What does the "tanh" activation function help achieve in RNNs?

The "tanh" activation function is particularly useful in recurrent neural networks (RNNs) because it transforms the output to a range between -1 and 1. This characteristic effectively centers the output around zero, allowing for improved learning dynamics. When the activation outputs are centered, it helps mitigate issues such as vanishing gradients and promotes faster convergence during training. This is especially important in RNNs that handle sequential data, as input data can vary widely and having outputs centered can provide more stable gradients for model training.

In contrast, other activation functions like sigmoid would squash outputs to a range of 0 to 1, which does not facilitate zero-centered outputs and can hinder the learning process in certain scenarios. Therefore, the "tanh" function's ability to center outputs around zero plays a critical role in enhancing the performance of RNNs.