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 presents a new framework called PROMST for optimizing prompts in multi-step tasks for Large Language Models (LLMs). Unlike previous methods, which work well for single-step tasks, PROMST is…
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 article presents a study on the use of Reinforcement Learning from Human Feedback (RLHF) to improve the performance of the GPT Neo 125M in the Community Question Answering (CQA)…
Continue readingThis research paper explores the potential for ‘jailbreak backdoors’ in large language models trained with Reinforcement Learning from Human Feedback (RLHF). It reveals that a malicious actor could potentially poison…
Continue readingThis academic article discusses the security vulnerabilities in Reinforcement Learning with Human Feedback (RLHF) in Large Language Models (LLMs). RLHF plays a crucial role in aligning LLMs with human preferences….
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 researchers from Peking University have proposed a novel algorithm, Safe Reinforcement Learning from Human Feedback (Safe RLHF), aimed at enhancing the safety and performance of Large Language Models (LLMs)….
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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