AutoGym framework generates verifiable agent gyms
Training reinforcement‑learning agents needs a gym that includes a task, an executable environment, and a verifier that can reliably distinguish success from failure. Building such gyms has been manual, costly, and static, and the task sets quickly become saturated as models improve.
Key points
- AutoGym automatically generates complete RL gyms from a minimal domain seed or prior model trajectories.
- Blueprint‑first generation sets solvability before environment creation, ensuring tasks are solvable by design.
- Active curriculum synthesis calibrates task distribution as model capabilities grow, producing gyms that challenge frontier models.
AutoGym proposes a framework that automatically generates complete gyms from a minimal domain seed or from prior model trajectories. It introduces three mechanisms. First, blueprint‑first generation specifies the valid solution space, environment requirements, and verification criteria before the environment is materialized, making solvability a construction prerequisite rather than a property verified after the fact. Second, explicit generation parameters control task topology, interaction depth, capability axes, question obfuscation, and distractor composition, allowing fine‑grained difficulty steering. Third, active curriculum synthesis uses performance‑informed calibration to adjust the distribution over these parameters as model capabilities evolve.
Across productivity and temporal‑reasoning settings, AutoGym produces gyms that span the capability spectrum, including instances that challenge frontier models.
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