#sglang

2 articles

01 ·Time Series·★ MEMBER·PAPER·7 min read Paper Explained: STEPQuant — When and Where Quantization Errors Matter in Delta-Rule Recurrent States Linear-attention states have a fixed size, yet under concurrent serving they can outgrow the model weights. Naive low-bit quantization lets errors pile up with every update. STEPQuant spends bits and scales according to how long an error survives and which row it lands on. Explained from scratch. 02 ·★ MEMBER·PAPER·17 min read Paper Walkthrough: Miles v0.1 — Anatomy of a Post-Training Stack Built to Survive Production A read-through of Miles, a full-stack system for agentic RL post-training of frontier MoE models: how it hunts down the numerical drift between the rollout engine and the trainer, and what a 744B model actually measured on 64 GPUs.