EPnG: Adaptive Expert Prune-and-Grow for Parameter-Efficient MoE Fine-tuning
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Computer Science > Machine Learning
Title:EPnG: Adaptive Expert Prune-and-Grow for Parameter-Efficient MoE Fine-tuning
Abstract:Mixture-of-Experts (MoE) models scale efficiently but remain costly to adapt due to redundant experts and uniform parameter allocation. Existing parameter-efficient fine-tuning (PEFT) methods such as LoRA ignore MoE routing dynamics, leading to suboptimal resource use. We propose EPnG, an adaptive prune-and-grow framework that reallocates LoRA capacity based on expert importance derived from router gate probabilities. EPnG prunes under-utilized experts and expands high-importance experts via rank growth with orthogonal initialization, while maintaining a fixed parameter budget. Across OLMoE and Qwen1.5-MoE, EPnG consistently outperforms LoRA under the same budget and achieves performance comparable to full fine-tuning while updating only 0.55%-0.72% of parameters (up to 140x-180x fewer). These results demonstrate that aligning PEFT with MoE routing yields a more effective and scalable fine-tuning strategy.
| Comments: | 6 pages. Accepted at MobiSys Workshop '26 |
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2607.01789 [cs.LG] |
| (or arXiv:2607.01789v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2607.01789
arXiv-issued DOI via DataCite (pending registration)
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| Journal reference: | In Proceedings of the 24th Annual International Conference on Mobile Systems, Applications and Services Workshops (MobiSys Workshop '26), pp. 93-98, 2026 |
| Related DOI: | https://doi.org/10.1145/3812836.3814761
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