DogeRM: Equipping Reward Models with Domain Knowledge through Model Merging

Hung-Yi Lee, Yun-Nung Chen, Chen-An Li, Tzu-Han Lin

arXiv · PDF · Open in the Atlas

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• MiuLab/DogeRM

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Abstract

Reinforcement learning from human feedback (RLHF) is a popular strategy for aligning large language models (LLMs) with desired behaviors. Reward modeling is a crucial step in RLHF. However, collecting paired preference data for training reward models is often costly and time-consuming, especially for domainspecific preferences requiring expert annotation. To address this challenge, we propose the Domain knowledge merged Reward Model (DogeRM), a novel framework that integrates domain-specific knowledge into a general reward model by model merging. The experiments demonstrate that DogeRM enhances performance across different benchmarks and provide a detailed analysis showcasing the effects of model merging, showing the great potential of facilitating model alignment. 1

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