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gsm8k_recovery_evaluation

gsm8k_recovery_evaluation

Run a bounded GSM8K verifier recovery harness with source provenance, generated-skill invocation logs, and solve-rate metrics.

Independent GitHub snapshot · Automated classification · Original documentation governs

Documentation de la Skill

GitHub

name: gsm8k_recovery_evaluation description: Run a bounded GSM8K verifier recovery harness with source provenance, generated-skill invocation logs, and solve-rate metrics.

GSM8K Recovery Evaluation

Use this skill to run the soft-mode recovery experiment for the GSM8K verifier paper. It orchestrates the generated answer, candidate, verifier-training, and verifier-search skills and emits validator-compatible recovery artifacts.

Do not run this skill against the original repository during recovery. Use only the runtime handoff's allowed current-attempt snapshot files.

Inputs

  • Attempt directory with module_plan.json.
  • Runtime handoff with allowed snapshot paths.
  • Generated skills root.

Outputs

  • Recovery result with numeric solve_rate.
  • Generated data item and training trace.
  • Command and generated-skill invocation logs.
  • Source manifest that excludes the original repository.

Workflow

  1. Read environment/runtime_handoff.json.
  2. Resolve the allowed GSM8K snapshot file.
  3. Generate reduced candidate records.
  4. Train the lightweight verifier and save loss/parameter trace.
  5. Rank candidates and produce predictions.
  6. Evaluate solve rate.
  7. Write mechanism checks and provenance artifacts.

Validation

Run the harness and then the Distiller recovery validator:

python scripts/run_recovery.py --attempt-dir /path/to/attempt --skills-root /path/to/generated_skills
python /share/project/yuyang/workspace/Paper2Skills/Distiller/skills/recover-paper/scripts/validate_recovery_experiment.py /path/to/attempt --output /path/to/attempt/recovery/experiment_validation.json

Limitations

This harness is a reduced proxy when full model checkpoints and GPT-3-family training infrastructure are unavailable. It must keep full-model booleans false and explicitly record reduced training as the accepted soft-mode scope.

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