AI智能体也需要“训练场”，这篇论文帮你选场地
大模型智能体（比如能帮你订酒店、写代码的AI）不是天生就会干活，它们需要在一个模拟环境里反复练习。但现有的环境五花八门，有的像游戏，有的像办公软件，选错了环境，智能体可能练了一身屠龙技。这篇综述把环境工程拆成四个环节：建模、合成、评估、应用，并梳理了8个属性（比如是否动态、是否多智能体）和8个领域（比如客服、编程）。它不是你明天能用上的工具，但如果你想理解为什么有些AI助手显得“聪明”，有些却“笨”，这篇论文给出了一个系统框架：智能体的能力上限，很大程度上取决于它被训练的环境有多丰富、多真实。
📄 原文摘要(英文)
Environments serve as interactive systems for large language model (LLM) based agents across diverse scenarios and play a crucial role in driving the continual evolution of model capabilities. Despite this importance, existing work lacks a systematic categorization and deep analysis. This paper systematically studies current researches on agentic environments from the perspective of the environment engineering lifecycle, covering their modeling, synthesis, evaluation and application. Specifically, the paper first introduces representative environments from the perspectives of eight attributes and eight domains, providing detailed analyses of their development paths and highlighting their core capabilities. Second, for automated environment synthesis, two paradigms are introduced, such as symbolic synthesis and neural synthesis. This paper also shows different environment evaluation methods in each paradigm. Thirdly, the corresponding environment applications from the perspective of agent-environment co-evolution are discussed. In specific, the paper characterizes the primary pathways for agent evolution in dynamic environments from four complementary perspectives: memory-centric experience evolution, orchestration-centric workflow evolution, trajectory-centric offline evolution, and exploration-centric online evolution. And three paradigms of environment evolution are identified, namely neural-driven, difficulty-driven, and scaling-driven approaches. At last, several promising future directions are discussed, including Environment-as-a-Service, Multi-agent Environments, and Neural-Symbolic Environments.