{"id":1176624,"date":"2026-06-22T16:05:11","date_gmt":"2026-06-22T23:05:11","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/when-are-experts-misrouted-counterfactual-routing-analysis-in-mixture-of-experts-language-models\/"},"modified":"2026-07-10T17:05:05","modified_gmt":"2026-07-11T00:05:05","slug":"when-are-experts-misrouted-counterfactual-routing-analysis-in-mixture-of-experts-language-models","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/when-are-experts-misrouted-counterfactual-routing-analysis-in-mixture-of-experts-language-models\/","title":{"rendered":"When Are Experts Misrouted? Counterfactual Routing Analysis in Mixture-of-Experts Language Models"},"content":{"rendered":"\n\n\n<p class=\"wp-block-paragraph\">Mixture-of-Experts (MoE) language models route each token to a small subset of experts, but whether the routes selected by a trained top-<math><mi>k<\/mi><\/math> router are good ones is rarely evaluated directly. Holding the model fixed, we compare each standard route against sampled equal-compute alternatives for the same token and score each by the next-token probability it assigns to the realized token in a verified reasoning trajectory. The result is sharply token-conditional: the standard router is well-aligned with route utility on confident tokens but uninformative on the fragile tokens that drive hard reasoning, where lower-loss equal-compute routes consistently exist inside the frozen model but are not selected. The same pattern holds across Qwen3-30B-A3B, GPT-OSS-20B, DeepSeek-V2-Lite, and OLMoE-1B-7B, and follows structurally from how standard top-<math><mi>k<\/mi><\/math> training evaluates routing decisions: the language modeling loss scores only the executed route, and load balancing depends only on aggregate routing statistics. A minimal router-only update to the final-layer router, leaving every expert and every other router frozen, is sufficient to shift pass@K on AIME 2024+2025 and HMMT 2025 for both Qwen3-30B-A3B and GPT-OSS-20B, suggesting that at least part of the failure reflects router-reachable misallocation rather than expert capacity alone.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Mixture-of-Experts (MoE) language models route each token to a small subset of experts, but whether the routes selected by a trained top-k router are good ones is rarely evaluated directly. Holding the model fixed, we compare each standard route against sampled equal-compute alternatives for the same token and score each by the next-token probability it [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":[{"type":"text","value":"Youngsik Yoon","user_id":0},{"type":"user_nicename","value":"Siwei Wang","user_id":"42321"},{"type":"user_nicename","value":"Wei Chen","user_id":"34795"},{"type":"text","value":"Jungseul 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