Weight training breakdown in machine learning I'm not sure if this exists. Is there such a situation where weights in gradient descent fail to work or break up? If so, how and when?
There is a situation called "exploding gradients" where very large error gradients accumulate and result in very large updates to neural network model weights during training.

This can result in an unstable model that cannot learn from the training data or even result in NaN weight values that can no longer be updated.