Systematic Multi-Level Acceleration for World Action Models

World Action Models (WAMs) are emerging as a promising alternative to Vision-Language-Action (VLA) models. Existing VLA acceleration methods primarily reduce VLM computation and are not directly applicable to diffusion-based WAMs. Moreover, WAM acceleration methods typically target redundancy along only a single computational axis, overlooking substantial redundancy across the full pipeline, resulting in either limited speedups or drops in success rate. To address these limitations, we propose MARS, a training-free multi-level acceleration framework that systematically exploits distinct sources of redundancy at three computational levels: (1) across Transformer blocks, we selectively skip redundant attention components; (2) across diffusion denoising steps, we perform action-aware routing to adaptively skip diffusion steps; (3) across consecutive action chunks, we reuse future visual tokens, thereby skipping the VAE encoding. These mechanisms are synergistically integrated in a plug-and-play manner. Extensive experiments across multiple benchmarks and base models demonstrate that our method achieves a superior efficiency-performance trade-off, delivering up to a 4.00$\times$/3.94$\times$ speedup in LIBERO/LIBERO-PRO, 2.10$\times$ in RoboCasa, and consistent speedups on real-world tasks, while maintaining comparable success rates.