45 citations · 57 across the 4 of their papers we have counts for
5 papers
PrefixFlow: Training-Time KV Caching for Schedule-Level Prefix Reuse in LLM RL Training
Pengbo Li, Feiyuan Zhang, Guangming Sheng +8
In reinforcement learning (RL) post-training, one prompt fans out into trajectories, yet standard trainers replay the shared prefix's forward and backward pass times; in lo…
Laminar: A Scalable Asynchronous RL Post-Training Framework
Guangming Sheng, Yuxuan Tong, Borui Wan +10
Reinforcement learning (RL) post-training for Large Language Models (LLMs) is now scaling to large clusters and running for extended durations to enhance model reasoning performanc…
HybridFlow: A Flexible and Efficient RLHF Framework
Guangming Sheng, Chi Zhang, Zilingfeng Ye +6
Reinforcement Learning from Human Feedback (RLHF) is widely used in Large Language Model (LLM) alignment. Traditional RL can be modeled as a dataflow, where each node represents co…
MSPipe: Efficient Temporal GNN Training via Staleness-Aware Pipeline
Guangming Sheng, Junwei Su, Chao Huang +1
Memory-based Temporal Graph Neural Networks (MTGNNs) are a class of temporal graph neural networks that utilize a node memory module to capture and retain long-term temporal depend…
GNNFlow: A Distributed Framework for Continuous Temporal GNN Learning on Dynamic Graphs
Yuchen Zhong, Guangming Sheng, Tianzuo Qin +3
Graph Neural Networks (GNNs) play a crucial role in various fields. However, most existing deep graph learning frameworks assume pre-stored static graphs and do not support trainin…