Open Vision Reasoner: Transferring Linguistic Cognitive Behavior for Visual Reasoning
Yana Wei, Liang Zhao, Jianjian Sun, Kangheng Lin, En Yu, jisheng yin, Jingcheng Hu, Yinmin Zhang, Qi Han, Haoran Lv, Zejia Weng, Jia Wang, Zheng Ge, Xiangyu Zhang, Daxin Jiang, Vishal Patel
Cognitive behaviors such as subgoal decomposition, reflection, and verification have emerged as key drivers of reasoning performance in large language models (LLMs) trained with reinforcement learning (RL). While recent studies have formalized these patterns and demonstrated their impact on generalization, it remains unclear whether such behaviors can transfer from language to vision in multimodal settings.In this work, we present a simple yet revealing observation: a vision-language model trained with language-only cold start exhibits surprising gains on visual reasoning tasks, along with emergent visual reflection behaviors. Motivated by DeepSeek-R1 and Open-Reasoner-Zero, we propose a three-stage training pipeline to systematically study cross-modal cognitive behavior transfer: (1) supervised fine-tuning on language-only reasoning data, (2) language-only RL to reinforce cognitive traits, and (3) multimodal RL for cross-modal adaptation.Our 7B model, \textbf{Open-Vision-Reasoner}, achieves strong performance across both language and vision benchmarks, including 94.1% on MATH500, 50.0% on MathVision, and 52.9% on MathVerse. Our visual cognitive behavior analysis further reveals the mechanisms behind these gains and provides a promising path toward general-purpose multimodal reasoning.