Reinforcement Learning (RL)

Reinforcement learning is a training approach where an AI agent learns optimal behavior through trial and error, receiving numerical rewards for good actions and penalties for bad ones. Unlike supervised learning where the model learns from labeled examples, RL agents discover effective strategies by interacting with an environment and maximizing cumulative reward. This makes RL particularly suited for sequential decision-making tasks.

In the context of large language models, Reinforcement Learning from Human Feedback (RLHF) is a critical training stage that aligns model behavior with human preferences. During RLHF, human raters compare different model outputs and indicate which is better. These preferences are used to train a reward model, which then guides the LLM to produce more helpful, accurate, and safe responses. Constitutional AI (used by Anthropic for Claude) is a variation where AI-generated feedback partially replaces human feedback.

Reinforcement learning has produced some of AI's most impressive achievements including DeepMind's AlphaGo defeating the world Go champion, game-playing agents that surpass human performance, and robotic control systems that learn complex physical tasks. In the LLM space, RL techniques are increasingly used to improve reasoning capabilities, reduce hallucinations, and align model behavior with complex human values and preferences.