Transformer reinforcement learning applies Transformer-based architectures to reinforcement learning problems. The approach uses attention mechanisms to model sequences of states, actions, rewards, and context for policy learning, planning, or decision making.
Transformer Reinforcement Learning (TRL) refers to the application of reinforcement learning (RL) techniques to train transformer-based models for generative tasks. This approach addresses limitations of traditional generative models, which are often trained on next-token inference without explicit optimization for desirable qualities like coherence, safety, and sensibility. TRL directly optimizes the language model on complex objectives by using RL, often incorporating human feedback (RLHF) to guide the learning process. Examples include fine-tuning models to generate less toxic content using reward models and using TRL to fine-tune Gemma, a generative model. For more information, see Hugging Face DLCs: Fine-tuning Gemma with Transformer Reinforcement Learning (TRL) on Vertex AI.