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We also examine A-Evolve-Training, an autonomous system that ran multiple rounds of post-training on a 30-billion-parameter AI model, designing experiments, analyzing results, and deciding what improvements to keep. Most surprisingly, it reportedly recognized when its own internal evaluation metric was misleading and changed its strategy. We’re not at fully autonomous recursive self-improvement or superintelligence yet. But AI agents are beginning to close parts of the improvement loop, potentially changing how future AGI, frontier AI models, and artificial intelligence systems are developed.
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