More Thought, Less Accuracy? On the Dual Nature of Reasoning in
  Vision-Language Models
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/
68dc984b4159d1f2418f9b70
Xinyu Tian
claimed_verified
2025-10-10T09:39:27.758Z
630c7d9ecb1d6a2384273f1a
/avatars/8ba76de6092e5d9fcc4f23d548befe9a.svg
Xinyu Tian
user
xytian1008
68dc984b4159d1f2418f9b71
Shu Zou
claimed_verified
2025-10-24T16:15:05.949Z
679de3b13d9e2d1228188899
/avatars/768e4dd418567ae066563f3d3a7217fd.svg
shuzou
user
treeZou
68dc984b4159d1f2418f9b72
Zhaoyuan Yang
68dc984b4159d1f2418f9b73
Mengqi He
68dc984b4159d1f2418f9b74
Fabian Waschkowski
68dc984b4159d1f2418f9b75
Lukas Wesemann
68dc984b4159d1f2418f9b76
Peter Tu
68dc984b4159d1f2418f9b77
Jing Zhang
More Thought, Less Accuracy? On the Dual Nature of Reasoning in
  Vision-Language Models
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/
VAPO-Thinker-7B enhances multimodal reasoning by anchoring the process to visual information, improving performance on visual tasks while maintaining logical inference.
Reinforcement Learning Group Relative Policy Optimization Vision-Language Models multimodal reasoning logical inference perceptual grounding visual forgetting Vision-Anchored Policy Optimization