Prototypical Reward Network for Data-Efficient RLHF

• URL: http://arxiv.org/abs/2406.06606v2
• Date: Sun, 7 Jul 2024 16:29:17 GMT
• Title: Prototypical Reward Network for Data-Efficient RLHF
• Authors: Jinghan Zhang, Xiting Wang, Yiqiao Jin, Changyu Chen, Xinhao Zhang, Kunpeng Liu,
• Abstract summary: A reward model for Reinforcement Learning from Human Feedback (RLHF) has proven effective in fine-tuning Large Language Models (LLMs) Our proposed framework Proto-RM leverages prototypical networks to enhance reward models under limited human feedback.
• Score: 17.220998116937444
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The reward model for Reinforcement Learning from Human Feedback (RLHF) has proven effective in fine-tuning Large Language Models (LLMs). Notably, collecting human feedback for RLHF can be resource-intensive and lead to scalability issues for LLMs and complex tasks. Our proposed framework Proto-RM leverages prototypical networks to enhance reward models under limited human feedback. By enabling stable and reliable structural learning from fewer samples, Proto-RM significantly enhances LLMs' adaptability and accuracy in interpreting human preferences. Extensive experiments on various datasets demonstrate that Proto-RM significantly improves the performance of reward models and LLMs in human feedback tasks, achieving comparable and usually better results than traditional methods, while requiring significantly less data. in data-limited scenarios. This research offers a promising direction for enhancing the efficiency of reward models and optimizing the fine-tuning of language models under restricted feedback conditions.

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