Over the last few years, there has been a surge in the use of learning techniques to improve the performance of optimization algorithms. In particular, the learning of branching rules in mixed integer linear programming has received a lot of attention, with most methodologies based on strong branching imitation. Recently, some advances have been made as well in the context of nonlinear programming, with some methodologies focusing on learning to select the best branching rule among a predefined set of rules, leading to promising results. In this paper, we explore, in the nonlinear setting, the limits on the improvements that might be achieved by the above two approaches when using reformulation-linearization technique-based relaxations for solving polynomial optimization problems: learning to select the best variable (strong branching) and learning to select the best rule (rule selection).