Verifier-free Test-Time Sampling for Vision Language Action Models
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 the 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, ensuring maximum 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 achieves significant
performance improvements, including a 28%/35% improvement in real-world
in-distribution/out-of-distribution tasks, along with a 168% relative gain on
RoboCasa pick-and-place tasks trained with 30 demonstrations.
68e64504975ac4c405ef2252
Suhyeok Jang
claimed_verified
2025-10-08T11:48:44.093Z
67041a46d1fb54bd91a478fd
/avatars/6414c7c501857948a36a55036b0e1f8b.svg
Suhyeok Jang
user
glory-hyeok
68e64504975ac4c405ef2253
Dongyoung Kim
68e64504975ac4c405ef2254
Changyeon Kim
68e64504975ac4c405ef2255
Youngsuk Kim
68e64504975ac4c405ef2256
Jinwoo Shin
Verifier-free Test-Time Sampling for Vision Language Action Models
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 the 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, ensuring maximum 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 achieves significant
performance improvements, including a 28%/35% improvement in real-world
in-distribution/out-of-distribution tasks, along with a 168% relative gain on
RoboCasa pick-and-place tasks trained with 30 demonstrations.
MG-Select, a novel test-time scaling framework for Vision-Language-Action models, improves performance by using KL divergence from a reference distribution generated with masked inputs, achieving significant gains in both in-distribution and out-of-distribution tasks.
Vision-Language-Action models test-time scaling KL divergence reference action token distribution masked states language conditions dropout RoboCasa pick-and-place tasks