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

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.0Download Report PDF
discrete diffusiontest-time scalingreward aligntment
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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This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
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Overview

Overall Novelty Assessment

The paper proposes a Sequential Monte Carlo framework for inference-time control of discrete diffusion models, deriving tractable importance weights and characterizing optimal proposals through gradient-based and amortized approximations. Within the taxonomy, it resides in the 'Sequential Monte Carlo and Importance Weighting' leaf under 'Inference-Time Guidance and Control Mechanisms,' alongside two sibling papers. This leaf represents a focused research direction within the broader field of inference-time control, which itself comprises four distinct guidance subcategories. The relatively small number of siblings suggests this is a specialized but not overcrowded area.

The taxonomy reveals that inference-time guidance methods span multiple paradigms: gradient-free approaches, gradient-based posterior prediction, tree search strategies, and SMC-based techniques. The paper's leaf sits within a branch that emphasizes principled probabilistic inference, contrasting with neighboring leaves that employ search-based or derivative-free guidance. The taxonomy's scope notes clarify that SMC methods focus on particle-based frameworks and importance weighting, distinguishing them from gradient-based guidance that directly steers generation via reward gradients. This positioning highlights the paper's methodological commitment to probabilistic reweighting rather than direct optimization.

Among thirty candidates examined, the first contribution (SMC framework with tractable importance weights) shows two refutable candidates out of ten examined, indicating some prior work in this specific area. The second contribution (approximately optimal proposals) has one refutable candidate among ten, suggesting moderate overlap with existing methods. The third contribution (versatile multi-domain demonstration) found no refutable candidates across ten examined papers, appearing more novel in its breadth of application. These statistics reflect a limited search scope—top-K semantic matches plus citation expansion—rather than exhaustive coverage, so the presence of refutable candidates signals overlap within a constrained candidate pool.

Given the limited search scale, the analysis suggests the paper occupies a methodologically distinct position within SMC-based guidance, though some foundational elements overlap with prior importance weighting and proposal design work. The multi-domain versatility appears less explored in the examined candidates, potentially offering incremental novelty. However, the search scope (thirty candidates) leaves open the possibility of additional relevant work beyond the examined set, particularly in adjacent probabilistic inference or particle filtering literature not captured by semantic search.

Taxonomy

Core-task Taxonomy Papers
44
3
Claimed Contributions
30
Contribution Candidate Papers Compared
3
Refutable Paper

Research Landscape Overview

Core task: inference-time control of discrete diffusion models. The field organizes around four main branches that reflect distinct methodological emphases. Inference-Time Guidance and Control Mechanisms explore how to steer generation without retraining, encompassing techniques such as sequential Monte Carlo methods, importance weighting, and corrector-based approaches that refine samples during the reverse process. Sampling and Inference Acceleration focuses on reducing computational cost through faster solvers, remasking strategies like Remasking Inference Scaling[1], and adaptive scheduling. Training-Based Approaches and Model Architecture Design address foundational model improvements, including architectural innovations such as ControlNet[22] and training objectives that better align generation with downstream constraints. Domain-Specific Applications demonstrate how these methods adapt to specialized settings like protein design, layout generation with LayoutDM[6], and multimodal tasks, highlighting the interplay between general-purpose control mechanisms and domain requirements. A particularly active line of work centers on probabilistic inference methods that treat guidance as a posterior correction problem. Techniques like Particle Gibbs Sampling[11] and Feynman-Kac Correctors[29] leverage sequential Monte Carlo frameworks to incorporate constraints through reweighting or resampling, while Soft Value Decoding[3] and Steering Posterior Prediction[2] explore alternative ways to bias the generative process toward desired properties. Importance Weighting Inference[0] sits within this cluster, emphasizing importance weighting to adjust the sampling distribution at inference time. Compared to Particle Gibbs Sampling[11], which iteratively refines particle sets, and Feynman-Kac Correctors[29], which apply corrector steps grounded in Feynman-Kac theory, Importance Weighting Inference[0] offers a complementary perspective on how to balance computational efficiency with the fidelity of constraint satisfaction, contributing to ongoing discussions about trade-offs between sample quality, diversity, and inference cost in discrete diffusion models.

Claimed Contributions

SMC framework for discrete diffusion models with tractable importance weights

The authors introduce a Sequential Monte Carlo framework specifically designed for discrete diffusion models that enables inference-time control through principled importance weighting. The framework derives tractable importance weights for intermediate target distributions, including product distributions and reward-tilting distributions, providing a general approach for test-time scaling.

10 retrieved papers
Can Refute
Two approximately optimal proposal distributions

The authors develop two practical approximations to the optimal SMC proposal: a gradient-based first-order approximation and an amortised neural proposal trained by minimising the log-variance of importance weights. These proposals aim to reduce variance in the SMC procedure and improve sampling efficiency.

10 retrieved papers
Can Refute
Versatile framework demonstrated across multiple domains

The authors validate their SMC framework across diverse applications spanning language modelling, biological sequence design, and text-to-image generation. The experiments demonstrate that the proposed methods consistently enhance controllability and sample quality across different domains.

10 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

SMC framework for discrete diffusion models with tractable importance weights

The authors introduce a Sequential Monte Carlo framework specifically designed for discrete diffusion models that enables inference-time control through principled importance weighting. The framework derives tractable importance weights for intermediate target distributions, including product distributions and reward-tilting distributions, providing a general approach for test-time scaling.

Contribution

Two approximately optimal proposal distributions

The authors develop two practical approximations to the optimal SMC proposal: a gradient-based first-order approximation and an amortised neural proposal trained by minimising the log-variance of importance weights. These proposals aim to reduce variance in the SMC procedure and improve sampling efficiency.

Contribution

Versatile framework demonstrated across multiple domains

The authors validate their SMC framework across diverse applications spanning language modelling, biological sequence design, and text-to-image generation. The experiments demonstrate that the proposed methods consistently enhance controllability and sample quality across different domains.

Table of Contents