RL4LMs, or Reinforcement Learning for Language Models, is a dynamic and open-source tool specifically tailored for Reinforcement Learning from Human Feedback (RLHF).
This library is designed to train transformer-based language models, offering a range of on-policy RL algorithms and actor-critic policies.
What sets RL4LMs apart is its customizable building blocks, providing developers with flexibility in fine-tuning language models.