  Diffusion-based generative methods have proven effective in modeling
trajectories with offline datasets. However, they often face computational
challenges and can falter in generalization, especially in capturing temporal
abstractions for long-horizon tasks. To overcome this, we introduce the
Hierarchical Diffuser, a simple, fast, yet surprisingly effective planning
method combining the advantages of hierarchical and diffusion-based planning.
Our model adopts a "jumpy" planning strategy at the higher level, which allows
it to have a larger receptive field but at a lower computational cost -- a
crucial factor for diffusion-based planning methods, as we have empirically
verified. Additionally, the jumpy sub-goals guide our low-level planner,
facilitating a fine-tuning stage and further improving our approach's
effectiveness. We conducted empirical evaluations on standard offline
reinforcement learning benchmarks, demonstrating our method's superior
performance and efficiency in terms of training and planning speed compared to
the non-hierarchical Diffuser as well as other hierarchical planning methods.
Moreover, we explore our model's generalization capability, particularly on how
our method improves generalization capabilities on compositional
out-of-distribution tasks.
