EnvACE 让模型自己脑补环境的反馈，省去搭建外部模拟器，四个基准上还赢了。想低成本练智能体的可以看看。
EnvACE 是一种面向大语言模型智能体的强化学习方法，训练时不再依赖真实或合成的可执行环境交互，而是让策略交替执行动作和扮演环境产生响应。该方法在 BFCL-v4、tau^2-Bench、VitaBench 和 FinMCP-Bench 四个基准上表现优异，整体评估超过环境扩展类基线。受控实验表明，世界预演在不同模型规模下都能稳定提升策略学习效果。测试阶段，内化的世界模型还可在提交执行前进行私有预演，在中等预演预算下获得额外增益。代码已在 GitHub 上公开。
EnvACE: Internalizing Environment Dynamics via World Rehearsal for Agentic Reinforcement Learning
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.