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，过去要么真给它一个能跑的环境(贵、难造)，要么用外部模拟器(和现实对不上)。这篇换了个思路：让 AI 自己演环境。它每调用一个工具，就自己扮演环境、生成工具返回的结果，再基于这个结果继续下一步，整个流程用最终任务是否成功来优化。结果在四个工具使用基准上，这种「脑内预演」比依赖真实环境的训练方法表现更好，而且模型越大提升越明显。测试时它还能先在心里预演几遍再真正执行，进一步提分。这不是你明天能用的功能，但它指向一个趋势：AI 训练可能不再被「有没有真实环境」卡住，模型自己就能把环境规律内化进参数里。
📄 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.