What can happen to gradients in RNNs during backpropagation through time?

In the context of Recurrent Neural Networks (RNNs), gradients during backpropagation through time can indeed face challenges, specifically the issue of vanishing gradients. This phenomenon occurs when the gradients are propagated back through many layers or time steps, leading them to become increasingly smaller. As the gradients diminish, they can reach a point where they are so close to zero that they fail to make significant updates to the weights of the network. This can severely hinder the network's ability to learn long-term dependencies from the input data, as the model struggles to capture the relevance of past states to future predictions.

The vanishing gradient problem is particularly pronounced in RNNs because they rely on sequences of data where information needs to be retained over time. If the gradients vanish, the model essentially stops learning, especially for earlier time steps where important information might be present.

This understanding highlights the critical challenge facing RNNs and is a primary reason for the development of alternatives, such as Long Short-Term Memory (LSTM) networks and Gated Recurrent Units (GRUs), which are designed to mitigate the effects of vanishing gradients.