2 citations · 2 across the 1 of their papers we have counts for
3 papers
cs.LG2024
Understanding and Alleviating Memory Consumption in RLHF for LLMs
Jin Zhou, Hanmei Yang, Steven +4
Fine-tuning with Reinforcement Learning with Human Feedback (RLHF) is essential for aligning large language models (LLMs). However, RLHF often encounters significant memory challen…
cs.DC2024
ProTrain: Efficient LLM Training via Memory-Aware Techniques
Hanmei Yang, Jin Zhou, Yao Fu +4
Memory pressure has emerged as a dominant constraint in scaling the training of large language models (LLMs), particularly in resource-constrained environments. While modern framew…
cs.PF2022★ 2 cited
CachePerf: A Unified Cache Miss Classifier via Hybrid Hardware Sampling
Jin Zhou, Steven, Tang +2
The cache plays a key role in determining the performance of applications, no matter for sequential or concurrent programs on homogeneous and heterogeneous architecture. Fixing cac…