MM-Eureka: Exploring Visual Aha Moment with Rule-based Large-scale
  Reinforcement Learning
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
67cf9cd037bc7273882147a3
Fanqing Meng
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Lingxiao Du
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Zongkai Liu
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kkkai
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Quanfeng Lu
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Botian Shi
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Wenhai Wang
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Junjun He
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Kaipeng Zhang
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KAIPENG ZHANG
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Ping Luo
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Wenqi Shao
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Wenqi Shao
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MM-Eureka: Exploring Visual Aha Moment with Rule-based Large-scale
  Reinforcement Learning
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
MM-Eureka extends rule-based reinforcement learning to multimodal reasoning, achieving strong capabilities in data-efficient multimodal tasks without supervised fine-tuning.
rule-based reinforcement learning multimodal reasoning DeepSeek-R1 accuracy reward response length reflection behaviors instruction-tuned pre-trained models data efficiency