Title: Inference-Time Scaling of Discrete Diffusion Models via Importance Weighting and Optimal Proposal Design
Primary area: generative models
Keywords: discrete diffusion, test-time scaling, reward aligntment

Abstract: Discrete diffusion models have become highly effective across various domains. However, real-world applications often require the generative process to adhere to certain constraints.
To this end, we propose a Sequential Monte Carlo (SMC) framework that enables scalable inference-time control of discrete diffusion models through principled importance weighting and optimal proposal construction.
Specifically, our approach derives tractable importance weights for a range of intermediate targets and characterises the optimal proposal, for which we develop two practical approximations: a first-order gradient-based approximation and an amortised proposal trained to minimise the log-variance of the importance weights.
Empirical results across synthetic tasks, language modelling, biology design, and text-to-image generation demonstrate that our framework enhances controllability and sample quality, highlighting the effectiveness of SMC as a versatile recipe for scaling discrete diffusion models at inference time.