Generalized Agent Iteration: One Formal Framework for Iterative Policy Improvement and Recursive Self-Improvement

2026-09-11

AI Summary

本文提出广义代理迭代框架，统一描述迭代策略改进与递归自我改进，通过调整机制和标准来区分不同实例，为分析设计新系统提供基础。

Abstract

When we speak of recursive self-improvement (RSI), are we speaking of a phenomenon, a mechanism, or a prospect? Towards autonomous and evolving intelligence, RSI is being claimed at many scales, while no single framework that formally describes these emerging instances exists. Its counterpart in the classical realm, iterative policy improvement, is characterized by generalized policy iteration (GPI), a framework of broad applicability with well-understood theoretical properties, but only where the update principle and the evaluation base lie outside the agent. In this paper, we propose Generalized Agent Iteration (GAI), a formal framework that describes iterative policy improvement and RSI as two cases of a single learning paradigm. GAI defines the agent as a configuration of modifiable components within a system and models the learning process as a cycle of agent evaluation and agent improvement. Two pivotal dials then distinguish the instances: whether the improving mechanism is part of the agent and whether the standard it is measured against is grounded outside it. The former dial delineates the boundary between GPI and RSI, and the latter determines a system's polarity as anchored, goal drift, or fully self-referential. Moreover, we use these coordinates to place existing systems on the same two axes and make the defects of recursive self-improvement statable one condition at a time. We see this paper as a first step toward exploring a formal characterization of RSI that rests on the classical account, makes existing systems comparable, and provides a principled basis for analyzing and designing new ones.

Problem

• Recursive Self-Improvement
• Iterative Policy Improvement
• Generalized Agent Iteration

Innovation

• Generalized Agent Iteration
• Iterative Policy Improvement
• Recursive Self-Improvement
• Agent Evaluation and Improvement