GLM-4.1V-Thinking: Towards Versatile Multimodal Reasoning with Scalable
  Reinforcement Learning
Authors: name: Wenyi Hong; name: Wenmeng Yu; name: Xiaotao Gu; name: Guo Wang; name: Guobing Gan; name: Haomiao Tang; name: Jiale Cheng; name: Ji Qi; name: Junhui Ji; name: Lihang Pan; name: Shuaiqi Duan; name: Weihan Wang; name: Yan Wang; name: Yean Cheng; name: Zehai He; name: Zhe Su; name: Zhen Yang; name: Ziyang Pan; name: Aohan Zeng; name: Baoxu Wang; name: Boyan Shi; name: Changyu Pang; name: Chenhui Zhang; name: Da Yin; name: Fan Yang; name: Guoqing Chen; name: Jiazheng Xu; name: Jiali Chen; name: Jing Chen; name: Jinhao Chen; name: Jinghao Lin; name: Jinjiang Wang; name: Junjie Chen; name: Leqi Lei; name: Leyi Pan; name: Mingzhi Zhang; name: Qinkai Zheng; name: Sheng Yang; name: Shi Zhong; name: Shiyu Huang; name: Shuyuan Zhao; name: Siyan Xue; name: Shangqin Tu; name: Shengbiao Meng; name: Tianshu Zhang; name: Tianwei Luo; name: Tianxiang Hao; name: Tianle Gong; name: Wenkai Li; name: Wei Jia; name: Xin Lyu; name: Xuancheng Huang; name: Yanling Wang; name: Yadong Xue; name: Yanfeng Wang; name: Yifan An; name: Yifan Du; name: Yiming Shi; name: Yiheng Huang; name: Yilin Niu; name: Yuan Wang; name: Yuanchang Yue; name: Yuchen Li; name: Yutao Zhang; name: Yuxuan Zhang; name: Zhanxiao Du; name: Zhenyu Hou; name: Zhao Xue; name: Zhengxiao Du; name: Zihan Wang; name: Peng Zhang; name: Debing Liu; name: Bin Xu; name: Juanzi Li; name: Minlie Huang; name: Yuxiao Dong; name: Jie Tang
Published: 2025-07-01 17:55:04

Abstract: We present GLM-4.1V-Thinking, a vision-language model (VLM) designed to
advance general-purpose multimodal reasoning. In this report, we share our key
findings in the development of the reasoning-centric training framework. We
first develop a capable vision foundation model with significant potential
through large-scale pre-training, which arguably sets the upper bound for the
final performance. Reinforcement Learning with Curriculum Sampling (RLCS) then
unlocks the full potential of the model, leading to comprehensive capability
enhancement across a diverse range of tasks, including STEM problem solving,
video understanding, content recognition, coding, grounding, GUI-based agents,
and long document understanding, among others. To facilitate research in this
field, we open-source GLM-4.1V-9B-Thinking, which achieves state-of-the-art
performance among models of comparable size. In a comprehensive evaluation
across 28 public benchmarks, our model outperforms Qwen2.5-VL-7B on nearly all
tasks and achieves comparable or even superior performance on 18 benchmarks
relative to the significantly larger Qwen2.5-VL-72B. Notably,
GLM-4.1V-9B-Thinking also demonstrates competitive or superior performance
compared to closed-source models such as GPT-4o on challenging tasks including
long document understanding and STEM reasoning, further underscoring its strong
capabilities. Code, models and more information are released at
https://github.com/THUDM/GLM-4.1V-Thinking.