Limitations and Considerations
- Vanishing Gradients: Simple RNNs have fundamental difficulty learning long-term dependencies due to gradient decay .
- Capacity: Without gates, RNNs may struggle with tasks requiring remembering far-back inputs. Training can be slow as it’s inherently sequential.
- Alternatives: In practice, gated RNNs (LSTM/GRU) or Transformers are often used for long-range dependencies. However, simple RNNs are still instructive and sometimes sufficient for short sequences  .
- Regularization: Weight decay or dropout (on inputs/states) can help generalization but must be applied carefully due to temporal correlations.
- Statefulness: For very long sequences, one can preserve hidden state across batches (stateful RNN) to avoid resetting memory.