Title: On the Reasoning Abilities of Masked Diffusion Language Models
Primary area: other topics in machine learning (i.e., none of the above)
Keywords: diffusion language models, formal language theory, boolean circuits, expressivity, transformers, masked diffusion models, chain of thought, looped transformers

Abstract: Masked diffusion models (MDMs) for text offer a compelling alternative to traditional autoregressive language models. Parallel generation makes them efficient, but their computational capabilities and the limitations inherent to their parallelism remain largely unexplored. To this end, we characterize what types of reasoning problems MDMs can provably solve and how efficiently. We do this by connecting MDMs to the well-understood reasoning frameworks of chain of thought (CoT) and padded looped transformers (PLTs) in the finite-precision log-width setting: We show that MDMs and polynomially-padded PLTs are, in fact, equivalent in this setting, and that MDMs can solve all problems that CoT-augmented transformers can. Moreover, we showcase classes of problems (including regular languages) for which MDMs are inherently more efficient than CoT transformers, where parallel generation allows for substantially faster reasoning.

TL;DR: We prove that masked text diffusion models are equivalent to padded looped transformers, can solve all problems that chain-of-thought transformers can, and are more efficient on certain problem classes due to their parallel generation mechanism.