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

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Conflicts of Interest

Identified Weaknesses

Rating Explanation

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Paper Summary

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LLMs Can Actually Plan (If You Teach Them to Think Step-by-Step with a Smart Coach!)

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 teaching them logical, step-by-step reasoning and verification. The approach achieved up to 94% planning accuracy on standard benchmarks, representing a substantial 66% absolute improvement over baseline models. A key limitation is that it focuses on "satisficing" rather than "optimal" plans and is currently limited to a subset of PDDL features.

Possible Conflicts of Interest

None identified

Identified Weaknesses

Not 100% Accuracy

The models still don't achieve perfect planning accuracy across all domains, indicating room for improvement and potential unreliability in highly critical tasks.

Focus on Satisficing, Not Optimal Plans

The framework prioritizes finding any valid plan that achieves the goal, rather than the most efficient or shortest plan. This limits its applicability in scenarios where resource optimization or speed is crucial.

Limited PDDL Feature Coverage

The approach currently only uses a subset of Planning Domain Definition Language (PDDL) features, explicitly simplifying the logical reasoning by excluding complex elements like conditional effects or durative actions. This means its capabilities might not directly transfer to more complex real-world planning problems.

Reliance on External Verifier

The system currently relies on an external verification module (VAL) to check the logical validity of generated plans, rather than the LLM being able to reliably self-correct its own reasoning. This dependence limits the autonomy and efficiency of the system.

Fixed Iteration Limits

The training process uses a fixed number of feedback loops (η=10 or 15), which might not be optimal for all problem complexities and could impact efficiency or final performance.

Limited Domain Coverage

The empirical evaluation was conducted on only three planning domains from PlanBench, which limits the generalizability of the findings to a wider variety of planning scenarios.

Rating Explanation

This is a strong research paper presenting a novel and effective instruction tuning framework that significantly advances LLM capabilities in symbolic planning, demonstrating substantial performance improvements. The methodology is sound, and the results are empirically validated across multiple domains. Key limitations (satisficing plans, limited PDDL features, external verifier) are clearly discussed by the authors and temper the rating from a 5.

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