| # 03 Code Fixes |
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| Date: 2026-06-19 UTC |
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| ## Installation Fixes |
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| - Moved `pyarrow` from required dependencies to optional `parquet` extra in `pyproject.toml`. |
| - Reason: this cluster resolves `pyarrow` to a dummy wheel that requires an external Arrow module and breaks core `pip install -e .`. |
| - Core JSONL dataset path remains fully supported. Parquet remains available with `pip install -e ".[parquet]"` in an environment with Arrow. |
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| ## New Reproducible Shell Commands |
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| Added: |
| - `scripts/smoke_test.sh` |
| - `scripts/run_train_debug.sh` |
| - `scripts/run_inference.sh` |
| - `scripts/run_eval.sh` |
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| These scripts run tiny toy-backend jobs with `OPENCLAUDE_MOCK=1` by default. |
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| ## New Inference Command |
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| Added: |
| - `scripts/infer_toy_policy.py` |
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| It loads a CIL dataset and checkpoint, runs model policy inference when torch/model weights are available, and otherwise emits a clearly labeled fallback action selection. Model-policy toy outputs are bound to actual group targets for readable/actionable JSON. |
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| ## Evaluation Fixes |
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| - Fixed CausalStress policy rollout crashes caused by decoded actions containing `predicted_target`. |
| - Added task-aware binding before toy simulator execution. |
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| ## Runtime Stability Fixes |
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| - Added `DOVLA_TORCH_THREADS` handling in trainer/eval with default `1` for stable CPU smoke runs on shared clusters. |
| - Full test suite dropped from a long/hanging run to `126 passed, 1 skipped in 16.21s`. |
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| ## README Updates |
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| - Added the four paper-audit shell commands to README quickstart. |
| - Documented that no-torch fallback paths are explicit and should not be interpreted as learned model results. |
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| ## Verification |
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| - `outputs/audit_venv/bin/python -m pip install -e .` succeeded. |
| - `DOVLA_TORCH_THREADS=1 outputs/audit_venv/bin/python -m pytest -q` passed: `126 passed, 1 skipped`. |
| - Smoke, debug train, inference, eval, and expert-only baseline scripts all ran successfully. |
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