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

Understand

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.