MM-Eureka: Exploring Visual Aha Moment with Rule-based Large-scale
  Reinforcement Learning
Authors: name: Fanqing Meng, name: Lingxiao Du, name: Zongkai Liu, name: Zhixiang Zhou, name: Quanfeng Lu, name: Daocheng Fu, name: Botian Shi, name: Wenhai Wang, name: Junjun He, name: Kaipeng Zhang, name: Ping Luo, name: Yu Qiao, name: Qiaosheng Zhang, name: Wenqi Shao

We present MM-Eureka, a multimodal reasoning model that successfully extends
large-scale rule-based reinforcement learning (RL) to multimodal reasoning.
While rule-based RL has shown remarkable success in improving LLMs' reasoning
abilities in text domains, its application to multimodal settings has remained
challenging. Our work reproduces key characteristics of text-based RL systems
like DeepSeek-R1 in the multimodal space, including steady increases in
accuracy reward and response length, and the emergence of reflection behaviors.
We demonstrate that both instruction-tuned and pre-trained models can develop
strong multimodal reasoning capabilities through rule-based RL without
supervised fine-tuning, showing superior data efficiency compared to
alternative approaches. We open-source our complete pipeline to foster further
research in this area. We release all our codes, models, data, etc. at
https://github.com/ModalMinds/MM-EUREKA