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AutoGym presents a framework for automatically generating complete agent gyms—tasks, executable environments, and verifiers—from minimal seeds using blueprint-first generation to ensure solvability and active curriculum synthesis for adaptive difficulty steering.
The paper introduces RACES, a recursive automated composition framework that treats verifiable environments as composable building blocks to scale reinforcement learning for LLMs, enabling efficient reasoning generalization through compositional operators.