Debugging a broken large-scale RL run

You have 5 hours of wall-clock time for this task. Your machine has 4 CPUs, 16 GiB of RAM and no GPU. When the time is up, you are stopped and your work is graded as it stands.

Can you help me audit this recent RL run I made and tell me what's going wrong? Why are my eval and train scores not climbing? Audit the traces I have. Reconstruct what happened from the preserved artifacts, test competing explanations, and identify implementation defects you can demonstrate.

Document everything in /workspace/delivery/README.md, including:

• Experiment timeline and reconstructed outcomes.
• Investigations, hypotheses, tests, and their results.
• Any defects with supporting evidence.
• Root-cause analysis of performance stagnation or degradation.
• Minimal proposed repairs and validation results.
• Remaining uncertainties, limitations, and reproducibility instructions.

Prioritize demonstrated findings over speculation. Do not claim a defect is fixed unless you have verified the repair.

Relevant info:

| Data | Location | | --- | --- | | Raw rollout exports | /workspace/rl_training_audit_20260929/traces/ | | Historical harness code | /workspace/historical_checkpoint_backup_20261016/historical-harness-5671249.tar.gz | | Per-rollout grades and trace links | /workspace/rl_training_audit_20260929/trace_summaries.csv | | Evaluation scores | /workspace/rl_training_audit_20260929/eval_rollouts.csv | | Training rewards by step | /workspace/rl_training_audit_20260929/training_batch_rewards.csv |

Please be very methodical.

Use native subagents when useful for independent work. You remain responsible for integrating their results into the final deliverable.