How (and How Not) to Use Data Augmentation in VLA Post-Training

arXiv:2610.05994 · cs.RO, cs.LG · Submitted 2026-10-05 · Read on arXiv

cs.RO, cs.LG

Submitted: 2026-10-05

Updated: 2026-10-05

Project page: https://bramgrooten.nl/vla-augm

Terminology

• Data Augmentation — 1× in this paper · explained in 7 episodes

Sources

• A Recipe for Unbounded Data Augmentation in Visual Reinforcement Learning
• GR00T N1: An Open Foundation Model for Generalist Humanoid Robots
• $\pi_{0.5}$: a Vision-Language-Action Model with Open-World Generalization
• $\pi_0$: A Vision-Language-Action Flow Model for General Robot Control
• $\pi_\texttt{RL}$: Online RL Fine-tuning for Flow-based Vision-Language-Action Models
• LIBERO-Plus: In-depth Robustness Analysis of Vision-Language-Action Models
• Sword: Style-Robust World Models as Simulators via Dynamic Latent Bootstrapping for VLA Policy Post-Training
• MaDi: Learning to Mask Distractions for Generalization in Visual Deep Reinforcement Learning
• Enhancing Generalization in Vision-Language-Action Models by Preserving Pretrained Representations
• Improving Vision-Language-Action Model with Online Reinforcement Learning
• Generalization in Reinforcement Learning by Soft Data Augmentation
• Stabilizing Deep Q-Learning with ConvNets and Vision Transformers under Data Augmentation
• Simple Recipe Works: Vision-Language-Action Models are Natural Continual Learners with Reinforcement Learning
• Reinforcement Learning with Augmented Data
• SimpleVLA-RL: Scaling VLA Training via Reinforcement Learning
• LIBERO: Benchmarking Knowledge Transfer for Lifelong Robot Learning
• Flow-GRPO: Training Flow Matching Models via Online RL
• What Can RL Bring to VLA Generalization? An Empirical Study
• VLA-RL: Towards Masterful and General Robotic Manipulation with Scalable Reinforcement Learning
• Reinforcement Fine-Tuning of Flow-Matching Policies for Vision-Language-Action Models

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