Inference-Time Alignment of Diffusion Models via Trust-Region Iterative Twisted Sequential Monte Carlo
arXiv:2605.25123v1 Announce Type: cross Abstract: We study inference-time alignment for diffusion-based generative models, aiming to steer a base model toward high-reward outputs without updating its weights. Recent Sequential Monte Carlo (SMC)-based steering methods approximate reward-tilted target distributions in a principled way, but their proposals remain largely tied to the base sampler. Since reward information is mainly used after propagation through particle reweighting and resampling, these methods can require large particle budgets and suffer from weight degeneracy and high-variance
This research addresses a critical challenge in steering diffusion models towards high-reward outputs, a timely focus given the increasing deployment of such generative AI systems.
Improved inference-time alignment for diffusion models will lead to more controllable and efficient generative AI, extending their utility across various applications without requiring model retraining.
The ability to steer generative models more effectively at inference time reduces the need for extensive retraining, offering a more agile approach to adapting AI outputs to specific goals.
- · AI developers
- · Generative AI applications
- · Content creation industries
- · Researchers in AI safety and alignment
- · Methods relying solely on retraining for model alignment
More efficient and targeted generation of AI-created content.
Accelerated development of AI agents capable of nuanced, goal-driven content production in complex environments.
Potential for new forms of personalized and adaptive AI assistance, where system outputs dynamically align with user intent without explicit reprogramming.
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Read at arXiv cs.CL