arXiv:2602.15008v2 Announce Type: replace Abstract: Diffusion models over discrete spaces have recently shown striking empirical success, yet their theoretical foundations remain incomplete. In this paper, we study the sampling efficiency of score-based discrete diffusion models under a continuous-time Markov chain (CTMC) formulation, with a focus on $\tau$-leaping-based samplers. We establish sharp convergence guarantees for attaining $\varepsilon$ accuracy in Kullback-Leibler (KL) divergence for both uniform and masking noising processes. For uniform discrete diffusion, we show that the $\ta
The rapid empirical success of discrete diffusion models necessitates a deeper theoretical understanding to optimize their application and overcome current limitations in sampling efficiency.
Improved sampling efficiency in discrete diffusion models is critical for advancing the performance and practical deployment of AI systems reliant on generative capabilities, making them more resource-efficient and robust.
This research provides a more rigorous theoretical foundation and guarantees for sampling efficiency in discrete diffusion models, paving the way for more optimized and predictable AI model development.
- · AI model developers
- · Machine learning researchers
- · Generative AI applications
- · Cloud computing providers
- · Compute-intensive, inefficient generative AI architectures
More efficient and reliable discrete diffusion models will accelerate progress in generative AI tasks like data synthesis and content creation.
Reduced computational costs for training and deploying these models could democratize access to advanced AI capabilities.
The theoretical advancements may inspire new algorithmic paradigms that transcend current diffusion model limitations, leading to unforeseen breakthroughs in AI.
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Read at arXiv cs.LG