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SimVTA: Simulation-Grounded Video-Trajectory Alignment for Driving World Action Models
Driving world action models (WAMs) learn from paired trajectories and future observations. Post-training with reinforcement learning allows the policy to explore alternative trajectories and improve planning using driving rewards. However, driving logs contain future videos only for the recorded tra
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