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name: jsrl_recovery_evaluation description: Evaluate Jump-Start Reinforcement Learning recovery evidence with mechanism checks for guide roll-in, curriculum handoff, value update, and proxy metric validity.

JSRL Recovery Evaluation

When To Use

Use this skill after a JSRL experiment has run and produced trajectories, training traces, and metrics. It is especially useful in soft-mode reduced recovery, where final scores alone are not enough to prove mechanism faithfulness.

Inputs

• Module plan target containing dataset, metric, and paper value.
• Recovery metrics for JSRL, vanilla exploration, and optional random switching.
• Trajectory summaries with controller labels.
• Training trace with loss and parameter changes.
• Source-boundary and generated-skill invocation evidence.

Outputs

• Primary metric such as success_rate_gain.
• Mechanism checks for guide use, exploration handoff, curriculum decrease, value update, random ablation, and source-boundary safety.
• Feedback flags for analysis.

Workflow

1. Confirm recovery metadata matches the module-plan target.
2. Compute JSRL success-rate gain over vanilla exploration.
3. Verify at least one trajectory contains both guide and exploration controllers.
4. Verify curriculum guide steps decrease or the random ablation samples a schedule.
5. Verify the value-update trace changed trainable parameters.
6. Mark reduced recovery explicitly and never label a Q-table proxy as full D4RL/IQL/QT-Opt training.

Validation

Run:

python scripts/evaluate_recovery.py --demo
python -m pytest tests

The demo emits a compact metric/mechanism bundle.

Limitations

This skill evaluates evidence; it does not run the environment itself. Recovery harnesses should invoke it after executing the rollout and update skills.