[2606.08015] Q-VGM: Q-Guided Value-Gradient Matching for Flow-Matching VLA Policies
[Submitted on 6 Jun 2026]
Title:Q-VGM: Q-Guided Value-Gradient Matching for Flow-Matching VLA Policies
View a PDF of the paper titled Q-VGM: Q-Guided Value-Gradient Matching for Flow-Matching VLA Policies, by Ziqian Wang and 4 other authors
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Abstract:We propose Q-Guided Value-Gradient Matching (Q-VGM), an off-policy reinforcement learning (RL) method that tackles a long-standing challenge in fine-tuning flow-matching vision-language-action (VLA) policies: efficiently improving an expressive flow-matching action expert with respect to a learned Q-function. Effective improvement must exploit the first-order (gradient) information of the critic, but this is difficult for flow policies, because directly back-propagating the value through their multi-step denoising process is numerically unstable at VLA scale, while the tractable action likelihoods required by policy-gradient methods are unavailable under iterative denoising. Existing value-based methods either backpropagate through the full denoising chain, use the critic only at test time without updating the policy, or distill critic-improved actions as terminal labels without supervising the velocity field. Q-VGM sidesteps these issues by leveraging VGG-Flow, a value-gradient view of flow alignment in generative modeling that transforms value gradient into a denoising-time value-gradient field rather than an unstable end-to-end objective. This requires no action likelihoods and no backpropagation through the denoising chain, and operates on a fixed replay buffer. The critic is an action-sensitive Cal-QL ensemble over compact RLT features with per-layer action injection. Q-VGM enables a practical few-shot initialization then learn-from-experience paradigm: starting from a few-shot-SFT pi0.5 VLA, the method leverages self-generated rollout data to substantially improve task performance without additional expert supervision. On LIBERO, Q-VGM raises the average success rate from 75.0% to 92.5%; on RoboTwin 2.0, from 76.4% to 87.2%; and on two real-robot tabletop tasks, from 40.0% to 67.5%, outperforming all same-backbone, same-critic baselines across all three settings.
Comments: 13 pages, 3 figures, 4 tables
Subjects:
Robotics (cs.RO)
Cite as: arXiv:2606.08015 [cs.RO]
(or arXiv:2606.08015v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2606.08015
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Ziqian Wang [view email] [v1] Sat, 6 Jun 2026 07:10:25 UTC (7,643 KB)
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