Generic Expert Coverage for Pruning SparseMixture-of-Experts Language Models
Sparsely activated Mixture-of-Experts (MoE) language models contain substantial structured redundancy among routed experts, but pruning them without downstream calibration data remains challenging. Existing expert-pruning methods typically rely on a single aggregated importance score, which can bias the retained set toward experts favored by dominant calibration patterns. We propose \textbf{Generic TB-Coverage}, a coverage-aware expert pruning method that uses only generic text corpora (WikiText2 and C4) for calibration. Instead of collapsing expert utility into one score, our method profiles per-expert utility separately on each corpus and enforces a fixed-budget coverage rule that prese...
| Authors | Yongqin Zeng, Sicheng Pan, Jiale Wang et al. |
|---|---|
| Primary category | cs.AI |
| Published | 2 July 2026 |
| Last updated at source | 2 July 2026 |
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