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This is a Quantitative Study study (also classified as: Experimental Study).

October 10, 2025Open Access

ParallelBench: Understanding the Trade-offs of Parallel Decoding in Diffusion LLMs

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Parallel decoding can significantly degrade generation quality in diffusion models due to strong token dependencies.
Evaluations showed that diffusion LLMs struggle in real-world scenarios compared to autoregressive models under parallel decoding.
Information-theoretic analysis and case studies illuminate the limitations of existing parallel decoding strategies.
The proposed ParallelBench benchmark addresses gaps in evaluating diffusion LLMs, facilitating more effective development.

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Evaluation reveals parallel decoding leads to quality degradation in diffusion LLMs, highlighting the need for better strategies.

Kang et al. (2025) studied this question.

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