mlsanityData to 7 Oct 2026

Fast and Precise: Adjusting Planning Horizon with Adaptive Subgoal Search

arXiv 2206.00702 ↗First posted 1 Jun 2022cs.AI cs.LGcited by 12

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Abstract (arXiv)

Complex reasoning problems contain states that vary in the computational cost required to determine a good action plan. Taking advantage of this property, we propose Adaptive Subgoal Search (AdaSubS), a search method that adaptively adjusts the planning horizon. To this end, AdaSubS generates diverse sets of subgoals at different distances. A verification mechanism is employed to filter out unreachable subgoals swiftly, allowing to focus on feasible further subgoals. In this way, AdaSubS benefits from the efficiency of planning with longer subgoals and the fine control with the shorter ones, and thus scales well to difficult planning problems. We show that AdaSubS significantly surpasses hierarchical planning algorithms on three complex reasoning tasks: Sokoban, the Rubik's Cube, and inequality proving benchmark INT.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 33 of its 57 references that are in the map. First posted on arXiv 1 Jun 2022.

Builds on

33 of 57 references.

References · 33

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21 more
  1. 2020What Matters In On-Policy Reinforcement Learning? A Large-Scale Empirical Studycited by 157
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 4; 2024: 3; 2025: 4; 2026: 1.

12 papers.