GLM 5 👋 Join our WeChat or Discord community. 📖 Check out the GLM 5 technical blog . 📍 Use GLM 5 API services on Z.ai API Platform. 👉 One click to GLM 5 . [ Paper ] [ GitHub ] Introduction We are launching GLM 5, targeting complex systems engineering and long horizon agentic tasks. Scaling is still one of the most important ways to improve the intelligence efficiency of Artificial General Intelligence (AGI). Compared to GLM 4.5, GLM 5 scales from 355B parameters (32B active) to 744B parameters (40B active), and increases pre training data from 23T to 28.5T tokens. GLM 5 also integrates DeepSeek Sparse Attention (DSA), largely reducing deployment cost while preserving long context capacity. Reinforcement learning aims to bridge the gap between competence and excellence in pre trained models. However, deploying it at scale for LLMs is a challenge due to the RL training inefficiency. To this end, we developed slime, a novel asynchronous RL infrastructure that substantially improves training throughput and efficiency, enabling more fine grained post training iterations. With advances in both pre training and post training, GLM 5 delivers significant improvement compared to GLM 4.7…
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