Direct-policy World Action Models (WAM) have demonstrated the benefits of decoupling action generation from explicit future visual prediction for efficient inference in autonomous driving and embodied
OSWAM enables real-time, high-quality action generation in autonomous driving and embodied control by initializing diffusion with structured imagination tokens and distilling future-conditioned privileged information into a single denoising step.
Direct-policy World Action Models (WAM) have demonstrated the benefits of decoupling action generation from explicit future visual prediction for efficient inference in autonomous driving and embodied control. However, they still require tens of denoising steps to achieve competitive action quality, incurring substantial latency and hindering real-time deployment. Simply reducing the number of denoising steps will collapse the nonlinear multi-step denoising trajectory into coarse updates, substantially degrading action quality. To reduce the denoising steps while enabling real-time action generation, we present an effective world action model with one-step action generation, namely OSWAM,…
Summary and abstract excerpt via Hugging Face Papers ↗ · arXiv.
OSWAM: An Effective World Action Model with One-Step Action Generation was first seen on Hugging Face · Daily papers on Nov 2, 2026. Since then it has appeared in 14 checks on 2 different days. Its best position was #9 of 30, reached on Nov 3, 03:28 UTC. It dropped off the list after Nov 3, 19:28 UTC.
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It was first seen on Nov 2, 2026 and has been on the list on 2 days (14 checks) since.