From Treats to Training: Understanding Reinforcement Learning with Human Feedback To understand reinforcement learning, it's important to first distinguish between supervised and unsupervised learning. Supervised learning relies on labeled data to train models to respond appropriately when encountering similar data in the future. In unsupervised learning, models learn independently by identifying patterns and inferring rules and behaviors from data without guidance. However, unsupervised learning alone may not be sufficient to produce answers that align with human values and needs. This is where reinforcement learning comes in, particularly in the context of the AISHE client system. Reinforcement learning is a powerful machine learning approach where models learn to solve problems through trial and error. Behaviors that optimize outputs are rewarded, while those that don't are punished and further refined through training. An analogy for reinforcement learning is how we train ...
