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cs.LG2026
TuneAhead: Predicting Fine-tuning Performance Before Full Training Begins
Yuxiang Luo, Haonan Long, Chen Wang +6
Fine-tuning large language models (LLMs) is compute-intensive and error-prone: model performance depends sensitively on data quality and hyperparameter choices, and naïve runs can…
cs.LG2025
LEAD: Iterative Data Selection for Efficient LLM Instruction Tuning
Xiaotian Lin, Yanlin Qi, Yizhang Zhu +4
Instruction tuning has emerged as a critical paradigm for improving the capabilities and alignment of large language models (LLMs). However, existing iterative model-aware data sel…