感知到推理：解耦感知与推理以实现细粒度视觉推理

摘要

细粒度视觉推理对视觉语言模型仍是挑战，尤其是当微小但关键的视觉线索隐藏在高分辨率图像中时。现有方法依赖重复裁剪或测试时视觉搜索以引入局部证据，但通常未能明确区分感知与推理。本文提出感知到推理（P2R）统一框架，将细粒度视觉推理表述为一个两阶段过程：模型首先作为感知器定位与问题相关的证据，然后作为推理器基于标注图像与裁剪区域回答问题。为进一步使训练与该解耦框架对齐，我们引入感知-推理交替GRPO（PRA-GRPO），一种角色感知的强化学习策略，仅在最终答案监督下交替进行感知聚焦与推理聚焦的更新。基于Qwen3-VL-Instruct-2B/4B/8B，P2R在多个模型规模上持续提升性能。其中，P2R-4B在V-Star上达到93.2%，在HR-Bench-4K上达到81.9%，在HR-Bench-8K上达到80.5%，大幅超越对应基线。进一步实验表明，P2R的优势不仅限于高分辨率基准，还延伸至更广泛的多模态推理任务。这些结果表明，显式解耦感知与推理为细粒度视觉推理提供了有效框架。

English

Fine-grained visual reasoning remains challenging for vision-language models, especially when small but critical visual cues are buried in high-resolution images. Existing approaches rely on repeated cropping or test-time visual search to introduce local evidence, but they typically do not explicitly distinguish perception from reasoning. In this paper, we propose Perceive-to-Reason (P2R), a unified framework that formulates fine-grained visual reasoning as a two-stage process: the model first localizes question-relevant evidence as a Perceiver, and then answers the question as a Reasoner based on the annotated image and cropped regions. To better align training with this decoupled formulation, we further introduce Perception-Reasoning Alternating GRPO (PRA-GRPO), a role-aware reinforcement learning strategy that alternates between perception-focused and reasoning-focused updates using only final-answer supervision. Built on top of Qwen3-VL-Instruct-2B/4B/8B, P2R consistently improves performance across model scales. In particular, P2R-4B achieves 93.2% on V-Star, 81.9% on HR-Bench-4K, and 80.5% on HR-Bench-8K, substantially outperforming its corresponding backbone. Further experiments show that the benefits of P2R extend beyond high-resolution benchmarks to broader multimodal reasoning tasks. These results suggest that explicitly decoupling perception from reasoning provides an effective framework for fine-grained visual reasoning.