Human-feedback-based reinforcement learning (RLHF) is poised to become an indispensable core technology for large-scale model training and intelligent upgrades in the AI field by 2025. This article comprehensively reviews the fundamental principles of RLHF, its differences from traditional RL, key training processes, and mainstream application tools, and delves into data bottlenecks, reward models, etc.
Overfitting is a core challenge in machine learning, referring to a model's excessive fit to training data, which reduces its predictive ability for new data. As AI becomes increasingly prevalent in industries such as healthcare, finance, and e-commerce, overfitting not only affects decision accuracy but can also pose significant risks. This….