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

The long-standing vision of general-purpose robots hinges on theirability to understand and act upon natural language instructions. Vision-Language-Action (VLA) models have made remarkable progress toward thisgoal, yet their generated actions can still misalign with the given instructions.In this paper, we investigate test-time verification as a means to shrinkthe “intention-action gap.” We first characterize the test-time scaling lawsfor embodied instruction following and demonstrate that jointly scaling thenumber of rephrased instructions and generated actions greatly increasestest-time sample diversity, often recovering correct actions more efficientlythan scaling each dimension independently. To capitalize on these scalinglaws, we present CoVer, a contrastive verifier for vision–language–actionalignment, and show that our architecture scales gracefully with additionalcomputational resources and data. We then introduce CoVer-VLA, a hierar-chical test-time verification pipeline using the trained verifier. At deployment,our framework precomputes a diverse set of rephrased instructions from aVision-Language-Model (VLM), repeatedly generates action candidates foreach instruction, and then uses the verifier to select the optimal high-levelprompt and low-level action chunks. Compared to scaling policy pre-training⋆ †Equal contribution. § Equal advising.on the same data, our verification approach yields 22% gains in-distributionand 13% out-of-distribution on the SIMPLER benchmark, with a further45% improvement in real-world experiments. On the PolaRiS benchmark,CoVer-VLA achieves 14% gains in task progress and 9% in success rate.