docs/REPRODUCE.md

Reproduce

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>.

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.

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.