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Towards Understanding Distilled Reasoning Models: A Representational Approach
This paper explores how model distillation affects reasoning features in large language models, revealing unique reasoning directions and structured representations that enhance AI transparency and reliability.
https://arxiv.org/abs//2503.03730
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 explores how model distillation affects reasoning features in large language models, revealing unique reasoning directions and structured representations that enhance AI transparency and reliability.
https://arxiv.org/abs//2503.03730
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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