Open Problems and Fundamental Limitations of Reinforcement Learning from
Human Feedback
- URL: http://arxiv.org/abs/2307.15217v2
- Date: Mon, 11 Sep 2023 17:25:24 GMT
- Title: Open Problems and Fundamental Limitations of Reinforcement Learning from
Human Feedback
- Authors: Stephen Casper, Xander Davies, Claudia Shi, Thomas Krendl Gilbert,
J\'er\'emy Scheurer, Javier Rando, Rachel Freedman, Tomasz Korbak, David
Lindner, Pedro Freire, Tony Wang, Samuel Marks, Charbel-Rapha\"el Segerie,
Micah Carroll, Andi Peng, Phillip Christoffersen, Mehul Damani, Stewart
Slocum, Usman Anwar, Anand Siththaranjan, Max Nadeau, Eric J. Michaud, Jacob
Pfau, Dmitrii Krasheninnikov, Xin Chen, Lauro Langosco, Peter Hase, Erdem
B{\i}y{\i}k, Anca Dragan, David Krueger, Dorsa Sadigh, Dylan Hadfield-Menell
- Abstract summary: Reinforcement learning from human feedback (RLHF) is a technique for training AI systems to align with human goals.
Our work emphasizes the limitations of RLHF and highlights the importance of a multi-faceted approach to the development of safer AI systems.
- Score: 46.701165912225086
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
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