👉 Trying computing, also known as reinforcement learning from human feedback (RLHF), is a method where an AI system learns to perform tasks by receiving feedback from humans on the quality of its actions. Unlike traditional supervised learning, which relies solely on labeled data, trying computing involves an iterative process where the AI generates actions, and humans evaluate these actions based on predefined criteria or preferences. This feedback is then used to adjust the AI's policy, guiding it towards more desirable outcomes. The process often includes a reward model that quantifies human preferences, and techniques like inverse reinforcement learning or preference learning are employed to refine the AI's behavior. This approach is particularly useful in complex, dynamic environments where explicit programming is impractical, enabling AI to adapt and improve through interaction and feedback.
