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This work shows that conventional hyperparameter scaling laws are insufficient for ultra-sparse MoEs: the optimal learning rate and batch size vary with activation ratio, and these shifts cannot be explained by either total or activated parameter count alone.
This work proposes SuperValid, a framework that synthesizes OOD, capability-aligned validation data by distilling core concepts from benchmarks within a capability domain and expanding them into diverse, knowledge-rich texts, which enables effective model selection, early stopping, and scaling decisions.
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