TBD-VLA: Temporal Block Diffusion Vision Language Action Model
Summary
TBD-VLA introduces a discrete vision-language-action framework that combines block diffusion with autoregressive generation to achieve efficient temporal action modeling and faster inference, significantly outperforming prior VLA approaches in simulation and real-world manipulation tasks.
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Paper page - TBD-VLA: Temporal Block Diffusion Vision Language Action Model
Source: https://huggingface.co/papers/2606.07895
Abstract
TBD-VLA is a discrete vision-language-action framework that combines block diffusion with autoregressive generation to achieve efficient temporal action modeling and faster inference.
Discrete Vision-Language-Action (VLA) models typically formulate action generation asnext-token predictionover discretized action spaces, conditioning each token autoregressively on prior context. While effective, this paradigm incurs high inference latency and largely ignores thetemporal structureinherent in action trajectories. Recent efforts introduceparallel decodingto improve efficiency, enabling faster inference, but lack explicit mechanisms for modeling token dependencies. We introduce TBD-VLA, a discrete token-based VLA framework that incorporatesblock diffusionto enabletemporal action generation. We partition action sequences into temporal blocks and performmasked discrete diffusionwithin each block, while maintainingautoregressive generationacross blocks. This design unifies temporal autoregression and parallel action decoding, achieving both strong temporal coherence and improved inference speed. In addition, the explicit temporal modeling enables asynchronous execution of action chunks (e.g.,Real-Time Chunking) viatemporal in-painting. TBD-VLA significantly outperforms prior VLA approaches in both simulation and real-world manipulation tasks, offering a scalable path toward fast, temporally aware, discrete VLA models. Project webpage: https://tbd-vla.github.io/
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