Reinforcement Learning from Human Feedback (RLHF) for LLMs | by Michał Oleszak | Sep, 2024
LLMs
Reinforcement Learning from Human Feedback (RLHF) has turned out to be the key to unlocking the full potential of today’s large language models (LLMs). There is arguably no better evidence for this than OpenAI’s GPT-3 model. It was released back in 2020, but it was only its RLHF-trained version dubbed ChatGPT that became an overnight sensation, capturing the attention of millions and setting a new standard for conversational AI.
Before RLHF, the LLM training process typically consisted of a pre-training stage in which the model learned the general structure of the language and a fine-tuning stage in which it learned to perform a specific task. By integrating human judgment as a third training stage, RLHF ensures that models not only produce coherent and useful outputs but also align more closely with human values, preferences, and expectations. It achieves this through a feedback loop where human evaluators rate or rank the model’s outputs, which is then used to adjust the model’s behavior.
This article explores the intricacies of RLHF. We will look at its importance for language modeling, analyze its inner workings in detail, and discuss the…
