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DPad: Efficient Diffusion Language Models with Suffix Dropout

arxiv.org · 10,329 words · saved by 1 readers

This is experimental HTML to improve accessibility. We invite you to report rendering errors. Use Alt+Y to toggle on accessible reporting links and Alt+Shift+Y to toggle off. Learn more about this project and help improve conversions. HTML conversions sometimes display errors due to content that did not convert correctly from the source. This paper uses the following packages that are not yet supported by the HTML conversion tool. Feedback on these issues are not necessary; they are known and are being worked on. Authors: achieve the best HTML results from your LaTeX submissions by following these best practices. Diffusion-based Large Language Models (dLLMs) parallelize text generation by framing decoding as a denoising process, but suffer from high computational overhead since they predict all future suffix tokens at each step while retaining only a small fraction. We propose Diffusion Scratchpad (DPad), a training-free method that restricts attention to a small set of nearby suffix

DPad: Efficient Diffusion Language Models with Suffix Dropout Xinhua Chen 1 1 1 1 Equal contribution Sitao Huang 1 1 1 footnotemark: 1 Cong Guo 1 1 1 footnotemark: 1 2 2 2 Corresponding author: Cong Guo (cong.guo@duke.edu) Chiyue Wei 1 Yintao He 1 Jianyi Zhang 1 Hai “Hellen” Li 1 Yiran Chen 1 1 Duke University Abstract Diffusion-based Large Language Models (dLLMs) parallelize text generation by framing decoding as a denoising process, but suffer from high computational overhead since they predict all future suffix tokens at each step while retaining only a small fraction. We propose Diffusion

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