---

name: bounded_pruning_recovery description: Run a bounded soft-mode recovery experiment for EL2N/GraNd data pruning with executable mechanism evidence.

Bounded Pruning Recovery

Use this skill when full CIFAR/ResNet retraining is infeasible but soft-mode recovery allows a declared reduced/proxy experiment. The experiment must still run the core paper mechanism: compute early EL2N scores from model probabilities, retain high-score examples, prune low-score examples, and execute a real optimizer step.

Inputs

• Attempt directory containing module_plan.json and environment/runtime_handoff.json.
• Generated early_example_scoring and score_based_subset_selection skill directories.
• Output recovery directory.

Outputs

• recovery/recovery_result.json.
• recovery/logs/training_trace.json.
• recovery/logs/generated_data_item.json.
• Generated skill invocation records suitable for the Distiller gate.

Workflow

1. Build a deterministic small supervised classification dataset inside the attempt.
2. Train a tiny standard-library softmax classifier for bounded steps.
3. Compute class probabilities and invoke the scoring skill to produce EL2N scores.
4. Invoke the selection skill to retain the high-score half.
5. Measure selected-minus-pruned mean EL2N gap.
6. Run and log an optimizer step with before/after loss and parameters.
7. Save mechanism checks distinguishing proxy execution from full CIFAR/ResNet execution.

Validation

Run the recovery harness, then run Distiller's recovery validator:

python recovery/run_recovery.py
python <distiller>/recover-paper/scripts/validate_recovery_experiment.py <attempt_dir> --output <attempt_dir>/recovery/experiment_validation.json

Limitations

• This is not a full paper reproduction unless CIFAR/ResNet data and training are explicitly used.
• Full-runtime flags must remain false for standard-library proxy runs.