Refining Compositional Diffusion for Reliable Long-Horizon Planning

Compositional diffusion planning generates long-horizon trajectories by stitching together overlapping short-horizon segments through score composition. However, when local plan distributions are multimodal, existing compositional methods suffer from mode-averaging, where averaging incompatible local modes leads to plans that are neither locally feasible nor globally coherent. We propose *Refining Compositional Diffusion* (RCD), a training-free guidance method that steers compositional sampling toward high-density, globally coherent plans. RCD leverages the *self-reconstruction error* of a pretrained diffusion model as a proxy for the log-density of composed plans, combined with an *overlap consistency* term that enforces consistency at segment boundaries. We show that the combined guidance concentrates sampling on high-density plans that mitigate mode-averaging. Experiments on challenging long-horizon tasks from OGBench, including locomotion, object manipulation, and pixel-based observations, demonstrate that RCD consistently outperforms existing methods. Project website at [https://refining-compositional-diffusion.github.io/](https://refining-compositional-diffusion.github.io/).