PPO-Based RLHF Implementation Guide and Reproduction Results

By reproducing OpenAI's 2019 RLHF (lm-human-preferences) code to match the training curves, this provides an essential technical checklist for implementation.

Key Implementation and Technical Details:

• Reward Model: Reward generation methods including Reward Normalization.
• Policy Training: Techniques such as Rejection Sampling and Reward Whitening.
• Optimization Issue: It highlights that PyTorch's Adam optimizer behaves numerically differently from TensorFlow, which can cause excessive updates during model training.

The reproduced code shows training curves nearly identical to OpenAI's results on style tasks such as sentiment analysis and descriptiveness. It is designed for educational purposes, and for large-scale models or cases requiring PEFT, using Hugging Face's trl library is recommended.