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offline_recovery_harness

offline_recovery_harness

Execute a bounded D4RL-style offline recovery proxy with fixed-dataset training and normalized-score evidence.

Independent GitHub snapshot · Automated classification · Original documentation governs

Skill 문서

GitHub

name: offline_recovery_harness description: Execute a bounded D4RL-style offline recovery proxy with fixed-dataset training and normalized-score evidence.

Offline Recovery Harness

Use this skill when full D4RL packages or datasets are unavailable but soft-mode recovery permits a declared mechanism-faithful proxy. The harness should still perform executable work: validate a fixed transition dataset, run a parameter update from that dataset, compute normalized scores, and save mechanism evidence.

Inputs

  • module_plan.json containing the fast recovery target.
  • environment/runtime_handoff.json describing package blockers and allowed sources.
  • Generated skill root containing dataset validation, score normalization, and taxonomy skills.
  • An output recovery directory.

Outputs

  • recovery_result.json with numeric metrics and target metadata.
  • logs/generated_data_item.json with transition content and provenance.
  • logs/training_trace.json with loss_before, loss_after, params_before, and params_after.
  • Invocation logs proving generated skills were used.

Workflow

  1. Read the module plan target and runtime handoff.
  2. Build a tiny fixed offline chain dataset with explicit synthetic-proxy provenance.
  3. Validate the dataset through offline_dataset_protocol.
  4. Classify proxy metadata through benchmark_property_taxonomy.
  5. Fit a one-parameter linear Q approximation with a deterministic gradient update.
  6. Compute raw and normalized return through d4rl_score_normalization.
  7. Save mechanism checks: no online collection, fixed dataset consumed, reduced training executed, optimizer step changed parameters, full D4RL packages unavailable, and normalized scoring used.
  8. Keep full-runtime flags false when the runtime handoff reports missing packages.

Validation

Run the harness script used by the recovery attempt, then run Distiller's validate_recovery_experiment.py on the attempt directory.

Limitations

This harness is a reduced proxy, not a full D4RL benchmark run. It should be accepted only in soft mode after full recovery is blocked and the experiment gate passes.

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