[2508.13876] Improved Generalized Planning with LLMs through Strategy Refinement and Reflection

Computer Science > Artificial Intelligence arXiv:2508.13876 (cs) [Submitted on 19 Aug 2025 (v1), last revised 20 Mar 2026 (this version, v2)] Title:Improved Generalized Planning with LLMs through Strategy Refinement and Reflection Authors:Katharina Stein, Nils Hodel, Daniel Fišer, Jörg Hoffmann, Michael Katz, Alexander Koller View a PDF of the paper titled Improved Generalized Planning with LLMs through Strategy Refinement and Reflection, by Katharina Stein and 4 other authors View PDF HTML (experimental) Abstract:LLMs have recently been used to generate Python programs representing generalized plans in PDDL planning, i.e., plans that generalize across the tasks of a given PDDL domain. Previous work proposed a framework consisting of three steps: the LLM first generates a summary and then a strategy for the domain, both in natural language, and then implements that strategy as a Python program, that gets debugged on example planning tasks. In that work, only one strategy is generated and passed directly to the program generation. If the strategy is incorrect, its implementation will therefore result in an incorrect generalized plan. Here, we introduce an approach that generates the strategy in the form of pseudocode and enables automatic debugging of the pseudocode, hence allowing us to identify and fix errors prior to the generation of the generalized plan itself. Additionally, we extend the Python debugging phase with a reflection step prompting the LLM to pinpoint the r...