Teaching LLMs to Plan: Logical Chain-of-Thought Instruction Tuning for Symbolic Planning
While Large Language Models excel at creative tasks, they often struggle with the logical precision required for symbolic planning. This episode explores PDDL-INSTRUCT, a novel framework that teaches LLMs to reason through complex plans using a logical "chain-of-thought" approach, verifying each step to ensure its validity. Tune in to learn how this method dramatically improves planning accuracy by up to 66%, bridging the gap between general AI reasoning and the formal logic of automated planning.