Joint Reward Modeling: Internalizing Chain-of-Thought for Efficient Visual Reward Models

• URL: http://arxiv.org/abs/2602.07533v1
• Date: Sat, 07 Feb 2026 13:09:41 GMT
• Title: Joint Reward Modeling: Internalizing Chain-of-Thought for Efficient Visual Reward Models
• Authors: Yankai Yang, Yancheng Long, Hongyang Wei, Wei Chen, Tianke Zhang, Kaiyu Jiang, Haonan Fan, Changyi Liu, Jiankang Chen, Kaiyu Tang, Bin Wen, Fan Yang, Tingting Gao, Han Li, Shuo Yang,
• Abstract summary: Generative reward models offer stronger semantic understanding and reasoning, but they are costly at inference time and difficult to align directly with human preferences.<n>We propose Joint Reward Modeling (JRM), which jointly optimize preference learning and language modeling on a shared vision-language backbone.<n>JRM achieves state-of-the-art results on MMRB2 and EditReward-Bench, and significantly improves stability and performance in downstream online reinforcement learning.
• Score: 22.77769800361136
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Reward models are critical for reinforcement learning from human feedback, as they determine the alignment quality and reliability of generative models. For complex tasks such as image editing, reward models are required to capture global semantic consistency and implicit logical constraints beyond local similarity. Existing reward modeling approaches have clear limitations. Discriminative reward models align well with human preferences but struggle with complex semantics due to limited reasoning supervision. Generative reward models offer stronger semantic understanding and reasoning, but they are costly at inference time and difficult to align directly with human preferences. To this end, we propose Joint Reward Modeling (JRM), which jointly optimizes preference learning and language modeling on a shared vision-language backbone. This approach internalizes the semantic and reasoning capabilities of generative models into efficient discriminative representations, enabling fast and accurate evaluation. JRM achieves state-of-the-art results on MMRB2 and EditReward-Bench, and significantly improves stability and performance in downstream online reinforcement learning. These results show that joint training effectively bridges efficiency and semantic understanding in reward modeling.

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