QPILOTS: Efficient Test-Time Q-Steering for Flow Policies
QPILOTS is a method that steers the denoising process at inference time using Q-values without modifying the original policy. It projects intermediate noisy states to an estimate of the final clean action to compute critic gradients, avoiding instability from direct backpropagation. It achieves 90% average success rate across 50 offline-to-online RL tasks and outperforms prior inference-time approaches on simulated manipulation tasks using a VLA model.
[2606.14801] QPILOTS: Efficient Test-Time Q-Steering for Flow Policies
[Submitted on 11 Jun 2026]
Title:QPILOTS: Efficient Test-Time Q-Steering for Flow Policies
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Abstract:Flow-matching and diffusion policies are expressive action generators, but optimizing them with temporal-difference reinforcement learning (RL) remains difficult. Effective policy extraction requires exploiting the critic's action gradient, yet directly backpropagating this signal through a multi-step denoising process can be numerically unstable. Existing methods work around this either by discarding gradient information, distilling the policy into a simpler one-step actor, or repeatedly fine-tuning the denoising policy as the critic improves. We propose QPILOTS, a method that leaves the original policy unmodified and steers the denoising process at inference time. At each denoising step, instead of evaluating the critic on the noisy intermediate action where critic predictions are unreliable, we first project that intermediate state to an estimate of the final clean action and compute the critic gradient there. We introduce two variants: QPILOTS-U uses a fast single-point approximation, while QPILOTS-M draws differentiable posterior samples via a learned auxiliary network. On a standard offline-to-online RL benchmark, QPILOTS achieves the best aggregate performance, reaching an average success rate of 90% across 50 tasks. We also apply QPILOTS to steer a large, frozen, pretrained Vision-Language Action (VLA) foundation model, outperforming or matching prior inference-time approaches across six manipulation tasks in simulation.
Comments: 10 pages, 7 figures
Subjects:
Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Robotics (cs.RO)
Cite as: arXiv:2606.14801 [cs.LG]
(or arXiv:2606.14801v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2606.14801
arXiv-issued DOI via DataCite
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From: Yifan Ruan [view email] [v1] Thu, 11 Jun 2026 18:22:03 UTC (3,441 KB)
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