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[QA] Beyond Fixed: Variable-Length Denoising for Diffusion Large Language Models
DAEDAL introduces a dynamic length expansion strategy for Diffusion Large Language Models, enhancing performance and efficiency by overcoming static length constraints during generation, outperforming fixed-length models.
https://arxiv.org/abs//2508.00819
YouTube: https://www.youtube.com/@ArxivPapers
TikTok: https://www.tiktok.com/@arxiv_papers
Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016
Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers
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By Igor Melnyk
5
33 ratings
DAEDAL introduces a dynamic length expansion strategy for Diffusion Large Language Models, enhancing performance and efficiency by overcoming static length constraints during generation, outperforming fixed-length models.
https://arxiv.org/abs//2508.00819
YouTube: https://www.youtube.com/@ArxivPapers
TikTok: https://www.tiktok.com/@arxiv_papers
Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016
Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers
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