Reinforcement Learning from Human Feedback: From Zero to chatGPT

In this talk, we will cover the basics of Reinforcement Learning from Human Feedback (RLHF) and how this technology is being used to enable state-of-the-art ML tools like ChatGPT. Most of the talk will be an overview of the interconnected ML models and cover the basics of Natural Language Processing and RL that one needs to understand how RLHF is used on large language models. It will conclude with open question in RLHF. RLHF Blogpost: https://huggingface.co/blog/rlhf The Deep RL Course: https://hf.co/deep-rl-course Slides from this talk: https://docs.google.com/presentation/d/1eI9PqRJTCFOIVihkig1voRM4MHDpLpCicX9lX1J2fqk/edit?usp=sharing Nathan Twitter: https://twitter.com/natolambert Thomas Twitter: https://twitter.com/thomassimonini Nathan Lambert is a Research Scientist at HuggingFace. He received his PhD from the University of California, Berkeley working at the intersection of machine learning and robotics. He was advised by Professor Kristofer Pister in the Berkeley Autonomous Microsystems Lab and Roberto Calandra at Meta AI Research. He was lucky to intern at Facebook AI and DeepMind during his Ph.D. Nathan was was awarded the UC Berkeley EECS Demetri Angelakos Memorial Achievement Award for Altruism for his efforts to better community norms.