# Methods

## Domain Types

### Dpo Hyperparameters

- `DpoHyperparameters object { batch_size, beta, learning_rate_multiplier, n_epochs }`

  The hyperparameters used for the DPO fine-tuning job.

  - `batch_size: optional "auto" or number`

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

    - `"auto"`

      - `"auto"`

    - `number`

  - `beta: optional "auto" or number`

    The beta value for the DPO method. A higher beta value will increase the weight of the penalty between the policy and reference model.

    - `"auto"`

      - `"auto"`

    - `number`

  - `learning_rate_multiplier: optional "auto" or number`

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

    - `"auto"`

      - `"auto"`

    - `number`

  - `n_epochs: optional "auto" or number`

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

    - `"auto"`

      - `"auto"`

    - `number`

### Dpo Method

- `DpoMethod object { hyperparameters }`

  Configuration for the DPO fine-tuning method.

  - `hyperparameters: optional DpoHyperparameters`

    The hyperparameters used for the DPO fine-tuning job.

    - `batch_size: optional "auto" or number`

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

      - `"auto"`

        - `"auto"`

      - `number`

    - `beta: optional "auto" or number`

      The beta value for the DPO method. A higher beta value will increase the weight of the penalty between the policy and reference model.

      - `"auto"`

        - `"auto"`

      - `number`

    - `learning_rate_multiplier: optional "auto" or number`

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

      - `"auto"`

        - `"auto"`

      - `number`

    - `n_epochs: optional "auto" or number`

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

      - `"auto"`

        - `"auto"`

      - `number`

### Reinforcement Hyperparameters

- `ReinforcementHyperparameters object { batch_size, compute_multiplier, eval_interval, 4 more }`

  The hyperparameters used for the reinforcement fine-tuning job.

  - `batch_size: optional "auto" or number`

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

    - `"auto"`

      - `"auto"`

    - `number`

  - `compute_multiplier: optional "auto" or number`

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

    - `"auto"`

      - `"auto"`

    - `number`

  - `eval_interval: optional "auto" or number`

    The number of training steps between evaluation runs.

    - `"auto"`

      - `"auto"`

    - `number`

  - `eval_samples: optional "auto" or number`

    Number of evaluation samples to generate per training step.

    - `"auto"`

      - `"auto"`

    - `number`

  - `learning_rate_multiplier: optional "auto" or number`

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

    - `"auto"`

      - `"auto"`

    - `number`

  - `n_epochs: optional "auto" or number`

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

    - `"auto"`

      - `"auto"`

    - `number`

  - `reasoning_effort: optional "default" or "low" or "medium" or "high"`

    Level of reasoning effort.

    - `"default"`

    - `"low"`

    - `"medium"`

    - `"high"`

### Reinforcement Method

- `ReinforcementMethod object { grader, hyperparameters }`

  Configuration for the reinforcement fine-tuning method.

  - `grader: StringCheckGrader or TextSimilarityGrader or PythonGrader or 2 more`

    The grader used for the fine-tuning job.

    - `StringCheckGrader object { input, name, operation, 2 more }`

      A StringCheckGrader object that performs a string comparison between input and reference using a specified operation.

      - `input: string`

        The input text. This may include template strings.

      - `name: string`

        The name of the grader.

      - `operation: "eq" or "ne" or "like" or "ilike"`

        The string check operation to perform. One of `eq`, `ne`, `like`, or `ilike`.

        - `"eq"`

        - `"ne"`

        - `"like"`

        - `"ilike"`

      - `reference: string`

        The reference text. This may include template strings.

      - `type: "string_check"`

        The object type, which is always `string_check`.

        - `"string_check"`

    - `TextSimilarityGrader object { evaluation_metric, input, name, 2 more }`

      A TextSimilarityGrader object which grades text based on similarity metrics.

      - `evaluation_metric: "cosine" or "fuzzy_match" or "bleu" or 8 more`

        The evaluation metric to use. One of `cosine`, `fuzzy_match`, `bleu`,
        `gleu`, `meteor`, `rouge_1`, `rouge_2`, `rouge_3`, `rouge_4`, `rouge_5`,
        or `rouge_l`.

        - `"cosine"`

        - `"fuzzy_match"`

        - `"bleu"`

        - `"gleu"`

        - `"meteor"`

        - `"rouge_1"`

        - `"rouge_2"`

        - `"rouge_3"`

        - `"rouge_4"`

        - `"rouge_5"`

        - `"rouge_l"`

      - `input: string`

        The text being graded.

      - `name: string`

        The name of the grader.

      - `reference: string`

        The text being graded against.

      - `type: "text_similarity"`

        The type of grader.

        - `"text_similarity"`

    - `PythonGrader object { name, source, type, image_tag }`

      A PythonGrader object that runs a python script on the input.

      - `name: string`

        The name of the grader.

      - `source: string`

        The source code of the python script.

      - `type: "python"`

        The object type, which is always `python`.

        - `"python"`

      - `image_tag: optional string`

        The image tag to use for the python script.

