AR-VLA: True Autoregressive Action Expert for Vision-Language-Action Models

• URL: http://arxiv.org/abs/2603.10126v1
• Date: Tue, 10 Mar 2026 18:03:29 GMT
• Title: AR-VLA: True Autoregressive Action Expert for Vision-Language-Action Models
• Abstract summary: We propose a standalone autoregressive (AR) Action Expert that generates actions as a continuous causal sequence.<n>Our work introduces a scalable, context-aware action generation schema that provides a robust structural foundation for training effective robotic policies.
• Score: 36.00004339916959
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose a standalone autoregressive (AR) Action Expert that generates actions as a continuous causal sequence while conditioning on refreshable vision-language prefixes. In contrast to existing Vision-Language-Action (VLA) models and diffusion policies that reset temporal context with each new observation and predict actions reactively, our Action Expert maintains its own history through a long-lived memory and is inherently context-aware. This structure addresses the frequency mismatch between fast control and slow reasoning, enabling efficient independent pretraining of kinematic syntax and modular integration with heavy perception backbones, naturally ensuring spatio-temporally consistent action generation across frames. To synchronize these asynchronous hybrid V-L-A modalities, we utilize a re-anchoring mechanism that mathematically accounts for perception staleness during both training and inference. Experiments on simulated and real-robot manipulation tasks demonstrate that the proposed method can effectively replace traditional chunk-based action heads for both specialist and generalist policies. AR-VLA exhibits superior history awareness and substantially smoother action trajectories while maintaining or exceeding the task success rates of state-of-the-art reactive VLAs. Overall, our work introduces a scalable, context-aware action generation schema that provides a robust structural foundation for training effective robotic policies.

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