Flash-WAM: Modality-Aware Distillation for World Action Models

World-action models (WAMs) jointly generate future video and robot actions through iterative diffusion, achieving strong performance on manipulation benchmarks but requiring tens of sequential denoising steps per chunk, a cost that precludes real-time control. Step distillation has emerged as the natural remedy, but off-the-shelf methods break down in the joint video-action setting. Video latents are high-dimensional and redundant, so they tolerate small errors, whereas actions are low-dimensional and precision-critical. Existing distillation methods are designed for a single modality and distill both streams in the same way, which causes task success to collapse. We introduce Flash-WAM, a modality-aware step-distillation framework inspired by consistency distillation. We show that the standard consistency function provides almost no learning signal at low noise levels, where actions receive their final refinement. Flash-WAM therefore derives a separate consistency function for each stream. The action stream uses a function whose learning signal scales linearly with the noise level, and video keeps the standard function. We apply Flash-WAM to two WAMs with different architectures, LingBot-VA and Motus, reducing inference to a single denoising step per modality. On LingBot-VA, this cuts per-chunk latency from $8.1$ s to $347$ ms on an NVIDIA L40S ($\text{23}\times$) while retaining $81.4\%$ success on RoboTwin 2.0, versus $36.3\%$ for naive consistency distillation, and $95.1\%$ on LIBERO. In real-world experiments on a Unitree G1 humanoid robot, Flash-WAM outperforms video-only distillation by $16.7$ points on Lingbot-VA. On Motus, a single step retains 77.0% success, compared with 23.0% for naive distillation. Code is available at https://anonymous.4open.science/r/Flash-WAM/.