Reinforcement Learning (RL) is a machine learning approach that enables intelligent agents to learn by interacting with their environment, using a reward-based system to guide decision-making. An extension of this, Reinforcement Learning from Human Feedback (RLHF), incorporates human feedback into the learning process, refining model outputs and improving convergence rates. This approach has been applied in fields like Computer Vision (CV) and Natural Language Processing (NLP), enhancing models for tasks such as object detection and language generation by allowing them to adapt to complex real-world scenarios more efficiently. RLHF has demonstrated its potential in applications like OpenAI's ChatGPT, where human preferences guide the model to produce more accurate and contextually appropriate responses. In CV, RLHF has improved segmentation and detection tasks, showing promise even in data-limited environments. This methodology offers a significant advantage by fine-tuning existing models without the need for extensive additional data, optimizing performance, and reducing computational costs.