A-RSI: Adaptive Task Refresh for Zero-Data Multimodal Self-Improvement
arXiv:2610.38568v1 Announce Type: new Abstract: Recursive self-improvement requires models not only to acquire useful training experience, but also to decide when their current experience has ceased to be useful. Existing methods often focus on what experience to generate next, while paying less attention to when fresh experience should be introduced. Many also construct training targets through self-consistency or majority voting, making supervision depend on the model's own uncertain predicti…