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Showing posts with the label reward modeling

Reinforcement Learning with Human Feedback: A Powerful Approach to AI Training

The unprecedented capabilities exhibited by the large language models (LLMs) such as ChatGPT and GPT-4 have created enormous excitement as well as concerns about the impact of AI on the society in near and far future. Behind the success of LLMs and AI in general lies among other techniques a learning approach called Reinforcement Learning with Human Feedback (RLHF). In this blog post, we will try to understand what RLHF is and why it offers a powerful approach to training AI models. However, before we do that, let's try to understand the concept of reinforcement learning (RL). What is Reinforcement Learning (RL)? RL, inspired by the principles of behavioral psychology, is a machine learning technique wherein the learner, called an agent , learns decision making by exploring an environment through a trial-and-error process to achieve its goal. Each action by the agent results in feedback in the form of a reward or punishment . While performing actions and receiving feedback, the a