Contrastive Distribution Matching for Amortized Sequential Monte Carlo in Discrete Diffusion
arXiv:2605.23346v1 Announce Type: new 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 fram
This research addresses a critical computational bottleneck in discrete diffusion models, a key component for generating structured categorical data, which are becoming more prevalent in AI.
Improving the efficiency of sampling from reward-tilted distributions in discrete diffusion models can accelerate R&D and deployment of advanced AI systems, particularly those dealing with complex, structured data.
The introduction of Contrastive Distribution Matching (CDM) offers a more computationally efficient method for handling a fundamental challenge in current discrete diffusion models.
- · AI researchers
- · Machine learning platform providers
- · Industries relying on structured data generation
- · Developers reliant on less efficient sampling methods
Increased efficiency in training and deploying discrete diffusion models for complex data tasks.
Faster development and application of AI models capable of generating highly structured outputs like code, molecular structures, or complex narratives.
Potential for new product categories or services based on more robust and efficient generative AI for discrete data.
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Read at arXiv cs.LG