Diffusion Language Models are Provably Optimal Parallel Samplers
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Recent research highlights the efficiency of diffusion language models (DLMs) in parallel token generation, challenging traditional autoregressive models. By formalizing a parallel sampling model, the study proves that DLMs with polynomial-length chain-of-thought can match optimal sequential steps of parallel algorithms. However, without modifications to revealed tokens, DLMs can have significant intermediate footprints. Introducing remasking or revision methods allows DLMs to maintain optimal space complexity and enhances their expressiveness. This research underscores the potential of DLMs as superior parallel samplers and advocates for incorporating revision capabilities.
Diffusion Language Models Demonstrate Optimal Parallel Sampling Capabilities
Recent research highlights the potential of diffusion language models (DLMs) as a superior alternative to traditional autoregressive models, particularly in faster inference through parallel token generation. A new study formalizes the advantages of DLMs, establishing a rigorous foundation for their efficiency in parallel sampling.
The study demonstrates that DLMs, when enhanced with polynomial-length chain-of-thought (CoT), can effectively simulate any parallel sampling algorithm while employing an optimal number of sequential steps. This indicates that for any target distribution generated using a limited number of sequential steps, a DLM can replicate this process with equal efficiency.
Efficiency and Limitations of DLMs
Despite their advantages, DLMs face limitations regarding the modification of previously revealed tokens, which can result in substantial intermediate footprints. The researchers proved that incorporating remasking—transforming unmasked tokens into masks—and revision—changing unmasked tokens to other unmasked tokens—allows DLMs to simulate any parallel sampling algorithm while optimizing space complexity.
This introduces a significant expressivity gap: DLMs that utilize revision or remasking are shown to be strictly more expressive than counterparts lacking these features. This underscores the importance of enabling revision within DLM frameworks, enhancing their performance and solidifying their position for efficient parallel sampling.
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📰 Original Source: https://arxiv.org/abs/2512.25014v1
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