Definition

RLHF is a way to teach AI by showing it what people think is a good or bad response. The model tries things, and humans give feedback—rewarding good answers and correcting bad ones. This helps the AI get better at being useful, safe, and aligned.

Example

“RLHF helps ChatGPT respond politely and avoid harmful suggestions by learning from human feedback.”

How It’s Used in AI

RLHF is used in tools like ChatGPT, Claude, and Bard to improve how they talk, reason, and stay on-topic. It’s one of the main ways companies train AI to follow instructions, avoid bias, and be more trustworthy.

Brief History

OpenAI popularized RLHF during the development of InstructGPT and later ChatGPT. The method became a key part of fine-tuning large models to behave more safely and align with user expectations.

Key Tools or Models

Models using RLHF include GPT-3.5, GPT-4, Claude, and Gemini. The method combines reinforcement learning with reward models trained on human rankings.

Pro Tip

RLHF helps models sound more human—but it can also make them avoid hard questions or play it too safe. Balance is key.