Title: On-Policy RL with Optimal Reward Baseline
Primary area: foundation or frontier models, including LLMs
Keywords: Reinforcement learning for large language models, On-policy optimization, Reward baseline, RL training stability, Policy entropy

Abstract: Reinforcement learning algorithms are fundamental to align large language models with human preferences and to enhance their reasoning capabilities. However, current reinforcement learning algorithms often suffer from training instability due to loose on-policy constraints and computational inefficiency due to auxiliary models. In this work, we propose On-Policy RL with Optimal reward baseline (OPO), a novel and simplified reinforcement learning algorithm designed to address these challenges. OPO emphasizes the importance of exact on-policy training, which empirically stabilizes the training process and enhances exploration. Moreover, OPO integrates a practically feasible formulation of the optimal reward baseline that minimizes gradient variance. We evaluate OPO on mathematical reasoning benchmarks. The results demonstrate its superior performance and training stability without additional models or regularization terms. Furthermore, OPO achieves lower policy shifts and higher output entropy, encouraging more diverse and less repetitive responses. These results highlight OPO as a promising direction for stable and effective reinforcement learning in large language model alignment and reasoning tasks. The OPO implementation is integrated into the VeRL library.

TL;DR: We introduce OPO, a novel RL algorithm that uses exact on-policy training and a practical optimal reward baseline to achieve superior performance, increased training stability, and diverse outputs.