Inference-Time Scaling of Discrete Diffusion Models via Importance Weighting and Optimal Proposal Design

Zijing Ou; Chinmay Pani; Yingzhen Li

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arXiv · 2505.22524

Inference-Time Scaling of Discrete Diffusion Models via Importance Weighting and Optimal Proposal Design

Zijing Ou·Chinmay Pani·Yingzhen Li

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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.

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Zijing Ou, Chinmay Pani, Yingzhen Li. 2026-03-15. Inference-Time Scaling of Discrete Diffusion Models via Importance Weighting and Optimal Proposal Design. https://arxiv.org/abs/2505.22524

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