Publish verified ACE Reactive and Responsive training snapshots

README.md

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+---
+library_name: pytorch
+tags:
+- motion-generation
+- dance
+---
+
+# ACE Reverse Dance
+
+Intermediate snapshots from the Reactive and Responsive runs started on 2026-11-11.
+
+| Run | Published snapshot | Weights for inference |
+| --- | --- | --- |
+| Reactive | 7,605 optimizer updates, epoch 182 in progress | Online |
+| Responsive | Epoch 1,800 complete, 12,600 optimizer updates | EMA |
+
+Both files include online and EMA state dictionaries, the native normalizer, and snapshot configuration.
+Use the matching ACE runtime and codecs from the training run. The Reactive normalizer requires the runtime's
+`dataset` module when loading the native PyTorch checkpoint. Only load checkpoints from trusted sources.
+
+Both runs were still training at export time. The reported losses are training losses. The run receipts
+do not contain held-out metrics or an acceptance review of generated motion. See `snapshot-manifest.json`
+for provenance and SHA-256 checksums.

reactive/model-step-007605.pt

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+size 798420023

responsive/model-e1800.pt

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+size 817700514

snapshot-manifest.json

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+{
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+      "sha256": "2c2f834081336253c964c5ff1b661da1cf86bf44e225d276c329bb8d5e74cdbb",
+      "progress": {
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+        "batch": 0,
+        "updates": 12600,
+        "examples": 15764400
+      },
+      "context": {
+        "request_id": "reverse-both-portable-20261111",
+        "task": "responsive",
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+        "raw_role_order": [
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+        "initialization": "scratch_seed42",
+        "experiment_interpretation": "New ordinary conditional diffusion recipe; original diffusion mechanism integrated with current task codecs and Shape/HF."
+      },
+      "source_sha256": "2c2f834081336253c964c5ff1b661da1cf86bf44e225d276c329bb8d5e74cdbb"
+    },
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+          600,
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+          800,
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+          "relation",
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+          "self_forcing",
+          "pose_anchors"
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+          "motion_attention_mode": "legacy_prefix",
+          "time_conditioning": "global_max",
+          "condition_visibility": "full",
+          "df_loss_scope": "future_uniform",
+          "self_conditioning": false,
+          "motion_adapter": false,
+          "cfg": false,
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+            "cfg_mode": "cond_only",
+            "cfg_scale": 1.0
+          },
+          "responsive": {
+            "nfe": 50,
+            "cfg_mode": "cond_only",
+            "cfg_scale": 1.0
+          }
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+        "physical_weights": {
+          "floor": 0.01,
+          "hand": 0.01
+        },
+        "initialization": "scratch_seed42",
+        "experiment_interpretation": "New ordinary conditional diffusion recipe; original diffusion mechanism integrated with current task codecs and Shape/HF."
+      },
+      "source_sha256": "be4f73420c2a228f455e922f4651a81c466ad9b28e031d76cb386ef7373d7fd4"
+    }
+  ],
+  "training": {
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+        "batch": 0,
+        "updates": 7644,
+        "examples": 1467648
+      },
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+      "complete": false,
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+      "evaluation": "No held-out result in the run receipts"
+    },
+    "responsive": {
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+        "batch": 0,
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+      "complete": false,
+      "target_epochs": 2000,
+      "target_updates": 14000,
+      "evaluation": "No held-out result in the run receipts"
+    }
+  }
+}