RLHF explained: how human feedback trains AI models
A clear explanation of reinforcement learning from human feedback, what evaluators actually do, how consistency is maintained and how to run an evaluation programme.
On this page
Large language models learn a great deal from text, but text alone does not teach them which answers people find helpful, accurate and safe. That is where reinforcement learning from human feedback, or RLHF, comes in.
The idea in one paragraph
A model produces several responses to the same prompt. Trained evaluators compare them and say which is better, often with scores against a rubric and a written reason. Those judgements become the signal used to adjust the model, so it produces more of what people prefer and less of what they do not.
What evaluators actually do
- Preference ranking: choose the better of two or more responses.
- Rubric scoring: rate responses on helpfulness, accuracy, tone, instruction following and safety.
- Rewriting: improve a weak response to show what a good one looks like, which is useful for supervised fine-tuning data.
- Prompt writing: create realistic and challenging prompts.
- Multi-turn evaluation: judge whole conversations, not just single replies.
- Side-by-side testing: compare a new model release against the previous one.
Why rationales matter
A score alone is hard to audit. A short written reason for every judgement explains what the evaluator saw, makes disagreements easy to investigate and gives model teams insight into failure patterns.
Consistency is everything
If two evaluators judge the same response differently, the model learns noise. Good programmes keep evaluators consistent through:
- 1Calibration on reference examples before production work starts;
- 2hidden reference items mixed into the queue to measure drift;
- 3senior review of a sample of judgements;
- 4re-calibration whenever the rubric changes, and regularly anyway.
Evaluators who drift are re-calibrated before they continue.
Specialist pools
General evaluation suits many tasks, but some need expertise: code, mathematics and reasoning, creative writing, dialogue and safety. Specialist pools with relevant degrees or experience produce more reliable judgements in these areas.
Wellbeing
Safety evaluation can involve disturbing content. Evaluators should only see it with consent, with exposure limits, rotation and wellbeing support in place.
Running a programme
- 1Rubric review: agree definitions and examples.
- 2Calibration: test the pool against your reference answers.
- 3Production: daily output with quality and agreement reporting.
- 4Insight: share patterns in model failures with your team.
A pilot pool can usually be stood up within days.
Frequently asked questions
How do you measure evaluator agreement?
With hidden reference items, agreement tracking and re-calibration whenever the rubric changes.
Can you evaluate code and maths?
Yes, with specialist pools who have computer science, engineering or mathematics backgrounds.
Learn more about our AI model evaluation and RLHF service.
Topics
- RLHF
- model evaluation
- LLMs
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Written by the people who recruit, train and run outsourced teams at Maxed BPO every day.
