it]
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Epoch 85 Training:  65%|██████▍   | 62/96 [01:22<00:45,  1.32s/it]
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Epoch 85 Training:  66%|██████▌   | 63/96 [01:23<00:43,  1.33s/it]
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Epoch 85 Training:  67%|██████▋   | 64/96 [01:24<00:42,  1.33s/it]
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Epoch 85 Training:  68%|██████▊   | 65/96 [01:26<00:41,  1.32s/it]
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Epoch 85 Training:  69%|██████▉   | 66/96 [01:27<00:39,  1.33s/it]
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Epoch 85 Training:  70%|██████▉   | 67/96 [01:28<00:38,  1.33s/it]
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Epoch 85 Training:  71%|███████   | 68/96 [01:30<00:37,  1.33s/it]
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Epoch 85 Training:  72%|███████▏  | 69/96 [01:31<00:35,  1.32s/it]
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Epoch 85 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s][2026-10-01 22:49:51] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0085 | Epoch 085] train/policy_loss=13.0791 train/unweighted_policy_loss=2.6158 train/loss=13.0791 train/grad_norm=0.0885
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+[2026-10-01 22:49:51] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0086 | Epoch 085] timing/rollout_seconds=147.5291 timing/feedback_seconds=5.4029 timing/optimization_seconds=127.7886 timing/cycle_seconds=278.8508
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+[2026-10-01 22:52:23] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0086 | Epoch 086] train/reward_dists_mean=0.9342 train/reward_dists_std=0.0191 train/reward_psnr_mean=30.7438 train/reward_psnr_std=2.2345 train/reward_ssim_mean=0.9348 train/reward_ssim_std=0.0230 train/reward_dists_group_std_mean=0.0027 train/reward_dists_group_std_max=0.0074 train/reward_dists_group_std_min=0.0010 train/reward_dists_group_mean_std=0.0189 train/reward_psnr_group_std_mean=0.8801 train/reward_psnr_group_std_max=2.5275 train/reward_psnr_group_std_min=0.2320 train/reward_psnr_group_mean_std=1.9995 train/reward_ssim_group_std_mean=0.0012 train/reward_ssim_group_std_max=0.0052 train/reward_ssim_group_std_min=0.0004 train/reward_ssim_group_mean_std=0.0229 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-7.8465 train/adv_max=6.0882 train/adv_abs_mean=1.8326
+
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Epoch 86 Training:  46%|████▌     | 44/96 [00:58<01:10,  1.36s/it]
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Epoch 86 Training:  47%|████▋     | 45/96 [01:00<01:09,  1.35s/it]
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Epoch 86 Training:  48%|████▊     | 46/96 [01:01<01:07,  1.34s/it]
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Epoch 86 Training:  49%|████▉     | 47/96 [01:02<01:05,  1.34s/it]
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Epoch 86 Training:  50%|█████     | 48/96 [01:04<01:04,  1.34s/it]
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Epoch 86 Training:  51%|█████     | 49/96 [01:05<01:02,  1.33s/it]
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Epoch 86 Training:  52%|█████▏    | 50/96 [01:06<01:01,  1.33s/it]
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Epoch 86 Training:  53%|█████▎    | 51/96 [01:08<00:59,  1.33s/it]
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Epoch 86 Training:  54%|█████▍    | 52/96 [01:09<00:58,  1.33s/it]
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Epoch 86 Training:  55%|█████▌    | 53/96 [01:10<00:57,  1.33s/it]
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Epoch 86 Training:  56%|█████▋    | 54/96 [01:12<00:55,  1.33s/it]
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Epoch 86 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 86 Training:  57%|█████▋    | 55/96 [01:13<00:54,  1.32s/it]
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Epoch 86 Training:  58%|█████▊    | 56/96 [01:14<00:52,  1.32s/it]
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Epoch 86 Training:  59%|█████▉    | 57/96 [01:16<00:51,  1.32s/it]
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Epoch 86 Training:  60%|██████    | 58/96 [01:17<00:50,  1.32s/it]
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Epoch 86 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 86 Training:  61%|██████▏   | 59/96 [01:18<00:48,  1.32s/it]
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Epoch 86 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 86 Training:  62%|██████▎   | 60/96 [01:19<00:47,  1.32s/it]
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Epoch 86 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 86 Training:  64%|██████▎   | 61/96 [01:21<00:46,  1.32s/it]
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Epoch 86 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 86 Training:  65%|██████▍   | 62/96 [01:22<00:44,  1.32s/it]
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Epoch 86 Training:  66%|██████▌   | 63/96 [01:23<00:43,  1.32s/it]
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Epoch 86 Training:  67%|██████▋   | 64/96 [01:25<00:42,  1.32s/it]
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Epoch 86 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 86 Training:  68%|██████▊   | 65/96 [01:26<00:40,  1.32s/it]
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Epoch 86 Training:  69%|██████▉   | 66/96 [01:27<00:39,  1.32s/it]
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Epoch 86 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 86 Training:  70%|██████▉   | 67/96 [01:29<00:38,  1.32s/it]
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Epoch 86 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 86 Training:  71%|███████   | 68/96 [01:30<00:37,  1.32s/it]
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Epoch 86 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 86 Training:  72%|███████▏  | 69/96 [01:31<00:35,  1.32s/it]
+
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Epoch 86 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 86 Training:  73%|███████▎  | 70/96 [01:33<00:34,  1.32s/it]
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Epoch 86 Training:  74%|███████▍  | 71/96 [01:34<00:33,  1.33s/it]
+
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Epoch 86 Training:  75%|███████▌  | 72/96 [01:35<00:31,  1.32s/it]
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Epoch 86 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 86 Training:  76%|███████▌  | 73/96 [01:37<00:30,  1.32s/it]
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Epoch 86 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 86 Training:  77%|███████▋  | 74/96 [01:38<00:29,  1.32s/it]
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Epoch 86 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 86 Training:  78%|███████▊  | 75/96 [01:39<00:27,  1.32s/it]
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Epoch 86 Training:  79%|███████▉  | 76/96 [01:41<00:26,  1.33s/it]
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Epoch 86 Training:  80%|████████  | 77/96 [01:42<00:25,  1.33s/it]
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Epoch 86 Training:  81%|████████▏ | 78/96 [01:43<00:23,  1.33s/it]
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Epoch 86 Training:  82%|████████▏ | 79/96 [01:45<00:22,  1.33s/it]
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Epoch 86 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 86 Training:  83%|████████▎ | 80/96 [01:46<00:21,  1.33s/it]
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Epoch 86 Training:  84%|████████▍ | 81/96 [01:47<00:19,  1.32s/it]
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Epoch 86 Training:  85%|████████▌ | 82/96 [01:49<00:18,  1.33s/it]
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Epoch 86 Training:  86%|████████▋ | 83/96 [01:50<00:17,  1.32s/it]
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Epoch 86 Training:  88%|████████▊ | 84/96 [01:51<00:15,  1.32s/it]
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Epoch 86 Training:  89%|████████▊ | 85/96 [01:53<00:14,  1.32s/it]
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Epoch 86 Training:  90%|████████▉ | 86/96 [01:54<00:13,  1.32s/it]
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Epoch 86 Training:  91%|█████████ | 87/96 [01:55<00:11,  1.32s/it]
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Epoch 86 Training:  92%|█████████▏| 88/96 [01:57<00:10,  1.32s/it]
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Epoch 86 Training:  93%|█████████▎| 89/96 [01:58<00:09,  1.32s/it]
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Epoch 86 Training:  94%|█████████▍| 90/96 [01:59<00:07,  1.32s/it]
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Epoch 86 Training:  95%|█████████▍| 91/96 [02:01<00:06,  1.32s/it]
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Epoch 86 Training:  96%|█████████▌| 92/96 [02:02<00:05,  1.32s/it]
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Epoch 86 Training:  97%|█████████▋| 93/96 [02:03<00:03,  1.32s/it]
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Epoch 86 Training:  98%|█████████▊| 94/96 [02:05<00:02,  1.32s/it]
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Epoch 86 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s][2026-10-01 22:54:31] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0086 | Epoch 086] train/policy_loss=12.6039 train/unweighted_policy_loss=2.5208 train/loss=12.6039 train/grad_norm=0.1030
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+[2026-10-01 22:54:31] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0087 | Epoch 086] timing/rollout_seconds=147.8281 timing/feedback_seconds=5.7543 timing/optimization_seconds=128.3871 timing/cycle_seconds=279.7655
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+[2026-10-01 22:57:04] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0087 | Epoch 087] train/reward_dists_mean=0.9369 train/reward_dists_std=0.0159 train/reward_psnr_mean=29.8670 train/reward_psnr_std=2.0595 train/reward_ssim_mean=0.9328 train/reward_ssim_std=0.0219 train/reward_dists_group_std_mean=0.0027 train/reward_dists_group_std_max=0.0058 train/reward_dists_group_std_min=0.0013 train/reward_dists_group_mean_std=0.0156 train/reward_psnr_group_std_mean=0.9799 train/reward_psnr_group_std_max=1.8873 train/reward_psnr_group_std_min=0.3081 train/reward_psnr_group_mean_std=1.7679 train/reward_ssim_group_std_mean=0.0009 train/reward_ssim_group_std_max=0.0019 train/reward_ssim_group_std_min=0.0003 train/reward_ssim_group_mean_std=0.0219 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-7.3959 train/adv_max=6.6503 train/adv_abs_mean=1.8134
+
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Epoch 87 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.32it/s]
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Epoch 87 Training:  18%|█▊        | 17/96 [00:22<01:44,  1.33s/it]
+
Epoch 87 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 87 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 87 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 87 Training:  19%|█▉        | 18/96 [00:23<01:43,  1.33s/it]
+
Epoch 87 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 87 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.31it/s]
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Epoch 87 Training:  20%|█▉        | 19/96 [00:25<01:42,  1.33s/it]
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Epoch 87 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 87 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.32it/s]
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Epoch 87 Training:  21%|██        | 20/96 [00:26<01:40,  1.33s/it]
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Epoch 87 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 87 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 87 Training:  22%|██▏       | 21/96 [00:27<01:39,  1.33s/it]
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Epoch 87 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 87 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.31it/s]
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Epoch 87 Training:  23%|██▎       | 22/96 [00:29<01:38,  1.33s/it]
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Epoch 87 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 87 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.33it/s]
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Epoch 87 Training:  24%|██▍       | 23/96 [00:30<01:36,  1.32s/it]
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Epoch 87 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 87 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.32it/s]
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Epoch 87 Training:  25%|██▌       | 24/96 [00:31<01:35,  1.32s/it]
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Epoch 87 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 87 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.31it/s]
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Epoch 87 Training:  26%|██▌       | 25/96 [00:33<01:33,  1.32s/it]
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Epoch 87 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 87 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.29it/s]
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Epoch 87 Training:  27%|██▋       | 26/96 [00:34<01:32,  1.32s/it]
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Epoch 87 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 87 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 87 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.32it/s]
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Epoch 87 Training:  28%|██▊       | 27/96 [00:35<01:31,  1.32s/it]
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Epoch 87 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 87 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.33it/s]
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Epoch 87 Training:  29%|██▉       | 28/96 [00:37<01:29,  1.32s/it]
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Epoch 87 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 87 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.33it/s]
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Epoch 87 Training:  30%|███       | 29/96 [00:38<01:28,  1.32s/it]
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Epoch 87 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 87 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.29it/s]
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Epoch 87 Training:  31%|███▏      | 30/96 [00:39<01:27,  1.32s/it]
+
Epoch 87 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 87 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 87 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.31it/s]
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Epoch 87 Training:  32%|███▏      | 31/96 [00:40<01:25,  1.32s/it]
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Epoch 87 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 87 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.32it/s]
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Epoch 87 Training:  33%|███▎      | 32/96 [00:42<01:24,  1.32s/it]
+
Epoch 87 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 87 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 87 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.31it/s]
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Epoch 87 Training:  34%|███▍      | 33/96 [00:43<01:23,  1.32s/it]
+
Epoch 87 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 87 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 87 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.30it/s]
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Epoch 87 Training:  35%|███▌      | 34/96 [00:44<01:22,  1.32s/it]
+
Epoch 87 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 87 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 87 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.29it/s]
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Epoch 87 Training:  36%|███▋      | 35/96 [00:46<01:20,  1.32s/it]
+
Epoch 87 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 87 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 87 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.21it/s]
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Epoch 87 Training:  38%|███▊      | 36/96 [00:47<01:20,  1.33s/it]
+
Epoch 87 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 87 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.29it/s]
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Epoch 87 Training:  39%|███▊      | 37/96 [00:48<01:18,  1.33s/it]
+
Epoch 87 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 87 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.30it/s]
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Epoch 87 Training:  40%|███▉      | 38/96 [00:50<01:17,  1.33s/it]
+
Epoch 87 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 87 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.32it/s]
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Epoch 87 Training:  41%|████      | 39/96 [00:51<01:15,  1.33s/it]
+
Epoch 87 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 87 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 87 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.31it/s]
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Epoch 87 Training:  42%|████▏     | 40/96 [00:52<01:14,  1.33s/it]
+
Epoch 87 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 87 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 87 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.31it/s]
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Epoch 87 Training:  43%|████▎     | 41/96 [00:54<01:12,  1.33s/it]
+
Epoch 87 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 87 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 87 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.29it/s]
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Epoch 87 Training:  44%|████▍     | 42/96 [00:55<01:11,  1.33s/it]
+
Epoch 87 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 87 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 87 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.34it/s]
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Epoch 87 Training:  45%|████▍     | 43/96 [00:56<01:10,  1.32s/it]
+
Epoch 87 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 87 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 87 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.33it/s]
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Epoch 87 Training:  46%|████▌     | 44/96 [00:58<01:08,  1.32s/it]
+
Epoch 87 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 87 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 87 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.31it/s]
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Epoch 87 Training:  47%|████▋     | 45/96 [00:59<01:07,  1.32s/it]
+
Epoch 87 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 87 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.29it/s]
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Epoch 87 Training:  48%|████▊     | 46/96 [01:00<01:06,  1.32s/it]
+
Epoch 87 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 87 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.32it/s]
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Epoch 87 Training:  49%|████▉     | 47/96 [01:02<01:04,  1.32s/it]
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Epoch 87 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 87 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.34it/s]
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Epoch 87 Training:  50%|█████     | 48/96 [01:03<01:03,  1.32s/it]
+
Epoch 87 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 87 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.33it/s]
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Epoch 87 Training:  51%|█████     | 49/96 [01:04<01:02,  1.32s/it]
+
Epoch 87 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 87 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.30it/s]
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Epoch 87 Training:  52%|█████▏    | 50/96 [01:06<01:00,  1.32s/it]
+
Epoch 87 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 87 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.30it/s]
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Epoch 87 Training:  53%|█████▎    | 51/96 [01:07<00:59,  1.32s/it]
+
Epoch 87 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 87 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.33it/s]
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Epoch 87 Training:  54%|█████▍    | 52/96 [01:08<00:58,  1.32s/it]
+
Epoch 87 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 87 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.34it/s]
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Epoch 87 Training:  55%|█████▌    | 53/96 [01:10<00:56,  1.32s/it]
+
Epoch 87 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 87 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.31it/s]
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Epoch 87 Training:  56%|█████▋    | 54/96 [01:11<00:55,  1.32s/it]
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Epoch 87 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 87 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.32it/s]
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Epoch 87 Training:  57%|█████▋    | 55/96 [01:12<00:54,  1.32s/it]
+
Epoch 87 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 87 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.32it/s]
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Epoch 87 Training:  58%|█████▊    | 56/96 [01:14<00:52,  1.32s/it]
+
Epoch 87 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 87 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.34it/s]
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Epoch 87 Training:  59%|█████▉    | 57/96 [01:15<00:51,  1.32s/it]
+
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Epoch 87 Training:  60%|██████    | 58/96 [01:16<00:50,  1.32s/it]
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Epoch 87 Training:  61%|██████▏   | 59/96 [01:18<00:48,  1.32s/it]
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Epoch 87 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.31it/s]
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Epoch 87 Training:  62%|██████▎   | 60/96 [01:19<00:47,  1.32s/it]
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Epoch 87 Training:  64%|██████▎   | 61/96 [01:20<00:46,  1.32s/it]
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Epoch 87 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.33it/s]
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Epoch 87 Training:  65%|██████▍   | 62/96 [01:22<00:44,  1.32s/it]
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Epoch 87 Training:  66%|██████▌   | 63/96 [01:23<00:43,  1.32s/it]
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Epoch 87 Training:  67%|██████▋   | 64/96 [01:24<00:42,  1.32s/it]
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Epoch 87 Training:  68%|██████▊   | 65/96 [01:25<00:41,  1.32s/it]
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Epoch 87 Training:  69%|██████▉   | 66/96 [01:27<00:39,  1.32s/it]
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Epoch 87 Training:  70%|██████▉   | 67/96 [01:28<00:38,  1.32s/it]
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Epoch 87 Training:  71%|███████   | 68/96 [01:29<00:37,  1.33s/it]
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Epoch 87 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s][2026-10-01 22:59:11] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0087 | Epoch 087] train/policy_loss=12.4077 train/unweighted_policy_loss=2.4815 train/loss=12.4077 train/grad_norm=0.1703
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+[2026-10-01 22:59:11] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0088 | Epoch 087] timing/rollout_seconds=147.6174 timing/feedback_seconds=5.8986 timing/optimization_seconds=128.7630 timing/cycle_seconds=279.8853
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Epoch 88 Sampling:  99%|█████████▉| 95/96 [02:23<00:01,  1.51s/it]
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Epoch 88 Sampling: 100%|██████████| 96/96 [02:25<00:00,  1.51s/it]
+
Pointwise Rewards: ssim:   0%|          | 0/6 [00:00<?, ?it/s]
Pointwise Rewards: ssim:  17%|█▋        | 1/6 [00:00<00:00,  6.87it/s]
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Pointwise Rewards: ssim:  67%|██████▋   | 4/6 [00:00<00:00,  6.52it/s]
Pointwise Rewards: ssim:  83%|████████▎ | 5/6 [00:00<00:00,  6.52it/s]
Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:00<00:00,  6.83it/s]
Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:00<00:00,  6.65it/s]
+
Pointwise Rewards: dists:   0%|          | 0/6 [00:00<?, ?it/s]
Pointwise Rewards: dists:  17%|█▋        | 1/6 [00:00<00:01,  3.96it/s]
Pointwise Rewards: dists:  33%|███▎      | 2/6 [00:00<00:01,  3.79it/s]
Pointwise Rewards: dists:  50%|█████     | 3/6 [00:00<00:00,  3.64it/s]
Pointwise Rewards: dists:  67%|██████▋   | 4/6 [00:01<00:00,  3.65it/s]
Pointwise Rewards: dists:  83%|████████▎ | 5/6 [00:01<00:00,  3.66it/s]
Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.68it/s]
Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.69it/s]
+
Pointwise Rewards: psnr:   0%|          | 0/6 [00:00<?, ?it/s]
Pointwise Rewards: psnr:  17%|█▋        | 1/6 [00:00<00:00,  5.79it/s]
Pointwise Rewards: psnr:  33%|███▎      | 2/6 [00:00<00:00,  5.38it/s]
Pointwise Rewards: psnr:  50%|█████     | 3/6 [00:00<00:00,  5.30it/s]
Pointwise Rewards: psnr:  67%|██████▋   | 4/6 [00:00<00:00,  5.81it/s]
Pointwise Rewards: psnr:  83%|████████▎ | 5/6 [00:00<00:00,  6.05it/s]
Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.54it/s]
Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.07it/s]
+[2026-10-01 23:01:43] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0088 | Epoch 088] train/reward_dists_mean=0.9376 train/reward_dists_std=0.0130 train/reward_psnr_mean=30.5174 train/reward_psnr_std=1.8104 train/reward_ssim_mean=0.9347 train/reward_ssim_std=0.0205 train/reward_dists_group_std_mean=0.0025 train/reward_dists_group_std_max=0.0042 train/reward_dists_group_std_min=0.0011 train/reward_dists_group_mean_std=0.0127 train/reward_psnr_group_std_mean=0.8801 train/reward_psnr_group_std_max=1.9335 train/reward_psnr_group_std_min=0.1673 train/reward_psnr_group_mean_std=1.5348 train/reward_ssim_group_std_mean=0.0009 train/reward_ssim_group_std_max=0.0031 train/reward_ssim_group_std_min=0.0004 train/reward_ssim_group_mean_std=0.0205 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-7.4087 train/adv_max=5.6680 train/adv_abs_mean=1.7326
+
Epoch 88 Training:   0%|          | 0/96 [00:00<?, ?it/s]
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Epoch 88 Training:   1%|          | 1/96 [00:01<01:55,  1.21s/it]
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Epoch 88 Training:   2%|▏         | 2/96 [00:02<02:00,  1.28s/it]
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Epoch 88 Training:   3%|▎         | 3/96 [00:03<02:00,  1.30s/it]
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Epoch 88 Training:   4%|▍         | 4/96 [00:05<02:00,  1.31s/it]
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Epoch 88 Training:   5%|▌         | 5/96 [00:06<01:59,  1.32s/it]
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Epoch 88 Training:   6%|▋         | 6/96 [00:07<01:58,  1.32s/it]
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Epoch 88 Training:   7%|▋         | 7/96 [00:09<01:57,  1.32s/it]
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Epoch 88 Training:   8%|▊         | 8/96 [00:10<01:56,  1.32s/it]
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Epoch 88 Training:   9%|▉         | 9/96 [00:11<01:55,  1.32s/it]
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Epoch 88 Training:  10%|█         | 10/96 [00:13<01:53,  1.33s/it]
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Epoch 88 Training:  11%|█▏        | 11/96 [00:14<01:52,  1.32s/it]
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Epoch 88 Training:  12%|█▎        | 12/96 [00:15<01:51,  1.33s/it]
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Epoch 88 Training:  14%|█▎        | 13/96 [00:17<01:49,  1.32s/it]
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Epoch 88 Training:  15%|█▍        | 14/96 [00:18<01:48,  1.33s/it]
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Epoch 88 Training:  16%|█▌        | 15/96 [00:19<01:47,  1.33s/it]
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Epoch 88 Training:  17%|█▋        | 16/96 [00:21<01:46,  1.33s/it]
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Epoch 88 Training:  18%|█▊        | 17/96 [00:22<01:44,  1.33s/it]
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Epoch 88 Training:  19%|█▉        | 18/96 [00:23<01:43,  1.33s/it]
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Epoch 88 Training:  20%|█▉        | 19/96 [00:25<01:42,  1.33s/it]
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Epoch 88 Training:  21%|██        | 20/96 [00:26<01:40,  1.33s/it]
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Epoch 88 Training:  22%|██▏       | 21/96 [00:27<01:39,  1.33s/it]
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Epoch 88 Training:  23%|██▎       | 22/96 [00:29<01:38,  1.33s/it]
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Epoch 88 Training:  24%|██▍       | 23/96 [00:30<01:36,  1.33s/it]
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Epoch 88 Training:  25%|██▌       | 24/96 [00:31<01:35,  1.33s/it]
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Epoch 88 Training:  26%|██▌       | 25/96 [00:33<01:34,  1.32s/it]
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Epoch 88 Training:  27%|██▋       | 26/96 [00:34<01:32,  1.33s/it]
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Epoch 88 Training:  28%|██▊       | 27/96 [00:35<01:31,  1.33s/it]
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Epoch 88 Training:  29%|██▉       | 28/96 [00:37<01:30,  1.33s/it]
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Epoch 88 Training:  30%|███       | 29/96 [00:38<01:28,  1.33s/it]
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Epoch 88 Training:  31%|███▏      | 30/96 [00:39<01:27,  1.33s/it]
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Epoch 88 Training:  32%|███▏      | 31/96 [00:41<01:26,  1.33s/it]
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Epoch 88 Training:  33%|███▎      | 32/96 [00:42<01:25,  1.33s/it]
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Epoch 88 Training:  34%|███▍      | 33/96 [00:43<01:23,  1.33s/it]
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Epoch 88 Training:  35%|███▌      | 34/96 [00:45<01:22,  1.33s/it]
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Epoch 88 Training:  36%|███▋      | 35/96 [00:46<01:21,  1.33s/it]
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Epoch 88 Training:  38%|███▊      | 36/96 [00:47<01:19,  1.33s/it]
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Epoch 88 Training:  39%|███▊      | 37/96 [00:48<01:18,  1.33s/it]
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Epoch 88 Training:  41%|████      | 39/96 [00:51<01:15,  1.33s/it]
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Epoch 88 Training:  43%|████▎     | 41/96 [00:54<01:12,  1.33s/it]
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Epoch 88 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 88 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 88 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 88 Training:  44%|████▍     | 42/96 [00:55<01:11,  1.33s/it]
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Epoch 88 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 88 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 88 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 88 Training:  45%|████▍     | 43/96 [00:56<01:10,  1.33s/it]
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Epoch 88 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 88 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 88 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 88 Training:  46%|████▌     | 44/96 [00:58<01:09,  1.33s/it]
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Epoch 88 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 88 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 88 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 88 Training:  47%|████▋     | 45/96 [00:59<01:07,  1.32s/it]
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Epoch 88 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 88 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 88 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.29it/s]
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Epoch 88 Training:  48%|████▊     | 46/96 [01:00<01:06,  1.32s/it]
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Epoch 88 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 88 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 88 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 88 Training:  49%|████▉     | 47/96 [01:02<01:04,  1.32s/it]
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Epoch 88 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 88 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 88 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.30it/s]
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Epoch 88 Training:  50%|█████     | 48/96 [01:03<01:03,  1.32s/it]
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Epoch 88 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 88 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 88 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 88 Training:  51%|█████     | 49/96 [01:04<01:02,  1.32s/it]
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Epoch 88 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 88 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 88 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 88 Training:  52%|█████▏    | 50/96 [01:06<01:00,  1.32s/it]
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Epoch 88 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 88 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.63it/s]
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Epoch 88 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 88 Training:  53%|█████▎    | 51/96 [01:07<00:59,  1.33s/it]
+
Epoch 88 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 88 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 88 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.29it/s]
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Epoch 88 Training:  54%|█████▍    | 52/96 [01:08<00:58,  1.32s/it]
+
Epoch 88 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 88 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 88 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 88 Training:  55%|█████▌    | 53/96 [01:10<00:57,  1.33s/it]
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Epoch 88 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 88 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 88 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 88 Training:  56%|█████▋    | 54/96 [01:11<00:55,  1.33s/it]
+
Epoch 88 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 88 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 88 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 88 Training:  57%|█████▋    | 55/96 [01:12<00:54,  1.33s/it]
+
Epoch 88 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 88 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 88 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.22it/s]
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Epoch 88 Training:  58%|█████▊    | 56/96 [01:14<00:53,  1.33s/it]
+
Epoch 88 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 88 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 88 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
+
                                                                
Epoch 88 Training:  59%|█████▉    | 57/96 [01:15<00:51,  1.33s/it]
+
Epoch 88 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 88 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 88 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
+
                                                                
Epoch 88 Training:  60%|██████    | 58/96 [01:16<00:50,  1.33s/it]
+
Epoch 88 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 88 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 88 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
+
                                                                
Epoch 88 Training:  61%|██████▏   | 59/96 [01:18<00:49,  1.33s/it]
+
Epoch 88 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 88 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 88 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.23it/s]
+
                                                                
Epoch 88 Training:  62%|██████▎   | 60/96 [01:19<00:47,  1.33s/it]
+
Epoch 88 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 88 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 88 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
+
                                                                
Epoch 88 Training:  64%|██████▎   | 61/96 [01:20<00:46,  1.33s/it]
+
Epoch 88 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 88 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 88 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
+
                                                                
Epoch 88 Training:  65%|██████▍   | 62/96 [01:22<00:45,  1.33s/it]
+
Epoch 88 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 88 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 88 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
+
                                                                
Epoch 88 Training:  66%|██████▌   | 63/96 [01:23<00:43,  1.33s/it]
+
Epoch 88 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 88 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 88 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
+
                                                                
Epoch 88 Training:  67%|██████▋   | 64/96 [01:24<00:42,  1.33s/it]
+
Epoch 88 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 88 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 88 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.36it/s]
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Epoch 88 Training:  68%|██████▊   | 65/96 [01:26<00:41,  1.33s/it]
+
Epoch 88 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 88 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 88 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
+
                                                                
Epoch 88 Training:  69%|██████▉   | 66/96 [01:27<00:39,  1.33s/it]
+
Epoch 88 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 88 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 88 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
+
                                                                
Epoch 88 Training:  70%|██████▉   | 67/96 [01:28<00:38,  1.33s/it]
+
Epoch 88 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 88 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 88 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
+
                                                                
Epoch 88 Training:  71%|███████   | 68/96 [01:30<00:37,  1.33s/it]
+
Epoch 88 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 88 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 88 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.29it/s]
+
                                                                
Epoch 88 Training:  72%|███████▏  | 69/96 [01:31<00:35,  1.33s/it]
+
Epoch 88 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 88 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 88 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 88 Training:  73%|███████▎  | 70/96 [01:32<00:34,  1.33s/it]
+
Epoch 88 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 88 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 88 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 88 Training:  74%|███████▍  | 71/96 [01:34<00:33,  1.33s/it]
+
Epoch 88 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 88 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 88 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 88 Training:  75%|███████▌  | 72/96 [01:35<00:31,  1.32s/it]
+
Epoch 88 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 88 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.63it/s]
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Epoch 88 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 88 Training:  76%|███████▌  | 73/96 [01:36<00:30,  1.32s/it]
+
Epoch 88 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 88 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 88 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
+
                                                                
