教LLM规划：面向符号规划的逻辑思维链指令微调
Teaching LLMs to Plan: Logical Chain-of-Thought Instruction Tuning for Symbolic Planning
AI总结：
提出PDDL-Instruct指令微调框架，通过逻辑思维链推理教导LLM严谨推理动作适用性与状态转换，在标准基准上实现94%规划准确率，较基线绝对提升66%。
摘要：
大型语言模型（LLMs）在各类任务中展现了出色的能力，但它们执行结构化符号规划的能力仍然有限，特别是在需要形式化表示的领域，如规划领域定义语言（PDDL）。本文提出了一个新颖的指令微调框架PDDL-Instruct，旨在通过逻辑思维链推理增强LLMs的符号规划能力。我们的方法侧重于教导模型使用显式的逻辑推理步骤，严谨地推理动作的适用性、状态转换和规划有效性。通过开发指令提示，引导模型完成确定何时能在给定状态下应用动作所需的精确逻辑推理，我们使LLMs能够通过结构化反思自我纠正其规划过程。该框架通过将规划过程分解为关于前提条件满足、效果应用和不变性保持的显式推理链，系统地构建了验证技能。在多个规划领域的实验结果表明，我们基于思维链推理的指令微调模型在规划方面表现显著更好，在标准基准上实现了高达94%的规划准确率，比基线模型绝对提升了66%。这项工作弥合了LLMs的一般推理能力与自动规划所需的逻辑精度之间的差距，为开发更好的AI规划系统提供了有希望的方向。
英文摘要：
Large language models (LLMs) have demonstrated impressive capabilities across diverse tasks, yet their ability to perform structured symbolic planning remains limited, particularly in domains requiring formal representations like the Planning Domain Definition Language (PDDL). In this paper, we present a novel instruction tuning framework, PDDL-Instruct, designed to enhance LLMs' symbolic planning capabilities through logical chain-of-thought reasoning. Our approach focuses on teaching models to rigorously reason about action applicability, state transitions, and plan validity using explicit logical inference steps. By developing instruction prompts that guide models through the precise logical reasoning required to determine when actions can be applied in a given state, we enable LLMs to self-correct their planning processes through structured reflection. The framework systematically builds verification skills by decomposing the planning process into explicit reasoning chains about precondition satisfaction, effect application, and invariant preservation. Experimental results on multiple planning domains show that our chain-of-thought reasoning based instruction-tuned models are significantly better at planning, achieving planning accuracy of up to 94% on standard benchmarks, representing a 66% absolute improvement over baseline models. This work bridges the gap between the general reasoning capabilities of LLMs and the logical precision required for automated planning, offering a promising direction for developing better AI planning systems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
