CompilerGym provides easy-to-use and performant reinforcement learning environments that frame compiler optimization problems in a way familiar to ML practitioners. It wraps compiler toolchains (LLVM, GCC, etc.) as interactive gym-style environments where agents can propose optimizations and receive feedback on metrics like instruction count, compile time, or binary size. This enables ML researchers to apply reinforcement learning, search algorithms, and other ML techniques to practical compiler optimization challenges.