Unified Reward Model for Multimodal Understanding and Generation
Authors: name: Yibin Wang, name: Yuhang Zang, name: Hao Li, name: Cheng Jin, name: Jiaqi Wang

Recent advances in human preference alignment have significantly enhanced
multimodal generation and understanding. A key approach is training reward
models to guide preference optimization. However, existing models are often
task-specific, limiting their adaptability across diverse visual applications.
We also argue that jointly learning to assess multiple tasks may foster a
synergistic effect, where improved image understanding enhances image
generation assessment, and refined image evaluation benefits video assessment
through better frame analysis. To this end, this paper proposes UnifiedReward,
the first unified reward model for multimodal understanding and generation
assessment, enabling both pairwise ranking and pointwise scoring, which can be
employed for vision model preference alignment. Specifically, (1) we first
develop UnifiedReward on our constructed large-scale human preference dataset,
including both image and video generation/understanding tasks. (2) Then, it is
utilized to automatically construct high-quality preference pair data based on
the vision models, fine-gradually filtering their outputs through pair ranking
and point sifting. (3) Finally, these data are used for their preference
alignment through Direct Preference Optimization (DPO). Experimental results
demonstrate that joint learning to assess diverse visual tasks can lead to
substantial mutual benefits and we apply our pipeline to both image and video
understanding/generation tasks, significantly improving the performance in each
domain.