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LIBERO-spatial π rollouts — 80% train split (RLinf format, upright)
pi_0.5 policy rollouts on LIBERO-spatial (10 tasks), converted to the RLinf world-model training format used to fine-tune WAN2.2-TI2V-5B. Frames are upright (robosuite's OpenGL vertical flip has been corrected at the source).
Split (seed-234 permutation torch.randperm(500)): train = perm[:400] (80%),
val = perm[400:408].
Layout
train_data/libero_spatial/ep****/rgb.npy # [T, 1, 3, 256, 256] uint8
train_data/libero_spatial/ep****/actions.npy # [T, 1, 7] float32 (OSC deltas + gripper)
val_data/libero_spatial/ep****/...
Point the WAN trainer's train_dataset_base_path at train_data/ and val_dataset_base_path at
val_data/ (the RLinfDataset scans <base>/<step>/<seed>/{rgb,actions}.npy).
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