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About this paper
GLM-5-Team, Aohan Zeng, Xin Lv, Zhenyu Hou, Zhengxiao Du, Qinkai Zheng, Bin Chen, Da Yin and 178 more
GLM-5 presents an agentic engineering system for exploring repositories, using tools, and adapting across long coding tasks. It combines long-context attention efficiency with asynchronous reinforcement learning, separating inference rollouts from training. On repo exploration, it reaches 65.6% Pass@1, versus 64.5% for Claude Opus 4.5, but scores 52.3% on chained tasks versus Claude’s 61.6%, limiting the comparison to selected evaluations.
Key insights
- Architectural innovations like DeepSeek Sparse Attention can drastically reduce computational costs for long contexts (e.g., 128K tokens at half GPU cost) without compromising model performance or long-range dependencies.
- A decoupled and asynchronous RL framework is highly effective for training agentic AI models, maximizing GPU utilization and enabling robust learning from complex, long-horizon interactions.
- Strategic hardware-software co-design and optimization for domestic GPU ecosystems are crucial for achieving competitive performance and ensuring technological sovereignty in advanced AI development.
Summary by alphaXiv.
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