When Load-Balancing Goes Too Far: Expert Pruning in Over-Dispersed Mixture-of-Experts Models

SummaryAI generated

On September 7, 2026, arXiv cs.AI published a research report stating that implementing expert pruning in an overly decentralized MoE model to optimize load balancing may lead to new performance issues. The study warned that when the load balancing strategy is too aggressive, it can cause imbalance in the model structure and reduce overall efficiency.

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Key entitiesKEY ENTITIES

Gemma-4-26B-A4BMixtral-8x7B-InstructOLMoE-1B-7Bgpt-oss-20B

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gpt-oss-20B1
Mixtral-8x7B-Instruct1
Gemma-4-26B-A4B1
OLMoE-1B-7B1

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A arXiv cs.AI en 2026-09-07 12:00

针对过分散路由下混合专家（MoE）模型中标准重要性假设失效的问题，研究提出最小最大专家分数分配（MESA）方法。在 GPT-OSS-20B 等模型上，过分散路由导致困惑度无法预测下游任务准确率，且单一评分指标无法同时优化数学推理与科学领域表现。MESA 通过迭代提升受负面影响最严重领域的专家重要性分数，以最小化最坏情况下的领域退化。在 25% 专家剪枝下，MESA 在 11 个基准测试中优于激活感知基线 7 项，实现了最小的跨领域性能下降及更低的内存占用，并成功泛化至 GPT-OSS-120B、Gemma-4-26B-A4B 和 OLMoE-1B-7B 模型。