Epoch 88 Training:  77%|███████▋  | 74/96 [01:38<00:29,  1.32s/it]
+
Epoch 88 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 88 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 88 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 88 Training:  78%|███████▊  | 75/96 [01:39<00:27,  1.33s/it]
+
Epoch 88 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 88 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 88 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 88 Training:  79%|███████▉  | 76/96 [01:40<00:26,  1.33s/it]
+
Epoch 88 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 88 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 88 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 88 Training:  80%|████████  | 77/96 [01:42<00:25,  1.32s/it]
+
Epoch 88 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 88 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 88 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 88 Training:  81%|████████▏ | 78/96 [01:43<00:23,  1.33s/it]
+
Epoch 88 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 88 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 88 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 88 Training:  82%|████████▏ | 79/96 [01:44<00:22,  1.32s/it]
+
Epoch 88 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 88 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 88 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 88 Training:  83%|████████▎ | 80/96 [01:46<00:21,  1.33s/it]
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Epoch 88 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 88 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 88 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 88 Training:  84%|████████▍ | 81/96 [01:47<00:19,  1.33s/it]
+
Epoch 88 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 88 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 88 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 88 Training:  85%|████████▌ | 82/96 [01:48<00:18,  1.33s/it]
+
Epoch 88 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 88 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 88 Training:  86%|████████▋ | 83/96 [01:50<00:17,  1.33s/it]
+
Epoch 88 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 88 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 88 Training:  88%|████████▊ | 84/96 [01:51<00:15,  1.33s/it]
+
Epoch 88 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 88 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 88 Training:  89%|████████▊ | 85/96 [01:52<00:14,  1.33s/it]
+
Epoch 88 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 88 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 88 Training:  90%|████████▉ | 86/96 [01:54<00:13,  1.33s/it]
+
Epoch 88 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 88 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 88 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 88 Training:  91%|█████████ | 87/96 [01:55<00:11,  1.33s/it]
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Epoch 88 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 88 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 88 Training:  92%|█████████▏| 88/96 [01:56<00:10,  1.33s/it]
+
Epoch 88 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 88 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 88 Training:  93%|█████████▎| 89/96 [01:57<00:09,  1.33s/it]
+
Epoch 88 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 88 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 88 Training:  94%|█████████▍| 90/96 [01:59<00:07,  1.33s/it]
+
Epoch 88 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 88 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 88 Training:  95%|█████████▍| 91/96 [02:00<00:06,  1.33s/it]
+
Epoch 88 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 88 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 88 Training:  96%|█████████▌| 92/96 [02:01<00:05,  1.33s/it]
+
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Epoch 88 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s][2026-10-01 23:03:50] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0088 | Epoch 088] train/policy_loss=12.4787 train/unweighted_policy_loss=2.4957 train/loss=12.4787 train/grad_norm=0.0920
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+[2026-10-01 23:03:50] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0089 | Epoch 088] timing/rollout_seconds=147.8000 timing/feedback_seconds=5.5694 timing/optimization_seconds=127.8720 timing/cycle_seconds=279.2238
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+
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+[2026-10-01 23:06:22] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0089 | Epoch 089] train/reward_dists_mean=0.9353 train/reward_dists_std=0.0317 train/reward_psnr_mean=30.9806 train/reward_psnr_std=1.9590 train/reward_ssim_mean=0.9383 train/reward_ssim_std=0.0210 train/reward_dists_group_std_mean=0.0027 train/reward_dists_group_std_max=0.0141 train/reward_dists_group_std_min=0.0011 train/reward_dists_group_mean_std=0.0315 train/reward_psnr_group_std_mean=0.8246 train/reward_psnr_group_std_max=3.5849 train/reward_psnr_group_std_min=0.2022 train/reward_psnr_group_mean_std=1.6771 train/reward_ssim_group_std_mean=0.0013 train/reward_ssim_group_std_max=0.0240 train/reward_ssim_group_std_min=0.0003 train/reward_ssim_group_mean_std=0.0207 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-8.7348 train/adv_max=5.7977 train/adv_abs_mean=1.7500
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Epoch 89 Training:  16%|█▌        | 15/96 [00:19<01:47,  1.33s/it]
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Epoch 89 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 89 Training:  17%|█▋        | 16/96 [00:21<01:46,  1.33s/it]
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Epoch 89 Training:  18%|█▊        | 17/96 [00:22<01:45,  1.33s/it]
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Epoch 89 Training:  19%|█▉        | 18/96 [00:23<01:43,  1.33s/it]
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Epoch 89 Training:  20%|█▉        | 19/96 [00:25<01:42,  1.33s/it]
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Epoch 89 Training:  21%|██        | 20/96 [00:26<01:41,  1.33s/it]
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Epoch 89 Training:  22%|██▏       | 21/96 [00:27<01:39,  1.33s/it]
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Epoch 89 Training:  23%|██▎       | 22/96 [00:29<01:38,  1.33s/it]
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Epoch 89 Training:  24%|██▍       | 23/96 [00:30<01:36,  1.33s/it]
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Epoch 89 Training:  25%|██▌       | 24/96 [00:31<01:35,  1.33s/it]
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Epoch 89 Training:  26%|██▌       | 25/96 [00:33<01:34,  1.33s/it]
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Epoch 89 Training:  27%|██▋       | 26/96 [00:34<01:32,  1.33s/it]
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Epoch 89 Training:  28%|██▊       | 27/96 [00:35<01:31,  1.33s/it]
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Epoch 89 Training:  29%|██▉       | 28/96 [00:37<01:30,  1.33s/it]
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Epoch 89 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 89 Training:  30%|███       | 29/96 [00:38<01:28,  1.33s/it]
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Epoch 89 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 89 Training:  31%|███▏      | 30/96 [00:39<01:27,  1.33s/it]
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Epoch 89 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 89 Training:  32%|███▏      | 31/96 [00:41<01:26,  1.33s/it]
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Epoch 89 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 89 Training:  33%|███▎      | 32/96 [00:42<01:24,  1.33s/it]
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Epoch 89 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 89 Training:  34%|███▍      | 33/96 [00:43<01:23,  1.33s/it]
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Epoch 89 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 89 Training:  35%|███▌      | 34/96 [00:45<01:22,  1.33s/it]
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Epoch 89 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 89 Training:  36%|███▋      | 35/96 [00:46<01:21,  1.33s/it]
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Epoch 89 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 89 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 89 Training:  38%|███▊      | 36/96 [00:47<01:19,  1.33s/it]
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Epoch 89 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 89 Training:  39%|███▊      | 37/96 [00:49<01:18,  1.33s/it]
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Epoch 89 Training:  40%|███▉      | 38/96 [00:50<01:16,  1.33s/it]
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Epoch 89 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 89 Training:  41%|████      | 39/96 [00:51<01:15,  1.33s/it]
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Epoch 89 Training:  42%|████▏     | 40/96 [00:53<01:14,  1.33s/it]
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Epoch 89 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 89 Training:  43%|████▎     | 41/96 [00:54<01:12,  1.33s/it]
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Epoch 89 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 89 Training:  44%|████▍     | 42/96 [00:55<01:11,  1.32s/it]
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Epoch 89 Training:  45%|████▍     | 43/96 [00:57<01:10,  1.33s/it]
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Epoch 89 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 89 Training:  46%|████▌     | 44/96 [00:58<01:09,  1.33s/it]
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Epoch 89 Training:  47%|████▋     | 45/96 [00:59<01:07,  1.33s/it]
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Epoch 89 Training:  48%|████▊     | 46/96 [01:01<01:06,  1.33s/it]
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Epoch 89 Training:  49%|████▉     | 47/96 [01:02<01:05,  1.33s/it]
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Epoch 89 Training:  50%|█████     | 48/96 [01:03<01:03,  1.33s/it]
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Epoch 89 Training:  51%|█████     | 49/96 [01:05<01:02,  1.33s/it]
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Epoch 89 Training:  52%|█████▏    | 50/96 [01:06<01:00,  1.33s/it]
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Epoch 89 Training:  53%|█████▎    | 51/96 [01:07<00:59,  1.33s/it]
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Epoch 89 Training:  54%|█████▍    | 52/96 [01:09<00:58,  1.33s/it]
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Epoch 89 Training:  55%|█████▌    | 53/96 [01:10<00:57,  1.33s/it]
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Epoch 89 Training:  56%|█████▋    | 54/96 [01:11<00:55,  1.33s/it]
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Epoch 89 Training:  57%|█████▋    | 55/96 [01:12<00:54,  1.33s/it]
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Epoch 89 Training:  58%|█████▊    | 56/96 [01:14<00:53,  1.33s/it]
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Epoch 89 Training:  59%|█████▉    | 57/96 [01:15<00:51,  1.33s/it]
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Epoch 89 Training:  60%|██████    | 58/96 [01:16<00:50,  1.33s/it]
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Epoch 89 Training:  61%|██████▏   | 59/96 [01:18<00:49,  1.33s/it]
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Epoch 89 Training:  62%|██████▎   | 60/96 [01:19<00:47,  1.33s/it]
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Epoch 89 Training:  64%|██████▎   | 61/96 [01:20<00:46,  1.33s/it]
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Epoch 89 Training:  65%|██████▍   | 62/96 [01:22<00:45,  1.33s/it]
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Epoch 89 Training:  66%|██████▌   | 63/96 [01:23<00:43,  1.33s/it]
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Epoch 89 Training:  67%|██████▋   | 64/96 [01:24<00:42,  1.33s/it]
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Epoch 89 Training:  68%|██████▊   | 65/96 [01:26<00:41,  1.33s/it]
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Epoch 89 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s][2026-10-01 23:08:29] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0089 | Epoch 089] train/policy_loss=13.2593 train/unweighted_policy_loss=2.6519 train/loss=13.2593 train/grad_norm=0.0970
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+[2026-10-01 23:08:29] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0090 | Epoch 089] timing/rollout_seconds=147.8000 timing/feedback_seconds=4.8898 timing/optimization_seconds=128.1027 timing/cycle_seconds=279.3506
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+[2026-10-01 23:09:13] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0090 | Epoch 090] eval/vedai_rl_full/reward_dists_mean=0.9263 eval/vedai_rl_full/reward_dists_std=0.0170 eval/vedai_rl_full/reward_psnr_mean=28.8995 eval/vedai_rl_full/reward_psnr_std=2.2659 eval/vedai_rl_full/reward_ssim_mean=0.9185 eval/vedai_rl_full/reward_ssim_std=0.0259
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+
Pointwise Rewards: ssim:   0%|          | 0/6 [00:00<?, ?it/s]
Pointwise Rewards: ssim:  17%|█▋        | 1/6 [00:00<00:00,  6.04it/s]
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Pointwise Rewards: ssim:  67%|██████▋   | 4/6 [00:00<00:00,  6.20it/s]
Pointwise Rewards: ssim:  83%|████████▎ | 5/6 [00:00<00:00,  6.35it/s]
Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:00<00:00,  6.58it/s]
Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:00<00:00,  6.37it/s]
+
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Pointwise Rewards: dists:  33%|███▎      | 2/6 [00:00<00:01,  3.76it/s]
Pointwise Rewards: dists:  50%|█████     | 3/6 [00:00<00:00,  3.71it/s]
Pointwise Rewards: dists:  67%|██████▋   | 4/6 [00:01<00:00,  3.55it/s]
Pointwise Rewards: dists:  83%|████████▎ | 5/6 [00:01<00:00,  3.59it/s]
Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.63it/s]
Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.64it/s]
+
Pointwise Rewards: psnr:   0%|          | 0/6 [00:00<?, ?it/s]
Pointwise Rewards: psnr:  17%|█▋        | 1/6 [00:00<00:00,  7.12it/s]
Pointwise Rewards: psnr:  33%|███▎      | 2/6 [00:00<00:00,  6.85it/s]
Pointwise Rewards: psnr:  50%|█████     | 3/6 [00:00<00:00,  6.84it/s]
Pointwise Rewards: psnr:  67%|██████▋   | 4/6 [00:00<00:00,  6.68it/s]
Pointwise Rewards: psnr:  83%|████████▎ | 5/6 [00:00<00:00,  6.69it/s]
Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.76it/s]
Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.77it/s]
+[2026-10-01 23:11:45] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0090 | Epoch 090] train/reward_dists_mean=0.9414 train/reward_dists_std=0.0117 train/reward_psnr_mean=30.4227 train/reward_psnr_std=1.5572 train/reward_ssim_mean=0.9367 train/reward_ssim_std=0.0192 train/reward_dists_group_std_mean=0.0024 train/reward_dists_group_std_max=0.0047 train/reward_dists_group_std_min=0.0012 train/reward_dists_group_mean_std=0.0115 train/reward_psnr_group_std_mean=0.7164 train/reward_psnr_group_std_max=1.9605 train/reward_psnr_group_std_min=0.2156 train/reward_psnr_group_mean_std=1.3323 train/reward_ssim_group_std_mean=0.0008 train/reward_ssim_group_std_max=0.0017 train/reward_ssim_group_std_min=0.0003 train/reward_ssim_group_mean_std=0.0191 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-8.9993 train/adv_max=5.9934 train/adv_abs_mean=1.8321
+
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Epoch 90 Training:   3%|▎         | 3/96 [00:03<02:01,  1.30s/it]
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Epoch 90 Training:   4%|▍         | 4/96 [00:05<02:00,  1.31s/it]
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Epoch 90 Training:   6%|▋         | 6/96 [00:07<01:59,  1.32s/it]
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Epoch 90 Training:   7%|▋         | 7/96 [00:09<01:57,  1.32s/it]
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Epoch 90 Training:   8%|▊         | 8/96 [00:10<01:56,  1.32s/it]
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Epoch 90 Training:   9%|▉         | 9/96 [00:11<01:55,  1.32s/it]
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Epoch 90 Training:  10%|█         | 10/96 [00:13<01:53,  1.32s/it]
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Epoch 90 Training:  11%|█▏        | 11/96 [00:14<01:52,  1.32s/it]
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Epoch 90 Training:  14%|█▎        | 13/96 [00:17<01:50,  1.33s/it]
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Epoch 90 Training:  15%|█▍        | 14/96 [00:18<01:48,  1.33s/it]
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Epoch 90 Training:  16%|█▌        | 15/96 [00:19<01:47,  1.33s/it]
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Epoch 90 Training:  17%|█▋        | 16/96 [00:21<01:46,  1.33s/it]
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Epoch 90 Training:  18%|█▊        | 17/96 [00:22<01:45,  1.33s/it]
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Epoch 90 Training:  19%|█▉        | 18/96 [00:23<01:43,  1.33s/it]
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Epoch 90 Training:  21%|██        | 20/96 [00:26<01:40,  1.33s/it]
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Epoch 90 Training:  22%|██▏       | 21/96 [00:27<01:39,  1.33s/it]
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Epoch 90 Training:  23%|██▎       | 22/96 [00:29<01:38,  1.33s/it]
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Epoch 90 Training:  26%|██▌       | 25/96 [00:33<01:34,  1.33s/it]
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Epoch 90 Training:  27%|██▋       | 26/96 [00:34<01:32,  1.33s/it]
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Epoch 90 Training:  28%|██▊       | 27/96 [00:35<01:31,  1.32s/it]
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Epoch 90 Training:  29%|██▉       | 28/96 [00:37<01:30,  1.32s/it]
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Epoch 90 Training:  33%|███▎      | 32/96 [00:42<01:24,  1.33s/it]
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Epoch 90 Training:  35%|███▌      | 34/96 [00:45<01:22,  1.33s/it]
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Epoch 90 Training:  36%|███▋      | 35/96 [00:46<01:20,  1.33s/it]
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Epoch 90 Training:  38%|███▊      | 36/96 [00:47<01:19,  1.33s/it]
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Epoch 90 Training:  39%|███▊      | 37/96 [00:48<01:18,  1.33s/it]
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Epoch 90 Training:  40%|███▉      | 38/96 [00:50<01:17,  1.33s/it]
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Epoch 90 Training:  41%|████      | 39/96 [00:51<01:15,  1.33s/it]
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Epoch 90 Training:  42%|████▏     | 40/96 [00:52<01:14,  1.33s/it]
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Epoch 90 Training:  43%|████▎     | 41/96 [00:54<01:13,  1.33s/it]
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Epoch 90 Training:  44%|████▍     | 42/96 [00:55<01:11,  1.33s/it]
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Epoch 90 Training:  45%|████▍     | 43/96 [00:56<01:10,  1.33s/it]
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Epoch 90 Training:  46%|████▌     | 44/96 [00:58<01:09,  1.33s/it]
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Epoch 90 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 90 Training:  47%|████▋     | 45/96 [00:59<01:07,  1.33s/it]
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Epoch 90 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 90 Training:  48%|████▊     | 46/96 [01:00<01:06,  1.33s/it]
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Epoch 90 Training:  49%|████▉     | 47/96 [01:02<01:05,  1.33s/it]
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Epoch 90 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 90 Training:  50%|█████     | 48/96 [01:03<01:03,  1.33s/it]
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Epoch 90 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 90 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 90 Training:  51%|█████     | 49/96 [01:04<01:02,  1.33s/it]
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Epoch 90 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 90 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 90 Training:  52%|█████▏    | 50/96 [01:06<01:00,  1.33s/it]
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Epoch 90 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 90 Training:  53%|█████▎    | 51/96 [01:07<00:59,  1.33s/it]
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Epoch 90 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 90 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 90 Training:  54%|█████▍    | 52/96 [01:08<00:58,  1.33s/it]
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Epoch 90 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 90 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 90 Training:  55%|█████▌    | 53/96 [01:10<00:57,  1.33s/it]
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Epoch 90 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.40it/s]
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Epoch 90 Training:  56%|█████▋    | 54/96 [01:11<00:55,  1.32s/it]
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Epoch 90 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 90 Training:  57%|█████▋    | 55/96 [01:12<00:54,  1.33s/it]
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Epoch 90 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.23it/s]
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Epoch 90 Training:  58%|█████▊    | 56/96 [01:14<00:53,  1.33s/it]
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Epoch 90 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.23it/s]
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Epoch 90 Training:  59%|█████▉    | 57/96 [01:15<00:51,  1.33s/it]
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Epoch 90 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 90 Training:  60%|██████    | 58/96 [01:16<00:50,  1.33s/it]
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Epoch 90 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 90 Training:  61%|██████▏   | 59/96 [01:18<00:49,  1.33s/it]
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Epoch 90 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 90 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 90 Training:  62%|██████▎   | 60/96 [01:19<00:47,  1.33s/it]
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Epoch 90 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 90 Training:  64%|██████▎   | 61/96 [01:20<00:46,  1.33s/it]
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Epoch 90 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 90 Training:  65%|██████▍   | 62/96 [01:22<00:45,  1.33s/it]
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Epoch 90 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.23it/s]
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Epoch 90 Training:  66%|██████▌   | 63/96 [01:23<00:43,  1.33s/it]
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Epoch 90 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 90 Training:  67%|██████▋   | 64/96 [01:24<00:42,  1.33s/it]
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Epoch 90 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.29it/s]
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Epoch 90 Training:  68%|██████▊   | 65/96 [01:26<00:41,  1.33s/it]
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Epoch 90 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.23it/s]
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Epoch 90 Training:  69%|██████▉   | 66/96 [01:27<00:39,  1.33s/it]
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Epoch 90 Training:  70%|██████▉   | 67/96 [01:28<00:38,  1.33s/it]
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Epoch 90 Training:  71%|███████   | 68/96 [01:30<00:37,  1.33s/it]
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Epoch 90 Training:  72%|███████▏  | 69/96 [01:31<00:35,  1.33s/it]
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Epoch 90 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 90 Training:  73%|███████▎  | 70/96 [01:32<00:34,  1.33s/it]
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Epoch 90 Training:  74%|███████▍  | 71/96 [01:34<00:33,  1.33s/it]
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Epoch 90 Training:  75%|███████▌  | 72/96 [01:35<00:31,  1.33s/it]
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Epoch 90 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.23it/s]
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Epoch 90 Training:  76%|███████▌  | 73/96 [01:36<00:30,  1.33s/it]
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Epoch 90 Training:  77%|███████▋  | 74/96 [01:38<00:29,  1.33s/it]
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Epoch 90 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 90 Training:  78%|███████▊  | 75/96 [01:39<00:27,  1.33s/it]
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Epoch 90 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 90 Training:  79%|███████▉  | 76/96 [01:40<00:26,  1.32s/it]
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Epoch 90 Training:  80%|████████  | 77/96 [01:42<00:25,  1.33s/it]
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Epoch 90 Training:  81%|████████▏ | 78/96 [01:43<00:23,  1.33s/it]
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Epoch 90 Training:  82%|████████▏ | 79/96 [01:44<00:22,  1.32s/it]
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Epoch 90 Training:  83%|████████▎ | 80/96 [01:46<00:21,  1.32s/it]
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Epoch 90 Training:  84%|████████▍ | 81/96 [01:47<00:19,  1.33s/it]
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Epoch 90 Training:  85%|████████▌ | 82/96 [01:48<00:18,  1.32s/it]
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Epoch 90 Training:  86%|████████▋ | 83/96 [01:50<00:17,  1.32s/it]
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Epoch 90 Training:  88%|████████▊ | 84/96 [01:51<00:15,  1.32s/it]
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Epoch 90 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s][2026-10-01 23:13:52] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0090 | Epoch 090] train/policy_loss=12.5657 train/unweighted_policy_loss=2.5131 train/loss=12.5657 train/grad_norm=0.0832
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+[2026-10-01 23:13:52] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0091 | Epoch 090] timing/rollout_seconds=147.7196 timing/feedback_seconds=5.0968 timing/optimization_seconds=127.8945 timing/cycle_seconds=279.1286
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+
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+[2026-10-01 23:16:24] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0091 | Epoch 091] train/reward_dists_mean=0.9351 train/reward_dists_std=0.0310 train/reward_psnr_mean=30.7015 train/reward_psnr_std=2.1551 train/reward_ssim_mean=0.9400 train/reward_ssim_std=0.0207 train/reward_dists_group_std_mean=0.0026 train/reward_dists_group_std_max=0.0099 train/reward_dists_group_std_min=0.0011 train/reward_dists_group_mean_std=0.0309 train/reward_psnr_group_std_mean=0.9272 train/reward_psnr_group_std_max=3.4423 train/reward_psnr_group_std_min=0.2274 train/reward_psnr_group_mean_std=1.8663 train/reward_ssim_group_std_mean=0.0010 train/reward_ssim_group_std_max=0.0129 train/reward_ssim_group_std_min=0.0002 train/reward_ssim_group_mean_std=0.0206 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-7.8747 train/adv_max=5.7892 train/adv_abs_mean=1.7590
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Epoch 91 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.23it/s]
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Epoch 91 Training:   7%|▋         | 7/96 [00:09<01:58,  1.33s/it]
+
Epoch 91 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 91 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 91 Training:   8%|▊         | 8/96 [00:10<01:56,  1.33s/it]
+
Epoch 91 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 91 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 91 Training:   9%|▉         | 9/96 [00:11<01:55,  1.33s/it]
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Epoch 91 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 91 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 91 Training:  10%|█         | 10/96 [00:13<01:54,  1.33s/it]
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Epoch 91 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 91 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 91 Training:  11%|█▏        | 11/96 [00:14<01:53,  1.33s/it]
+
Epoch 91 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 91 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 91 Training:  12%|█▎        | 12/96 [00:15<01:51,  1.33s/it]
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Epoch 91 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 91 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 91 Training:  14%|█▎        | 13/96 [00:17<01:50,  1.33s/it]
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Epoch 91 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 91 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 91 Training:  15%|█▍        | 14/96 [00:18<01:49,  1.33s/it]
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Epoch 91 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 91 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.22it/s]
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Epoch 91 Training:  16%|█▌        | 15/96 [00:19<01:48,  1.33s/it]
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Epoch 91 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 91 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 91 Training:  17%|█▋        | 16/96 [00:21<01:46,  1.33s/it]
+
Epoch 91 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 91 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.23it/s]
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Epoch 91 Training:  18%|█▊        | 17/96 [00:22<01:45,  1.33s/it]
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Epoch 91 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 91 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 91 Training:  19%|█▉        | 18/96 [00:23<01:43,  1.33s/it]
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Epoch 91 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 91 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 91 Training:  20%|█▉        | 19/96 [00:25<01:42,  1.33s/it]
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Epoch 91 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 91 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 91 Training:  21%|██        | 20/96 [00:26<01:41,  1.33s/it]
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Epoch 91 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 91 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 91 Training:  22%|██▏       | 21/96 [00:27<01:39,  1.33s/it]
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Epoch 91 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 91 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 91 Training:  23%|██▎       | 22/96 [00:29<01:38,  1.33s/it]
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Epoch 91 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 91 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.23it/s]
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Epoch 91 Training:  24%|██▍       | 23/96 [00:30<01:37,  1.33s/it]
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Epoch 91 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 91 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 91 Training:  25%|██▌       | 24/96 [00:31<01:35,  1.33s/it]
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Epoch 91 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 91 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 91 Training:  26%|██▌       | 25/96 [00:33<01:34,  1.33s/it]
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Epoch 91 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 91 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.23it/s]
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Epoch 91 Training:  27%|██▋       | 26/96 [00:34<01:33,  1.33s/it]
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Epoch 91 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 91 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 91 Training:  28%|██▊       | 27/96 [00:35<01:31,  1.33s/it]
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Epoch 91 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 91 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 91 Training:  29%|██▉       | 28/96 [00:37<01:30,  1.33s/it]
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Epoch 91 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 91 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 91 Training:  30%|███       | 29/96 [00:38<01:29,  1.33s/it]
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Epoch 91 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 91 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.23it/s]
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Epoch 91 Training:  31%|███▏      | 30/96 [00:39<01:27,  1.33s/it]
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Epoch 91 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 91 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 91 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 91 Training:  32%|███▏      | 31/96 [00:41<01:26,  1.33s/it]
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Epoch 91 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 91 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 91 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 91 Training:  33%|███▎      | 32/96 [00:42<01:25,  1.33s/it]
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Epoch 91 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 91 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 91 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.23it/s]
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Epoch 91 Training:  34%|███▍      | 33/96 [00:43<01:23,  1.33s/it]
+
Epoch 91 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 91 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 91 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.21it/s]
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Epoch 91 Training:  35%|███▌      | 34/96 [00:45<01:22,  1.33s/it]
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Epoch 91 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 91 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 91 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 91 Training:  36%|███▋      | 35/96 [00:46<01:21,  1.33s/it]
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Epoch 91 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 91 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 91 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.22it/s]
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Epoch 91 Training:  38%|███▊      | 36/96 [00:47<01:20,  1.33s/it]
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Epoch 91 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 91 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 91 Training:  39%|███▊      | 37/96 [00:49<01:18,  1.33s/it]
+
Epoch 91 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 91 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 91 Training:  40%|███▉      | 38/96 [00:50<01:17,  1.33s/it]
+
Epoch 91 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 91 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 91 Training:  41%|████      | 39/96 [00:51<01:15,  1.33s/it]
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Epoch 91 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 91 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 91 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.23it/s]
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Epoch 91 Training:  42%|████▏     | 40/96 [00:53<01:14,  1.33s/it]
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Epoch 91 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 91 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 91 Training:  43%|████▎     | 41/96 [00:54<01:13,  1.33s/it]
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Epoch 91 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 91 Training:  44%|████▍     | 42/96 [00:55<01:11,  1.33s/it]
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Epoch 91 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 91 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.22it/s]
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Epoch 91 Training:  45%|████▍     | 43/96 [00:57<01:10,  1.33s/it]
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Epoch 91 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 91 Training:  46%|████▌     | 44/96 [00:58<01:09,  1.33s/it]
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Epoch 91 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 91 Training:  47%|████▋     | 45/96 [00:59<01:07,  1.33s/it]
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Epoch 91 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 91 Training:  48%|████▊     | 46/96 [01:01<01:06,  1.33s/it]
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Epoch 91 Training:  49%|████▉     | 47/96 [01:02<01:05,  1.33s/it]
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Epoch 91 Training:  50%|█████     | 48/96 [01:03<01:03,  1.33s/it]
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Epoch 91 Training:  51%|█████     | 49/96 [01:05<01:02,  1.33s/it]
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Epoch 91 Training:  52%|█████▏    | 50/96 [01:06<01:01,  1.33s/it]
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Epoch 91 Training:  53%|█████▎    | 51/96 [01:07<00:59,  1.33s/it]
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Epoch 91 Training:  54%|█████▍    | 52/96 [01:09<00:58,  1.33s/it]
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Epoch 91 Training:  55%|█████▌    | 53/96 [01:10<00:57,  1.33s/it]
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Epoch 91 Training:  56%|█████▋    | 54/96 [01:11<00:55,  1.33s/it]
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Epoch 91 Training:  57%|█████▋    | 55/96 [01:13<00:54,  1.33s/it]
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Epoch 91 Training:  58%|█████▊    | 56/96 [01:14<00:53,  1.33s/it]
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Epoch 91 Training:  59%|█████▉    | 57/96 [01:15<00:51,  1.33s/it]
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Epoch 91 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s][2026-10-01 23:18:32] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0091 | Epoch 091] train/policy_loss=13.0717 train/unweighted_policy_loss=2.6143 train/loss=13.0717 train/grad_norm=0.0806
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+[2026-10-01 23:18:32] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0092 | Epoch 091] timing/rollout_seconds=147.7271 timing/feedback_seconds=5.1995 timing/optimization_seconds=128.3053 timing/cycle_seconds=279.5516
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+
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Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:00<00:00,  6.47it/s]
+
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Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.63it/s]
+
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Pointwise Rewards: psnr:  33%|███▎      | 2/6 [00:00<00:00,  6.65it/s]
Pointwise Rewards: psnr:  50%|█████     | 3/6 [00:00<00:00,  6.92it/s]
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Pointwise Rewards: psnr:  83%|████████▎ | 5/6 [00:00<00:00,  6.58it/s]
Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.51it/s]
Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.61it/s]
+[2026-10-01 23:21:04] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0092 | Epoch 092] train/reward_dists_mean=0.9395 train/reward_dists_std=0.0138 train/reward_psnr_mean=30.4685 train/reward_psnr_std=1.7322 train/reward_ssim_mean=0.9383 train/reward_ssim_std=0.0186 train/reward_dists_group_std_mean=0.0023 train/reward_dists_group_std_max=0.0061 train/reward_dists_group_std_min=0.0013 train/reward_dists_group_mean_std=0.0136 train/reward_psnr_group_std_mean=0.8633 train/reward_psnr_group_std_max=1.6572 train/reward_psnr_group_std_min=0.1896 train/reward_psnr_group_mean_std=1.4624 train/reward_ssim_group_std_mean=0.0008 train/reward_ssim_group_std_max=0.0033 train/reward_ssim_group_std_min=0.0003 train/reward_ssim_group_mean_std=0.0186 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-8.4275 train/adv_max=5.5485 train/adv_abs_mean=1.8292
+
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Epoch 92 Training:   7%|▋         | 7/96 [00:09<02:03,  1.39s/it]
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Epoch 92 Training:  32%|███▏      | 31/96 [00:41<01:26,  1.33s/it]
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Epoch 92 Training:  33%|███▎      | 32/96 [00:42<01:25,  1.33s/it]
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Epoch 92 Training:  34%|███▍      | 33/96 [00:44<01:23,  1.33s/it]
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Epoch 92 Training:  35%|███▌      | 34/96 [00:45<01:22,  1.33s/it]
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Epoch 92 Training:  36%|███▋      | 35/96 [00:46<01:20,  1.33s/it]
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Epoch 92 Training:  38%|███▊      | 36/96 [00:48<01:19,  1.33s/it]
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Epoch 92 Training:  39%|███▊      | 37/96 [00:49<01:18,  1.33s/it]
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Epoch 92 Training:  40%|███▉      | 38/96 [00:50<01:17,  1.33s/it]
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Epoch 92 Training:  41%|████      | 39/96 [00:52<01:15,  1.33s/it]
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Epoch 92 Training:  42%|████▏     | 40/96 [00:53<01:14,  1.33s/it]
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Epoch 92 Training:  43%|████▎     | 41/96 [00:54<01:13,  1.33s/it]
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Epoch 92 Training:  44%|████▍     | 42/96 [00:56<01:11,  1.33s/it]
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Epoch 92 Training:  45%|████▍     | 43/96 [00:57<01:10,  1.33s/it]
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Epoch 92 Training:  46%|████▌     | 44/96 [00:58<01:09,  1.33s/it]
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Epoch 92 Training:  47%|████▋     | 45/96 [01:00<01:07,  1.33s/it]
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Epoch 92 Training:  48%|████▊     | 46/96 [01:01<01:06,  1.33s/it]
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Epoch 92 Training:  49%|████▉     | 47/96 [01:02<01:05,  1.33s/it]
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Epoch 92 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 92 Training:  50%|█████     | 48/96 [01:04<01:03,  1.33s/it]
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Epoch 92 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 92 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 92 Training:  51%|█████     | 49/96 [01:05<01:02,  1.33s/it]
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Epoch 92 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 92 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 92 Training:  52%|█████▏    | 50/96 [01:06<01:01,  1.33s/it]
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Epoch 92 Training:  53%|█████▎    | 51/96 [01:08<00:59,  1.33s/it]
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Epoch 92 Training:  54%|█████▍    | 52/96 [01:09<00:58,  1.33s/it]
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Epoch 92 Training:  55%|█████▌    | 53/96 [01:10<00:57,  1.33s/it]
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Epoch 92 Training:  56%|█████▋    | 54/96 [01:12<00:55,  1.33s/it]
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Epoch 92 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.23it/s]
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Epoch 92 Training:  57%|█████▋    | 55/96 [01:13<00:54,  1.33s/it]
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Epoch 92 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 92 Training:  58%|█████▊    | 56/96 [01:14<00:53,  1.33s/it]
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Epoch 92 Training:  59%|█████▉    | 57/96 [01:16<00:51,  1.33s/it]
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Epoch 92 Training:  60%|██████    | 58/96 [01:17<00:50,  1.33s/it]
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Epoch 92 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.23it/s]
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Epoch 92 Training:  61%|██████▏   | 59/96 [01:18<00:49,  1.33s/it]
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Epoch 92 Training:  62%|██████▎   | 60/96 [01:20<00:47,  1.33s/it]
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Epoch 92 Training:  64%|██████▎   | 61/96 [01:21<00:46,  1.33s/it]
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Epoch 92 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 92 Training:  65%|██████▍   | 62/96 [01:22<00:45,  1.33s/it]
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Epoch 92 Training:  66%|██████▌   | 63/96 [01:24<00:43,  1.33s/it]
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Epoch 92 Training:  67%|██████▋   | 64/96 [01:25<00:42,  1.33s/it]
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Epoch 92 Training:  68%|██████▊   | 65/96 [01:26<00:41,  1.33s/it]
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Epoch 92 Training:  69%|██████▉   | 66/96 [01:28<00:39,  1.33s/it]
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Epoch 92 Training:  70%|██████▉   | 67/96 [01:29<00:38,  1.33s/it]
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Epoch 92 Training:  71%|███████   | 68/96 [01:30<00:37,  1.33s/it]
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Epoch 92 Training:  72%|███████▏  | 69/96 [01:31<00:35,  1.33s/it]
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Epoch 92 Training:  73%|███████▎  | 70/96 [01:33<00:34,  1.33s/it]
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Epoch 92 Training:  74%|███████▍  | 71/96 [01:34<00:33,  1.33s/it]
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Epoch 92 Training:  75%|███████▌  | 72/96 [01:35<00:31,  1.33s/it]
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Epoch 92 Training:  76%|███████▌  | 73/96 [01:37<00:30,  1.33s/it]
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Epoch 92 Training:  77%|███████▋  | 74/96 [01:38<00:29,  1.33s/it]
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Epoch 92 Training:  78%|███████▊  | 75/96 [01:39<00:27,  1.33s/it]
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Epoch 92 Training:  79%|███████▉  | 76/96 [01:41<00:26,  1.33s/it]
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Epoch 92 Training:  80%|████████  | 77/96 [01:42<00:25,  1.33s/it]
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Epoch 92 Training:  81%|████████▏ | 78/96 [01:43<00:23,  1.33s/it]
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Epoch 92 Training:  82%|████████▏ | 79/96 [01:45<00:22,  1.33s/it]
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Epoch 92 Training:  83%|████████▎ | 80/96 [01:46<00:21,  1.33s/it]
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Epoch 92 Training:  84%|████████▍ | 81/96 [01:47<00:19,  1.32s/it]
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Epoch 92 Training:  85%|████████▌ | 82/96 [01:49<00:18,  1.33s/it]
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Epoch 92 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s][2026-10-01 23:23:12] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0092 | Epoch 092] train/policy_loss=12.4988 train/unweighted_policy_loss=2.4998 train/loss=12.4988 train/grad_norm=0.0652
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+[2026-10-01 23:23:12] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0093 | Epoch 092] timing/rollout_seconds=147.7514 timing/feedback_seconds=5.2387 timing/optimization_seconds=128.4643 timing/cycle_seconds=279.7116
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+[2026-10-01 23:25:43] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0093 | Epoch 093] train/reward_dists_mean=0.9372 train/reward_dists_std=0.0178 train/reward_psnr_mean=30.4214 train/reward_psnr_std=1.8729 train/reward_ssim_mean=0.9350 train/reward_ssim_std=0.0231 train/reward_dists_group_std_mean=0.0024 train/reward_dists_group_std_max=0.0051 train/reward_dists_group_std_min=0.0009 train/reward_dists_group_mean_std=0.0176 train/reward_psnr_group_std_mean=0.7805 train/reward_psnr_group_std_max=1.5993 train/reward_psnr_group_std_min=0.2547 train/reward_psnr_group_mean_std=1.6639 train/reward_ssim_group_std_mean=0.0008 train/reward_ssim_group_std_max=0.0024 train/reward_ssim_group_std_min=0.0003 train/reward_ssim_group_mean_std=0.0231 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-6.9263 train/adv_max=5.1878 train/adv_abs_mean=1.7582
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Epoch 93 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 93 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 93 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 93 Training:   5%|▌         | 5/96 [00:06<02:07,  1.40s/it]
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Epoch 93 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 93 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 93 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 93 Training:   6%|▋         | 6/96 [00:08<02:03,  1.38s/it]
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Epoch 93 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 93 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 93 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.23it/s]
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Epoch 93 Training:   7%|▋         | 7/96 [00:09<02:01,  1.36s/it]
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Epoch 93 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 93 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 93 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 93 Training:   8%|▊         | 8/96 [00:10<01:58,  1.35s/it]
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Epoch 93 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 93 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 93 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 93 Training:   9%|▉         | 9/96 [00:12<01:56,  1.34s/it]
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Epoch 93 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 93 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 93 Training:  10%|█         | 10/96 [00:13<01:54,  1.34s/it]
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Epoch 93 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 93 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 93 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.19it/s]
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Epoch 93 Training:  11%|█▏        | 11/96 [00:14<01:53,  1.34s/it]
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Epoch 93 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 93 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 93 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 93 Training:  12%|█▎        | 12/96 [00:16<01:52,  1.34s/it]
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Epoch 93 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 93 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 93 Training:  14%|█▎        | 13/96 [00:17<01:50,  1.33s/it]
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Epoch 93 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 93 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 93 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.22it/s]
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Epoch 93 Training:  15%|█▍        | 14/96 [00:18<01:49,  1.34s/it]
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Epoch 93 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 93 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 93 Training:  16%|█▌        | 15/96 [00:20<01:48,  1.33s/it]
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Epoch 93 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 93 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 93 Training:  17%|█▋        | 16/96 [00:21<01:46,  1.33s/it]
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Epoch 93 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 93 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 93 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.22it/s]
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Epoch 93 Training:  18%|█▊        | 17/96 [00:22<01:45,  1.33s/it]
+
Epoch 93 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 93 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 93 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 93 Training:  19%|█▉        | 18/96 [00:24<01:43,  1.33s/it]
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Epoch 93 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 93 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 93 Training:  20%|█▉        | 19/96 [00:25<01:42,  1.33s/it]
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Epoch 93 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 93 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 93 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.23it/s]
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Epoch 93 Training:  21%|██        | 20/96 [00:26<01:41,  1.33s/it]
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Epoch 93 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 93 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 93 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 93 Training:  22%|██▏       | 21/96 [00:28<01:39,  1.33s/it]
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Epoch 93 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 93 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 93 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 93 Training:  23%|██▎       | 22/96 [00:29<01:38,  1.33s/it]
+
Epoch 93 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 93 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 93 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 93 Training:  24%|██▍       | 23/96 [00:30<01:36,  1.33s/it]
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Epoch 93 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 93 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.23it/s]
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Epoch 93 Training:  25%|██▌       | 24/96 [00:32<01:35,  1.33s/it]
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Epoch 93 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 93 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 93 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 93 Training:  26%|██▌       | 25/96 [00:33<01:34,  1.33s/it]
+
Epoch 93 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 93 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 93 Training:  27%|██▋       | 26/96 [00:34<01:32,  1.33s/it]
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Epoch 93 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 93 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 93 Training:  28%|██▊       | 27/96 [00:36<01:31,  1.33s/it]
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Epoch 93 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 93 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 93 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.22it/s]
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Epoch 93 Training:  29%|██▉       | 28/96 [00:37<01:30,  1.33s/it]
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Epoch 93 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 93 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 93 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 93 Training:  30%|███       | 29/96 [00:38<01:28,  1.33s/it]
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Epoch 93 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 93 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 93 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 93 Training:  31%|███▏      | 30/96 [00:40<01:27,  1.33s/it]
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Epoch 93 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 93 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 93 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.23it/s]
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Epoch 93 Training:  32%|███▏      | 31/96 [00:41<01:26,  1.33s/it]
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Epoch 93 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 93 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 93 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 93 Training:  33%|███▎      | 32/96 [00:42<01:24,  1.33s/it]
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Epoch 93 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 93 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 93 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 93 Training:  34%|███▍      | 33/96 [00:44<01:23,  1.33s/it]
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Epoch 93 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 93 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 93 Training:  35%|███▌      | 34/96 [00:45<01:22,  1.33s/it]
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Epoch 93 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 93 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.22it/s]
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Epoch 93 Training:  36%|███▋      | 35/96 [00:46<01:20,  1.33s/it]
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Epoch 93 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 93 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 93 Training:  38%|███▊      | 36/96 [00:48<01:19,  1.33s/it]
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Epoch 93 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 93 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 93 Training:  39%|███▊      | 37/96 [00:49<01:18,  1.33s/it]
+
Epoch 93 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 93 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 93 Training:  40%|███▉      | 38/96 [00:50<01:16,  1.33s/it]
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Epoch 93 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 93 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.23it/s]
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Epoch 93 Training:  41%|████      | 39/96 [00:52<01:15,  1.33s/it]
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Epoch 93 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 93 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 93 Training:  42%|████▏     | 40/96 [00:53<01:14,  1.33s/it]
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Epoch 93 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 93 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 93 Training:  43%|████▎     | 41/96 [00:54<01:12,  1.33s/it]
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Epoch 93 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 93 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.21it/s]
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Epoch 93 Training:  44%|████▍     | 42/96 [00:56<01:11,  1.33s/it]
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Epoch 93 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 93 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.35it/s]
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Epoch 93 Training:  45%|████▍     | 43/96 [00:57<01:10,  1.33s/it]
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Epoch 93 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 93 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 93 Training:  46%|████▌     | 44/96 [00:58<01:09,  1.33s/it]
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Epoch 93 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 93 Training:  47%|████▋     | 45/96 [01:00<01:07,  1.33s/it]
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Epoch 93 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 93 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.22it/s]
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Epoch 93 Training:  48%|████▊     | 46/96 [01:01<01:06,  1.33s/it]
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Epoch 93 Training:  49%|████▉     | 47/96 [01:02<01:05,  1.33s/it]
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Epoch 93 Training:  50%|█████     | 48/96 [01:04<01:03,  1.33s/it]
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Epoch 93 Training:  51%|█████     | 49/96 [01:05<01:02,  1.33s/it]
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Epoch 93 Training:  52%|█████▏    | 50/96 [01:06<01:01,  1.33s/it]
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Epoch 93 Training:  53%|█████▎    | 51/96 [01:07<00:59,  1.33s/it]
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Epoch 93 Training:  54%|█████▍    | 52/96 [01:09<00:58,  1.33s/it]
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Epoch 93 Training:  55%|█████▌    | 53/96 [01:10<00:57,  1.33s/it]
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Epoch 93 Training:  56%|█████▋    | 54/96 [01:11<00:55,  1.33s/it]
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Epoch 93 Training:  57%|█████▋    | 55/96 [01:13<00:54,  1.33s/it]
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Epoch 93 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s][2026-10-01 23:27:51] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0093 | Epoch 093] train/policy_loss=11.9611 train/unweighted_policy_loss=2.3922 train/loss=11.9611 train/grad_norm=0.1056
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+[2026-10-01 23:27:51] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0094 | Epoch 093] timing/rollout_seconds=147.5080 timing/feedback_seconds=5.2008 timing/optimization_seconds=128.3641 timing/cycle_seconds=279.3141
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+
Pointwise Rewards: ssim:   0%|          | 0/6 [00:00<?, ?it/s]
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Pointwise Rewards: ssim:  67%|██████▋   | 4/6 [00:00<00:00,  6.34it/s]
Pointwise Rewards: ssim:  83%|████████▎ | 5/6 [00:00<00:00,  6.43it/s]
Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:00<00:00,  6.38it/s]
Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:00<00:00,  6.31it/s]
+
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Pointwise Rewards: dists:  67%|██████▋   | 4/6 [00:01<00:00,  3.66it/s]
Pointwise Rewards: dists:  83%|████████▎ | 5/6 [00:01<00:00,  3.65it/s]
Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.67it/s]
Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.66it/s]
+
Pointwise Rewards: psnr:   0%|          | 0/6 [00:00<?, ?it/s]
Pointwise Rewards: psnr:  17%|█▋        | 1/6 [00:00<00:00,  6.68it/s]
Pointwise Rewards: psnr:  33%|███▎      | 2/6 [00:00<00:00,  6.64it/s]
Pointwise Rewards: psnr:  50%|█████     | 3/6 [00:00<00:00,  6.54it/s]
Pointwise Rewards: psnr:  67%|██████▋   | 4/6 [00:00<00:00,  6.73it/s]
Pointwise Rewards: psnr:  83%|████████▎ | 5/6 [00:00<00:00,  6.57it/s]
Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.61it/s]
Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.61it/s]
+[2026-10-01 23:30:23] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0094 | Epoch 094] train/reward_dists_mean=0.9384 train/reward_dists_std=0.0132 train/reward_psnr_mean=30.6346 train/reward_psnr_std=2.2550 train/reward_ssim_mean=0.9388 train/reward_ssim_std=0.0197 train/reward_dists_group_std_mean=0.0025 train/reward_dists_group_std_max=0.0101 train/reward_dists_group_std_min=0.0013 train/reward_dists_group_mean_std=0.0129 train/reward_psnr_group_std_mean=0.7579 train/reward_psnr_group_std_max=1.6374 train/reward_psnr_group_std_min=0.1591 train/reward_psnr_group_mean_std=2.0892 train/reward_ssim_group_std_mean=0.0009 train/reward_ssim_group_std_max=0.0025 train/reward_ssim_group_std_min=0.0002 train/reward_ssim_group_mean_std=0.0197 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-6.6790 train/adv_max=6.5926 train/adv_abs_mean=1.8436
+
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Epoch 94 Training:   3%|▎         | 3/96 [00:04<02:06,  1.36s/it]
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Epoch 94 Training:   4%|▍         | 4/96 [00:05<02:03,  1.35s/it]
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Epoch 94 Training:   5%|▌         | 5/96 [00:06<02:01,  1.34s/it]
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Epoch 94 Training:   6%|▋         | 6/96 [00:08<01:59,  1.33s/it]
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Epoch 94 Training:   7%|▋         | 7/96 [00:09<02:05,  1.41s/it]
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Epoch 94 Training:   8%|▊         | 8/96 [00:10<02:01,  1.38s/it]
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Epoch 94 Training:   9%|▉         | 9/96 [00:12<01:58,  1.36s/it]
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Epoch 94 Training:  10%|█         | 10/96 [00:13<01:56,  1.35s/it]
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Epoch 94 Training:  11%|█▏        | 11/96 [00:14<01:54,  1.35s/it]
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Epoch 94 Training:  12%|█▎        | 12/96 [00:16<01:52,  1.34s/it]
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Epoch 94 Training:  14%|█▎        | 13/96 [00:17<01:51,  1.34s/it]
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Epoch 94 Training:  15%|█▍        | 14/96 [00:18<01:49,  1.33s/it]
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Epoch 94 Training:  16%|█▌        | 15/96 [00:20<01:47,  1.33s/it]
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Epoch 94 Training:  17%|█▋        | 16/96 [00:21<01:46,  1.33s/it]
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Epoch 94 Training:  18%|█▊        | 17/96 [00:22<01:44,  1.33s/it]
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Epoch 94 Training:  19%|█▉        | 18/96 [00:24<01:43,  1.33s/it]
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Epoch 94 Training:  21%|██        | 20/96 [00:26<01:40,  1.33s/it]
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Epoch 94 Training:  23%|██▎       | 22/96 [00:29<01:38,  1.33s/it]
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Epoch 94 Training:  26%|██▌       | 25/96 [00:33<01:34,  1.33s/it]
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Epoch 94 Training:  27%|██▋       | 26/96 [00:34<01:32,  1.33s/it]
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Epoch 94 Training:  28%|██▊       | 27/96 [00:36<01:33,  1.35s/it]
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Epoch 94 Training:  29%|██▉       | 28/96 [00:37<01:31,  1.34s/it]
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Epoch 94 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 94 Training:  30%|███       | 29/96 [00:38<01:29,  1.34s/it]
+
Epoch 94 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 94 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 94 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 94 Training:  31%|███▏      | 30/96 [00:40<01:28,  1.33s/it]
+
Epoch 94 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 94 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 94 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.23it/s]
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Epoch 94 Training:  32%|███▏      | 31/96 [00:41<01:26,  1.33s/it]
+
Epoch 94 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 94 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 94 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 94 Training:  33%|███▎      | 32/96 [00:42<01:25,  1.33s/it]
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Epoch 94 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 94 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 94 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.23it/s]
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Epoch 94 Training:  34%|███▍      | 33/96 [00:44<01:23,  1.33s/it]
+
Epoch 94 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 94 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 94 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 94 Training:  35%|███▌      | 34/96 [00:45<01:22,  1.33s/it]
+
Epoch 94 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 94 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 94 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
+
                                                                
