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Scaling linear attention models with Sparse Delta Memory leads to significant improvements in long-context recall and reasoning without the computational burden of larger state sizes.
Sobolev training of diffusion policies slashes trajectory optimization time by up to 20x by preventing compounding errors, even when learning from limited data.