Short-term data selectors in multi-stage LLM fine-tuning can slow future learning and increase forgetting, formalized as myopic selection with a proposed LHAS objective to address it.
Utility-diversity aware online batch selection for llm supervised fine-tuning.arXiv preprint arXiv:2510.16882, 2025a
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The Long-Term Effects of Data Selection in LLM Fine-Tuning
Short-term data selectors in multi-stage LLM fine-tuning can slow future learning and increase forgetting, formalized as myopic selection with a proposed LHAS objective to address it.