When to use learning rate finder
Reading the paper '
Cyclical Learning Rates for Training Neural Networks' https://arxiv.org/abs/1506.01186

Does it make sense to use the learning rate finder if the model is over-fitting ? Other than reduce the number of iterations before the model overfit's will using the learning finder prevent over-fitting ?

From reading the paper there is no suggestion this method of reduces over-fitting, is my interpretation correct ?

I don't think changing the learning rate reduces over-fit. To avoid over-fitting you might want to use L1/L2 regularization and drop-out or some of its variant.  
