This paper introduces temporal straightening, a technique for improving representation learning in latent planning with world models. By using a curvature regularizer to encourage straightened latent trajectories in a JEPA architecture, the method makes Euclidean distance a better proxy for geodesic distance and improves planning stability, achieving higher success rates on goal-reaching tasks.