batch_size: Optional[Union[Literal["auto"], int, null]]

Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance.

One of the following:
Literal["auto"]
int
compute_multiplier: Optional[Union[Literal["auto"], float, null]]

Multiplier on amount of compute used for exploring search space during training.

One of the following:
Literal["auto"]
float
eval_interval: Optional[Union[Literal["auto"], int, null]]

The number of training steps between evaluation runs.

One of the following:
Literal["auto"]
int
eval_samples: Optional[Union[Literal["auto"], int, null]]

Number of evaluation samples to generate per training step.

One of the following:
Literal["auto"]
int
learning_rate_multiplier: Optional[Union[Literal["auto"], float, null]]

Scaling factor for the learning rate. A smaller learning rate may be useful to avoid overfitting.

One of the following:
Literal["auto"]
float
n_epochs: Optional[Union[Literal["auto"], int, null]]

The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset.

One of the following:
Literal["auto"]
int
reasoning_effort: Optional[Literal["default", "low", "medium", "high"]]

Level of reasoning effort.

One of the following:
"default"
"low"
"medium"
"high"