DogeRM: Equipping Reward Models with Domain Knowledge through Model Merging

• URL: http://arxiv.org/abs/2407.01470v2
• Date: Sat, 05 Oct 2024 17:48:41 GMT
• Title: DogeRM: Equipping Reward Models with Domain Knowledge through Model Merging
• Authors: Tzu-Han Lin, Chen-An Li, Hung-yi Lee, Yun-Nung Chen,
• Abstract summary: We propose textbfDomain knowledtextbfge merged textbfReward textbfModel (DogeRM), a novel framework that integrates domain-specific knowledge into a general reward model by model merging.
• Score: 65.41765072566287
• License:
• 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 domain-specific preferences requiring expert annotation. To address this challenge, we propose the \textbf{Do}main knowled\textbf{ge} merged \textbf{R}eward \textbf{M}odel (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.

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