---

name: rnd_proxy_recovery_harness description: Run a bounded Random Network Distillation proxy recovery experiment with executable evidence and generated-skill invocation logs.

RND Proxy Recovery Harness

Use this skill when full Atari-scale RND training is blocked by runtime cost but soft-mode recovery permits a reduced, mechanism-faithful proxy. The harness should reproduce the paper's MNIST-style novelty intuition: a predictor distills a deterministic random target, and held-out target-class error decreases when more target examples are included.

Inputs

• Attempt directory containing module_plan.json and generated skills.
• Runtime handoff JSON from environment preparation.
• Generated RND module skill paths.

Outputs

• recovery/recovery_result.json with novelty_mse_reduction_fraction.
• recovery/logs/training_trace.json with loss and parameter changes.
• recovery/logs/generated_data_item.json describing proxy data.
• recovery/logs/generated_skill_invocations.json proving module usage.

Workflow

1. Read the module-plan target and runtime handoff.
2. Create deterministic synthetic vectors for a frequent seen class and an under-sampled target class.
3. Normalize observations via rnd_observation_normalization.
4. Train a predictor against a frozen random target via rnd_bonus_model in low-target and high-target regimes.
5. Cross-check reward scaling and dual-return contracts with their generated skill scripts.
6. Write recovery artifacts and mechanism checks, marking the result as proxy/reduced.
7. Run the recovery experiment validator after the command completes.

Validation

Run python scripts/run_proxy_recovery.py --attempt-dir <attempt_dir> --skills-root <generated_skills_root>. The command should produce all recovery artifacts and a positive reduction fraction.

Limitations

This is not full Atari PPO training and must not be reported as full reproduction. It is acceptable only under soft recovery mode after recording the full-runtime blocker.