9 citations · 11 across the 6 of their papers we have counts for
6 papers
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…
RL in the Wild: Characterizing RLVR Training in LLM Deployment
Jiecheng Zhou, Qinghao Hu, Yuyang Jin +7
Large Language Models (LLMs) are now widely used across many domains. With their rapid development, Reinforcement Learning with Verifiable Rewards (RLVR) has surged in recent month…
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…
Towards Efficient Pre-training: Exploring FP4 Precision in Large Language Models
Jiecheng Zhou, Ding Tang, Rong Fu +8
The burgeoning computational demands for training large language models (LLMs) necessitate efficient methods, including quantized training, which leverages low-bit arithmetic opera…
GTSinger: A Global Multi-Technique Singing Corpus with Realistic Music Scores for All Singing Tasks
Yu Zhang, Changhao Pan, Wenxiang Guo +15
The scarcity of high-quality and multi-task singing datasets significantly hinders the development of diverse controllable and personalized singing tasks, as existing singing datas…
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…