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arXiv AI By Tian Wang, Yongding Tao, Yihong Dong, Xiaoming Wu, Ying Guo, Ge Li

Experts Exhibit Structured Redundancy: Structure-Preserving Expert Pruning for Mixture-of-Experts Large Language Models

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The paper introduces a structure‑preserving approach to prune experts in Mixture‑of‑Experts large language models, addressing memory overhead while maintaining performance. It observes that expert outputs exhibit structured redundancy, with stable rank and entropy effective rank far below the nominal number of experts. Two pruning methods—Projection Energy Expert Pruning (PEEP) and Distributional Coverage Expert Pruning (DCEP)—use router‑derived weights to preserve global structural relationships and local importance, achieving superior results on DeepSeek‑V2‑Lite and Qwen3‑30B‑A3B at 75% and 50% expert retention.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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