More Thought, Less Accuracy? On the Dual Nature of Reasoning in
  Vision-Language Models
Authors: name: Xinyu Tian, name: Shu Zou, name: Zhaoyuan Yang, name: Mengqi He, name: Fabian Waschkowski, name: Lukas Wesemann, name: Peter Tu, name: Jing Zhang

Reasoning has emerged as a pivotal capability in Large Language Models
(LLMs). Through Reinforcement Learning (RL), typically Group Relative Policy
Optimization (GRPO), these models are able to solve complex tasks such as
mathematics and code generation. Building on these advances, recent research
has sought to extend reasoning to Vision-Language Models (VLMs), yielding
promising results across diverse visual tasks. Despite this progress, our study
uncovers the dual nature of multimodal reasoning: while it substantially
enhances logical inference and facilitates performance on challenging problems,
it may gradually impair perceptual grounding, leading to recognition failures
on otherwise basic visual questions. Through further analysis, we attribute
this phenomenon to visual forgetting, wherein prolonged reasoning causes the
model to increasingly disregard visual input. To address this, we propose
Vision-Anchored Policy Optimization (VAPO), a simple yet effective method that
explicitly steers the reasoning process toward visually grounded trajectories.
Our result model, VAPO-Thinker-7B, significantly strengthens the model's
reliance on visual information and achieves new state-of-the-art results on a
wide range of established benchmarks. Project page:
https://xytian1008.github.io/VAPO/