2026
TuneAhead: Predicting Fine-tuning Performance Before Training Begins
ICML 2026poster
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 even degrade model performance. This raises a fundamental question: Can we predict fine-tuning performance before traini…