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reduced_recovery_harness

reduced_recovery_harness

Run a bounded DDPM proxy experiment that exercises generated schedule, epsilon-loss, and reverse-step skills.

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Documentation de la Skill

GitHub

name: reduced_recovery_harness description: Run a bounded DDPM proxy experiment that exercises generated schedule, epsilon-loss, and reverse-step skills.

Reduced Recovery Harness

Use this skill when full DDPM image training is blocked by runtime limits but soft-mode recovery permits a declared mechanism-faithful proxy. The harness must import or call the generated DDPM module skills rather than duplicating their core equations silently. Do not use it to claim full CIFAR-10 FID or Inception Score reproduction.

Inputs

  • Attempt directory containing module_plan.json and environment/runtime_handoff.json.
  • Generated skill root containing diffusion_schedule, epsilon_objective, and reverse_denoising_step.
  • Deterministic hyperparameters for a tiny synthetic Gaussian-mixture experiment.

Outputs

  • recovery/logs/generated_data_item.json.
  • recovery/logs/training_trace.json with params_before and params_after.
  • recovery/logs/generated_skill_invocations.json.
  • recovery/source_manifest.json.
  • recovery/recovery_result.json.

Workflow

  1. Build a deterministic one-dimensional mixture and fixed Gaussian-noise values.
  2. Import the schedule skill and construct x_t using closed-form DDPM noising.
  3. Train a tiny linear epsilon predictor with gradient descent and record parameter changes.
  4. Import the epsilon-objective skill to score loss before and after the optimizer update.
  5. Import the reverse-step skill to compute a deterministic denoising mean.
  6. Save command-readable JSON artifacts for the recovery validators.

Validation

Run the harness script, then run:

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

A valid soft-mode proxy must report is_proxy: true, reduced_training_executed: true, optimizer_step_executed: true, and training_step_executed: false.

Limitations

The harness validates DDPM mechanism execution on a reduced synthetic dataset. It is not full image generation, does not estimate FID, and must be interpreted as proxy evidence only.

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