Using a new discrete Wasserstein distance and action functional, the paper proves polynomial convergence rates for annealed Glauber dynamics in mean-field Ising and Potts models.
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2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
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DRIFT enables stable offline-to-online fine-tuning of CTMC policies in discrete RL via advantage-weighted discrete flow matching, path-space regularization, and candidate-set approximation.
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Discrete Optimal Transport: Rapid Convergence of Simulated Annealing Algorithms
Using a new discrete Wasserstein distance and action functional, the paper proves polynomial convergence rates for annealed Glauber dynamics in mean-field Ising and Potts models.
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Discrete Flow Matching for Offline-to-Online Reinforcement Learning
DRIFT enables stable offline-to-online fine-tuning of CTMC policies in discrete RL via advantage-weighted discrete flow matching, path-space regularization, and candidate-set approximation.