3 papers
cs.CL2026
Mechanism-Driven Monitors for Preemptive Detection of LLM Training Instability
Ruixuan Huang, Yipei Wang, Wenyi Fang +7
Frontier large language model training consumes massive accelerator fleets and long wall-clock computation, making stability failures costly when they occur. After a numerical or a…
cs.DC2025
AsyncHZP: Hierarchical ZeRO Parallelism with Asynchronous Scheduling for Scalable LLM Training
Huawei Bai, Yifan Huang, Wenqi Shi +4
The training efficiency and scalability of language models on massive clusters currently remain a critical bottleneck. Mainstream approaches like ND parallelism are often cumbersom…
cs.LG2025
AsyncFlow: An Asynchronous Streaming RL Framework for Efficient LLM Post-Training
Zhenyu Han, Ansheng You, Haibo Wang +16
Reinforcement learning (RL) has become a pivotal technology in the post-training phase of large language models (LLMs). Traditional task-colocated RL frameworks suffer from signifi…