Towards Understanding the Influence of Reward Margin on Preference Model Performance
This work addresses a bottleneck in aligning language models with human preferences, offering an incremental improvement over existing methods.
The paper tackles the problem of reward models in RLHF struggling to distinguish between responses effectively, and shows that incorporating margin values into training significantly improves reward prediction accuracy.
Reinforcement Learning from Human Feedback (RLHF) is a widely used framework for the training of language models. However, the process of using RLHF to develop a language model that is well-aligned presents challenges, especially when it comes to optimizing the reward model. Our research has found that existing reward models, when trained using the traditional ranking objective based on human preference data, often struggle to effectively distinguish between responses that are more or less favorable in real-world scenarios. To bridge this gap, our study introduces a novel method to estimate the preference differences without the need for detailed, exhaustive labels from human annotators. Our experimental results provide empirical evidence that incorporating margin values into the training process significantly improves the effectiveness of reward models. This comparative analysis not only demonstrates the superiority of our approach in terms of reward prediction accuracy but also highlights its effectiveness in practical applications.