Recent advancements in Large Language Models (LLMs) have garnered wide attention and led to successful products such as ChatGPT and GPT-4. Their proficiency in adhering to instructions and delivering harmless, helpful, and honest (3H) responses can largely be attributed to the technique of Reinforcement Learning from Human Feedback (RLHF). In this paper, we aim to link the research in conventional RL to RL techniques used in LLM research. Demystify this technique by discussing why, when, and how RL excels. Furthermore, we explore potential future avenues that could either benefit from or contribute to RLHF research. Highlighted Takeaways: 1. RLHF is Online Inverse RL with Offline Demonstration Data. 2. RLHF $>$ SFT because Imitation Learning (and Inverse RL) $>$ Behavior Cloning (BC) by alleviating the problem of compounding error. 3. The RM step in RLHF generates a proxy of the expensive human feedback, such an insight can be generalized to other LLM tasks such as prompting evaluation and optimization where feedback is also expensive. 4. The policy learning in RLHF is more challenging than conventional problems studied in IRL due to their high action dimensionality and feedback sparsity. 5. The main superiority of PPO over off-policy value-based methods is its stability gained from (almost) on-policy data and conservative policy updates.

最近大型语言模型（LLMs）的最新进展，引起了广泛关注，并导致了成功产品，如ChatGPT和GPT-4。它们在遵循指导和提供无害、有益和诚实（3H）回答方面的熟练程度，主要归功于人类反馈强化学习（RLHF）技术。本文旨在将传统RL研究与LLM研究中使用的RL技术联系起来。通过讨论RL的优点，探索为RLHF研究带来或贡献的潜在未来方向。

LLM时代的强化学习: 什么是必要的? 什么是需要的? 强化学习在RLHF, Prompting和更多方面的视角