albertalonghini8/weaver-marlowe-robolab-full-left-s0

WEAVER fine-tuned on RoboLab (Marlowe full-data run) — model card

Fine-tune of the released WEAVER action-conditioned multi-view world model on Pi0.5 RoboLab demos, trained on Stanford's Marlowe H100 cluster (plan 011). This is the clean re-encode + full-data run that plan 009 could not complete on GCP (a full 250 GB disk killed 3 of 12 encode batches there).

Training data

• 1,045 train + 55 val episodes from 29 RoboLab tasks (the 92 non-empty raw HDF5 files of the original 178-file collection; 86 were empty stubs — see the plan-011 audit).
• Encoded once to SD3 latents + CLIP text features on Marlowe, 4-way GPU-sharded, --ext1 left (over_shoulder_left in panel 0, aligned with the Pi0 policy's exterior_image_1_left).
• The val split (55 episodes) is frozen (val_ids.txt) so any future rung shares it byte-for-byte.

Training

• 4× H100 DDP (torchrun), global batch 24 (per-GPU 6), 16,000 steps, LR 2e-5, compile off, seed 0.
• ~2.6 h wall at ~0.57 s/step.

Final validation (step 16,000)

Camera	FVD ↓	FID ↓	LPIPS ↓
exterior_1_left	136.4	29.0	0.070
wrist_left	383.5	52.8	0.240

(Wrist is the harder view. PSNR/SSIM not enabled in this eval config.)

⚠️ Reward / critic heads are a PLACEHOLDER — not usable for planning (yet)

The exporter wrote a success-gated linear ramp into reward_progress, not RoboMeter (plan 010), and this run did not freeze the base heads. So the reward and critic heads learned a clock, not a real value signal — do not use them for best-of-N or value-based planning. The video-prediction metrics above are unaffected (driven by the Flow loss). A RoboMeter-labelled reward/critic fine-tune is the planned follow-up (plan 010/011).