RLHF
Reinforcement Learning from Human Feedback, a training technique where AI learns to improve its responses based on human ratings and preferences.
Why this matters
RLHF is the secret sauce behind most modern chatbots feeling so much better than their predecessors. The basic idea is straightforward. You have humans rate AI responses, then use those ratings to teach the model which answers people actually prefer. It's like having thousands of teachers constantly grading papers and the student actually learning from the feedback.
The process typically works in stages. First, you collect examples of good and bad responses from humans. Then you train a separate "reward model" that learns to predict what humans would rate highly. Finally, you use that reward model to fine-tune the main AI, pushing it toward responses that score well. It's elegant in theory, messy in practice.
One limitation is that RLHF depends heavily on the humans doing the rating. If your raters have biases or miss subtle problems, the AI learns those same blind spots. There's also the question of whose preferences matter. A response that one person loves might annoy someone else. Companies have to make judgment calls about what "good" means.
Despite its quirks, RLHF has been a genuine breakthrough. Models trained with it are noticeably more helpful, less likely to say harmful things, and better at following instructions. It's not magic, but it's moved the needle in real ways. Most major AI labs use some version of this approach, though they're always experimenting with improvements.