AnyStep-WAM: Budget-Aligned Distillation and Adaptive Inference for World Action Models
AnyStep-WAM cuts world-action denoising steps by up to 85% while holding RoboTwin success rates.
World-action models typically use a fixed denoising budget even when action chunks differ in error sensitivity. AnyStep-WAM distills interval-conditioned flow maps from a frozen teacher and uses a risk-benefit scheduler to choose the smallest budget that meets fidelity needs. On Motus, FastWAM, and LingBotVA with RoboTwin 2.0, average denoising steps drop 60.2%, 49.8%, and 85.28% while baseline success holds. One-step success rises by 7.07%, 12.08%, and 8.94%, and six real-world manipulation tasks also validate the method.
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