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Do Language Models Use Their Depth Efficiently?
The study analyzes Llama 3.1 and Qwen 3 models, finding deeper layers contribute less and do not perform new computations, explaining diminishing returns in stacked Transformer architectures.
https://arxiv.org/abs//2505.13898
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
The study analyzes Llama 3.1 and Qwen 3 models, finding deeper layers contribute less and do not perform new computations, explaining diminishing returns in stacked Transformer architectures.
https://arxiv.org/abs//2505.13898
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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