Before it
Reinforcement learning needed a hand-written reward function.
The problem
Many goals are easy to judge and hard to specify. You know good behaviour when you see it.
What it introduced
Learn a reward model from human comparisons between pairs of behaviours, then optimise against it.
Why it matters
The whole alignment stack starts here. InstructGPT, ChatGPT and Claude are this method applied to language.
What modern models inherit
Reward models learned from preference comparisons.
What came next
Ziegler applied it to language models in 2019, then summarisation, then instruction following.