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arXiv · 2603.18091

Action Draft and Verify: A Self-Verifying Framework for Vision-Language-Action Model

Chen Zhao·Zhuoran Wang·Haoyang Li·Shifeng Bao·Guanlin Li·Youhe Feng·Yang Li·Jie Tang·Jing Zhang
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Abstract

Vision-Language-Action (VLA) models have recently demonstrated strong performance across embodied tasks. Modern VLAs commonly employ diffusion action experts to efficiently generate high-precision continuous action chunks, while auto-regressive generation can be slower and less accurate at low-level control. Yet auto-regressive paradigms still provide complementary priors that can improve robustness and generalization in out-of-distribution environments. To leverage both paradigms, we propose Action-Draft-and-Verify (ADV): diffusion action expert drafts multiple candidate action chunks, and the VLM selects one by scoring all candidates in a single forward pass with a perplexity-style metric. Under matched backbones, training data, and action-chunk length, ADV improves success rate by +4.3 points in simulation and +19.7 points in real-world over diffusion-based baseline, with a single-pass VLM reranking overhead.

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Chen Zhao, Zhuoran Wang, Haoyang Li, Shifeng Bao, Guanlin Li, Youhe Feng, Yang Li, Jie Tang, Jing Zhang. 2026-03-18. Action Draft and Verify: A Self-Verifying Framework for Vision-Language-Action Model. https://arxiv.org/abs/2603.18091

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