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arXiv — cs.AI

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标题为英语How To Train Your World Model: Fine-tuning vs RAG for LM-based World Modeling

World models (WMs) simulate the transition dynamics of environments, enabling agents to plan over the consequences of their actions. In text-based environments, fine-tuning a Language Model (LM) to serve as a WM has emerged as a dominant paradigm. However, despite the widespread…

arXiv — cs.AI作者: Dhananjay Ashok, Shantanu Agarwal, Vivek Datla, Jonathan May, Alfy Samuel2026年10月5日 (10月5日)
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  • World models (WMs) simulate the transition dynamics of environments, enabling agents to plan over the consequences of their actions
  • In text-based environments, fine-tuning a Language Model (LM) to serve as a WM has emerged as a dominant paradigm
  • However, despite the widespread…
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How To Train Your World Model: Fine-tuning vs RAG for LM-based World Modeling

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