Open Vision Reasoner: Transferring Linguistic Cognitive Behavior for
  Visual Reasoning
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.
6871194fc8391850d60979b0
Yana Wei
claimed_verified
2025-07-15T19:10:59.530Z
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Yana Wei
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Liang Zhao
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Jianjian Sun
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Kangheng Lin
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Jisheng Yin
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Jingcheng Hu
claimed_verified
2025-07-17T08:21:29.127Z
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Jingcheng Hu
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Yinmin Zhang
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En Yu
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Haoran Lv
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Zejia Weng
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Jia Wang
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Chunrui Han
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Yuang Peng
claimed_verified
2025-07-15T19:10:57.683Z
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Yuang Peng
user
yuangpeng
6871194fc8391850d60979bd
Qi Han
6871194fc8391850d60979be
Zheng Ge
6871194fc8391850d60979bf
Xiangyu Zhang
6871194fc8391850d60979c0
Daxin Jiang
6871194fc8391850d60979c1
Vishal M. Patel
Open Vision Reasoner: Transferring Linguistic Cognitive Behavior for
  Visual Reasoning
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.
A two-stage paradigm using Qwen2.5-VL-7B and multimodal reinforcement learning achieves state-of-the-art performance in visual reasoning benchmarks.
large language models Multimodal LLMs Qwen2.5-VL-7B massive linguistic cold-start fine-tuning multimodal reinforcement learning Open-Vision-Reasoner MATH500 MathVision MathVerse