EasyRL Framework Proposes Data-Efficient Reinforcement Learning for LLMs

A recent study presents EasyRL, an innovative method for reinforcement learning tailored for large language models, designed to address the shortcomings of prior techniques. The framework, outlined in arXiv preprint 2604.18639, tackles challenges such as high annotation expenses, model collapse, and reward hacking that have affected previous RL applications in LLMs. Drawing inspiration from cognitive learning theory, EasyRL mimics human knowledge acquisition by merging effective transfer from simple labeled data with a systematic divide-and-conquer approach for complex unlabeled data. It starts with a warm-up model trained via supervised RL using a few labeled examples, followed by a pseudo-labeling technique for difficult unlabeled data, utilizing consistency-based selection for low-uncertainty instances. This strategy marks a notable shift from conventional supervised learning methods that depend on extensive annotation or unsupervised techniques based on voting or entropy-based rewards. The findings indicate that easy samples can effectively support self-evolving language models through efficient reinforcement learning strategies.