This paper addresses critical challenges in autonomous driving trajectory prediction where minor initial deviations in open-loop models cascade into compounding errors, leading to out-of-distribution states. The authors identify a shortcut learning problem in differentiable closed-loop simulators where gradients inadvertently leak future ground truth information into previous predictions, causing non-causal regret instead of genuine recovery. To solve this, they propose a detached receding horizon rollout that severs computation graphs between simulation steps, forcing the model to learn authentic reactive recovery behaviors from drifted states. Comprehensive evaluations on the nuScenes and DeepScenario autonomous driving datasets demonstrate the effectiveness of their approach in achieving genuine trajectory rectification without temporal information leakage.