Reinforcement Learning from Human Feedback (RLHF) utilizes transformer-based language models.
These language models, such as those used in TRLX and RL4LMs, are built on transformer architecture, a type of neural network that excels in processing sequential data.
Transformer-based models have proven highly effective in capturing complex language patterns, allowing RLHF libraries to fine-tune and optimize large language models through advanced reinforcement learning techniques.