# How To Train Your World Model: Fine-tuning vs RAG for LM-based World Modeling

> 全文为英语

**Published:** 2026年10月5日
**Author:** Dhananjay Ashok, Shantanu Agarwal, Vivek Datla, Jonathan May, Alfy Samuel
**Source:** arXiv — cs.AI
**Language:** en-US
**HTML:** https://aiqazaqstan.com/zh/articles/how-to-train-your-world-model-fine-tuning-vs-rag-for-lm-based-world-mode

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…

> 内容来自原始来源： **arXiv — cs.AI**
> [在 arXiv — cs.AI 阅读全文](https://arxiv.org/abs/2610.02542)

**标签：** #arXiv

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