Microsoft, in collaboration with the University of Southern California and the University of Pennsylvania, has introduced a new training approach for artificial intelligence called Experiential Reinforcement Learning (ERL). This methodology allows AI models to learn more effectively by incorporating a self-reflection mechanism after initial attempts at tasks. When the model fails and receives feedback, it reflects on the experience, generates a note for improvement, and applies this learning in a subsequent attempt, significantly enhancing learning efficiency. This advancement addresses the limitations of standard reinforcement learning, which often struggles with sparse feedback and can lead to inefficient exploration in complex multi-step environments. The integration of explicit self-reflection not only stabilizes the optimization process but also enables the model to internalize improvements for future applications.
Microsoft introduces Experiential Reinforcement Learning to optimize AI training
