Reinforcement Learning from Human Feedback (RLHF)

Reinforcement learning from human feedback (RLHF) is widely used to fine-tune pretrained models to deliver outputs that align with human preferences. New work aligns pretrained models without the cumbersome step of reinforcement learning.

Training an agent that controls a robot arm to perform a task — say, opening a door — that involves a sequence of motions (reach, grasp, turn, pull, release) can take from tens of thousands to millions of examples...

Do language models have their own opinions about politically charged issues? Yes — and they probably don’t match yours. Shibani Santurkar and colleagues at Stanford compared opinion-poll responses of large language models with those of various human groups.