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arXiv · 2509.19931

Documentation Retrieval Improves Planning Language Generation

Renxiang Wang·Li Zhang
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Abstract

Certain strong LLMs have shown promise for zero-shot formal planning by generating planning languages like PDDL. Yet, the performance of most open-source models under 50B parameters has been reported to be close to zero due to the low-resource nature of these languages. We significantly improve their performance via a series of lightweight pipelines that integrates documentation retrieval with modular code generation and error refinement. With models like Llama-4-Maverick, our best pipeline improves plan correctness from 0% to over 80% on the common BlocksWorld domain. However, while syntactic errors are substantially reduced, semantic errors persist in more challenging domains, revealing fundamental limitations in current models' reasoning capabilities.

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Renxiang Wang, Li Zhang. 2025-09-24. Documentation Retrieval Improves Planning Language Generation. https://arxiv.org/abs/2509.19931

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