Generalized Agent Iteration: One Formal Framework for Iterative Policy Improvement and Recursive Self-Improvement
Read the original on arXiv AI →The paper introduces Generalized Agent Iteration (GAI), a formal framework that unifies iterative policy improvement and recursive self‑improvement (RSI) under a single learning paradigm. GAI treats an agent as a configuration of modifiable components and models learning as a cycle of evaluation and improvement, with two key dials: whether the improving mechanism is part of the agent and whether the evaluation standard is external. These dials distinguish between generalized policy iteration (GPI) and RSI, and classify systems as anchored, goal‑drift, or fully self‑referential, allowing existing systems to be mapped and RSI defects to be analyzed systematically.
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