Routing-Aligned Fine-Tuning for Multilingual Downstream Tasks in Mixture-of-Experts Models 事件

Routing-Aligned Fine-Tuning for Multilingual Downstream Tasks in Mixture-of-Experts Models arXiv:2605.28306v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) models have emerged as a dominant paradigm for efficient LLM scaling, yet adapting them to non-English downstream tasks remains challenging. Existing fine-tuning approaches treat MoE models as monolithic learners, ignoring the heterogeneous routing structure that develops during pretraining. We validate across multiple MoE models and