DriftLite: Lightweight Drift Control for Inference-Time Scaling of Diffusion Models

• URL: http://arxiv.org/abs/2509.21655v1
• Date: Thu, 25 Sep 2025 22:21:59 GMT
• Title: DriftLite: Lightweight Drift Control for Inference-Time Scaling of Diffusion Models
• Authors: Yinuo Ren, Wenhao Gao, Lexing Ying, Grant M. Rotskoff, Jiequn Han,
• Abstract summary: We study inference-time scaling for diffusion models, where the goal is to adapt a pre-trained model to new target distributions without retraining.<n>We introduce DriftLite, a lightweight, training-free particle-based approach that steers the inference dynamics on the fly with provably optimal stability.
• Score: 22.823183347642132
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We study inference-time scaling for diffusion models, where the goal is to adapt a pre-trained model to new target distributions without retraining. Existing guidance-based methods are simple but introduce bias, while particle-based corrections suffer from weight degeneracy and high computational cost. We introduce DriftLite, a lightweight, training-free particle-based approach that steers the inference dynamics on the fly with provably optimal stability control. DriftLite exploits a previously unexplored degree of freedom in the Fokker-Planck equation between the drift and particle potential, and yields two practical instantiations: Variance- and Energy-Controlling Guidance (VCG/ECG) for approximating the optimal drift with minimal overhead. Across Gaussian mixture models, particle systems, and large-scale protein-ligand co-folding problems, DriftLite consistently reduces variance and improves sample quality over pure guidance and sequential Monte Carlo baselines. These results highlight a principled, efficient route toward scalable inference-time adaptation of diffusion models.

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