True or False: Larger batch sizes require smaller learning rates.

The assertion that larger batch sizes require smaller learning rates is true due to the dynamics of how learning rates and batch sizes interact during the training process of machine learning models.

When using larger batch sizes, the model is exposed to a more comprehensive representation of the training dataset in each update step. This results in less noisy gradients, which can make the optimization landscape appear smoother. Because of this reduced noise, a smaller learning rate is often more appropriate with larger batch sizes to ensure that the model does not overshoot the minima during gradient descent. If too large a learning rate is applied, it can lead to instability in the model training and may prevent convergence or even cause divergence.

Conversely, smaller batch sizes typically introduce more noise in the gradient estimates, which can benefit from larger learning rates due to this variability. The fluctuations in the gradient can help the model escape local minima and explore the optimization landscape more effectively.

The statement accurately reflects this relationship, emphasizing the need to tune learning rates based on batch sizes to promote effective and stable training of machine learning models.