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RefGRPO demonstrates that agent self-assessment can be substantially improved without any external annotation or reward model, enabling agents to act as their own verifiers grounded in environment feedback.
The report documents a concrete inversion — from AI writing a negligible share of Anthropic's code to authoring the overwhelming majority in roughly 15 months — while simultaneously warning, from inside a leading AI lab, that recursive self-improvement is outpacing the control mechanisms designed to govern it.
The paper provides a concrete, criteria-based framework for evaluating claims of recursive self-design in AI systems, grounding the discussion in publicly verifiable evidence from systems like DGM rather than treating MetaAI as an established paradigm.