Efficient Reinforcement Learning for Large Language Models with Intrinsic Exploration

TL;DR

This paper introduces PREPO, a method that enhances data efficiency in reinforcement learning for large language models by using intrinsic properties like prompt perplexity and rollout discrepancy, reducing training cost while maintaining performance.

Contribution

The paper proposes PREPO, a novel approach combining prompt perplexity and relative entropy to improve data efficiency in RLVR for large language models, with theoretical and empirical validation.

Findings

Achieves up to 3x fewer rollouts on reasoning benchmarks

Maintains competitive performance with reduced training data

Provides theoretical analysis of the method's effectiveness

Abstract

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 free benefit 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 that exhibit a higher degree of exploration. Together, these mechanisms reduce rollout demand while preserving competitive performance. On the Qwen and Llama models, PREPO achieves effective results on mathematical reasoning benchmarks with up to 3 times fewer rollouts than the 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.