Nested sequential Monte Carlo methods provide a principled way to steer discrete diffusion language models at inference time, outperforming simpler sampling approaches by better managing the exploration-exploitation tradeoff without model retraining.
This paper improves how language models can be steered toward desired outputs (like less toxic or more fluent text) during generation without retraining.