REINFORCE++: A Simple and Efficient Approach for Aligning Large Language
  Models
Authors: name: Jian Hu

Reinforcement Learning from Human Feedback (RLHF) has emerged as a critical
approach for aligning large language models with human preferences, witnessing
rapid algorithmic evolution through methods such as Proximal Policy
Optimization (PPO), Direct Preference Optimization (DPO), REINFORCE Leave
One-Out (RLOO), ReMax, and Group Relative Policy Optimization (GRPO). We
present REINFORCE++, an enhanced variant of the classical REINFORCE algorithm
that incorporates key optimization techniques from PPO while eliminating the
need for a critic network. REINFORCE++ achieves three primary objectives: (1)
simplicity (2) enhanced training stability, and (3) reduced computational
overhead. Through extensive empirical evaluation, we demonstrate that
REINFORCE++ exhibits superior stability compared to GRPO and achieves greater
computational efficiency than PPO while maintaining comparable performance. The
implementation is available at https://github.com/OpenRLHF/OpenRLHF.