RLHF

Reinforcement Learning with Human Feedback keeps people actively involved in shaping AI behavior. Instead of assuming systems are finished, humans remain part of an ongoing feedback loop.

Outputs are reviewed, compared, and refined. Subtle failures are identified. Ambiguities are examined. This process reflects a simple truth: human judgment still matters.

RLHF mirrors dialogue — a system responds, humans question, and improvements follow. It is a modern extension of learning through examination rather than blind acceptance.

By keeping humans in the loop, RLHF helps ensure that safety, ethics, and alignment remain active priorities rather than afterthoughts.