Open Vision Reasoner: Transferring Linguistic Cognitive Behavior for
  Visual Reasoning
Authors: name: Yana Wei; name: Liang Zhao; name: Jianjian Sun; name: Kangheng Lin; name: Jisheng Yin; name: Jingcheng Hu; name: Yinmin Zhang; name: En Yu; name: Haoran Lv; name: Zejia Weng; name: Jia Wang; name: Chunrui Han; name: Yuang Peng; name: Qi Han; name: Zheng Ge; name: Xiangyu Zhang; name: Daxin Jiang; name: Vishal M. Patel
Published: 2025-07-07 17:59:03

Abstract: The remarkable reasoning capability of large language models (LLMs) stems
from cognitive behaviors that emerge through reinforcement with verifiable
rewards. This work investigates how to transfer this principle to Multimodal
LLMs (MLLMs) to unlock advanced visual reasoning. We introduce a two-stage
paradigm built on Qwen2.5-VL-7B: a massive linguistic cold-start fine-tuning,
followed by multimodal reinforcement learning (RL) spanning nearly 1,000 steps,
surpassing all previous open-source efforts in scale. This pioneering work
reveals three fundamental insights: 1) Behavior transfer emerges surprisingly
early in cold start due to linguistic mental imagery. 2) Cold start broadly
memorizes visual behaviors, while RL critically discerns and scales up
effective patterns. 3) Transfer strategically favors high-utility behaviors
such as visual reflection. Our resulting model, Open-Vision-Reasoner (OVR),
achieves state-of-the-art performance on a suite of reasoning benchmarks,
including 95.3% on MATH500, 51.8% on MathVision and 54.6% on MathVerse. We
release our model, data, and training dynamics to catalyze the development of
more capable, behavior-aligned multimodal reasoners.