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

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.