This article discusses the application of Reinforcement Learning from Human Feedback (RLHF) in large language models (LLMs). It critically examines the use of Proximal Policy Optimization (PPO), which though popular,…
Continue readingThe article focuses on understanding the mechanisms of alignment algorithms, particularly Direct Preference Optimization (DPO), and how they reduce toxicity in language models like GPT2. The researchers first study how…
Continue readingThe paper discusses the challenges of training large language models (LLMs) using Reinforcement Learning from Human Feedback (RLHF). The authors identify three important properties in RLHF tasks: fast simulation, deterministic…
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