Stanford's Hinted Scaling Laws Predict Benchmark Scores at 1/5600 Compute

A Stanford team has released an arXiv paper, "Hinted Scaling Laws," introducing a new method for predicting a model's final benchmark accuracy at extremely low compute. Authors include rckpudi (their final PhD paper), with the project led by @suzeva's group and collaborators including Percy Liang, Tatsunori Hashimoto, and John Duchi; the work is related to Delphi.

Confirmed

• Core approach: transform a benchmark (e.g., GSM8K) into a series of easier hinted versions, jointly modeling compute scale C and hint level H, so that scores can be extrapolated to the full benchmark (H=0) even from models that score zero on it.
• Results: 5600x compute savings compared to running the full benchmark, with prediction error within 3 points.
• The method addresses the failure of traditional scaling-law extrapolation on benchmark accuracy.

Why it matters

• Evaluating large models on full benchmarks is typically very expensive. This approach lets researchers forecast a model's final scores with a tiny compute budget, directly cutting evaluation costs and informing training decisions.

2026-11-03 ~ 2026-11-04 · 6 related posts

• Stanford's Hinted Scaling Laws cut benchmark forecast error from 21 to 4.3 points — heghbalz · 2026-11-03
• [source] Stanford's Hinted Scaling Laws forecast GSM8K accuracy within 3 pts using 5600x less compute — StanfordAILab · 2026-11-04
• PhD paper forecasts GSM8K accuracy within 3 pts using 5600x less compute via hinted benchmarks — kenziyuliu · 2026-11-04
• New method forecasts GSM8K accuracy within 3 points using 5600x less compute — kenziyuliu · 2026-11-04
• Forecasting GSM8K Accuracy Within 3 Points Using 5600x Less Compute — josh_wills · 2026-11-04
• Stanford team forecasts GSM8K accuracy within 3 pts using 5600x less compute — stanfordnlp · 2026-11-04