Gated Recurrent Units (GRUs) are defined by two key distinguishing features that govern how they manage sequences:

• Reset Gates: These help the model capture short-term dependencies in sequences by controlling how much of the previous state should be ignored when computing the new candidate state.
• Update Gates: These help the model capture long-term dependencies in sequences by deciding how much of the previous hidden state should be preserved in the final hidden state.

Notably, GRUs contain basic (simple) RNNs as their extreme case: whenever the reset gate is fully activated (switched on), the candidate state computation becomes equivalent to a standard RNN update. GRUs can also effectively skip subsequences by activating the update gate, which causes the hidden state to be copied from the previous time step with minimal modification.