System Behavior: Training
Living document. Updated by
/archive-specwhen features are completed. Last archived: F006 on 2026-03-28
Training Pipeline
Training notebook produces a trained model from one-click execution
The system accepts a notebooks/train_grpo.ipynb notebook that, when run end-to-end, downloads a HuggingFace model, trains it on SQLEnv episodes using GRPO, and saves the trained weights to a configurable output directory.
Training produces a learning curve showing reward improvement
After training completes, the notebook displays a matplotlib plot of reward over training steps, showing whether the model learned to improve its SQL exploration strategy over the course of training.
Training produces side-by-side episode transcripts
After training completes, the notebook displays episode transcripts comparing random-action baseline episodes against trained-model episodes on the same questions, showing the difference in exploration behavior.
Rollout function plays SQLEnv episodes via model generation
The system accepts a batch of question prompts and returns episode completions by playing full SQLEnv episodes: resetting the environment, generating actions with HF model.generate(), parsing them into SQLActions, and stepping the environment until the episode ends.
Reward functions return per-completion scores for GRPO training
The system accepts TRL-format completion batches and returns float reward lists from three independent callables: correctness (binary 0/1), progress (normalized cumulative progress), and operational (sum of per-step L1 signals).
Unparseable model output falls back to QUERY action
When the model produces text that cannot be parsed as ACTION_TYPE: argument format, the system defaults to a QUERY action with the raw text as the argument, allowing the episode to continue rather than crashing.