Tag
Robert Nishihara highlights a paper on disaggregating RL workloads, showing that using compute-optimized H800s for prefill and bandwidth-optimized H20s for decode can cut rollout times by 21-51% and 47% respectively, emphasizing that no single hardware type fits all stages.
RollArt presents a disaggregated architecture for large-scale reinforcement learning, demonstrating significant improvements in efficiency and scalability.
Discusses the nuanced reality of prefill-decode disaggregation in LLM serving at scale, based on customer patterns and validated on AMD with vLLM.
Delta-compressed weight sync technique merged into slime, enabling lossless delta sync for Megatron ↔ SGLang disaggregation, enhancing reinforcement learning at scale.