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Temporal Straightening for Latent Planning Author's Explanation: https://t.co/UqNLJ8hqCk Overview: Temporal straightening improves representation learning for latent planning by applying a curvature regularizer that enforces locally straightened latent trajectories. This method aligns Euclidean distance in latent space with geodesic distance, which stabilizes gradient-based planning. Experimental results show significant performance gains in success rates across goal-reaching tasks compared to standard representation learning techniques for LLMs and world models. Paper: https://t.co/UjSsITfefu

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