GLM-5.3团队分享递归自改进RSI实战：模型优化自身推理系统
A brilliant post from the GLM-5.3 team on RSI (recursive self-improvement).
这是罕见的模型直接优化自身底层推理系统的硬核工程复盘，给出了具体的性能瓶颈分析与可复现的优化思路，对做AI Infra的同学极具参考价值。
A brilliant post from the GLM-5.3 team on RSI (recursive self-improvement).
GLM-5.3 was already used to optimize the infrastructure that runs GLM itself, including production kernel and concurrency fixes. It helped triple GLM-5.3-Flash throughput on 100,000+ accelerators.
Their early self-improvement loop: the model improves its serving system, that system runs the model, and the engineering knowledge accumulates for the next optimization cycle.
Engineers still set objectives and boundaries, while the agent handled analysis, hypotheses, code changes, and experiments.
In one test, Prefill plus KV Transfer lagged Prefill alone by over 20%, and the agent traced the slowdown to the Python GIL, and releasing that lock cut the gap below 1%
Another kernel change reached a 1.71x speedup over the prior version by eliminating repeated FP32 normalization and gating work.
更进一步：量化金融体系
看懂新闻只是起点——沿量化金融路径，把它变成能交付的工程能力