arXiv:2603.14504v2 Announce Type: replace-cross Abstract: 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
The increasing prevalence of diffusion and flow models necessitates more efficient and robust alignment methods, particularly for black-box reward functions.
This development offers a more generalized and efficient approach to aligning generative AI models with desired outcomes, impacting a wide range of AI applications.
Optimization of generative model noise no longer requires differentiable or cheap reward models, allowing for broader application and performance improvements in areas like reinforcement learning from human feedback.
- · AI developers
- · Reinforcement learning practitioners
- · Generative AI applications
- · Companies using black-box reward systems
- · Inefficient alignment methods
- · Companies heavily reliant on differentiable rewards
Improved performance and broader applicability of AI models aligned with complex or non-differentiable objectives.
Faster development cycles for generative AI applications requiring human-in-the-loop or black-box feedback.
Acceleration of autonomous AI agent capabilities through more effective alignment with real-world, complex goals.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.AI