RLHF Deciphered: A Critical Analysis of Reinforcement Learning from Human Feedback for LLMs

arXiv:2404.08555v1 Announce Type: new
Abstract: State-of-the-art large language models (LLMs) have become indispensable tools for various tasks. However, training LLMs to serve as effective assistants for humans requires careful consideration. A promising approach is reinforcement learning from human feedback (RLHF), which leverages human feedback to update the model in accordance with human preferences and mitigate issues like toxicity and hallucinations. Yet, an understanding of RLHF for LLMs is largely entangled with initial design choices that popularized the method and current …

abstract analysis art arxiv assistants become cs.ai cs.cl cs.lg feedback however human human feedback humans language language models large language large language models llms reinforcement reinforcement learning rlhf serve state tasks tools training training llms type update