MENTAL MODEL · M1901

RLHF

BusinessHigh supportManagement

Included

Version 1.0.0 · Updated 2026-07-30

CORE DEFINITION

Reinforcement Learning from Human Feedback. First, let the AI say anything, then humans score its responses (good/bad), train a reward model, and then let the AI adjust its strategy to get high scores. This is the key to making AI 'speak human' and 'align with values'.

SCAFFOLDING EFFECT

psychology

Reduce cognitive load

- Alignment mechanism: not only for AI, but also for management. What kind of employees do you want? Not only give JD (instructions), but also give frequent feedback (RLHF), let them fine-tune their behavior through your rewards and punishments, and finally 'align' with your values.

anchor

Anchor fast decisions

Complex goals ("speak human", "useful", "safe") are difficult to write as rules / loss functions. RLHF first collects human preference rankings of AI outputs, trains a reward model to approximate human preferences, then uses reinforcement learning to maximize that reward, aligning model behavior with human values. Essentially, it converts "unwritable goals" into "learnable signals".

MINIMUM ACTION

In progress 0/4

Practice this model in one real situation:

Use supervised fine-tuning to get a base model.
Humans rank several outputs, train a reward model (RM).
Use reinforcement learning like PPO, with RM as the reward to optimize the policy.
Iteratively collect feedback, retrain RM, prevent reward hacking.

Check to track your progress (stored locally)

Learning progress0%

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Source support: Explicit

link
en.wikipedia.orghttps://en.wikipedia.org/wiki/Reinforcement_learning_from_human_feedbackZH · Explicit
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