GLM-5 achieves autonomous software engineering through sparse attention and efficient training adaptation.
We present GLM-5, a next-generation foundation model designed to transition the paradigm of vibe coding to agentic engineering. Building upon the agentic, reasoning, and coding (ARC) capabilities of its predecessor, GLM-5 adopts DSA to significantly reduce training and inference costs while maintaining long-context fidelity. To advance model alignment and autonomy, we implement a new asynchronous reinforcement learning infrastructure that drastically improves post-training efficiency by decoupling generation from training. Furthermore, we propose novel asynchronous agent RL algorithms that further improve RL quality, enabling the model to learn from complex, long-horizon interactions more effectively. Through these innovations, GLM-5 achieves state-of-the-art performance on major open benchmarks. Most critically, GLM-5 demonstrates unprecedented capability in real-world coding tasks, surpassing previous baselines in handling end-to-end software engineering challenges. Code, models, and more information are available at https://github.com/zai-org/GLM-5.
GLM-5 moves from human-guided vibe coding to fully autonomous agentic engineering that handles end-to-end software tasks.
DeepSeek Sparse Attention reduces computational complexity by intelligently selecting relevant tokens instead of attending to all.
Dense warm-up followed by sparse adaptation dramatically reduces training costs while maintaining performance on long-context tasks.
Training massive models with 744 billion parameters still requires millions of dollars in computational resources.
Sparse attention implementation required custom architectural innovations like Muon Split to avoid performance gaps with standard optimizers.
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