FASTer: Toward Efficient Autoregressive Vision Language Action Modeling via Neural Action Tokenization

UCSD Figure 1: F AST er combines a learnable action tokenizer (FASTerVQ) and an autoregressive VLA model (FASTerVLA), achieving efficient compression, fast control, and strong performance across eight real and simulated embodiments. Autoregressive vision-language-action (VLA) models have recently demonstrated strong capabilities in robotic manipulation. However, their core process of action tokenization often involves a trade-off between reconstruction fidelity and inference efficiency. We introduce F AST er, a unified framework for efficient and generalizable robot learning that integrates a learnable tokenizer with an autore-gressive policy built upon it. FASTerVLA builds on this tokenizer with block-wise autore-gressive decoding and a lightweight action expert, achieving both faster inference and higher task performance. Extensive experiments across simulated and real-world benchmarks show that FASTerVQ delivers superior reconstruction quality, high token utilization, and strong cross-task and cross-embodiment generalization, while FASTerVLA further improves overall capability, surpassing previous state-of-the-art VLA models in both inference speed and task performance. Vision-Language-Action (VLA) models represent a paradigm shift in robotics, embodying generalist robot policies trained on increasingly large-scale robotic datasets (Chenjia Bai, 2024). These models are categorized primarily by their method of robot action prediction, with the most prominent approaches being diffusion-based (Team et al., 2024; Black et al., 2024) and autoregressive VLA (Belkhale & Sadigh, 2024; Kim et al., 2024; Pertsch et al., 2025; Zhou et al., 2025) models. While diffusion-based models have demonstrated superior precision in manipulation tasks, they often exhibit a notable deficiency in leveraging critical visual and linguistic cues (Pertsch et al., 2025; Dong et al., 2025). In contrast, recent research indicates that a carefully designed autoregres-sive VLA model can increasingly bridge the performance gap with its diffusion-based counterparts, while simultaneously offering enhanced instruction-following capabilities (Pertsch et al., 2025; Intelligence et al., 2025; Hancock et al., 2025), superior scene generalization (Pertsch et al., 2025), and effective transfer of common-sense knowledge (Brohan et al., 2023). Most importantly, autoregres-sive VLA models share the most architectural similarity to the highly successful Vision-Language Models (VLMs), suggesting significant potential for future advancements. A pivotal challenge within autoregressive VLA models is the development of an appropriate tok-enization scheme to discretize continuous robot action sequence into action tokens (Wang et al., 2025c; Pertsch et al., 2025). Numerous sequence modeling studies, including LLMs and Speech-LLMs, have demonstrated that tokenizer quality directly determines model performance (Radford et al., 2019; Zhang et al., 2023; Gong et al., 2025).