title: MiMo-VL Technical Report
summary: We open-source MiMo-VL-7B-SFT and MiMo-VL-7B-RL, two powerful vision-language
  models delivering state-of-the-art performance in both general visual
  understanding and multimodal reasoning. MiMo-VL-7B-RL outperforms Qwen2.5-VL-7B
  on 35 out of 40 evaluated tasks, and scores 59.4 on OlympiadBench, surpassing
  models with up to 78B parameters. For GUI grounding applications, it sets a new
  standard with 56.1 on OSWorld-G, even outperforming specialized models such as
  UI-TARS. Our training combines four-stage pre-training (2.4 trillion tokens)
  with Mixed On-policy Reinforcement Learning (MORL) integrating diverse reward
  signals. We identify the importance of incorporating high-quality reasoning
  data with long Chain-of-Thought into pre-training stages, and the benefits of
  mixed RL despite challenges in simultaneous multi-domain optimization. We also
  contribute a comprehensive evaluation suite covering 50+ tasks to promote
  reproducibility and advance the field. The model checkpoints and full
  evaluation suite are available at https://github.com/XiaomiMiMo/MiMo-VL.
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paper_authors.name: Xing Zhang
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paper_authors.name: Xin Zhang
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paper_authors.name: Weiji Zhuang
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paper_authors.name: Wei Liu
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paper_authors.name: Sirui Deng
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paper_authors.name: Shuo Liu
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paper_authors.name: Shande Wang
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paper_authors.name: Rui Ma
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paper_authors.name: Qiantong Wang
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paper_authors.name: Peng Wang
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paper_authors.name: Nuo Chen
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paper_authors.name: Menghang Zhu
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paper_authors.name: Kai Fang
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paper_authors.name: Hongshen Xu
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paper_authors.name: Heng Qu
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paper_authors.name: Haochen Zhao
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paper_authors.name: Hanglong Lv
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paper_authors.name: Guoan Wang
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paper_authors.name: Duo Zhang
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paper_authors.name: Dong Zhang
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paper_authors.name: Di Zhang
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paper_authors.name: Chong Ma
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paper_authors.name: Chang Liu
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paper_authors.name: Can Cai
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paper_authors.name: Bingquan Xia
paper_title: MiMo-VL Technical Report
paper_summary: We open-source MiMo-VL-7B-SFT and MiMo-VL-7B-RL, two powerful vision-language
  models delivering state-of-the-art performance in both general visual
  understanding and multimodal reasoning. MiMo-VL-7B-RL outperforms Qwen2.5-VL-7B
  on 35 out of 40 evaluated tasks, and scores 59.4 on OlympiadBench, surpassing
  models with up to 78B parameters. For GUI grounding applications, it sets a new
  standard with 56.1 on OSWorld-G, even outperforming specialized models such as
  UI-TARS. Our training combines four-stage pre-training (2.4 trillion tokens)
  with Mixed On-policy Reinforcement Learning (MORL) integrating diverse reward
  signals. We identify the importance of incorporating high-quality reasoning
  data with long Chain-of-Thought into pre-training stages, and the benefits of
  mixed RL despite challenges in simultaneous multi-domain optimization. We also
  contribute a comprehensive evaluation suite covering 50+ tasks to promote
  reproducibility and advance the field. The model checkpoints and full
  evaluation suite are available at https://github.com/XiaomiMiMo/MiMo-VL.
paper_ai_summary: MiMo-VL-7B-SFT and MiMo-VL-7B-RL provide state-of-the-art general visual understanding and multimodal reasoning through four-stage pre-training and Mixed On-policy Reinforcement Learning, outperforming models with up to 78B parameters.
paper_ai_keywords: vision-language models multimodal reasoning four-stage pre-training Mixed On-policy Reinforcement Learning MORL Chain-of-Thought reproducibility