Symbolic reasoning is a well understood and effective approach to handling reasoning over formally represented knowledge; however, simple symbolic inference systems necessarily slow as complexity and ground facts grow. As automated approaches to ontology-building become more prevalent and sophisticated, knowledge base systems become larger and more complex, necessitating techniques for faster inference. This work uses reinforcement learning, a statistical machine learning technique, to learn control laws which guide inference. We implement our learning method in ResearchCyc, a very large knowledge base with millions of assertions. A large set of test queries, some of which require tens of thousands of inference steps to answer, can be answered faster after training over an independent set of training queries. Furthermore, this learned inference module outperforms ResearchCyc's integrated inference module, a module that has been hand-tuned with considerable effort.