train

name	train
description	Execute a training experiment from a recipe. Use when user says "开始训练", "run experiment", "train model", or wants to execute a recipe.
argument-hint	["recipe-path"]
allowed-tools	Bash(*), Read, Write, Grep, Glob

Train — Experiment Executor

Execute training experiment from: $ARGUMENTS

Pipeline

1. Load — Read and validate the recipe JSON file
2. Compile — Use recipes/compiler.py to compile recipe into training config
3. Prepare — Set up data, model, and environment
4. Train — Execute training:
   • SFT → trainers/sft/trainer.py (TRL backend)
   • RL/GRPO → trainers/rl/trainer.py (veRL backend)
5. Evaluate — Run benchmarks from recipe.eval on the trained checkpoint
6. Judge — Submit results to experiment judge for verdict
7. Store — Save results to result DB
8. Report — Print summary with metrics, verdict, and next steps

Constraints

• ALWAYS set seeds before training (reproducibility)
• ALWAYS run baseline comparison if no baseline exists for this config
• NEVER skip evaluation — every training run must be evaluated
• Log all outputs to outputs/<recipe-id>/