trl_md_files/reward_trainer.mdx

# Reward Modeling

TRL supports custom reward modeling for anyone to perform reward modeling on their dataset and model.

Check out a complete flexible example at [`examples/scripts/reward_modeling.py`](https://github.com/huggingface/trl/tree/main/examples/scripts/reward_modeling.py).

## Expected dataset format

The [`RewardTrainer`] expects a very specific format for the dataset since the model will be trained on pairs of examples to predict which of the two is preferred. We provide an example from the [`Anthropic/hh-rlhf`](https://huggingface.co/datasets/Anthropic/hh-rlhf) dataset below:

<div style="text-align: center">
<img src="https://huggingface.co/datasets/trl-internal-testing/example-images/resolve/main/images/rlhf-antropic-example.png", width="50%">
</div>

Therefore the final dataset object should contain two 4 entries at least if you use the default [`RewardDataCollatorWithPadding`] data collator. The entries should be named:

-   `input_ids_chosen`
-   `attention_mask_chosen`
-   `input_ids_rejected`
-   `attention_mask_rejected`

## Using the `RewardTrainer`

After preparing your dataset, you can use the [`RewardTrainer`] in the same way as the `Trainer` class from 🤗 Transformers.
You should pass an `AutoModelForSequenceClassification` model to the [`RewardTrainer`], along with a [`RewardConfig`] which configures the hyperparameters of the training.

### Leveraging 🤗 PEFT to train a reward model

Just pass a `peft_config` in the keyword arguments of [`RewardTrainer`], and the trainer should automatically take care of converting the model into a PEFT model!

```python
from peft import LoraConfig, TaskType
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from trl import RewardTrainer, RewardConfig

model = AutoModelForSequenceClassification.from_pretrained("gpt2")
peft_config = LoraConfig(
    task_type=TaskType.SEQ_CLS,
    inference_mode=False,
    r=8,
    lora_alpha=32,
    lora_dropout=0.1,
)

...

trainer = RewardTrainer(
    model=model,
    args=training_args,
    tokenizer=tokenizer,
    train_dataset=dataset,
    peft_config=peft_config,
)

trainer.train()

```

### Adding a margin to the loss

As in the [Llama 2 paper](https://huggingface.co/papers/2307.09288), you can add a margin to the loss by adding a `margin` column to the dataset. The reward collator will automatically pass it through and the loss will be computed accordingly.

```python
def add_margin(row):
    # Assume you have a score_chosen and score_rejected columns that you want to use to compute the margin
    return {'margin': row['score_chosen'] - row['score_rejected']}

dataset = dataset.map(add_margin)
```

### Centering rewards

In many scenarios, it's preferable to ensure that a reward model's output is mean zero. This is often done by first calculating the model's average score and then subtracting it.

[[Eisenstein et al., 2023]](https://huggingface.co/papers/2312.09244) proposed an auxiliary loss function designed to directly learn a centered reward model. This auxiliary loss minimizes the squared sum of the rewards, encouraging the model to naturally produce mean-zero outputs:

$$\Big( R(p, r_1) + R(p, r_2) \Big)^2 $$

This auxiliary loss is combined with the main loss function, weighted by the parameter `center_rewards_coefficient` in the `[RewardConfig]`. By default, this feature is deactivated (`center_rewards_coefficient = None`).

```python
reward_config = RewardConfig(
    center_rewards_coefficient=0.01,
    ...
)
```

For reference results, please refer PR [#1932](https://github.com/huggingface/trl/pull/1932).

## RewardConfig

[[autodoc]] RewardConfig

## RewardTrainer

[[autodoc]] RewardTrainer