Epoch 94 Training:  36%|███▋      | 35/96 [00:46<01:20,  1.33s/it]
+
Epoch 94 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 94 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 94 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 94 Training:  38%|███▊      | 36/96 [00:48<01:19,  1.33s/it]
+
Epoch 94 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 94 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 94 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.23it/s]
+
                                                                
Epoch 94 Training:  39%|███▊      | 37/96 [00:49<01:18,  1.33s/it]
+
Epoch 94 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 94 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 94 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
+
                                                                
Epoch 94 Training:  40%|███▉      | 38/96 [00:50<01:16,  1.33s/it]
+
Epoch 94 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 94 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 94 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
+
                                                                
Epoch 94 Training:  41%|████      | 39/96 [00:52<01:15,  1.33s/it]
+
Epoch 94 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 94 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 94 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.32it/s]
+
                                                                
Epoch 94 Training:  42%|████▏     | 40/96 [00:53<01:14,  1.33s/it]
+
Epoch 94 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 94 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 94 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
+
                                                                
Epoch 94 Training:  43%|████▎     | 41/96 [00:54<01:12,  1.33s/it]
+
Epoch 94 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 94 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 94 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
+
                                                                
Epoch 94 Training:  44%|████▍     | 42/96 [00:56<01:11,  1.33s/it]
+
Epoch 94 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 94 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 94 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
+
                                                                
Epoch 94 Training:  45%|████▍     | 43/96 [00:57<01:10,  1.32s/it]
+
Epoch 94 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 94 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 94 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.23it/s]
+
                                                                
Epoch 94 Training:  46%|████▌     | 44/96 [00:58<01:09,  1.33s/it]
+
Epoch 94 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 94 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 94 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
+
                                                                
Epoch 94 Training:  47%|████▋     | 45/96 [01:00<01:07,  1.33s/it]
+
Epoch 94 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 94 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 94 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
+
                                                                
Epoch 94 Training:  48%|████▊     | 46/96 [01:01<01:06,  1.33s/it]
+
Epoch 94 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 94 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 94 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
+
                                                                
Epoch 94 Training:  49%|████▉     | 47/96 [01:02<01:04,  1.32s/it]
+
Epoch 94 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 94 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 94 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.23it/s]
+
                                                                
Epoch 94 Training:  50%|█████     | 48/96 [01:04<01:03,  1.33s/it]
+
Epoch 94 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 94 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 94 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
+
                                                                
Epoch 94 Training:  51%|█████     | 49/96 [01:05<01:02,  1.33s/it]
+
Epoch 94 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 94 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 94 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
+
                                                                
Epoch 94 Training:  52%|█████▏    | 50/96 [01:06<01:00,  1.32s/it]
+
Epoch 94 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 94 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 94 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
+
                                                                
Epoch 94 Training:  53%|█████▎    | 51/96 [01:08<00:59,  1.32s/it]
+
Epoch 94 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 94 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 94 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.23it/s]
+
                                                                
Epoch 94 Training:  54%|█████▍    | 52/96 [01:09<00:58,  1.33s/it]
+
Epoch 94 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 94 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 94 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
+
                                                                
Epoch 94 Training:  55%|█████▌    | 53/96 [01:10<00:56,  1.33s/it]
+
Epoch 94 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 94 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 94 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
+
                                                                
Epoch 94 Training:  56%|█████▋    | 54/96 [01:11<00:55,  1.32s/it]
+
Epoch 94 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 94 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 94 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
+
                                                                
Epoch 94 Training:  57%|█████▋    | 55/96 [01:13<00:54,  1.32s/it]
+
Epoch 94 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 94 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 94 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.23it/s]
+
                                                                
Epoch 94 Training:  58%|█████▊    | 56/96 [01:14<00:53,  1.33s/it]
+
Epoch 94 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 94 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
+
Epoch 94 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
+
                                                                
Epoch 94 Training:  59%|█████▉    | 57/96 [01:15<00:51,  1.32s/it]
+
Epoch 94 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 94 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 94 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.29it/s]
+
                                                                
Epoch 94 Training:  60%|██████    | 58/96 [01:17<00:50,  1.32s/it]
+
Epoch 94 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 94 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 94 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
+
                                                                
Epoch 94 Training:  61%|██████▏   | 59/96 [01:18<00:49,  1.33s/it]
+
Epoch 94 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 94 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 94 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.23it/s]
+
                                                                
Epoch 94 Training:  62%|██████▎   | 60/96 [01:19<00:47,  1.33s/it]
+
Epoch 94 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 94 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 94 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
+
                                                                
Epoch 94 Training:  64%|██████▎   | 61/96 [01:21<00:46,  1.33s/it]
+
Epoch 94 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 94 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 94 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
+
                                                                
Epoch 94 Training:  65%|██████▍   | 62/96 [01:22<00:45,  1.33s/it]
+
Epoch 94 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 94 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 94 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.29it/s]
+
                                                                
Epoch 94 Training:  66%|██████▌   | 63/96 [01:23<00:43,  1.32s/it]
+
Epoch 94 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 94 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 94 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.20it/s]
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Epoch 94 Training:  67%|██████▋   | 64/96 [01:25<00:42,  1.33s/it]
+
Epoch 94 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 94 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 94 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 94 Training:  68%|██████▊   | 65/96 [01:26<00:41,  1.33s/it]
+
Epoch 94 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 94 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 94 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 94 Training:  69%|██████▉   | 66/96 [01:27<00:39,  1.33s/it]
+
Epoch 94 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 94 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 94 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 94 Training:  70%|██████▉   | 67/96 [01:29<00:38,  1.33s/it]
+
Epoch 94 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 94 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 94 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.23it/s]
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Epoch 94 Training:  71%|███████   | 68/96 [01:30<00:37,  1.33s/it]
+
Epoch 94 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 94 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 94 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.22it/s]
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Epoch 94 Training:  72%|███████▏  | 69/96 [01:31<00:35,  1.33s/it]
+
Epoch 94 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 94 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 94 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 94 Training:  73%|███████▎  | 70/96 [01:33<00:34,  1.33s/it]
+
Epoch 94 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 94 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 94 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 94 Training:  74%|███████▍  | 71/96 [01:34<00:33,  1.33s/it]
+
Epoch 94 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 94 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 94 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 94 Training:  75%|███████▌  | 72/96 [01:35<00:31,  1.33s/it]
+
Epoch 94 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 94 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 94 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 94 Training:  76%|███████▌  | 73/96 [01:37<00:30,  1.33s/it]
+
Epoch 94 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 94 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 94 Training:  77%|███████▋  | 74/96 [01:38<00:29,  1.33s/it]
+
Epoch 94 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 94 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 94 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 94 Training:  78%|███████▊  | 75/96 [01:39<00:27,  1.33s/it]
+
Epoch 94 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 94 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 94 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 94 Training:  79%|███████▉  | 76/96 [01:41<00:26,  1.33s/it]
+
Epoch 94 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 94 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 94 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 94 Training:  80%|████████  | 77/96 [01:42<00:25,  1.33s/it]
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Epoch 94 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 94 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 94 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 94 Training:  81%|████████▏ | 78/96 [01:43<00:23,  1.33s/it]
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Epoch 94 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 94 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 94 Training:  82%|████████▏ | 79/96 [01:45<00:22,  1.33s/it]
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Epoch 94 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 94 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 94 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s][2026-10-01 23:32:31] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0094 | Epoch 094] train/policy_loss=11.7515 train/unweighted_policy_loss=2.3503 train/loss=11.7515 train/grad_norm=0.0797
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+[2026-10-01 23:32:31] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0095 | Epoch 094] timing/rollout_seconds=147.9009 timing/feedback_seconds=5.7894 timing/optimization_seconds=128.3550 timing/cycle_seconds=279.7640
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+
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+
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+[2026-10-01 23:35:03] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0095 | Epoch 095] train/reward_dists_mean=0.9346 train/reward_dists_std=0.0165 train/reward_psnr_mean=30.4667 train/reward_psnr_std=1.9353 train/reward_ssim_mean=0.9350 train/reward_ssim_std=0.0207 train/reward_dists_group_std_mean=0.0026 train/reward_dists_group_std_max=0.0057 train/reward_dists_group_std_min=0.0010 train/reward_dists_group_mean_std=0.0162 train/reward_psnr_group_std_mean=0.8269 train/reward_psnr_group_std_max=1.7632 train/reward_psnr_group_std_min=0.2345 train/reward_psnr_group_mean_std=1.7107 train/reward_ssim_group_std_mean=0.0009 train/reward_ssim_group_std_max=0.0022 train/reward_ssim_group_std_min=0.0003 train/reward_ssim_group_mean_std=0.0207 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-7.7216 train/adv_max=5.4245 train/adv_abs_mean=1.8471
+
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Epoch 95 Training:   2%|▏         | 2/96 [00:02<02:00,  1.28s/it]
+
Epoch 95 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 95 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.21it/s]
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Epoch 95 Training:   3%|▎         | 3/96 [00:03<02:01,  1.31s/it]
+
Epoch 95 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 95 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 95 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
+
                                                                
Epoch 95 Training:   4%|▍         | 4/96 [00:05<02:00,  1.31s/it]
+
Epoch 95 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 95 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 95 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 95 Training:   5%|▌         | 5/96 [00:06<01:59,  1.32s/it]
+
Epoch 95 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 95 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 95 Training:   6%|▋         | 6/96 [00:07<01:58,  1.32s/it]
+
Epoch 95 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 95 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 95 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.23it/s]
+
                                                                
Epoch 95 Training:   7%|▋         | 7/96 [00:09<01:57,  1.32s/it]
+
Epoch 95 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 95 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 95 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
+
                                                                
Epoch 95 Training:   8%|▊         | 8/96 [00:10<01:56,  1.32s/it]
+
Epoch 95 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 95 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 95 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
+
                                                                
Epoch 95 Training:   9%|▉         | 9/96 [00:11<01:55,  1.32s/it]
+
Epoch 95 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 95 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 95 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
+
                                                                
Epoch 95 Training:  10%|█         | 10/96 [00:13<01:53,  1.32s/it]
+
Epoch 95 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 95 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 95 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
+
                                                                
Epoch 95 Training:  11%|█▏        | 11/96 [00:14<01:52,  1.33s/it]
+
Epoch 95 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 95 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 95 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
+
                                                                
Epoch 95 Training:  12%|█▎        | 12/96 [00:15<01:51,  1.33s/it]
+
Epoch 95 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 95 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 95 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
+
                                                                
Epoch 95 Training:  14%|█▎        | 13/96 [00:17<01:50,  1.33s/it]
+
Epoch 95 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 95 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 95 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
+
                                                                
Epoch 95 Training:  15%|█▍        | 14/96 [00:18<01:48,  1.33s/it]
+
Epoch 95 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 95 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 95 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
+
                                                                
Epoch 95 Training:  16%|█▌        | 15/96 [00:19<01:47,  1.33s/it]
+
Epoch 95 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 95 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 95 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
+
                                                                
Epoch 95 Training:  17%|█▋        | 16/96 [00:21<01:46,  1.33s/it]
+
Epoch 95 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 95 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 95 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
+
                                                                
Epoch 95 Training:  18%|█▊        | 17/96 [00:22<01:44,  1.33s/it]
+
Epoch 95 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 95 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 95 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
+
                                                                
Epoch 95 Training:  19%|█▉        | 18/96 [00:23<01:43,  1.33s/it]
+
Epoch 95 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 95 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 95 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
+
                                                                
Epoch 95 Training:  20%|█▉        | 19/96 [00:25<01:42,  1.33s/it]
+
Epoch 95 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 95 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 95 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
+
                                                                
Epoch 95 Training:  21%|██        | 20/96 [00:26<01:40,  1.33s/it]
+
Epoch 95 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 95 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 95 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.23it/s]
+
                                                                
Epoch 95 Training:  22%|██▏       | 21/96 [00:27<01:39,  1.33s/it]
+
Epoch 95 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 95 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 95 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.23it/s]
+
                                                                
Epoch 95 Training:  23%|██▎       | 22/96 [00:29<01:38,  1.33s/it]
+
Epoch 95 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 95 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 95 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
+
                                                                
Epoch 95 Training:  24%|██▍       | 23/96 [00:30<01:36,  1.33s/it]
+
Epoch 95 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 95 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.63it/s]
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Epoch 95 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
+
                                                                
Epoch 95 Training:  25%|██▌       | 24/96 [00:31<01:35,  1.33s/it]
+
Epoch 95 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 95 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 95 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.23it/s]
+
                                                                
Epoch 95 Training:  26%|██▌       | 25/96 [00:33<01:34,  1.33s/it]
+
Epoch 95 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 95 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 95 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.23it/s]
+
                                                                
Epoch 95 Training:  27%|██▋       | 26/96 [00:34<01:32,  1.33s/it]
+
Epoch 95 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 95 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 95 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
+
                                                                
Epoch 95 Training:  28%|██▊       | 27/96 [00:35<01:31,  1.33s/it]
+
Epoch 95 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 95 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 95 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
+
                                                                
Epoch 95 Training:  29%|██▉       | 28/96 [00:37<01:30,  1.33s/it]
+
Epoch 95 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 95 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 95 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.22it/s]
+
                                                                
Epoch 95 Training:  30%|███       | 29/96 [00:38<01:29,  1.33s/it]
+
Epoch 95 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 95 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 95 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.23it/s]
+
                                                                
Epoch 95 Training:  31%|███▏      | 30/96 [00:39<01:27,  1.33s/it]
+
Epoch 95 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 95 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 95 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
+
                                                                
Epoch 95 Training:  32%|███▏      | 31/96 [00:41<01:26,  1.33s/it]
+
Epoch 95 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 95 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 95 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 95 Training:  33%|███▎      | 32/96 [00:42<01:25,  1.33s/it]
+
Epoch 95 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 95 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 95 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 95 Training:  34%|███▍      | 33/96 [00:43<01:23,  1.33s/it]
+
Epoch 95 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 95 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 95 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 95 Training:  35%|███▌      | 34/96 [00:45<01:22,  1.33s/it]
+
Epoch 95 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 95 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 95 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 95 Training:  36%|███▋      | 35/96 [00:46<01:21,  1.33s/it]
+
Epoch 95 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 95 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 95 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.32it/s]
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Epoch 95 Training:  38%|███▊      | 36/96 [00:47<01:19,  1.33s/it]
+
Epoch 95 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 95 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 95 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 95 Training:  39%|███▊      | 37/96 [00:49<01:18,  1.33s/it]
+
Epoch 95 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 95 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 95 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 95 Training:  40%|███▉      | 38/96 [00:50<01:17,  1.33s/it]
+
Epoch 95 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 95 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 95 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 95 Training:  41%|████      | 39/96 [00:51<01:15,  1.33s/it]
+
Epoch 95 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 95 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 95 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 95 Training:  42%|████▏     | 40/96 [00:52<01:14,  1.33s/it]
+
Epoch 95 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 95 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 95 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 95 Training:  43%|████▎     | 41/96 [00:54<01:13,  1.33s/it]
+
Epoch 95 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 95 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 95 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 95 Training:  44%|████▍     | 42/96 [00:55<01:11,  1.33s/it]
+
Epoch 95 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 95 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 95 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 95 Training:  45%|████▍     | 43/96 [00:56<01:10,  1.33s/it]
+
Epoch 95 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 95 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 95 Training:  46%|████▌     | 44/96 [00:58<01:08,  1.33s/it]
+
Epoch 95 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 95 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 95 Training:  47%|████▋     | 45/96 [00:59<01:07,  1.33s/it]
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Epoch 95 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 95 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.23it/s]
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Epoch 95 Training:  48%|████▊     | 46/96 [01:00<01:06,  1.33s/it]
+
Epoch 95 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 95 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 95 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.22it/s]
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Epoch 95 Training:  49%|████▉     | 47/96 [01:02<01:05,  1.33s/it]
+
Epoch 95 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 95 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 95 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.21it/s]
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Epoch 95 Training:  50%|█████     | 48/96 [01:03<01:04,  1.33s/it]
+
Epoch 95 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 95 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 95 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.18it/s]
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Epoch 95 Training:  51%|█████     | 49/96 [01:04<01:02,  1.34s/it]
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Epoch 95 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 95 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 95 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 95 Training:  52%|█████▏    | 50/96 [01:06<01:01,  1.34s/it]
+
Epoch 95 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 95 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 95 Training:  53%|█████▎    | 51/96 [01:07<00:59,  1.33s/it]
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Epoch 95 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 95 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 95 Training:  54%|█████▍    | 52/96 [01:08<00:58,  1.33s/it]
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Epoch 95 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 95 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 95 Training:  55%|█████▌    | 53/96 [01:10<00:57,  1.33s/it]
+
Epoch 95 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 95 Training:  82%|████████▏ | 79/96 [01:44<00:22,  1.33s/it]
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Epoch 95 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s][2026-10-01 23:37:10] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0095 | Epoch 095] train/policy_loss=11.7011 train/unweighted_policy_loss=2.3402 train/loss=11.7011 train/grad_norm=0.1073
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+[2026-10-01 23:37:10] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0096 | Epoch 095] timing/rollout_seconds=147.7227 timing/feedback_seconds=5.6634 timing/optimization_seconds=128.1507 timing/cycle_seconds=279.3437
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Epoch 96 Sampling: 100%|██████████| 96/96 [02:25<00:00,  1.51s/it]
+
Pointwise Rewards: ssim:   0%|          | 0/6 [00:00<?, ?it/s]
Pointwise Rewards: ssim:  17%|█▋        | 1/6 [00:00<00:00,  6.71it/s]
Pointwise Rewards: ssim:  33%|███▎      | 2/6 [00:00<00:00,  6.85it/s]
Pointwise Rewards: ssim:  50%|█████     | 3/6 [00:00<00:00,  6.51it/s]
Pointwise Rewards: ssim:  67%|██████▋   | 4/6 [00:00<00:00,  6.46it/s]
Pointwise Rewards: ssim:  83%|████████▎ | 5/6 [00:00<00:00,  6.43it/s]
Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:00<00:00,  6.37it/s]
Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:00<00:00,  6.46it/s]
+
Pointwise Rewards: dists:   0%|          | 0/6 [00:00<?, ?it/s]
Pointwise Rewards: dists:  17%|█▋        | 1/6 [00:00<00:01,  3.84it/s]
Pointwise Rewards: dists:  33%|███▎      | 2/6 [00:00<00:01,  3.74it/s]
Pointwise Rewards: dists:  50%|█████     | 3/6 [00:00<00:00,  3.69it/s]
Pointwise Rewards: dists:  67%|██████▋   | 4/6 [00:01<00:00,  3.69it/s]
Pointwise Rewards: dists:  83%|████████▎ | 5/6 [00:01<00:00,  3.68it/s]
Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.66it/s]
Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.68it/s]
+
Pointwise Rewards: psnr:   0%|          | 0/6 [00:00<?, ?it/s]
Pointwise Rewards: psnr:  17%|█▋        | 1/6 [00:00<00:00,  7.30it/s]
Pointwise Rewards: psnr:  33%|███▎      | 2/6 [00:00<00:00,  7.15it/s]
Pointwise Rewards: psnr:  50%|█████     | 3/6 [00:00<00:00,  7.01it/s]
Pointwise Rewards: psnr:  67%|██████▋   | 4/6 [00:00<00:00,  6.89it/s]
Pointwise Rewards: psnr:  83%|████████▎ | 5/6 [00:00<00:00,  6.85it/s]
Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.77it/s]
Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.87it/s]
+[2026-10-01 23:39:42] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0096 | Epoch 096] train/reward_dists_mean=0.9382 train/reward_dists_std=0.0170 train/reward_psnr_mean=30.6786 train/reward_psnr_std=2.1498 train/reward_ssim_mean=0.9364 train/reward_ssim_std=0.0232 train/reward_dists_group_std_mean=0.0023 train/reward_dists_group_std_max=0.0055 train/reward_dists_group_std_min=0.0008 train/reward_dists_group_mean_std=0.0168 train/reward_psnr_group_std_mean=0.7322 train/reward_psnr_group_std_max=1.9018 train/reward_psnr_group_std_min=0.2097 train/reward_psnr_group_mean_std=1.9859 train/reward_ssim_group_std_mean=0.0007 train/reward_ssim_group_std_max=0.0017 train/reward_ssim_group_std_min=0.0002 train/reward_ssim_group_mean_std=0.0232 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-7.4827 train/adv_max=5.6602 train/adv_abs_mean=1.7382
+
Epoch 96 Training:   0%|          | 0/96 [00:00<?, ?it/s]
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Epoch 96 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.50it/s]
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Epoch 96 Training:   1%|          | 1/96 [00:01<01:55,  1.21s/it]
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Epoch 96 Training:   2%|▏         | 2/96 [00:02<02:00,  1.28s/it]
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Epoch 96 Training:   3%|▎         | 3/96 [00:03<02:01,  1.30s/it]
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Epoch 96 Training:   4%|▍         | 4/96 [00:05<02:00,  1.31s/it]
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Epoch 96 Training:   5%|▌         | 5/96 [00:06<01:59,  1.32s/it]
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Epoch 96 Training:   6%|▋         | 6/96 [00:07<01:58,  1.32s/it]
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Epoch 96 Training:   7%|▋         | 7/96 [00:09<01:58,  1.33s/it]
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Epoch 96 Training:   8%|▊         | 8/96 [00:10<01:56,  1.33s/it]
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Epoch 96 Training:   9%|▉         | 9/96 [00:11<01:55,  1.33s/it]
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Epoch 96 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 96 Training:  10%|█         | 10/96 [00:13<01:54,  1.33s/it]
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Epoch 96 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 96 Training:  11%|█▏        | 11/96 [00:14<01:52,  1.33s/it]
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Epoch 96 Training:  12%|█▎        | 12/96 [00:15<01:51,  1.33s/it]
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Epoch 96 Training:  14%|█▎        | 13/96 [00:17<01:50,  1.33s/it]
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Epoch 96 Training:  15%|█▍        | 14/96 [00:18<01:48,  1.33s/it]
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Epoch 96 Training:  16%|█▌        | 15/96 [00:19<01:47,  1.33s/it]
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Epoch 96 Training:  17%|█▋        | 16/96 [00:21<01:46,  1.33s/it]
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Epoch 96 Training:  18%|█▊        | 17/96 [00:22<01:44,  1.33s/it]
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Epoch 96 Training:  19%|█▉        | 18/96 [00:23<01:43,  1.33s/it]
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Epoch 96 Training:  20%|█▉        | 19/96 [00:25<01:42,  1.33s/it]
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Epoch 96 Training:  21%|██        | 20/96 [00:26<01:41,  1.33s/it]
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Epoch 96 Training:  22%|██▏       | 21/96 [00:27<01:39,  1.33s/it]
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Epoch 96 Training:  23%|██▎       | 22/96 [00:29<01:38,  1.33s/it]
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Epoch 96 Training:  24%|██▍       | 23/96 [00:30<01:36,  1.33s/it]
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Epoch 96 Training:  25%|██▌       | 24/96 [00:31<01:35,  1.33s/it]
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Epoch 96 Training:  26%|██▌       | 25/96 [00:33<01:34,  1.33s/it]
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Epoch 96 Training:  27%|██▋       | 26/96 [00:34<01:32,  1.32s/it]
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Epoch 96 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 96 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 96 Training:  28%|██▊       | 27/96 [00:35<01:31,  1.32s/it]
+
Epoch 96 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 96 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 96 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.23it/s]
+
                                                                
