Stanford team forecasts GSM8K accuracy within 3 pts using 5600x less compute via hinted evals
stanfordnlp · x · 2026-11-04
Tatsu Hashimoto highlights new eval-scaling research: advanced LM capabilities lack scaling laws, so instead of scaling up models, why not scale down evals?
The team (co-leads Luke Bailey and rckpudi) converts a benchmark into a series of easier hinted versions. Jointly modeling model scale C and hint level H enables forecasting GSM8K accuracy (H=0) within 3 points using 5600x less compute.
The result offers a cheap way to predict advanced-capability performance, addressing the 'can't improve what we can't measure' gap in LM safety and capability development.
More from Research
- NRGPT paper reimagines GPT inference as gradient descent on an explicit energy landscape — burkov · 2026-11-04
- UCLA Study Seeks Users of 'Griefbots' to Interview on AI and Mourning ($30 Gift Card) — Interesting-Cat-7838 · 2026-11-04
- Tiny LoRA adapter beats DNA model Carbon-X on RareBench, exposing generalization limits — danielmckinn0n · 2026-11-04
- QuickProof: formal verification for C with specs written as plain C tests — thomasahle · 2026-11-04
- RSNA 2026 Deep Learning Lab to teach medical imaging VLM dataset building from scratch — PRouzrokh · 2026-11-04
- RSNA lab details: build medical VLM datasets with EHR cohorts, de-ID and QC in Colab — PRouzrokh · 2026-11-04