Fast-Slow Thinking for Large Vision-Language Model Reasoning

• URL: http://arxiv.org/abs/2504.18458v1
• Date: Fri, 25 Apr 2025 16:11:23 GMT
• Title: Fast-Slow Thinking for Large Vision-Language Model Reasoning
• Authors: Wenyi Xiao, Leilei Gan, Weilong Dai, Wanggui He, Ziwei Huang, Haoyuan Li, Fangxun Shu, Zhelun Yu, Peng Zhang, Hao Jiang, Fei Wu,
• Abstract summary: We present textbfFAST, a framework that adapts reasoning depth based on question characteristics.<n>FAST achieves state-of-the-art accuracy with over 10% relative improvement compared to the base model.
• Score: 22.084891053164686
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent advances in large vision-language models (LVLMs) have revealed an \textit{overthinking} phenomenon, where models generate verbose reasoning across all tasks regardless of questions. To address this issue, we present \textbf{FAST}, a novel \textbf{Fa}st-\textbf{S}low \textbf{T}hinking framework that dynamically adapts reasoning depth based on question characteristics. Through empirical analysis, we establish the feasibility of fast-slow thinking in LVLMs by investigating how response length and data distribution affect performance. We develop FAST-GRPO with three components: model-based metrics for question characterization, an adaptive thinking reward mechanism, and difficulty-aware KL regularization. Experiments across seven reasoning benchmarks demonstrate that FAST achieves state-of-the-art accuracy with over 10\% relative improvement compared to the base model, while reducing token usage by 32.7-67.3\% compared to previous slow-thinking approaches, effectively balancing reasoning length and accuracy.

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