Epoch 96 Training:  29%|██▉       | 28/96 [00:37<01:30,  1.33s/it]
+
Epoch 96 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 96 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 96 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
+
                                                                
Epoch 96 Training:  30%|███       | 29/96 [00:38<01:28,  1.32s/it]
+
Epoch 96 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 96 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 96 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
+
                                                                
Epoch 96 Training:  31%|███▏      | 30/96 [00:39<01:27,  1.32s/it]
+
Epoch 96 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 96 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 96 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 96 Training:  32%|███▏      | 31/96 [00:41<01:26,  1.32s/it]
+
Epoch 96 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 96 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 96 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 96 Training:  33%|███▎      | 32/96 [00:42<01:24,  1.33s/it]
+
Epoch 96 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 96 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 96 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.37it/s]
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Epoch 96 Training:  34%|███▍      | 33/96 [00:43<01:23,  1.33s/it]
+
Epoch 96 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 96 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 96 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
+
                                                                
Epoch 96 Training:  35%|███▌      | 34/96 [00:45<01:22,  1.32s/it]
+
Epoch 96 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 96 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 96 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
+
                                                                
Epoch 96 Training:  36%|███▋      | 35/96 [00:46<01:20,  1.32s/it]
+
Epoch 96 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 96 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 96 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.23it/s]
+
                                                                
Epoch 96 Training:  38%|███▊      | 36/96 [00:47<01:19,  1.33s/it]
+
Epoch 96 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 96 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 96 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
+
                                                                
Epoch 96 Training:  39%|███▊      | 37/96 [00:48<01:18,  1.32s/it]
+
Epoch 96 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 96 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 96 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
+
                                                                
Epoch 96 Training:  40%|███▉      | 38/96 [00:50<01:16,  1.32s/it]
+
Epoch 96 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 96 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 96 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
+
                                                                
Epoch 96 Training:  41%|████      | 39/96 [00:51<01:15,  1.32s/it]
+
Epoch 96 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 96 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 96 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
+
                                                                
Epoch 96 Training:  42%|████▏     | 40/96 [00:52<01:14,  1.32s/it]
+
Epoch 96 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 96 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 96 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
+
                                                                
Epoch 96 Training:  43%|████▎     | 41/96 [00:54<01:12,  1.32s/it]
+
Epoch 96 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 96 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 96 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
+
                                                                
Epoch 96 Training:  44%|████▍     | 42/96 [00:55<01:11,  1.32s/it]
+
Epoch 96 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 96 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 96 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
+
                                                                
Epoch 96 Training:  45%|████▍     | 43/96 [00:56<01:10,  1.32s/it]
+
Epoch 96 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 96 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 96 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
+
                                                                
Epoch 96 Training:  46%|████▌     | 44/96 [00:58<01:08,  1.33s/it]
+
Epoch 96 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 96 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 96 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
+
                                                                
Epoch 96 Training:  47%|████▋     | 45/96 [00:59<01:07,  1.32s/it]
+
Epoch 96 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 96 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 96 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
+
                                                                
Epoch 96 Training:  48%|████▊     | 46/96 [01:00<01:06,  1.32s/it]
+
Epoch 96 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 96 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 96 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
+
                                                                
Epoch 96 Training:  49%|████▉     | 47/96 [01:02<01:04,  1.32s/it]
+
Epoch 96 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 96 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 96 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
+
                                                                
Epoch 96 Training:  50%|█████     | 48/96 [01:03<01:03,  1.33s/it]
+
Epoch 96 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 96 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 96 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
+
                                                                
Epoch 96 Training:  51%|█████     | 49/96 [01:04<01:02,  1.33s/it]
+
Epoch 96 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 96 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 96 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
+
                                                                
Epoch 96 Training:  52%|█████▏    | 50/96 [01:06<01:00,  1.32s/it]
+
Epoch 96 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 96 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 96 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
+
                                                                
Epoch 96 Training:  53%|█████▎    | 51/96 [01:07<00:59,  1.32s/it]
+
Epoch 96 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 96 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
+
Epoch 96 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.19it/s]
+
                                                                
Epoch 96 Training:  54%|█████▍    | 52/96 [01:08<00:58,  1.33s/it]
+
Epoch 96 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 96 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 96 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
+
                                                                
Epoch 96 Training:  55%|█████▌    | 53/96 [01:10<00:57,  1.33s/it]
+
Epoch 96 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 96 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 96 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
+
                                                                
Epoch 96 Training:  56%|█████▋    | 54/96 [01:11<00:55,  1.32s/it]
+
Epoch 96 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 96 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.63it/s]
+
Epoch 96 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
+
                                                                
Epoch 96 Training:  57%|█████▋    | 55/96 [01:12<00:54,  1.32s/it]
+
Epoch 96 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 96 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 96 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
+
                                                                
Epoch 96 Training:  58%|█████▊    | 56/96 [01:14<00:53,  1.33s/it]
+
Epoch 96 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 96 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 96 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
+
                                                                
Epoch 96 Training:  59%|█████▉    | 57/96 [01:15<00:51,  1.32s/it]
+
Epoch 96 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 96 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
+
Epoch 96 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
+
                                                                
Epoch 96 Training:  60%|██████    | 58/96 [01:16<00:50,  1.32s/it]
+
Epoch 96 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 96 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 96 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
+
                                                                
Epoch 96 Training:  61%|██████▏   | 59/96 [01:18<00:48,  1.32s/it]
+
Epoch 96 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 96 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 96 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.23it/s]
+
                                                                
Epoch 96 Training:  62%|██████▎   | 60/96 [01:19<00:47,  1.32s/it]
+
Epoch 96 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 96 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 96 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
+
                                                                
Epoch 96 Training:  64%|██████▎   | 61/96 [01:20<00:46,  1.32s/it]
+
Epoch 96 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
+
Epoch 96 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 96 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
+
                                                                
Epoch 96 Training:  65%|██████▍   | 62/96 [01:22<00:44,  1.32s/it]
+
Epoch 96 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 96 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 96 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
+
                                                                
Epoch 96 Training:  66%|██████▌   | 63/96 [01:23<00:43,  1.32s/it]
+
Epoch 96 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 96 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 96 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.22it/s]
+
                                                                
Epoch 96 Training:  67%|██████▋   | 64/96 [01:24<00:42,  1.33s/it]
+
Epoch 96 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 96 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 96 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
+
                                                                
Epoch 96 Training:  68%|██████▊   | 65/96 [01:26<00:41,  1.33s/it]
+
Epoch 96 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 96 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 96 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
+
                                                                
Epoch 96 Training:  69%|██████▉   | 66/96 [01:27<00:39,  1.33s/it]
+
Epoch 96 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 96 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 96 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
+
                                                                
Epoch 96 Training:  70%|██████▉   | 67/96 [01:28<00:38,  1.32s/it]
+
Epoch 96 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 96 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 96 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.23it/s]
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Epoch 96 Training:  71%|███████   | 68/96 [01:30<00:37,  1.33s/it]
+
Epoch 96 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 96 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 96 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.23it/s]
+
                                                                
Epoch 96 Training:  72%|███████▏  | 69/96 [01:31<00:35,  1.33s/it]
+
Epoch 96 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 96 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 96 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 96 Training:  73%|███████▎  | 70/96 [01:32<00:34,  1.33s/it]
+
Epoch 96 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 96 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 96 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 96 Training:  74%|███████▍  | 71/96 [01:34<00:33,  1.33s/it]
+
Epoch 96 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 96 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 96 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.23it/s]
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Epoch 96 Training:  75%|███████▌  | 72/96 [01:35<00:31,  1.33s/it]
+
Epoch 96 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 96 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 96 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
+
                                                                
