Recall that when [exponentially decaying](https://www.tensorflow.org/versions/r0.9/api_docs/python/train.html#decaying-the-learning-rate) the learning rate in TensorFlow one does:

> 
> decayed\_learning\_rate = learning\_rate \*
>  decay\_rate ^ (global\_step / decay\_steps)
> 
> 
> 

the docs mention this staircase option as:

> 
> If the argument staircase is True, then global\_step /decay\_steps is an
>  integer division and the decayed learning rate follows a staircase
>  function.
> 
> 
> 

when is it better to decay every X number of steps and follow at stair case function rather than a smoother version that decays more and more with every step?