title: MM-Eureka: Exploring Visual Aha Moment with Rule-based Large-scale
    Reinforcement Learning
summary: 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
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paper_authors.name: Junjun He
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paper_title: MM-Eureka: Exploring Visual Aha Moment with Rule-based Large-scale
    Reinforcement Learning
paper_summary: 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
paper_ai_keywords: multimodal reasoning rule-based reinforcement learning (RL) large-scale rule-based reinforcement learning (RL) DeepSeek-R1 multimodal space accuracy reward response length reflection behaviors instruction-tuned pre-trained models multimodal reasoning capabilities rule-based RL supervised fine-tuning data efficiency