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Phi-4-Mini-Reasoning: Exploring the Limits of Small Reasoning Language Models in Math
This work presents a systematic training recipe for Small Language Models, enhancing their reasoning capabilities using Chain-of-Thought data, outperforming larger models in math reasoning tasks.
https://arxiv.org/abs//2504.21233
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 work presents a systematic training recipe for Small Language Models, enhancing their reasoning capabilities using Chain-of-Thought data, outperforming larger models in math reasoning tasks.
https://arxiv.org/abs//2504.21233
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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