Before it
Preference-based RL had been shown on robotics and games, not language.
The problem
Language generation has no reward signal, and likelihood is a poor proxy for what people want.
What it introduced
Apply the reward-model-plus-RL recipe to fine-tuning a language model, on GPT-2.
Why it matters
The bridge between the 2017 RLHF idea and the assistants of 2022. Everything after is refinement and scale.
What modern models inherit
The three-stage pipeline: pretrain, learn a reward model, optimise the policy.
What came next
Summarisation from human feedback in 2020, then InstructGPT in 2022.