MiMo-VL Technical Report
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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Xiaomi LLM-Core Team
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Zihao Yue
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Zihao Yue
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Zhenru Lin
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Yifan Song
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Weikun Wang
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Weikun Wang
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Shuhuai Ren
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Shuhuai Ren
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Shuhao Gu
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Shicheng Li
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Li Shicheng
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Peidian Li
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Liang Zhao
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Liang Zhao
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Lei Li
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Lei Li
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Kainan Bao
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Hao Tian
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Hailin Zhang
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Gang Wang
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Dawei Zhu
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Dawei Zhu
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Cici
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Chenhong He
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Bowen Ye
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Bowen Shen
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Zihan Zhang
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Zihan Jiang
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Zihan Jiang
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Zhixian Zheng
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Zhichao Song
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Zhenbo Luo
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Yue Yu
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Yudong Wang
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Yuanyuan Tian
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Yu Tu
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Yihan Yan
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Yi Huang
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Xinzhe Xu
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Xingchen Song
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Xing Zhang
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Xing Yong
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Xin Zhang
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Xiangwei Deng
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Wenyu Yang
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Wenhan Ma
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Weiwei Lv
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Weiji Zhuang
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Wei Liu
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Shuo Liu
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Shimao Chen
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Ezio Chen
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Shihua Yu
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Shande Wang
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Qiantong Wang
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Peng Wang
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Nuo Chen
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Menghang Zhu
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Kangyang Zhou
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Kang Zhou
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Kai Fang
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Jun Shi
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Jinhao Dong
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Jiaming Xu
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Huaqiu Liu
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Huaqiu Liu
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Hongshen Xu
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Heng Qu
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Haochen Zhao
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Hanglong Lv
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Guoan Wang
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Duo Zhang
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Dong Zhang
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Di Zhang
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Chong Ma
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Chang Liu
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Can Cai
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Bingquan Xia
MiMo-VL Technical Report
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.
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.
vision-language models multimodal reasoning four-stage pre-training Mixed On-policy Reinforcement Learning MORL Chain-of-Thought reproducibility