GLM-5: from Vibe Coding to Agentic Engineering
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...
| Authors | GLM-5-Team, :, Aohan Zeng et al. |
|---|---|
| Primary category | cs.LG |
| Published | 17 February 2026 |
| Last updated at source | 24 February 2026 |
Models and datasets referencing this paper
- zai-org/GLM-5.3-FlashModel
- zai-org/GLM-5.3Model
- unsloth/GLM-5.3-Flash-GGUFModel
- zai-org/GLM-5Model
- zai-org/GLM-5.1Model
- zai-org/GLM-5.2Model
- unsloth/GLM-5.3-GGUFModel
- unsloth/GLM-5.2-GGUFModel
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