🎁 RewardTrainer refactor

README.md

@@ -136,23 +136,13 @@ trainer.train()
 Here is a basic example of how to use the [`RewardTrainer`](https://huggingface.co/docs/trl/reward_trainer):
 
 ```python
-from trl import RewardConfig, RewardTrainer
+from trl import RewardTrainer
 from datasets import load_dataset
-from transformers import AutoModelForSequenceClassification, AutoTokenizer
-
-tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")
-model = AutoModelForSequenceClassification.from_pretrained(
-    "Qwen/Qwen2.5-0.5B-Instruct", num_labels=1
-)
-model.config.pad_token_id = tokenizer.pad_token_id
 
 dataset = load_dataset("trl-lib/ultrafeedback_binarized", split="train")
 
-training_args = RewardConfig(output_dir="Qwen2.5-0.5B-Reward", per_device_train_batch_size=2)
 trainer = RewardTrainer(
-    args=training_args,
-    model=model,
-    processing_class=tokenizer,
+    model="Qwen/Qwen2.5-0.5B-Instruct",
     train_dataset=dataset,
 )
 trainer.train()

docs/source/clis.md

@@ -9,6 +9,7 @@ Currently supported commands are:
 - `trl dpo`: fine-tune a LLM with DPO
 - `trl grpo`: fine-tune a LLM with GRPO
 - `trl kto`: fine-tune a LLM with KTO
+- `trl reward`: train a Reward Model
 - `trl rloo`: fine-tune a LLM with RLOO
 - `trl sft`: fine-tune a LLM with SFT
 
@@ -41,6 +42,15 @@ trl dpo \
   --dataset_name anthropic/hh-rlhf
 ```
 
+</hfoption>
+<hfoption id="Reward">
+
+```bash
+trl reward \
+  --model_name_or_path Qwen/Qwen2.5-0.5B \
+  --dataset_name trl-lib/ultrafeedback_binarized
+```
+
 </hfoption>
 </hfoptions>
 
@@ -78,6 +88,21 @@ Launch with:
 trl dpo --config dpo_config.yaml
 ```
 
+</hfoption>
+<hfoption id="Reward">
+
+```yaml
+# reward_config.yaml
+model_name_or_path: Qwen/Qwen2.5-0.5B
+dataset_name: trl-lib/ultrafeedback_binarized
+```
+
+Launch with:
+
+```bash
+trl reward --config reward_config.yaml
+```
+
 </hfoption>
 </hfoptions>
 
@@ -138,6 +163,33 @@ Launch with:
 ```bash
 trl dpo --config dpo_config.yaml
 ```
+
+</hfoption>
+<hfoption id="Reward inline">
+
+```bash
+trl reward \
+  --model_name_or_path Qwen/Qwen2.5-0.5B \
+  --dataset_name trl-lib/ultrafeedback_binarized \
+  --num_processes 4
+```
+
+</hfoption>
+<hfoption id="Reward w/ config file">
+
+```yaml
+# reward_config.yaml
+model_name_or_path: Qwen/Qwen2.5-0.5B
+dataset_name: trl-lib/ultrafeedback_binarized
+num_processes: 4
+```
+
+Launch with:
+
+```bash
+trl reward --config reward_config.yaml
+```
+
 </hfoption>
 </hfoptions>
 
@@ -217,14 +269,41 @@ Launch with:
 ```bash
 trl dpo --config dpo_config.yaml
 ```
+
+</hfoption>
+<hfoption id="Reward inline">
+
+```bash
+trl reward \
+  --model_name_or_path Qwen/Qwen2.5-0.5B \
+  --dataset_name trl-lib/ultrafeedback_binarized \
+  --accelerate_config zero2  # or path/to/my/accelerate/config.yaml
+```
+
+</hfoption>
+<hfoption id="Reward w/ config file">
+
+```yaml
+# reward_config.yaml
+model_name_or_path: Qwen/Qwen2.5-0.5B
+dataset_name: trl-lib/ultrafeedback_binarized
+accelerate_config: zero2  # or path/to/my/accelerate/config.yaml
+```
+
+Launch with:
+
+```bash
+trl reward --config reward_config.yaml
+```
+
 </hfoption>
 </hfoptions>
 
 ### Using dataset mixtures
 
 You can use dataset mixtures to combine multiple datasets into a single training dataset. This is useful for training on diverse data sources or when you want to mix different types of data.
 
-<hfoptions id="accelerate_config">
+<hfoptions id="dataset_mixtures">
 <hfoption id="SFT">
 
