AI 导读
Zai 表示 GLM-5.3 参与搭建并优化了支撑 GLM-5.3-Flash 的推理基础设施，系统从首次跑通到生产就绪不到两周，端到端吞吐量达到初始基线的三倍。其关键做法是依赖本地正确性测试、执行轨迹、微基准和端到端测量构成的密集反馈，进行有针对性的假设验证，而非只看聚合性能指标。Zai 称这属于迈向递归自我改进的早期一步。
正文
While the US is discussing a slowdown, China is currently putting full force into its RSI.
Zai says GLM is taking early steps toward recursive self-improvement: AI helping build and optimize the infrastructure that runs AI.
GLM-5.3 helped build the infrastructure running GLM-5.3-Flash, with engineers and the agent tripling inference throughput *in under two weeks*. They say this work will “directly change how the next generation of models is trained.”
We’re sharing how GLM-5.3 helped build and optimize the inference infrastructure serving GLM-5.3-Flash. The system went from its first successful run to production readiness in less than two weeks, with end-to-end throughput tripling relative to the initial baseline. The key was dense feedback: local correctness tests, execution traces, microbenchmarks, and end-to-end measurements that enabled targeted hypothesis testing rather than reliance on aggregate performance metrics alone. https://t.co/yUf6OpJD7c在 X 查看被引用的帖子
来源：@kimmonismus · x.com