GLM-4.1V-Thinking: Towards Versatile Multimodal Reasoning with Scalable
  Reinforcement Learning
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.
68649318d59a9eda59024a6f
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Jie Tang
GLM-4.1V-Thinking: Towards Versatile Multimodal Reasoning with Scalable
  Reinforcement Learning
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.
A vision-language model (VLM) named GLM-4.1V-Thinking, developed with a reasoning-centric training framework, achieves state-of-the-art performance across various tasks, including STEM problem solving, video understanding, and long document understanding, outperforming larger models on many benchmarks.
vision-language model VLM reasoning-centric training framework large-scale pre-training Reinforcement Learning with Curriculum Sampling RLCS STEM problem solving video understanding content recognition coding grounding GUI-based agents long document understanding state-of-the-art performance public benchmarks Qwen2.5-VL-7B Qwen2.5-VL-72B GPT-4o