Aligning AI with Humanity: The Role of Reinforcement Learning in Language Model Alignment
Published:
In this work, we look into the prominent applications of Reinforcement Learning (RL) in the field of Natural Language Processing (NLP) with a focus on Language Models (LM). First, we examine one of the initial applications of Reinforcement Learning with Human Feedback (RLHF) in NLP. Then, we discuss how this method evolves to be applied in a more general AI and becomes a fundamental aspect of Large Language Model (LLM) training. Also, we discuss the risks, challenges, and potential problems associated with RLHF, offering insights into how these issues might be addressed and mitigated. Furthermore, we explore the emerging field of Reinforcement Learning with AI Feedback (RLAIF), assessing its position in current research. Our investigation shows that RLHF training is a very effective tool for language model alignment. This method cannot only improve the performance of the overall model in NLP benchmarks but also help with problems such as hallucination. In addition, we showed that methods like Constitutional AI can improve the LLMs’ safety by increasing harmlessness while keeping high levels of helpfulness.