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From 128K to 4M: Efficient Training of Ultra-Long Context Large Language Models
This paper presents an efficient training method for ultra-long context LLMs, extending context lengths to 4M tokens while maintaining performance on both long and short context tasks.
https://arxiv.org/abs//2504.06214
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
This paper presents an efficient training method for ultra-long context LLMs, extending context lengths to 4M tokens while maintaining performance on both long and short context tasks.
https://arxiv.org/abs//2504.06214
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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