Supervised Fine-tuning (SFT), Reward Modeling (RM), and Proximal Policy Optimization (PPO) are all essential components of the TRL (Transfomer-language-RL) library. TRL is a full-stack library developed to make it easy for researchers to train transformer language models and stable diffusion models with Reinforcement Learning. The library extends Hugging Face’s transformers collection, making it simple to load pre-trained language models directly via transformers. The TRL library empowers users to optimize transformer language models for a wide range of tasks, making it more efficient and resistant to noise and adversarial inputs than conventional techniques. With the newly introduced TextEnvironments, TRL sets to transform the way we use transformer language models to solve tasks reliably. Check out the GitHub page for more details and examples!