Humans cannot write a reward function for "good assistant response," but they can compare two responses and pick the better one. Fit a reward model to those comparisons, then RL the language model against it. Christiano 2017. InstructGPT 2022. The recipe that turned GPT-3 into ChatGPT. In 2026 it is mostly being replaced by DPO — but the mental model stays.
You trained a language model on the next-token-prediction objective. It writes grammatical English. It also lies, rambles, and refuses to refuse. You cannot fix this with more pretraining — web text is the problem, not the cure.
You want a scalar reward that says "response A is better than response B for instruction X." Writing that reward function by hand is impossible. "Helpfulness" is not a closed-form expression over tokens. But humans can compare two outputs and mark a preference. That is cheap to collect at scale.
RLHF (Christiano et al. 2017; Ouyang et al. 2022) converts preferences into a reward model, then optimizes the LM via PPO against that reward. In three steps: SFT → RM → PPO. It is the recipe that shipped ChatGPT, Claude, Gemini, and every other aligned-LLM in 2023–2025.
In 2026 the PPO step is mostly replaced by DPO (Phase 10 · 08) because it is cheaper and nearly as good for alignment tuning. But the reward model piece still underlies every Best-of-N sampler, every RL-from-verifiable-rewards pipeline, and every reasoning model using a process reward model. Understand RLHF and you understand the entire alignment stack.
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