Automatic Generation of High-Performance RL Environments

ArXi:2603.12145v1 Announce Type: cross Translating complex reinforcement learning (RL) environments into high-performance implementations has traditionally required months of specialized engineering. We present a reusable recipe - a generic prompt template, hierarchical verification, and iterative agent-assisted repair - that produces semantically equivalent high-performance environments for <$10 in compute cost. We nstrate three distinct workflows across five environments.