GRU Architecture › GRU Architecture

In the previous chapters, we explored how LSTMs solve the vanishing gradient problem through their cell state and gating mechanisms. LSTMs work remarkably well, but their complexity comes at a cost: four separate weight matrices, two state vectors, and a substantial parameter count. In 2014, Cho et al. asked a natural question: can we achieve similar performance with a simpler architecture?

The Gated Recurrent Unit (GRU) is their answer. GRUs retain the core insight of LSTMs, using gates to control information flow, but streamline the design by merging the cell state and hidden state into a single vector and reducing three gates to two. This simplification often achieves comparable performance to LSTMs while training faster and using fewer parameters. Understanding when and why to choose GRUs over LSTMs is an essential skill for any sequence modeling practitioner.