GFRIEND: Generative Few-shot Reward Inference through EfficieNt DPO

• URL: http://arxiv.org/abs/2506.08965v1
• Date: Tue, 10 Jun 2025 16:37:13 GMT
• Title: GFRIEND: Generative Few-shot Reward Inference through EfficieNt DPO
• Authors: Yiyang Zhao, Huiyu Bai, Xuejiao Zhao,
• Abstract summary: The ability to train high-performing reward models with few-shot data is critical for enhancing the efficiency and scalability of Reinforcement Learning from Human Feedback.<n>We propose a data augmentation and expansion framework that enables generative reward models trained on small datasets to achieve comparable performance to those trained on large-scale datasets.
• Score: 3.189559302776161
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The ability to train high-performing reward models with few-shot data is critical for enhancing the efficiency and scalability of Reinforcement Learning from Human Feedback (RLHF). We propose a data augmentation and expansion framework that enables generative reward models trained on small datasets to achieve comparable performance to those trained on large-scale datasets. Traditional methods to train a generative reward model, such as Direct Preference Optimization (DPO), are constrained by inefficiencies in sample pairing and limited data diversity. This work introduces preference refinement, which employs Chain-of-Thought (CoT) sampling to uncover diverse and high-quality preference relationships. It also incorporates a perplexity-based scoring mechanism to assign nuanced preference levels and utilizes Multi-level Direct Preference Optimization (M-DPO) to enable the model to capture finer-grained preference differences between samples. Experimental results demonstrate that the proposed method significantly enhances data efficiency and model performance, enabling reward models trained in a few-shot setting to achieve results on par with those trained on large-scale datasets. This study underscores the potential of data-efficient strategies in advancing reward model optimization, offering a robust solution for low-resource RLHF applications.

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