Subgoal Search For Complex Reasoning Tasks

Konrad Czechowski; Tomasz Odrzygóźdź; Marek Zbysiński; Michał Zawalski; Krzysztof Olejnik; Yuhuai Wu; Łukasz Kuciński; Piotr Miłoś

arXiv · 2108.11204

Subgoal Search For Complex Reasoning Tasks

Konrad Czechowski·Tomasz Odrzygóźdź·Marek Zbysiński·Michał Zawalski·Krzysztof Olejnik·Yuhuai Wu·Łukasz Kuciński·Piotr Miłoś

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Abstract

Humans excel in solving complex reasoning tasks through a mental process of moving from one idea to a related one. Inspired by this, we propose Subgoal Search (kSubS) method. Its key component is a learned subgoal generator that produces a diversity of subgoals that are both achievable and closer to the solution. Using subgoals reduces the search space and induces a high-level search graph suitable for efficient planning. In this paper, we implement kSubS using a transformer-based subgoal module coupled with the classical best-first search framework. We show that a simple approach of generating $k$-th step ahead subgoals is surprisingly efficient on three challenging domains: two popular puzzle games, Sokoban and the Rubik's Cube, and an inequality proving benchmark INT. kSubS achieves strong results including state-of-the-art on INT within a modest computational budget.

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Konrad Czechowski, Tomasz Odrzygóźdź, Marek Zbysiński, Michał Zawalski, Krzysztof Olejnik, Yuhuai Wu, Łukasz Kuciński, Piotr Miłoś. 2021-08-25. Subgoal Search For Complex Reasoning Tasks. https://arxiv.org/abs/2108.11204

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