[Submitted on 29 Sep 2026]
Title:DriftOPD: Sequence-Level Reverse-KL Distillation for One-Step VLA Policies
View a PDF of the paper titled DriftOPD: Sequence-Level Reverse-KL Distillation for One-Step VLA Policies, by Youngjun Jun and 6 other authors
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Abstract:Vision-Language-Action (VLA) models increasingly rely on action experts that generate short action chunks under receding-horizon control. While chunk-level training is convenient across robot embodiments, it optimizes local action likelihood without explicitly accounting for long-horizon task success. Sequence-level reinforcement learning can address this limitation, but typically requires policy rollouts and closed-loop interaction, which are costly for real-robot manipulation. We introduce DriftOPD, a teacher-free, rollout-free framework for sequence-level on-policy distillation of continuous VLA action experts. We show that the sequence-level reverse Kullback-Leibler (KL) divergence decomposes into a chunk-level reverse-KL term and a future-potential term that captures the long-horizon effect of the current action. DriftOPD optimizes these two terms using a one-step drifting objective and a Q-function critic learned from offline demonstrations, respectively, enabling sequence-level optimization with only offline data and one-step action generation. Across multiple VLA architectures in simulation and real-world manipulation, DriftOPD generally outperforms existing one-step distillation baselines while achieving task success performance comparable to multi-step teacher policies. These results demonstrate that long-horizon behavior can be effectively distilled into one-step VLA action experts without online interaction or a separate teacher.
Comments: Preprint
Subjects:
Robotics (cs.RO); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
Cite as: arXiv:2610.00317 [cs.RO]
(or arXiv:2610.00317v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2610.00317
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Jong Chul Ye [view email] [v1] Tue, 29 Sep 2026 02:55:12 UTC (6,646 KB)
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