  Large language models (LLMs) have demonstrated impressive task-solving
capabilities, achieved through either prompting techniques or system designs.
However, concerns have arisen regarding their proficiency in planning tasks, as
they often struggle to generate valid plans. This paper investigates the impact
of fine-tuning on LLMs' planning capabilities. Our findings indicate that LLMs
can achieve good performance in planning through substantial (thousands of
specific examples) fine-tuning. However, fine-tuning is associated with
significant economic and computational costs. To address this challenge, we
propose the Maximum Diversity Fine-Tuning (MDFT) strategy to improve the sample
efficiency of fine-tuning in the planning domain. Specifically, our algorithm,
referred to as MDFT-g, encodes the planning task instances with their graph
representations and selects a subset of samples in the vector space that
maximizes data diversity. We empirically demonstrate that MDFT-g consistently
outperforms existing baselines at various scales across multiple benchmark
domains.
