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OSWAM: A World Action Model with One-Step Action Generation

OSWAM proposes a world action model that generates actions with just one diffusion step, for autonomous driving and embodied control. The authors initialize the diffusion process with imagination tokens to provide physical priors and action-conditioned representations, and introduce privileged information distillation with future conditioning, making action quality comparable to or better than direct-policy WAM and Joint-WAM, which require tens of steps. It is about 3 times faster than the direct-policy WAM baseline, with compiled inference latency as low as 40ms on an NVIDIA RTX 5090 GPU.

Part of the event: OSWAM: A World Action Model for One-Step Diffusion Action Generation

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