Reinforcement learning from human feedback (RLHF)
Training that uses people's ratings of model answers. It helps steer the model toward replies people prefer.
The fuller definition
RLHF uses human feedback to help shape a reward signal that guides model training toward preferred behavior.
Supply instructions and examples each time. Nothing in the model changes.
Fetch current documents and put them in context. Facts can change daily.
Train the parameters on examples. Changes behaviour and style; poor at keeping facts current.
Most legal tasks need the first two. Fine-tuning is a project, with data, evaluation, and maintenance, and it is the wrong tool for facts that change.
A concrete example
People compare candidate responses; a training process uses that feedback to improve subsequent behavior.
The distinction that matters
A thumbs-up in an ordinary chat does not necessarily update that model immediately or solely for you.
Related terms
Training
Adjusting a model with examples and a learning rule. The aim is to improve how it handles tasks.
Training & generationReward model
A helper model that scores answers so the main model can learn what people like.
Training & generationAlignment
Work to make a model act as people intend. For example, it should follow rules and avoid harm. This is a goal, not a promise.
Where it is taught
How models learn from examplesSource and further reading
Hugging Face · LLM courseThe definition and example are written for this library. The link gives background, official documentation or legal authority as labelled. Legal scope depends on the jurisdiction and current text; product features depend on release and settings.