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On the Generalization of SFT: A Reinforcement Learning Perspective with Reward Rectification
We introduce Dynamic Fine-Tuning (DFT), enhancing Supervised Fine-Tuning for Large Language Models by improving generalization through dynamic gradient updates, outperforming standard methods across benchmarks.
https://arxiv.org/abs//2508.05629
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
We introduce Dynamic Fine-Tuning (DFT), enhancing Supervised Fine-Tuning for Large Language Models by improving generalization through dynamic gradient updates, outperforming standard methods across benchmarks.
https://arxiv.org/abs//2508.05629
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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