In deep RNNs, gradients shrink exponentially through time — early timesteps get near-zero updates.

In deep RNNs, gradients shrink exponentially through time — early timesteps get near-zero updates. LSTM introduces a cell state with additive updates (not multiplicative) and gates (input, forget, output) that regulate what to remember. The forget gate can stay near 1 to preserve gradient flow, avoiding vanishing. GRU is a simpler variant with the same key idea.