Illustrating Reinforcement Learning from Human Feedback (RLHF)

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This article has been translated to Chinese 简体中文 and Vietnamese đọc tiếng việt. Language models have shown impressive capabilities in the past few years by generating diverse and compelling text from human input prompts. However, what makes a "good" text is inherently hard to define as it is subjective and context dependent. There are many applications such as writing stories where you want creativity, pieces of informative text which should be truthful, or code snippets that we want to be executable. Writing a loss function to capture these attributes seems intractable and most language…

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related reading

• RLHF: Reinforcement Learning from Human Feedbackhuyenchip.com
• A simple technical explanation of RLH(AI)F | Kairos.fmkairos.fm
• [2309.00267] RLAIF vs. RLHF: Scaling Reinforcement Learning from Human Feedback with AI Feedbackarxiv.org
• [2305.18290] Direct Preference Optimization: Your Language Model is Secretly a Reward Modelarxiv.org
• RLHF | John Lambertjohnwlambert.github.io
• Introduction | RLHF and Post-Training Book by Nathan Lambertrlhfbook.com
• The N Implementation Details of RLHF with PPO | ICLR Blogposts 2024iclr-blogposts.github.io
• Reinforcement learning from human feedback - Wikipediaen.wikipedia.org
• LLM Training: RLHF and Its Alternativesmagazine.sebastianraschka.com
• rlhfbook.com/book.pdfrlhfbook.com
• How RLHF actually works - by Nathan Lambertinterconnects.ai
• RLHF Bookrlhfbook.com