We propose Q-Guided Value-Gradient Matching (Q-VGM), an offline-to-online reinforcement learning (RL) method for fine-tuning flow-matching vision-language-action (VLA) policies with a learned Q-function. Classical off-policy actor-critic methods improve a policy by following the critic gradient $\nabla_A Q$, but applying this update to flow policies requires backpropagation through the multi-step denoising process (BPTT), which is costly and unstable at VLA scale. Existing BPTT-free approaches mostly reduce policy improvement to critic-supervised imitation learning through filtering or reweighting sampled behaviors, or rely on test-time selection and guidance, leaving the underlying policy unchanged. Q-VGM instead formulates policy improvement as optimal control over the denoising dynamics, where the optimal residual velocity is the gradient of a denoising-time value function. Specifically, we train an action-sensitive chunk critic on compact latent states from the frozen VLA backbone, with IQL in the offline phase and TD learning in the online phase. Clean-action estimates improved by iterative Q-gradient ascent are then converted into residual velocity targets that directly supervise the velocity field. Training thus avoids both action-likelihood estimation and the BPTT problem, while requiring no critic at inference time. Starting from a few-shot-SFT $π_{0.5}$ policy on LIBERO, offline Q-VGM improves the average success rate across the Spatial, Object, Goal and Long suites from 84.6% to 90.7% with 150 rollout episodes per task. Offline-to-online training reaches 98.5%, surpassing PPO fine-tuning (97.4%) with approximately $6\times$ fewer rollout episodes. On three real-world bimanual manipulation tasks, offline Q-VGM improves the average success rate from 66.7% to 98.3%.

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