The Last AI Built by Humans: Toward Genuine Recursive Self-Improvement
This paper addresses the fundamental challenge of enabling AI systems to autonomously improve themselves, which is a foundational problem for the entire field of AI.
The paper introduces the concept of Recursive Self-Improvement (RSI) for AI systems, which allows them to improve their capabilities and future improvement processes based on experience and feedback. It outlines a development roadmap for RSI, progressing through various levels of autonomy, and discusses its application across different scenarios.
Recursive self-improvement (RSI) enables AI systems to turn experience and feedback into persistent changes that improve both their capabilities and the process of future improvement. We first use the Headroom-Closed Index (HCI) to reveal the problems of existing LLMs, then introduce the RSI concept and its development roadmap: from improvement-execution autonomy, improvement-strategy autonomy, experience-acquisition autonomy, and environment-adaptation autonomy, to recursive meta-improvement. Next we examine RSI across scenarios (e.g., scientific discovery, embodied intelligence, software engineering), highlighting their distinct requirements and development speeds. Drawing on diverse industry practices and preliminary empirical evidence, we connect RSI research with practical systems and identify key challenges to achieving genuine RSI.