Harness Engineering for Self-Improvement
Lilian Weng 1 week ago
Recursive self-improvement in AI systems, first theorized by Good in 1965 and formalized by Yudkowsky in 2008, describes a feedback loop where AI models enhance their own cognitive processes to create better successor models. The concept encompasses direct weight modification or broader improvements to training and deployment systems, with frontier labs like Anthropic and OpenAI demonstrating accelerated research development through such mechanisms. This capability could enable faster iteration cycles in AI development as improved models continuously refine the systems that produce them.