Unified Reward Model for Multimodal Understanding and Generation
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.
67ce37239f9aaaae837f3894
Yibin Wang
admin_assigned
2025-03-10T09:37:51.835Z
654c6845bac6e6e49895a5b5
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/QwUOUujUAyiV4tAhB_moO.png
SII-Yibin Wang
user
CodeGoat24
67ce37239f9aaaae837f3895
Yuhang Zang
claimed_verified
2025-03-10T08:01:24.660Z
63859cf3b2906edaf83af9f0
https://cdn-avatars.huggingface.co/v1/production/uploads/63859cf3b2906edaf83af9f0/mCgynEtoXdILvzyoPexii.png
Yuhang Zang
user
yuhangzang
67ce37239f9aaaae837f3896
Hao Li
67ce37239f9aaaae837f3897
Cheng Jin
67ce37239f9aaaae837f3898
Jiaqi Wang
admin_assigned
2025-03-10T09:38:17.938Z
64638c4d51fa6e63060521b5
/avatars/c863ace5b1dc788a341bcf4ddbdfaec1.svg
JIaqi
user
Jiaqiwang
Unified Reward Model for Multimodal Understanding and Generation
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.
A unified reward model for multimodal understanding and generation improves the assessment and preference alignment in image and video tasks through joint learning and direct preference optimization.
UnifiedReward reward models preference optimization pairwise ranking pointwise scoring vision model preference alignment Direct Preference Optimization (DPO)