2025
Weaver: Shrinking the Generation-Verification Gap by Scaling Compute for Verification
Jon Saad-Falcon, E. Kelly Buchanan, Mayee F Chen, Tzu-Heng Huang, Brendan McLaughlin, Tanvir Bhathal +6
NeurIPS 2025poster
Verifiers can improve language model (LM) capabilities by providing feedback or selecting the best response from a pool of generated candidates. Currently, high-quality verifiers are either unscalable (e.g., humans) or limited in utility (e.g., tools like Lean for formal proofs). While LM judges and…