# Rolling-WAM

Rolling-WAM introduces a rolling world action model that keeps a sliding window of video-action chunks at staggered noise levels, fully denoising only the imminent action chunk each replanning cycle while partially refining farther-future chunks, thereby distributing joint denoising computation across control cycles; it achieves competitive manipulation success on LIBERO, RoboTwin 2.0, and real-world Unitree G1 humanoid tasks while delivering a 4.5x steady-state replanning speedup over standard joint WAMs.

Rolling-WAM introduces a rolling world action model that keeps a sliding window of video-action chunks at staggered noise levels, fully denoising only the imminent action chunk each replanning cycle while partially refining farther-future chunks, thereby distributing joint denoising computation across control cycles; it achieves competitive manipulation success on LIBERO, RoboTwin 2.0, and real-world Unitree G1 humanoid tasks while delivering a 4.5x steady-state replanning speedup over standard joint WAMs.