Ziang Li

A stable, hardware-native NVFP4 RL recipe from @humansand is now open source, and Miles is the trainer behind it! The blog calls it the 4-bitter lesson, that the hard problems live in the interactions between pieces, not the pieces themselves, which we've internalized deeply building Miles and SGLang. Miles & SGLang keep quantization bit-exact across both sides: dequantized backward, 4/6 adaptive scaling at zero extra rollout latency, and selective precision that holds from checkpoint to rollout. Reward curves come out identical to BF16, while unlocking up to 9x tensor core FLOPs on next-gen NVIDIA GPUs. Congrats @humansand, @zianglih, and proud to build this together with @nvidia!

At humans&, we train models from the long-term impacts of their interactions with people. This requires prioritizing long-horizon multi-agent RL. We've developed and are excited to share an open-source, hardware-native 4-bit RL recipe, significantly accelerating training

Loved seeing how @LinkedIn is using SGLang to accelerate large-scale RecSys ranking, and contributing major features back to both SGLang and @NVIDIA’s FlashInfer open-source stack. SGLang is now powering LinkedIn’s latency-critical ranking workflows with: • Massive prefill speedups • Efficient multi-item scoring • Production-ready FA3 attention • Accurate and fast FP8 kernels • End-to-end latency hiding for real-time recommendations From multi-item scoring to FA3 to per-token FP8, this work shows what’s possible when strong engineering meets an open ecosystem. See the full blog 👇 Huge thanks to the @LinkedInEng team, @NVIDIAAIDev , FlashInfer and HazyResearch for pushing LLM serving forward together.