VLA Model Post-Training

VLA: Vision-Language-Action

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16 papers1 paper in the last four weeks, down 75% on the four weeks before. 0.0% of all new papers.

SimPT: An Automated Simulation Framework for Intervention-Based VLA Post-Training
Oct 21, 2026Caicheng Wang, Zipeng Ye, Zhexuan Zhou +3VLA Model Post-Training Vision-Language-Action Models
Meituan
VLAQuantBench: Closed-Loop Evaluation of Post-Training Quantization for Vision-Language-Action Models
Sep 21, 2026Jiuyi Xu, Qing Jin, Meida Chen +3Mixed-Precision Quantization Closed-Loop Control of VLA Models
Colorado School of Mines · Independent Researcher · University of Central Florida +2
Towards High-DoF Dexterous Manipulation through VLA Post-Training
Sep 17, 2026Junlei Zhu, Shenzhe Yao, Chaogui Huang +6Latent Action Learning VLA Model Post-Training
Wuji Technology · School of Information Science and Technology, ShanghaiTech University, China
RoboDrop: Curating VLA Post-Training Data via Local Gradient Compatibility
Sep 9, 2026Runze Xu, Yuanfan Xu, Cuijie Xu +4Robotic Data Collection VLA Model Post-Training
Tsinghua University · Striding AI
WISE: World-model-guided Imagination Scheduling for Efficient Post-training of Vision-Language-Action Models
Sep 3, 2026Chenhao Zhang, Hanyu Zhao, Hang Cheng +2Robustness of VLA Models World Models for Robotics
1Tsinghua University · 2Beijing Academy of Artificial Intelligence (BAAI)
From Recovery to Drop-off: How Action Post-training Reduces a VLM's Late-Layer Depth Decodability
Aug 9, 2026Alexander Hackett, Arnaud Denis-Remillard, Axel CassouVLM Interpretability VLA Model Post-Training
New York University · Reflex · Université de Montréal +1
Beyond Flat Policies: Hierarchical Post-Training for Embodied Agents in Robotic Manipulation
Aug 6, 2026He Kong, Zengjue Chen, Qi Wang +6Long-Horizon Robotic Manipulation Hierarchical RL
School of Artificial Intelligence, Jilin University · College of computer science and technology, Jilin University · Joy Future Academy, JD
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)
JoyNexus: Service-Oriented Multi-Tenant Post-Training for VLA Models
Jul 17, 2026Haoran Sun, Wentao Zhang, Junyang Hua +18Efficient VLA Model Inference VLA Model Post-Training
JDT AI Infra · Peking University · Beihang University +2
CLAP: Direct VLM-to-VLA Adaptation via Language-Action Grounding
Jul 9, 2026Yuri Ishitoya, Jeremy Siburian, Masashi Hamaya +3Robotic Manipulation VLM Adaptation
Ochanomizu University · OMRON SINIC X Corporation · University of Tokyo +1
Ω-QVLA: Robust Quantization for Vision-Language-Action Models via Composite Rotation and Per-step Scaling
May 27, 2026Xinyu Wang, Mingze Li, Sicheng Lyu +6Post-Training Quantization Efficient VLA Model Inference
McGill University · Université de Montréal · Mila – Quebec AI Institute +2
CrossVLA: Cross-Paradigm Post-Training and Inference Optimization for Vision-Language-Action Models
May 21, 2026Zhi LiuDirect Preference Optimization VLA Model Post-Training
Tianjin University
ActQuant: Sub-4-bit Action-Guided Quantization for Vision-Language-Action Models
May 19, 2026Arash Akbari, Arman Akbari, Masih Eskandar +11Neural Network Quantization VLM Quantization
1Northeastern University · University of Georgia · 3Cisco Systems
Breaking Lock-In: Preserving Steerability under Low-Data VLA Post-Training
Apr 25, 2026Suning Huang, Jiaqi Shao, Ke Wang +5VLA Model Post-Training Vision-Language Grounding
Stanford University
World2Act: Latent Action Post-Training from World Model Dynamics
Mar 11, 2026An Dinh Vuong, Tuan Van Vo, Abdullah Sohail +6VLA Model Post-Training Vision-Language-Action Models
MBZUAI
QuantVLA: Scale-Calibrated Post-Training Quantization for Vision-Language-Action Models
Feb 23, 2026Jingxuan Zhang, Yunta Hsieh, Zhongwei Wan +5Efficient VLA Model Inference VLA Model Post-Training
The Ohio State University · University of Michigan · City University of Hong Kong

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SimPT: An Automated Simulation Framework for Intervention-Based VLA Post-Training

Caicheng Wang, Zipeng Ye, Zhexuan Zhou, Zilong Huang+2

Meituan

VLA Model Post-Training Vision-Language-Action Models Interactive Imitation Learning Robotic Manipulation Robot Imitation Learning

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Human-in-the-loop (HIL) post-training improves vision-language-action (VLA) policies through expert corrections, but costly human supervision and repeated real-robot evaluation limit algorithm exploration and development. We introduce SimPT (Simulation-based Post-Training), a simulation framework that automates intervention-based VLA post-training by integrating policy rollouts, corrective data collection, policy training, and standalone evaluation. To automatically determine intervention timing and recovery strategies, we develop an Agent-Compiled Progress Graph that tracks task progress using privileged simulator state and retains stable snapshots during policy execution. After a rollout fails, a CuRobo-based expert attempts recovery from these snapshots, starting at more advanced task stages and falling back to earlier ones as needed to generate corrective trajectories. We implement and compare four post-training methods, RECAP, HG-DAgger, DPO+SFT, and FlowPRO, across seven RoboTwin manipulation tasks. Relative to a fixed-interval snapshot baseline, the Progress Graph reduces recovery attempts by 75.0% and increases recovery success from 93.4% to 98.9%. Using SimPT, we develop RECAP-CF-LPF, a refinement of RECAP that combines cross-fitting and low-pass filtering of value predictions. This refinement increases the overall success rate from 76.50% to 79.36% in held-out simulation trials. In real-robot HIL post-training, it increases the success rate from 72% to 96% across 25 scenarios of an Ethernet cable-insertion task. These results demonstrate the utility of SimPT for exploring and developing VLA post-training algorithms.