  Many machine learning strategies designed to automate mathematical tasks
leverage neural networks to search large combinatorial spaces of mathematical
symbols. In contrast to traditional evolutionary approaches, using a neural
network at the core of the search allows learning higher-level symbolic
patterns, providing an informed direction to guide the search. When no labeled
data is available, such networks can still be trained using reinforcement
learning. However, we demonstrate that this approach can suffer from an early
commitment phenomenon and from initialization bias, both of which limit
exploration. We present two exploration methods to tackle these issues,
building upon ideas of entropy regularization and distribution initialization.
We show that these techniques can improve the performance, increase sample
efficiency, and lower the complexity of solutions for the task of symbolic
regression.
