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Think, Prune, Train, Improve: Scaling Reasoning Without Scaling Models
The paper presents a framework, Think, Prune, Train, enhancing LLM performance through iterative fine-tuning on self-generated reasoning traces, achieving significant improvements in programming and mathematical tasks.
https://arxiv.org/abs//2504.18116
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 paper presents a framework, Think, Prune, Train, enhancing LLM performance through iterative fine-tuning on self-generated reasoning traces, achieving significant improvements in programming and mathematical tasks.
https://arxiv.org/abs//2504.18116
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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