Stanford's Hinted Scaling Laws forecast GSM8K accuracy within 3 pts using 5600x less compute
StanfordAILab · x · 2026-11-04
Stanford researchers (including Percy Liang, Tatsunori Hashimoto, John Duchi) propose Hinted Scaling Laws: convert a benchmark into a series of easier hinted versions, then jointly model compute scale C and hint level H. This lets them forecast GSM8K accuracy (H=0) within 3 points using 5600x less compute, where standard scaling laws diverge badly in the zero-score regime. A new tool for cheap evaluation forecasting and training decisions.
Related event: Stanford's Hinted Scaling Laws Predict Benchmark Scores at 1/5600 Compute(4 posts)→
More from Research
- Vector and Waterloo's Chrysalis makes students teach the AI to finish assignments — VectorInst · 2026-11-04
- Neuro for AI & AI for Neuro workshop at AAAI 2027 opens call for papers, due Nov 20, 2026 — naturecomputes · 2026-11-04
- Multimodal benchmarks should undergo blind filtering by default, researcher argues — cwolferesearch · 2026-11-04
- Larger tokenizers suffer more multi-epoch regret, small vocabs survive repeats — SonglinYang4 · 2026-11-04
- Reddit debates claim that 3-SAT and #3-SAT are solvable in subexponential time — burny_tech · 2026-11-04
- Dataset of 2k+ LLM chats with real voters released to build better decision evals — logangraham · 2026-11-04