 ```yaml
@@ -258,6 +337,23 @@ Launch with:
 trl dpo --config dpo_config.yaml
 ```
 
+</hfoption>
+<hfoption id="Reward">
+
+```yaml
+# reward_config.yaml
+model_name_or_path: Qwen/Qwen2.5-0.5B
+datasets:
+  - path: trl-lib/tldr-preference
+  - path: trl-lib/lm-human-preferences-sentiment
+```
+
+Launch with:
+
+```bash
+trl reward --config reward_config.yaml
+```
+
 </hfoption>
 </hfoptions>
 

docs/source/paper_index.md

@@ -533,3 +533,53 @@ training_args = CPOConfig(
     ...
 )
 ```
+
+## Reward Modeling
+
+Papers relating to the [`RewardTrainer`]
+
+### Helping or Herding? Reward Model Ensembles Mitigate but do not Eliminate Reward Hacking
+
+**📜 Paper**: https://huggingface.co/papers/2312.09244
+
+This paper 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 and thereby resolving the issue of underdetermination.
+
+$$
+\mathcal{L}(\theta) = - \mathbb{E}_{(x,y^+,y^-) \sim \mathcal{D}} \left[ \log \sigma(r_\theta(x, y^+) - r_\theta(x, y^-)) \textcolor{red}{- \eta \cdot (r_\theta(x, y^+) + r_\theta(x, y^-))^2} \right].
+$$
+
+To use this auxiliary loss with [`RewardTrainer`], you can use the `center_rewards_coefficient` argument in [`RewardConfig`] as follows:
+
+```python
+from trl import RewardConfig
+
+training_args = RewardConfig(
+    center_rewards_coefficient=0.01,  # η in the paper
+    ...
+)
+```
+
+### Llama 2: Open Foundation and Fine-Tuned Chat Models
+
+**📜 Paper**: https://huggingface.co/papers/2307.09288
+
+In this paper, the authors propose to leverage their preference ratings being decomposed as a scale of four points (e.g., _significantly better_) to provide more informative feedback to the reward model. This is done by adding a margin to the loss function, which encourages the reward model to assign larger gaps in scores for pairs with higher preference ratings.
+
+$$
+\mathcal{L}(\theta) = - \mathbb{E}_{(x,y^+,y^-,\textcolor{red}{m}) \sim \mathcal{D}} \left[ \log \sigma(r_\theta(x, y^+) - r_\theta(x, y^-) \textcolor{red}{- m}) \right].
+$$
+
+You can add a margin to the loss by adding a `margin` column to the dataset. The following example shows how to set up a the "Margin Small" setting of the paper.
+
+```python
+def add_margin(example):
+    preference_to_margin = {
+        "significantly better": 1.0,
+        "better": 2.0/3.0,
+        "slightly better": 1.0/3.0,
+        "negligibly better / unsure": 0.0,
+    }
+    return {"margin": preference_to_margin[example["preference_label"]]}
+
+dataset = dataset.map(add_margin)
+```

docs/source/quickstart.md

@@ -1,6 +1,6 @@
 # Quickstart
 
-TRL is a comprehensive library for post-training foundation models using techniques like Supervised Fine-Tuning (SFT), Group Relative Policy Optimization (GRPO),  Direct Preference Optimization (DPO).
+TRL is a comprehensive library for post-training foundation models using techniques like Supervised Fine-Tuning (SFT), Group Relative Policy Optimization (GRPO), Direct Preference Optimization (DPO).
 
 ## Quick Examples
 
@@ -51,6 +51,21 @@ trainer = DPOTrainer(
 trainer.train()
 ```
 
+### Reward Modeling
+
+```python
+from trl import RewardTrainer
+from datasets import load_dataset
+
+dataset = load_dataset("trl-lib/ultrafeedback_binarized", split="train")
+
+trainer = RewardTrainer(
+    model="Qwen/Qwen2.5-0.5B-Instruct",
+    train_dataset=dataset,
+)
+trainer.train()
+```
+
 ## Command Line Interface
 
 Skip the code entirely - train directly from your terminal:
@@ -63,6 +78,10 @@ trl sft --model_name_or_path Qwen/Qwen2.5-0.5B \
 # DPO: Align with preferences  
 trl dpo --model_name_or_path Qwen/Qwen2.5-0.5B-Instruct \
     --dataset_name trl-lib/ultrafeedback_binarized
+
+# Reward: Train a reward model
+trl reward --model_name_or_path Qwen/Qwen2.5-0.5B-Instruct \
+    --dataset_name trl-lib/ultrafeedback_binarized
 ```
 
 ## What's Next?