title: R1-ShareVL: Incentivizing Reasoning Capability of Multimodal Large
    Language Models via Share-GRPO
summary: In this work, we aim to incentivize the reasoning ability of Multimodal Large
  Language Models (MLLMs) via reinforcement learning (RL) and develop an
  effective approach that mitigates the sparse reward and advantage vanishing
  issues during RL. To this end, we propose Share-GRPO, a novel RL approach that
  tackle these issues by exploring and sharing diverse reasoning trajectories
  over expanded question space. Specifically, Share-GRPO first expands the
  question space for a given question via data transformation techniques, and
  then encourages MLLM to effectively explore diverse reasoning trajectories over
  the expanded question space and shares the discovered reasoning trajectories
  across the expanded questions during RL. In addition, Share-GRPO also shares
  reward information during advantage computation, which estimates solution
  advantages hierarchically across and within question variants, allowing more
  accurate estimation of relative advantages and improving the stability of
  policy training. Extensive evaluations over six widely-used reasoning
  benchmarks showcase the superior performance of our method. Code will be
  available at https://github.com/HJYao00/R1-ShareVL.
paper_authors._id: 6836164664810fd39f82cf6e
paper_authors.name: Huanjin Yao
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-28T12:36:18.475Z
paper_authors._id: 6590e03454f8826173ed5ee6
paper_authors.avatarUrl: /avatars/f5e59d3e58c28a99f2ff39267ca51cdb.svg
paper_authors.fullname: Huanjin Yao
paper_authors.type: user
paper_authors.user: HuanjinYao
paper_authors._id: 6836164664810fd39f82cf6f
paper_authors.name: Qixiang Yin
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-28T12:36:25.597Z
paper_authors._id: 66841df7f0236757f437be00
paper_authors.avatarUrl: /avatars/930d58451152bfd90a0e5549552790c0.svg
paper_authors.fullname: Qixiang Yin
paper_authors.type: user
paper_authors.user: Lucasqixiang
paper_authors._id: 6836164664810fd39f82cf70
paper_authors.name: Jingyi Zhang
paper_authors._id: 6836164664810fd39f82cf71
paper_authors.name: Min Yang
paper_authors._id: 6836164664810fd39f82cf72
paper_authors.name: Yibo Wang
paper_authors._id: 6836164664810fd39f82cf73
paper_authors.name: Wenhao Wu
paper_authors._id: 6836164664810fd39f82cf74
paper_authors.name: Fei Su
paper_authors._id: 6836164664810fd39f82cf75
paper_authors.name: Li Shen
paper_authors._id: 6836164664810fd39f82cf76
paper_authors.name: Minghui Qiu
paper_authors._id: 6836164664810fd39f82cf77
paper_authors.name: Dacheng Tao
paper_authors._id: 6836164664810fd39f82cf78
paper_authors.name: Jiaxing Huang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-28T12:37:33.188Z
paper_authors._id: 67f3a9ec327c0599a0e7c2f0
paper_authors.avatarUrl: /avatars/41674aad656c49487cb18fdd2a44c07c.svg
paper_authors.fullname: Huang jiaxing
paper_authors.type: user
paper_authors.user: WisteriaWilson
paper_title: R1-ShareVL: Incentivizing Reasoning Capability of Multimodal Large
    Language Models via Share-GRPO
paper_summary: In this work, we aim to incentivize the reasoning ability of Multimodal Large
  Language Models (MLLMs) via reinforcement learning (RL) and develop an
  effective approach that mitigates the sparse reward and advantage vanishing
  issues during RL. To this end, we propose Share-GRPO, a novel RL approach that
  tackle these issues by exploring and sharing diverse reasoning trajectories
  over expanded question space. Specifically, Share-GRPO first expands the
  question space for a given question via data transformation techniques, and
  then encourages MLLM to effectively explore diverse reasoning trajectories over
  the expanded question space and shares the discovered reasoning trajectories
  across the expanded questions during RL. In addition, Share-GRPO also shares
  reward information during advantage computation, which estimates solution
  advantages hierarchically across and within question variants, allowing more
  accurate estimation of relative advantages and improving the stability of
  policy training. Extensive evaluations over six widely-used reasoning
  benchmarks showcase the superior performance of our method. Code will be
  available at https://github.com/HJYao00/R1-ShareVL.
paper_ai_summary: Share-GRPO, a novel reinforcement learning approach, enhances Multimodal Large Language Models by expanding the question space, sharing diverse reasoning trajectories, and hierarchical advantage computation.
paper_ai_keywords: Multimodal Large Language Models reinforcement learning sparse reward advantage vanishing Share-GRPO data transformation techniques reasoning trajectories question space hierarchical advantage computation