| # Reproduce |
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|
| 1. Clone RoboFactory at `5868242322414a91454e22f1dd9641f613ba1bcf` and install its environment. |
| 2. Install the dependencies with `pip install -r requirements.txt`. |
| 3. Run `python scripts/download_artifacts.py` (the dataset requires roughly 180 GB). |
| 4. Verify checkpoint hashes against `artifacts/Stereo-CoRE/SHA256SUMS.json`. |
| 5. Export the official DeFM checkpoint path as `DEFM_CHECKPOINT`; DINOv3 access follows its |
| upstream Hugging Face license and authentication requirements. |
| 6. Run `bash scripts/audit_data.sh`, then `bash scripts/train_stereo_core.sh` or |
| `bash scripts/evaluate_frozen100.sh <checkpoint> <task>`. |
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| The training entry point exactly reproduces the released main configuration: All-5 data, |
| 120k optimizer updates, global batch 40, weighted item sampling, capability target every four |
| updates, and checkpoints at 60k/80k/100k/120k. Set `DATA_ROOT`, `MODEL_ROOT`, `OUTPUT`, and |
| `WORKERS` to override paths or loader count without changing the method. Training uses one GPU, |
| matching the released run; independent evaluations can safely occupy the remaining GPUs. |
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| The exact train/held-out episode split and first-20 manifests are in `protocol/`. The formal main |
| metric is single-rollout frozen-seed SR@1. Recovery@3 is supplementary and is never substituted |
| for SR@1 in the raw results. |
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