Epoch 96 Training:  76%|███████▌  | 73/96 [01:36<00:30,  1.33s/it]
+
Epoch 96 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 96 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 96 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 96 Training:  77%|███████▋  | 74/96 [01:38<00:29,  1.33s/it]
+
Epoch 96 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 96 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 96 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 96 Training:  78%|███████▊  | 75/96 [01:39<00:27,  1.33s/it]
+
Epoch 96 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 96 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 96 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.23it/s]
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Epoch 96 Training:  79%|███████▉  | 76/96 [01:40<00:26,  1.33s/it]
+
Epoch 96 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 96 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 96 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 96 Training:  80%|████████  | 77/96 [01:41<00:25,  1.33s/it]
+
Epoch 96 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 96 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 96 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s][2026-10-01 23:41:49] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0096 | Epoch 096] train/policy_loss=11.6909 train/unweighted_policy_loss=2.3382 train/loss=11.6909 train/grad_norm=0.0845
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+[2026-10-01 23:41:49] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0097 | Epoch 096] timing/rollout_seconds=147.4365 timing/feedback_seconds=5.4320 timing/optimization_seconds=127.8104 timing/cycle_seconds=278.7353
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Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.79it/s]
+[2026-10-01 23:44:21] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0097 | Epoch 097] train/reward_dists_mean=0.9364 train/reward_dists_std=0.0162 train/reward_psnr_mean=30.8223 train/reward_psnr_std=2.0412 train/reward_ssim_mean=0.9354 train/reward_ssim_std=0.0193 train/reward_dists_group_std_mean=0.0022 train/reward_dists_group_std_max=0.0043 train/reward_dists_group_std_min=0.0012 train/reward_dists_group_mean_std=0.0160 train/reward_psnr_group_std_mean=0.8234 train/reward_psnr_group_std_max=1.7160 train/reward_psnr_group_std_min=0.3049 train/reward_psnr_group_mean_std=1.8292 train/reward_ssim_group_std_mean=0.0008 train/reward_ssim_group_std_max=0.0026 train/reward_ssim_group_std_min=0.0003 train/reward_ssim_group_mean_std=0.0193 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-7.4247 train/adv_max=6.5578 train/adv_abs_mean=1.8207
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Epoch 97 Training:  82%|████████▏ | 79/96 [01:44<00:22,  1.33s/it]
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Epoch 97 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s][2026-10-01 23:46:28] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0097 | Epoch 097] train/policy_loss=11.5354 train/unweighted_policy_loss=2.3071 train/loss=11.5354 train/grad_norm=0.0952
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+[2026-10-01 23:46:28] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0098 | Epoch 097] timing/rollout_seconds=147.7541 timing/feedback_seconds=5.6336 timing/optimization_seconds=128.0313 timing/cycle_seconds=279.3024
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+
Pointwise Rewards: ssim:   0%|          | 0/6 [00:00<?, ?it/s]
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Pointwise Rewards: ssim:  83%|████████▎ | 5/6 [00:00<00:00,  6.51it/s]
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Pointwise Rewards: ssim: 100%|██████████| 6/6 [00:00<00:00,  6.39it/s]
+
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Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.65it/s]
Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.57it/s]
+
Pointwise Rewards: psnr:   0%|          | 0/6 [00:00<?, ?it/s]
Pointwise Rewards: psnr:  17%|█▋        | 1/6 [00:00<00:00,  6.65it/s]
Pointwise Rewards: psnr:  33%|███▎      | 2/6 [00:00<00:00,  6.74it/s]
Pointwise Rewards: psnr:  50%|█████     | 3/6 [00:00<00:00,  6.56it/s]
Pointwise Rewards: psnr:  67%|██████▋   | 4/6 [00:00<00:00,  6.56it/s]
Pointwise Rewards: psnr:  83%|████████▎ | 5/6 [00:00<00:00,  6.65it/s]
Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.65it/s]
Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.63it/s]
+[2026-10-01 23:49:00] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0098 | Epoch 098] train/reward_dists_mean=0.9387 train/reward_dists_std=0.0164 train/reward_psnr_mean=30.3713 train/reward_psnr_std=1.7842 train/reward_ssim_mean=0.9356 train/reward_ssim_std=0.0171 train/reward_dists_group_std_mean=0.0023 train/reward_dists_group_std_max=0.0095 train/reward_dists_group_std_min=0.0011 train/reward_dists_group_mean_std=0.0162 train/reward_psnr_group_std_mean=0.7068 train/reward_psnr_group_std_max=2.6329 train/reward_psnr_group_std_min=0.1950 train/reward_psnr_group_mean_std=1.5910 train/reward_ssim_group_std_mean=0.0008 train/reward_ssim_group_std_max=0.0053 train/reward_ssim_group_std_min=0.0003 train/reward_ssim_group_mean_std=0.0170 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-9.1840 train/adv_max=5.6490 train/adv_abs_mean=1.8061
+
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Epoch 98 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 98 Training:  23%|██▎       | 22/96 [00:29<01:37,  1.32s/it]
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Epoch 98 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 98 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 98 Training:  24%|██▍       | 23/96 [00:30<01:36,  1.32s/it]
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Epoch 98 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 98 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 98 Training:  25%|██▌       | 24/96 [00:31<01:35,  1.32s/it]
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Epoch 98 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 98 Training:  26%|██▌       | 25/96 [00:33<01:34,  1.33s/it]
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Epoch 98 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 98 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.39it/s]
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Epoch 98 Training:  27%|██▋       | 26/96 [00:34<01:32,  1.32s/it]
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Epoch 98 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 98 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 98 Training:  28%|██▊       | 27/96 [00:35<01:31,  1.32s/it]
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Epoch 98 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 98 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 98 Training:  29%|██▉       | 28/96 [00:36<01:29,  1.32s/it]
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Epoch 98 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 98 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.23it/s]
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Epoch 98 Training:  30%|███       | 29/96 [00:38<01:28,  1.33s/it]
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Epoch 98 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 98 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 98 Training:  31%|███▏      | 30/96 [00:39<01:27,  1.32s/it]
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Epoch 98 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 98 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 98 Training:  32%|███▏      | 31/96 [00:40<01:25,  1.32s/it]
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Epoch 98 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 98 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 98 Training:  33%|███▎      | 32/96 [00:42<01:24,  1.32s/it]
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Epoch 98 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 98 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 98 Training:  34%|███▍      | 33/96 [00:43<01:23,  1.32s/it]
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Epoch 98 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 98 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 98 Training:  35%|███▌      | 34/96 [00:44<01:22,  1.32s/it]
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Epoch 98 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 98 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 98 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 98 Training:  36%|███▋      | 35/96 [00:46<01:20,  1.32s/it]
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Epoch 98 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 98 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 98 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 98 Training:  38%|███▊      | 36/96 [00:47<01:19,  1.32s/it]
+
Epoch 98 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 98 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 98 Training:  39%|███▊      | 37/96 [00:48<01:18,  1.32s/it]
+
Epoch 98 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 98 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 98 Training:  40%|███▉      | 38/96 [00:50<01:16,  1.32s/it]
+
Epoch 98 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 98 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 98 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 98 Training:  41%|████      | 39/96 [00:51<01:15,  1.32s/it]
+
Epoch 98 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 98 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s]
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Epoch 98 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 98 Training:  42%|████▏     | 40/96 [00:52<01:14,  1.33s/it]
+
Epoch 98 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 98 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 98 Training:  43%|████▎     | 41/96 [00:54<01:12,  1.33s/it]
+
Epoch 98 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 98 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.23it/s]
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Epoch 98 Training:  44%|████▍     | 42/96 [00:55<01:11,  1.33s/it]
+
Epoch 98 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 98 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 98 Training:  45%|████▍     | 43/96 [00:56<01:10,  1.33s/it]
+
Epoch 98 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 98 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 98 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 98 Training:  46%|████▌     | 44/96 [00:58<01:08,  1.32s/it]
+
Epoch 98 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 98 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.21it/s]
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Epoch 98 Training:  47%|████▋     | 45/96 [00:59<01:07,  1.33s/it]
+
Epoch 98 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 98 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 98 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 98 Training:  48%|████▊     | 46/96 [01:00<01:06,  1.33s/it]
+
Epoch 98 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 98 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 98 Training:  49%|████▉     | 47/96 [01:02<01:05,  1.33s/it]
+
Epoch 98 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 98 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 98 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
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Epoch 98 Training:  50%|█████     | 48/96 [01:03<01:03,  1.33s/it]
+
Epoch 98 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 98 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.64it/s]
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Epoch 98 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 98 Training:  51%|█████     | 49/96 [01:04<01:02,  1.33s/it]
+
Epoch 98 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 98 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 98 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 98 Training:  52%|█████▏    | 50/96 [01:06<01:01,  1.33s/it]
+
Epoch 98 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 98 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 98 Training:  53%|█████▎    | 51/96 [01:07<00:59,  1.33s/it]
+
Epoch 98 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 98 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 98 Training:  54%|█████▍    | 52/96 [01:08<00:58,  1.32s/it]
+
Epoch 98 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 98 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s]
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Epoch 98 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 98 Training:  55%|█████▌    | 53/96 [01:10<00:56,  1.32s/it]
+
Epoch 98 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 98 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 98 Training:  56%|█████▋    | 54/96 [01:11<00:55,  1.33s/it]
+
Epoch 98 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 98 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 98 Training:  57%|█████▋    | 55/96 [01:12<00:54,  1.33s/it]
+
Epoch 98 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 98 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.22it/s]
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Epoch 98 Training:  58%|█████▊    | 56/96 [01:14<00:53,  1.33s/it]
+
Epoch 98 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 98 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.22it/s]
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Epoch 98 Training:  59%|█████▉    | 57/96 [01:15<00:51,  1.33s/it]
+
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Epoch 98 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 98 Training:  60%|██████    | 58/96 [01:16<00:50,  1.33s/it]
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Epoch 98 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 98 Training:  61%|██████▏   | 59/96 [01:18<00:49,  1.33s/it]
+
Epoch 98 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 98 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 98 Training:  62%|██████▎   | 60/96 [01:19<00:47,  1.33s/it]
+
Epoch 98 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 98 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 98 Training:  64%|██████▎   | 61/96 [01:20<00:46,  1.33s/it]
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Epoch 98 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 98 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 98 Training:  65%|██████▍   | 62/96 [01:22<00:45,  1.33s/it]
+
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Epoch 98 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 98 Training:  66%|██████▌   | 63/96 [01:23<00:43,  1.33s/it]
+
Epoch 98 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 98 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 98 Training:  67%|██████▋   | 64/96 [01:24<00:42,  1.33s/it]
+
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Epoch 98 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 98 Training:  68%|██████▊   | 65/96 [01:26<00:41,  1.33s/it]
+
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Epoch 98 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 98 Training:  69%|██████▉   | 66/96 [01:27<00:39,  1.33s/it]
+
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Epoch 98 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
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Epoch 98 Training:  70%|██████▉   | 67/96 [01:28<00:38,  1.33s/it]
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Epoch 98 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.24it/s]
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Epoch 98 Training:  71%|███████   | 68/96 [01:30<00:37,  1.33s/it]
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Epoch 98 Timestep:   0%|          | 0/2 [00:00<?, ?it/s]
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Epoch 98 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 98 Training:  72%|███████▏  | 69/96 [01:31<00:35,  1.32s/it]
+
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Epoch 98 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.27it/s]
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Epoch 98 Training:  73%|███████▎  | 70/96 [01:32<00:34,  1.32s/it]
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Epoch 98 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.22it/s]
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Epoch 98 Training:  74%|███████▍  | 71/96 [01:34<00:33,  1.33s/it]
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Epoch 98 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.26it/s]
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Epoch 98 Training:  75%|███████▌  | 72/96 [01:35<00:31,  1.33s/it]
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Epoch 98 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.65it/s][2026-10-01 23:51:07] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0098 | Epoch 098] train/policy_loss=11.0300 train/unweighted_policy_loss=2.2060 train/loss=11.0300 train/grad_norm=0.0714
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+[2026-10-01 23:51:07] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0099 | Epoch 098] timing/rollout_seconds=147.6639 timing/feedback_seconds=5.8560 timing/optimization_seconds=127.7307 timing/cycle_seconds=279.1586
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Pointwise Rewards: dists:  83%|████████▎ | 5/6 [00:01<00:00,  3.66it/s]
Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.65it/s]
Pointwise Rewards: dists: 100%|██████████| 6/6 [00:01<00:00,  3.66it/s]
+
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Pointwise Rewards: psnr:  17%|█▋        | 1/6 [00:00<00:00,  6.99it/s]
Pointwise Rewards: psnr:  33%|███▎      | 2/6 [00:00<00:00,  6.76it/s]
Pointwise Rewards: psnr:  50%|█████     | 3/6 [00:00<00:00,  6.73it/s]
Pointwise Rewards: psnr:  67%|██████▋   | 4/6 [00:00<00:00,  6.85it/s]
Pointwise Rewards: psnr:  83%|████████▎ | 5/6 [00:00<00:00,  6.79it/s]
Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.70it/s]
Pointwise Rewards: psnr: 100%|██████████| 6/6 [00:00<00:00,  6.75it/s]
+[2026-10-01 23:53:39] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0099 | Epoch 099] train/reward_dists_mean=0.9362 train/reward_dists_std=0.0171 train/reward_psnr_mean=30.9279 train/reward_psnr_std=2.0747 train/reward_ssim_mean=0.9375 train/reward_ssim_std=0.0189 train/reward_dists_group_std_mean=0.0026 train/reward_dists_group_std_max=0.0154 train/reward_dists_group_std_min=0.0009 train/reward_dists_group_mean_std=0.0168 train/reward_psnr_group_std_mean=0.8903 train/reward_psnr_group_std_max=5.8964 train/reward_psnr_group_std_min=0.1309 train/reward_psnr_group_mean_std=1.6631 train/reward_ssim_group_std_mean=0.0010 train/reward_ssim_group_std_max=0.0168 train/reward_ssim_group_std_min=0.0003 train/reward_ssim_group_mean_std=0.0187 train/reward_dists_zero_std_ratio=0 train/reward_psnr_zero_std_ratio=0 train/reward_ssim_zero_std_ratio=0 train/batch_norm_mean=0 train/batch_norm_std=1 train/adv_min=-7.8089 train/adv_max=5.0410 train/adv_abs_mean=1.7576
+
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Epoch 99 Training:  15%|█▍        | 14/96 [00:18<01:48,  1.33s/it]
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Epoch 99 Training:  26%|██▌       | 25/96 [00:33<01:34,  1.33s/it]
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Epoch 99 Training:  28%|██▊       | 27/96 [00:35<01:31,  1.33s/it]
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Epoch 99 Training:  29%|██▉       | 28/96 [00:37<01:30,  1.33s/it]
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Epoch 99 Training:  33%|███▎      | 32/96 [00:42<01:24,  1.32s/it]
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Epoch 99 Training:  49%|████▉     | 47/96 [01:02<01:04,  1.33s/it]
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Epoch 99 Training:  51%|█████     | 49/96 [01:04<01:02,  1.33s/it]
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Epoch 99 Training:  52%|█████▏    | 50/96 [01:06<01:01,  1.33s/it]
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Epoch 99 Training:  53%|█████▎    | 51/96 [01:07<00:59,  1.33s/it]
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Epoch 99 Training:  54%|█████▍    | 52/96 [01:08<00:58,  1.33s/it]
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Epoch 99 Training:  55%|█████▌    | 53/96 [01:10<00:57,  1.33s/it]
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Epoch 99 Training:  56%|█████▋    | 54/96 [01:11<00:55,  1.33s/it]
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Epoch 99 Training:  57%|█████▋    | 55/96 [01:12<00:54,  1.33s/it]
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Epoch 99 Training:  58%|█████▊    | 56/96 [01:14<00:53,  1.33s/it]
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Epoch 99 Training:  59%|█████▉    | 57/96 [01:15<00:51,  1.33s/it]
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Epoch 99 Training:  60%|██████    | 58/96 [01:16<00:50,  1.33s/it]
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Epoch 99 Training:  61%|██████▏   | 59/96 [01:18<00:49,  1.33s/it]
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Epoch 99 Training:  62%|██████▎   | 60/96 [01:19<00:47,  1.33s/it]
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Epoch 99 Training:  64%|██████▎   | 61/96 [01:20<00:46,  1.33s/it]
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Epoch 99 Training:  65%|██████▍   | 62/96 [01:22<00:45,  1.33s/it]
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Epoch 99 Training:  66%|██████▌   | 63/96 [01:23<00:43,  1.33s/it]
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Epoch 99 Training:  67%|██████▋   | 64/96 [01:24<00:42,  1.33s/it]
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Epoch 99 Training:  68%|██████▊   | 65/96 [01:26<00:41,  1.33s/it]
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Epoch 99 Training:  69%|██████▉   | 66/96 [01:27<00:39,  1.33s/it]
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Epoch 99 Training:  70%|██████▉   | 67/96 [01:28<00:38,  1.32s/it]
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Epoch 99 Training:  71%|███████   | 68/96 [01:30<00:37,  1.33s/it]
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Epoch 99 Training:  72%|███████▏  | 69/96 [01:31<00:35,  1.33s/it]
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Epoch 99 Training:  73%|███████▎  | 70/96 [01:32<00:34,  1.32s/it]
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Epoch 99 Training:  74%|███████▍  | 71/96 [01:34<00:33,  1.32s/it]
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Epoch 99 Training:  75%|███████▌  | 72/96 [01:35<00:31,  1.33s/it]
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Epoch 99 Training:  76%|███████▌  | 73/96 [01:36<00:30,  1.33s/it]
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Epoch 99 Training:  77%|███████▋  | 74/96 [01:38<00:29,  1.33s/it]
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Epoch 99 Training:  78%|███████▊  | 75/96 [01:39<00:27,  1.33s/it]
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Epoch 99 Training:  79%|███████▉  | 76/96 [01:40<00:26,  1.33s/it]
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Epoch 99 Training:  80%|████████  | 77/96 [01:42<00:25,  1.33s/it]
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Epoch 99 Training:  81%|████████▏ | 78/96 [01:43<00:23,  1.33s/it]
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Epoch 99 Training:  82%|████████▏ | 79/96 [01:44<00:22,  1.33s/it]
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Epoch 99 Training:  83%|████████▎ | 80/96 [01:46<00:21,  1.33s/it]
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Epoch 99 Training:  84%|████████▍ | 81/96 [01:47<00:19,  1.33s/it]
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Epoch 99 Training:  85%|████████▌ | 82/96 [01:48<00:18,  1.32s/it]
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Epoch 99 Training:  86%|████████▋ | 83/96 [01:49<00:17,  1.33s/it]
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Epoch 99 Training:  88%|████████▊ | 84/96 [01:51<00:15,  1.33s/it]
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Epoch 99 Training:  89%|████████▊ | 85/96 [01:52<00:14,  1.33s/it]
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Epoch 99 Training:  90%|████████▉ | 86/96 [01:53<00:13,  1.33s/it]
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Epoch 99 Training:  91%|█████████ | 87/96 [01:55<00:11,  1.33s/it]
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Epoch 99 Training:  92%|█████████▏| 88/96 [01:56<00:10,  1.33s/it]
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Epoch 99 Training:  93%|█████████▎| 89/96 [01:57<00:09,  1.33s/it]
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Epoch 99 Training:  94%|█████████▍| 90/96 [01:59<00:07,  1.33s/it]
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Epoch 99 Training:  95%|█████████▍| 91/96 [02:00<00:06,  1.33s/it]
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Epoch 99 Training:  96%|█████████▌| 92/96 [02:01<00:05,  1.33s/it]
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Epoch 99 Training:  97%|█████████▋| 93/96 [02:03<00:03,  1.33s/it]
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Epoch 99 Training:  98%|█████████▊| 94/96 [02:04<00:02,  1.33s/it]
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Epoch 99 Training:  99%|█████████▉| 95/96 [02:05<00:01,  1.33s/it]
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Epoch 99 Timestep:  50%|█████     | 1/2 [00:00<00:00,  2.66it/s][2026-10-01 23:55:47] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0099 | Epoch 099] train/policy_loss=11.3420 train/unweighted_policy_loss=2.2684 train/loss=11.3420 train/grad_norm=0.1088
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Epoch 99 Timestep: 100%|██████████| 2/2 [00:00<00:00,  2.09it/s]
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Epoch 99 Training: 100%|██████████| 96/96 [02:07<00:00,  1.34s/it]
Epoch 99 Training: 100%|██████████| 96/96 [02:07<00:00,  1.33s/it]
+[2026-10-01 23:55:47] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0100 | Epoch 099] timing/rollout_seconds=147.7519 timing/feedback_seconds=5.7882 timing/optimization_seconds=127.8563 timing/cycle_seconds=279.1503
+[2026-10-01 23:55:47] [Rank 0] [INFO] [flow_factory.models.abc]: Saving LoRA weights for transformer role=base to /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/runs/klein4b_nft_vedai_full_ssim_dists_psnr/saves/klein4b_nft_vedai_full_ssim_dists_psnr/checkpoints/checkpoint-100
+[2026-10-01 23:55:47] [Rank 0] [INFO] [flow_factory.models.abc]: Checkpoint saved successfully to /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/runs/klein4b_nft_vedai_full_ssim_dists_psnr/saves/klein4b_nft_vedai_full_ssim_dists_psnr/checkpoints/checkpoint-100
+
Eval/vedai_rl_full:   0%|          | 0/8 [00:00<?, ?it/s]
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Eval/vedai_rl_full: 100%|██████████| 8/8 [00:43<00:00,  5.38s/it]
+
Pointwise Rewards: ssim:   0%|          | 0/1 [00:00<?, ?it/s]
Pointwise Rewards: ssim: 100%|██████████| 1/1 [00:00<00:00,  8.80it/s]
Pointwise Rewards: ssim: 100%|██████████| 1/1 [00:00<00:00,  8.76it/s]
+
Pointwise Rewards: psnr:   0%|          | 0/1 [00:00<?, ?it/s]
Pointwise Rewards: psnr: 100%|██████████| 1/1 [00:00<00:00, 13.74it/s]
+
Pointwise Rewards: dists:   0%|          | 0/1 [00:00<?, ?it/s]
Pointwise Rewards: dists: 100%|██████████| 1/1 [00:00<00:00,  9.05it/s]
Pointwise Rewards: dists: 100%|██████████| 1/1 [00:00<00:00,  9.00it/s]
+[2026-10-01 23:56:31] [Rank 0] [INFO] [flow_factory.trainers.abc]: [Step 0100 | Epoch 100] eval/vedai_rl_full/reward_dists_mean=0.9294 eval/vedai_rl_full/reward_dists_std=0.0164 eval/vedai_rl_full/reward_psnr_mean=29.2820 eval/vedai_rl_full/reward_psnr_std=2.4443 eval/vedai_rl_full/reward_ssim_mean=0.9227 eval/vedai_rl_full/reward_ssim_std=0.0254
+
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Epoch 100 Sampling:  32%|███▏      | 31/96 [00:47<01:38,  1.51s/it]W1001 23:57:20.854000 2490833 site-packages/torch/distributed/elastic/agent/server/api.py:739] Received Signals.SIGTERM death signal, shutting down workers
+W1001 23:57:20.894000 2490833 site-packages/torch/distributed/elastic/multiprocessing/api.py:1010] Sending process 2491050 closing signal SIGTERM
+W1001 23:57:20.895000 2490833 site-packages/torch/distributed/elastic/multiprocessing/api.py:1010] Sending process 2491051 closing signal SIGTERM
+W1001 23:57:20.895000 2490833 site-packages/torch/distributed/elastic/multiprocessing/api.py:1010] Sending process 2491052 closing signal SIGTERM
+W1001 23:57:20.896000 2490833 site-packages/torch/distributed/elastic/multiprocessing/api.py:1010] Sending process 2491053 closing signal SIGTERM
+W1001 23:57:20.896000 2490833 site-packages/torch/distributed/elastic/multiprocessing/api.py:1010] Sending process 2491054 closing signal SIGTERM
+W1001 23:57:20.897000 2490833 site-packages/torch/distributed/elastic/multiprocessing/api.py:1010] Sending process 2491055 closing signal SIGTERM
+W1001 23:57:20.897000 2490833 site-packages/torch/distributed/elastic/multiprocessing/api.py:1010] Sending process 2491056 closing signal SIGTERM
+W1001 23:57:20.897000 2490833 site-packages/torch/distributed/elastic/multiprocessing/api.py:1010] Sending process 2491057 closing signal SIGTERM
+Traceback (most recent call last):
+  File "/opt/conda/envs/ff-qi21/bin/accelerate", line 6, in <module>
+    sys.exit(main())
+  File "/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/accelerate/commands/accelerate_cli.py", line 50, in main
+    args.func(args)
+  File "/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/accelerate/commands/launch.py", line 1392, in launch_command
+    deepspeed_launcher(args)
+  File "/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/accelerate/commands/launch.py", line 1076, in deepspeed_launcher
+    distrib_run.run(args)
+  File "/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torch/distributed/run.py", line 982, in run
+    elastic_launch(
+  File "/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torch/distributed/launcher/api.py", line 170, in __call__
+    return launch_agent(self._config, self._entrypoint, list(args))
+  File "/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torch/distributed/launcher/api.py", line 308, in launch_agent
+    result = agent.run()
+  File "/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torch/distributed/elastic/metrics/api.py", line 134, in wrapper
+    result = f(*args, **kwargs)
+  File "/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torch/distributed/elastic/agent/server/api.py", line 731, in run
+    result = self._invoke_run(role)
+  File "/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torch/distributed/elastic/agent/server/api.py", line 908, in _invoke_run
+    time.sleep(monitor_interval)
+  File "/opt/conda/envs/ff-qi21/lib/python3.10/site-packages/torch/distributed/elastic/multiprocessing/api.py", line 86, in _terminate_process_handler
+    raise SignalException(f"Process {os.getpid()} got signal: {sigval}", sigval=sigval)
+torch.distributed.elastic.multiprocessing.api.SignalException: Process 2490833 got signal: 15
diff --git a/runs/klein4b_nft_vedai_full_ssim_dists_psnr_from_ep144/saves/klein4b_nft_vedai_full_ssim_dists_psnr_from_ep144/checkpoints/checkpoint-0/README.md b/runs/klein4b_nft_vedai_full_ssim_dists_psnr_from_ep144/saves/klein4b_nft_vedai_full_ssim_dists_psnr_from_ep144/checkpoints/checkpoint-0/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..2b78a7a995873d5db3090fca56885fe89cdf97e1
--- /dev/null
+++ b/runs/klein4b_nft_vedai_full_ssim_dists_psnr_from_ep144/saves/klein4b_nft_vedai_full_ssim_dists_psnr_from_ep144/checkpoints/checkpoint-0/README.md
@@ -0,0 +1,204 @@
+---
+base_model: /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/flow_factory_gpu_gate_models/FLUX.2-klein-base-4B/transformer
+library_name: peft
+tags:
+- lora
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]
+### Framework versions
+
+- PEFT 0.20.0
\ No newline at end of file
diff --git a/runs/klein4b_nft_vedai_full_ssim_dists_psnr_from_ep144/saves/klein4b_nft_vedai_full_ssim_dists_psnr_from_ep144/checkpoints/checkpoint-0/adapter_config.json b/runs/klein4b_nft_vedai_full_ssim_dists_psnr_from_ep144/saves/klein4b_nft_vedai_full_ssim_dists_psnr_from_ep144/checkpoints/checkpoint-0/adapter_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..a348ffb9f29d944341b08508746b10918bae6013
--- /dev/null
+++ b/runs/klein4b_nft_vedai_full_ssim_dists_psnr_from_ep144/saves/klein4b_nft_vedai_full_ssim_dists_psnr_from_ep144/checkpoints/checkpoint-0/adapter_config.json
@@ -0,0 +1,59 @@
+{
+  "alora_invocation_tokens": null,
+  "alpha_pattern": {},
+  "arrow_config": null,
+  "auto_mapping": {
+    "base_model_class": "Flux2Transformer2DModel",
+    "parent_library": "diffusers.models.transformers.transformer_flux2"
+  },
+  "base_model_name_or_path": null,
+  "bias": "none",
+  "corda_config": null,
+  "ensure_weight_tying": false,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": "gaussian",
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 128,
+  "lora_bias": false,
+  "lora_dropout": 0.0,
+  "lora_ga_config": null,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "monteclora_config": null,
+  "peft_type": "LORA",
+  "peft_version": "0.20.0",
+  "qalora_group_size": 16,
+  "r": 64,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "attn.add_k_proj",
+    "attn.to_qkv_mlp_proj",
+    "attn.add_v_proj",
+    "attn.to_out.0",
+    "ff.linear_out",
+    "ff_context.linear_in",
+    "ff_context.linear_out",
+    "attn.to_q",
+    "attn.to_add_out",
+    "attn.to_v",
+    "ff.linear_in",
+    "attn.to_k",
+    "attn.add_q_proj"
+  ],
+  "target_parameters": null,
+  "task_type": null,
+  "trainable_token_indices": null,
+  "use_bdlora": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false,
+  "velora_config": null
+}
\ No newline at end of file
diff --git a/runs/klein4b_nft_vedai_full_ssim_dists_psnr_from_ep144/saves/klein4b_nft_vedai_full_ssim_dists_psnr_from_ep144/checkpoints/checkpoint-0/manifest.json b/runs/klein4b_nft_vedai_full_ssim_dists_psnr_from_ep144/saves/klein4b_nft_vedai_full_ssim_dists_psnr_from_ep144/checkpoints/checkpoint-0/manifest.json
new file mode 100644
index 0000000000000000000000000000000000000000..4443fd995e0f6f3bd9b7e3dacea93df8d345ecfb
--- /dev/null
