This paper introduces RLPHF, which aligns large language models with personalized human preferences via multi-objective RL and parameter merging.
This paper is under CC 4.0 license. available on arxiv Authors: Joel Jang, CarperAI,University of Washington & Allen Institute for AI; Seungone Kim, KAIST AI; Yizhong Wang, University of Washington; Jack Hessel, University of Washington; Luke Zettlemoyer, Aleph Alpha; Hannaneh Hajishirzi, University of Washington & Allen Institute for AI; Yejin Choi, UC San Diego.
Multi-objective Reinforcement Learning Previous work has aimed to alleviate these problems through novel MORL methods . Other work aims to solve complex problems such as water management, military purchasing, wind farm control, etc. by converting the single-objective RL problem into a MORL problem.
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