Refining Compositional Diffusion for Reliable Long-Horizon Planning

• URL: http://arxiv.org/abs/2605.03075v1
• Date: Mon, 04 May 2026 18:44:19 GMT
• Title: Refining Compositional Diffusion for Reliable Long-Horizon Planning
• Abstract summary: We propose a training-free guidance method that steers compositional sampling toward high-density, globally coherent plans.<n> Experiments on challenging long-horizon tasks from OGBench, including locomotion, object manipulation, and pixel-based observations, demonstrate that RCD consistently outperforms existing methods.
• Score: 31.40044829548484
• License: http://creativecommons.org/licenses/by/4.0/
• 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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