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

A Close Look At World Model Recovery In Supervised Fine-Tuned LLM Planners 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? Due to the relative complexity of classical planning problems and the challenge that end-to-end plan generation poses for LLMs, it has been difficult to explore this questi