R1-Onevision: Advancing Generalized Multimodal Reasoning through
  Cross-Modal Formalization
Large Language Models have demonstrated remarkable reasoning capability in
complex textual tasks. However, multimodal reasoning, which requires
integrating visual and textual information, remains a significant challenge.
Existing visual-language models often struggle to effectively analyze and
reason visual content, resulting in suboptimal performance on complex reasoning
tasks. Moreover, the absence of comprehensive benchmarks hinders the accurate
assessment of multimodal reasoning capabilities. In this paper, we introduce
R1-Onevision, a multimodal reasoning model designed to bridge the gap between
visual perception and deep reasoning. To achieve this, we propose a cross-modal
reasoning pipeline that transforms images into formal textural representations,
enabling precise language-based reasoning. Leveraging this pipeline, we
construct the R1-Onevision dataset which provides detailed, step-by-step
multimodal reasoning annotations across diverse domains. We further develop the
R1-Onevision model through supervised fine-tuning and reinforcement learning to
cultivate advanced reasoning and robust generalization abilities. To
comprehensively evaluate multimodal reasoning performance across different
grades, we introduce R1-Onevision-Bench, a benchmark aligned with human
educational stages, covering exams from junior high school to university and
beyond. Experimental results show that R1-Onevision achieves state-of-the-art
performance, outperforming models such as GPT-4o and Qwen2.5-VL on multiple
challenging multimodal reasoning benchmarks.
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Yi Yang
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Xiaoxuan He
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shreddedpork
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Hongkun Pan
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Xiyan Jiang
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Jiang Xiyan
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Yan Deng
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Xingtao Yang
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Haoyu Lu
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Haoyu Lu
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Nealeon
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Dacheng Yin
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Dacheng Yin
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dcyin
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Fengyun Rao
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Minfeng Zhu
admin_assigned
2025-03-14T11:10:30.044Z
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Minfeng Zhu
user
twilightsnow
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Bo Zhang
67d3aa3f2f42ed5552e8ea17
Wei Chen
R1-Onevision: Advancing Generalized Multimodal Reasoning through
  Cross-Modal Formalization
Large Language Models have demonstrated remarkable reasoning capability in
complex textual tasks. However, multimodal reasoning, which requires
integrating visual and textual information, remains a significant challenge.
Existing visual-language models often struggle to effectively analyze and
reason visual content, resulting in suboptimal performance on complex reasoning
tasks. Moreover, the absence of comprehensive benchmarks hinders the accurate
assessment of multimodal reasoning capabilities. In this paper, we introduce
R1-Onevision, a multimodal reasoning model designed to bridge the gap between
visual perception and deep reasoning. To achieve this, we propose a cross-modal
reasoning pipeline that transforms images into formal textural representations,
enabling precise language-based reasoning. Leveraging this pipeline, we
construct the R1-Onevision dataset which provides detailed, step-by-step
multimodal reasoning annotations across diverse domains. We further develop the
R1-Onevision model through supervised fine-tuning and reinforcement learning to
cultivate advanced reasoning and robust generalization abilities. To
comprehensively evaluate multimodal reasoning performance across different
grades, we introduce R1-Onevision-Bench, a benchmark aligned with human
educational stages, covering exams from junior high school to university and
beyond. Experimental results show that R1-Onevision achieves state-of-the-art
performance, outperforming models such as GPT-4o and Qwen2.5-VL on multiple
challenging multimodal reasoning benchmarks.
R1-Onevision is a multimodal reasoning model that integrates visual and textual information using a cross-modal reasoning pipeline, achieving state-of-the-art performance on diverse multimodal reasoning tasks.
Large Language Models multimodal reasoning visual-language models formal textural representations cross-modal reasoning pipeline supervised fine-tuning reinforcement learning R1-Onevision dataset R1-Onevision-Bench GPT-4o Qwen2.5-VL