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Beyond `Aha!': Toward Systematic Meta-Abilities Alignment in Large Reasoning Models
The paper presents a method to enhance large reasoning models' performance by aligning them with deduction, induction, and abduction, improving reasoning reliability and scalability through a structured pipeline.
https://arxiv.org/abs//2505.10554
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 method to enhance large reasoning models' performance by aligning them with deduction, induction, and abduction, improving reasoning reliability and scalability through a structured pipeline.
https://arxiv.org/abs//2505.10554
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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