Formulates pre-hoc fine-tuning prediction as stochastic estimation, proves lower bound on optimization variance decay rate, and introduces a three-regime predictability phase diagram.
Enhancing Data Quality through Simple De-duplication: Navigating Responsible Computational Social Science Research
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TUNEAHEAD predicts fine-tuning performance from meta-features and short probes, reporting RMSE 1.47 and 95.1% of predictions within 3 points on 370 held-out runs of Qwen2.5-7B.
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A Risk Decomposition Framework for Pre-Hoc Fine-Tuning Prediction
Formulates pre-hoc fine-tuning prediction as stochastic estimation, proves lower bound on optimization variance decay rate, and introduces a three-regime predictability phase diagram.
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TuneAhead: Predicting Fine-tuning Performance Before Full Training Begins
TUNEAHEAD predicts fine-tuning performance from meta-features and short probes, reporting RMSE 1.47 and 95.1% of predictions within 3 points on 370 held-out runs of Qwen2.5-7B.