AutoEnvScaling：用终端智能体自动化数据飞轮

递归式自我改进需要能够构建自身训练环境的智能体。但研究人员仍需留在循环之中，这使得规模化变得困难。 我们提出 AutoEnvScaling，它将数据飞轮转化为一项终端任务：由 proposer 智能体构建完整的 Harbor 任务和沙箱。它的构建方式与人类研究者如出一辙。这为终端智能体实现了自博弈强化学习：同一个模型在作为构建者和求解者的过程中不断提升，无需人类参与循环。

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Recursive self-improvement needs agents that can build their own training environments. But researchers still part of the loop, making it hard to scale. We introduce AutoEnvScaling, which turns Data Flywheel into a terminal task: a proposer agent builds complete Harbor tasks and sandbox. It builds just like human researchers. This enables self-play RL for terminal agents: one model improves as both builder and solver, without human-in-the-loop.