Reinforcement Learning (RL) from Human Feedback (RLHF)

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• Reinforcement Learning (RL)
• ChatGPT
• Illustrating Reinforcement Learning from Human Feedback (RLHF) | N. Lambert, L. Castricato, L. von Werra, and A. Havrilla - Hugging Face

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 Reinforcement Learning (RL) that one needs to understand how Reinforcement Learning (RL) from Human Feedback (RLHF) is used on large language models. It will conclude with open question in RLHF. RLHF Blogpost The Deep RL Course Slides from this talk Nathan Twitter Thomas Twitter 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.

I challenged ChatGPT to code and hack (Are we doomed?) Are we doomed? Will AI like ChatGPT replace us? I put it to the test and challenged it to write C code, Python hacking scripts,