Generalized Agent Iteration: a formal framework unifying iterative policy improvement and recursive self-improvement
This arXiv preprint proposes Generalized Agent Iteration (GAI), a formal framework that models learning as cycles of agent evaluation and agent improvement and treats iterative policy improvement (GPI) and recursive self-improvement (RSI) as two instances on the same two-axis space (whether the improver is part of the agent, and whether the evaluation standard is external). The paper uses these coordinates to locate existing systems and to make the potential defects of RSI expressible as specific conditions.