Reinforcement Fine-Tuning Powers Reasoning Capability of Multimodal Large Language Models
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arXiv
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| Autori principali: | , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866908378620493824 |
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| author | Sun, Haoyuan Wu, Jiaqi Xia, Bo Luo, Yifu Zhao, Yifei Qin, Kai Lv, Xufei Zhang, Tiantian Chang, Yongzhe Wang, Xueqian |
| author_facet | Sun, Haoyuan Wu, Jiaqi Xia, Bo Luo, Yifu Zhao, Yifei Qin, Kai Lv, Xufei Zhang, Tiantian Chang, Yongzhe Wang, Xueqian |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_18536 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Reinforcement Fine-Tuning Powers Reasoning Capability of Multimodal Large Language Models Sun, Haoyuan Wu, Jiaqi Xia, Bo Luo, Yifu Zhao, Yifei Qin, Kai Lv, Xufei Zhang, Tiantian Chang, Yongzhe Wang, Xueqian Computation and Language Artificial Intelligence Computer Vision and Pattern Recognition 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. |
| title | Reinforcement Fine-Tuning Powers Reasoning Capability of Multimodal Large Language Models |
| topic | Computation and Language Artificial Intelligence Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2505.18536 |