The combination of the expressiveness of Probabilistic Logic Programming with the possibility of managing constraints between random variables allows users to develop simple yet powerful models to describe many real-world situations. In this paper, we propose the class of Probabilistic Reducible Logic Programs, in which the goal is to minimize the number of facts while preserving the validity of the constraints on the distribution induced by the program. Furthermore, we propose a practical algorithm to perform this task.