How do gated recurrent units (GRUs) enhance the processing of real-time data in recurrent neural networks (RNNs)?

Gated recurrent units add two gates—an update gate and a reset gate—to the hidden state update, letting the network decide how much past information to keep or forget. This selective memory prevents the vanishing‑gradient problem that plagues vanilla RNNs, allowing the model to capture long‑term patterns in streaming data. The update gate blends the old hidden state with new candidate values, so the network can smoothly carry information forward while discarding irrelevant details. For example, when processing a live speech stream, a GRU can retain the context of a speaker’s name across many words while discarding filler words, improving real‑time transcription accuracy. As a result, GRUs enable RNNs to handle real‑time sequences more reliably and efficiently.