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.

The statement is false. RNNs keep a hidden state that lets them remember past inputs, which is useful for sequences, but this does not automatically make them more efficient than feed‑forward nets; they often require more computation per step and can be slower to train because each time step depends on the previous one. Feed‑forward networks can still handle sequences by using tricks like sliding windows or 1‑D convolutions, which can be parallelized and trained faster. For example, a 1‑D CNN can process a sentence in one pass, while an RNN must process each word sequentially, adding latency. Thus, RNNs are not inherently more efficient, they just offer a different way to capture temporal dependencies.