RLHF

 

Reinforcement Learning from Human Feedback (RLHF) has been employed to train and fine-tune large language models to achieve better performance for artificial general intelligence (AGI).  Here are the explanations of RLHF (1) one from ChatGPT at simple level, and (2) the other from Brian Roemmele (@BrianRoemmele) that is concise and descriptive.

1)  From ChatGPT:

2)  From @BrianRoemmele:

RLHF stands for "Reinforcement Learning from Human Feedback."  It's a technique used in machine learning where a model, typically an AI, learns from feedback given by humans rather than solely relying on predefined datasets. 

This method allows the AI to adapt to more complex, nuanced tasks that are difficult to encapsulate with traditional training data.

In RLHF AI initially learns from a standard dataset and then its performance is iteratively improved based on human feedbacks. 

The feedback can come in various forms, such as corrections, rankings of different outputs, or direct instructions.  The AI uses this feedback to adjust its algorithms and improve its responses or actions. 

This approach is particularly useful in domains where defining explicit rules or providing exhaustive examples is challenging, such as natural language processing, complex decision-making tasks, or creative endeavors.