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arXiv · 2506.07822

Accelerating Diffusion Planners in Offline RL via Reward-Aware Consistency Trajectory Distillation

Xintong Duan·Yutong He·Fahim Tajwar·Ruslan Salakhutdinov·J. Zico Kolter·Jeff Schneider
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Abstract

Although diffusion models have achieved strong results in decision-making tasks, their slow inference speed remains a key limitation. While consistency models offer a potential solution, existing applications to decision-making either struggle with suboptimal demonstrations under behavior cloning or rely on complex concurrent training of multiple networks under the actor-critic framework. In this work, we propose a novel approach to consistency distillation for offline reinforcement learning that directly incorporates reward optimization into the distillation process. Our method achieves single-step sampling while generating higher-reward action trajectories through decoupled training and noise-free reward signals. Empirical evaluations on the Gym MuJoCo, FrankaKitchen, and long horizon planning benchmarks demonstrate that our approach can achieve a 9.7% improvement over previous state-of-the-art while offering up to 142x speedup over diffusion counterparts in inference time.

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Xintong Duan, Yutong He, Fahim Tajwar, Ruslan Salakhutdinov, J. Zico Kolter, Jeff Schneider. 2026-02-06. Accelerating Diffusion Planners in Offline RL via Reward-Aware Consistency Trajectory Distillation. https://arxiv.org/abs/2506.07822

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