Exploring Expert Concentration for Parameter-efficient Fine-tuning of Mixture-of-Expert LLMs

Scaling large language models (LLMs) with the Mixture-of-Experts (MoE) architecture has emerged as a powerful alternative to dense models. However, fine-tuning MoE models for domain- or task-specific adaptation remains challenging: full-model tuning is prohibitively expensive, while existing parameter-efficient fine-tuning (PEFT) methods, mostly adapted from dense models, suffer from unstable optimization due to MoE’s sparse expert activation. In this work, we conduct an empirical study on the fine-tuning dynamics of MoE models. We first introduce the Domain Advantage Score (DAS), a simple yet effective metric for identifying domain-relevant experts. Our findings uncover an expert concentration phenomenon: during domain-specific fine-tuning, the overall DAS of the top experts consistently increases, indicating a progressive enhancement of domain concentration. Building on this, we propose a lightweight two-stage PEFT framework: (1) fine-tuning only the attention and router layers to sharpen expert specialization, and (2) selectively fine-tuning parameters on the identified experts. This approach updates only a small fraction of parameters while achieving performance on par with full fine-tuning, and it effectively preserves the model's general capabilities. Experiments on nine benchmarks show the effectiveness and efficiency of our method. Our code and data will be publicly released.