from modal_training_gym.common.train import TrainConfigCompose dataset, model, and recipe into one training entrypoint.
Fields
Section titled “Fields”| Field | Type | Default | Description |
|---|---|---|---|
dataset | DatasetConfig | ||
model | ModelConfig | ||
recipe | modal_training_gym.train_recipes.base.BaseTrainRecipe | ||
checkpoint | modal_training_gym.common.checkpoint.Checkpoint | None | None | |
detach | bool | True | |
_stable_id | str | None | None |
Methods
Section titled “Methods”recipe_param_summary(self) -> dict[str, dict[str, typing.Any]]
Section titled “recipe_param_summary(self) -> dict[str, dict[str, typing.Any]]”train(self) -> modal_training_gym.common.train_result.TrainResult
Section titled “train(self) -> modal_training_gym.common.train_result.TrainResult”Build the app, run training, and return the TrainResult.
Related Tutorials
Section titled “Related Tutorials”- Qwen3-4B haiku evaluation with verifiable rewards — serve, evaluate, train, compare
- Code RL with Harbor hello-world and sandboxed verification
- Multi-turn number-guessing RL with custom generate and reward functions
- On-policy distillation on math — Qwen3-8B teacher, Qwen3-4B student
- DAPO on math with Qwen3-4B
- Audio GRPO on Qwen3-ASR-1.7B — transcribe LibriSpeech, reward −WER
- Windowed-FIFO rollout scheduling for over-sampled RL