TuneAhead: Predicting Fine-tuning Performance Before Full Training Begins

TUNEAHEAD is a lightweight framework designed to predict the performance of fine-tuning large language models (LLMs) before full training, addressing the challenges of compute intensity and sensitivity to hyperparameters. By encoding candidate runs as meta-feature vectors that integrate static dataset descriptors and dynamic probe features, TUNEAHEAD achieves an RMSE of 1.47 percentage points across 1,300+ fine-tuning runs on the Qwen2.5-7B-Instruct model, outperforming baselines like Early-Stop Extrapolation and ProxyLM. This capability allows practitioners to implement effective go/no-go screening policies, minimizing unnecessary fine-tuning while focusing on the most promising configurations.