Verifier-free Test-Time Sampling for Vision Language Action Models

ICLR 2026 Conference Submission19696 Authors

Keywords: Vision-Language-Action Models, Test-Time Scaling, Robotic Manipulation

TL;DR: We propose MG-Select, a masking distribution–guided test-time scaling method that enhances precision of Vision-Language-Action models in robotic manipulation.

Abstract:

Vision-Language-Action models (VLAs) have demonstrated remarkable performance in robot control. However, they remain fundamentally limited in tasks that require high precision due to their single-inference paradigm. While test-time scaling approaches using external verifiers have shown promise, they require additional training and fail to generalize to unseen conditions. We propose Masking
Distribution Guided Selection (MG-Select), a novel test-time scaling framework for VLAs that leverages the model's internal properties without requiring additional training or external modules. Our approach utilizes KL divergence from a reference action token distribution as a confidence metric for selecting optimal action from multiple candidates. We introduce a reference distribution generated by the same VLA but with randomly masked states and language conditions as inputs, providing action uncertainty while remaining aligned with the target task distribution. Additionally, we propose a joint training strategy that enables the model to learn both conditional and unconditional distributions by applying dropout to state and language conditions, thereby further improving the quality of the reference distribution. Our experiments demonstrate that MG-Select provides a reliable reference for action selection through task-relevant condition masking
and consistently improves base models across diverse simulation and real-world benchmarks.

Primary Area: applications to robotics, autonomy, planning

Submission Number: 19696