What is Reinforcement Learning (RL)?
Reinforcement Learning (RL) — Training AI by rewarding desired behaviors and punishing undesired ones.
RL trains agents through trial and error — rewarding good actions and penalizing bad ones. It powered AlphaGo’s victory over world champions and is used in RLHF (Reinforcement Learning from Human Feedback) to align LLMs with human preferences.
Frequently Asked Questions
How is RL used in LLMs?
RLHF (Reinforcement Learning from Human Feedback) trains models to generate responses that humans rate as helpful, harmless, and honest. It is the step that makes raw language models into useful assistants.
What are the limitations of RL?
RL requires carefully designed reward functions. Poorly defined rewards can lead to unexpected or harmful behavior as the agent finds loopholes to maximize its score.
Does RL need a lot of data?
RL needs a lot of interactions with an environment rather than static data. For real-world applications, simulation environments are often used to generate these interactions safely.