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About this paper
Zixian Guo, Ming Liu, Qilong Wang, Zhilong Ji, Jinfeng Bai, Lei Zhang, Wangmeng Zuo
Researchers from Harbin Institute of Technology, The Hong Kong Polytechnic University, and Tianjin University propose a decoupled vision-language reasoning framework, separating visual interpretation from linguistic reasoning. This approach improved accuracy on various multi-modal benchmarks, outperforming LLaVA-CoT-11B by 6.3 percentage points on MathVerse and achieving competitive results with significantly larger models in visually intensive math problems.
Key insights
- Decoupling visual interpretation from linguistic reasoning allows leveraging the distinct strengths of specialized models, leading to improved performance in complex, visually intensive tasks.
- Reinforcement learning, when guided by outcome-based rewards, effectively optimizes both the visual interpretation module's description generation and the linguistic reasoning module's problem-solving capabilities.
- Preprocessing image descriptions to improve conciseness and logical structure significantly enhances the supervised fine-tuning stage and reduces the visual interpretation model's tendency to generate verbose or hallucinated content.
Summary by alphaXiv.
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