PerPO: Perceptual Preference Optimization via Discriminative Rewarding
Keywords: MLLMs; RLHF; DPO
TL;DR: Perceptual Preference Optimization enhances MLLMs' visual comprehension ability through discriminative rewarding.
Abstract: This paper presents Perceptual Preference Optimization (PerPO), a perception alignment method aimed at addressing the visual discrimination challenges in generative pre-trained multimodal large language models (MLLMs). PerPO employs discriminative rewarding and listwise preference optimization to align MLLMs with human visual perception processes. By utilizing the reward as a quantitative margin for ranking, our method effectively bridges generative preference optimization and discriminative empirical risk minimization. PerPO significantly enhances MLLMs’ visual discrimination capabilities while maintaining their generative strengths, mitigates image-unconditional reward hacking, and ensures consistent performance across visual tasks. This work marks a crucial step towards more perceptually aligned and versatile MLLMs. We also anticipate that PerPO will inspire the community to reconsider MLLM alignment strategies.
Primary Area: foundation or frontier models, including LLMs
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Submission Number: 1779
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