+++ b/runs/klein4b_nft_vedai_full_ssim_dists_psnr_from_ep144/saves/klein4b_nft_vedai_full_ssim_dists_psnr_from_ep144/checkpoints/checkpoint-0/manifest.json
@@ -0,0 +1,16 @@
+{
+  "base_role": "base",
+  "entries": [
+    {
+      "component": "transformer",
+      "path": ".",
+      "role": "base"
+    }
+  ],
+  "finetune_type": "lora",
+  "format_version": 1,
+  "primary": {
+    "component": "transformer",
+    "path": "."
+  }
+}
diff --git a/runs/klein4b_nft_vedai_full_ssim_dists_psnr_from_ep144/saves/klein4b_nft_vedai_full_ssim_dists_psnr_from_ep144/checkpoints/checkpoint-80/README.md b/runs/klein4b_nft_vedai_full_ssim_dists_psnr_from_ep144/saves/klein4b_nft_vedai_full_ssim_dists_psnr_from_ep144/checkpoints/checkpoint-80/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..2b78a7a995873d5db3090fca56885fe89cdf97e1
--- /dev/null
+++ b/runs/klein4b_nft_vedai_full_ssim_dists_psnr_from_ep144/saves/klein4b_nft_vedai_full_ssim_dists_psnr_from_ep144/checkpoints/checkpoint-80/README.md
@@ -0,0 +1,204 @@
+---
+base_model: /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/flow_factory_gpu_gate_models/FLUX.2-klein-base-4B/transformer
+library_name: peft
+tags:
+- lora
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]
+### Framework versions
+
+- PEFT 0.20.0
\ No newline at end of file
diff --git a/runs/klein4b_nft_vedai_full_ssim_dists_psnr_from_ep144/saves/klein4b_nft_vedai_full_ssim_dists_psnr_from_ep144/checkpoints/checkpoint-80/adapter_config.json b/runs/klein4b_nft_vedai_full_ssim_dists_psnr_from_ep144/saves/klein4b_nft_vedai_full_ssim_dists_psnr_from_ep144/checkpoints/checkpoint-80/adapter_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..a348ffb9f29d944341b08508746b10918bae6013
--- /dev/null
+++ b/runs/klein4b_nft_vedai_full_ssim_dists_psnr_from_ep144/saves/klein4b_nft_vedai_full_ssim_dists_psnr_from_ep144/checkpoints/checkpoint-80/adapter_config.json
@@ -0,0 +1,59 @@
+{
+  "alora_invocation_tokens": null,
+  "alpha_pattern": {},
+  "arrow_config": null,
+  "auto_mapping": {
+    "base_model_class": "Flux2Transformer2DModel",
+    "parent_library": "diffusers.models.transformers.transformer_flux2"
+  },
+  "base_model_name_or_path": null,
+  "bias": "none",
+  "corda_config": null,
+  "ensure_weight_tying": false,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": "gaussian",
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 128,
+  "lora_bias": false,
+  "lora_dropout": 0.0,
+  "lora_ga_config": null,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "monteclora_config": null,
+  "peft_type": "LORA",
+  "peft_version": "0.20.0",
+  "qalora_group_size": 16,
+  "r": 64,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "attn.add_k_proj",
+    "attn.to_qkv_mlp_proj",
+    "attn.add_v_proj",
+    "attn.to_out.0",
+    "ff.linear_out",
+    "ff_context.linear_in",
+    "ff_context.linear_out",
+    "attn.to_q",
+    "attn.to_add_out",
+    "attn.to_v",
+    "ff.linear_in",
+    "attn.to_k",
+    "attn.add_q_proj"
+  ],
+  "target_parameters": null,
+  "task_type": null,
+  "trainable_token_indices": null,
+  "use_bdlora": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false,
+  "velora_config": null
+}
\ No newline at end of file
diff --git a/runs/klein4b_nft_vedai_full_ssim_dists_psnr_from_ep144/saves/klein4b_nft_vedai_full_ssim_dists_psnr_from_ep144/checkpoints/checkpoint-80/manifest.json b/runs/klein4b_nft_vedai_full_ssim_dists_psnr_from_ep144/saves/klein4b_nft_vedai_full_ssim_dists_psnr_from_ep144/checkpoints/checkpoint-80/manifest.json
new file mode 100644
index 0000000000000000000000000000000000000000..4443fd995e0f6f3bd9b7e3dacea93df8d345ecfb
--- /dev/null
+++ b/runs/klein4b_nft_vedai_full_ssim_dists_psnr_from_ep144/saves/klein4b_nft_vedai_full_ssim_dists_psnr_from_ep144/checkpoints/checkpoint-80/manifest.json
@@ -0,0 +1,16 @@
+{
+  "base_role": "base",
+  "entries": [
+    {
+      "component": "transformer",
+      "path": ".",
+      "role": "base"
+    }
+  ],
+  "finetune_type": "lora",
+  "format_version": 1,
+  "primary": {
+    "component": "transformer",
+    "path": "."
+  }
+}
diff --git a/runs/klein4b_sft_flir_full_r64_lr1e-4/saves/klein4b_sft_flir_full_r64_lr1e-4/checkpoints/checkpoint-12/README.md b/runs/klein4b_sft_flir_full_r64_lr1e-4/saves/klein4b_sft_flir_full_r64_lr1e-4/checkpoints/checkpoint-12/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..2b78a7a995873d5db3090fca56885fe89cdf97e1
--- /dev/null
+++ b/runs/klein4b_sft_flir_full_r64_lr1e-4/saves/klein4b_sft_flir_full_r64_lr1e-4/checkpoints/checkpoint-12/README.md
@@ -0,0 +1,204 @@
+---
+base_model: /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/flow_factory_gpu_gate_models/FLUX.2-klein-base-4B/transformer
+library_name: peft
+tags:
+- lora
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]
+### Framework versions
+
+- PEFT 0.20.0
\ No newline at end of file
diff --git a/runs/klein4b_sft_flir_full_r64_lr1e-4/saves/klein4b_sft_flir_full_r64_lr1e-4/checkpoints/checkpoint-12/adapter_config.json b/runs/klein4b_sft_flir_full_r64_lr1e-4/saves/klein4b_sft_flir_full_r64_lr1e-4/checkpoints/checkpoint-12/adapter_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..65d87c3d2902e4931761f9764e2e78eb09d28dcc
--- /dev/null
+++ b/runs/klein4b_sft_flir_full_r64_lr1e-4/saves/klein4b_sft_flir_full_r64_lr1e-4/checkpoints/checkpoint-12/adapter_config.json
@@ -0,0 +1,59 @@
+{
+  "alora_invocation_tokens": null,
+  "alpha_pattern": {},
+  "arrow_config": null,
+  "auto_mapping": {
+    "base_model_class": "Flux2Transformer2DModel",
+    "parent_library": "diffusers.models.transformers.transformer_flux2"
+  },
+  "base_model_name_or_path": null,
+  "bias": "none",
+  "corda_config": null,
+  "ensure_weight_tying": false,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": "gaussian",
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 64,
+  "lora_bias": false,
+  "lora_dropout": 0.0,
+  "lora_ga_config": null,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "monteclora_config": null,
+  "peft_type": "LORA",
+  "peft_version": "0.20.0",
+  "qalora_group_size": 16,
+  "r": 64,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "ff.linear_out",
+    "attn.add_v_proj",
+    "attn.to_v",
+    "attn.to_q",
+    "attn.to_add_out",
+    "attn.add_k_proj",
+    "ff_context.linear_in",
+    "ff.linear_in",
+    "attn.add_q_proj",
+    "attn.to_out.0",
+    "attn.to_qkv_mlp_proj",
+    "ff_context.linear_out",
+    "attn.to_k"
+  ],
+  "target_parameters": null,
+  "task_type": null,
+  "trainable_token_indices": null,
+  "use_bdlora": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false,
+  "velora_config": null
+}
\ No newline at end of file
diff --git a/runs/klein4b_sft_flir_full_r64_lr1e-4/saves/klein4b_sft_flir_full_r64_lr1e-4/checkpoints/checkpoint-12/manifest.json b/runs/klein4b_sft_flir_full_r64_lr1e-4/saves/klein4b_sft_flir_full_r64_lr1e-4/checkpoints/checkpoint-12/manifest.json
new file mode 100644
index 0000000000000000000000000000000000000000..4443fd995e0f6f3bd9b7e3dacea93df8d345ecfb
--- /dev/null
+++ b/runs/klein4b_sft_flir_full_r64_lr1e-4/saves/klein4b_sft_flir_full_r64_lr1e-4/checkpoints/checkpoint-12/manifest.json
@@ -0,0 +1,16 @@
+{
+  "base_role": "base",
+  "entries": [
+    {
+      "component": "transformer",
+      "path": ".",
+      "role": "base"
+    }
+  ],
+  "finetune_type": "lora",
+  "format_version": 1,
+  "primary": {
+    "component": "transformer",
+    "path": "."
+  }
+}
diff --git a/runs/klein4b_sft_flir_full_r64a128_lr1e-4/saves/klein4b_sft_flir_full_r64a128_lr1e-4/checkpoints/checkpoint-12/README.md b/runs/klein4b_sft_flir_full_r64a128_lr1e-4/saves/klein4b_sft_flir_full_r64a128_lr1e-4/checkpoints/checkpoint-12/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..2b78a7a995873d5db3090fca56885fe89cdf97e1
--- /dev/null
+++ b/runs/klein4b_sft_flir_full_r64a128_lr1e-4/saves/klein4b_sft_flir_full_r64a128_lr1e-4/checkpoints/checkpoint-12/README.md
@@ -0,0 +1,204 @@
+---
+base_model: /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/flow_factory_gpu_gate_models/FLUX.2-klein-base-4B/transformer
+library_name: peft
+tags:
+- lora
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]
+### Framework versions
+
+- PEFT 0.20.0
\ No newline at end of file
diff --git a/runs/klein4b_sft_flir_full_r64a128_lr1e-4/saves/klein4b_sft_flir_full_r64a128_lr1e-4/checkpoints/checkpoint-12/adapter_config.json b/runs/klein4b_sft_flir_full_r64a128_lr1e-4/saves/klein4b_sft_flir_full_r64a128_lr1e-4/checkpoints/checkpoint-12/adapter_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..30077bbef5b4c16035fac290d0ca57f8d8a47900
--- /dev/null
+++ b/runs/klein4b_sft_flir_full_r64a128_lr1e-4/saves/klein4b_sft_flir_full_r64a128_lr1e-4/checkpoints/checkpoint-12/adapter_config.json
@@ -0,0 +1,59 @@
+{
+  "alora_invocation_tokens": null,
+  "alpha_pattern": {},
+  "arrow_config": null,
+  "auto_mapping": {
+    "base_model_class": "Flux2Transformer2DModel",
+    "parent_library": "diffusers.models.transformers.transformer_flux2"
+  },
+  "base_model_name_or_path": null,
+  "bias": "none",
+  "corda_config": null,
+  "ensure_weight_tying": false,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": "gaussian",
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 128,
+  "lora_bias": false,
+  "lora_dropout": 0.0,
+  "lora_ga_config": null,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "monteclora_config": null,
+  "peft_type": "LORA",
+  "peft_version": "0.20.0",
+  "qalora_group_size": 16,
+  "r": 64,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "attn.add_q_proj",
+    "attn.to_k",
+    "ff_context.linear_out",
+    "attn.add_k_proj",
+    "attn.to_out.0",
+    "attn.to_add_out",
+    "ff.linear_in",
+    "attn.to_v",
+    "ff.linear_out",
+    "attn.to_qkv_mlp_proj",
+    "attn.add_v_proj",
+    "ff_context.linear_in",
+    "attn.to_q"
+  ],
+  "target_parameters": null,
+  "task_type": null,
+  "trainable_token_indices": null,
+  "use_bdlora": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false,
+  "velora_config": null
+}
\ No newline at end of file
diff --git a/runs/klein4b_sft_flir_full_r64a128_lr1e-4/saves/klein4b_sft_flir_full_r64a128_lr1e-4/checkpoints/checkpoint-12/manifest.json b/runs/klein4b_sft_flir_full_r64a128_lr1e-4/saves/klein4b_sft_flir_full_r64a128_lr1e-4/checkpoints/checkpoint-12/manifest.json
new file mode 100644
index 0000000000000000000000000000000000000000..4443fd995e0f6f3bd9b7e3dacea93df8d345ecfb
--- /dev/null
+++ b/runs/klein4b_sft_flir_full_r64a128_lr1e-4/saves/klein4b_sft_flir_full_r64a128_lr1e-4/checkpoints/checkpoint-12/manifest.json
@@ -0,0 +1,16 @@
+{
+  "base_role": "base",
+  "entries": [
+    {
+      "component": "transformer",
+      "path": ".",
+      "role": "base"
+    }
+  ],
+  "finetune_type": "lora",
+  "format_version": 1,
+  "primary": {
+    "component": "transformer",
+    "path": "."
+  }
+}
diff --git a/runs/klein4b_sft_flir_full_r64a128_lr1e-4_gas1_ep144/saves/klein4b_sft_flir_full_r64a128_lr1e-4_gas1_ep144/checkpoints/checkpoint-144/README.md b/runs/klein4b_sft_flir_full_r64a128_lr1e-4_gas1_ep144/saves/klein4b_sft_flir_full_r64a128_lr1e-4_gas1_ep144/checkpoints/checkpoint-144/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..2b78a7a995873d5db3090fca56885fe89cdf97e1
--- /dev/null
+++ b/runs/klein4b_sft_flir_full_r64a128_lr1e-4_gas1_ep144/saves/klein4b_sft_flir_full_r64a128_lr1e-4_gas1_ep144/checkpoints/checkpoint-144/README.md
@@ -0,0 +1,204 @@
+---
+base_model: /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/flow_factory_gpu_gate_models/FLUX.2-klein-base-4B/transformer
+library_name: peft
+tags:
+- lora
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]
+### Framework versions
+
+- PEFT 0.20.0
\ No newline at end of file
diff --git a/runs/klein4b_sft_flir_full_r64a128_lr1e-4_gas1_ep144/saves/klein4b_sft_flir_full_r64a128_lr1e-4_gas1_ep144/checkpoints/checkpoint-144/adapter_config.json b/runs/klein4b_sft_flir_full_r64a128_lr1e-4_gas1_ep144/saves/klein4b_sft_flir_full_r64a128_lr1e-4_gas1_ep144/checkpoints/checkpoint-144/adapter_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..bf93a848ccac37e9a8694106e4fac3bf21edf514
--- /dev/null
+++ b/runs/klein4b_sft_flir_full_r64a128_lr1e-4_gas1_ep144/saves/klein4b_sft_flir_full_r64a128_lr1e-4_gas1_ep144/checkpoints/checkpoint-144/adapter_config.json
@@ -0,0 +1,59 @@
+{
+  "alora_invocation_tokens": null,
+  "alpha_pattern": {},
+  "arrow_config": null,
+  "auto_mapping": {
+    "base_model_class": "Flux2Transformer2DModel",
+    "parent_library": "diffusers.models.transformers.transformer_flux2"
+  },
+  "base_model_name_or_path": null,
+  "bias": "none",
+  "corda_config": null,
+  "ensure_weight_tying": false,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": "gaussian",
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 128,
+  "lora_bias": false,
+  "lora_dropout": 0.0,
+  "lora_ga_config": null,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "monteclora_config": null,
+  "peft_type": "LORA",
+  "peft_version": "0.20.0",
+  "qalora_group_size": 16,
+  "r": 64,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "attn.to_out.0",
+    "attn.add_v_proj",
+    "attn.add_k_proj",
+    "attn.to_v",
+    "ff.linear_out",
+    "ff_context.linear_out",
+    "attn.to_add_out",
+    "ff_context.linear_in",
+    "attn.to_q",
+    "attn.add_q_proj",
+    "attn.to_qkv_mlp_proj",
+    "ff.linear_in",
+    "attn.to_k"
+  ],
+  "target_parameters": null,
+  "task_type": null,
+  "trainable_token_indices": null,
+  "use_bdlora": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false,
+  "velora_config": null
+}
\ No newline at end of file
diff --git a/runs/klein4b_sft_flir_full_r64a128_lr1e-4_gas1_ep144/saves/klein4b_sft_flir_full_r64a128_lr1e-4_gas1_ep144/checkpoints/checkpoint-144/manifest.json b/runs/klein4b_sft_flir_full_r64a128_lr1e-4_gas1_ep144/saves/klein4b_sft_flir_full_r64a128_lr1e-4_gas1_ep144/checkpoints/checkpoint-144/manifest.json
new file mode 100644
index 0000000000000000000000000000000000000000..4443fd995e0f6f3bd9b7e3dacea93df8d345ecfb
--- /dev/null
+++ b/runs/klein4b_sft_flir_full_r64a128_lr1e-4_gas1_ep144/saves/klein4b_sft_flir_full_r64a128_lr1e-4_gas1_ep144/checkpoints/checkpoint-144/manifest.json
@@ -0,0 +1,16 @@
+{
+  "base_role": "base",
+  "entries": [
+    {
+      "component": "transformer",
+      "path": ".",
+      "role": "base"
+    }
+  ],
+  "finetune_type": "lora",
+  "format_version": 1,
+  "primary": {
+    "component": "transformer",
+    "path": "."
+  }
+}
diff --git a/runs/klein4b_sft_flir_full_r64a128_lr1e-4_gas1_ep36/saves/klein4b_sft_flir_full_r64a128_lr1e-4_gas1_ep36/checkpoints/checkpoint-36/README.md b/runs/klein4b_sft_flir_full_r64a128_lr1e-4_gas1_ep36/saves/klein4b_sft_flir_full_r64a128_lr1e-4_gas1_ep36/checkpoints/checkpoint-36/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..2b78a7a995873d5db3090fca56885fe89cdf97e1
--- /dev/null
+++ b/runs/klein4b_sft_flir_full_r64a128_lr1e-4_gas1_ep36/saves/klein4b_sft_flir_full_r64a128_lr1e-4_gas1_ep36/checkpoints/checkpoint-36/README.md
@@ -0,0 +1,204 @@
+---
+base_model: /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/flow_factory_gpu_gate_models/FLUX.2-klein-base-4B/transformer
+library_name: peft
+tags:
+- lora
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]
+### Framework versions
+
+- PEFT 0.20.0
\ No newline at end of file
diff --git a/runs/klein4b_sft_flir_full_r64a128_lr1e-4_gas1_ep36/saves/klein4b_sft_flir_full_r64a128_lr1e-4_gas1_ep36/checkpoints/checkpoint-36/adapter_config.json b/runs/klein4b_sft_flir_full_r64a128_lr1e-4_gas1_ep36/saves/klein4b_sft_flir_full_r64a128_lr1e-4_gas1_ep36/checkpoints/checkpoint-36/adapter_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..8690f20ad56909acb9cc5e2ad1201c9eac159ca6
--- /dev/null
+++ b/runs/klein4b_sft_flir_full_r64a128_lr1e-4_gas1_ep36/saves/klein4b_sft_flir_full_r64a128_lr1e-4_gas1_ep36/checkpoints/checkpoint-36/adapter_config.json
@@ -0,0 +1,59 @@
+{
+  "alora_invocation_tokens": null,
+  "alpha_pattern": {},
+  "arrow_config": null,
+  "auto_mapping": {
+    "base_model_class": "Flux2Transformer2DModel",
+    "parent_library": "diffusers.models.transformers.transformer_flux2"
+  },
+  "base_model_name_or_path": null,
+  "bias": "none",
+  "corda_config": null,
+  "ensure_weight_tying": false,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": "gaussian",
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 128,
+  "lora_bias": false,
+  "lora_dropout": 0.0,
+  "lora_ga_config": null,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "monteclora_config": null,
+  "peft_type": "LORA",
+  "peft_version": "0.20.0",
+  "qalora_group_size": 16,
+  "r": 64,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "ff_context.linear_out",
+    "attn.to_q",
+    "attn.add_k_proj",
+    "attn.to_add_out",
+    "attn.to_k",
+    "ff_context.linear_in",
+    "attn.to_v",
+    "attn.add_q_proj",
+    "ff.linear_out",
+    "attn.add_v_proj",
+    "attn.to_qkv_mlp_proj",
+    "ff.linear_in",
+    "attn.to_out.0"
+  ],
+  "target_parameters": null,
+  "task_type": null,
+  "trainable_token_indices": null,
+  "use_bdlora": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false,
+  "velora_config": null
+}
\ No newline at end of file
diff --git a/runs/klein4b_sft_flir_full_r64a128_lr1e-4_gas1_ep36/saves/klein4b_sft_flir_full_r64a128_lr1e-4_gas1_ep36/checkpoints/checkpoint-36/manifest.json b/runs/klein4b_sft_flir_full_r64a128_lr1e-4_gas1_ep36/saves/klein4b_sft_flir_full_r64a128_lr1e-4_gas1_ep36/checkpoints/checkpoint-36/manifest.json
new file mode 100644
index 0000000000000000000000000000000000000000..4443fd995e0f6f3bd9b7e3dacea93df8d345ecfb
--- /dev/null
+++ b/runs/klein4b_sft_flir_full_r64a128_lr1e-4_gas1_ep36/saves/klein4b_sft_flir_full_r64a128_lr1e-4_gas1_ep36/checkpoints/checkpoint-36/manifest.json
@@ -0,0 +1,16 @@
+{
+  "base_role": "base",
+  "entries": [
+    {
+      "component": "transformer",
+      "path": ".",
+      "role": "base"
+    }
+  ],
+  "finetune_type": "lora",
+  "format_version": 1,
+  "primary": {
+    "component": "transformer",
+    "path": "."
+  }
+}
diff --git a/runs/klein4b_sft_flir_full_r64a128_lr1e-4_gas1_ep48_n3584/saves/klein4b_sft_flir_full_r64a128_lr1e-4_gas1_ep48_n3584/checkpoints/checkpoint-48/README.md b/runs/klein4b_sft_flir_full_r64a128_lr1e-4_gas1_ep48_n3584/saves/klein4b_sft_flir_full_r64a128_lr1e-4_gas1_ep48_n3584/checkpoints/checkpoint-48/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..2b78a7a995873d5db3090fca56885fe89cdf97e1
--- /dev/null
+++ b/runs/klein4b_sft_flir_full_r64a128_lr1e-4_gas1_ep48_n3584/saves/klein4b_sft_flir_full_r64a128_lr1e-4_gas1_ep48_n3584/checkpoints/checkpoint-48/README.md
@@ -0,0 +1,204 @@
+---
+base_model: /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/flow_factory_gpu_gate_models/FLUX.2-klein-base-4B/transformer
+library_name: peft
+tags:
+- lora
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]
+### Framework versions
+
+- PEFT 0.20.0
\ No newline at end of file
diff --git a/runs/klein4b_sft_flir_full_r64a128_lr1e-4_gas1_ep48_n3584/saves/klein4b_sft_flir_full_r64a128_lr1e-4_gas1_ep48_n3584/checkpoints/checkpoint-48/adapter_config.json b/runs/klein4b_sft_flir_full_r64a128_lr1e-4_gas1_ep48_n3584/saves/klein4b_sft_flir_full_r64a128_lr1e-4_gas1_ep48_n3584/checkpoints/checkpoint-48/adapter_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..9909909921792083c33687cf01e86434601cf17c
--- /dev/null
+++ b/runs/klein4b_sft_flir_full_r64a128_lr1e-4_gas1_ep48_n3584/saves/klein4b_sft_flir_full_r64a128_lr1e-4_gas1_ep48_n3584/checkpoints/checkpoint-48/adapter_config.json
@@ -0,0 +1,59 @@
+{
+  "alora_invocation_tokens": null,
+  "alpha_pattern": {},
+  "arrow_config": null,
+  "auto_mapping": {
+    "base_model_class": "Flux2Transformer2DModel",
+    "parent_library": "diffusers.models.transformers.transformer_flux2"
+  },
+  "base_model_name_or_path": null,
+  "bias": "none",
+  "corda_config": null,
+  "ensure_weight_tying": false,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": "gaussian",
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 128,
+  "lora_bias": false,
+  "lora_dropout": 0.0,
+  "lora_ga_config": null,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "monteclora_config": null,
+  "peft_type": "LORA",
+  "peft_version": "0.20.0",
+  "qalora_group_size": 16,
+  "r": 64,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "attn.add_k_proj",
+    "attn.to_qkv_mlp_proj",
+    "attn.to_k",
+    "ff.linear_out",
+    "attn.add_q_proj",
+    "attn.to_v",
+    "ff_context.linear_out",
+    "attn.to_out.0",
+    "ff.linear_in",
+    "attn.to_q",
+    "ff_context.linear_in",
+    "attn.add_v_proj",
+    "attn.to_add_out"
+  ],
+  "target_parameters": null,
+  "task_type": null,
+  "trainable_token_indices": null,
+  "use_bdlora": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false,
+  "velora_config": null
+}
\ No newline at end of file
diff --git a/runs/klein4b_sft_flir_full_r64a128_lr1e-4_gas1_ep48_n3584/saves/klein4b_sft_flir_full_r64a128_lr1e-4_gas1_ep48_n3584/checkpoints/checkpoint-48/manifest.json b/runs/klein4b_sft_flir_full_r64a128_lr1e-4_gas1_ep48_n3584/saves/klein4b_sft_flir_full_r64a128_lr1e-4_gas1_ep48_n3584/checkpoints/checkpoint-48/manifest.json
new file mode 100644
index 0000000000000000000000000000000000000000..4443fd995e0f6f3bd9b7e3dacea93df8d345ecfb
--- /dev/null
+++ b/runs/klein4b_sft_flir_full_r64a128_lr1e-4_gas1_ep48_n3584/saves/klein4b_sft_flir_full_r64a128_lr1e-4_gas1_ep48_n3584/checkpoints/checkpoint-48/manifest.json
@@ -0,0 +1,16 @@
+{
+  "base_role": "base",
+  "entries": [
+    {
+      "component": "transformer",
+      "path": ".",
+      "role": "base"
+    }
+  ],
+  "finetune_type": "lora",
+  "format_version": 1,
+  "primary": {
+    "component": "transformer",
+    "path": "."
+  }
+}
diff --git a/runs/klein4b_sft_flir_full_r64a128_lr1e-4_gas1_ep72/saves/klein4b_sft_flir_full_r64a128_lr1e-4_gas1_ep72/checkpoints/checkpoint-72/README.md b/runs/klein4b_sft_flir_full_r64a128_lr1e-4_gas1_ep72/saves/klein4b_sft_flir_full_r64a128_lr1e-4_gas1_ep72/checkpoints/checkpoint-72/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..2b78a7a995873d5db3090fca56885fe89cdf97e1
--- /dev/null
+++ b/runs/klein4b_sft_flir_full_r64a128_lr1e-4_gas1_ep72/saves/klein4b_sft_flir_full_r64a128_lr1e-4_gas1_ep72/checkpoints/checkpoint-72/README.md
@@ -0,0 +1,204 @@
+---
+base_model: /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/flow_factory_gpu_gate_models/FLUX.2-klein-base-4B/transformer
+library_name: peft
+tags:
+- lora
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]
+### Framework versions
+
+- PEFT 0.20.0
\ No newline at end of file
diff --git a/runs/klein4b_sft_flir_full_r64a128_lr1e-4_gas1_ep72/saves/klein4b_sft_flir_full_r64a128_lr1e-4_gas1_ep72/checkpoints/checkpoint-72/adapter_config.json b/runs/klein4b_sft_flir_full_r64a128_lr1e-4_gas1_ep72/saves/klein4b_sft_flir_full_r64a128_lr1e-4_gas1_ep72/checkpoints/checkpoint-72/adapter_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..8f98119371456740b2401997f4b2cca91a91e96b
--- /dev/null
+++ b/runs/klein4b_sft_flir_full_r64a128_lr1e-4_gas1_ep72/saves/klein4b_sft_flir_full_r64a128_lr1e-4_gas1_ep72/checkpoints/checkpoint-72/adapter_config.json
@@ -0,0 +1,59 @@
+{
+  "alora_invocation_tokens": null,
+  "alpha_pattern": {},
+  "arrow_config": null,
+  "auto_mapping": {
+    "base_model_class": "Flux2Transformer2DModel",
+    "parent_library": "diffusers.models.transformers.transformer_flux2"
+  },
+  "base_model_name_or_path": null,
+  "bias": "none",
+  "corda_config": null,
+  "ensure_weight_tying": false,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": "gaussian",
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 128,
+  "lora_bias": false,
+  "lora_dropout": 0.0,
+  "lora_ga_config": null,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "monteclora_config": null,
+  "peft_type": "LORA",
+  "peft_version": "0.20.0",
+  "qalora_group_size": 16,
+  "r": 64,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "attn.add_k_proj",
+    "attn.to_v",
+    "attn.add_v_proj",
+    "attn.add_q_proj",
+    "attn.to_qkv_mlp_proj",
+    "ff_context.linear_out",
+    "ff.linear_in",
+    "attn.to_add_out",
+    "attn.to_out.0",
+    "attn.to_q",
+    "ff.linear_out",
+    "attn.to_k",
+    "ff_context.linear_in"
+  ],
+  "target_parameters": null,
+  "task_type": null,
+  "trainable_token_indices": null,
+  "use_bdlora": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false,
+  "velora_config": null
+}
\ No newline at end of file
diff --git a/runs/klein4b_sft_flir_full_r64a128_lr1e-4_gas1_ep72/saves/klein4b_sft_flir_full_r64a128_lr1e-4_gas1_ep72/checkpoints/checkpoint-72/manifest.json b/runs/klein4b_sft_flir_full_r64a128_lr1e-4_gas1_ep72/saves/klein4b_sft_flir_full_r64a128_lr1e-4_gas1_ep72/checkpoints/checkpoint-72/manifest.json
new file mode 100644
index 0000000000000000000000000000000000000000..4443fd995e0f6f3bd9b7e3dacea93df8d345ecfb
--- /dev/null
+++ b/runs/klein4b_sft_flir_full_r64a128_lr1e-4_gas1_ep72/saves/klein4b_sft_flir_full_r64a128_lr1e-4_gas1_ep72/checkpoints/checkpoint-72/manifest.json
@@ -0,0 +1,16 @@
+{
+  "base_role": "base",
+  "entries": [
+    {
+      "component": "transformer",
+      "path": ".",
+      "role": "base"
+    }
+  ],
+  "finetune_type": "lora",
+  "format_version": 1,
+  "primary": {
+    "component": "transformer",
+    "path": "."
+  }
+}
diff --git a/runs/klein4b_sft_flir_full_r64a128_lr1e-4_gas4_ep48_n3584/saves/klein4b_sft_flir_full_r64a128_lr1e-4_gas4_ep48_n3584/checkpoints/checkpoint-48/README.md b/runs/klein4b_sft_flir_full_r64a128_lr1e-4_gas4_ep48_n3584/saves/klein4b_sft_flir_full_r64a128_lr1e-4_gas4_ep48_n3584/checkpoints/checkpoint-48/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..2b78a7a995873d5db3090fca56885fe89cdf97e1
--- /dev/null
+++ b/runs/klein4b_sft_flir_full_r64a128_lr1e-4_gas4_ep48_n3584/saves/klein4b_sft_flir_full_r64a128_lr1e-4_gas4_ep48_n3584/checkpoints/checkpoint-48/README.md
@@ -0,0 +1,204 @@
+---
+base_model: /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/flow_factory_gpu_gate_models/FLUX.2-klein-base-4B/transformer
+library_name: peft
+tags:
+- lora
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]
+### Framework versions
+
+- PEFT 0.20.0
\ No newline at end of file
diff --git a/runs/klein4b_sft_flir_full_r64a128_lr1e-4_gas4_ep48_n3584/saves/klein4b_sft_flir_full_r64a128_lr1e-4_gas4_ep48_n3584/checkpoints/checkpoint-48/adapter_config.json b/runs/klein4b_sft_flir_full_r64a128_lr1e-4_gas4_ep48_n3584/saves/klein4b_sft_flir_full_r64a128_lr1e-4_gas4_ep48_n3584/checkpoints/checkpoint-48/adapter_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..88a12e925306bf8f5daa68cbd330a2b64d7a41e6
--- /dev/null
+++ b/runs/klein4b_sft_flir_full_r64a128_lr1e-4_gas4_ep48_n3584/saves/klein4b_sft_flir_full_r64a128_lr1e-4_gas4_ep48_n3584/checkpoints/checkpoint-48/adapter_config.json
@@ -0,0 +1,59 @@
+{
+  "alora_invocation_tokens": null,
+  "alpha_pattern": {},
+  "arrow_config": null,
+  "auto_mapping": {
+    "base_model_class": "Flux2Transformer2DModel",
+    "parent_library": "diffusers.models.transformers.transformer_flux2"
+  },
+  "base_model_name_or_path": null,
+  "bias": "none",
+  "corda_config": null,
+  "ensure_weight_tying": false,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": "gaussian",
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 128,
+  "lora_bias": false,
+  "lora_dropout": 0.0,
+  "lora_ga_config": null,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "monteclora_config": null,
+  "peft_type": "LORA",
+  "peft_version": "0.20.0",
+  "qalora_group_size": 16,
+  "r": 64,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "attn.add_q_proj",
+    "attn.add_v_proj",
+    "ff_context.linear_in",
+    "attn.to_qkv_mlp_proj",
+    "attn.to_k",
+    "attn.to_out.0",
+    "ff.linear_in",
+    "attn.to_v",
+    "ff.linear_out",
+    "attn.to_q",
+    "attn.to_add_out",
+    "ff_context.linear_out",
+    "attn.add_k_proj"
+  ],
+  "target_parameters": null,
+  "task_type": null,
+  "trainable_token_indices": null,
+  "use_bdlora": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false,
+  "velora_config": null
+}
\ No newline at end of file
diff --git a/runs/klein4b_sft_flir_full_r64a128_lr1e-4_gas4_ep48_n3584/saves/klein4b_sft_flir_full_r64a128_lr1e-4_gas4_ep48_n3584/checkpoints/checkpoint-48/manifest.json b/runs/klein4b_sft_flir_full_r64a128_lr1e-4_gas4_ep48_n3584/saves/klein4b_sft_flir_full_r64a128_lr1e-4_gas4_ep48_n3584/checkpoints/checkpoint-48/manifest.json
new file mode 100644
index 0000000000000000000000000000000000000000..4443fd995e0f6f3bd9b7e3dacea93df8d345ecfb
--- /dev/null
+++ b/runs/klein4b_sft_flir_full_r64a128_lr1e-4_gas4_ep48_n3584/saves/klein4b_sft_flir_full_r64a128_lr1e-4_gas4_ep48_n3584/checkpoints/checkpoint-48/manifest.json
@@ -0,0 +1,16 @@
+{
+  "base_role": "base",
+  "entries": [
+    {
+      "component": "transformer",
+      "path": ".",
+      "role": "base"
+    }
+  ],
+  "finetune_type": "lora",
+  "format_version": 1,
+  "primary": {
+    "component": "transformer",
+    "path": "."
+  }
+}
diff --git a/runs/klein4b_sft_flir_full_r64a128_lr1e-4_gas8_ep48_n3584/saves/klein4b_sft_flir_full_r64a128_lr1e-4_gas8_ep48_n3584/checkpoints/checkpoint-48/README.md b/runs/klein4b_sft_flir_full_r64a128_lr1e-4_gas8_ep48_n3584/saves/klein4b_sft_flir_full_r64a128_lr1e-4_gas8_ep48_n3584/checkpoints/checkpoint-48/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..2b78a7a995873d5db3090fca56885fe89cdf97e1
--- /dev/null
+++ b/runs/klein4b_sft_flir_full_r64a128_lr1e-4_gas8_ep48_n3584/saves/klein4b_sft_flir_full_r64a128_lr1e-4_gas8_ep48_n3584/checkpoints/checkpoint-48/README.md
@@ -0,0 +1,204 @@
+---
+base_model: /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/flow_factory_gpu_gate_models/FLUX.2-klein-base-4B/transformer
+library_name: peft
+tags:
+- lora
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]
+### Framework versions
+
+- PEFT 0.20.0
\ No newline at end of file
diff --git a/runs/klein4b_sft_flir_full_r64a128_lr1e-4_gas8_ep48_n3584/saves/klein4b_sft_flir_full_r64a128_lr1e-4_gas8_ep48_n3584/checkpoints/checkpoint-48/adapter_config.json b/runs/klein4b_sft_flir_full_r64a128_lr1e-4_gas8_ep48_n3584/saves/klein4b_sft_flir_full_r64a128_lr1e-4_gas8_ep48_n3584/checkpoints/checkpoint-48/adapter_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..a0214d61e61f59eccd36e7e36e30c99dcf55e949
--- /dev/null
+++ b/runs/klein4b_sft_flir_full_r64a128_lr1e-4_gas8_ep48_n3584/saves/klein4b_sft_flir_full_r64a128_lr1e-4_gas8_ep48_n3584/checkpoints/checkpoint-48/adapter_config.json
@@ -0,0 +1,59 @@
+{
+  "alora_invocation_tokens": null,
+  "alpha_pattern": {},
+  "arrow_config": null,
+  "auto_mapping": {
+    "base_model_class": "Flux2Transformer2DModel",
+    "parent_library": "diffusers.models.transformers.transformer_flux2"
+  },
+  "base_model_name_or_path": null,
+  "bias": "none",
+  "corda_config": null,
+  "ensure_weight_tying": false,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": "gaussian",
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 128,
+  "lora_bias": false,
+  "lora_dropout": 0.0,
+  "lora_ga_config": null,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "monteclora_config": null,
+  "peft_type": "LORA",
+  "peft_version": "0.20.0",
+  "qalora_group_size": 16,
+  "r": 64,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "attn.add_v_proj",
+    "ff.linear_in",
+    "attn.to_k",
+    "attn.to_q",
+    "ff.linear_out",
+    "attn.add_k_proj",
+    "attn.to_add_out",
+    "attn.add_q_proj",
+    "attn.to_out.0",
+    "attn.to_qkv_mlp_proj",
+    "ff_context.linear_out",
+    "attn.to_v",
+    "ff_context.linear_in"
+  ],
+  "target_parameters": null,
+  "task_type": null,
+  "trainable_token_indices": null,
+  "use_bdlora": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false,
+  "velora_config": null
+}
\ No newline at end of file
diff --git a/runs/klein4b_sft_flir_full_r64a128_lr1e-4_gas8_ep48_n3584/saves/klein4b_sft_flir_full_r64a128_lr1e-4_gas8_ep48_n3584/checkpoints/checkpoint-48/manifest.json b/runs/klein4b_sft_flir_full_r64a128_lr1e-4_gas8_ep48_n3584/saves/klein4b_sft_flir_full_r64a128_lr1e-4_gas8_ep48_n3584/checkpoints/checkpoint-48/manifest.json
new file mode 100644
index 0000000000000000000000000000000000000000..4443fd995e0f6f3bd9b7e3dacea93df8d345ecfb
--- /dev/null
+++ b/runs/klein4b_sft_flir_full_r64a128_lr1e-4_gas8_ep48_n3584/saves/klein4b_sft_flir_full_r64a128_lr1e-4_gas8_ep48_n3584/checkpoints/checkpoint-48/manifest.json
@@ -0,0 +1,16 @@
+{
+  "base_role": "base",
+  "entries": [
+    {
+      "component": "transformer",
+      "path": ".",
+      "role": "base"
+    }
+  ],
+  "finetune_type": "lora",
+  "format_version": 1,
+  "primary": {
+    "component": "transformer",
+    "path": "."
+  }
+}
diff --git a/runs/klein4b_sft_flir_sel_r64a128_lr1e-4/saves/klein4b_sft_flir_sel_r64a128_lr1e-4/checkpoints/checkpoint-120/README.md b/runs/klein4b_sft_flir_sel_r64a128_lr1e-4/saves/klein4b_sft_flir_sel_r64a128_lr1e-4/checkpoints/checkpoint-120/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..2b78a7a995873d5db3090fca56885fe89cdf97e1
--- /dev/null
+++ b/runs/klein4b_sft_flir_sel_r64a128_lr1e-4/saves/klein4b_sft_flir_sel_r64a128_lr1e-4/checkpoints/checkpoint-120/README.md
@@ -0,0 +1,204 @@
+---
+base_model: /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/flow_factory_gpu_gate_models/FLUX.2-klein-base-4B/transformer
+library_name: peft
+tags:
+- lora
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]
+### Framework versions
+
+- PEFT 0.20.0
\ No newline at end of file
diff --git a/runs/klein4b_sft_flir_sel_r64a128_lr1e-4/saves/klein4b_sft_flir_sel_r64a128_lr1e-4/checkpoints/checkpoint-120/adapter_config.json b/runs/klein4b_sft_flir_sel_r64a128_lr1e-4/saves/klein4b_sft_flir_sel_r64a128_lr1e-4/checkpoints/checkpoint-120/adapter_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..6ea53abc535184a7f8c2fabf8c4256ab35d28243
--- /dev/null
+++ b/runs/klein4b_sft_flir_sel_r64a128_lr1e-4/saves/klein4b_sft_flir_sel_r64a128_lr1e-4/checkpoints/checkpoint-120/adapter_config.json
@@ -0,0 +1,59 @@
+{
+  "alora_invocation_tokens": null,
+  "alpha_pattern": {},
+  "arrow_config": null,
+  "auto_mapping": {
+    "base_model_class": "Flux2Transformer2DModel",
+    "parent_library": "diffusers.models.transformers.transformer_flux2"
+  },
+  "base_model_name_or_path": null,
+  "bias": "none",
+  "corda_config": null,
+  "ensure_weight_tying": false,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": "gaussian",
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 128,
+  "lora_bias": false,
+  "lora_dropout": 0.0,
+  "lora_ga_config": null,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "monteclora_config": null,
