Unverified50% confidenceFactExact time
MoE routing may introduce additional scheduling overhead, and when training is insufficient, tokens may be assigned to experts that are not good at them, causing fluctuations in performance on long-chain tasks.
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50%
Confidence
Long-term
Relevance
10/15/2026
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Related Claims
Unverified对于MoE模型，路由层中两个专家得分极为接近时，微小数值差异可能翻转专家选择结果，导致分布式训练崩溃75% similarVerifiedUnbalanced expert load is the most typical training challenge in MoE, which can lead to the phenomenon of 'expert collapse' and is typically addressed by introducing auxiliary load-balancing loss or token-dropping strategies.75% similarUnverifiedFor teams using MoE architectures, it is recommended to monitor expert load distribution and token coverage early in training; once routing collapse occurs, it is very difficult to fix later by continuing training.74% similar
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