Efficiently tackling combinatorial reasoning problems, particularly the notorious NP-hard tasks, remains a significant challenge for AI research. Recent efforts have sought to enhance planning by incorporating hierarchical high-level search strategies, known as subgoal methods. While promising, their performance against traditional low-level planners is inconsistent, raising questions about their application contexts. In this study, we conduct an in-depth exploration of subgoal-planning methods for combinatorial reasoning. We identify the attributes pivotal for leveraging the advantages of high-level search: hard-to-learn value functions, complex action spaces, presence of dead ends in the environment, or using data collected from diverse experts. We propose a consistent evaluation methodology to achieve meaningful comparisons between methods and reevaluate the state-of-the-art algorithms.
View on arXiv@article{zawalski2025_2406.03361,
title={ What Matters in Hierarchical Search for Combinatorial Reasoning Problems? },
author={ Michał Zawalski and Gracjan Góral and Michał Tyrolski and Emilia Wiśnios and Franciszek Budrowski and Marek Cygan and Łukasz Kuciński and Piotr Miłoś },
journal={arXiv preprint arXiv:2406.03361},
year={ 2025 }
}