🔥 GLM-5: from Vibe Coding to Agentic Engineering

💡 The paper introduces GLM-5, a next-generation foundation model that advances the field of coding and software engineering. The current paradigm of vibe coding, which relies on intuitive and often imprecise coding practices, is limited in its ability to handle complex real-world software engineering tasks. To address this, the authors propose GLM-5, which builds upon the agentic, reasoning, and coding capabilities of its predecessor and incorporates several key innovations.

The method used to develop GLM-5 involves the adoption of DSA, which significantly reduces training and inference costs while maintaining long-context fidelity. Additionally, the authors implement a new asynchronous reinforcement learning infrastructure that improves post-training efficiency by decoupling generation from training. Novel asynchronous agent RL algorithms are also proposed to further improve RL quality, enabling the model to learn from complex, long-horizon interactions more effectively.

The results of the paper demonstrate the effectiveness of GLM-5, which achieves state-of-the-art performance on major open benchmarks. Most notably, GLM-5 demonstrates unprecedented capability in real-world coding tasks, surpassing previous baselines in handling end-to-end software engineering challenges. The model's ability to handle complex coding tasks and its potential to transition the paradigm of vibe coding to agentic engineering make it a significant contribution to the field of data science and software engineering. Overall, the paper presents a major advancement in foundation models and has the potential to impact the way software engineering is practiced.

📅 Published on Feb 17

🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2602.15763
• PDF: https://arxiv.org/pdf/2602.15763
• Project Page: https://huggingface.co/spaces/GenAISecurityProject/OWASP-AIBOM-Generator

🤖 Models citing this paper:
• https://huggingface.co/zai-org/GLM-5
• https://huggingface.co/zai-org/GLM-5.1
• https://huggingface.co/zai-org/GLM-5.2

📊 Datasets citing this paper:
• https://huggingface.co/datasets/zai-org/terminal-bench-2-verified
• https://huggingface.co/datasets/harithoppil/terminal-bench-2-verified

🚀 Spaces citing this paper:
• https://huggingface.co/spaces/pliny-the-prompter/obliteratus
• https://huggingface.co/spaces/akhaliq/anycoder
• https://huggingface.co/spaces/GenAISecurityProject/OWASP-AIBOM-Generator

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📢 By: https://xn--r1a.website/PaperNexus

#AgenticEngineering #FoundationModels #VibeCoding #SoftwareEngineeringInnovations #ReinforcementLearningTechniques