On the Robustness of Langevin Dynamics to Score Function Error
We consider the robustness of score-based generative modeling to errors in the estimate of the score function. In particular, we show that Langevin dynamics is not robust to the $L^2$ errors (more generally $L^p$ errors) in the estimate of the score function. It is well-established that with small $L^2$ errors in the estimate of the score function, diffusion models can sample faithfully from the target distribution under fairly mild regularity assumptions in a polynomial time horizon. In contrast, our work shows that even for simple distributions in high dimensions, Langevin dynamics run for any polynomial time horizon will produce a distribution far from the target distribution in Total ...
| Authors | Daniel Yiming Cao, August Y. Chen, Karthik Sridharan et al. |
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| Primary category | cs.LG |
| Published | 11 March 2026 |
| Last updated at source | 4 June 2026 |
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