arXiv:2606.13355v1 Announce Type: cross Abstract: Real-time execution, enabled by asynchronous inference that ensures both smooth action trajectories and fast reactivity, is critical for realistic deployments of large-scale Vision-Language-Action models. However, recent work on real-time execution primarily focuses on variants of diffusion policies, even though it is more critical for autoregressive policies given their slower rollout speed in synchronous inference. In contrast, we demonstrate that autoregressive policies can achieve real-time execution by adjusting the tokenization horizon an
The increasing complexity of AI models, particularly Vision-Language-Action models, necessitates advancements in real-time execution to enable practical deployment.
Achieving real-time execution for autoregressive policies could significantly broaden the applicability and performance of advanced AI systems in robotics and other dynamic environments.
Autoregressive policies, previously hindered by slower synchronous inference, can now achieve real-time performance through tokenization horizon adjustment, closing a critical performance gap with diffusion policies.
- · AI developers
- · Robotics sector
- · Autonomous systems manufacturers
- · Companies reliant on less efficient synchronous AI inference
More sophisticated and responsive AI agents can be deployed in diverse real-world applications.
Accelerated development and adoption of humanoid robotics and other real-time AI-powered devices.
The blurring of lines between human-controlled and AI-controlled operations as AI becomes more seamlessly integrated into physical actions.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.AI