Slowly Annealed Langevin Dynamics: Theory and Applications to Training-Free Guided Generation
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866918489695977472 |
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| author | Nitanda, Atsushi Bu, Dake Lyu, Yueming Veeravalli, Tanya |
| author_facet | Nitanda, Atsushi Bu, Dake Lyu, Yueming Veeravalli, Tanya |
| contents | 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 through contraction of intermediate targets and the complexity of the path. Motivated by training-free guided generation with pretrained score-based generative models, we further introduce Velocity-Aware SALD (VA-SALD), which explicitly incorporates the underlying marginal distributions of the pretrained model and uses slowdown to correct the additional deviation induced by guidance. This yields a principled framework for training-free guided generation for diffusion-based and related generative model families, together with convergence guarantees that clarify the roles of intermediate functional inequalities and guidance bias. Code is available at https://github.com/anitan0925/sald. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_07950 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Slowly Annealed Langevin Dynamics: Theory and Applications to Training-Free Guided Generation Nitanda, Atsushi Bu, Dake Lyu, Yueming Veeravalli, Tanya Machine Learning 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 through contraction of intermediate targets and the complexity of the path. Motivated by training-free guided generation with pretrained score-based generative models, we further introduce Velocity-Aware SALD (VA-SALD), which explicitly incorporates the underlying marginal distributions of the pretrained model and uses slowdown to correct the additional deviation induced by guidance. This yields a principled framework for training-free guided generation for diffusion-based and related generative model families, together with convergence guarantees that clarify the roles of intermediate functional inequalities and guidance bias. Code is available at https://github.com/anitan0925/sald. |
| title | Slowly Annealed Langevin Dynamics: Theory and Applications to Training-Free Guided Generation |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2605.07950 |