Reinforcement Learning (RL)

Executive Definition: Reinforcement Learning (RL) — A machine learning technique where an agent learns to make decisions by performing actions and optimizing for a reward signal.

Reinforcement Learning (RL) trains an agent through trial and error, optimizing for cumulative reward. It is commonly used in robotics, gaming, and aligning large language models via Reinforcement Learning from Human Feedback (RLHF).

Frequently Asked Questions

How is RL used in LLMs?

Through RLHF (Reinforcement Learning from Human Feedback), models are trained to align with human preferences, making their responses more helpful, harmless, and honest.

What are the limitations of RL?

It requires carefully designed reward functions. Poorly defined rewards can lead to reward hacking, where the model finds unintended shortcuts to maximize its score.

Does RL require a lot of data?

RL requires a large number of interactions with an environment. To achieve this safely and efficiently, training often occurs within simulated environments.