Open Problems and Fundamental Limitations of Reinforcement Learning from
  Human Feedback
Authors: name: Stephen Casper, name: Xander Davies, name: Claudia Shi, name: Thomas Krendl Gilbert, name: Jérémy Scheurer, name: Javier Rando, name: Rachel Freedman, name: Tomasz Korbak, name: David Lindner, name: Pedro Freire, name: Tony Wang, name: Samuel Marks, name: Charbel-Raphaël Segerie, name: Micah Carroll, name: Andi Peng, name: Phillip Christoffersen, name: Mehul Damani, name: Stewart Slocum, name: Usman Anwar, name: Anand Siththaranjan, name: Max Nadeau, name: Eric J. Michaud, name: Jacob Pfau, name: Dmitrii Krasheninnikov, name: Xin Chen, name: Lauro Langosco, name: Peter Hase, name: Erdem Bıyık, name: Anca Dragan, name: David Krueger, name: Dorsa Sadigh, name: Dylan Hadfield-Menell

Reinforcement learning from human feedback (RLHF) is a technique for training
AI systems to align with human goals. RLHF has emerged as the central method
used to finetune state-of-the-art large language models (LLMs). Despite this
popularity, there has been relatively little public work systematizing its
flaws. In this paper, we (1) survey open problems and fundamental limitations
of RLHF and related methods; (2) overview techniques to understand, improve,
and complement RLHF in practice; and (3) propose auditing and disclosure
standards to improve societal oversight of RLHF systems. Our work emphasizes
the limitations of RLHF and highlights the importance of a multi-faceted
approach to the development of safer AI systems.