+  "peft_type": "LORA",
+  "peft_version": "0.20.0",
+  "qalora_group_size": 16,
+  "r": 64,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "attn.to_k",
+    "ff_context.linear_in",
+    "ff.linear_out",
+    "ff.linear_in",
+    "ff_context.linear_out",
+    "attn.add_v_proj",
+    "attn.to_out.0",
+    "attn.add_q_proj",
+    "attn.to_qkv_mlp_proj",
+    "attn.to_q",
+    "attn.add_k_proj",
+    "attn.to_v",
+    "attn.to_add_out"
+  ],
+  "target_parameters": null,
+  "task_type": null,
+  "trainable_token_indices": null,
+  "use_bdlora": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false,
+  "velora_config": null
+}
\ No newline at end of file
diff --git a/runs/klein4b_sft_flir_sel_r64a128_lr1e-4/saves/klein4b_sft_flir_sel_r64a128_lr1e-4/checkpoints/checkpoint-120/manifest.json b/runs/klein4b_sft_flir_sel_r64a128_lr1e-4/saves/klein4b_sft_flir_sel_r64a128_lr1e-4/checkpoints/checkpoint-120/manifest.json
new file mode 100644
index 0000000000000000000000000000000000000000..4443fd995e0f6f3bd9b7e3dacea93df8d345ecfb
--- /dev/null
+++ b/runs/klein4b_sft_flir_sel_r64a128_lr1e-4/saves/klein4b_sft_flir_sel_r64a128_lr1e-4/checkpoints/checkpoint-120/manifest.json
@@ -0,0 +1,16 @@
+{
+  "base_role": "base",
+  "entries": [
+    {
+      "component": "transformer",
+      "path": ".",
+      "role": "base"
+    }
+  ],
+  "finetune_type": "lora",
+  "format_version": 1,
+  "primary": {
+    "component": "transformer",
+    "path": "."
+  }
+}
diff --git a/runs/klein4b_sft_flir_sel_r64a128_lr1e-4/saves/klein4b_sft_flir_sel_r64a128_lr1e-4/checkpoints/checkpoint-40/README.md b/runs/klein4b_sft_flir_sel_r64a128_lr1e-4/saves/klein4b_sft_flir_sel_r64a128_lr1e-4/checkpoints/checkpoint-40/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..2b78a7a995873d5db3090fca56885fe89cdf97e1
--- /dev/null
+++ b/runs/klein4b_sft_flir_sel_r64a128_lr1e-4/saves/klein4b_sft_flir_sel_r64a128_lr1e-4/checkpoints/checkpoint-40/README.md
@@ -0,0 +1,204 @@
+---
+base_model: /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/flow_factory_gpu_gate_models/FLUX.2-klein-base-4B/transformer
+library_name: peft
+tags:
+- lora
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]
+### Framework versions
+
+- PEFT 0.20.0
\ No newline at end of file
diff --git a/runs/klein4b_sft_flir_sel_r64a128_lr1e-4/saves/klein4b_sft_flir_sel_r64a128_lr1e-4/checkpoints/checkpoint-40/adapter_config.json b/runs/klein4b_sft_flir_sel_r64a128_lr1e-4/saves/klein4b_sft_flir_sel_r64a128_lr1e-4/checkpoints/checkpoint-40/adapter_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..6ea53abc535184a7f8c2fabf8c4256ab35d28243
--- /dev/null
+++ b/runs/klein4b_sft_flir_sel_r64a128_lr1e-4/saves/klein4b_sft_flir_sel_r64a128_lr1e-4/checkpoints/checkpoint-40/adapter_config.json
@@ -0,0 +1,59 @@
+{
+  "alora_invocation_tokens": null,
+  "alpha_pattern": {},
+  "arrow_config": null,
+  "auto_mapping": {
+    "base_model_class": "Flux2Transformer2DModel",
+    "parent_library": "diffusers.models.transformers.transformer_flux2"
+  },
+  "base_model_name_or_path": null,
+  "bias": "none",
+  "corda_config": null,
+  "ensure_weight_tying": false,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": "gaussian",
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 128,
+  "lora_bias": false,
+  "lora_dropout": 0.0,
+  "lora_ga_config": null,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "monteclora_config": null,
+  "peft_type": "LORA",
+  "peft_version": "0.20.0",
+  "qalora_group_size": 16,
+  "r": 64,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "attn.to_k",
+    "ff_context.linear_in",
+    "ff.linear_out",
+    "ff.linear_in",
+    "ff_context.linear_out",
+    "attn.add_v_proj",
+    "attn.to_out.0",
+    "attn.add_q_proj",
+    "attn.to_qkv_mlp_proj",
+    "attn.to_q",
+    "attn.add_k_proj",
+    "attn.to_v",
+    "attn.to_add_out"
+  ],
+  "target_parameters": null,
+  "task_type": null,
+  "trainable_token_indices": null,
+  "use_bdlora": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false,
+  "velora_config": null
+}
\ No newline at end of file
diff --git a/runs/klein4b_sft_flir_sel_r64a128_lr1e-4/saves/klein4b_sft_flir_sel_r64a128_lr1e-4/checkpoints/checkpoint-40/manifest.json b/runs/klein4b_sft_flir_sel_r64a128_lr1e-4/saves/klein4b_sft_flir_sel_r64a128_lr1e-4/checkpoints/checkpoint-40/manifest.json
new file mode 100644
index 0000000000000000000000000000000000000000..4443fd995e0f6f3bd9b7e3dacea93df8d345ecfb
--- /dev/null
+++ b/runs/klein4b_sft_flir_sel_r64a128_lr1e-4/saves/klein4b_sft_flir_sel_r64a128_lr1e-4/checkpoints/checkpoint-40/manifest.json
@@ -0,0 +1,16 @@
+{
+  "base_role": "base",
+  "entries": [
+    {
+      "component": "transformer",
+      "path": ".",
+      "role": "base"
+    }
+  ],
+  "finetune_type": "lora",
+  "format_version": 1,
+  "primary": {
+    "component": "transformer",
+    "path": "."
+  }
+}
diff --git a/runs/klein4b_sft_flir_sel_r64a128_lr1e-4_bs1_gas4_ep240/saves/klein4b_sft_flir_sel_r64a128_lr1e-4_bs1_gas4_ep240/checkpoints/checkpoint-80/README.md b/runs/klein4b_sft_flir_sel_r64a128_lr1e-4_bs1_gas4_ep240/saves/klein4b_sft_flir_sel_r64a128_lr1e-4_bs1_gas4_ep240/checkpoints/checkpoint-80/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..2b78a7a995873d5db3090fca56885fe89cdf97e1
--- /dev/null
+++ b/runs/klein4b_sft_flir_sel_r64a128_lr1e-4_bs1_gas4_ep240/saves/klein4b_sft_flir_sel_r64a128_lr1e-4_bs1_gas4_ep240/checkpoints/checkpoint-80/README.md
@@ -0,0 +1,204 @@
+---
+base_model: /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/flow_factory_gpu_gate_models/FLUX.2-klein-base-4B/transformer
+library_name: peft
+tags:
+- lora
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]
+### Framework versions
+
+- PEFT 0.20.0
\ No newline at end of file
diff --git a/runs/klein4b_sft_flir_sel_r64a128_lr1e-4_bs1_gas4_ep240/saves/klein4b_sft_flir_sel_r64a128_lr1e-4_bs1_gas4_ep240/checkpoints/checkpoint-80/adapter_config.json b/runs/klein4b_sft_flir_sel_r64a128_lr1e-4_bs1_gas4_ep240/saves/klein4b_sft_flir_sel_r64a128_lr1e-4_bs1_gas4_ep240/checkpoints/checkpoint-80/adapter_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..00a43d1358d299f89dda73cfc7eeeef5e63d1048
--- /dev/null
+++ b/runs/klein4b_sft_flir_sel_r64a128_lr1e-4_bs1_gas4_ep240/saves/klein4b_sft_flir_sel_r64a128_lr1e-4_bs1_gas4_ep240/checkpoints/checkpoint-80/adapter_config.json
@@ -0,0 +1,59 @@
+{
+  "alora_invocation_tokens": null,
+  "alpha_pattern": {},
+  "arrow_config": null,
+  "auto_mapping": {
+    "base_model_class": "Flux2Transformer2DModel",
+    "parent_library": "diffusers.models.transformers.transformer_flux2"
+  },
+  "base_model_name_or_path": null,
+  "bias": "none",
+  "corda_config": null,
+  "ensure_weight_tying": false,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": "gaussian",
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 128,
+  "lora_bias": false,
+  "lora_dropout": 0.0,
+  "lora_ga_config": null,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "monteclora_config": null,
+  "peft_type": "LORA",
+  "peft_version": "0.20.0",
+  "qalora_group_size": 16,
+  "r": 64,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "attn.to_v",
+    "ff_context.linear_in",
+    "attn.add_k_proj",
+    "attn.to_out.0",
+    "attn.to_k",
+    "attn.to_add_out",
+    "ff_context.linear_out",
+    "attn.to_q",
+    "ff.linear_out",
+    "ff.linear_in",
+    "attn.add_q_proj",
+    "attn.add_v_proj",
+    "attn.to_qkv_mlp_proj"
+  ],
+  "target_parameters": null,
+  "task_type": null,
+  "trainable_token_indices": null,
+  "use_bdlora": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false,
+  "velora_config": null
+}
\ No newline at end of file
diff --git a/runs/klein4b_sft_flir_sel_r64a128_lr1e-4_bs1_gas4_ep240/saves/klein4b_sft_flir_sel_r64a128_lr1e-4_bs1_gas4_ep240/checkpoints/checkpoint-80/manifest.json b/runs/klein4b_sft_flir_sel_r64a128_lr1e-4_bs1_gas4_ep240/saves/klein4b_sft_flir_sel_r64a128_lr1e-4_bs1_gas4_ep240/checkpoints/checkpoint-80/manifest.json
new file mode 100644
index 0000000000000000000000000000000000000000..4443fd995e0f6f3bd9b7e3dacea93df8d345ecfb
--- /dev/null
+++ b/runs/klein4b_sft_flir_sel_r64a128_lr1e-4_bs1_gas4_ep240/saves/klein4b_sft_flir_sel_r64a128_lr1e-4_bs1_gas4_ep240/checkpoints/checkpoint-80/manifest.json
@@ -0,0 +1,16 @@
+{
+  "base_role": "base",
+  "entries": [
+    {
+      "component": "transformer",
+      "path": ".",
+      "role": "base"
+    }
+  ],
+  "finetune_type": "lora",
+  "format_version": 1,
+  "primary": {
+    "component": "transformer",
+    "path": "."
+  }
+}
diff --git a/runs/klein4b_sft_flir_sel_r64a128_lr1e-4_gas1_ep360/saves/klein4b_sft_flir_sel_r64a128_lr1e-4_gas1_ep360/checkpoints/checkpoint-360/README.md b/runs/klein4b_sft_flir_sel_r64a128_lr1e-4_gas1_ep360/saves/klein4b_sft_flir_sel_r64a128_lr1e-4_gas1_ep360/checkpoints/checkpoint-360/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..2b78a7a995873d5db3090fca56885fe89cdf97e1
--- /dev/null
+++ b/runs/klein4b_sft_flir_sel_r64a128_lr1e-4_gas1_ep360/saves/klein4b_sft_flir_sel_r64a128_lr1e-4_gas1_ep360/checkpoints/checkpoint-360/README.md
@@ -0,0 +1,204 @@
+---
+base_model: /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/flow_factory_gpu_gate_models/FLUX.2-klein-base-4B/transformer
+library_name: peft
+tags:
+- lora
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]
+### Framework versions
+
+- PEFT 0.20.0
\ No newline at end of file
diff --git a/runs/klein4b_sft_flir_sel_r64a128_lr1e-4_gas1_ep360/saves/klein4b_sft_flir_sel_r64a128_lr1e-4_gas1_ep360/checkpoints/checkpoint-360/adapter_config.json b/runs/klein4b_sft_flir_sel_r64a128_lr1e-4_gas1_ep360/saves/klein4b_sft_flir_sel_r64a128_lr1e-4_gas1_ep360/checkpoints/checkpoint-360/adapter_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..a614e1fe5d0e68278b51cd0b233821bbe7b955e4
--- /dev/null
+++ b/runs/klein4b_sft_flir_sel_r64a128_lr1e-4_gas1_ep360/saves/klein4b_sft_flir_sel_r64a128_lr1e-4_gas1_ep360/checkpoints/checkpoint-360/adapter_config.json
@@ -0,0 +1,59 @@
+{
+  "alora_invocation_tokens": null,
+  "alpha_pattern": {},
+  "arrow_config": null,
+  "auto_mapping": {
+    "base_model_class": "Flux2Transformer2DModel",
+    "parent_library": "diffusers.models.transformers.transformer_flux2"
+  },
+  "base_model_name_or_path": null,
+  "bias": "none",
+  "corda_config": null,
+  "ensure_weight_tying": false,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": "gaussian",
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 128,
+  "lora_bias": false,
+  "lora_dropout": 0.0,
+  "lora_ga_config": null,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "monteclora_config": null,
+  "peft_type": "LORA",
+  "peft_version": "0.20.0",
+  "qalora_group_size": 16,
+  "r": 64,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "attn.to_q",
+    "attn.to_add_out",
+    "ff.linear_out",
+    "ff_context.linear_out",
+    "attn.add_v_proj",
+    "attn.to_v",
+    "attn.to_out.0",
+    "attn.add_q_proj",
+    "attn.add_k_proj",
+    "attn.to_k",
+    "attn.to_qkv_mlp_proj",
+    "ff.linear_in",
+    "ff_context.linear_in"
+  ],
+  "target_parameters": null,
+  "task_type": null,
+  "trainable_token_indices": null,
+  "use_bdlora": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false,
+  "velora_config": null
+}
\ No newline at end of file
diff --git a/runs/klein4b_sft_flir_sel_r64a128_lr1e-4_gas1_ep360/saves/klein4b_sft_flir_sel_r64a128_lr1e-4_gas1_ep360/checkpoints/checkpoint-360/manifest.json b/runs/klein4b_sft_flir_sel_r64a128_lr1e-4_gas1_ep360/saves/klein4b_sft_flir_sel_r64a128_lr1e-4_gas1_ep360/checkpoints/checkpoint-360/manifest.json
new file mode 100644
index 0000000000000000000000000000000000000000..4443fd995e0f6f3bd9b7e3dacea93df8d345ecfb
--- /dev/null
+++ b/runs/klein4b_sft_flir_sel_r64a128_lr1e-4_gas1_ep360/saves/klein4b_sft_flir_sel_r64a128_lr1e-4_gas1_ep360/checkpoints/checkpoint-360/manifest.json
@@ -0,0 +1,16 @@
+{
+  "base_role": "base",
+  "entries": [
+    {
+      "component": "transformer",
+      "path": ".",
+      "role": "base"
+    }
+  ],
+  "finetune_type": "lora",
+  "format_version": 1,
+  "primary": {
+    "component": "transformer",
+    "path": "."
+  }
+}
diff --git a/runs/klein4b_sft_vedai_full_r64_lr1e-4/saves/klein4b_sft_vedai_full_r64_lr1e-4/checkpoints/checkpoint-48/README.md b/runs/klein4b_sft_vedai_full_r64_lr1e-4/saves/klein4b_sft_vedai_full_r64_lr1e-4/checkpoints/checkpoint-48/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..2b78a7a995873d5db3090fca56885fe89cdf97e1
--- /dev/null
+++ b/runs/klein4b_sft_vedai_full_r64_lr1e-4/saves/klein4b_sft_vedai_full_r64_lr1e-4/checkpoints/checkpoint-48/README.md
@@ -0,0 +1,204 @@
+---
+base_model: /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/flow_factory_gpu_gate_models/FLUX.2-klein-base-4B/transformer
+library_name: peft
+tags:
+- lora
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]
+### Framework versions
+
+- PEFT 0.20.0
\ No newline at end of file
diff --git a/runs/klein4b_sft_vedai_full_r64_lr1e-4/saves/klein4b_sft_vedai_full_r64_lr1e-4/checkpoints/checkpoint-48/adapter_config.json b/runs/klein4b_sft_vedai_full_r64_lr1e-4/saves/klein4b_sft_vedai_full_r64_lr1e-4/checkpoints/checkpoint-48/adapter_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..51db10dae3c0d3a13f5337431b93556a4df30440
--- /dev/null
+++ b/runs/klein4b_sft_vedai_full_r64_lr1e-4/saves/klein4b_sft_vedai_full_r64_lr1e-4/checkpoints/checkpoint-48/adapter_config.json
@@ -0,0 +1,59 @@
+{
+  "alora_invocation_tokens": null,
+  "alpha_pattern": {},
+  "arrow_config": null,
+  "auto_mapping": {
+    "base_model_class": "Flux2Transformer2DModel",
+    "parent_library": "diffusers.models.transformers.transformer_flux2"
+  },
+  "base_model_name_or_path": null,
+  "bias": "none",
+  "corda_config": null,
+  "ensure_weight_tying": false,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": "gaussian",
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 64,
+  "lora_bias": false,
+  "lora_dropout": 0.0,
+  "lora_ga_config": null,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "monteclora_config": null,
+  "peft_type": "LORA",
+  "peft_version": "0.20.0",
+  "qalora_group_size": 16,
+  "r": 64,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "ff.linear_out",
+    "ff_context.linear_out",
+    "attn.add_k_proj",
+    "attn.to_v",
+    "attn.to_q",
+    "ff.linear_in",
+    "attn.add_q_proj",
+    "attn.to_out.0",
+    "attn.to_k",
+    "ff_context.linear_in",
+    "attn.to_add_out",
+    "attn.to_qkv_mlp_proj",
+    "attn.add_v_proj"
+  ],
+  "target_parameters": null,
+  "task_type": null,
+  "trainable_token_indices": null,
+  "use_bdlora": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false,
+  "velora_config": null
+}
\ No newline at end of file
diff --git a/runs/klein4b_sft_vedai_full_r64_lr1e-4/saves/klein4b_sft_vedai_full_r64_lr1e-4/checkpoints/checkpoint-48/manifest.json b/runs/klein4b_sft_vedai_full_r64_lr1e-4/saves/klein4b_sft_vedai_full_r64_lr1e-4/checkpoints/checkpoint-48/manifest.json
new file mode 100644
index 0000000000000000000000000000000000000000..4443fd995e0f6f3bd9b7e3dacea93df8d345ecfb
--- /dev/null
+++ b/runs/klein4b_sft_vedai_full_r64_lr1e-4/saves/klein4b_sft_vedai_full_r64_lr1e-4/checkpoints/checkpoint-48/manifest.json
@@ -0,0 +1,16 @@
+{
+  "base_role": "base",
+  "entries": [
+    {
+      "component": "transformer",
+      "path": ".",
+      "role": "base"
+    }
+  ],
+  "finetune_type": "lora",
+  "format_version": 1,
+  "primary": {
+    "component": "transformer",
+    "path": "."
+  }
+}
diff --git a/runs/klein4b_sft_vedai_full_r64a128_lr1e-4/saves/klein4b_sft_vedai_full_r64a128_lr1e-4/checkpoints/checkpoint-48/README.md b/runs/klein4b_sft_vedai_full_r64a128_lr1e-4/saves/klein4b_sft_vedai_full_r64a128_lr1e-4/checkpoints/checkpoint-48/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..2b78a7a995873d5db3090fca56885fe89cdf97e1
--- /dev/null
+++ b/runs/klein4b_sft_vedai_full_r64a128_lr1e-4/saves/klein4b_sft_vedai_full_r64a128_lr1e-4/checkpoints/checkpoint-48/README.md
@@ -0,0 +1,204 @@
+---
+base_model: /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/flow_factory_gpu_gate_models/FLUX.2-klein-base-4B/transformer
+library_name: peft
+tags:
+- lora
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]
+### Framework versions
+
+- PEFT 0.20.0
\ No newline at end of file
diff --git a/runs/klein4b_sft_vedai_full_r64a128_lr1e-4/saves/klein4b_sft_vedai_full_r64a128_lr1e-4/checkpoints/checkpoint-48/adapter_config.json b/runs/klein4b_sft_vedai_full_r64a128_lr1e-4/saves/klein4b_sft_vedai_full_r64a128_lr1e-4/checkpoints/checkpoint-48/adapter_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..486ccbfa1e972a3bdae31aacc3211e219c75b19c
--- /dev/null
+++ b/runs/klein4b_sft_vedai_full_r64a128_lr1e-4/saves/klein4b_sft_vedai_full_r64a128_lr1e-4/checkpoints/checkpoint-48/adapter_config.json
@@ -0,0 +1,59 @@
+{
+  "alora_invocation_tokens": null,
+  "alpha_pattern": {},
+  "arrow_config": null,
+  "auto_mapping": {
+    "base_model_class": "Flux2Transformer2DModel",
+    "parent_library": "diffusers.models.transformers.transformer_flux2"
+  },
+  "base_model_name_or_path": null,
+  "bias": "none",
+  "corda_config": null,
+  "ensure_weight_tying": false,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": "gaussian",
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 128,
+  "lora_bias": false,
+  "lora_dropout": 0.0,
+  "lora_ga_config": null,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "monteclora_config": null,
+  "peft_type": "LORA",
+  "peft_version": "0.20.0",
+  "qalora_group_size": 16,
+  "r": 64,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "attn.to_qkv_mlp_proj",
+    "attn.to_q",
+    "attn.add_q_proj",
+    "attn.to_v",
+    "attn.to_k",
+    "ff.linear_in",
+    "ff_context.linear_in",
+    "attn.add_k_proj",
+    "attn.to_out.0",
+    "ff.linear_out",
+    "attn.to_add_out",
+    "ff_context.linear_out",
+    "attn.add_v_proj"
+  ],
+  "target_parameters": null,
+  "task_type": null,
+  "trainable_token_indices": null,
+  "use_bdlora": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false,
+  "velora_config": null
+}
\ No newline at end of file
diff --git a/runs/klein4b_sft_vedai_full_r64a128_lr1e-4/saves/klein4b_sft_vedai_full_r64a128_lr1e-4/checkpoints/checkpoint-48/manifest.json b/runs/klein4b_sft_vedai_full_r64a128_lr1e-4/saves/klein4b_sft_vedai_full_r64a128_lr1e-4/checkpoints/checkpoint-48/manifest.json
new file mode 100644
index 0000000000000000000000000000000000000000..4443fd995e0f6f3bd9b7e3dacea93df8d345ecfb
--- /dev/null
+++ b/runs/klein4b_sft_vedai_full_r64a128_lr1e-4/saves/klein4b_sft_vedai_full_r64a128_lr1e-4/checkpoints/checkpoint-48/manifest.json
@@ -0,0 +1,16 @@
+{
+  "base_role": "base",
+  "entries": [
+    {
+      "component": "transformer",
+      "path": ".",
+      "role": "base"
+    }
+  ],
+  "finetune_type": "lora",
+  "format_version": 1,
+  "primary": {
+    "component": "transformer",
+    "path": "."
+  }
+}
diff --git a/runs/klein4b_sft_vedai_full_r64a128_lr1e-4_gas1_ep144/saves/klein4b_sft_vedai_full_r64a128_lr1e-4_gas1_ep144/checkpoints/checkpoint-144/README.md b/runs/klein4b_sft_vedai_full_r64a128_lr1e-4_gas1_ep144/saves/klein4b_sft_vedai_full_r64a128_lr1e-4_gas1_ep144/checkpoints/checkpoint-144/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..2b78a7a995873d5db3090fca56885fe89cdf97e1
--- /dev/null
+++ b/runs/klein4b_sft_vedai_full_r64a128_lr1e-4_gas1_ep144/saves/klein4b_sft_vedai_full_r64a128_lr1e-4_gas1_ep144/checkpoints/checkpoint-144/README.md
@@ -0,0 +1,204 @@
+---
+base_model: /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/flow_factory_gpu_gate_models/FLUX.2-klein-base-4B/transformer
+library_name: peft
+tags:
+- lora
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]
+### Framework versions
+
+- PEFT 0.20.0
\ No newline at end of file
diff --git a/runs/klein4b_sft_vedai_full_r64a128_lr1e-4_gas1_ep144/saves/klein4b_sft_vedai_full_r64a128_lr1e-4_gas1_ep144/checkpoints/checkpoint-144/adapter_config.json b/runs/klein4b_sft_vedai_full_r64a128_lr1e-4_gas1_ep144/saves/klein4b_sft_vedai_full_r64a128_lr1e-4_gas1_ep144/checkpoints/checkpoint-144/adapter_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..f27a5732697d242a840481f1fd1a513346ffdf32
--- /dev/null
+++ b/runs/klein4b_sft_vedai_full_r64a128_lr1e-4_gas1_ep144/saves/klein4b_sft_vedai_full_r64a128_lr1e-4_gas1_ep144/checkpoints/checkpoint-144/adapter_config.json
@@ -0,0 +1,59 @@
+{
+  "alora_invocation_tokens": null,
+  "alpha_pattern": {},
+  "arrow_config": null,
+  "auto_mapping": {
+    "base_model_class": "Flux2Transformer2DModel",
+    "parent_library": "diffusers.models.transformers.transformer_flux2"
+  },
+  "base_model_name_or_path": null,
+  "bias": "none",
+  "corda_config": null,
+  "ensure_weight_tying": false,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": "gaussian",
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 128,
+  "lora_bias": false,
+  "lora_dropout": 0.0,
+  "lora_ga_config": null,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "monteclora_config": null,
+  "peft_type": "LORA",
+  "peft_version": "0.20.0",
+  "qalora_group_size": 16,
+  "r": 64,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "attn.to_q",
+    "ff.linear_in",
+    "attn.add_v_proj",
+    "attn.to_k",
+    "attn.to_qkv_mlp_proj",
+    "attn.to_v",
+    "ff_context.linear_out",
+    "ff.linear_out",
+    "attn.add_k_proj",
+    "ff_context.linear_in",
+    "attn.to_add_out",
+    "attn.to_out.0",
+    "attn.add_q_proj"
+  ],
+  "target_parameters": null,
+  "task_type": null,
+  "trainable_token_indices": null,
+  "use_bdlora": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false,
+  "velora_config": null
+}
\ No newline at end of file
diff --git a/runs/klein4b_sft_vedai_full_r64a128_lr1e-4_gas1_ep144/saves/klein4b_sft_vedai_full_r64a128_lr1e-4_gas1_ep144/checkpoints/checkpoint-144/manifest.json b/runs/klein4b_sft_vedai_full_r64a128_lr1e-4_gas1_ep144/saves/klein4b_sft_vedai_full_r64a128_lr1e-4_gas1_ep144/checkpoints/checkpoint-144/manifest.json
new file mode 100644
index 0000000000000000000000000000000000000000..4443fd995e0f6f3bd9b7e3dacea93df8d345ecfb
--- /dev/null
+++ b/runs/klein4b_sft_vedai_full_r64a128_lr1e-4_gas1_ep144/saves/klein4b_sft_vedai_full_r64a128_lr1e-4_gas1_ep144/checkpoints/checkpoint-144/manifest.json
@@ -0,0 +1,16 @@
+{
+  "base_role": "base",
+  "entries": [
+    {
+      "component": "transformer",
+      "path": ".",
+      "role": "base"
+    }
+  ],
+  "finetune_type": "lora",
+  "format_version": 1,
+  "primary": {
+    "component": "transformer",
+    "path": "."
+  }
+}
diff --git a/runs/klein4b_sft_vedai_full_r64a128_lr1e-4_gas1_ep288/saves/klein4b_sft_vedai_full_r64a128_lr1e-4_gas1_ep288/checkpoints/checkpoint-288/README.md b/runs/klein4b_sft_vedai_full_r64a128_lr1e-4_gas1_ep288/saves/klein4b_sft_vedai_full_r64a128_lr1e-4_gas1_ep288/checkpoints/checkpoint-288/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..2b78a7a995873d5db3090fca56885fe89cdf97e1
--- /dev/null
+++ b/runs/klein4b_sft_vedai_full_r64a128_lr1e-4_gas1_ep288/saves/klein4b_sft_vedai_full_r64a128_lr1e-4_gas1_ep288/checkpoints/checkpoint-288/README.md
@@ -0,0 +1,204 @@
+---
+base_model: /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/flow_factory_gpu_gate_models/FLUX.2-klein-base-4B/transformer
+library_name: peft
+tags:
+- lora
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]
+### Framework versions
+
+- PEFT 0.20.0
\ No newline at end of file
diff --git a/runs/klein4b_sft_vedai_full_r64a128_lr1e-4_gas1_ep288/saves/klein4b_sft_vedai_full_r64a128_lr1e-4_gas1_ep288/checkpoints/checkpoint-288/adapter_config.json b/runs/klein4b_sft_vedai_full_r64a128_lr1e-4_gas1_ep288/saves/klein4b_sft_vedai_full_r64a128_lr1e-4_gas1_ep288/checkpoints/checkpoint-288/adapter_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..de40d6d6cc759336a8fdb746215df1ee1cb493b2
--- /dev/null
+++ b/runs/klein4b_sft_vedai_full_r64a128_lr1e-4_gas1_ep288/saves/klein4b_sft_vedai_full_r64a128_lr1e-4_gas1_ep288/checkpoints/checkpoint-288/adapter_config.json
@@ -0,0 +1,59 @@
+{
+  "alora_invocation_tokens": null,
+  "alpha_pattern": {},
+  "arrow_config": null,
+  "auto_mapping": {
+    "base_model_class": "Flux2Transformer2DModel",
+    "parent_library": "diffusers.models.transformers.transformer_flux2"
+  },
+  "base_model_name_or_path": null,
+  "bias": "none",
+  "corda_config": null,
+  "ensure_weight_tying": false,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": "gaussian",
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 128,
+  "lora_bias": false,
+  "lora_dropout": 0.0,
+  "lora_ga_config": null,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "monteclora_config": null,
+  "peft_type": "LORA",
+  "peft_version": "0.20.0",
+  "qalora_group_size": 16,
+  "r": 64,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "attn.to_qkv_mlp_proj",
+    "ff.linear_out",
+    "ff.linear_in",
+    "attn.to_add_out",
+    "attn.add_k_proj",
+    "attn.to_q",
+    "attn.add_q_proj",
+    "ff_context.linear_out",
+    "attn.to_out.0",
+    "attn.to_v",
+    "attn.add_v_proj",
+    "ff_context.linear_in",
+    "attn.to_k"
+  ],
+  "target_parameters": null,
+  "task_type": null,
+  "trainable_token_indices": null,
+  "use_bdlora": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false,
+  "velora_config": null
+}
\ No newline at end of file
diff --git a/runs/klein4b_sft_vedai_full_r64a128_lr1e-4_gas1_ep288/saves/klein4b_sft_vedai_full_r64a128_lr1e-4_gas1_ep288/checkpoints/checkpoint-288/manifest.json b/runs/klein4b_sft_vedai_full_r64a128_lr1e-4_gas1_ep288/saves/klein4b_sft_vedai_full_r64a128_lr1e-4_gas1_ep288/checkpoints/checkpoint-288/manifest.json
new file mode 100644
index 0000000000000000000000000000000000000000..4443fd995e0f6f3bd9b7e3dacea93df8d345ecfb
--- /dev/null
+++ b/runs/klein4b_sft_vedai_full_r64a128_lr1e-4_gas1_ep288/saves/klein4b_sft_vedai_full_r64a128_lr1e-4_gas1_ep288/checkpoints/checkpoint-288/manifest.json
@@ -0,0 +1,16 @@
+{
+  "base_role": "base",
+  "entries": [
+    {
+      "component": "transformer",
+      "path": ".",
+      "role": "base"
+    }
+  ],
+  "finetune_type": "lora",
+  "format_version": 1,
+  "primary": {
+    "component": "transformer",
+    "path": "."
+  }
+}
diff --git a/runs/klein4b_sft_vedai_sel_r64_lr1e-4/saves/klein4b_sft_vedai_sel_r64_lr1e-4/checkpoints/checkpoint-20/README.md b/runs/klein4b_sft_vedai_sel_r64_lr1e-4/saves/klein4b_sft_vedai_sel_r64_lr1e-4/checkpoints/checkpoint-20/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..2b78a7a995873d5db3090fca56885fe89cdf97e1
--- /dev/null
+++ b/runs/klein4b_sft_vedai_sel_r64_lr1e-4/saves/klein4b_sft_vedai_sel_r64_lr1e-4/checkpoints/checkpoint-20/README.md
@@ -0,0 +1,204 @@
+---
+base_model: /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/flow_factory_gpu_gate_models/FLUX.2-klein-base-4B/transformer
+library_name: peft
+tags:
+- lora
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]
+### Framework versions
+
+- PEFT 0.20.0
\ No newline at end of file
diff --git a/runs/klein4b_sft_vedai_sel_r64_lr1e-4/saves/klein4b_sft_vedai_sel_r64_lr1e-4/checkpoints/checkpoint-20/adapter_config.json b/runs/klein4b_sft_vedai_sel_r64_lr1e-4/saves/klein4b_sft_vedai_sel_r64_lr1e-4/checkpoints/checkpoint-20/adapter_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..17c265c2455622b58700f7d11d0bd5cca9d37a10
--- /dev/null
+++ b/runs/klein4b_sft_vedai_sel_r64_lr1e-4/saves/klein4b_sft_vedai_sel_r64_lr1e-4/checkpoints/checkpoint-20/adapter_config.json
@@ -0,0 +1,59 @@
+{
+  "alora_invocation_tokens": null,
+  "alpha_pattern": {},
+  "arrow_config": null,
+  "auto_mapping": {
+    "base_model_class": "Flux2Transformer2DModel",
+    "parent_library": "diffusers.models.transformers.transformer_flux2"
+  },
+  "base_model_name_or_path": null,
+  "bias": "none",
+  "corda_config": null,
+  "ensure_weight_tying": false,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": "gaussian",
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 64,
+  "lora_bias": false,
+  "lora_dropout": 0.0,
+  "lora_ga_config": null,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "monteclora_config": null,
+  "peft_type": "LORA",
+  "peft_version": "0.20.0",
+  "qalora_group_size": 16,
+  "r": 64,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "attn.add_k_proj",
+    "attn.to_v",
+    "ff.linear_out",
+    "attn.add_q_proj",
+    "ff_context.linear_out",
+    "ff.linear_in",
+    "attn.to_q",
+    "ff_context.linear_in",
+    "attn.add_v_proj",
+    "attn.to_qkv_mlp_proj",
+    "attn.to_add_out",
+    "attn.to_out.0",
+    "attn.to_k"
+  ],
+  "target_parameters": null,
+  "task_type": null,
+  "trainable_token_indices": null,
+  "use_bdlora": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false,
+  "velora_config": null
+}
\ No newline at end of file
diff --git a/runs/klein4b_sft_vedai_sel_r64_lr1e-4/saves/klein4b_sft_vedai_sel_r64_lr1e-4/checkpoints/checkpoint-20/manifest.json b/runs/klein4b_sft_vedai_sel_r64_lr1e-4/saves/klein4b_sft_vedai_sel_r64_lr1e-4/checkpoints/checkpoint-20/manifest.json
new file mode 100644
index 0000000000000000000000000000000000000000..4443fd995e0f6f3bd9b7e3dacea93df8d345ecfb
--- /dev/null
+++ b/runs/klein4b_sft_vedai_sel_r64_lr1e-4/saves/klein4b_sft_vedai_sel_r64_lr1e-4/checkpoints/checkpoint-20/manifest.json
@@ -0,0 +1,16 @@
+{
+  "base_role": "base",
+  "entries": [
+    {
+      "component": "transformer",
+      "path": ".",
+      "role": "base"
+    }
+  ],
+  "finetune_type": "lora",
+  "format_version": 1,
+  "primary": {
+    "component": "transformer",
+    "path": "."
+  }
+}
diff --git a/runs/klein4b_sft_vedai_sel_r64a128_lr1e-4/saves/klein4b_sft_vedai_sel_r64a128_lr1e-4/checkpoints/checkpoint-120/README.md b/runs/klein4b_sft_vedai_sel_r64a128_lr1e-4/saves/klein4b_sft_vedai_sel_r64a128_lr1e-4/checkpoints/checkpoint-120/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..2b78a7a995873d5db3090fca56885fe89cdf97e1
--- /dev/null
+++ b/runs/klein4b_sft_vedai_sel_r64a128_lr1e-4/saves/klein4b_sft_vedai_sel_r64a128_lr1e-4/checkpoints/checkpoint-120/README.md
@@ -0,0 +1,204 @@
+---
+base_model: /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/flow_factory_gpu_gate_models/FLUX.2-klein-base-4B/transformer
+library_name: peft
+tags:
+- lora
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]
+### Framework versions
+
+- PEFT 0.20.0
\ No newline at end of file
diff --git a/runs/klein4b_sft_vedai_sel_r64a128_lr1e-4/saves/klein4b_sft_vedai_sel_r64a128_lr1e-4/checkpoints/checkpoint-120/adapter_config.json b/runs/klein4b_sft_vedai_sel_r64a128_lr1e-4/saves/klein4b_sft_vedai_sel_r64a128_lr1e-4/checkpoints/checkpoint-120/adapter_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..ac810b0fccff93b71c1bf83947d07b82470d85f5
--- /dev/null
+++ b/runs/klein4b_sft_vedai_sel_r64a128_lr1e-4/saves/klein4b_sft_vedai_sel_r64a128_lr1e-4/checkpoints/checkpoint-120/adapter_config.json
@@ -0,0 +1,59 @@
+{
+  "alora_invocation_tokens": null,
+  "alpha_pattern": {},
+  "arrow_config": null,
+  "auto_mapping": {
+    "base_model_class": "Flux2Transformer2DModel",
+    "parent_library": "diffusers.models.transformers.transformer_flux2"
+  },
+  "base_model_name_or_path": null,
+  "bias": "none",
+  "corda_config": null,
+  "ensure_weight_tying": false,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": "gaussian",
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 128,
+  "lora_bias": false,
+  "lora_dropout": 0.0,
+  "lora_ga_config": null,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "monteclora_config": null,
+  "peft_type": "LORA",
+  "peft_version": "0.20.0",
+  "qalora_group_size": 16,
+  "r": 64,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "attn.add_v_proj",
+    "attn.to_qkv_mlp_proj",
+    "ff_context.linear_out",
+    "attn.add_q_proj",
+    "ff.linear_out",
+    "ff_context.linear_in",
+    "attn.add_k_proj",
+    "attn.to_k",
+    "attn.to_v",
+    "attn.to_add_out",
+    "attn.to_out.0",
+    "ff.linear_in",
+    "attn.to_q"
+  ],
+  "target_parameters": null,
+  "task_type": null,
+  "trainable_token_indices": null,
+  "use_bdlora": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false,
+  "velora_config": null
+}
\ No newline at end of file
diff --git a/runs/klein4b_sft_vedai_sel_r64a128_lr1e-4/saves/klein4b_sft_vedai_sel_r64a128_lr1e-4/checkpoints/checkpoint-120/manifest.json b/runs/klein4b_sft_vedai_sel_r64a128_lr1e-4/saves/klein4b_sft_vedai_sel_r64a128_lr1e-4/checkpoints/checkpoint-120/manifest.json
new file mode 100644
index 0000000000000000000000000000000000000000..4443fd995e0f6f3bd9b7e3dacea93df8d345ecfb
--- /dev/null
+++ b/runs/klein4b_sft_vedai_sel_r64a128_lr1e-4/saves/klein4b_sft_vedai_sel_r64a128_lr1e-4/checkpoints/checkpoint-120/manifest.json
@@ -0,0 +1,16 @@
+{
+  "base_role": "base",
+  "entries": [
+    {
+      "component": "transformer",
+      "path": ".",
+      "role": "base"
+    }
+  ],
+  "finetune_type": "lora",
+  "format_version": 1,
+  "primary": {
+    "component": "transformer",
+    "path": "."
+  }
+}
diff --git a/runs/klein4b_sft_vedai_sel_r64a128_lr1e-4_gas1_ep360/saves/klein4b_sft_vedai_sel_r64a128_lr1e-4_gas1_ep360/checkpoints/checkpoint-360/README.md b/runs/klein4b_sft_vedai_sel_r64a128_lr1e-4_gas1_ep360/saves/klein4b_sft_vedai_sel_r64a128_lr1e-4_gas1_ep360/checkpoints/checkpoint-360/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..2b78a7a995873d5db3090fca56885fe89cdf97e1
--- /dev/null
+++ b/runs/klein4b_sft_vedai_sel_r64a128_lr1e-4_gas1_ep360/saves/klein4b_sft_vedai_sel_r64a128_lr1e-4_gas1_ep360/checkpoints/checkpoint-360/README.md
@@ -0,0 +1,204 @@
+---
+base_model: /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/flow_factory_gpu_gate_models/FLUX.2-klein-base-4B/transformer
+library_name: peft
+tags:
+- lora
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]
+### Framework versions
+
+- PEFT 0.20.0
\ No newline at end of file
diff --git a/runs/klein4b_sft_vedai_sel_r64a128_lr1e-4_gas1_ep360/saves/klein4b_sft_vedai_sel_r64a128_lr1e-4_gas1_ep360/checkpoints/checkpoint-360/adapter_config.json b/runs/klein4b_sft_vedai_sel_r64a128_lr1e-4_gas1_ep360/saves/klein4b_sft_vedai_sel_r64a128_lr1e-4_gas1_ep360/checkpoints/checkpoint-360/adapter_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..7f2493244b9a29965c35f2ce643ad458925ad031
--- /dev/null
+++ b/runs/klein4b_sft_vedai_sel_r64a128_lr1e-4_gas1_ep360/saves/klein4b_sft_vedai_sel_r64a128_lr1e-4_gas1_ep360/checkpoints/checkpoint-360/adapter_config.json
@@ -0,0 +1,59 @@
+{
+  "alora_invocation_tokens": null,
+  "alpha_pattern": {},
+  "arrow_config": null,
+  "auto_mapping": {
+    "base_model_class": "Flux2Transformer2DModel",
+    "parent_library": "diffusers.models.transformers.transformer_flux2"
+  },
+  "base_model_name_or_path": null,
