499 citations · 593 across the 4 of their papers we have counts for
4 papers
Reward learning from human preferences and demonstrations in Atari
Borja Ibarz, Jan Leike, Tobias Pohlen +3
To solve complex real-world problems with reinforcement learning, we cannot rely on manually specified reward functions. Instead, we can have humans communicate an objective to the…
Supervising strong learners by amplifying weak experts
Paul Christiano, Buck Shlegeris, Dario Amodei
Many real world learning tasks involve complex or hard-to-specify objectives, and using an easier-to-specify proxy can lead to poor performance or misaligned behavior. One solution…
AI safety via debate
Geoffrey Irving, Paul Christiano, Dario Amodei
To make AI systems broadly useful for challenging real-world tasks, we need them to learn complex human goals and preferences. One approach to specifying complex goals asks humans…
Deep reinforcement learning from human preferences
Paul Christiano, Jan Leike, Tom B. Brown +3
For sophisticated reinforcement learning (RL) systems to interact usefully with real-world environments, we need to communicate complex goals to these systems. In this work, we exp…