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Contrastive learning's entanglement problem may be solved: BayesNCL disentangles representations by selectively filtering task-irrelevant features, leading to a 142% boost in semantic consistency.
LLMs can safely guide DRL exploration in autonomous driving, leading to faster convergence and improved performance compared to traditional DRL methods.
