RL

Reinforcement Learning (RL) optimizes agent behavior through a system of rewards and penalties to master complex environments like Go or LLM reasoning.

RL shifts the paradigm from static datasets to dynamic interaction. By utilizing algorithms like PPO (Proximal Policy Optimization) and Q-Learning, agents explore state spaces to maximize a cumulative reward signal. This tech powered AlphaGo's 4-1 victory over Lee Sedol and remains the backbone of RLHF (Reinforcement Learning from Human Feedback), the process that aligns models like GPT-4 with human intent. It is the definitive framework for sequential decision-making where the optimal path is discovered through trial, error, and high-compute simulation.

https://openai.com/index/learning-from-human-feedback/