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

Understand

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 research focuses on augmenting those choices rather than fundamentally improving the framework.