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

• URL: http://arxiv.org/abs/2604.24622v2
• Date: Tue, 28 Apr 2026 15:05:04 GMT
• Title: CF-VLA: Efficient Coarse-to-Fine Action Generation for Vision-Language-Action Policies
• Abstract summary: Flow-based vision-language-action (VLA) policies offer strong expressivity for action generation, but suffer from a fundamental inefficiency.<n>We propose CF-VLA, a coarse-to-fine two-stage formulation that restructures action generation into a coarse-to-fine step.<n>We show that our method establishes a strong efficiency-performance frontier under low-NFE (Number of Function Evaluations) regimes.
• Score: 26.36935074374452
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Flow-based vision-language-action (VLA) policies offer strong expressivity for action generation, but suffer from a fundamental inefficiency: multi-step inference is required to recover action structure from uninformative Gaussian noise, leading to a poor efficiency-quality trade-off under real-time constraints. We address this issue by rethinking the role of the starting point in generative action modeling. Instead of shortening the sampling trajectory, we propose CF-VLA, a coarse-to-fine two-stage formulation that restructures action generation into a coarse initialization step that constructs an action-aware starting point, followed by a single-step local refinement that corrects residual errors. Concretely, the coarse stage learns a conditional posterior over endpoint velocity to transform Gaussian noise into a structured initialization, while the fine stage performs a fixed-time refinement from this initialization. To stabilize training, we introduce a stepwise strategy that first learns a controlled coarse predictor and then performs joint optimization. Experiments on CALVIN and LIBERO show that our method establishes a strong efficiency-performance frontier under low-NFE (Number of Function Evaluations) regimes: it consistently outperforms existing NFE=2 methods, matches or surpasses the NFE=10 $π_{0.5}$ baseline on several metrics, reduces action sampling latency by 75.4%, and achieves the best average real-robot success rate of 83.0%, outperforming MIP by 19.5 points and $π_{0.5}$ by 4.0 points. These results suggest that structured, coarse-to-fine generation enables both strong performance and efficient inference. Our code is available at https://github.com/EmbodiedAI-RoboTron/CF-VLA.

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