Scaling Verification Can Be More Effective than Scaling Policy Learning for Vision-Language-Action Alignment

• URL: http://arxiv.org/abs/2602.12281v2
• Date: Wed, 18 Feb 2026 03:42:48 GMT
• Title: Scaling Verification Can Be More Effective than Scaling Policy Learning for Vision-Language-Action Alignment
• Authors: Jacky Kwok, Xilun Zhang, Mengdi Xu, Yuejiang Liu, Azalia Mirhoseini, Chelsea Finn, Marco Pavone,
• Abstract summary: CoVer-VLA is a hierarchical test-time verification pipeline using the trained verifier.<n>Our framework precomputes a diverse set of rephrased instructions from a Vision-Language-Model.<n>It repeatedly generates action candidates for each instruction, and then uses the verifier to select the optimal high-level prompt and low-level action chunks.
• Score: 58.93227458806748
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The long-standing vision of general-purpose robots hinges on their ability to understand and act upon natural language instructions. Vision-Language-Action (VLA) models have made remarkable progress toward this goal, yet their generated actions can still misalign with the given instructions. In this paper, we investigate test-time verification as a means to shrink the "intention-action gap." We first characterize the test-time scaling laws for embodied instruction following and demonstrate that jointly scaling the number of rephrased instructions and generated actions greatly increases test-time sample diversity, often recovering correct actions more efficiently than scaling each dimension independently. To capitalize on these scaling laws, we present CoVer, a contrastive verifier for vision-language-action alignment, and show that our architecture scales gracefully with additional computational resources and data. We then introduce CoVer-VLA, a hierarchical test-time verification pipeline using the trained verifier. At deployment, our framework precomputes a diverse set of rephrased instructions from a Vision-Language-Model (VLM), repeatedly generates action candidates for each instruction, and then uses the verifier to select the optimal high-level prompt and low-level action chunks. Compared to scaling policy pre-training on the same data, our verification approach yields 22% gains in-distribution and 13% out-of-distribution on the SIMPLER benchmark, with a further 45% improvement in real-world experiments. On the PolaRiS benchmark, CoVer-VLA achieves 14% gains in task progress and 9% in success rate.

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