SimWAM: A Simple World Action Model for End-to-End Autonomous Driving

TL;DR - SimWAM is a world-action model for end-to-end autonomous driving that uses video generation only as a training signal, so the video branch can be dropped at inference — yielding a self-contained trajectory planner with state-of-the-art accuracy at much lower latency.

• Co-trains a pretrained video expert and a lightweight action expert with joint flow matching; an isolated attention mask keeps action prediction independent of future frames, so no costly future generation is needed at inference.
• The two experts share no parameters and interact only through a unified attention interface, letting the video backbone be swapped or the action expert scaled without changing the objective or inference pipeline.
• Adds reinforcement learning on a compositional driving reward to go beyond pure trajectory imitation.
• Reports 91.5 PDMS on NAVSIM, surpassing prior WAM-based planners with substantially lower latency, plus zero-shot transfer to nuScenes; code and weights released.