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While Reinforcement Learning from Human Feedback (RLHF) aligns Large LanguageModels (LLMs) with general, aggregate human preferences, it is suboptimal forlearning diverse, individual perspectives. In this work, we study ReinforcementLearning from Personalized Human Feedback (RLPHF) problem, wherein LLMs arealigned to multiple (sometimes conflicting) preferences by modeling alignmentas a Multi-Objective Reinforcement Learning (MORL) problem. Compared to strongsingle-objective baselines, we show that we can achieve personalized alignmentby decomposing preferences into multiple dimensions. These dimensions aredefined based on personalizations that are declared as desirable by the user.In this work, we show that they can be efficiently trained independently in adistributed manner and combined effectively post-hoc through parameter merging.The code is available at https://github.com/joeljang/RLPHF.

Personalized Soups: Personalized Large Language Model Alignment via Post-hoc Parameter Merging