What is gradient noise scale and its impact on cost function convergence?

Gradient noise measures stochasticity in gradient estimates affecting convergence stability.

Gradient noise arises because mini-batch gradients are noisy estimates of full gradients. High noise can help escape sharp minima but slows convergence. Low noise improves stability but may trap optimization in poor minima. The gradient noise scale influences optimal batch size selection and learning rate scheduling.