title: Vision-R1: Incentivizing Reasoning Capability in Multimodal Large
    Language Models
summary: DeepSeek-R1-Zero has successfully demonstrated the emergence of reasoning
  capabilities in LLMs purely through Reinforcement Learning (RL). Inspired by
  this breakthrough, we explore how RL can be utilized to enhance the reasoning
  capability of MLLMs. However, direct training with RL struggles to activate
  complex reasoning capabilities such as questioning and reflection in MLLMs, due
  to the absence of substantial high-quality multimodal reasoning data. To
  address this issue, we propose the reasoning MLLM, Vision-R1, to improve
  multimodal reasoning capability. Specifically, we first construct a
  high-quality multimodal CoT dataset without human annotations by leveraging an
  existing MLLM and DeepSeek-R1 through modality bridging and data filtering to
  obtain a 200K multimodal CoT dataset, Vision-R1-cold dataset. It serves as
  cold-start initialization data for Vision-R1. To mitigate the optimization
  challenges caused by overthinking after cold start, we propose Progressive
  Thinking Suppression Training (PTST) strategy and employ Group Relative Policy
  Optimization (GRPO) with the hard formatting result reward function to
  gradually refine the model's ability to learn correct and complex reasoning
  processes on a 10K multimodal math dataset. Comprehensive experiments show our
  model achieves an average improvement of sim6% across various multimodal
  math reasoning benchmarks. Vision-R1-7B achieves a 73.5% accuracy on the widely
  used MathVista benchmark, which is only 0.4% lower than the leading reasoning
  model, OpenAI O1. The datasets and code will be released in:
  https://github.com/Osilly/Vision-R1 .
paper_authors._id: 67cfb6495944a8e54f24cd9a
paper_authors.name: Wenxuan Huang
paper_authors.status: claimed_verified
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paper_authors.fullname: Wenxuan Huang
paper_authors.type: user
paper_authors.user: Osilly
paper_authors._id: 67cfb6495944a8e54f24cd9b
paper_authors.name: Bohan Jia
paper_authors.status: admin_assigned
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paper_authors.fullname: Bohan Jia
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paper_authors.user: BohanJia
paper_authors._id: 67cfb6495944a8e54f24cd9c
paper_authors.name: Zijie Zhai
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paper_authors.fullname: Zijie Zhai
paper_authors.type: user
paper_authors.user: skyz1010
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paper_authors.name: Shaosheng Cao
paper_authors._id: 67cfb6495944a8e54f24cd9e
paper_authors.name: Zheyu Ye
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-11T14:33:09.475Z
paper_authors._id: 61eeecc16f01506a837664ce
paper_authors.avatarUrl: /avatars/61ef427943e2945f512e6e3c705991c7.svg
paper_authors.fullname: Zheyu Ye
paper_authors.type: user
paper_authors.user: zheyuye
paper_authors._id: 67cfb6495944a8e54f24cd9f
paper_authors.name: Fei Zhao
paper_authors._id: 67cfb6495944a8e54f24cda0
paper_authors.name: Yao Hu
paper_authors._id: 67cfb6495944a8e54f24cda1
paper_authors.name: Shaohui Lin
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-11T14:33:21.819Z
paper_authors._id: 6481ace6578646b5c2370b7e
paper_authors.avatarUrl: /avatars/218ffacc705c1fe0e23ef1217f76b374.svg
paper_authors.fullname: linshaohui
paper_authors.type: user
paper_authors.user: kimlin123
paper_title: Vision-R1: Incentivizing Reasoning Capability in Multimodal Large
    Language Models
paper_summary: DeepSeek-R1-Zero has successfully demonstrated the emergence of reasoning
  capabilities in LLMs purely through Reinforcement Learning (RL). Inspired by
  this breakthrough, we explore how RL can be utilized to enhance the reasoning
  capability of MLLMs. However, direct training with RL struggles to activate
  complex reasoning capabilities such as questioning and reflection in MLLMs, due
  to the absence of substantial high-quality multimodal reasoning data. To
  address this issue, we propose the reasoning MLLM, Vision-R1, to improve
  multimodal reasoning capability. Specifically, we first construct a
  high-quality multimodal CoT dataset without human annotations by leveraging an
  existing MLLM and DeepSeek-R1 through modality bridging and data filtering to
  obtain a 200K multimodal CoT dataset, Vision-R1-cold dataset. It serves as
  cold-start initialization data for Vision-R1. To mitigate the optimization
  challenges caused by overthinking after cold start, we propose Progressive
  Thinking Suppression Training (PTST) strategy and employ Group Relative Policy
  Optimization (GRPO) with the hard formatting result reward function to
  gradually refine the model's ability to learn correct and complex reasoning
  processes on a 10K multimodal math dataset. Comprehensive experiments show our
  model achieves an average improvement of sim6% across various multimodal
  math reasoning benchmarks. Vision-R1-7B achieves a 73.5% accuracy on the widely
  used MathVista benchmark, which is only 0.4% lower than the leading reasoning
  model, OpenAI O1. The datasets and code will be released in:
  https://github.com/Osilly/Vision-R1 .
paper_ai_summary: A reasoning MLLM called Vision-R1 is proposed to enhance multimodal reasoning capabilities using Reinforcement Learning and a constructed multimodal CoT dataset, achieving high performance on multimodal math reasoning benchmarks.
paper_ai_keywords: LLMs RL MLLMs multimodal reasoning Vision-R1 CoT dataset modality bridging data filtering Vision-R1-cold dataset Progressive Thinking Suppression Training (PTST) Group Relative Policy Optimization (GRPO) hard formatting result reward function MathVista benchmark