Interest in applying Reinforcement Learning (RL) techniques to compiler optimizations is increasing rapidly, but compiler research has a high entry barrier. Unlike in other domains, compiler and RL researchers do not have access to the infrastructure and datasets that enable fast iteration and development of ideas, and getting started requires a significant engineering investment.

We present CompilerGym, a community infrastructure for exposing compiler optimizations as RL environments, and initial results in applying RL to these environments. Our findings suggest two key challenges in RL for compilers is representation learning and transfer learning between program domains.