Easy Samples Are All You Need: Self-Evolving LLMs via Data-Efficient Reinforcement Learning

This research introduces EasyRL, a novel data-efficient reinforcement learning approach for self-evolving LLMs, designed to overcome high annotation costs and performance issues in existing methods. Inspired by cognitive learning theory, EasyRL integrates knowledge transfer from easy labeled data with a progressive divide-and-conquer strategy for difficult unlabeled data.