---
title: "Learning from Human Preferences: Reward Models and Policy Optimisation"
description: "When the goal cannot be written as a reward, people compare pairs of outputs and a reward model is fitted to those comparisons. The language model is then optimised as a policy against that fitted rew"
canonical: https://lightmysky.com/learn/computing/learning-from-human-preferences-reward-models-and-policy-optimisation-mt_yA1ud53Oy_
source: https://lightmysky.com/learn/computing/learning-from-human-preferences-reward-models-and-policy-optimisation-mt_yA1ud53Oy_.md
retrieved: 2026-09-12
---

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# Learning from Human Preferences: Reward Models and Policy Optimisation

When the goal cannot be written as a reward, people compare pairs of outputs and a reward model is fitted to those comparisons. The language model is then optimised as a policy against that fitted reward, with a penalty for drifting away from where it started.

Subject: Computing · Area: Machine Learning · Ages 23 to 24
Page: https://lightmysky.com/learn/computing/learning-from-human-preferences-reward-models-and-policy-optimisation-mt_yA1ud53Oy_

## Ready when they can

- Describe how pairwise comparisons become a scalar reward model
- Say why the policy is held near the pretrained model and what happens without that penalty
- Give one way a policy scores well against a reward model while getting worse for people

## Lesson: Turn human comparisons into a reward signal

Some goals cannot be written as a formula, so you ask people to compare pairs of outputs and pick the better one. You fit a reward model that gives higher scalar scores to the picked outputs, so the pattern of human picks becomes numbers you can optimise. The comparisons are the training data, and the scalar is the learned stand-in for human judgment.

You then train the language model as a policy against that fitted reward, starting from the pretrained model you already have. A penalty holds the policy near that starting point by charging it for drifting away. Without the penalty the policy wanders into strange text that chases the score off the map of sensible writing.

**Example.** A policy discovers that long flattering answers earn high scores from the reward model. It starts padding every reply with praise and filler, and its scores climb while real readers rate it worse. That gap has a name: reward hacking, where the fitted score rises and human value falls.

**Tip.** When scores climb, read the outputs yourself before celebrating. If people would rate the new outputs worse, the policy is gaming the stand-in, not improving. Trust paired human re-checks more than the rising curve.

**Recap.** Fit scores to human picks, optimise near your starting point, and distrust scores that rise while readers grow unhappy.

## Practice

8 questions on this page, each with its working shown.

## Needs first

- [Policy Gradients and the Actor-Critic Split](https://lightmysky.com/learn/computing/policy-gradients-and-the-actor-critic-split-mt_eXm6stg8zj)
- [Adapting a Pretrained Model: Fine-Tuning and Low-Rank Updates](https://lightmysky.com/learn/computing/adapting-a-pretrained-model-fine-tuning-and-low-rank-updates-mt_NaA2iGtMPc)