GLM-4.5: Agentic, Reasoning, and Coding (ARC) Foundation Models
GLM-4. 5 Team, Aohan Zeng, Xin Lv, Qinkai Zheng, Zhenyu Hou, Bin Chen, Chengxing Xie, Cunxiang Wang, Da Yin, Hao Zeng, Jiajie Zhang, Kedong Wang, Lucen Zhong, Mingdao Liu, Rui Lu, Shulin Cao, Xiaohan Zhang, Xuancheng Huang, Yao Wei, Yean Cheng, Yifan An, Yilin Niu
2025-08-11
Summary
This paper talks about GLM-4.5, a huge advanced language model with 355 billion parameters that is designed to be very good at tasks involving thinking, making decisions like an agent, and writing computer code.
What's the problem?
The problem is that building AI models that can do many complex tasks like reasoning and coding quickly and efficiently is very difficult because these models require huge amounts of data, computing power, and clever training techniques.
What's the solution?
The solution was to create GLM-4.5 using a special architecture called Mixture-of-Experts that activates only part of the model to save computation, combined with multi-stage training and reinforcement learning to teach it to think carefully, solve problems step-by-step, and write code effectively.
Why it matters?
This matters because GLM-4.5 can perform complex reasoning and coding tasks better and faster than many other large models, making it a powerful tool for developing smarter AI assistants and applications that can understand and interact with the world more like humans.
Abstract
GLM-4.5, a Mixture-of-Experts large language model with 355B parameters, achieves strong performance across agentic, reasoning, and coding tasks using multi-stage training and reinforcement learning.