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This is a Experimental Study study.

October 8, 2025Open Access

Accelerating Diffusion LLMs via Adaptive Parallel Decoding

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Authors

DIDaniel IsraelLos Alamos National Laboratory GBGuy Van den BroeckUniversity of California, Los Angeles AGAditya GroverUniversity of California, Los Angeles

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Implication

Novel adaptive parallel decoding method improves token generation throughput in diffusion large language models, suggesting a solution to the autoregressive bottleneck.

Key Points

APD significantly increases throughput while maintaining minimal quality loss.
The method dynamically adjusts the number of tokens sampled in parallel, enhancing efficiency.
Incorporating KV caching and limiting masked input size further optimizes performance.
Tunable parameters in APD allow a flexible tradeoff between speed and output quality.

Cite This Study

Israel et al. (2025) studied this question.

synapsesocial.com/papers/68e6d7971ffa7aa7d63d174f https://doi.org/10.48550/arxiv.2506.00413

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Implication

Novel adaptive parallel decoding method improves token generation throughput in diffusion large language models, suggesting a solution to the autoregressive bottleneck.

Accelerating Diffusion LLMs via Adaptive Parallel Decoding

Discussion

Member takes

Implication

Key Points

Cite This Study

Accelerating Diffusion LLMs via Adaptive Parallel Decoding

Discussion

Member takes

Implication

Key Points

Cite This Study

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