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Discrete diffusion models can now generate samples in polylogarithmic time, thanks to a novel Gibbs-based corrector that avoids costly retraining.
Optimism is the key to stable and convergent safe RLHF, according to a new primal-dual framework that unifies existing alignment algorithms.
