Experience is the Best Teacher: Motivating Effective Exploration in Reinforcement Learning for LLMs

ArXi:2603.20046v1 Announce Type: new Reinforcement Learning (RL) with rubric-based rewards has recently shown remarkable progress in enhancing general reasoning capabilities of Large Language Models (LLMs), yet still suffers from ineffective exploration confined to curent policy distribution. In fact, RL optimization can be viewed as steering the policy toward an ideal distribution that maximizes the rewards, while effective exploration should align efforts with desired target.