Reinforcement Fine-Tuning Powers Reasoning Capability of Multimodal
  Large Language Models
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.
68351f7a06b4dae20a214442
Haoyuan Sun
admin_assigned
2025-05-27T12:36:26.780Z
65e2d43f9fb58a5115253049
/avatars/46bd4ae27eaa23802cef3d91626897b5.svg
Haoyuan Sun
user
xiaonengmiao
68351f7a06b4dae20a214443
Jiaqi Wu
68351f7a06b4dae20a214444
Bo Xia
68351f7a06b4dae20a214445
Yifu Luo
admin_assigned
2025-05-27T12:36:42.662Z
681d7bc1e36b83d25c8c4581
/avatars/f3a28ec01dc1c24ad50e4248497dec22.svg
Yifu Luo
user
happyyifu
68351f7a06b4dae20a214446
Yifei Zhao
68351f7a06b4dae20a214447
Kai Qin
68351f7a06b4dae20a214448
Xufei Lv
68351f7a06b4dae20a214449
Tiantian Zhang
68351f7a06b4dae20a21444a
Yongzhe Chang
68351f7a06b4dae20a21444b
Xueqian Wang
Reinforcement Fine-Tuning Powers Reasoning Capability of Multimodal
  Large Language Models
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.
Reinforcement fine-tuning significantly enhances the reasoning capabilities of multimodal large language models through diverse modalities, tasks, algorithms, benchmarks, and frameworks.
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