Haonan Wang

Can language models sustain improvement by generating their own training tasks? This talk explores the promise of recursive self-improvement through a systematic study of self-evolving reinforcement learning. We discuss what current approaches achieve, what our findings reveal about the limits of that progress, and why the results so far do not establish a sustained cycle of self-improvement. We will also discuss possible directions for addressing these limitations.