9 citations · 11 across the 6 of their papers we have counts for
Showing cs.DCShow all
3 papers · 1 filter
cs.DC2026
Belayer: Efficient Fault Tolerance for LLM Agentic RL Training
Jiecheng Zhou, Qinghao Hu, Peng Sun +2
Large language model (LLM) agents are increasingly trained with reinforcement learning in long-horizon, sandboxed environments. Unlike conventional RL, agentic RL couples GPU-inten…
cs.DC2025
H2:Towards Efficient Large-Scale LLM Training on Hyper-Heterogeneous Cluster over 1,000 Chips
Ding Tang, Jiecheng Zhou, Jiakai Hu +5
Recent advancements in large language models (LLMs) necessitate extensive computational resources, prompting the use of diverse hardware accelerators from multiple vendors. However…
cs.DC2024★ 1 cited
ZeroPP: Unleashing Exceptional Parallelism Efficiency through Tensor-Parallelism-Free Methodology
Ding Tang, Lijuan Jiang, Jiecheng Zhou +5
Large-scale models rely heavily on 3D parallelism for distributed training, which utilizes tensor parallelism (TP) as the intra-operator parallelism to partition model states acros…