Contrastive Distribution Matching for Amortized Sequential Monte Carlo in Discrete Diffusion

• URL: http://arxiv.org/abs/2605.23346v1
• Date: Fri, 22 May 2026 08:06:52 GMT
• Title: Contrastive Distribution Matching for Amortized Sequential Monte Carlo in Discrete Diffusion
• Abstract summary: We introduce Contrastive Distribution Matching (CDM), a novel framework that amortizes the cost of SMC inference by learning a parameterized twist function via positive and negative samples.<n>In practice, evaluating our learned twist function incurs less than 5% additional computational overhead compared to a single forward pass of the base model.<n>We validate the effectiveness and versatility of our approach across a diverse range of applications, including toxic text generation, regulatory DNA sequence design, protein designability, and diffusion large language model alignment.
• Score: 33.94204857658877
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Discrete diffusion models have emerged as powerful frameworks for generating structured categorical data. However, efficiently sampling from reward-tilted distributions remains a fundamental challenge. While Twisted Sequential Monte Carlo (SMC) offers asymptotic exactness for this task, estimating the optimal twist function in discrete state spaces necessitates costly Monte Carlo approximations, resulting a severe computational bottleneck at inference. To overcome this limitation, we introduce Contrastive Distribution Matching (CDM), a novel framework that amortizes the cost of SMC inference by learning a parameterized twist function via positive and negative samples. For efficient training, we reformulate the gradient estimator to leverage the closed-form forward kernels of discrete diffusion models. In practice, evaluating our learned twist function incurs less than 5% additional computational overhead compared to a single forward pass of the base model. Through extensive empirical evaluations, we demonstrate that CDM consistently outperforms existing baselines under matched wall-clock time. We validate the effectiveness and versatility of our approach across a diverse range of applications, including toxic text generation, regulatory DNA sequence design, protein designability, and diffusion large language model alignment.

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