In a new paper, researchers examine how approaches to recursive self-improvement (RSI) could help tackle the rising financial and computational costs of developing ever-more advanced AI models.
This recursive feedback loop could eventually enable AI systems to develop newer, more powerful generations of models with minimal human input.
At the lower levels, development remains largely human-led. As AI systems move up the scale, they become increasingly capable of identifying their weaknesses and deciding how to address them, eventually gaining the ability to make their own “meta-improvements” — improving how they improve themselves.
In one study earlier this year, an AI system called A-Evolve-Training was tasked with improving the performance of a 30 billion-parameter large language model (LLM).
Peter Bentley, a professor of digital biology at University College London, questioned whether such experiments offer a credible route to RSI.
Bentley pointed out that self-improving computer systems have been around for decades. Evolutionary computation and reinforcement learning, for instance, are both based on the principle of systems refining their performance over time in response to feedback.
Could AI really self-improve? Some experts are skeptical. (Image credit: Mina De La O/Getty Images)"You can't 'recursively self-improve' unless you can also enhance your own training, enhance your hardware, and test it exhaustively in real world settings," Bentley added.
The challenge of true RSI
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Kayla Blomquist, an AI researcher at the University of Oxford and co-founder of the Oxford China Policy Lab, said that, while the paper doesn't provide a practical road map to RSI, it could help developers and policymakers understand the technical challenges and safety risks associated with increasingly autonomous AI systems.
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