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Slowly Annealed Langevin Dynamics: Theory and Applications to Training-Free Guided Generation
We study Slowly Annealed Langevin Dynamics (SALD), a sampler for tracking a path of moving target distributions and approximating the terminal target through time slowdown. We establish non-asymptotic convergence guarantees via a KL differential inequality, showing that slowdown improves tracking th
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