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  1. outputs/yw002-1-sage-baseline/rl_samples/0000001/group_0/index_tensor([6771, 6771])_group_0_rewards.json +62 -0
  2. outputs/yw002-1-sage-baseline/rl_samples/0000001/group_1/index_tensor([3217, 3217])_group_1_rewards.json +62 -0
  3. outputs/yw002-1-sage-baseline/rl_samples/0000001/group_10/index_tensor([6903, 6903])_group_10_rewards.json +62 -0
  4. outputs/yw002-1-sage-baseline/rl_samples/0000001/group_11/index_tensor([3841, 3841])_group_11_rewards.json +62 -0
  5. outputs/yw002-1-sage-baseline/rl_samples/0000001/group_12/index_tensor([5051, 5051])_group_12_rewards.json +62 -0
  6. outputs/yw002-1-sage-baseline/rl_samples/0000001/group_13/index_tensor([587, 587])_group_13_rewards.json +62 -0
  7. outputs/yw002-1-sage-baseline/rl_samples/0000001/group_14/index_tensor([6642, 6642])_group_14_rewards.json +62 -0
  8. outputs/yw002-1-sage-baseline/rl_samples/0000001/group_15/index_tensor([9841, 9841])_group_15_rewards.json +62 -0
  9. outputs/yw002-1-sage-baseline/rl_samples/0000001/group_2/index_tensor([2519, 2519])_group_2_rewards.json +62 -0
  10. outputs/yw002-1-sage-baseline/rl_samples/0000001/group_3/index_tensor([6359, 6359])_group_3_rewards.json +62 -0
  11. outputs/yw002-1-sage-baseline/rl_samples/0000001/group_4/index_tensor([6929, 6929])_group_4_rewards.json +62 -0
  12. outputs/yw002-1-sage-baseline/rl_samples/0000001/group_5/index_tensor([5756, 5756])_group_5_rewards.json +62 -0
  13. outputs/yw002-1-sage-baseline/rl_samples/0000001/group_6/index_tensor([5897, 5897])_group_6_rewards.json +62 -0
  14. outputs/yw002-1-sage-baseline/rl_samples/0000001/group_7/index_tensor([9764, 9764])_group_7_rewards.json +62 -0
  15. outputs/yw002-1-sage-baseline/rl_samples/0000001/group_8/index_tensor([5307, 5307])_group_8_rewards.json +62 -0
  16. outputs/yw002-1-sage-baseline/rl_samples/0000001/group_9/index_tensor([6113, 6113])_group_9_rewards.json +62 -0
  17. outputs/yw002-1-sage-baseline/rl_samples/0000020/group_0/index_5415_seed_11619_dp_0_rank_0_sample_0_slot_0.mp4 +3 -0
  18. outputs/yw002-1-sage-baseline/rl_samples/0000020/group_0/index_5415_seed_11620_dp_0_rank_0_sample_1_slot_1.mp4 +3 -0
  19. outputs/yw002-1-sage-baseline/rl_samples/0000020/group_0/index_5415_seed_11621_dp_1_rank_2_sample_2_slot_0.mp4 +3 -0
  20. outputs/yw002-1-sage-baseline/rl_samples/0000020/group_0/index_5415_seed_11622_dp_1_rank_2_sample_3_slot_1.mp4 +3 -0
  21. outputs/yw002-1-sage-baseline/rl_samples/0000020/group_1/index_148_seed_22588_dp_2_rank_4_sample_0_slot_0.mp4 +3 -0
  22. outputs/yw002-1-sage-baseline/rl_samples/0000020/group_1/index_148_seed_22589_dp_2_rank_4_sample_1_slot_1.mp4 +3 -0
  23. outputs/yw002-1-sage-baseline/rl_samples/0000020/group_1/index_148_seed_22590_dp_3_rank_6_sample_2_slot_0.mp4 +3 -0
  24. outputs/yw002-1-sage-baseline/rl_samples/0000020/group_1/index_148_seed_22591_dp_3_rank_6_sample_3_slot_1.mp4 +3 -0
  25. outputs/yw002-1-sage-baseline/rl_samples/0000020/group_10/index_7886_seed_1910_dp_20_rank_40_sample_0_slot_0.mp4 +3 -0
  26. outputs/yw002-1-sage-baseline/rl_samples/0000020/group_10/index_7886_seed_1911_dp_20_rank_40_sample_1_slot_1.mp4 +3 -0
  27. outputs/yw002-1-sage-baseline/rl_samples/0000020/group_10/index_7886_seed_1912_dp_21_rank_42_sample_2_slot_0.mp4 +3 -0
  28. outputs/yw002-1-sage-baseline/rl_samples/0000020/group_10/index_7886_seed_1913_dp_21_rank_42_sample_3_slot_1.mp4 +3 -0
  29. outputs/yw002-1-sage-baseline/rl_samples/0000020/group_11/index_7039_seed_23289_dp_22_rank_44_sample_0_slot_0.mp4 +3 -0
  30. outputs/yw002-1-sage-baseline/rl_samples/0000020/group_11/index_7039_seed_23290_dp_22_rank_44_sample_1_slot_1.mp4 +3 -0
  31. outputs/yw002-1-sage-baseline/rl_samples/0000020/group_11/index_7039_seed_23291_dp_23_rank_46_sample_2_slot_0.mp4 +3 -0
  32. outputs/yw002-1-sage-baseline/rl_samples/0000020/group_11/index_7039_seed_23292_dp_23_rank_46_sample_3_slot_1.mp4 +3 -0
  33. outputs/yw002-1-sage-baseline/rl_samples/0000020/group_12/index_1891_seed_18721_dp_24_rank_48_sample_0_slot_0.mp4 +3 -0
  34. outputs/yw002-1-sage-baseline/rl_samples/0000020/group_12/index_1891_seed_18722_dp_24_rank_48_sample_1_slot_1.mp4 +3 -0
