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cs.LG2026
Alloc-MoE: Budget-Aware Expert Activation Allocation for Efficient Mixture-of-Experts Inference
Baihui Liu, Kaiyuan Tian, Wei Wang +3
Mixture-of-Experts (MoE) has become a dominant architecture for scaling large language models due to their sparse activation mechanism. However, the substantial number of expert ac…
cs.LG2025
ParaDySe: A Parallel-Strategy Switching Framework for Dynamic Sequence Lengths in Transformer
Zhixin Ou, Peng Liang, Jianchen Han +2
Dynamic sequences with varying lengths have been widely used in the training of Transformer-based large language models (LLMs). However, current training frameworks adopt a pre-def…
cs.LG2025★ 2 cited
A Survey on Memory-Efficient Transformer-Based Model Training in AI for Science
Kaiyuan Tian, Linbo Qiao, Baihui Liu +3
Scientific research faces high costs and inefficiencies with traditional methods, but the rise of deep learning and large language models (LLMs) offers innovative solutions. This s…