3 papers
cs.LG2026
AIMER: Calibration-Free Task-Agnostic MoE Expert Pruning
Zongfang Liu, Guangyi Chen, Shengkun Tang +3
Mixture-of-Experts (MoE) language models increase parameter capacity without proportional per-token computation, yet deployment still requires storing the full expert pool, making…
cs.LG2026
How to Score Experts for One-Shot MoE Expert Pruning: A Unified Formulation and Selection Principle
Zongfang Liu, Jinghui Zhang, Zijian Ma +2
Mixture-of-Experts (MoE) language models reduce per-token computation through sparse expert activation, yet deployment still requires storing the full expert pool, making one-shot…
cs.LG2026
EvoESAP: Non-Uniform Expert Pruning for Sparse MoE
Zongfang Liu, Shengkun Tang, Boyang Sun +2
Sparse Mixture-of-Experts (SMoE) language models achieve strong capability at low per-token compute, yet deployment remains constrained by memory footprint and throughput because t…