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refactor: move training code to scripts/, add train/eval split, tune GRPO hyperparams
- Relocate NeuralTunerOpenEnv and training utilities from server/ to scripts/ - Add 80/20 train/eval scenario split (TRAIN_FRACTION, SPLIT_SEED) for proper held-out evaluation - Refactor post_training_logs to evaluate held-out set against random/baseline/heuristic baselines - Switch oracle from run_baseline_episode to run_heuristic_episode (stronger ceiling) - Tune GRPO: lr 1e-6→2e-6, max_completion_length 384→256, NUM_STEPS 1→20, TRAIN_EPISODES 100→80 - Add prune_layer to ALLOWED_ACTIONS in inference.py - Replace weave with standard W&B; add WANDB_PROJECT env var and conditional report_to - Add device smoke check cell for MPS/CPU validation before trainingfad16c9 -
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