Learn the Operator, Not the Future: State-Anchored Distillation of World Action Model Dynamics for Autonomous Driving

World action models(WAM) offer end-to-end driving a glimpse of the future, yet directly feeding WAM-predicted features into a planner suffers from feature misalignment, error accumulation in closed-loop rollouts, and high runtime cost. Moreover, generic video WAMs model how scenes evolve but are not explicitly aligned with driving behavior. We propose state-anchored transition transfer, which turns the WAM from a runtime feature provider into a training-time source of transferable transition knowledge. A compact transition operator learns to transform current scene features toward future WAM representations and modulates the planner through a lightweight FiLM-style mechanism, while planning remains anchored to fresh observations. Importantly, the approach requires no future-frame observations during training and relies solely on the pretrained WAM to provide future representations, while the WAM is completely removed at inference. This keeps hallucinated futures off the control path and eliminates the computational cost of WAM deployment. To further align the learned transitions with driving behavior, we use a rule-supervised scorer trained with a metric simulator to guide transition learning. On the NAVSIM-v2 navhard benchmark, our method improves the baseline EPDMS by 13.7% (from 48.3 to 54.9), and under zero-shot closed-loop evaluation on HUGSIM it further gains 25.4% in driving score, achieving state-of-the-art performance with negligible inference overhead.