AI Seminar Series 2023: Stephen Montes Casper

The AI Seminar is a weekly meeting at the University of Alberta where researchers interested in artificial intelligence (AI) can share their research. Presenters include both local speakers from the University of Alberta and visitors from other institutions. Topics can be related in any way to artificial intelligence, from foundational theoretical work to innovative applications of AI techniques to new fields and problems.

On September 1, Stephen Montes Casper —a PhD Candidate at MIT — presented “Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback" at the AI Seminar.

Abstract:
Reinforcement Learning from Human Feedback (RLHF) has emerged as the central alignment technique used to finetune state-of-the-art AI systems such as GPT-4, Claude, Bard, and Llama-2. Given RLHF's status as the default industry alignment technique, there is a need to carefully study how we got here and what challenges persist in today's state of the art.  We review open challenges and fundamental limitations with RLHF with a focus on applications in large language models. Technical progress in some respects is tractable, and this should be seen as a cause for concerted work and optimism. However, other problems with RLHF cannot fully be solved and instead must be avoided or compensated for with non-RLHF approaches.

Watch the full presentation below:

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