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

TL;DR

This paper demonstrates that test-time verification, combined with scaling laws, significantly improves vision-language-action alignment in embodied instruction following, outperforming traditional policy scaling methods.

Contribution

The authors introduce CoVer, a contrastive verifier, and CoVer-VLA, a hierarchical verification pipeline, leveraging test-time scaling laws to enhance action alignment in vision-language models.

Findings

Scaling instructions and actions jointly improves diversity and correctness.

Verification-based approach outperforms policy pre-training scaling.

Significant gains on SIMPLER and PolaRiS benchmarks.

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.