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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Online-Zugang: | https://arxiv.org/abs/2508.05383 |
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| _version_ | 1866915433771171840 |
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| author | Zhang, Xiangxiang Wei, Jingxuan Zhong, Donghong Chen, Qi Jia, Caijun Tan, Cheng Gu, Jinming Qin, Xiaobo Liu, Zhiping Hu, Liang Sun, Tong Wu, Yuchen Sun, Zewei Lou, Chenwei Zheng, Hua Zhan, Tianyang Wang, Changbao Wu, Shuangzhi Lin, Zefa Guo, Chang Yuan, Sihang Chen, Riwei Zhao, Shixiong Zhang, Yingping Wu, Gaowei Yu, Bihui Wu, Jiahui Zhao, Zhehui Liu, Qianqian Tang, Ruofeng Huang, Xingyue Zhao, Bing Zhang, Mengyang Zhou, Youqiang |
| author_facet | Zhang, Xiangxiang Wei, Jingxuan Zhong, Donghong Chen, Qi Jia, Caijun Tan, Cheng Gu, Jinming Qin, Xiaobo Liu, Zhiping Hu, Liang Sun, Tong Wu, Yuchen Sun, Zewei Lou, Chenwei Zheng, Hua Zhan, Tianyang Wang, Changbao Wu, Shuangzhi Lin, Zefa Guo, Chang Yuan, Sihang Chen, Riwei Zhao, Shixiong Zhang, Yingping Wu, Gaowei Yu, Bihui Wu, Jiahui Zhao, Zhehui Liu, Qianqian Tang, Ruofeng Huang, Xingyue Zhao, Bing Zhang, Mengyang Zhou, Youqiang |
| contents | Existing Vision-Language Models often struggle with complex, multi-question reasoning tasks where partial correctness is crucial for effective learning. Traditional reward mechanisms, which provide a single binary score for an entire response, are too coarse to guide models through intricate problems with multiple sub-parts. To address this, we introduce StructVRM, a method that aligns multimodal reasoning with Structured and Verifiable Reward Models. At its core is a model-based verifier trained to provide fine-grained, sub-question-level feedback, assessing semantic and mathematical equivalence rather than relying on rigid string matching. This allows for nuanced, partial credit scoring in previously intractable problem formats. Extensive experiments demonstrate the effectiveness of StructVRM. Our trained model, Seed-StructVRM, achieves state-of-the-art performance on six out of twelve public multimodal benchmarks and our newly curated, high-difficulty STEM-Bench. The success of StructVRM validates that training with structured, verifiable rewards is a highly effective approach for advancing the capabilities of multimodal models in complex, real-world reasoning domains. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_05383 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | StructVRM: Aligning Multimodal Reasoning with Structured and Verifiable Reward Models Zhang, Xiangxiang Wei, Jingxuan Zhong, Donghong Chen, Qi Jia, Caijun Tan, Cheng Gu, Jinming Qin, Xiaobo Liu, Zhiping Hu, Liang Sun, Tong Wu, Yuchen Sun, Zewei Lou, Chenwei Zheng, Hua Zhan, Tianyang Wang, Changbao Wu, Shuangzhi Lin, Zefa Guo, Chang Yuan, Sihang Chen, Riwei Zhao, Shixiong Zhang, Yingping Wu, Gaowei Yu, Bihui Wu, Jiahui Zhao, Zhehui Liu, Qianqian Tang, Ruofeng Huang, Xingyue Zhao, Bing Zhang, Mengyang Zhou, Youqiang Artificial Intelligence Existing Vision-Language Models often struggle with complex, multi-question reasoning tasks where partial correctness is crucial for effective learning. Traditional reward mechanisms, which provide a single binary score for an entire response, are too coarse to guide models through intricate problems with multiple sub-parts. To address this, we introduce StructVRM, a method that aligns multimodal reasoning with Structured and Verifiable Reward Models. At its core is a model-based verifier trained to provide fine-grained, sub-question-level feedback, assessing semantic and mathematical equivalence rather than relying on rigid string matching. This allows for nuanced, partial credit scoring in previously intractable problem formats. Extensive experiments demonstrate the effectiveness of StructVRM. Our trained model, Seed-StructVRM, achieves state-of-the-art performance on six out of twelve public multimodal benchmarks and our newly curated, high-difficulty STEM-Bench. The success of StructVRM validates that training with structured, verifiable rewards is a highly effective approach for advancing the capabilities of multimodal models in complex, real-world reasoning domains. |
| title | StructVRM: Aligning Multimodal Reasoning with Structured and Verifiable Reward Models |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2508.05383 |