VANE: Reliable Test-Time Training for Vision-Language-Action Models via Future Visual Representation Prediction

Hongjin Ji; Guoyang Xia; Luoyang Sun; Fangxiang Feng; Lei Ren

cs.ROAug 10, 2026

VANE: Reliable Test-Time Training for Vision-Language-Action Models via Future Visual Representation Prediction

Authors: Hongjin Ji, Guoyang Xia, Luoyang Sun, Fangxiang Feng, Lei Ren

Organizations: The Chinese University of Hong Kong, Shenzhen · Li Auto Inc. · Beijing University of Posts and Telecommunications · Institute of Automation, Chinese Academy of Sciences · University of Chinese Academy of Sciences

Abstract

Test-time training (TTT) offers a lightweight way to adapt vision--language--action (VLA) policies from unlabeled deployment streams, but it remains difficult to use reliably in closed-loop manipulation. A shared adaptation space can mix incompatible task corrections, while an online update can alter subsequent actions before its consequences are known. We introduce a reliable TTT framework for VLA policies (VANE). VANE conditions prompt adaptation on the current vision--language context and learns from the future visual consequences of executed actions. Candidate updates are isolated from the live policy, evaluated on subsequent observations, and committed only when supported by future evidence, making adaptation selective and reversible. On SimplerEnv WidowX, VANE improves average success by $3.2$ percentage points over the corresponding TTT baseline. Results on Google Robot further show that deployment-time gains remain task- and embodiment-dependent. Together, these results demonstrate a constrained, evidence-based approach to adapting VLA policies during interaction.

Explore similar work

TTT-VLA: Test-Time Latent Prompt Optimization for Vision-Language-Action Models
Jun 2, 2026Wenbo Zhang, Jianxiong Li, Shuai Yang +4Steering
ICI-VLA: In-Context Imitation with Spatiotemporally Aligned Demonstrations for Vision-Language-Action Models
Sep 7, 2026Songhua Yang, Ziyu Liu, Xuetao Li +3Imitation Learning Multimodal Fusion
VLA-ATTC: Adaptive Test-Time Compute for VLA Models with Relative Action Critic Model
May 2, 2026Wenhao Li, Xiu Su, Yichao Cao +5Vision-Language-Action Models Critic-Free Reinforcement Learning

Jun 2, 2026 · cs.ROJ/K move · Enter open · S save

TTT-VLA: Test-Time Latent Prompt Optimization for Vision-Language-Action Models

Wenbo Zhang, Jianxiong Li, Shuai Yang, Sijin Chen+3

ByteDance Seed · The University of Adelaide · Tsinghua University · Zhejiang University · The University of Hong Kong

Vision-Language-Action (VLA) models trained on large-scale data have made remarkable progress, but they remain vulnerable to distribution shifts at deployment time. Recent VLA models suggest that prompts can serve as an efficient interface for steering policy behavior, but existing prompt-based steering typically relies on external guidance. This raises a natural question: can test-time training (TTT) for VLA be achieved by optimizing a prompt, so that the steering interface itself can be learned and adapted from interaction? We address this question with TTT-VLA, a test-time training framework based on Latent Prompt Optimization (LPO). During training, the latent prompt is learned with an additional proxy task, providing an extra learned conditioning signal for policy learning. At test time, TTT is performed by collecting interaction data from the current environment and optimizing only the latent prompt on those data using the proxy task's self-supervised signal, without modifying the policy itself. Experiments on SimplerEnv demonstrate that the proposed method consistently improves task success rates in both single- and multi-embodiment settings. Further analysis shows that the gains arise primarily from correcting a small number of critical decisions rather than globally altering policy behavior. These results suggest that LPO provides an effective and practical pathway for deployment-time improvement of foundation manipulation policies.

Steering

Open paper Ask about it PDF

Abstract

Explore similar work

TTT-VLA: Test-Time Latent Prompt Optimization for Vision-Language-Action Models

ICI-VLA: In-Context Imitation with Spatiotemporally Aligned Demonstrations for Vision-Language-Action Models

VLA-ATTC: Adaptive Test-Time Compute for VLA Models with Relative Action Critic Model