New paper trains Lean-grounded verifiers to spot errors in natural-language math proofs
Pavel_Izmailov · x · 2026-11-07
Pavel Izmailov announces a new paper that generates Lean-grounded synthetic data to train verifiers capable of finding mistakes in natural-language math proofs. The broader hope: use Lean and formal-to-informal generalization as part of the solution to supervising superhuman models. Full details in the companion LeanPRM thread.
Related event: LeanPRM Trains Math Proof Verifiers on Lean-Generated Synthetic Data(3 posts)→
More from Research
- Unverified claims: frontier labs allegedly gaming ICLR reviews with malicious reviews — examachine · 2026-11-07
- SSF teaches robots to reason about likely hiding spots when searching for hidden objects — _krishna_murthy · 2026-11-07
- 'Die getting Schmidhubered or live long enough to become one': scaling law priority drama — teortaxesTex · 2026-11-07
- Stanford and Together AI propose Next-Concept Modeling to replace next-token prediction — bendee983 · 2026-11-07
- New benchmark shows some LLMs refuse over 50% of legitimate biology queries — nlarusstone · 2026-11-07
- From AI-designed phage DNA to patient care: the ecosystem behind the model — exnx · 2026-11-07