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.

翻译：我们提出GLM-5，这是一个旨在将范式从氛围编码过渡到智能体工程的下一代基础模型。GLM-5在其前代模型的智能体、推理与编码能力基础上，采用DSA显著降低了训练和推理成本，同时保持了长上下文保真度。为推进模型对齐与自主性，我们实现了一种新的异步强化学习基础设施，通过将生成与训练解耦，大幅提升了后训练效率。此外，我们提出了新颖的异步智能体强化学习算法，进一步提升了强化学习质量，使模型能够更有效地从复杂、长周期的交互中学习。通过这些创新，GLM-5在主要开放基准测试中取得了最先进的性能。最关键的是，GLM-5在实际编码任务中展现出前所未有的能力，在处理端到端软件工程挑战方面超越了以往的基线。代码、模型及更多信息可在 https://github.com/zai-org/GLM-5 获取。