Teaching LLMs to Plan: Logical Chain-of-Thought Instruction Tuning for Symbolic Planning

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Conflicts of Interest

Identified Weaknesses

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Paper Summary

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AI's New Homework: How Logical Steps and Cheating (with a Validator) Make LLMs Super Planners!

This paper introduces PDDL-INSTRUCT, a novel instruction tuning framework that significantly enhances Large Language Models' (LLMs) ability to perform structured symbolic planning. By explicitly training LLMs with logical chain-of-thought reasoning and external verification feedback, the framework enables them to generate and validate plans with up to 94% accuracy in complex planning domains, representing a 66% absolute improvement over baseline models. The findings demonstrate a promising direction for developing more trustworthy AI planning systems by bridging the gap between general LLM reasoning and the logical precision needed for automated planning.

Possible Conflicts of Interest

None identified

Identified Weaknesses

Limited PDDL Feature Coverage

The framework currently simplifies logical reasoning by using only a subset of PDDL features. This might limit its applicability to more complex, real-world planning problems that often involve advanced features like conditional effects, derived predicates, action costs, or temporal constraints.

Focus on Satisficing, Not Optimal Planning

The current work prioritizes finding any valid plan that achieves the goal (satisficing) rather than the shortest or most efficient plan (optimal). For many real-world applications, resource efficiency and optimality are critical considerations that are not addressed here.

Reliance on External Verifier

The approach currently relies on an external verifier (VAL) for ground-truth feedback and self-correction. While robust, this external dependency limits the LLM's inherent self-verification capabilities, potentially reducing autonomy and efficiency in deployment if seamless integration is not achieved.

Fixed Iteration Limits

The study uses fixed iteration limits (η) for the feedback loops during CoT instruction tuning. This fixed approach may not be optimal for all problem complexities, and dynamically determining the appropriate number of iterations could improve efficiency and performance.

Limited Domain Coverage in Evaluation

The empirical evaluation is conducted on three planning domains (Blocksworld, Mystery Blocksworld, Logistics). While these domains present varying challenges, a wider and more diverse set of planning domains would provide a more comprehensive assessment of the approach's generalizability.

High Computational Cost

Finetuning large language models, especially with iterative feedback loops and detailed reasoning chains, involves significant economic, time, and computational resources, which can be a barrier to wider adoption and experimentation.

Rating Explanation

The paper presents a novel and effective instruction tuning framework that significantly enhances LLMs' symbolic planning capabilities. The methodology is robust, providing substantial empirical improvements (66% absolute gain over baselines) across challenging domains. The limitations are clearly discussed and primarily relate to the scope of PDDL features covered, the focus on satisficing rather than optimal planning, and reliance on an external verifier, which are areas for future work rather than critical flaws in the current findings.

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