  Diffusion planning has been recognized as an effective decision-making
paradigm in various domains. The capability of conditionally generating
high-quality long-horizon trajectories makes it a promising research direction.
However, existing diffusion planning methods suffer from low decision-making
frequencies due to the expensive iterative sampling cost. To address this
issue, we introduce DiffuserLite, a super fast and lightweight diffusion
planning framework. DiffuserLite employs a planning refinement process (PRP) to
generate coarse-to-fine-grained trajectories, significantly reducing the
modeling of redundant information and leading to notable increases in
decision-making frequency. Our experimental results demonstrate that
DiffuserLite achieves a decision-making frequency of $122$Hz ($112.7$x faster
than previous mainstream frameworks) and reaches state-of-the-art performance
on D4RL benchmarks. In addition, our neat DiffuserLite framework can serve as a
flexible plugin to enhance decision frequency in other diffusion planning
algorithms, providing a structural design reference for future works. More
details and visualizations are available at https://diffuserlite.github.io/.
