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Jialiang Cheng

2 accepted papers

2026

SERE: Similarity-based Expert Re-routing for Efficient Batch Decoding in MoE Models

ICLR 2026poster

Mixture-of-Experts (MoE) architectures employ sparse activation to deliver faster training and inference with higher accuracy than dense LLMs. However, in production serving, MoE models require batch inference to optimize hardware efficiency, which may cause excessive expert activation and thus slow…

Cited by 0SourceScholar
2025

EDiT: A Local-SGD-Based Efficient Distributed Training Method for Large Language Models

ICLR 2025poster

Distributed training methods are crucial for large language models (LLMs). However, existing distributed training methods often suffer from communication bottlenecks, stragglers, and limited elasticity, particularly in heterogeneous or large-scale environments. Local SGD methods have been proposed t…