论文提出SARA方法，用语义锚对齐MoE路由，让低资源语言也能用好专家能力，Global-MMLU提升0.8%-1.2%。
稀疏MoE（Mixture-of-Experts）模型在多语言场景下，低资源语言因数据稀缺导致路由与高资源语言不一致，限制跨语言知识共享。为此提出SARA（Semantically Anchored Routing Alignment）框架，利用对称JS散度对齐路由分布。在Qwen3-30B-A3B和Phi-3.5-MoE-instruct两个模型上，针对5种低资源语言和3个基准测试，SARA在Global-MMLU上分别提升0.8%和1.2%。该方法不依赖输出logits蒸馏，直接对齐内部路由机制，有效缓解低资源语言瓶颈。
SARA: Unlocking Multilingual Knowledge in Mixture-of-Experts via Semantically Anchored Routing Alignment
Sparse Mixture-of-Experts (MoE) architectures have emerged as an increasingly influential paradigm as they offer a strategic balance between parameter scalability and computational efficiency. However, low-resource languages, which suffer from a scarcity of high-quality training data, often have their tokens routed to different experts than those predominantly activated by high-resource inputs, which limits cross-lingual expert sharing. This cross-lingual routing divergence consequently hinders their efficacy in multilingual contexts. To address this issue, we propose SARA (Semantically Anchored Routing Alignment), a framework designed to transfer specialized capabilities from high-resource languages as anchors to low-resource languages. SARA explicitly aligns the routing distribution of multilingual inputs with high-resource semantic anchors using a symmetric Jensen-Shannon (JS) divergence constraint. Unlike traditional distillation methods that operate on output logits, SARA directly aligns the internal routing distributions of MoE layers, encouraging mechanistic consistency in expert selection across languages. We conduct experiments on 2 LLMs across 5 low-resource languages and 3 benchmarks. Experiment results demonstrate that SARA outperforms standard instruction tuning, e.g., +0.8% on Qwen3-30B-A3B and +1.2% on Phi-3.5-MoE-instruct on Global-MMLU. Further analyses show that SARA effectively addresses performance bottlenecks in low-resource languages, providing a scalable pathway to enhance multilingual capabilities in sparse architectures.