AI RESEARCH
On the optimization dynamics of RLVR: Gradient gap and step size thresholds
arXiv CS.LG
•
ArXi:2510.08539v4 Announce Type: replace Reinforcement Learning with Verifiable Rewards (RLVR), which uses simple binary feedback to post-train large language models, has found significant empirical success. However, a principled understanding of why it works is lacking. This paper builds a theoretical foundation for RLVR by analyzing its