Gradient-Free Sampling from Generative Models via Stochastic Bounded Extremum Seeking
We introduce a sampling approach for energy- and score-based generative models that requires no gradient evaluations of the model. Replacing the drift term that would normally contain the score $\nabla_\mathbf{x} \log p_θ(\bf{x})$ with a high-frequency dithered cosine of the model's \textit{value}, $\sqrt{αω}\,\cos(ωt + k \log p_θ(\bf{x}))$, produces, in the high-frequency averaging limit, Langevin Markov chain Monte Carlo for energy-based models and the reverse-time SDE of score-based diffusion. We prove that trajectories of the dithered Itô SDE converge to those of the target SDE, driven by the same Brownian motion, uniformly on compact time intervals in probability, by an averaging arg...
| Authors | Alexander Scheinker |
|---|---|
| Primary category | stat.ML |
| Published | 3 October 2026 |
| Last updated at source | 3 October 2026 |
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