Hinted Scaling Laws

Researchers introduce hinted scaling laws, a method using privileged information to transform downstream benchmarks into a continuum of easier versions where small models score above zero. This enables smooth and predictable accuracy trends that operate below the noise threshold. The approach significantly reduces forecasting errors on tasks like GSM8K, AIME, and HLE, while also reducing the number of test examples needed to compare model performance. Open article →