AutoGym makes agent training gyms verifiable and easier to scale
AutoGym could reduce the manual work of building reinforcement-learning environments by generating the task, executable environment, and success checker together. It first defines what valid solutions look like and how they will be verified, then builds the environment, avoiding a post-hoc judgment by an unreliable language model. Its performance-informed curriculum adjusts task difficulty as models improve, with experiments spanning productivity and temporal reasoning, including challenges for frontier models.