1.3k citations · 2.9k across the 32 of their papers we have counts for
7 papers · 1 filter
Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback
Stephen Casper, Xander Davies, Claudia Shi +29
Reinforcement learning from human feedback (RLHF) is a technique for training AI systems to align with human goals. RLHF has emerged as the central method used to finetune state-of…
Thinker: Learning to Plan and Act
Stephen Chung, Ivan Anokhin, David Krueger
We propose the Thinker algorithm, a novel approach that enables reinforcement learning agents to autonomously interact with and utilize a learned world model. The Thinker algorithm…
Investigating the Nature of 3D Generalization in Deep Neural Networks
Shoaib Ahmed Siddiqui, David Krueger, Thomas Breuel
Visual object recognition systems need to generalize from a set of 2D training views to novel views. The question of how the human visual system can generalize to novel views has b…
Characterizing Manipulation from AI Systems
Micah Carroll, Alan Chan, Henry Ashton +1
Manipulation is a common concern in many domains, such as social media, advertising, and chatbots. As AI systems mediate more of our interactions with the world, it is important to…
Harms from Increasingly Agentic Algorithmic Systems
Alan Chan, Rebecca Salganik, Alva Markelius +19
Research in Fairness, Accountability, Transparency, and Ethics (FATE) has established many sources and forms of algorithmic harm, in domains as diverse as health care, finance, pol…
Blockwise Self-Supervised Learning at Scale
Shoaib Ahmed Siddiqui, David Krueger, Yann LeCun +1
Current state-of-the-art deep networks are all powered by backpropagation. In this paper, we explore alternatives to full backpropagation in the form of blockwise learning rules, l…