The study examines two problems in aligning language models to human preferences using reward models. Firstly, it questions if a certain monotone transformation of the reward model can preserve preference…
Continue readingThe study focuses on Reinforcement Learning from Human Feedback (RLHF) and how it can be optimized. Traditionally, RLHF involves generating completions from a language model in response to a query…
Continue readingThe paper investigates the impact of preference agreement on the efficacy of Reinforcement Learning from Human Feedback (RLHF) in text summarization. The authors demonstrate that including a diverse range of…
Continue readingThe paper focuses on the issue of overoptimization in large language models (LLMs) which are optimized to align with human preferences. The authors highlight that human preferences are multi-faceted and…
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