train.py is the only file your agent modifies. Everything it wants to try goes here.
Inside, you'll find: the GPT-like transformer model architecture (default DEPTH of layers), a hybrid Muon and AdamW optimizer configuration, the full training loop, attention patterns (like WINDOW_PATTERN: "SSSL" vs "L"), warmup and cooldown schedules, batch sizes, learning rates, and vocab_size.
The model you're optimizing is nanochat, a small GPT-like transformer language model. Every hyperparameter, every architectural choice in this file is fair game for your agent to change.