GRU

GRU was developed with goals similar to LSTM, but with fewer gates and a simpler internal structure. Because it uses fewer parameters, it can provide faster training with comparable performance in some scenarios. It may be particularly advantageous in resource-constrained settings or when a more compact model is preferred. Which structure works better often depends on the data and the problem itself.