GRU Explained: How Two Gates Rival the LSTM in Sequence Modeling

The Gated Recurrent Unit (GRU), introduced by Cho and colleagues in 2014, was designed to address the vanishing-gradient problem that makes vanilla RNNs ineffective at learning long-range dependencies. Unlike the LSTM, which uses three gates and a separate cell state, the GRU achieves similar results with just two gates — an update gate and a reset gate — and a single hidden state. The update gate controls how much of the previous hidden state is carried forward versus replaced by a new candidate, enabling gradients to flow back over many steps without shrinking. The reset gate determines how much past context influences the candidate computation, allowing the model to selectively ignore history at appropriate points. With roughly 25% fewer parameters than an LSTM for the same hidden size, the GRU trains faster and is generally recommended as a default choice for sequence tasks.