The problem is at the beginning of the training, we have no prior knowledge about the noise pattern, thus, both T and p are less reliable, making the training procedure be likely to trap into some poor local optimum.
we require a technique to guide our model to gradually adapt to the noisy training data, e.g., learning something simple first, and then trying to deal with noises.