View a PDF of the paper titled Improved Generalized Planning with LLMs through Strategy Refinement and Reflection, authored by Katharina Stein and four additional collaborators.
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Abstract: LLMs have recently been utilized to generate Python programs that represent generalized plans in PDDL (Planning Domain Definition Language) planning. These plans are aimed at providing a framework for tasks within specific PDDL domains. The previously established methodology comprises three steps: first, the LLM produces a summary, followed by a strategic outline in natural language, and ultimately, the implementation of that strategy as a Python program. This program is then debugged against example planning tasks. However, prior attempts only generated a singular strategy, which, if flawed, directly led to an erroneous generalized plan implementation. In our work, we introduce a novel approach that involves crafting the strategy in the form of pseudocode. This allows for the automatic debugging of the pseudocode, enabling the identification and rectification of errors before the actual generalized plan generation. Moreover, we enhance the Python debugging phase by incorporating a reflection step that prompts the LLM to identify reasons behind any plan failures. Inspired by LLM code generation, we also produce several program variants, allowing us to select the optimal one. Experimental results across 17 benchmark domains, utilizing two reasoning LLMs and two non-reasoning LLMs, indicate that our enhancements significantly improve the quality of generalized plans, with our best performing configuration achieving an average coverage of 82% across the domains.
Submission History
From: Katharina Stein [view email]
[v1] Tue, 19 Aug 2025 14:42:18 UTC (2,476 KB)
[v2] Fri, 20 Mar 2026 15:30:50 UTC (10,763 KB)
### Understanding Generalized Planning and LLMs
Generalized planning, in the domain of artificial intelligence, involves creating plans that can be universally applied across various tasks within a given framework, typically defined by PDDL. Recent advancements have seen the introduction of large language models (LLMs) as powerful tools for generating these generalized plans.
LLMs can formulate strategies and corresponding Python programs that automate task planning. However, challenges remain not only in generating effective strategies but also in ensuring the accuracy of their implementation. The nuances of human language and the complexity of programming introduce risks of inaccuracies that can lead to flawed outcomes.
### The Framework: From Strategy to Implementation
The traditional framework for generalized planning with LLMs can be delineated into three primary steps. Initially, the LLM generates a summary of the planning domain. This phase is crucial, as it sets the groundwork for understanding the specific tasks at hand. Next, a strategy is crafted in natural language, detailing the approach for handling the tasks within that domain.
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The final step involves the conversion of that strategy into a concrete Python program. However, in past iterations of this method, the strategy was singular and static. Should the strategy be incorrect, the resulting implementation would undoubtedly reflect those flaws.
### Introducing Pseudocode for Enhanced Debugging
To address the limitations of earlier methods, the innovative approach discussed in the paper revolves around using pseudocode for strategy generation. This crucial shift allows for pre-emptive debugging, which is essential for identifying potential errors even before the final plan is generated.
By debugging the pseudocode, practitioners can scrutinize their strategies and rectify them accordingly. This new process not only minimizes the chances of creating flawed plans but also enhances the overall efficacy of the planning task.
### Reflection Step: A Deeper Understanding of Failures
One of the notable extensions introduced in the current framework is the reflection step added to the Python debugging phase. This step prompts the LLM to consider the underlying reasons for any observed failures in the planning process. By doing so, it not only enables pinpointing of specific issues but also enhances the learning aspect of the model, fostering improved future performance.
### Generating Program Variants for Optimization
Another advancement highlighted in this research is the generation of multiple program variants. This strategy is inspired by the LLM’s inherent capabilities in code generation. Rather than settling for a single program outcome, exploring various implementations allows for an analytical approach in selecting the most effective version. This iterative process significantly contributes to achieving higher quality plans.
### Experimental Results and Impact
The research showcases its practical implications through extensive experiments conducted across 17 benchmark domains. Utilizing both reasoning and non-reasoning LLMs, the results demonstrate a substantial improvement in the quality and effectiveness of the generalized plans produced. The best-performing configuration achieved an impressive average coverage of 82% across the domains, highlighting the efficacy of the introduced methodologies.
### Conclusion
The landscape of artificial intelligence in planning continues to evolve, with advancements like those proposed by Katharina Stein and her collaborators paving the way for more efficient and reliable generalized planning solutions. The integration of pseudocode, coupled with reflection and program variation strategies, exemplifies the ongoing quest for higher accuracy and functionality in automated task planning.
For those interested in diving deeper into the intricacies of this research, the full paper is available for review, showcasing a significant leap in generalized planning with LLMs.
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