title: Advancing Multimodal Reasoning: From Optimized Cold Start to Staged
    Reinforcement Learning
summary: Inspired by the remarkable reasoning capabilities of Deepseek-R1 in complex
  textual tasks, many works attempt to incentivize similar capabilities in
  Multimodal Large Language Models (MLLMs) by directly applying reinforcement
  learning (RL). However, they still struggle to activate complex reasoning. In
  this paper, rather than examining multimodal RL in isolation, we delve into
  current training pipelines and identify three crucial phenomena: 1) Effective
  cold start initialization is critical for enhancing MLLM reasoning.
  Intriguingly, we find that initializing with carefully selected text data alone
  can lead to performance surpassing many recent multimodal reasoning models,
  even before multimodal RL. 2) Standard GRPO applied to multimodal RL suffers
  from gradient stagnation, which degrades training stability and performance. 3)
  Subsequent text-only RL training, following the multimodal RL phase, further
  enhances multimodal reasoning. This staged training approach effectively
  balances perceptual grounding and cognitive reasoning development. By
  incorporating the above insights and addressing multimodal RL issues, we
  introduce ReVisual-R1, achieving a new state-of-the-art among open-source 7B
  MLLMs on challenging benchmarks including MathVerse, MathVision, WeMath,
  LogicVista, DynaMath, and challenging AIME2024 and AIME2025.
paper_authors._id: 684117e22db29aa7b403af8d
paper_authors.name: Shuang Chen
paper_authors._id: 684117e22db29aa7b403af8e
paper_authors.name: Yue Guo
paper_authors._id: 684117e22db29aa7b403af8f
paper_authors.name: Zhaochen Su
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-06-05T07:26:45.759Z
paper_authors._id: 64264095ba51f8a2136946a0
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/64264095ba51f8a2136946a0/FR33boVpkDXcrvGMBmprF.jpeg
paper_authors.fullname: Zhaochen Su
paper_authors.type: user
paper_authors.user: Warrieryes
paper_authors._id: 684117e22db29aa7b403af90
paper_authors.name: Yafu Li
paper_authors._id: 684117e22db29aa7b403af91
paper_authors.name: Yulun Wu
paper_authors._id: 684117e22db29aa7b403af92
paper_authors.name: Jiacheng Chen
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-06-05T07:48:38.463Z
paper_authors._id: 65352acb7139c5dd8d9a8590
paper_authors.avatarUrl: /avatars/e2ff22b596aee45cdfb8f68dc15572f9.svg
paper_authors.fullname: JiachengChen
paper_authors.type: user
paper_authors.user: JC-Chen
paper_authors._id: 684117e22db29aa7b403af93
paper_authors.name: Jiayu Chen
paper_authors._id: 684117e22db29aa7b403af94
paper_authors.name: Weijie Wang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-06-06T07:41:59.293Z
paper_authors._id: 66699aa8a33847217b5a49c7
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/u8Z-6U8U7ARXOpdBDI7Qm.png
paper_authors.fullname: Weijie Wang
paper_authors.type: user
paper_authors.user: lhmd
paper_authors._id: 684117e22db29aa7b403af95
paper_authors.name: Xiaoye Qu
paper_authors._id: 684117e22db29aa7b403af96
paper_authors.name: Yu Cheng
paper_title: Advancing Multimodal Reasoning: From Optimized Cold Start to Staged
    Reinforcement Learning
paper_summary: Inspired by the remarkable reasoning capabilities of Deepseek-R1 in complex
  textual tasks, many works attempt to incentivize similar capabilities in
  Multimodal Large Language Models (MLLMs) by directly applying reinforcement
  learning (RL). However, they still struggle to activate complex reasoning. In
  this paper, rather than examining multimodal RL in isolation, we delve into
  current training pipelines and identify three crucial phenomena: 1) Effective
  cold start initialization is critical for enhancing MLLM reasoning.
  Intriguingly, we find that initializing with carefully selected text data alone
  can lead to performance surpassing many recent multimodal reasoning models,
  even before multimodal RL. 2) Standard GRPO applied to multimodal RL suffers
  from gradient stagnation, which degrades training stability and performance. 3)
  Subsequent text-only RL training, following the multimodal RL phase, further
  enhances multimodal reasoning. This staged training approach effectively
  balances perceptual grounding and cognitive reasoning development. By
  incorporating the above insights and addressing multimodal RL issues, we
  introduce ReVisual-R1, achieving a new state-of-the-art among open-source 7B
  MLLMs on challenging benchmarks including MathVerse, MathVision, WeMath,
  LogicVista, DynaMath, and challenging AIME2024 and AIME2025.