Discrete Diffusion Inference-Time Control with Nested Sequential Monte Carlo

Abstract:We study inference-time control for text generation in discrete diffusion language models, where the goal is to steer sampling toward sequence-level rewards without retraining. Prior work in this domain has focused on particle-based methods such as best-of-$n$ sampling and bootstrap sequential Monte Carlo, which may suffer from overoptimism and weight degeneracy, respectively. We address these limitations using \emph{nested} sequential Monte Carlo methods. We formulate nested SMC (NSMC) and fully-adapted nested SMC (FA-NSMC) for Feynman--Kac steering, identifying and correcting errors in prior formulations that lead to biased final estimates. We evaluate these methods on toxicity and fluency steering tasks, showing that NSMC and FA-NSMC consistently outperform best-of-$n$ and bootstrap SMC.

Machine Learning (stat.ML); Machine Learning (cs.LG)

arXiv:2608.20123 [stat.ML]

(or arXiv:2608.20123v1 [stat.ML] for this version)

https://doi.org/10.48550/arXiv.2608.20123 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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