标题： 开放视觉推理器：转移语言认知行为用于视觉推理 显示英文标题

标题： Open Vision Reasoner: Transferring Linguistic Cognitive Behavior for Visual Reasoning

摘要： 大型语言模型（LLMs）卓越的推理能力源于通过可验证奖励进行强化后出现的认知行为。 这项工作研究如何将这一原理转移到多模态大型语言模型（MLLMs）中，以解锁高级视觉推理能力。 我们引入了一个基于Qwen2.5-VL-7B的两阶段范式：首先进行大规模语言冷启动微调，然后进行跨越近1000步的多模态强化学习（RL），其规模超过了所有之前开源努力。 这项开创性工作揭示了三个基本见解：1）由于语言心理意象，行为转移在冷启动阶段意外地早期出现。 2）冷启动广泛记忆视觉行为，而强化学习则关键地区分并扩展有效的模式。 3）转移策略性地偏向高效行为，如视觉反思。 我们提出的模型Open-Vision-Reasoner（OVR）在一系列推理基准测试中取得了最先进的性能，包括MATH500上的95.3%，MathVision上的51.8%和MathVerse上的54.6%。 我们发布我们的模型、数据和训练动态，以推动更强大、行为对齐的多模态推理器的发展。 显示英文摘要

摘要： 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.