Discrete diffusion samplers and bridges: Off-policy algorithms and applications in latent spaces
arXiv:2602.05961v2 Announce Type: replace Abstract: Sampling from a distribution $p(x) \propto e^{-\mathcal{E}(x)}$ known up to a normalising constant is an important and challenging problem in statistics. Recent years have seen the rise of a new family of amortised sampling algorithms, commonly referred to as diffusion samplers, that enable fast and efficient sampling from an unnormalised density. Such algorithms have been widely studied for continuous-space sampling tasks; however, their application to problems in discrete space remains largely unexplored. Although some progress has been mad
This paper addresses a known limitation in current diffusion models, extending their applicability to discrete spaces, which is crucial for areas like natural language processing and other symbolic AI tasks.
Improving discrete diffusion samplers can unlock new capabilities for generative AI in domains like protein design, drug discovery, and code generation, where discrete outputs are fundamental.
The development of 'off-policy' algorithms for discrete diffusion offers more flexible and potentially robust sampling methods, bridging a gap in the theoretical and practical application of these powerful generative models.
- · AI researchers
- · Generative AI companies
- · Biotech and pharma (for discrete structure generation)
- · Natural Language Processing sector
More efficient and versatile generative AI models become possible across a wider range of data types.
This could lead to breakthroughs in areas requiring discrete output generation, such as automated drug discovery or novel material design.
The enhanced capability for discrete data generation might accelerate the development of more complex and reliable AI agents operating in symbolic environments.
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Read at arXiv cs.LG