The challenges faced by traditional RNNs during training are explored, specifically the vanishing gradients and exploding gradients problems. These issues can significantly hinder the training process, especially for long sequences.

• Vanishing gradients: during backpropagation, the gradients (used to adjust weights) can become very small, causing the model to stop learning or update its weights very slowly. This problem is most noticeable in long sequences, where the effect of the initial input fades as the network progresses through many layers;
• Exploding gradients: this occurs when the gradients grow exponentially during backpropagation, leading to large updates to the weights. This can cause the model to become unstable and result in numerical overflow;
• Impact on training: both vanishing and exploding gradients make training deep networks difficult. For vanishing gradients, the model struggles to capture long-term dependencies, while exploding gradients can cause erratic and unpredictable learning;
• Solutions to the problem: there are various techniques like long short-term memory (LSTM) or gated recurrent units (GRU) that are designed to handle these problems more effectively.

In summary, the vanishing and exploding gradients problems can prevent traditional RNNs from learning effectively. However, with the right techniques and alternative RNN architectures, these challenges can be addressed to improve model performance.