True or False: Recurrent Neural Networks (RNNs) are inherently more efficient than traditional feedforward networks in processing sequential data due to their ability to maintain hidden states across time steps.

False. RNNs can model sequences because they keep a hidden state that carries information over time, but this does not automatically make them computationally more efficient than feed‑forward nets; in fact, back‑propagation through time is often slower and more memory‑intensive. A feed‑forward network can still process a sequence by treating the whole sequence as a single long input, which can be faster if the sequence is short or if parallel computation is used. The hidden state gives RNNs a powerful inductive bias for temporal patterns, yet training them requires many sequential operations that limit parallelism. For example, a simple 5‑step sequence can be processed by an RNN in five sequential steps, whereas a feed‑forward net can compute all steps at once, showing that efficiency depends on the specific task and hardware.