3 citations · 3 across the 2 of their papers we have counts for
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cs.DC2026
psRL: Efficient Training for Agentic AI via Training-Time Prefix Sharing
Mianjie Yu, Zizhao Mo, Huanyu Qu +8
In modern agentic AI training, the system bottleneck is shifting from rollout to update. Emerging sampling strategies such as tree-structured and step-wise RL greatly increase trai…
cs.DC2024
LoHan: Low-Cost High-Performance Framework to Fine-Tune 100B Model on a Consumer GPU
Changyue Liao, Mo Sun, Zihan Yang +5
Nowadays, AI researchers become more and more interested in fine-tuning a pre-trained LLM, whose size has grown to up to over 100B parameters, for their downstream tasks. One appro…
cs.DC2023★ 3 cited
Helios: An Efficient Out-of-core GNN Training System on Terabyte-scale Graphs with In-memory Performance
Jie Sun, Mo Sun, Zheng Zhang +6
Training graph neural networks (GNNs) on large-scale graph data holds immense promise for numerous real-world applications but remains a great challenge. Several disk-based GNN sys…