In the context of Recurrent Neural Networks (RNNs), what phenomenon occurs when gradients converge to zero, making it difficult to learn long-range dependencies?

Vanishing gradient problem

This occurs because the chain rule involves repeated multiplications of derivatives, which can become very small if the values are less than one. Architectures like LSTMs and GRUs were specifically designed to combat this using gating mechanisms.