# Reward Modelling – Axolotl

# Reward Modelling

Reward models are used to guide models towards behaviors which is preferred by humans, by training over large datasets annotated with human preferences.

### Overview

Reward modelling is a technique used to train models to predict the reward or value of a given input. This is particularly useful in reinforcement learning scenarios where the model needs to evaluate the quality of its actions or predictions. We support the reward modelling techniques supported by `trl`.

### (Outcome) Reward Models

Outcome reward models are trained using data which contains preference annotations for an entire interaction between the user and model (e.g. rather than per-turn or per-step).

    base_model: google/gemma-2-2b
    model_type: AutoModelForSequenceClassification
    num_labels: 1
    tokenizer_type: AutoTokenizer

    reward_model: true
    chat_template: gemma
    datasets:
      - path: argilla/distilabel-intel-orca-dpo-pairs
        type: bradley_terry.chat_template

    val_set_size: 0.1
    eval_steps: 100

Bradley-Terry chat templates expect single-turn conversations in the following format:

    {
        "system": "...", // optional
        "input": "...",
        "chosen": "...",
        "rejected": "..."
    }

### Process Reward Models (PRM)

Tip

Check out our PRM blog.

Process reward models are trained using data which contains preference annotations for each step in a series of interactions. Typically, PRMs are trained to provide reward signals over each step of a reasoning trace and are used for downstream reinforcement learning.

    base_model: Qwen/Qwen2.5-3B
    model_type: AutoModelForTokenClassification
    num_labels: 2

    process_reward_model: true
    datasets:
      - path: trl-lib/math_shepherd
        type: stepwise_supervised
        split: train

    val_set_size: 0.1
    eval_steps: 100

Please see stepwise_supervised for more details on the dataset format.