How can one use a validation set to reduce overfitting Naive Bayes?
It is not much about the algorithm you use. It is about the fact that you learned so much details from your training set so just tune the parameter inside a loop in which training and validation errors are calculated each time. In NB case, you do not have many parameters in that sense. Probably the features can be inspected instead of parameter. Then see the famous point at which the validation error is the minimum.

Do not use fixed splits. For each calculation in loop do $n$ times splitting + evaluation and take the mean and std of errors. Gives you better impression about the stability of the results and effectiveness of your algorithm.