70 citations · 127 across the 6 of their papers we have counts for
Showing cs.DCShow all
3 papers · 1 filter
cs.DC2026
AInfer-PD: Communication-Safe In-Place Prefill-Decode Multiplexing for Distributed MoE Rollouts
Guowei Wang, Chaokun Yang, Zhenxuan Pan +6
Rollout inference often dominates the wall-clock time of large-scale reinforcement learning (RL). In agentic RL, each trajectory alternates between model generation and environment…
cs.DC2026
RoutePack: Replay-Guided Expert Placement and Rollout Trajectory Scheduling for MoE Reinforcement Learning
Yibo Shen, Xudong Han, Xiaowei Zhu +2
Reinforcement learning (RL) post-training of Mixture-of-Experts (MoE) models couples two load-balancing problems. Rollout-trajectory composition determines attention and dense feed…
cs.DC2021★ 1 cited
DFOGraph: An I/O- and Communication-Efficient System for Distributed Fully-out-of-Core Graph Processing
Jiping Yu, Wei Qin, Xiaowei Zhu +4
With the magnitude of graph-structured data continually increasing, graph processing systems that can scale-out and scale-up are needed to handle extreme-scale datasets. While exis…