Function main

scripts/train.py:512–552

()

# ── Main ─────────────────────────────────────────────────────────────────────

def main() -> None:
    args = parse_args()
    cfg = load_config(args)

    print(f"\n{'='*60}")
    print(f"  SkillOpt — Executive Strategy for Self-Evolving Agent Skills")
    print(f"{'='*60}")
    print(f"  env:            {cfg.get('env')}")
    print(f"  optimizer_model:  {cfg.get('optimizer_model')}")
    print(f"  target_model:  {cfg.get('target_model')}")
    print(f"  optimizer_backend:{cfg.get('optimizer_backend', 'openai_chat')}")
    print(f"  target_backend:{cfg.get('target_backend', 'openai_chat')}")
    print(f"  reasoning:      {cfg.get('reasoning_effort') or 'off'}")
    print(f"  rewrite_effort: {cfg.get('rewrite_reasoning_effort') or 'off'}")
    print(f"  epochs:         {cfg.get('num_epochs')}")
    print(f"  train_size:     {cfg.get('train_size') or 'from dataset'}")
    print(f"  steps/epoch:    auto")
    print(f"  batch_size:     {cfg.get('batch_size')}")
    print(f"  edit_budget:    {cfg.get('edit_budget')}")
    print(f"  lr_scheduler:   {cfg.get('lr_scheduler', 'constant')}")
    print(f"  update_mode:    {cfg.get('skill_update_mode', 'patch')}")
    print(f"  min_edit_budget:{cfg.get('min_edit_budget', 2)}")
    print(f"  minibatch_size: {cfg.get('minibatch_size')}")
    print(f"  seed:           {cfg.get('seed')}")
    print(f"  meta_skill:     {cfg.get('use_meta_skill', False)}")
    print(f"  skill_aware_reflection: {cfg.get('use_skill_aware_reflection', False)}")
    print(f"  slow_update:    {cfg.get('use_slow_update', False)}")
    print(f"  out_root:       {cfg.get('out_root')}")
    print(f"{'='*60}\n")

    # Build adapter
    adapter = get_adapter(cfg)

    # Build trainer and run
    from skillopt.engine.trainer import ReflACTTrainer
    trainer = ReflACTTrainer(cfg, adapter)
    summary = trainer.train()

    print(f"\n  Output saved to: {cfg['out_root']}")
    if summary.get("test_hard") is not None:
        print(f"  Final test: {summary['test_hard']:.4f}")


if __name__ == "__main__":