AI 学会在脑子里预演工具操作，不再依赖真实环境
训练 AI 用工具，过去要么给它真实环境（贵、难造），要么用外部模拟器（对不上现实）。这篇让 AI 自己演：它先想一个操作，然后自己扮演环境，猜这个操作会得到什么结果，再基于这个猜出来的结果继续想下一步。整个过程只用最终任务成败来打分，两个角色一起优化。结果在四个基准上，这种「脑内预演」比堆更多真实环境数据还强，而且模型越小提升越明显。测试时它还能先私下预演几轮再真正动手，效果更好。这不是你明天能用的功能，但它指向一个方向：AI 可能不再需要那么多真实世界的试错，靠想象就能学会用工具。
📄 原文摘要(英文)
Training large language model agents for long-horizon tool use typically relies on interactions with real or synthesized executable environments, whose construction and verification are costly, or on external simulators that are difficult to ground. We introduce EnvACE, an agentic reinforcement learning method that replaces external environment interaction during training with world rehearsal. The policy alternates between acting and rehearsal: it first generates a tool call, then plays the role of the environment to produce the response induced by that action, and conditions subsequent decisions on the rehearsed response. Both roles are jointly optimized end-to-end using task-success rewards. Through world rehearsal, the policy internalizes the relationship between actions and their environment responses in its parameters, yielding an agent world model that directly supports decision making. Across BFCL-v4, tau^2-Bench, VitaBench, and FinMCP-Bench, EnvACE achieves strong and transferable performance, outperforming environment-scaling baselines in the overall evaluation. Controlled studies further show that world rehearsal consistently improves policy learning across model scales. At test time, the internalized world model enables private rehearsal before committed execution, yielding further gains under a moderate rehearsal budget without additional external interaction. Our findings establish world rehearsal as a new path toward scaling LLM agent training beyond the constraints of external environments. Our code is publicly available at https://github.com/Within-yao/EnvACE.