This work asks when hierarchical search is preferable to planning directly with atomic actions. Controlled experiments isolate several relevant conditions: inaccurate value functions, complex action spaces, dead ends, heterogeneous training data, and out-of-distribution tasks.
The paper also proposes a consistent methodology for comparing hierarchical and low-level planners. The accompanying CaRL framework supports these reproducible comparisons.