Yeonbum Kim

Flat-RLVR: Sharpness-Aware Reinforcement Learning with Verifiable Rewards for Reasoning Capacity in LLMs

This work theoretically links flat reward RLVR to reduced distribution sharpening, and empirically shows improved sample diversity while maintaining single-attempt accuracy, in a method called \texttt{Flat}-RLVR that searches for flat reward regions via sharpness-aware minimization during LLM post-training, combining t...