VLA Model Adaptation

VLA: Vision-Language-Action

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48 papers22 papers in the last four weeks, up 175% on the four weeks before. 0.1% of all new papers.

Alignment-Guided Flow Transformer for Efficient Vision-Language-Action Policy Learning
Sep 28, 2026Shengchao Hu, Peng Wang, Qiyang Zhou +5VLA Model Adaptation Efficient VLA Model Inference
Shanghai Jiao Tong University · Shenzhen Campus of Sun Yat-sen University · Nanyang Technological University
Not All Layers Need Tuning: Diagnosing and Directing Adaptation in Vision-Language-Action Models
Sep 16, 2026Shahram Najam Syed, Arthur Jakobsson, Prayuj Sachdev +1VLA Model Adaptation Low-Rank Adaptation
Robotics Institute, Carnegie Mellon University, Pittsburgh, USA · Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, USA
Dense to MoE Adaptation for Compact Vision Language Action Policies
Sep 15, 2026Muchun Niu, Shuang Chen, Yuzhou Wu +5Robot Policy Adaptation Efficient VLA Models
Shanghai Jiao Tong University, Shanghai, China · Tianji KernalMind Co., Ltd.
Your Model Already Knows Don't Teach It, Learn to Ask It: Soft Prompting for Few-Shot Adaptation of Vision-Language Models
Sep 10, 2026Gautam Rajendrakumar Gare, Siyi Li, Hewei Wang +5Few-Shot Object Detection Few-Shot Domain Adaptation
Carnegie Mellon University · Apple
ICI-VLA: In-Context Imitation with Spatiotemporally Aligned Demonstrations for Vision-Language-Action Models
Sep 7, 2026Songhua Yang, Ziyu Liu, Xuetao Li +3Imitation Learning Robotic Manipulation
Wuhan University · Institute of Technological Sciences, Wuhan University
StellaVLA: In-Context Structured Demonstration for Generalizable Vision-Language-Action Models
Aug 12, 2026Siyu Xu, Yunke Wang, Zijian Wang +6Reasoning for VLA Models Continual Learning for VLA Models
StellarEdge AI Technical Team
In-Context VLA: Endowing Vision-Language-Action Models with Language via In-Context Post-Training and Agentic Tool Use
Aug 6, 2026Jiarui Yang, Wen Huang, Jiale Zhang +2Reasoning for VLA Models ICL for Robotics
Nankai University · Tsinghua University · Swiss Federal Institute of Technology in Lausanne (EPFL)
Generalizable VLA Finetuning via Representation Anchoring and Language-Action Alignment
Jul 15, 2026Dwip Dalal, Shivansh Patel, Chahit Jain +8VLA Model Training Vision-Language-Action Models
University of Illinois Urbana-Champaign · Texas A&M University · University of California, Irvine
CLAP: Direct VLM-to-VLA Adaptation via Language-Action Grounding
Jul 9, 2026Yuri Ishitoya, Jeremy Siburian, Masashi Hamaya +5Robotic Manipulation VLM Adaptation
Ochanomizu University · University of Tokyo · OMRON SINIC X Corp.
Domain Arithmetic: One-Shot VLA Adaptation under Environmental Shifts
Jul 1, 2026Taewook Kang, Taeheon Kim, Donghyun Shin +1Domain Adaptation Vision-Language-Action Models
Seoul National University · ECE, IPAI and ASRI in SNU
ROAD-VLA: Robust Online Adaptation via Self-Distillation for Vision-Language-Action Models
Jun 24, 2026Kejing Wang, Toan Nguyen, Minh Hoang Nguyen +2Robustness of VLA Models Robot Policy Adaptation
Applied Artificial Intelligence Initiative Deakin University Geelong, Australia · Air Force Research Laboratory USA
FOCA: Future-Oriented Conditioning for Data-Efficient Vision-Language-Action Adaptation
Jun 18, 2026Duc Minh Nguyen, Nghiem Tuong Diep, Binh Gia Nguyen +20VLM Adaptation Efficient VLA Models
VinRobotics, Vietnam · Center for AI Research, VinUniversity, Vietnam · University of Utah, USA +4
Retrieve, Don't Retrain: Extending Vision Language Action Models to New Tasks at Test Time
Jun 14, 2026Jeongeun Park, Juhan Park, Taekyung Kim +3Test-Time Adaptation Visuomotor Policy Learning
NAVER AI Lab · Korea University
StableVLA: Towards Robust Vision-Language-Action Models without Extra Data
May 18, 2026Yiyang Fu, Chubin Zhang, Shukai Gong +7Image Corruption Robustness Vision-Language-Action Models
Peking University · Tsinghua University · Astribot +2
PriorVLA: Prior-Preserving Adaptation for Vision-Language-Action Models
May 11, 2026Xinyu Guo, Bin Xie, Wei Chai +4VLA Model Adaptation Robot Policy Adaptation
Institute of Automation, Chinese Academy of Sciences · Dexmal · Nanjing University of Aeronautics and Astronautics +1
RL Token: Bootstrapping Online RL with Vision-Language-Action Models
Apr 24, 2026Charles Xu, Jost Tobias Springenberg, Michael Equi +4RL Fine-Tuning Vision-Language-Action Models
Physical Intelligence
Simple Recipe Works: Vision-Language-Action Models are Natural Continual Learners with Reinforcement Learning
Mar 12, 2026Jiaheng Hu, Jay Shim, Chen Tang +4Continual Learning for VLA Models VLA Model Adaptation
UT Austin · UCLA · NTU +1
Adaptive Capacity Allocation for Vision Language Action Fine-tuning
Mar 8, 2026Donghoon Kim, Minji Bae, Unghui Nam +4VLA Model Training Nonlinear LoRA
Department of Electrical and Computer Engineering, Seoul National University, Seoul 08826, Republic of Korea · Department of Computer Science and Engineering, Sungkyunkwan University, Suwon 16419, Republic of Korea

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Alignment-Guided Flow Transformer for Efficient Vision-Language-Action Policy Learning

Shengchao Hu, Peng Wang, Qiyang Zhou, Guodong Zheng+4

Shanghai Jiao Tong University · Shenzhen Campus of Sun Yat-sen University · Nanyang Technological University

VLA Model Adaptation Efficient VLA Model Inference Vision-Language-Action Models

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Recent advances in Vision-Language-Action (VLA) models point toward general-purpose robotic intelligence by unifying perception, instruction, and control. Despite impressive progress, existing VLA models often adapt poorly due to tri-modal misalignment among vision, language, and action, which weakens action grounding and hurts generalization and fine-tuning efficiency. In this work, we present Alignment-Guided Flow Transformer (AGFT), a novel framework that explicitly enforces tri-modal alignment through a dedicated alignment loss, bridging the representational gap across modalities and enhancing task adaptation. While prior research has predominantly emphasized bi-modal vision--language alignment, we systematically formalize and study tri-modal alignment in VLA models, and provide both ablations and analysis to isolate its role in improving adaptation and robustness. To further accelerate deployment, we adopt a flow-matching objective, enabling substantially fewer inference steps than diffusion-based policies while maintaining accuracy. Theoretically, we establish a quantitative connection between the tri-modal alignment gap and the optimization tightness of flow matching; empirically, experiments on the extensive benchmark show that AGFT achieves superior success rates and lower inference latency compared to SOTA baselines, underscoring tri-modal alignment as a key ingredient for scaling robust VLA manipulation.