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Reweighted normalizing flows enable exponential convergence to arbitrary Boltzmann distributions via a novel WFR JKO scheme, bypassing common structural assumptions.
Text generation evaluation can now directly measure distributional divergence using hidden states and double sliced Wasserstein distance, bypassing perplexity's limitations for non-autoregressive models.
SympFormer achieves faster convergence in attention blocks by drawing inspiration from inertial Nesterov acceleration, offering a potential speedup without additional computational cost.
Forget fixed schedules: this new discrete diffusion model learns when to stop, adapting computation to the complexity of each reasoning problem.
