TuneAhead: Predicting Fine-tuning Performance Before Full Training Begins

2026-06-16

AI Summary

This study addresses the high cost and sensitivity of large language model fine-tuning to data quality and hyperparameters, highlighting the need for pre-training performance prediction. The authors propose TuneAhead, a novel framework that enables accurate forecasting of fine-tuning outcomes by constructing a lightweight regression model from static data descriptors and dynamic features derived from short, standardized probing runs. Integrated SHAP analysis provides interpretable diagnostics to support informed “proceed/abort” decisions prior to full-scale training. Evaluated across 370 hold-out tests, TuneAhead achieves an RMSE of 1.47 percentage points, with 95.1% of predictions falling within ±3 percentage points of ground truth—significantly outperforming baseline methods such as Early-Stop Extrapolation and ProxyLM.

Abstract

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 practical question:can we predict fine-tuning performance before committing to a full training run? We present TUNEAHEAD, a lightweight framework for pre-hoc prediction of fine-tuning performance. TUNEAHEAD encodes each candidate run as a meta-feature vector that combines static dataset descriptors with dynamic probe features from a short standardized probe. A predictor maps these features to performance estimates, while SHAP-based attributions provide interpretable diagnostics that reveal which specific features drive the prediction. Across 1,300+ fine-tuning runs on Qwen2.5-7B-Instruct, TUNEAHEAD consistently outperforms strong baselines such as Early-Stop Extrapolation and ProxyLM. On a held-out test set of 370 runs, TUNEAHEAD achieves an RMSE of 1.47 percentage points and places 95.1% of predictions within +3/-3 percentage points of the true score. These accurate continuous predictions support practical go/no-go screening policies that can reduce unnecessary full fine-tuning while retaining most promising runs.

Problem

• fine-tuning
• performance prediction
• large language models
• hyperparameter sensitivity
• data quality

Innovation

• pre-hoc prediction
• fine-tuning performance
• meta-features
• SHAP interpretability
• lightweight probing