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This paper studies nonconvex stochastic optimization under Blum-Gladyshev noise, where gradient variance grows with distance from initialization. It proves convergence guarantees for normalized SGD with momentum and a variance-reduced STORM method, achieving minimax optimal rates under certain conditions.