3 papers
cs.LG2026
Data-efficient pre-training by scaling synthetic megadocs
Konwoo Kim, Suhas Kotha, Yejin Choi +3
Synthetic data augmentation has emerged as a promising solution when pre-training is constrained by data rather than compute. We study how to design synthetic data algorithms that…
cs.LG2025
Goedel-Prover-V2: Scaling Formal Theorem Proving with Scaffolded Data Synthesis and Self-Correction
Yong Lin, Shange Tang, Bohan Lyu +17
We introduce Goedel-Prover-V2, a series of open-source language models that set a new state-of-the-art in automated theorem proving. Built on the standard expert iteration and rein…
cs.CY2025
Advancing Science- and Evidence-based AI Policy
Rishi Bommasani, Sanjeev Arora, Jennifer Chayes +17
AI policy should advance AI innovation by ensuring that its potential benefits are responsibly realized and widely shared. To achieve this, AI policymaking should place a premium o…