title: REINFORCE++: A Simple and Efficient Approach for Aligning Large Language
    Models
summary: 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.
paper_authors._id: 677e18a67edb3025daa99e09
paper_authors.name: Jian Hu
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2025-06-05T00:20:53.154Z
paper_authors._id: 63f6c04ac96958470d1e9043
paper_authors.avatarUrl: /avatars/da46cdd9e21498e120ca91b67bfbfb5e.svg
paper_authors.fullname: Jian Hu
paper_authors.type: user
paper_authors.user: chuyi777
paper_title: REINFORCE++: A Simple and Efficient Approach for Aligning Large Language
    Models
paper_summary: 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.
paper_ai_summary: REINFORCE++ is an enhanced reinforcement learning algorithm that combines PPO techniques without a critic network, offering simplicity, stability, and efficiency.
paper_ai_keywords: Reinforcement Learning from Human Feedback (RLHF) Proximal Policy Optimization (PPO) Direct Preference Optimization (DPO) REINFORCE Leave One-Out (RLOO) ReMax Group Relative Policy Optimization (GRPO) REINFORCE critic network