What is Reinforcement Learning (RL)?

Reinforcement Learning (RL) is a machine learning paradigm in which an agent learns optimal behavior by interacting with an environment, taking actions, and receiving feedback in the form of rewards or penalties. Unlike supervised learning, where the model learns from labeled examples, RL agents discover effective strategies through trial and error — exploring different actions and learning which sequences of decisions lead to the best outcomes. This paradigm is inspired by how humans and animals learn from the consequences of their actions.

The core components of a reinforcement learning system include the agent (the learner), the environment (the world the agent interacts with), states (the agent's observations of the environment), actions (what the agent can do), and rewards (feedback signals indicating how good an action was). The agent's goal is to learn a policy — a mapping from states to actions — that maximizes cumulative reward over time. Key algorithms include Q-learning, policy gradient methods, and actor-critic architectures. Deep reinforcement learning combines RL with deep neural networks, enabling agents to learn from raw sensory inputs like pixels or text.

Reinforcement learning has produced some of AI's most celebrated achievements. DeepMind's AlphaGo defeated the world champion Go player using deep RL. OpenAI Five competed against professional Dota 2 players. More practically, RL powers robotics control systems, autonomous vehicle decision-making, recommendation engine optimization, and resource allocation in data centers. In the context of large language models, Reinforcement Learning from Human Feedback (RLHF) has become a critical training technique, using human preference ratings to fine-tune models to be more helpful, harmless, and honest.

RLHF deserves special attention because of its central role in modern AI development. After pre-training an LLM on text data, RLHF collects human ratings of model outputs, trains a reward model on these preferences, and then uses RL (typically Proximal Policy Optimization, or PPO) to fine-tune the LLM to maximize the reward model's score. This process is what transforms a raw language model into a helpful assistant. Variants like Direct Preference Optimization (DPO) and Constitutional AI offer alternative approaches to alignment that build on RLHF's foundations.