Since the absolute value of this term monotonically decreases as γ 2 increases, it can be regarded as a correction factor for the learning rate, fully based on skewness. When the skewness is small, the derivative value approaches -1 2 , allowing for larger search steps during the early training epochs. Conversely, when the skewness is large, the derivative value approaches 0, contributing to the stable convergence of the loss function minimization.