Reinforcement learning is a branch of machine learning concerned with using experience gained through interacting with the world and evaluative feedback to improve a system's ability to make ...

Interview with the creators of InstructGPT, one of the first major applications of reinforcement learning with human feedback (RLHF) to train large language models that influenced subsequent LLM ...

If your AI can’t learn from its mistakes, it’s not intelligent — it’s obsolete. Logging isn’t a risk. It's the price of ...

How reinforcement learning with human feedback helps ensure that businesses are building ethical generative AI models.

To this end, reinforcement learning has been particularly useful with robotics. For example, OpenAI has used this technique for a robotic arm that was able to solve the Rubik’s cube.

DeepSeek-R1’s Monday release has sent shockwaves through the AI community, disrupting assumptions about what’s required to achieve cutting-edge AI performance. This story focuses on exactly ...

The "reward-is-enough" hypothesis suggests that reinforcement learning alone could lead to AGI.

Motion imitation approaches can achieve highly dynamic motions, but are limited by the complexity of the system and lack of adaptability to task objectives. Techniques based on reinforcement learning ...

Reinforcement learning techniques could be the keys to integrating robots — who use machine learning to output more than words — into the real world.