StarVLA-OFT — G1 + Inspire piston pick-and-place

Contents

• final_model/pytorch_model.pt — bf16 weights (whole model, self-contained)
• config.yaml / config.full.yaml / dataset_statistics.json — the run layout StarVLA's policy_wrapper expects (config + stats one level above the weights)
• starvla_files/ — drop-ins for your StarVLA checkout:
  • data_config.py → .../train_files/data_registry/ (registers the unitree_g1_piston embodiment + unitree_g1_piston_pnp mixture this checkpoint references)
  • starvla_qwenoft_g1_piston_v2.yaml — the exact training config
  • policy_norm_processor_PATCHED.py — required to serve this checkpoint: upstream's PolicyNormProcessor crashes (Video key ego_view not found) on any data config whose transform includes video augmentation, as this one does. Replace deployment/model_server/policy_norm_processor.py.

Recipe (single RTX 4090, ~2h15m; transfers to bigger GPUs)

1. include_state: false on purpose. With state enabled on a stationary task, the model regresses actions from proprioception and ignores the camera (looks converged, fails closed-loop). StarVLA has no state-dropout to mitigate this — leave state off, or wire the dormant StateActionDropout transform yourself.
2. Never use gradient_accumulation_steps > 1 with DeepSpeed in train_starvla.py — the trainer steps the LR scheduler per micro-batch, silently compressing your schedule. On a single GPU, drop DeepSpeed entirely (ZeRO shards nothing across one rank) and accumulation via ACCELERATE_GRADIENT_ACCUMULATION_STEPS works correctly.
3. Full fine-tune, don't freeze the VLM. QwenOFT has no projector — freezing the backbone leaves a 42M head learning manipulation from web-pretrained features that never adapt. We verified a frozen-VLM sibling run converges onto the state shortcut.

Notes