It depends. ADAM updates any parameter with an individual learning rate. This means that every parameter in the network has a specific learning rate associated.
But the single learning rate for each parameter is computed using lambda (the initial learning rate) as an upper limit. This means that every single learning rate can vary from 0 (no update) to lambda (maximum update).
It’s true, that the learning rates adapt themselves during training steps, but if you want to be sure that every update step doesn’t exceed lambda you can than lower lambda using exponential decay or whatever.
It can help to reduce loss during the latest step of training, when the computed loss with the previously associated lambda parameter has stopped to decrease.