Recursive Self-Improvement
Overview
Recursive self-improvement asks how AI systems can use their own experience to make future versions more capable, efficient, or safe. Approaches include revising agent prompts, tools, memory, and code; evolving training data or models; and using search, world models, or learned evaluators to guide changes. Most demonstrated progress comes from bounded tasks with external or held-out checks, such as coding, reasoning, robotics, and scientific research benchmarks. A central challenge is ensuring that gains persist beyond the feedback used to produce them, without benchmark overfitting, unsafe regressions, or goal drift.
Latest papers
84 papers40 papers in the last four weeks, up 167% on the four weeks before. 0.2% of all new papers.