  35. outputs/yw002-1-sage-baseline/rl_samples/0000020/group_12/index_1891_seed_18723_dp_25_rank_50_sample_2_slot_0.mp4 +3 -0
  36. outputs/yw002-1-sage-baseline/rl_samples/0000020/group_12/index_1891_seed_18724_dp_25_rank_50_sample_3_slot_1.mp4 +3 -0
  37. outputs/yw002-1-sage-baseline/rl_samples/0000020/group_13/index_4883_seed_5496_dp_26_rank_52_sample_0_slot_0.mp4 +3 -0
  38. outputs/yw002-1-sage-baseline/rl_samples/0000020/group_13/index_4883_seed_5497_dp_26_rank_52_sample_1_slot_1.mp4 +3 -0
  39. outputs/yw002-1-sage-baseline/rl_samples/0000020/group_13/index_4883_seed_5498_dp_27_rank_54_sample_2_slot_0.mp4 +3 -0
  40. outputs/yw002-1-sage-baseline/rl_samples/0000020/group_13/index_4883_seed_5499_dp_27_rank_54_sample_3_slot_1.mp4 +3 -0
  41. outputs/yw002-1-sage-baseline/rl_samples/0000020/group_14/index_287_seed_11781_dp_28_rank_56_sample_0_slot_0.mp4 +3 -0
  42. outputs/yw002-1-sage-baseline/rl_samples/0000020/group_14/index_287_seed_11782_dp_28_rank_56_sample_1_slot_1.mp4 +3 -0
  43. outputs/yw002-1-sage-baseline/rl_samples/0000020/group_14/index_287_seed_11783_dp_29_rank_58_sample_2_slot_0.mp4 +3 -0
  44. outputs/yw002-1-sage-baseline/rl_samples/0000020/group_14/index_287_seed_11784_dp_29_rank_58_sample_3_slot_1.mp4 +3 -0
  45. outputs/yw002-1-sage-baseline/rl_samples/0000020/group_15/index_7943_seed_2336_dp_30_rank_60_sample_0_slot_0.mp4 +3 -0
  46. outputs/yw002-1-sage-baseline/rl_samples/0000020/group_15/index_7943_seed_2337_dp_30_rank_60_sample_1_slot_1.mp4 +3 -0
  47. outputs/yw002-1-sage-baseline/rl_samples/0000020/group_15/index_7943_seed_2338_dp_31_rank_62_sample_2_slot_0.mp4 +3 -0
  48. outputs/yw002-1-sage-baseline/rl_samples/0000020/group_15/index_7943_seed_2339_dp_31_rank_62_sample_3_slot_1.mp4 +3 -0
  49. outputs/yw002-1-sage-baseline/rl_samples/0000020/group_2/index_8949_seed_21966_dp_4_rank_8_sample_0_slot_0.mp4 +3 -0
  50. outputs/yw002-1-sage-baseline/rl_samples/0000020/group_2/index_8949_seed_21967_dp_4_rank_8_sample_1_slot_1.mp4 +3 -0
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outputs/yw002-1-sage-baseline/rl_samples/0000001/group_11/index_tensor([3841, 3841])_group_11_rewards.json ADDED
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+ [
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+ "seed": 712,
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+ "video_path": "./outputs/yw002-1-sage-baseline/rl_samples/0000001/group_11/index_3841_seed_712_dp_23_rank_46_sample_2_slot_0.mp4",
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+ "prompt": "static, medium-long shot, shot from a horizontal angle, captures an elderly man with wispy silver hair as he rides a faded, dark green, 28-style bicycle. He wears a washed-out light blue cotton shirt, a dark brown knitted vest, and loose, dark gray trousers, the cuffs of which are carefully rolled up, revealing a pair of worn brown leather shoes. The scene is set in a quiet, tree-lined alley in the old city. Warm afternoon sunlight filters through the dense foliage, casting dappled, swaying shadows on the bluestone pavement. The backdrop is the blue brick and gray tiled walls of the old-fashioned residential buildings. The elderly man, hands steady on the polished handlebars, pedals with a surprisingly steady rhythm, slowly and leisurely gliding backwards, gradually moving away from the camera. As he retreats, he tilts his head slightly, looking back over his shoulder. His face, marked by the marks of time, bears a serene, farewell smile, his kind eyes filled with nostalgia. As he gradually moves away, his figure becomes smaller and smaller at the end of the alley, and finally slowly disappears into the warm golden halo and deep shadows in the distance. The whole picture is filled with a tranquil and slightly sad nostalgic atmosphere.",
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+ "seed": 713,
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+ "video_path": "./outputs/yw002-1-sage-baseline/rl_samples/0000001/group_11/index_3841_seed_713_dp_23_rank_46_sample_3_slot_1.mp4",
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+ [
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outputs/yw002-1-sage-baseline/rl_samples/0000001/group_13/index_tensor([587, 587])_group_13_rewards.json ADDED
