Reinforcement Fine-Tuning Powers Reasoning Capability of Multimodal
  Large Language Models
Authors: name: Haoyuan Sun, name: Jiaqi Wu, name: Bo Xia, name: Yifu Luo, name: Yifei Zhao, name: Kai Qin, name: Xufei Lv, name: Tiantian Zhang, name: Yongzhe Chang, name: Xueqian Wang

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.