让大模型学会预判动作条件和结果，实现虚拟世界模拟
Making Large Language Models into World Models with Precondition and Effect Knowledge
- 用合成数据微调两个大模型，分别预测动作前提和执行后果
- 人类实验验证模型理解与人类认知一致，能支持连贯动作规划
- 适合对智能体决策、自动规划感兴趣的开发者与研究者
世界模型是智能体理解动作如何影响环境的基础。本文探索大语言模型（LLMs）作为世界模型的潜力。尽管LLMs并非专为建模真实世界动态而设计，我们证明可通过微调两个独立的LLM——一个用于预判动作前提，另一个用于预测动作结果——使其完成两项关键任务。方法依赖于合成数据生成技术。通过人类参与实验，验证了模型生成的前提与效果知识与人类对世界动态的理解高度一致。此外，分析表明，基于合成数据训练的世界模型所推导出的状态空间，具备支持动作链生成的能力，这是规划所需的必要特性。
原文摘要 · Abstract (English)
World models, which encapsulate the dynamics of how actions affect environments, are foundational to the functioning of intelligent agents. In this work, we explore the potential of Large Language Models (LLMs) to operate as world models. Although LLMs are not inherently designed to model real-world dynamics, we show that they can be induced to perform two critical world model functions: determining the applicability of an action based on a given world state, and predicting the resulting world state upon action execution. This is achieved by fine-tuning two separate LLMs-one for precondition prediction and another for effect prediction-while leveraging synthetic data generation techniques. Through human-participant studies, we validate that the precondition and effect knowledge generated by our models aligns with human understanding of world dynamics. We also analyze the extent to which the world model trained on our synthetic data results in an inferred state space that supports the creation of action chains, a necessary property for planning.
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