What This Research Area Covers
Reinforcement learning (RL) research studies how an agent learns to make decisions through trial and error, guided by reward signals, rather than learning from a fixed labeled dataset — foundational to both robotics and modern LLM alignment techniques like RLHF.
Why It Matters
RL underlies some of AI's most publicly recognized achievements (like AlphaGo) and is also the technique behind RLHF, which shapes the behavior of most current consumer-facing language models.
Current Research Directions
Applying RL to improve LLM reasoning capability (as explored in DeepSeek-R1's published approach), improving sample efficiency, and better techniques for specifying reward signals that genuinely capture intended behavior are active areas.
Related Pages
Frequently Asked
What is RLHF?
Reinforcement Learning from Human Feedback — using human preference data as the reward signal to align a language model's behavior, widely used across major chat assistants.
Is reinforcement learning only used for robotics?
No, it's also foundational to LLM alignment (via RLHF) and has been applied to training reasoning capability in language models.
What was AlphaGo's significance?
A landmark reinforcement learning achievement, the first program to defeat a world champion Go player — see our DeepMind research page.
Where can I learn more about reasoning models trained with RL?
See our Reasoning Models and DeepSeek-R1 pages.