Reinforcement Learning

Reinforcement learning (RL) trains AI agents to make sequential decisions by maximizing cumulative reward signals. Unlike supervised learning, RL doesn't require labeled examples — the agent explores its environment, takes actions, and learns from the outcomes.

RL has produced breakthroughs in game playing (AlphaGo, Atari), robotics control, and — critically — language model alignment. Reinforcement learning from human feedback (RLHF) is the primary technique used to align LLMs with human preferences, making models more helpful and less harmful.

RL expertise is valued in robotics companies, game AI studios, autonomous vehicle teams, and increasingly at LLM companies where alignment and safety are priorities. Roles requiring RL knowledge often intersect with research and typically demand strong mathematical foundations.