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Low-degree rational approximations enable MLPs to be linearized into efficient, composable kernel representations that outperform polynomial approximations.
Overcome the generalization failures plaguing offline goal-conditioned RL, especially in long-horizon tasks, with a new latent representation alignment method that achieves SOTA results.
