Source note

CF-VLA: Efficient Coarse-to-Fine Action Generation for Vision-Language-Action Policies

Authors: Fan Du, Feng Yan, Jianxiong Wu, Xinrun Xu, Weiye Zhang, Weinong Wang, et al.
Source: arXiv
Published: 2026-04-27
Collection: Embodied AI

Vision Language ActionRobot Foundation ModelFlow MatchingEfficient Inference Robot Manipulation

Summary

CF-VLA makes flow-based VLA action sampling faster by replacing long iterative generation from Gaussian noise with a two-step coarse initialization and fine correction. It targets real-time robot manipulation where each extra action-generation step adds latency.

Problem

Flow-based VLA policies such as $\pi_{0.5}$ can model continuous, multimodal robot actions, but they need many inference steps to turn Gaussian noise into valid actions.
Low-step sampling hurts action quality because one short trajectory must both find the action manifold and correct local errors.
This matters for closed-loop robot control, where action latency can reduce success on contact-rich, long-horizon, or bimanual tasks.

Approach

CF-VLA splits action generation into two stages with NFE=2: a coarse stage builds an action-prior-guided starting point, and a fine stage applies one local correction.
The coarse stage samples Gaussian noise $\epsilon_1$ , predicts a conditional posterior over endpoint velocity $q_\theta(u \mid o, \epsilon_1)$ , and forms $\tilde{\epsilon}=\epsilon_1-\hat{u}$ .
The coarse posterior is trained with a KL loss against a target Gaussian centered on the endpoint velocity $u_{t_1}=\epsilon_1-a$ with variance $\sigma_{noise}^2I$ .
The fine stage predicts the residual update from $\tilde{\epsilon}$ to the ground-truth action using an MSE loss at a fixed refinement time $t_f$ .
Training uses two phases: a warm-up phase with endpoint and proxy-refinement losses, then joint training with $\mathcal{L}_{fine}+\lambda\mathcal{L}_{coarse}$ .

Results

On LIBERO with NFE=2, CF-VLA reports 96.5 average success, compared with 93.6 for reproduced MIP on the $\pi_{0.5}$ architecture, 92.7 for MIP on the $\pi_0$ architecture, and 94.8 for reproduced $\pi_{0.5}$ at NFE=2.
LIBERO suite scores for CF-VLA are 98.0 Spatial, 99.2 Object, 96.6 Goal, and 92.0 Long.
Against $\pi_{0.5}$ at NFE=10 on LIBERO, CF-VLA has a lower average score, 96.5 vs 96.9, but exceeds it on Object, 99.2 vs 98.2, while using 2 function evaluations instead of 10.
The paper claims a 75.4% reduction in action sampling latency.
In real-robot experiments, CF-VLA reports 83.0 average success, beating MIP by 19.5 percentage points and $\pi_{0.5}$ by 4.0 points.
The excerpt states that CF-VLA matches or surpasses the NFE=10 $\pi_{0.5}$ baseline on several CALVIN and LIBERO metrics, but the provided CALVIN table is truncated, so exact CALVIN numbers are unavailable here.

Link

https://arxiv.org/abs/2604.24622v2