    - `ScoreModelGrader object { input, model, name, 3 more }`

      A ScoreModelGrader object that uses a model to assign a score to the input.

      - `input: array of object { content, role, type }`

        The input messages evaluated by the grader. Supports text, output text, input image, and input audio content blocks, and may include template strings.

        - `content: string or ResponseInputText or object { text, type }  or 3 more`

          Inputs to the model - can contain template strings. Supports text, output text, input images, and input audio, either as a single item or an array of items.

          - `TextInput = string`

            A text input to the model.

          - `ResponseInputText object { text, type }`

            A text input to the model.

            - `text: string`

              The text input to the model.

            - `type: "input_text"`

              The type of the input item. Always `input_text`.

              - `"input_text"`

          - `OutputText object { text, type }`

            A text output from the model.

            - `text: string`

              The text output from the model.

            - `type: "output_text"`

              The type of the output text. Always `output_text`.

              - `"output_text"`

          - `InputImage object { image_url, type, detail }`

            An image input block used within EvalItem content arrays.

            - `image_url: string`

              The URL of the image input.

            - `type: "input_image"`

              The type of the image input. Always `input_image`.

              - `"input_image"`

            - `detail: optional string`

              The detail level of the image to be sent to the model. One of `high`, `low`, or `auto`. Defaults to `auto`.

          - `ResponseInputAudio object { input_audio, type }`

            An audio input to the model.

            - `input_audio: object { data, format }`

              - `data: string`

                Base64-encoded audio data.

              - `format: "mp3" or "wav"`

                The format of the audio data. Currently supported formats are `mp3` and
                `wav`.

                - `"mp3"`

                - `"wav"`

            - `type: "input_audio"`

              The type of the input item. Always `input_audio`.

              - `"input_audio"`

          - `GraderInputs = array of string or ResponseInputText or object { text, type }  or 2 more`

            A list of inputs, each of which may be either an input text, output text, input
            image, or input audio object.

            - `TextInput = string`

              A text input to the model.

            - `ResponseInputText object { text, type }`

              A text input to the model.

            - `OutputText object { text, type }`

              A text output from the model.

              - `text: string`

                The text output from the model.

              - `type: "output_text"`

                The type of the output text. Always `output_text`.

                - `"output_text"`

            - `InputImage object { image_url, type, detail }`

              An image input block used within EvalItem content arrays.

              - `image_url: string`

                The URL of the image input.

              - `type: "input_image"`

                The type of the image input. Always `input_image`.

                - `"input_image"`

              - `detail: optional string`

                The detail level of the image to be sent to the model. One of `high`, `low`, or `auto`. Defaults to `auto`.

            - `ResponseInputAudio object { input_audio, type }`

              An audio input to the model.

        - `role: "user" or "assistant" or "system" or "developer"`

          The role of the message input. One of `user`, `assistant`, `system`, or
          `developer`.

          - `"user"`

          - `"assistant"`

          - `"system"`

          - `"developer"`

        - `type: optional "message"`

          The type of the message input. Always `message`.

          - `"message"`

      - `model: string`

        The model to use for the evaluation.

      - `name: string`

        The name of the grader.

      - `type: "score_model"`

        The object type, which is always `score_model`.

        - `"score_model"`

      - `range: optional array of number`

        The range of the score. Defaults to `[0, 1]`.

      - `sampling_params: optional object { max_completions_tokens, reasoning_effort, seed, 2 more }`

        The sampling parameters for the model.

        - `max_completions_tokens: optional number`

          The maximum number of tokens the grader model may generate in its response.

        - `reasoning_effort: optional ReasoningEffort`

          Constrains effort on reasoning for
          [reasoning models](https://platform.openai.com/docs/guides/reasoning).
          Currently supported values are `none`, `minimal`, `low`, `medium`, `high`, and `xhigh`. Reducing
          reasoning effort can result in faster responses and fewer tokens used
          on reasoning in a response.

          - `gpt-5.1` defaults to `none`, which does not perform reasoning. The supported reasoning values for `gpt-5.1` are `none`, `low`, `medium`, and `high`. Tool calls are supported for all reasoning values in gpt-5.1.
          - All models before `gpt-5.1` default to `medium` reasoning effort, and do not support `none`.
          - The `gpt-5-pro` model defaults to (and only supports) `high` reasoning effort.
          - `xhigh` is supported for all models after `gpt-5.1-codex-max`.

          - `"none"`

          - `"minimal"`

          - `"low"`

          - `"medium"`

          - `"high"`

          - `"xhigh"`

        - `seed: optional number`

          A seed value to initialize the randomness, during sampling.

        - `temperature: optional number`

          A higher temperature increases randomness in the outputs.

        - `top_p: optional number`

          An alternative to temperature for nucleus sampling; 1.0 includes all tokens.

    - `MultiGrader object { calculate_output, graders, name, type }`

      A MultiGrader object combines the output of multiple graders to produce a single score.

      - `calculate_output: string`

        A formula to calculate the output based on grader results.

