Discrete Diffusion Inference-Time Control with Nested Sequential Monte Carlo
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TL;DR - This paper introduces nested sequential Monte Carlo methods for steering discrete diffusion language models toward sequence-level rewards at inference time, without retraining. The methods address biases and sampling weaknesses in prior approaches and improve toxicity and fluency control in the reported experiments.
- Formulates nested SMC (NSMC) and fully adapted nested SMC (FA-NSMC) for Feynman–Kac steering.
- Identifies and corrects errors in earlier formulations that produce biased final estimates.
- Targets limitations of best-of-(n) sampling and bootstrap SMC, including overoptimism and weight degeneracy.
- NSMC and FA-NSMC consistently outperform both baselines on toxicity and fluency steering tasks.
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Discrete Diffusion Inference-Time Control with Nested Sequential Monte Carlo
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TL;DR - This paper introduces nested sequential Monte Carlo methods for steering discrete diffusion language models toward sequence-level rewards at inference time, without retraining. The methods address biases and sampling weaknesses in prior approaches and improve toxicity and fluency control in the reported experiments.
- Formulates nested SMC (NSMC) and fully adapted nested SMC (FA-NSMC) for Feynman–Kac steering.
- Identifies and corrects errors in earlier formulations that produce biased final estimates.
- Targets limitations of best-of-(n) sampling and bootstrap SMC, including overoptimism and weight degeneracy.
- NSMC and FA-NSMC consistently outperform both baselines on toxicity and fluency steering tasks.