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arXiv AI By Patrick Emami, Nan Qiang, Peter Graf

A Close Look At World Model Recovery In Supervised Fine-Tuned LLM Planners

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arXiv:2606. 03685v1 Announce Type: cross Abstract: Supervised fine-tuning (SFT) improves end-to-end classical planning in large language models (LLMs), but do these models also learn to represent and reason about the planning problems they are solving?

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