Trust-Region Noise Search for Black-Box Alignment of Diffusion and Flow Models
Optimizing the noise samples of diffusion and flow models is an increasingly popular approach to align these models to target rewards at inference time. However, we observe that these approaches are usually restricted to differentiable or cheap reward models, the formulation of the underlying pretrained generative model, or are memory/compute inefficient. We instead propose a simple trust-region based search algorithm (TRS) which treats the pre-trained generative and reward models as a black-box and only optimizes the source noise. Our approach achieves a good balance between global exploration and local exploitation, and is versatile and easily adaptable to various generative settings an...
| Authors | Niklas Schweiger, Daniel Cremers, Karnik Ram |
|---|---|
| Primary category | cs.LG |
| Published | 15 March 2026 |
| Last updated at source | 4 July 2026 |
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