GLM-5: from Vibe Coding to Agentic Engineering

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Summary

GLM-5 introduces DSA for cost reduction, asynchronous reinforcement learning for alignment, and enhanced coding capabilities, achieving state-of-the-art performance on benchmarks and real-world software engineering tasks.

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.

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Paper page - GLM-5: from Vibe Coding to Agentic Engineering

Source: https://huggingface.co/papers/2602.15763 Published on Feb 17

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Abstract

GLM-5 advances foundation models with DSA for cost reduction, asynchronous reinforcement learning for improved alignment, and enhanced coding capabilities for real-world software engineering.

We present GLM-5, a next-generation foundation model designed to transition the paradigm ofvibe codingtoagentic engineering. Building upon the agentic, reasoning, and coding (ARC) capabilities of its predecessor, GLM-5 adoptsDSAto significantly reduce training and inference costs while maintaining long-context fidelity. To advancemodel alignmentand autonomy, we implement a newasynchronous reinforcement learninginfrastructure that drastically improvespost-training efficiencyby 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 majoropen benchmarks. Most critically, GLM-5 demonstrates unprecedented capability in real-world coding tasks, surpassing previous baselines in handling end-to-endsoftware engineeringchallenges. Code, models, and more information are available at https://github.com/zai-org/GLM-5.

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