title: R1-Onevision: Advancing Generalized Multimodal Reasoning through
    Cross-Modal Formalization
summary: 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.
paper_authors._id: 67d3aa3f2f42ed5552e8ea0c
paper_authors.name: Yi Yang
paper_authors._id: 67d3aa3f2f42ed5552e8ea0d
paper_authors.name: Xiaoxuan He
paper_authors._id: 67d3aa3f2f42ed5552e8ea0e
paper_authors.name: Hongkun Pan
paper_authors._id: 67d3aa3f2f42ed5552e8ea0f
paper_authors.name: Xiyan Jiang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-14T11:11:04.147Z
paper_authors._id: 674972973dc92067bd606877
paper_authors.avatarUrl: /avatars/8366448b45baf7d7f3d3d2b8793479ed.svg
paper_authors.fullname: Jiang Xiyan
paper_authors.type: user
paper_authors.user: Emilia515
paper_authors._id: 67d3aa3f2f42ed5552e8ea10
paper_authors.name: Yan Deng
paper_authors._id: 67d3aa3f2f42ed5552e8ea11
paper_authors.name: Xingtao Yang
paper_authors._id: 67d3aa3f2f42ed5552e8ea12
paper_authors.name: Haoyu Lu
paper_authors._id: 67d3aa3f2f42ed5552e8ea13
paper_authors.name: Dacheng Yin
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-14T11:10:44.422Z
paper_authors._id: 650a932519f6f953cb5798ef
paper_authors.avatarUrl: /avatars/c3b4bdc6d4dc13642b69664876532af9.svg
paper_authors.fullname: Dacheng Yin
paper_authors.type: user
paper_authors.user: dcyin
paper_authors._id: 67d3aa3f2f42ed5552e8ea14
paper_authors.name: Fengyun Rao
paper_authors._id: 67d3aa3f2f42ed5552e8ea15
paper_authors.name: Minfeng Zhu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-14T11:10:30.044Z
paper_authors._id: 66771ddf6a685a4ffb0fbf36
paper_authors.avatarUrl: /avatars/55c9f32c710aadab0f71d53180a464a2.svg
paper_authors.fullname: Minfeng Zhu
paper_authors.type: user
paper_authors.user: twilightsnow
paper_authors._id: 67d3aa3f2f42ed5552e8ea16
paper_authors.name: Bo Zhang
paper_authors._id: 67d3aa3f2f42ed5552e8ea17
paper_authors.name: Wei Chen
paper_title: R1-Onevision: Advancing Generalized Multimodal Reasoning through
    Cross-Modal Formalization
paper_summary: 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.
paper_ai_summary: 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.
paper_ai_keywords: 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