RLHF
Reinforcement Learning from Human Feedback—training AI using human preferences.
Definition
RLHF (Reinforcement Learning from Human Feedback) is a training technique that improves AI model behavior using human preference data. Rather than learning purely from text prediction, models trained with RLHF learn to generate outputs humans prefer by comparing response pairs and optimizing toward preferred characteristics.
This technique has been crucial in making large language models more helpful, honest, and harmless. RLHF helps models learn nuanced preferences that are difficult to specify programmatically, like tone, helpfulness, and avoiding problematic content.
Why It Matters
Understanding RLHF explains why modern AI assistants behave differently from pure prediction models. RLHF shapes how models respond, their safety properties, and their alignment with user needs.
For businesses fine-tuning models, RLHF principles inform how to gather feedback data and improve model outputs for specific use cases.
Examples in Practice
A company preparing to fine-tune an AI assistant collected preference data from their customer service team, identifying which response styles best matched their brand voice for RLHF optimization.
Understanding RLHF helped a team recognize why their AI sometimes refused reasonable requests—overly cautious RLHF training had made the model too conservative for their use case.
An AI vendor's documentation about their RLHF process helped a buyer understand why model behavior would differ from competitors and how those differences aligned with their needs.