      - `graders: StringCheckGrader or TextSimilarityGrader or PythonGrader or 2 more`

        A StringCheckGrader object that performs a string comparison between input and reference using a specified operation.

        - `StringCheckGrader object { input, name, operation, 2 more }`

          A StringCheckGrader object that performs a string comparison between input and reference using a specified operation.

        - `TextSimilarityGrader object { evaluation_metric, input, name, 2 more }`

          A TextSimilarityGrader object which grades text based on similarity metrics.

        - `PythonGrader object { name, source, type, image_tag }`

          A PythonGrader object that runs a python script on the input.

        - `ScoreModelGrader object { input, model, name, 3 more }`

          A ScoreModelGrader object that uses a model to assign a score to the input.

        - `LabelModelGrader object { input, labels, model, 3 more }`

          A LabelModelGrader object which uses a model to assign labels to each item
          in the evaluation.

          - `input: array of object { content, role, type }`

            - `content: string or ResponseInputText or object { text, type }  or 3 more`

              Inputs to the model - can contain template strings. Supports text, output text, input images, and input audio, either as a single item or an array of items.

              - `TextInput = string`

                A text input to the model.

              - `ResponseInputText object { text, type }`

                A text input to the model.

              - `OutputText object { text, type }`

                A text output from the model.

                - `text: string`

                  The text output from the model.

                - `type: "output_text"`

                  The type of the output text. Always `output_text`.

                  - `"output_text"`

              - `InputImage object { image_url, type, detail }`

                An image input block used within EvalItem content arrays.

                - `image_url: string`

                  The URL of the image input.

                - `type: "input_image"`

                  The type of the image input. Always `input_image`.

                  - `"input_image"`

                - `detail: optional string`

                  The detail level of the image to be sent to the model. One of `high`, `low`, or `auto`. Defaults to `auto`.

              - `ResponseInputAudio object { input_audio, type }`

                An audio input to the model.

              - `GraderInputs = array of string or ResponseInputText or object { text, type }  or 2 more`

                A list of inputs, each of which may be either an input text, output text, input
                image, or input audio object.

            - `role: "user" or "assistant" or "system" or "developer"`

              The role of the message input. One of `user`, `assistant`, `system`, or
              `developer`.

              - `"user"`

              - `"assistant"`

              - `"system"`

              - `"developer"`

            - `type: optional "message"`

              The type of the message input. Always `message`.

              - `"message"`

          - `labels: array of string`

            The labels to assign to each item in the evaluation.

          - `model: string`

            The model to use for the evaluation. Must support structured outputs.

          - `name: string`

            The name of the grader.

          - `passing_labels: array of string`

            The labels that indicate a passing result. Must be a subset of labels.

          - `type: "label_model"`

            The object type, which is always `label_model`.

            - `"label_model"`

      - `name: string`

        The name of the grader.

      - `type: "multi"`

        The object type, which is always `multi`.

        - `"multi"`

  - `hyperparameters: optional ReinforcementHyperparameters`

    The hyperparameters used for the reinforcement fine-tuning job.

    - `batch_size: optional "auto" or number`

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

      - `"auto"`

        - `"auto"`

      - `number`

    - `compute_multiplier: optional "auto" or number`

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

      - `"auto"`

        - `"auto"`

      - `number`

    - `eval_interval: optional "auto" or number`

      The number of training steps between evaluation runs.

      - `"auto"`

        - `"auto"`

      - `number`

    - `eval_samples: optional "auto" or number`

      Number of evaluation samples to generate per training step.

      - `"auto"`

        - `"auto"`

      - `number`

    - `learning_rate_multiplier: optional "auto" or number`

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

      - `"auto"`

        - `"auto"`

      - `number`

    - `n_epochs: optional "auto" or number`

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

      - `"auto"`

        - `"auto"`

      - `number`

    - `reasoning_effort: optional "default" or "low" or "medium" or "high"`

      Level of reasoning effort.

      - `"default"`

      - `"low"`

      - `"medium"`

      - `"high"`

### Supervised Hyperparameters

- `SupervisedHyperparameters object { batch_size, learning_rate_multiplier, n_epochs }`

  The hyperparameters used for the fine-tuning job.

  - `batch_size: optional "auto" or number`

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

    - `"auto"`

      - `"auto"`

    - `number`

  - `learning_rate_multiplier: optional "auto" or number`

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

    - `"auto"`

      - `"auto"`

    - `number`

  - `n_epochs: optional "auto" or number`

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

    - `"auto"`

      - `"auto"`

    - `number`

### Supervised Method

- `SupervisedMethod object { hyperparameters }`

  Configuration for the supervised fine-tuning method.

  - `hyperparameters: optional SupervisedHyperparameters`

    The hyperparameters used for the fine-tuning job.

    - `batch_size: optional "auto" or number`

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

      - `"auto"`

        - `"auto"`

      - `number`

    - `learning_rate_multiplier: optional "auto" or number`

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

      - `"auto"`

        - `"auto"`

      - `number`

    - `n_epochs: optional "auto" or number`

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

      - `"auto"`

        - `"auto"`

      - `number`