A fine-tuning method that trains on a model's own correct answers, with gold or paraphrased answers otherwise, improves task accuracy and cuts generalization loss versus standard SFT.
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Selective Self-to-Supervised Fine-Tuning for Generalization in Large Language Models
A fine-tuning method that trains on a model's own correct answers, with gold or paraphrased answers otherwise, improves task accuracy and cuts generalization loss versus standard SFT.