Feb. 9, 2024, 5:43 a.m. | Xinyu Li Zachary C. Lipton Liu Leqi

cs.LG updates on arXiv.org arxiv.org

Reinforcement Learning from Human Feedback (RLHF) is the current dominating framework to fine-tune large language models to better align with human preferences. However, the underlying premise of algorithms developed under this framework can be problematic when user preferences encoded in human feedback are diverse. In this work, we aim to address this problem by developing methods for building personalized language models. We first formally introduce the task of learning from personalized human feedback and explain why vanilla RLHF can be …

aim algorithms cs.ai cs.cl cs.lg current diverse feedback framework human human feedback language language models large language large language models modeling personalized reinforcement reinforcement learning rlhf work

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