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Eso-LMs combine autoregressive and masked diffusion models, improving perplexity and inference efficiency with KV caching, achieving state-of-the-art performance and significantly faster inference rates. Code and checkpoints available online.
https://arxiv.org/abs//2506.01928
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
By Igor Melnyk5
33 ratings
Eso-LMs combine autoregressive and masked diffusion models, improving perplexity and inference efficiency with KV caching, achieving state-of-the-art performance and significantly faster inference rates. Code and checkpoints available online.
https://arxiv.org/abs//2506.01928
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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