AutoGym: blueprint-first framework to synthesize verifiable agent gyms
The paper (arXiv:2609.22592v1) introduces AutoGym, a framework that automatically generates complete reinforcement-learning gyms—tasks, executable environments, and verifiers—starting from a minimal domain seed or prior trajectories. AutoGym combines (1) blueprint-first generation that specifies valid solution spaces and verification criteria before materializing environments, (2) explicit generation parameters for fine-grained difficulty and topology control, and (3) active curriculum synthesis that adapts parameter distributions based on model performance; authors report generation of gyms across the capability spectrum, including instances that challenge frontier models in productivity and temporal-reasoning settings.