The vanishing gradients problem is a challenge in training deep neural networks where gradients of the loss function become exceedingly small, impeding effective learning and weight updates in earlier layers. This issue can lead to slow convergence or the network failing to learn altogether, often necessitating alternative architectures or optimization techniques to mitigate its effects.