Definition
Reinforcement learning (RL) is a way of training an AI system by letting it try actions and rewarding the ones that lead to good outcomes, the way you might train an animal or coach a player. Reinforcement learning from human feedback (RLHF) applies this to language models: people compare model answers, their preferences train a reward model, and the language model is then optimised to produce answers humans rate higher. This is how a raw text predictor becomes a helpful, polite assistant.
You will not run reinforcement learning in your company, but you use its results every day. The reason an AI assistant follows instructions, refuses harmful requests, formats answers neatly and admits uncertainty is RLHF and its successors. Reinforcement learning also appears in operations: optimising delivery routes, dynamic pricing, warehouse robotics and energy management, wherever a system takes repeated decisions and outcomes can be measured.
Reinforcement learning has become the key ingredient behind reasoning models: labs train models with rewards for solving verifiable problems such as maths, code and tool use, producing systems that think step by step and correct themselves. The misconception is that RLHF makes a model truthful. It makes a model produce answers people prefer, which is related but not identical, and it explains why models can sound confident while being wrong.
In practice
A parcel network uses reinforcement learning to decide, every minute, how to reroute vans as traffic and new pickups arrive. The system was rewarded for on-time deliveries and fuel saved, and now beats the hand-written rules it replaced.
Why it matters
Reinforcement learning is why AI assistants behave well and why reasoning models improved so fast. Knowing that behaviour is trained, not guaranteed, keeps expectations realistic.
Frequently asked questions
- What does RLHF mean?
- RLHF stands for reinforcement learning from human feedback. Humans rank different model answers, those rankings train a reward model, and the language model is optimised to produce the kind of answers humans preferred. It is the main technique that turns a raw language model into a usable assistant.
- What is the difference between reinforcement learning and supervised learning?
- Supervised learning learns from examples with known correct answers, such as labelled invoices. Reinforcement learning learns from rewards after taking actions, without being told the right answer in advance. It suits problems where success is measurable but the path is not, such as games, routing or multi-step reasoning.