title: Reinforcement Fine-Tuning Powers Reasoning Capability of Multimodal
    Large Language Models
summary: Standing in 2025, at a critical juncture in the pursuit of Artificial General
  Intelligence (AGI), reinforcement fine-tuning (RFT) has demonstrated
  significant potential in enhancing the reasoning capability of large language
  models (LLMs) and has led to the development of cutting-edge AI models such as
  OpenAI-o1 and DeepSeek-R1. Moreover, the efficient application of RFT to
  enhance the reasoning capability of multimodal large language models (MLLMs)
  has attracted widespread attention from the community. In this position paper,
  we argue that reinforcement fine-tuning powers the reasoning capability of
  multimodal large language models. To begin with, we provide a detailed
  introduction to the fundamental background knowledge that researchers
  interested in this field should be familiar with. Furthermore, we meticulously
  summarize the improvements of RFT in powering reasoning capability of MLLMs
  into five key points: diverse modalities, diverse tasks and domains, better
  training algorithms, abundant benchmarks and thriving engineering frameworks.
  Finally, we propose five promising directions for future research that the
  community might consider. We hope that this position paper will provide
  valuable insights to the community at this pivotal stage in the advancement
  toward AGI. Summary of works done on RFT for MLLMs is available at
  https://github.com/Sun-Haoyuan23/Awesome-RL-based-Reasoning-MLLMs.
paper_authors._id: 68351f7a06b4dae20a214442
paper_authors.name: Haoyuan Sun
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-27T12:36:26.780Z
paper_authors._id: 65e2d43f9fb58a5115253049
paper_authors.avatarUrl: /avatars/46bd4ae27eaa23802cef3d91626897b5.svg
paper_authors.fullname: Haoyuan Sun
paper_authors.type: user
paper_authors.user: xiaonengmiao
paper_authors._id: 68351f7a06b4dae20a214443
paper_authors.name: Jiaqi Wu
paper_authors._id: 68351f7a06b4dae20a214444
paper_authors.name: Bo Xia
paper_authors._id: 68351f7a06b4dae20a214445
paper_authors.name: Yifu Luo
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-27T12:36:42.662Z
paper_authors._id: 681d7bc1e36b83d25c8c4581
paper_authors.avatarUrl: /avatars/f3a28ec01dc1c24ad50e4248497dec22.svg
paper_authors.fullname: Yifu Luo
paper_authors.type: user
paper_authors.user: happyyifu
paper_authors._id: 68351f7a06b4dae20a214446
paper_authors.name: Yifei Zhao
paper_authors._id: 68351f7a06b4dae20a214447
paper_authors.name: Kai Qin
paper_authors._id: 68351f7a06b4dae20a214448
paper_authors.name: Xufei Lv
paper_authors._id: 68351f7a06b4dae20a214449
paper_authors.name: Tiantian Zhang
paper_authors._id: 68351f7a06b4dae20a21444a
paper_authors.name: Yongzhe Chang
paper_authors._id: 68351f7a06b4dae20a21444b
paper_authors.name: Xueqian Wang
paper_title: Reinforcement Fine-Tuning Powers Reasoning Capability of Multimodal
    Large Language Models
paper_summary: Standing in 2025, at a critical juncture in the pursuit of Artificial General
  Intelligence (AGI), reinforcement fine-tuning (RFT) has demonstrated
  significant potential in enhancing the reasoning capability of large language
  models (LLMs) and has led to the development of cutting-edge AI models such as
  OpenAI-o1 and DeepSeek-R1. Moreover, the efficient application of RFT to
  enhance the reasoning capability of multimodal large language models (MLLMs)
  has attracted widespread attention from the community. In this position paper,
  we argue that reinforcement fine-tuning powers the reasoning capability of
  multimodal large language models. To begin with, we provide a detailed
  introduction to the fundamental background knowledge that researchers
  interested in this field should be familiar with. Furthermore, we meticulously
  summarize the improvements of RFT in powering reasoning capability of MLLMs
  into five key points: diverse modalities, diverse tasks and domains, better
  training algorithms, abundant benchmarks and thriving engineering frameworks.
  Finally, we propose five promising directions for future research that the
  community might consider. We hope that this position paper will provide
  valuable insights to the community at this pivotal stage in the advancement
  toward AGI. Summary of works done on RFT for MLLMs is available at
  https://github.com/Sun-Haoyuan23/Awesome-RL-based-Reasoning-MLLMs.
paper_ai_summary: Reinforcement fine-tuning significantly enhances the reasoning capabilities of multimodal large language models through diverse modalities, tasks, algorithms, benchmarks, and frameworks.
paper_ai_keywords: reinforcement fine-tuning multimodal large language models OpenAI-o1 DeepSeek-R1 diverse modalities diverse tasks and domains better training algorithms abundant benchmarks thriving engineering frameworks