1 code implementation • NeurIPS 2020 • Nisan Stiennon, Long Ouyang, Jeffrey Wu, Daniel Ziegler, Ryan Lowe, Chelsea Voss, Alec Radford, Dario Amodei, Paul F. Christiano
We collect a large, high-quality dataset of human comparisons between summaries, train a model to predict the human-preferred summary, and use that model as a reward function to fine-tune a summarization policy using reinforcement learning.
no code implementations • 17 Aug 2015 • Benja Fallenstein, Jessica Taylor, Paul F. Christiano
This can be seen as providing a foundation for classical game theory in which players aren't special.
