Stanford AI Lab's hinted scaling laws forecast GSM8K accuracy with 5600x less compute
Predicting benchmark scores without training large models changes how labs and buyers estimate model quality before committing compute budgets. The 5600x compute saving makes accuracy forecasting accessible to academic groups and startups that cannot run full-scale training runs. Teams evaluating whether to invest in a model capability can apply this method to get defensible projections at a fraction of the usual cost.
Stanford AI Lab researchers led by Suze van Adrichem, Luke Bailey and Rckpudi introduced hinted scaling laws for forecasting benchmark accuracy. The method converts a benchmark into a series of easier versions by adding hints, then jointly models compute scale C and hint level H. This approach forecasts GSM8K accuracy at hint level H=0 within 3 points using 5600x less compute. The work predicts benchmark performance even from models that currently score zero.
Hinted scaling laws allow you to make better benchmark accuracy forecasts! Suze van Adrichem @suze_va Can we forecast benchmark accuracy from models that score zero? We (co-leads @LukeBailey181 @rckpudi ) turn a benchmark into a series of easier hinted versions. Jointly modeling scale C and hint level H enables forecasting GSM8K acc (H=0) within 3 pts from 5600x less compute 🧵 🔗 View Quoted Tweet 💬 0 🔄 2 ❤️ 3 👀 503 📊 1 ⚡