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1
+ [
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+ "prompt": "Thrilling first-person camerawork plunges the viewer directly into the action, simulating a skier's high-speed descent. The camera, representing the skier's eyes, moves with dynamic instability, realistically shaking and tilting as they traverse a dense forest of snow-covered pine trees. At the bottom of the frame, the tips of vibrant red skis are intermittently visible as quick and decisive S-turns plunge into deep, untouched powder, sending crystalline snow spraying into the air and catching the light. Wearing black techno gloves, the skier grips the handles of his ski poles tightly, occasionally digging them into the snow for balance. The setting is a breathtaking winter wonderland; towering pine trees, their branches dusted with fresh snow, rush by in motion blur, creating a palpable sense of speed and danger. Low-angle winter sunlight filters brightly through the treetops, casting long, striking blue shadows across the pristine white landscape and creating dazzling moments of lens flare as the camera pans around. The overall vibe is one of pure adrenaline and focused excitement, conveyed through rapid movement, narrow obstacle avoidance, and a crisp, cool color palette of white, blue, and dark forest green.",
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+ "prompt": "Thrilling first-person camerawork plunges the viewer directly into the action, simulating a skier's high-speed descent. The camera, representing the skier's eyes, moves with dynamic instability, realistically shaking and tilting as they traverse a dense forest of snow-covered pine trees. At the bottom of the frame, the tips of vibrant red skis are intermittently visible as quick and decisive S-turns plunge into deep, untouched powder, sending crystalline snow spraying into the air and catching the light. Wearing black techno gloves, the skier grips the handles of his ski poles tightly, occasionally digging them into the snow for balance. The setting is a breathtaking winter wonderland; towering pine trees, their branches dusted with fresh snow, rush by in motion blur, creating a palpable sense of speed and danger. Low-angle winter sunlight filters brightly through the treetops, casting long, striking blue shadows across the pristine white landscape and creating dazzling moments of lens flare as the camera pans around. The overall vibe is one of pure adrenaline and focused excitement, conveyed through rapid movement, narrow obstacle avoidance, and a crisp, cool color palette of white, blue, and dark forest green.",
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+ "prompt": "long, slightly upward-angled tracking shot steadily follows a scholar in white as he walks through the bustling, vibrant atmosphere of an ancient Chinese marketplace. The scholar has a delicate face, long black hair tied back with a simple wooden hairpin. He wears a pure white robe of fine fabric, the hems of which sway gently with his steady, deliberate steps. One hand hangs naturally at his side, while the other carefully holds several yellowed ancient tomes. Bright afternoon sunlight filters through the gaps between the eaves of the shops lining the street, casting dappled shadows on the time-worn bluestone pavement. Fine dust particles hang in the air, visible in the beams of light. Surrounding him is a constant stream of people: peddlers carrying loads and shouting their wares, young girls in colorful clothes playing and laughing. His white figure stands out against the vibrant surroundings, yet blends harmoniously into this vibrant scene. He moved nimbly through the crowded crowd, occasionally turning his body slightly to avoid oncoming people, but his eyes were always firmly fixed on the front, walking steadily towards the magnificent tall archway with carved beams and painted rafters at the end of the market. Against the noisy background, the whole scene created a peaceful atmosphere of movement and stillness, and clear goals, because of his focused expression.",
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