EvoESAP: Non-Uniform Expert Pruning for Sparse MoE
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 the full expert pool must still be stored and served. Post-training expert pruning reduces this cost, but most methods focus on which experts to prune within each layer and default to a uniform layer-wise sparsity allocation, even though the layer-wise allocation can strongly affect performance. We decouple pruning into within-layer expert ranking and across-layer budget allocation, and introduce \textbf{E}xpected \textbf{S}peculative \textbf{A}cceptance \textbf{P}roxy (\textbf{ESAP}), a speculative-decoding-insp...
| Authors | Zongfang Liu, Shengkun Tang, Boyang Sun et al. |
|---|---|
| Primary category | cs.LG |
| Published | 6 March 2026 |
| Last updated at source | 11 April 2026 |
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