Recursive Training

Recursive training involves iteratively training models on data generated by previously trained models, aiming to improve performance or understand model behavior under repeated application. Current research focuses on applying this technique to diverse areas, including combinatorial optimization (using graph neural networks with recurrent feature updates), generative AI image models (analyzing stability under recursive inpainting), and acoustic howling suppression (integrating neural networks into closed-loop systems). Understanding and mitigating issues like model collapse, where recursive training leads to performance degradation, is a key challenge with significant implications for the reliability and robustness of various AI applications.