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arXiv · 2605.03075

Refining Compositional Diffusion for Reliable Long-Horizon Planning

Kyowoon Lee·Yunhao Luo·Anh Tong·Jaesik Choi
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Abstract

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.

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Kyowoon Lee, Yunhao Luo, Anh Tong, Jaesik Choi. 2026-05-04. Refining Compositional Diffusion for Reliable Long-Horizon Planning. https://arxiv.org/abs/2605.03075

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