Verifiable Hidden Dynamics Play: Generating Agentic RL Environments from Solved Mechanisms
Language-model agents increasingly face long-horizon tasks with evolving state, interdependent decisions, and delayed outcomes, and scale-matched training retains gains as mechanism size and horizon grow, indicating the potential for an evolving training substrate.