This September 2025 paper analyzes the theoretical benefits and limitations of Masked Diffusion Models (MDMs) for text generation, contrasting them with auto-regressive models. While MDMs can sample multiple tokens in parallel, offering a potential for efficiency, the research demonstrates that their actual performance depends heavily on the evaluation metric. Specifically, MDMs can achieve near-optimal fluency (low Token Error Rate) with a constant number of sampling steps, regardless of sequence length. However, when assessed for correctness (low Sequence Error Rate), particularly for tasks requiring logical reasoning, MDMs necessitate a number of sampling steps that scales linearly with sequence length, effectively negating their efficiency advantage. Empirical results using formal languages and large open-sourced MDMs support these theoretical findings, indicating that MDMs are better suited for fluent text generation but less so for accuracy-critical reasoning tasks.Source:https://arxiv.org/pdf/2502.09622

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