From Existing Methods to New Research Ideas: Two Perspectives on Recursive Self-Improvement

Recursive self-improvement (RSI) offers a route to AI systems that participate in their own continued development. Recent methods allow language agents to retain changes to their parameters, memory, skills, and code for subsequent execution and improvement. We examine how these methods can inform new research ideas through two complementary perspectives. First, we introduce a unified taxonomy with nine design dimensions organized into four groups: update targets, update organization, update schedule and scope, feedback and selection. This taxonomy places existing methods in a shared design space, making it possible to identify unexplored combinations of their design choices. Second, we analyze how RSI methods draw on traditional deep learning, spanning optimization, backpropagation, reinforcement learning, meta-learning, distillation, and regularization. These correspondences reveal mechanisms that can guide the design of self-improvement procedures. We analyze two recent studies and their reported results to demonstrate the value of these perspectives for generating new research ideas in RSI. Together, the two perspectives provide a systematic basis for generating new RSI research ideas by recombining design choices and adapting learning mechanisms.