Author

Zheng-Ping Qian

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Book Open access Sep 2026

A Few GPUs, A Whole Lotta Scale: Faithful LLM Training Emulation with CrystalLLM

Large language model (LLM) training today runs on clusters spanning thousands of GPUs. While this scale enables rapid model advances, developing, debugging, and performance-tuning, the training framework inevitably becomes complex and costly. This is because engineers often need to reproduce production behaviors to dia...

Shao-Ke Xi, ChonLam Lao, Bo-Yi Jia et al. · Proceedings of the ACM SIGOP... · 0 citations

#machine learning Preprint Oct 2026

ReFlow: Replay-Driven Decoupling for Efficient RL Post-Training

Reinforcement learning (RL) has been a critical paradigm for improving large language model (LLM) reasoning and agentic capabilities, but its efficiency remains a major bottleneck. Existing RL systems commonly rely on asynchronous execution to overlap rollout generation with policy optimization for enhanced efficiency....

Yu Tian, Junwei Su, Hao Xu et al. · 0 citations