RLHF Training

Reinforcement learning from human feedback (RLHF) aims to align large language models (LLMs) with human preferences, improving their helpfulness and safety. Current research focuses on mitigating challenges like reward hacking and catastrophic forgetting through techniques such as decoupling reward and cost models, advantage model training, and selective data rehearsal to stabilize the training process. These advancements are crucial for making RLHF training more efficient and accessible, enabling broader development and deployment of safer and more effective LLMs across various applications.