In the realm of Machine Learning, Reinforcement Learning with Human Feedback (RLHF) stands out as an innovative technique where human trainers play a crucial role in guiding the learning process of models. Unlike traditional reinforcement learning, which relies solely on pre-defined rewards, RLHF incorporates human judgment to shape the training environment. This method can have significant implications, especially when it comes to ensuring that models consistently favor certain outcomes over others. In this blog, we’ll delve into how trainers can influence models using RLHF, highlighting both the potential benefits and pitfalls. Human trainers can introduce biases, whether consciously or