Parameter-Efficient Routed Fine-Tuning: Mixture-of-Experts Demands Mixture of Adaptation Modules

2025-08-04

AI Summary

Existing PEFT methods neglect the dynamic expert routing mechanism inherent in Mixture-of-Experts (MoE) models, leading to architectural misalignment between adaptation modules and the underlying MoE structure. To address this, we propose *Routed-PEFT*, the first PEFT framework that explicitly incorporates expert routing into adapter design—dynamically assigning dedicated adapters to individual experts, thereby enabling joint optimization of expert specialization and task-specific adaptation. We systematically evaluate Routed-PEFT on OLMoE and Mixtral architectures, integrating it with LoRA, Adapter, and other PEFT variants under diverse routing strategies across commonsense and mathematical reasoning benchmarks. With only 0.1%–0.5% additional trainable parameters, Routed-PEFT achieves average accuracy gains of 2.3–5.7 percentage points over standard PEFT baselines. Moreover, our analysis uncovers task-dependent optimal routing configurations, establishing a novel paradigm for efficient fine-tuning of MoE models.

Technology Category

• Machine Learning: Mixture of Experts (MoE)
• Planning, Routing, and Scheduling: Planning with Language Models
• Search and Optimization: Learning to Search

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Mixture-of-Experts (MoE) benefits from a dynamic routing mechanism among their specialized experts, which existing Parameter- Efficient Fine-Tuning (PEFT) strategies fail to leverage. This motivates us to investigate whether adaptation modules themselves should incorporate routing mechanisms to align with MoE's multi-expert architecture. We analyze dynamics of core components when applying PEFT to MoE language models and examine how different routing strategies affect adaptation effectiveness. Extensive experiments adapting OLMoE-1B-7B and Mixtral-8x7B on various commonsense and math reasoning tasks validate the performance and efficiency of our routed approach. We identify the optimal configurations for different scenarios and provide empirical analyses with practical insights to facilitate better PEFT and MoE applications.

Problem

• Investigates routing mechanisms for adaptation modules in MoE models
• Analyzes PEFT impact on MoE language model components
• Validates routed adaptation performance on reasoning tasks

Innovation

• Routed adaptation modules for MoE
• Dynamic routing in PEFT strategies
• Optimal configurations for MoE adaptation