[2510.13117] On the Reasoning Abilities of Masked Diffusion Language Models

Computer Science > Machine Learning arXiv:2510.13117 (cs) [Submitted on 15 Oct 2025 (v1), last revised 2 Mar 2026 (this version, v2)] Title:On the Reasoning Abilities of Masked Diffusion Language Models Authors:Anej Svete, Ashish Sabharwal View a PDF of the paper titled On the Reasoning Abilities of Masked Diffusion Language Models, by Anej Svete and 1 other authors View PDF HTML (experimental) 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 in 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. Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL) Cite as: arXiv:2510.13117 [cs.LG]   (or arXiv:2510.131...