Title: FASTer: Toward Powerful and Efficient Autoregressive Vision–Language–Action Models with Learnable Action Tokenizer and Block-wise Decoding
Primary area: applications to robotics, autonomy, planning
Keywords: VLA, embodied AI, robotics

Abstract: Autoregressive vision-language-action (VLA) models have shown strong capabilities in robotic manipulation. However, their core component—action tokenization—often suffers from a trade-off between reconstruction accuracy and inference efficiency.
We present \underline{\textbf{F}}lexible \underline{\textbf{A}}ction \underline{\textbf{S}}equence \underline{\textbf{T}}okenization for \underline{\textbf{e}}fficient infe\underline{\textbf{r}}ence (\textbf{FASTer}), a vector-quantization-based learnable tokenizer framework. FASTer represents action chunks as single-channel images to capture global spatio-temporal relationships. Combining a transformer backbone with residual vector quantization, it models cross-dimensional dependencies and regulates code length, thereby preserving structured action dependencies while enabling flexible code organization for downstream VLA models.
Building on FASTer, we propose FASTerVLA, which integrates a block-wise autoregressive decoding paradigm and an autoregressive action expert to fully exploit the strengths of autoregressive VLAs. FASTerVLA surpasses existing state-of-the-art VLA models in both performance and inference speed.
We construct a systematic evaluation framework for action tokenization and, through comprehensive analysis, demonstrate the performance, efficiency, and flexibility of FASTer across models, tasks, and embodiments. Furthermore, extensive experiments show that FASTerVLA further enhances overall capability, surpassing previous state-of-the-art VLA models in both inference speed and task performance across diverse simulated and real-world settings.

TL;DR: We proposed a novel action tokenization method and powerup the autoregressive VLA, both in performance and inference speed.