RA-MoE: Routing-Aligned Fine-Tuning for Multilingual Adaptation of Mixture-of-Experts Models
Mixture-of-Experts (MoE) models enable efficient LLM scaling, yet adapting them to non-English downstream tasks remains challenging. Standard multilingual fine-tuning largely ignores their heterogeneous routing structure. Across multiple MoE models and tasks, we find strong cross-lingual routing alignment in middle layers, with routing divergence associated with target-language performance gaps. Motivated by this observation, we propose RA-MoE (Routing-Aligned MoE Fine-Tuning), a three-stage framework for multilingual MoE adaptation. RA-MoE categorizes parallel examples into four correctness groups (cc/ci/ic/ii) and identifies task-relevant experts in middle layers. It then selectively al...
| Authors | Guanzhi Deng, Kuan Wu, Haibo Wang et al. |
|---|---|
| Primary category | cs.CL |
| Published | 27 May 2026 |
| Last updated at source | 30 September 2026 |
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