← Back to papers
Paper Summary
Paperzilla title
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.
Explain Like I'm Five
Researchers taught AI language models to plan complex tasks by showing them how to think through each step logically, like solving a puzzle, and then had a smart computer check their work. This made the AI much better at planning.
Possible Conflicts of Interest
None identified
Identified Limitations
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.
Good to know
This is the Starter analysis. Paperzilla Pro fact-checks every citation, researches author backgrounds and funding sources, and uses advanced AI reasoning for more thorough insights.
Explore Pro →
File Information
Original Title:
Teaching LLMs to Plan: Logical Chain-of-Thought Instruction Tuning for Symbolic Planning
Uploaded:
October 01, 2025 at 03:36 PM
Privacy:
Public