Definition
A training technique where AI models are refined based on human preferences and evaluations of their outputs.
In Depth
RLHF involves humans ranking multiple model outputs, then training a reward model on those preferences. The language model is then optimized to maximize this learned reward signal, producing outputs that align better with human expectations.
Example Usage
This is how ChatGPT and Claude were trained to be helpful, harmless, and honest rather than just predicting the next word.
Business Context
RLHF is why modern chatbots feel natural to interact with. Understanding this helps set realistic expectations for AI behavior and identify when custom alignment might be needed.
Related Terms
AI Safety
The field focused on ensuring AI systems behave as intended, don't cause harm, and remain aligned wi...
Attention Mechanism
A neural network component that allows models to focus on the most relevant parts of the input when ...
Fine-tuning
Adapting a pre-trained AI model to specific tasks or domains by training it on specialized data.
Large Language Model
An AI model trained on vast amounts of text data capable of understanding and generating human-like ...
Supervised Learning
A machine learning approach where models learn from labeled training data: input-output pairs that t...
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