Reward-aware Preference Optimization: A Unified Mathematical Framework for Model Alignment

• URL: http://arxiv.org/abs/2502.00203v2
• Date: Fri, 07 Feb 2025 20:38:07 GMT
• Title: Reward-aware Preference Optimization: A Unified Mathematical Framework for Model Alignment
• Authors: Shengyang Sun, Yian Zhang, Alexander Bukharin, David Mosallanezhad, Jiaqi Zeng, Soumye Singhal, Gerald Shen, Adithya Renduchintala, Tugrul Konuk, Yi Dong, Zhilin Wang, Dmitry Chichkov, Olivier Delalleau, Oleksii Kuchaiev,
• Abstract summary: This paper introduces Reward-Aware Preference Optimization (RPO), a mathematical framework that unifies popular preference optimization techniques. RPO provides a structured approach to disentangle and systematically study the impact of various design choices. We propose a new experimental setup that enables the clean and direct ablation of such design choices.
• Score: 45.45508377432791
• License:
• Abstract: The rapid development of large language model (LLM) alignment algorithms has resulted in a complex and fragmented landscape, with limited clarity on the effectiveness of different methods and their inter-connections. This paper introduces Reward-Aware Preference Optimization (RPO), a mathematical framework that unifies popular preference optimization techniques in LLM alignment, including DPO, IPO, SimPO, and REINFORCE (LOO), among others. RPO provides a structured approach to disentangle and systematically study the impact of various design choices, such as the optimization objective, the number of responses per prompt, and the use of implicit versus explicit reward models, on LLM preference optimization. We additionally propose a new experimental setup that enables the clean and direct ablation of such design choices. Through an extensive series of ablation studies within the RPO framework, we gain insights into the critical factors shaping model alignment, offering practical guidance on the most effective strategies for improving LLM alignment.

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