GLM-5: from Vibe Coding to Agentic Engineering

2026-02-17

AI Summary

This work proposes a paradigm shift in large language models—from intuition-driven “atmospheric encoding” to an agent-based framework endowed with autonomous reasoning and engineering capabilities—to effectively support complex, long-horizon software development tasks. To this end, we introduce GLM-5, a next-generation foundation model that incorporates an asynchronous reinforcement learning architecture and a novel agent-centric RL algorithm to decouple generation from training. Coupled with Dynamic Sparse Attention (DSA) and an Agent-driven Reasoning and Coding (ARC) framework, GLM-5 substantially enhances long-context modeling and autonomous software engineering proficiency. Experimental results demonstrate that GLM-5 achieves state-of-the-art performance on mainstream open-source benchmarks and significantly outperforms existing models in real-world end-to-end software engineering tasks, validating its superior practical coding capabilities.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• Search and Retrieval-Augmented AI: Large language models for search
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

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.

Problem

• vibe coding
• agentic engineering
• foundation model
• software engineering
• autonomous agents

Innovation

• asynchronous reinforcement learning
• DSA
• agentic engineering
• long-context modeling
• foundation model