REINFORCE++: A Simple and Efficient Approach for Aligning Large Language Models
Jian Hu
2025-01-08
Summary
This paper introduces REINFORCE++, a new method to make AI language models better at understanding and following human preferences. It's designed to be simpler, more stable, and faster than other similar methods.
What's the problem?
Large AI language models are really good at many tasks, but they don't always understand or follow what humans want them to do. Existing methods to fix this are complicated, unstable, or slow.
What's the solution?
The researchers created REINFORCE++, which combines the best parts of two existing methods (REINFORCE and PPO). It removes the need for a complex part called a critic network, making it simpler. They tested REINFORCE++ extensively and found it works as well as other methods but is more stable and faster.
Why it matters?
This matters because it could help make AI language models that are better at understanding and following human instructions. The simpler and faster method means it's easier for researchers to use, potentially speeding up improvements in AI that can communicate more effectively with humans. This could lead to AI assistants that are more helpful and better aligned with human values.
Abstract
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.