DR-EFT: Exploring and reloading domain-representative experts for the memory-constrained fine-tuning of MoE large...

Mixture of Experts (MoE) Large Language Models (LLMs) have demonstrated exceptional performance in recent years. However, their significantly increased parameter count poses substantial challenges for achieving a fine-tuned model without modifying the MoE architecture or quantity, particularly under memory-constrained conditions. Previous studies have shown that MoE tends to have a subset of representative experts in a specific domain. This inherent characteristic creates the possibility of fine-tuning such models exclusively during the training phase by loading and training only a targeted subset of experts. To address the challenge, we propose an algorithm framework named DR-EFT (Domain-Representative Experts for Fine-Tuning), which explores and loads the domain-representative experts for subsequent retraining and reincorporation. DR-EFT operates based on a structured two-stage learning mechanism. Firstly, it achieves the representative experts via quantized model fine-tuning to remove the obstacle brought by the observed phenomenon of easily overlooked expert activation drift during fine-tuning. Then it enables continuous fine-tuning through denoting a novel MoE training dynamic. It finds that secondary relevant experts play a crucial role and should be included in the representative subset, which differs from existing pruning strategies that focus on the most relevant experts, thus enriching the theoretical framework of domain-specific expert. We propose two strategies of static fine-tuning or expert switching to achieve continuous adaptation of the retrained experts. Extensive experiments on multiple downstream tasks show that the proposed DR-EFT framework reduces the memory consumption of MoEs by close to 50% with only a marginal performance loss. Furthermore, our method demonstrates robustness through validations on popular MoE LLMs, including Qwen, DeepSeek, and Ernie.