Title: Inference-Time Reward-Guided Langevin Refinement for Diffusion Models
Primary area: generative models
Keywords: diffusion models, Langevin dynamics, inference-time alignment

Abstract: We study test-time alignment of pretrained diffusion models to task-specific rewards for ODE-based samplers. Starting from a DPO-inspired, energy-based distribution, we introduce an inference-time Langevin refinement that interleaves standard deterministic denoising updates with reward-guided corrections applied in image/latent space. The procedure is plug-and-play for common ODE solvers, requires no additional training or data, and works with arbitrary differentiable reward models—including human-preference reward models, aesthetic or safety scores, and CLIP-based rewards. Empirically, across multiple datasets and rewards, the method consistently increases reward at test time. The result is a lightweight “align-as-you-sample” approach that turns pretrained diffusion models into reward-seeking generators to increase quality without finetuning data, extra training, or architectural changes.

TL;DR: An inference-time method for ODE-based diffusion model that inserts small reward-guided Langevin corrections into diffusion sampling to raise task-specific rewards without extra training, data, or architecture changes