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RL fine-tuning unlocks a 6x performance gain for in-place trajectory editing in autonomous driving, demonstrating the power of aligning diffusion planners with reinforcement learning.
A surprisingly simple change to the motion latent space鈥攔epresenting each body joint with its own token鈥攄ramatically improves text-to-motion generation quality, outperforming monolithic latent vector approaches.