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feat: add episode trace, refresh training dataset, and update eval metrics
- Add rollout_eval.py --trace flag to generate side-by-side random vs heuristic episode trace (artifacts/eval/episode_trace.md) - Replace training prompts with expanded curriculum-focused scenarios (easy-heavy, edge cases, fast-path patterns) and remove hard/medium scenarios that may have been causing instability - Correct oracle ceiling from 0.786 → 0.6428 (heuristic agent, not theoretical max) - Add train/heldout split metrics to eval_metrics.json for cleaner generalization tracking - Update training plots and metrics with latest 403s training run - Expand README with model zoo table, reward component breakdown, episode trace comparison, updated submission checklist, and HF Space link - Add training_utils.pya422c8d -
feat: add structured pruning action and random baseline policy
Extends NeuralTunerAction with a new prune_layer op (LOW/MEDIUM/HIGH sparsity) alongside quantization, so the LLM agent can now compress models via both axes simultaneously. Pruning and quantization effects stack multiplicatively for latency/memory and additively for accuracy penalty in the simulator, matching real Snapdragon HTP behaviour. Key changes: - models.py: add prune_layer action_type and sparsity field (LOW=25%, MEDIUM=50%, HIGH=75%) - server/simulator.py: PRUNE_CONFIGS table, simulate() and get_benchmark_report() now accept a prune_map; profile report includes per-layer pruning advice based on sensitivity - server/neural_tuner_env_environment.py: _prune_map state, _handle_prune() handler, revert_layer resets both dtype and sparsity, benchmark passes prune_map through to simulator - client.py: forward sparsity field in action payload - rollout_eval.py: add run_random_episode() (zero-knowledge baseline, expected reward 0.20–0.45), run_policy_sweep() to run all thr…d064b19 -
refactor: harden imports, add training extras, and rewrite README
- Wrap all openenv and relative imports in try/except so the package works both as an installed module and when run from the repo root without openenv present (models.py, client.py, server/app.py, server/neural_tuner_env_environment.py, __init__.py) - Add optional `training` dependency group to pyproject.toml (trl, transformers, datasets, accelerate, jupyter, matplotlib, pandas) - Wire main.py CLI entrypoint to rollout_eval harness - Rewrite README to describe the actual NeuralTuner RL environment (loop, reward design, quick-start, test commands) instead of the generic echo-env template - Extend .gitignore with .env.local, .hf_cache, .pytest_cache5dd60b9