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R2RDreamer achieves spatial generalization improvements in manipulation tasks by leveraging 3D-aware data augmentation without the pitfalls of complex scene setups or sim-to-real gaps.
LLMs can beat state-of-the-art tensor compilers on individual subgraphs, but struggle with consistency, revealing a path to unlock their full potential through targeted training.
