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KineBench reveals that embodied world models exhibit task-complexity-bounded nonlinear scaling, challenging existing assumptions about model performance and data requirements.
LLMs can learn better from human feedback by exploring more creatively, thanks to a simple coin-flip counting method that encourages them to try new things.
