3 papers
cs.LG2026
Less is MoE: Trimming Experts in Domain-Specialist Language Models
Haoze He, Xinkai Zou, Xuan Jiang +4
Mixture-of-Experts (MoE) models achieve strong performance through conditional computation, but their large parameter footprint poses deployment challenges. Prior MoE compression a…
cs.LG2026
Preserving Long-Tailed Expert Information in Mixture-of-Experts Tuning
Haoze He, Xingyuan Ding, Xuan Jiang +5
Despite MoE models leading many benchmarks, supervised fine-tuning (SFT) for the MoE architectures remains difficult because its router layers are fragile. Methods such as DenseMix…
cs.CL2025
Sparse Matrix in Large Language Model Fine-tuning
Haoze He, Juncheng Billy Li, Xuan Jiang +1
LoRA and its variants have become popular parameter-efficient fine-tuning (PEFT) methods due to their ability to avoid excessive computational costs. However, an accuracy gap often…