Efficient Reinforcement Learning for Large Language Models with Intrinsic Exploration

Reinforcement learning with verifiable rewards (RLVR) has improved the reasoning ability of large language models, yet training remains costly because many rollouts contribute little to optimization, considering the amount of computation required. This study investigates how simply leveraging intrinsic data properties, almost a free lunch during training, can improve data efficiency for RLVR. We propose PREPO with two complementary components. First, we adopt prompt perplexity as an indicator of model adaptability in learning, enabling the model to progress from well-understood contexts to more challenging ones. Second, we amplify the discrepancy among the rollouts by differentiating their relative entropy, and prioritize sequences with more out-of-the-box exploration. Together, these mechanisms reduce rollout demand while preserving competitive performance. On Qwen and Llama models, PREPO achieves effective results on mathematical reasoning benchmarks with up to 3× fewer rollouts than baselines. Beyond empirical gains, we provide theoretical and in-depth analyses explaining the underlying rationale of our method to improve the data efficiency of RLVR.