Inference-Time Scaling in Diffusion Models through Iterative Partial Refinement

ArXi:2605.19317v1 Announce Type: cross Inference-time scaling has emerged as a major approach for improving reasoning capabilities, and has been increasingly applied to diffusion models. However, existing inference-time scaling methods for diffusion models typically rely on external verifiers or reward models to rank and select samples, limiting their scalability to settings where such evaluators are available and reliable.