# Why does pre-training help avoid the vanishing gradient problem?

I read that a problem with the Classic approach to deep NN is the vanishing gradient, which is caused by the derivative of the logistic activation function - broadly speaking, the update flowing down through the network becomes ever more small.
In fact, the value of the logistic's derivative is at most $$0.25$$, and across many layers this upper bound is factored in many times.
So, why does pre-training help to avoid this effect? The derivative of logistic function is always at most $$0.25$$ and, in fine tuning phase, I always use back propagation with my even smaller derivative flowing in the net. Why the initial weights setting should change this behavior?