RNNs (Recurrent Neural Networks) have some problems that can affect their performance:

1. Forgetting Distant Information: RNNs struggle to remember information from earlier steps in long sequences, making it difficult for them to capture long-term dependencies.

2. Gradients Vanishing or Exploding: The gradients used to update the weights can become very small (vanishing gradient) or very large (exploding gradient), leading to slow learning or unstable training.

3. Difficulty with Variable-Length Inputs: RNNs expect fixed-length inputs, so handling sequences of varying lengths requires additional techniques like padding or truncation.

4. Limited Context Understanding: RNNs process input sequentially, which can limit their ability to understand global patterns or context that spans across the entire sequence.

5. Computational and Memory Constraints: RNNs can be computationally expensive and memory-intensive, especially for long sequences or large models.

To address these issues, variations of RNNs, such as LSTM and GRU, were introduced with better memory and gating mechanisms. Other models like Transformers have also emerged as alternatives for capturing long-range dependencies more effectively.