Reinforcement Learning from Human Feedback (RLHF) is a relatively new yet significant machine learning technique that can be applied to large generative AI models like ChatGPT to improve performance and enable more effective collaboration between humans and AI systems.
In this article, we’ll explain what RLHF is, how it works, and key benefits of using it to train machine learning models.
RLHF is a new approach to training AI models. Based on the standard technique of developing a reward and punishment mechanism, RLHF specifically involves collecting input from human experts to improve model performance. RLHF aims to enable AI models to learn from real human feedback rather than relying solely on predefined objectives or rewards.
RLHF is an iterative process that involves continually collecting feedback from humans in the loop and plugging in that data to refine the AI model’s performance over time.
The steps of RLHF can vary depending on the specific implementation. However, the general process involves the following stages:
The benefits of using RLHF to train generative AI models include:
Overall, RLHF has the potential to make generative AI models more reliable, accurate, efficient, flexible, and safe. TaskUs has the expertise, technology, and infrastructure to support Reinforcement Learning from Human Feedback (RLHF) workflows by providing access to a large pool of highly skilled human annotators. We can collect high-quality human feedback data for the most specific use cases, leading to more accurate and effective AI models.