VReST: Enhancing Reasoning in Large Vision-Language Models through Tree Search and Self-Reward Mechanism
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866913887949946880 |
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| author | Zhang, Congzhi Peng, Jiawei Wang, Zhenglin Lai, Yilong Sun, Haowen Chang, Heng Ma, Fei Yu, Weijiang |
| author_facet | Zhang, Congzhi Peng, Jiawei Wang, Zhenglin Lai, Yilong Sun, Haowen Chang, Heng Ma, Fei Yu, Weijiang |
| contents | Large Vision-Language Models (LVLMs) have shown exceptional performance in multimodal tasks, but their effectiveness in complex visual reasoning is still constrained, especially when employing Chain-of-Thought prompting techniques. In this paper, we propose VReST, a novel training-free approach that enhances Reasoning in LVLMs through Monte Carlo Tree Search and Self-Reward mechanisms. VReST meticulously traverses the reasoning landscape by establishing a search tree, where each node encapsulates a reasoning step, and each path delineates a comprehensive reasoning sequence. Our innovative multimodal Self-Reward mechanism assesses the quality of reasoning steps by integrating the utility of sub-questions, answer correctness, and the relevance of vision-language clues, all without the need for additional models. VReST surpasses current prompting methods and secures state-of-the-art performance across three multimodal mathematical reasoning benchmarks. Furthermore, it substantiates the efficacy of test-time scaling laws in multimodal tasks, offering a promising direction for future research. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2506_08691 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | VReST: Enhancing Reasoning in Large Vision-Language Models through Tree Search and Self-Reward Mechanism Zhang, Congzhi Peng, Jiawei Wang, Zhenglin Lai, Yilong Sun, Haowen Chang, Heng Ma, Fei Yu, Weijiang Computer Vision and Pattern Recognition Large Vision-Language Models (LVLMs) have shown exceptional performance in multimodal tasks, but their effectiveness in complex visual reasoning is still constrained, especially when employing Chain-of-Thought prompting techniques. In this paper, we propose VReST, a novel training-free approach that enhances Reasoning in LVLMs through Monte Carlo Tree Search and Self-Reward mechanisms. VReST meticulously traverses the reasoning landscape by establishing a search tree, where each node encapsulates a reasoning step, and each path delineates a comprehensive reasoning sequence. Our innovative multimodal Self-Reward mechanism assesses the quality of reasoning steps by integrating the utility of sub-questions, answer correctness, and the relevance of vision-language clues, all without the need for additional models. VReST surpasses current prompting methods and secures state-of-the-art performance across three multimodal mathematical reasoning benchmarks. Furthermore, it substantiates the efficacy of test-time scaling laws in multimodal tasks, offering a promising direction for future research. |
| title | VReST: Enhancing Reasoning in Large Vision-Language Models through Tree Search and Self-Reward Mechanism |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2506.08691 |