Recursive Environment Evolution for Agent Training

AutoEnvScaling and WorkRover present a developing infrastructure pattern in which solver rollouts generate or repair executable environments, verifiers validate outcomes, and dependency-closed replay feeds long-horizon agent training. Reported gains could address environment-generation and credit-assignment bottlenecks, but benchmark contamination, verifier reliability, task difficulty, proposer-solver coupling, and reproducibility remain unresolved.