+  "bias": "none",
+  "corda_config": null,
+  "ensure_weight_tying": false,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": "gaussian",
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 128,
+  "lora_bias": false,
+  "lora_dropout": 0.0,
+  "lora_ga_config": null,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "monteclora_config": null,
+  "peft_type": "LORA",
+  "peft_version": "0.20.0",
+  "qalora_group_size": 16,
+  "r": 64,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "attn.to_v",
+    "attn.add_q_proj",
+    "ff_context.linear_out",
+    "attn.to_q",
+    "attn.to_qkv_mlp_proj",
+    "ff_context.linear_in",
+    "attn.to_out.0",
+    "attn.add_k_proj",
+    "ff.linear_out",
+    "ff.linear_in",
+    "attn.to_k",
+    "attn.to_add_out",
+    "attn.add_v_proj"
+  ],
+  "target_parameters": null,
+  "task_type": null,
+  "trainable_token_indices": null,
+  "use_bdlora": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false,
+  "velora_config": null
+}
\ No newline at end of file
diff --git a/runs/klein4b_sft_vedai_sel_r64a128_lr1e-4_gas1_ep360/saves/klein4b_sft_vedai_sel_r64a128_lr1e-4_gas1_ep360/checkpoints/checkpoint-360/manifest.json b/runs/klein4b_sft_vedai_sel_r64a128_lr1e-4_gas1_ep360/saves/klein4b_sft_vedai_sel_r64a128_lr1e-4_gas1_ep360/checkpoints/checkpoint-360/manifest.json
new file mode 100644
index 0000000000000000000000000000000000000000..4443fd995e0f6f3bd9b7e3dacea93df8d345ecfb
--- /dev/null
+++ b/runs/klein4b_sft_vedai_sel_r64a128_lr1e-4_gas1_ep360/saves/klein4b_sft_vedai_sel_r64a128_lr1e-4_gas1_ep360/checkpoints/checkpoint-360/manifest.json
@@ -0,0 +1,16 @@
+{
+  "base_role": "base",
+  "entries": [
+    {
+      "component": "transformer",
+      "path": ".",
+      "role": "base"
+    }
+  ],
+  "finetune_type": "lora",
+  "format_version": 1,
+  "primary": {
+    "component": "transformer",
+    "path": "."
+  }
+}
diff --git a/runs/qwen21_dmd2_2_flir_from_nft/saves/qwen21_dmd2_2_flir_from_nft/checkpoints/checkpoint-150/README.md b/runs/qwen21_dmd2_2_flir_from_nft/saves/qwen21_dmd2_2_flir_from_nft/checkpoints/checkpoint-150/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..25ff3714c9b592e7e41482e6d43f42bbc019ff38
--- /dev/null
+++ b/runs/qwen21_dmd2_2_flir_from_nft/saves/qwen21_dmd2_2_flir_from_nft/checkpoints/checkpoint-150/README.md
@@ -0,0 +1,204 @@
+---
+base_model: /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/models/qwen21_nft_flir_ep36_ckpt60_merged/transformer
+library_name: peft
+tags:
+- lora
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]
+### Framework versions
+
+- PEFT 0.20.0
\ No newline at end of file
diff --git a/runs/qwen21_dmd2_2_flir_from_nft/saves/qwen21_dmd2_2_flir_from_nft/checkpoints/checkpoint-150/adapter_config.json b/runs/qwen21_dmd2_2_flir_from_nft/saves/qwen21_dmd2_2_flir_from_nft/checkpoints/checkpoint-150/adapter_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..68271550eb271c0437d703e63213daa591a6847c
--- /dev/null
+++ b/runs/qwen21_dmd2_2_flir_from_nft/saves/qwen21_dmd2_2_flir_from_nft/checkpoints/checkpoint-150/adapter_config.json
@@ -0,0 +1,53 @@
+{
+  "alora_invocation_tokens": null,
+  "alpha_pattern": {},
+  "arrow_config": null,
+  "auto_mapping": {
+    "base_model_class": "QwenImage21Transformer2DModel",
+    "parent_library": "diffusers.models.transformers.transformer_qwenimage21"
+  },
+  "base_model_name_or_path": null,
+  "bias": "none",
+  "corda_config": null,
+  "ensure_weight_tying": false,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": "gaussian",
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 128,
+  "lora_bias": false,
+  "lora_dropout": 0.0,
+  "lora_ga_config": null,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "monteclora_config": null,
+  "peft_type": "LORA",
+  "peft_version": "0.20.0",
+  "qalora_group_size": 16,
+  "r": 64,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "attn.to_k",
+    "attn.to_q",
+    "img_mlp.out",
+    "attn.to_out.0",
+    "attn.to_v",
+    "img_mlp.proj",
+    "img_mlp.gate_layer"
+  ],
+  "target_parameters": null,
+  "task_type": null,
+  "trainable_token_indices": null,
+  "use_bdlora": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false,
+  "velora_config": null
+}
\ No newline at end of file
diff --git a/runs/qwen21_dmd2_2_flir_from_nft/saves/qwen21_dmd2_2_flir_from_nft/checkpoints/checkpoint-150/manifest.json b/runs/qwen21_dmd2_2_flir_from_nft/saves/qwen21_dmd2_2_flir_from_nft/checkpoints/checkpoint-150/manifest.json
new file mode 100644
index 0000000000000000000000000000000000000000..36e2c39fb45c2aba7867fcd33a35c043a2309413
--- /dev/null
+++ b/runs/qwen21_dmd2_2_flir_from_nft/saves/qwen21_dmd2_2_flir_from_nft/checkpoints/checkpoint-150/manifest.json
@@ -0,0 +1,21 @@
+{
+  "base_role": "generator",
+  "entries": [
+    {
+      "component": "transformer",
+      "path": ".",
+      "role": "generator"
+    },
+    {
+      "component": "transformer",
+      "path": "roles/fake",
+      "role": "fake"
+    }
+  ],
+  "finetune_type": "lora",
+  "format_version": 1,
+  "primary": {
+    "component": "transformer",
+    "path": "."
+  }
+}
diff --git a/runs/qwen21_dmd2_2_flir_from_nft/saves/qwen21_dmd2_2_flir_from_nft/checkpoints/checkpoint-150/roles/fake/README.md b/runs/qwen21_dmd2_2_flir_from_nft/saves/qwen21_dmd2_2_flir_from_nft/checkpoints/checkpoint-150/roles/fake/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..25ff3714c9b592e7e41482e6d43f42bbc019ff38
--- /dev/null
+++ b/runs/qwen21_dmd2_2_flir_from_nft/saves/qwen21_dmd2_2_flir_from_nft/checkpoints/checkpoint-150/roles/fake/README.md
@@ -0,0 +1,204 @@
+---
+base_model: /apdcephfs_fsgm3/share_305110755/hunyuan/bowenping/ir_engine/models/qwen21_nft_flir_ep36_ckpt60_merged/transformer
+library_name: peft
+tags:
+- lora
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]
+### Framework versions
+
+- PEFT 0.20.0
\ No newline at end of file
diff --git a/runs/qwen21_dmd2_2_flir_from_nft/saves/qwen21_dmd2_2_flir_from_nft/checkpoints/checkpoint-150/roles/fake/adapter_config.json b/runs/qwen21_dmd2_2_flir_from_nft/saves/qwen21_dmd2_2_flir_from_nft/checkpoints/checkpoint-150/roles/fake/adapter_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..68271550eb271c0437d703e63213daa591a6847c
--- /dev/null
+++ b/runs/qwen21_dmd2_2_flir_from_nft/saves/qwen21_dmd2_2_flir_from_nft/checkpoints/checkpoint-150/roles/fake/adapter_config.json
@@ -0,0 +1,53 @@
+{
+  "alora_invocation_tokens": null,
+  "alpha_pattern": {},
+  "arrow_config": null,
+  "auto_mapping": {
+    "base_model_class": "QwenImage21Transformer2DModel",
+    "parent_library": "diffusers.models.transformers.transformer_qwenimage21"
+  },
+  "base_model_name_or_path": null,
+  "bias": "none",
+  "corda_config": null,
+  "ensure_weight_tying": false,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": "gaussian",
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 128,
+  "lora_bias": false,
+  "lora_dropout": 0.0,
+  "lora_ga_config": null,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "monteclora_config": null,
+  "peft_type": "LORA",
+  "peft_version": "0.20.0",
+  "qalora_group_size": 16,
+  "r": 64,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "attn.to_k",
+    "attn.to_q",
+    "img_mlp.out",
+    "attn.to_out.0",
+    "attn.to_v",
+    "img_mlp.proj",
+    "img_mlp.gate_layer"
+  ],
+  "target_parameters": null,
+  "task_type": null,
+  "trainable_token_indices": null,
+  "use_bdlora": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false,
+  "velora_config": null
+}
\ No newline at end of file
diff --git a/runs/qwen21_nft_flir_full_ssim_dists_from_ep36/saves/qwen21_nft_flir_full_ssim_dists_from_ep36/checkpoints/checkpoint-0/README.md b/runs/qwen21_nft_flir_full_ssim_dists_from_ep36/saves/qwen21_nft_flir_full_ssim_dists_from_ep36/checkpoints/checkpoint-0/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..a0abc14ecc8be3e58515fbb6909b943165a4ce89
--- /dev/null
+++ b/runs/qwen21_nft_flir_full_ssim_dists_from_ep36/saves/qwen21_nft_flir_full_ssim_dists_from_ep36/checkpoints/checkpoint-0/README.md
@@ -0,0 +1,204 @@
+---
+base_model: /root/models/Qwen-Image-2.1/transformer
+library_name: peft
+tags:
+- lora
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]
+### Framework versions
+
+- PEFT 0.20.0
\ No newline at end of file
diff --git a/runs/qwen21_nft_flir_full_ssim_dists_from_ep36/saves/qwen21_nft_flir_full_ssim_dists_from_ep36/checkpoints/checkpoint-0/adapter_config.json b/runs/qwen21_nft_flir_full_ssim_dists_from_ep36/saves/qwen21_nft_flir_full_ssim_dists_from_ep36/checkpoints/checkpoint-0/adapter_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..1fc2d25f12bd2e5ee060ddf5b13b0a9290b63b69
--- /dev/null
+++ b/runs/qwen21_nft_flir_full_ssim_dists_from_ep36/saves/qwen21_nft_flir_full_ssim_dists_from_ep36/checkpoints/checkpoint-0/adapter_config.json
@@ -0,0 +1,53 @@
+{
+  "alora_invocation_tokens": null,
+  "alpha_pattern": {},
+  "arrow_config": null,
+  "auto_mapping": {
+    "base_model_class": "QwenImage21Transformer2DModel",
+    "parent_library": "diffusers.models.transformers.transformer_qwenimage21"
+  },
+  "base_model_name_or_path": null,
+  "bias": "none",
+  "corda_config": null,
+  "ensure_weight_tying": false,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": "gaussian",
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 128,
+  "lora_bias": false,
+  "lora_dropout": 0.0,
+  "lora_ga_config": null,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "monteclora_config": null,
+  "peft_type": "LORA",
+  "peft_version": "0.20.0",
+  "qalora_group_size": 16,
+  "r": 64,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "img_mlp.gate_layer",
+    "attn.to_out.0",
+    "img_mlp.out",
+    "attn.to_q",
+    "attn.to_k",
+    "img_mlp.proj",
+    "attn.to_v"
+  ],
+  "target_parameters": null,
+  "task_type": null,
+  "trainable_token_indices": null,
+  "use_bdlora": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false,
+  "velora_config": null
+}
\ No newline at end of file
diff --git a/runs/qwen21_nft_flir_full_ssim_dists_from_ep36/saves/qwen21_nft_flir_full_ssim_dists_from_ep36/checkpoints/checkpoint-0/manifest.json b/runs/qwen21_nft_flir_full_ssim_dists_from_ep36/saves/qwen21_nft_flir_full_ssim_dists_from_ep36/checkpoints/checkpoint-0/manifest.json
new file mode 100644
index 0000000000000000000000000000000000000000..4443fd995e0f6f3bd9b7e3dacea93df8d345ecfb
--- /dev/null
+++ b/runs/qwen21_nft_flir_full_ssim_dists_from_ep36/saves/qwen21_nft_flir_full_ssim_dists_from_ep36/checkpoints/checkpoint-0/manifest.json
@@ -0,0 +1,16 @@
+{
+  "base_role": "base",
+  "entries": [
+    {
+      "component": "transformer",
+      "path": ".",
+      "role": "base"
+    }
+  ],
+  "finetune_type": "lora",
+  "format_version": 1,
+  "primary": {
+    "component": "transformer",
+    "path": "."
+  }
+}
diff --git a/runs/qwen21_nft_flir_full_ssim_dists_from_ep36/saves/qwen21_nft_flir_full_ssim_dists_from_ep36/checkpoints/checkpoint-60/README.md b/runs/qwen21_nft_flir_full_ssim_dists_from_ep36/saves/qwen21_nft_flir_full_ssim_dists_from_ep36/checkpoints/checkpoint-60/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..a0abc14ecc8be3e58515fbb6909b943165a4ce89
--- /dev/null
+++ b/runs/qwen21_nft_flir_full_ssim_dists_from_ep36/saves/qwen21_nft_flir_full_ssim_dists_from_ep36/checkpoints/checkpoint-60/README.md
@@ -0,0 +1,204 @@
+---
+base_model: /root/models/Qwen-Image-2.1/transformer
+library_name: peft
+tags:
+- lora
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]
+### Framework versions
+
+- PEFT 0.20.0
\ No newline at end of file
diff --git a/runs/qwen21_nft_flir_full_ssim_dists_from_ep36/saves/qwen21_nft_flir_full_ssim_dists_from_ep36/checkpoints/checkpoint-60/adapter_config.json b/runs/qwen21_nft_flir_full_ssim_dists_from_ep36/saves/qwen21_nft_flir_full_ssim_dists_from_ep36/checkpoints/checkpoint-60/adapter_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..1fc2d25f12bd2e5ee060ddf5b13b0a9290b63b69
--- /dev/null
+++ b/runs/qwen21_nft_flir_full_ssim_dists_from_ep36/saves/qwen21_nft_flir_full_ssim_dists_from_ep36/checkpoints/checkpoint-60/adapter_config.json
@@ -0,0 +1,53 @@
+{
+  "alora_invocation_tokens": null,
+  "alpha_pattern": {},
+  "arrow_config": null,
+  "auto_mapping": {
+    "base_model_class": "QwenImage21Transformer2DModel",
+    "parent_library": "diffusers.models.transformers.transformer_qwenimage21"
+  },
+  "base_model_name_or_path": null,
+  "bias": "none",
+  "corda_config": null,
+  "ensure_weight_tying": false,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": "gaussian",
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 128,
+  "lora_bias": false,
+  "lora_dropout": 0.0,
+  "lora_ga_config": null,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "monteclora_config": null,
+  "peft_type": "LORA",
+  "peft_version": "0.20.0",
+  "qalora_group_size": 16,
+  "r": 64,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "img_mlp.gate_layer",
+    "attn.to_out.0",
+    "img_mlp.out",
+    "attn.to_q",
+    "attn.to_k",
+    "img_mlp.proj",
+    "attn.to_v"
+  ],
+  "target_parameters": null,
+  "task_type": null,
+  "trainable_token_indices": null,
+  "use_bdlora": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false,
+  "velora_config": null
+}
\ No newline at end of file
diff --git a/runs/qwen21_nft_flir_full_ssim_dists_from_ep36/saves/qwen21_nft_flir_full_ssim_dists_from_ep36/checkpoints/checkpoint-60/manifest.json b/runs/qwen21_nft_flir_full_ssim_dists_from_ep36/saves/qwen21_nft_flir_full_ssim_dists_from_ep36/checkpoints/checkpoint-60/manifest.json
new file mode 100644
index 0000000000000000000000000000000000000000..4443fd995e0f6f3bd9b7e3dacea93df8d345ecfb
--- /dev/null
+++ b/runs/qwen21_nft_flir_full_ssim_dists_from_ep36/saves/qwen21_nft_flir_full_ssim_dists_from_ep36/checkpoints/checkpoint-60/manifest.json
@@ -0,0 +1,16 @@
+{
+  "base_role": "base",
+  "entries": [
+    {
+      "component": "transformer",
+      "path": ".",
+      "role": "base"
+    }
+  ],
+  "finetune_type": "lora",
+  "format_version": 1,
+  "primary": {
+    "component": "transformer",
+    "path": "."
+  }
+}
diff --git a/runs/qwen21_nft_vedai_full_ssim_dists_psnr_from_ep144/saves/qwen21_nft_vedai_full_ssim_dists_psnr_from_ep144/checkpoints/checkpoint-0/README.md b/runs/qwen21_nft_vedai_full_ssim_dists_psnr_from_ep144/saves/qwen21_nft_vedai_full_ssim_dists_psnr_from_ep144/checkpoints/checkpoint-0/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..a0abc14ecc8be3e58515fbb6909b943165a4ce89
--- /dev/null
+++ b/runs/qwen21_nft_vedai_full_ssim_dists_psnr_from_ep144/saves/qwen21_nft_vedai_full_ssim_dists_psnr_from_ep144/checkpoints/checkpoint-0/README.md
@@ -0,0 +1,204 @@
+---
+base_model: /root/models/Qwen-Image-2.1/transformer
+library_name: peft
+tags:
+- lora
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]
+### Framework versions
+
+- PEFT 0.20.0
\ No newline at end of file
diff --git a/runs/qwen21_nft_vedai_full_ssim_dists_psnr_from_ep144/saves/qwen21_nft_vedai_full_ssim_dists_psnr_from_ep144/checkpoints/checkpoint-0/adapter_config.json b/runs/qwen21_nft_vedai_full_ssim_dists_psnr_from_ep144/saves/qwen21_nft_vedai_full_ssim_dists_psnr_from_ep144/checkpoints/checkpoint-0/adapter_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..d9dfee7367b3ef95e1b2b7b9a9552579de3eddf0
--- /dev/null
+++ b/runs/qwen21_nft_vedai_full_ssim_dists_psnr_from_ep144/saves/qwen21_nft_vedai_full_ssim_dists_psnr_from_ep144/checkpoints/checkpoint-0/adapter_config.json
@@ -0,0 +1,53 @@
+{
+  "alora_invocation_tokens": null,
+  "alpha_pattern": {},
+  "arrow_config": null,
+  "auto_mapping": {
+    "base_model_class": "QwenImage21Transformer2DModel",
+    "parent_library": "diffusers.models.transformers.transformer_qwenimage21"
+  },
+  "base_model_name_or_path": null,
+  "bias": "none",
+  "corda_config": null,
+  "ensure_weight_tying": false,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": "gaussian",
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 128,
+  "lora_bias": false,
+  "lora_dropout": 0.0,
+  "lora_ga_config": null,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "monteclora_config": null,
+  "peft_type": "LORA",
+  "peft_version": "0.20.0",
+  "qalora_group_size": 16,
+  "r": 64,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "attn.to_k",
+    "img_mlp.gate_layer",
+    "img_mlp.proj",
+    "attn.to_q",
+    "img_mlp.out",
+    "attn.to_out.0",
+    "attn.to_v"
+  ],
+  "target_parameters": null,
+  "task_type": null,
+  "trainable_token_indices": null,
+  "use_bdlora": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false,
+  "velora_config": null
+}
\ No newline at end of file
diff --git a/runs/qwen21_nft_vedai_full_ssim_dists_psnr_from_ep144/saves/qwen21_nft_vedai_full_ssim_dists_psnr_from_ep144/checkpoints/checkpoint-0/manifest.json b/runs/qwen21_nft_vedai_full_ssim_dists_psnr_from_ep144/saves/qwen21_nft_vedai_full_ssim_dists_psnr_from_ep144/checkpoints/checkpoint-0/manifest.json
new file mode 100644
index 0000000000000000000000000000000000000000..4443fd995e0f6f3bd9b7e3dacea93df8d345ecfb
--- /dev/null
+++ b/runs/qwen21_nft_vedai_full_ssim_dists_psnr_from_ep144/saves/qwen21_nft_vedai_full_ssim_dists_psnr_from_ep144/checkpoints/checkpoint-0/manifest.json
@@ -0,0 +1,16 @@
+{
+  "base_role": "base",
+  "entries": [
+    {
+      "component": "transformer",
+      "path": ".",
+      "role": "base"
+    }
+  ],
+  "finetune_type": "lora",
+  "format_version": 1,
+  "primary": {
+    "component": "transformer",
+    "path": "."
+  }
+}
diff --git a/runs/qwen21_nft_vedai_full_ssim_dists_psnr_from_ep144/saves/qwen21_nft_vedai_full_ssim_dists_psnr_from_ep144/checkpoints/checkpoint-60/README.md b/runs/qwen21_nft_vedai_full_ssim_dists_psnr_from_ep144/saves/qwen21_nft_vedai_full_ssim_dists_psnr_from_ep144/checkpoints/checkpoint-60/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..a0abc14ecc8be3e58515fbb6909b943165a4ce89
--- /dev/null
+++ b/runs/qwen21_nft_vedai_full_ssim_dists_psnr_from_ep144/saves/qwen21_nft_vedai_full_ssim_dists_psnr_from_ep144/checkpoints/checkpoint-60/README.md
@@ -0,0 +1,204 @@
+---
+base_model: /root/models/Qwen-Image-2.1/transformer
+library_name: peft
+tags:
+- lora
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]
+### Framework versions
+
+- PEFT 0.20.0
\ No newline at end of file
diff --git a/runs/qwen21_nft_vedai_full_ssim_dists_psnr_from_ep144/saves/qwen21_nft_vedai_full_ssim_dists_psnr_from_ep144/checkpoints/checkpoint-60/adapter_config.json b/runs/qwen21_nft_vedai_full_ssim_dists_psnr_from_ep144/saves/qwen21_nft_vedai_full_ssim_dists_psnr_from_ep144/checkpoints/checkpoint-60/adapter_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..d9dfee7367b3ef95e1b2b7b9a9552579de3eddf0
--- /dev/null
+++ b/runs/qwen21_nft_vedai_full_ssim_dists_psnr_from_ep144/saves/qwen21_nft_vedai_full_ssim_dists_psnr_from_ep144/checkpoints/checkpoint-60/adapter_config.json
@@ -0,0 +1,53 @@
+{
+  "alora_invocation_tokens": null,
+  "alpha_pattern": {},
+  "arrow_config": null,
+  "auto_mapping": {
+    "base_model_class": "QwenImage21Transformer2DModel",
+    "parent_library": "diffusers.models.transformers.transformer_qwenimage21"
+  },
+  "base_model_name_or_path": null,
+  "bias": "none",
+  "corda_config": null,
+  "ensure_weight_tying": false,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": "gaussian",
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 128,
+  "lora_bias": false,
+  "lora_dropout": 0.0,
+  "lora_ga_config": null,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "monteclora_config": null,
+  "peft_type": "LORA",
+  "peft_version": "0.20.0",
+  "qalora_group_size": 16,
+  "r": 64,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "attn.to_k",
+    "img_mlp.gate_layer",
+    "img_mlp.proj",
+    "attn.to_q",
+    "img_mlp.out",
+    "attn.to_out.0",
+    "attn.to_v"
+  ],
+  "target_parameters": null,
+  "task_type": null,
+  "trainable_token_indices": null,
+  "use_bdlora": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false,
+  "velora_config": null
+}
\ No newline at end of file
diff --git a/runs/qwen21_nft_vedai_full_ssim_dists_psnr_from_ep144/saves/qwen21_nft_vedai_full_ssim_dists_psnr_from_ep144/checkpoints/checkpoint-60/manifest.json b/runs/qwen21_nft_vedai_full_ssim_dists_psnr_from_ep144/saves/qwen21_nft_vedai_full_ssim_dists_psnr_from_ep144/checkpoints/checkpoint-60/manifest.json
new file mode 100644
index 0000000000000000000000000000000000000000..4443fd995e0f6f3bd9b7e3dacea93df8d345ecfb
--- /dev/null
+++ b/runs/qwen21_nft_vedai_full_ssim_dists_psnr_from_ep144/saves/qwen21_nft_vedai_full_ssim_dists_psnr_from_ep144/checkpoints/checkpoint-60/manifest.json
@@ -0,0 +1,16 @@
+{
+  "base_role": "base",
+  "entries": [
+    {
+      "component": "transformer",
+      "path": ".",
+      "role": "base"
+    }
+  ],
+  "finetune_type": "lora",
+  "format_version": 1,
+  "primary": {
+    "component": "transformer",
+    "path": "."
+  }
+}
diff --git a/runs/qwen21_sft_flir_full_r64a128_lr1e-4_bs1_gas1_ep12/saves/qwen21_sft_flir_full_r64a128_lr1e-4_bs1_gas1_ep12/checkpoints/checkpoint-12/README.md b/runs/qwen21_sft_flir_full_r64a128_lr1e-4_bs1_gas1_ep12/saves/qwen21_sft_flir_full_r64a128_lr1e-4_bs1_gas1_ep12/checkpoints/checkpoint-12/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..a0abc14ecc8be3e58515fbb6909b943165a4ce89
--- /dev/null
+++ b/runs/qwen21_sft_flir_full_r64a128_lr1e-4_bs1_gas1_ep12/saves/qwen21_sft_flir_full_r64a128_lr1e-4_bs1_gas1_ep12/checkpoints/checkpoint-12/README.md
@@ -0,0 +1,204 @@
+---
+base_model: /root/models/Qwen-Image-2.1/transformer
+library_name: peft
+tags:
+- lora
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]
+### Framework versions
+
+- PEFT 0.20.0
\ No newline at end of file
diff --git a/runs/qwen21_sft_flir_full_r64a128_lr1e-4_bs1_gas1_ep12/saves/qwen21_sft_flir_full_r64a128_lr1e-4_bs1_gas1_ep12/checkpoints/checkpoint-12/adapter_config.json b/runs/qwen21_sft_flir_full_r64a128_lr1e-4_bs1_gas1_ep12/saves/qwen21_sft_flir_full_r64a128_lr1e-4_bs1_gas1_ep12/checkpoints/checkpoint-12/adapter_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..6d64d55b69a2a2536d1926e8ee385f1f1c4078c5
--- /dev/null
+++ b/runs/qwen21_sft_flir_full_r64a128_lr1e-4_bs1_gas1_ep12/saves/qwen21_sft_flir_full_r64a128_lr1e-4_bs1_gas1_ep12/checkpoints/checkpoint-12/adapter_config.json
@@ -0,0 +1,53 @@
+{
+  "alora_invocation_tokens": null,
+  "alpha_pattern": {},
+  "arrow_config": null,
+  "auto_mapping": {
+    "base_model_class": "QwenImage21Transformer2DModel",
+    "parent_library": "diffusers.models.transformers.transformer_qwenimage21"
+  },
+  "base_model_name_or_path": null,
+  "bias": "none",
+  "corda_config": null,
+  "ensure_weight_tying": false,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": "gaussian",
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 128,
+  "lora_bias": false,
+  "lora_dropout": 0.0,
+  "lora_ga_config": null,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "monteclora_config": null,
+  "peft_type": "LORA",
+  "peft_version": "0.20.0",
+  "qalora_group_size": 16,
+  "r": 64,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "attn.to_k",
+    "attn.to_out.0",
+    "img_mlp.out",
+    "attn.to_q",
+    "img_mlp.proj",
+    "img_mlp.gate_layer",
+    "attn.to_v"
+  ],
+  "target_parameters": null,
+  "task_type": null,
+  "trainable_token_indices": null,
+  "use_bdlora": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false,
+  "velora_config": null
+}
\ No newline at end of file
diff --git a/runs/qwen21_sft_flir_full_r64a128_lr1e-4_bs1_gas1_ep12/saves/qwen21_sft_flir_full_r64a128_lr1e-4_bs1_gas1_ep12/checkpoints/checkpoint-12/manifest.json b/runs/qwen21_sft_flir_full_r64a128_lr1e-4_bs1_gas1_ep12/saves/qwen21_sft_flir_full_r64a128_lr1e-4_bs1_gas1_ep12/checkpoints/checkpoint-12/manifest.json
new file mode 100644
index 0000000000000000000000000000000000000000..4443fd995e0f6f3bd9b7e3dacea93df8d345ecfb
--- /dev/null
+++ b/runs/qwen21_sft_flir_full_r64a128_lr1e-4_bs1_gas1_ep12/saves/qwen21_sft_flir_full_r64a128_lr1e-4_bs1_gas1_ep12/checkpoints/checkpoint-12/manifest.json
@@ -0,0 +1,16 @@
+{
+  "base_role": "base",
+  "entries": [
+    {
+      "component": "transformer",
+      "path": ".",
+      "role": "base"
+    }
+  ],
+  "finetune_type": "lora",
+  "format_version": 1,
+  "primary": {
+    "component": "transformer",
+    "path": "."
+  }
+}
diff --git a/runs/qwen21_sft_flir_full_r64a128_lr1e-4_bs1_gas1_ep36/saves/qwen21_sft_flir_full_r64a128_lr1e-4_bs1_gas1_ep36/checkpoints/checkpoint-36/README.md b/runs/qwen21_sft_flir_full_r64a128_lr1e-4_bs1_gas1_ep36/saves/qwen21_sft_flir_full_r64a128_lr1e-4_bs1_gas1_ep36/checkpoints/checkpoint-36/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..a0abc14ecc8be3e58515fbb6909b943165a4ce89
--- /dev/null
+++ b/runs/qwen21_sft_flir_full_r64a128_lr1e-4_bs1_gas1_ep36/saves/qwen21_sft_flir_full_r64a128_lr1e-4_bs1_gas1_ep36/checkpoints/checkpoint-36/README.md
@@ -0,0 +1,204 @@
+---
+base_model: /root/models/Qwen-Image-2.1/transformer
+library_name: peft
+tags:
+- lora
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]
+### Framework versions
+
+- PEFT 0.20.0
\ No newline at end of file
diff --git a/runs/qwen21_sft_flir_full_r64a128_lr1e-4_bs1_gas1_ep36/saves/qwen21_sft_flir_full_r64a128_lr1e-4_bs1_gas1_ep36/checkpoints/checkpoint-36/adapter_config.json b/runs/qwen21_sft_flir_full_r64a128_lr1e-4_bs1_gas1_ep36/saves/qwen21_sft_flir_full_r64a128_lr1e-4_bs1_gas1_ep36/checkpoints/checkpoint-36/adapter_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..2180907d12b40ffd83250459f63fb7e8f71594e8
--- /dev/null
+++ b/runs/qwen21_sft_flir_full_r64a128_lr1e-4_bs1_gas1_ep36/saves/qwen21_sft_flir_full_r64a128_lr1e-4_bs1_gas1_ep36/checkpoints/checkpoint-36/adapter_config.json
@@ -0,0 +1,53 @@
+{
+  "alora_invocation_tokens": null,
+  "alpha_pattern": {},
+  "arrow_config": null,
+  "auto_mapping": {
+    "base_model_class": "QwenImage21Transformer2DModel",
+    "parent_library": "diffusers.models.transformers.transformer_qwenimage21"
+  },
+  "base_model_name_or_path": null,
+  "bias": "none",
+  "corda_config": null,
+  "ensure_weight_tying": false,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": "gaussian",
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 128,
+  "lora_bias": false,
+  "lora_dropout": 0.0,
+  "lora_ga_config": null,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "monteclora_config": null,
+  "peft_type": "LORA",
+  "peft_version": "0.20.0",
+  "qalora_group_size": 16,
+  "r": 64,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "attn.to_out.0",
+    "attn.to_q",
+    "img_mlp.out",
+    "img_mlp.proj",
+    "attn.to_v",
+    "img_mlp.gate_layer",
+    "attn.to_k"
+  ],
+  "target_parameters": null,
+  "task_type": null,
+  "trainable_token_indices": null,
+  "use_bdlora": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false,
+  "velora_config": null
+}
\ No newline at end of file
diff --git a/runs/qwen21_sft_flir_full_r64a128_lr1e-4_bs1_gas1_ep36/saves/qwen21_sft_flir_full_r64a128_lr1e-4_bs1_gas1_ep36/checkpoints/checkpoint-36/manifest.json b/runs/qwen21_sft_flir_full_r64a128_lr1e-4_bs1_gas1_ep36/saves/qwen21_sft_flir_full_r64a128_lr1e-4_bs1_gas1_ep36/checkpoints/checkpoint-36/manifest.json
new file mode 100644
index 0000000000000000000000000000000000000000..4443fd995e0f6f3bd9b7e3dacea93df8d345ecfb
--- /dev/null
+++ b/runs/qwen21_sft_flir_full_r64a128_lr1e-4_bs1_gas1_ep36/saves/qwen21_sft_flir_full_r64a128_lr1e-4_bs1_gas1_ep36/checkpoints/checkpoint-36/manifest.json
@@ -0,0 +1,16 @@
+{
+  "base_role": "base",
+  "entries": [
+    {
+      "component": "transformer",
+      "path": ".",
+      "role": "base"
+    }
+  ],
+  "finetune_type": "lora",
+  "format_version": 1,
+  "primary": {
+    "component": "transformer",
+    "path": "."
+  }
+}
diff --git a/runs/qwen21_sft_vedai_full_r64a128_lr1e-4_bs1_gas1_ep144/saves/qwen21_sft_vedai_full_r64a128_lr1e-4_bs1_gas1_ep144/checkpoints/checkpoint-144/README.md b/runs/qwen21_sft_vedai_full_r64a128_lr1e-4_bs1_gas1_ep144/saves/qwen21_sft_vedai_full_r64a128_lr1e-4_bs1_gas1_ep144/checkpoints/checkpoint-144/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..a0abc14ecc8be3e58515fbb6909b943165a4ce89
--- /dev/null
+++ b/runs/qwen21_sft_vedai_full_r64a128_lr1e-4_bs1_gas1_ep144/saves/qwen21_sft_vedai_full_r64a128_lr1e-4_bs1_gas1_ep144/checkpoints/checkpoint-144/README.md
@@ -0,0 +1,204 @@
+---
+base_model: /root/models/Qwen-Image-2.1/transformer
+library_name: peft
+tags:
+- lora
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]
+### Framework versions
+
+- PEFT 0.20.0
\ No newline at end of file
diff --git a/runs/qwen21_sft_vedai_full_r64a128_lr1e-4_bs1_gas1_ep144/saves/qwen21_sft_vedai_full_r64a128_lr1e-4_bs1_gas1_ep144/checkpoints/checkpoint-144/adapter_config.json b/runs/qwen21_sft_vedai_full_r64a128_lr1e-4_bs1_gas1_ep144/saves/qwen21_sft_vedai_full_r64a128_lr1e-4_bs1_gas1_ep144/checkpoints/checkpoint-144/adapter_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..61e900fa7ce0979ecf86be5db85a7b318bc67b8e
--- /dev/null
+++ b/runs/qwen21_sft_vedai_full_r64a128_lr1e-4_bs1_gas1_ep144/saves/qwen21_sft_vedai_full_r64a128_lr1e-4_bs1_gas1_ep144/checkpoints/checkpoint-144/adapter_config.json
@@ -0,0 +1,53 @@
+{
+  "alora_invocation_tokens": null,
+  "alpha_pattern": {},
+  "arrow_config": null,
+  "auto_mapping": {
+    "base_model_class": "QwenImage21Transformer2DModel",
+    "parent_library": "diffusers.models.transformers.transformer_qwenimage21"
+  },
+  "base_model_name_or_path": null,
+  "bias": "none",
+  "corda_config": null,
+  "ensure_weight_tying": false,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": "gaussian",
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 128,
+  "lora_bias": false,
+  "lora_dropout": 0.0,
+  "lora_ga_config": null,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "monteclora_config": null,
+  "peft_type": "LORA",
+  "peft_version": "0.20.0",
+  "qalora_group_size": 16,
+  "r": 64,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "attn.to_q",
+    "img_mlp.proj",
+    "attn.to_v",
+    "img_mlp.gate_layer",
+    "attn.to_out.0",
+    "img_mlp.out",
+    "attn.to_k"
+  ],
+  "target_parameters": null,
+  "task_type": null,
+  "trainable_token_indices": null,
+  "use_bdlora": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false,
+  "velora_config": null
+}
\ No newline at end of file
diff --git a/runs/qwen21_sft_vedai_full_r64a128_lr1e-4_bs1_gas1_ep144/saves/qwen21_sft_vedai_full_r64a128_lr1e-4_bs1_gas1_ep144/checkpoints/checkpoint-144/manifest.json b/runs/qwen21_sft_vedai_full_r64a128_lr1e-4_bs1_gas1_ep144/saves/qwen21_sft_vedai_full_r64a128_lr1e-4_bs1_gas1_ep144/checkpoints/checkpoint-144/manifest.json
new file mode 100644
index 0000000000000000000000000000000000000000..4443fd995e0f6f3bd9b7e3dacea93df8d345ecfb
--- /dev/null
+++ b/runs/qwen21_sft_vedai_full_r64a128_lr1e-4_bs1_gas1_ep144/saves/qwen21_sft_vedai_full_r64a128_lr1e-4_bs1_gas1_ep144/checkpoints/checkpoint-144/manifest.json
@@ -0,0 +1,16 @@
+{
+  "base_role": "base",
+  "entries": [
+    {
+      "component": "transformer",
+      "path": ".",
+      "role": "base"
+    }
+  ],
+  "finetune_type": "lora",
+  "format_version": 1,
+  "primary": {
+    "component": "transformer",
+    "path": "."
+  }
+}
diff --git a/runs/qwen21_sft_vedai_full_r64a128_lr1e-4_bs1_gas1_ep48/saves/qwen21_sft_vedai_full_r64a128_lr1e-4_bs1_gas1_ep48/checkpoints/checkpoint-48/README.md b/runs/qwen21_sft_vedai_full_r64a128_lr1e-4_bs1_gas1_ep48/saves/qwen21_sft_vedai_full_r64a128_lr1e-4_bs1_gas1_ep48/checkpoints/checkpoint-48/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..a0abc14ecc8be3e58515fbb6909b943165a4ce89
--- /dev/null
+++ b/runs/qwen21_sft_vedai_full_r64a128_lr1e-4_bs1_gas1_ep48/saves/qwen21_sft_vedai_full_r64a128_lr1e-4_bs1_gas1_ep48/checkpoints/checkpoint-48/README.md
@@ -0,0 +1,204 @@
+---
+base_model: /root/models/Qwen-Image-2.1/transformer
+library_name: peft
+tags:
+- lora
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]
+### Framework versions
+
+- PEFT 0.20.0
\ No newline at end of file
diff --git a/runs/qwen21_sft_vedai_full_r64a128_lr1e-4_bs1_gas1_ep48/saves/qwen21_sft_vedai_full_r64a128_lr1e-4_bs1_gas1_ep48/checkpoints/checkpoint-48/adapter_config.json b/runs/qwen21_sft_vedai_full_r64a128_lr1e-4_bs1_gas1_ep48/saves/qwen21_sft_vedai_full_r64a128_lr1e-4_bs1_gas1_ep48/checkpoints/checkpoint-48/adapter_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..8f8b9c74fb24efbbd3c1fb8c0f3a57afc5e5de1a
--- /dev/null
+++ b/runs/qwen21_sft_vedai_full_r64a128_lr1e-4_bs1_gas1_ep48/saves/qwen21_sft_vedai_full_r64a128_lr1e-4_bs1_gas1_ep48/checkpoints/checkpoint-48/adapter_config.json
@@ -0,0 +1,53 @@
+{
+  "alora_invocation_tokens": null,
+  "alpha_pattern": {},
+  "arrow_config": null,
+  "auto_mapping": {
+    "base_model_class": "QwenImage21Transformer2DModel",
+    "parent_library": "diffusers.models.transformers.transformer_qwenimage21"
+  },
+  "base_model_name_or_path": null,
+  "bias": "none",
+  "corda_config": null,
+  "ensure_weight_tying": false,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": "gaussian",
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 128,
+  "lora_bias": false,
+  "lora_dropout": 0.0,
+  "lora_ga_config": null,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "monteclora_config": null,
+  "peft_type": "LORA",
+  "peft_version": "0.20.0",
+  "qalora_group_size": 16,
+  "r": 64,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "attn.to_v",
+    "attn.to_out.0",
+    "img_mlp.proj",
+    "img_mlp.out",
+    "attn.to_k",
+    "attn.to_q",
+    "img_mlp.gate_layer"
+  ],
+  "target_parameters": null,
+  "task_type": null,
+  "trainable_token_indices": null,
+  "use_bdlora": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false,
+  "velora_config": null
+}
\ No newline at end of file
diff --git a/runs/qwen21_sft_vedai_full_r64a128_lr1e-4_bs1_gas1_ep48/saves/qwen21_sft_vedai_full_r64a128_lr1e-4_bs1_gas1_ep48/checkpoints/checkpoint-48/manifest.json b/runs/qwen21_sft_vedai_full_r64a128_lr1e-4_bs1_gas1_ep48/saves/qwen21_sft_vedai_full_r64a128_lr1e-4_bs1_gas1_ep48/checkpoints/checkpoint-48/manifest.json
new file mode 100644
index 0000000000000000000000000000000000000000..4443fd995e0f6f3bd9b7e3dacea93df8d345ecfb
--- /dev/null
+++ b/runs/qwen21_sft_vedai_full_r64a128_lr1e-4_bs1_gas1_ep48/saves/qwen21_sft_vedai_full_r64a128_lr1e-4_bs1_gas1_ep48/checkpoints/checkpoint-48/manifest.json
@@ -0,0 +1,16 @@
+{
+  "base_role": "base",
+  "entries": [
+    {
+      "component": "transformer",
+      "path": ".",
+      "role": "base"
+    }
+  ],
+  "finetune_type": "lora",
+  "format_version": 1,
+  "primary": {
+    "component": "transformer",
+    "path": "."
+  }
+}