Learning Adaptive Reasoning Paths for Efficient Visual Reasoning

• URL: http://arxiv.org/abs/2604.14568v1
• Date: Thu, 16 Apr 2026 02:59:30 GMT
• Title: Learning Adaptive Reasoning Paths for Efficient Visual Reasoning
• Abstract summary: We propose textbfAVR, an adaptive visual reasoning framework.<n>It decomposes visual reasoning into three cognitive functions: visual perception,text reasoning, and answer application.<n>Experiments show that it reduces token usage by 50--90% while maintaining overall accuracy, especially in perception-intensive tasks.
• Score: 23.47120476765963
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Visual reasoning models (VRMs) have recently shown strong cross-modal reasoning capabilities by integrating visual perception with language reasoning. However, they often suffer from overthinking, producing unnecessarily long reasoning chains for any tasks. We attribute this issue to \textbf{Reasoning Path Redundancy} in visual reasoning: many visual questions do not require the full reasoning process. To address this, we propose \textbf{AVR}, an adaptive visual reasoning framework that decomposes visual reasoning into three cognitive functions: visual perception, logical reasoning, and answer application. It further enables models to dynamically choose among three response formats: Full Format, Perception-Only Format, and Direct Answer. AVR is trained with FS-GRPO, an adaptation of Group Relative Policy Optimization that encourages the model to select the most efficient reasoning format while preserving correctness. Experiments on multiple vision-language benchmarks show that AVR reduces token usage by 50--90\% while maintaining overall accuracy, especially in perception-intensive tasks. These results demonstrate that adaptive visual reasoning can effectively mitigate overthinking in VRMs. Code and data are available at: https://github.com/RunRiotComeOn/AVR.

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