Router Sensitivity Under Lightweight Fine-Tuning Identifies Prunable Experts in Mixture-of-Experts Models
Mixture-of-Experts (MoE) models decouple parameter count from per-token compute, but deployment still requires hosting every expert in memory. Recent theory shows that experts whose router weights change least during fine-tuning can be pruned with provable accuracy preservation, yet the guarantee assumes full fine-tuning. We show that the signal can be elicited through a brief parameter-efficient adaptation. We fine-tune with a lightweight adapter, rank experts by the induced router change, and prune the least-changed experts in one shot. On Mixtral-8$\times$7B-Instruct, router-only LoRA trains 0.002% of parameters and retains 27.54% MMLU-Pro accuracy with half the experts removed, agains...
| Authors | Ali Janati, Kaoutar El Maghraoui, Chengke Zou et al. |
|---|---|
| Primary category | cs.LG |
| Published | 8 August 2026 |
| Last updated at source | 10 October 2026 |
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