Preference tuning

As explained in Build a Reasoning Model (From Scratch)

A post-training method that adjusts a model to produce responses aligned with preferred outputs or styles.

Preference tuning is a post-training step that refines an instruction-tuned model toward preferred response styles and behaviors. It follows pretraining and supervised fine-tuning: instruction tuning improves task following, while preference tuning adjusts how answers are expressed. section 1.2 section 1.8

A common implementation, reinforcement learning with human feedback (RLHF), proceeds in two stages:

• Collect human rankings for multiple candidate responses to the same prompt. section 6.1.1
• Train a reward model to assign preference scores to responses. section 6.1.1
• Use those scores in an RL objective to update the target model, encouraging preferred outputs and discouraging less desirable ones. section 6.1.1

Choosing a programming laptop

Given a prompt about buying a laptop for programming and everyday use, annotators may prefer a concrete recommendation about CPU, RAM, SSD, keyboard, battery, and GPU relevance over a broader checklist. Repeated rankings teach the model to favor specificity and usefulness for the stated use case. section 6.1.1

Figure 6.5 A two-stage overview of reinforcement learning with human feedback (RLHF). First, a reward model is trained on human-ranked responses to assign a preference score to each. Second, the LLM is updated using these reward scores within an RL objective to encourage preferred responses and discourage less desirable ones.

Use the two-stage RLHF workflow to trace how human-ranked outputs become reward scores and then model updates. section 6.1.1

How Build a Reasoning Model (From Scratch) covers it

How deep it goes

Explains RLHF through rankings, reward-model training, and RL updates without detailing the complete optimization algorithm.

Examples

Uses ranked laptop recommendations and staged sleep-health summaries to illustrate specificity, tone, and user-centered responses.

What it assumes

Assumes pretraining and instruction tuning, while distinguishing both from preference alignment and interactive chat orchestration.

What you can do after

Evaluate/understand how human rankings become reward signals that steer a language model toward preferred whole responses.

How it connects

Connects preference tuning with pretraining, supervised fine-tuning, reward models, reinforcement learning, and reasoning methods.

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Also explained in

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  Reinforcement Learning from Human FeedbackPreference optimization is model training that adjusts behavior toward preferred outputs using preference-based objectives or rewards.

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Related concepts

• Direct Preference Optimizationuses · Direct Preference Optimization is a technique for training a model directly from preference data, such as preferred and dispreferred outputs, without separately fitting a reward model.
• reinforcement learning with human feedbackuses · Reinforcement learning with human feedback is a model-training approach that uses human evaluations or preferences to guide optimization of model behavior.
• Group Relative Policy Optimizationuses · Group Relative Policy Optimization is a reinforcement-learning algorithm that updates a policy using relative rewards or advantages estimated from groups of sampled outputs or actions.
• preference dataused by · Preference data is a collection of judgments or comparisons indicating the relative preference or quality of alternatives, often model outputs.

This explainer was generated by Manning's knowledge graph from the text of Build a Reasoning Model (From Scratch). The section links point to the parts of the book it draws on.