AI RESEARCH
The Long-Term Effects of Data Selection in LLM Fine-Tuning
arXiv CS.LG
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ArXi:2605.30537v1 Announce Type: new Data selection is increasingly used to reduce the cost of large language model (LLM) fine-tuning, with recent methods prioritizing samples by current utility, diversity, quality, or influence. This paper studies a different question: when fine-tuning occurs over multiple stages, can selection strategies that look optimal now make the model less adaptable later? We