Test-Time Adaptation of VLA Models

VLA: Vision-Language-Action

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13 papers in the last four weeks, up 160% on the four weeks before. 0.1% of all new papers.

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Agentic-VLA: Efficient Online Adaptation for Vision-Language-Action Models
May 21, 2026Ruofan Jin, Zaixi ZhangAgentic RL VLM Adaptation
Scinetics.
Retrieve-then-Steer: Online Success Memory for Test-Time Adaptation of Generative VLAs
May 11, 2026Jianchao Zhao, Huoren Yang, Yusong Hu +6Continual Test-Time Adaptation Robotic Manipulation
College of Artificial Intelligence, Xi’an Jiaotong University · 2One Robotics · 3Shenzhen University of Advanced Technology
LoopVLA: Learning Sufficiency in Recurrent Refinement for Vision-Language-Action Models
May 11, 2026Boyang Shen, Kaixiang Yang, Hao Wang +4Efficient VLM Inference Efficient VLA Model Inference
1Huazhong University of Science and Technology · 2Wuhan United Imaging Surgical Co.,Ltd. (UIS)
Test-Time Training for Visual Foresight Vision-Language-Action Models
May 6, 2026Sangwu Park, Wonjoong Kim, Yeonjun In +3VLM Robustness Test-Time Training
KAIST, Seoul, South Korea.
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 Test-Time Adaptation of VLA Models
University of Sydney · Central South University · University of Science and Technology of China +2
Test-Time Perturbation Learning with Delayed Feedback for Vision-Language-Action Models
Apr 20, 2026Zehua Zang, Xi Wang, Fuchun Sun +4Test-Time Adaptation Test-Time Adaptation of VLMs
Institute of Software, Chinese Academy of Sciences, Beijing, China · University of Chinese Academy of Sciences, Beijing, China · Tsinghua University, Beijing, China +2

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Agentic-VLA: Efficient Online Adaptation for Vision-Language-Action Models

Ruofan Jin, Zaixi Zhang

Scinetics.

Agentic RL VLM Adaptation Vision-Language-Action Models Test-Time Adaptation of VLA Models Robot Learning

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Vision-Language-Action (VLA) models have emerged as a promising paradigm for robotic manipulation by leveraging pre-trained vision-language representations. However, current VLA training methods suffer from two critical limitations: poor generalization to novel environments and low training efficiency requiring extensive demonstrations. We introduce Agentic-VLA, an agentic training framework that enables VLAs to efficiently adapt online through three key innovations: (1) Adaptive Reward Synthesis, which dynamically generates and adjusts reward functions based on the VLA's current capabilities and task complexity, decomposing complex tasks into learnable sub-goals for curriculum learning; (2) Language-Guided Exploration, where a critic model provides structured guidance for systematic exploration rather than random sampling; and (3) Experience Memory,which stores and retrieves task-relevant policy weights for warm-starting adaptation to similar tasks. We evaluate Agentic-VLA on the LIBERO benchmark, achieving substantial improvements: +12.3% on long-horizon tasks, +28.5% in 1-shot learning, and enabling cross-task transfer from 0% to 31.2% without task-specific demonstrations. Our framework also demonstrates 2.4x faster convergence compared to existing online adaptation methods. Beyond LIBERO, Agentic-VLA retains its advantage on the dual-arm RoboTwin 2.0 benchmark, including under its randomized Hard setting. These results establish Agentic-VLA as a significant step toward truly adaptive VLA systems capable of continuous learning in deployment.

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Related topics

Latest papers 36

Agentic-VLA: Efficient Online Adaptation for Vision-Language-Action Models

Retrieve-then-Steer: Online Success Memory for Test-Time Adaptation of Generative VLAs

LoopVLA: Learning Sufficiency in Recurrent Refinement for Vision-Language-Action Models

Test-Time Training for Visual Foresight Vision-Language-Action Models

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

Test-Time Perturbation Learning with Delayed Feedback for Vision-Language-Action Models