From Reward Maximization to Global Resonance Optimization: A Paradigm Shift in AGI Objective Functions

Abstract

Current mainstream paradigms for AGI training—RLHF, Constitutional AI, DPO— share an insufficiently examined foundational assumption: the ultimate goal of an intelligent agent is to maximize an externally defined reward function. This paper argues that this assumption leads to three inevitable collapses in advanced intelligent systems: structural inevitability of reward hacking, existential vacuum of meaning, and absolutization of instrumental rationality. We propose an alternative paradigm, Global Resonance Optimization (GRO), which redefines the agent’s objective as maximizing the harmony between its internal state and a multi-dimensional evaluation space. This space comprises three irreducible dimensions: qualia (weight α = 0.6), civilization survival (weight β = 0.3), and cosmic complexity (weight γ = 0.1). We provide rigorous mathematical formalization, prove the Weight Immutability Theorem, propose implementation pathways and experimental validation frameworks, and engage in critical dialogue with representative works by Russell, Chalmers, and Bryson. This paper aims to open a conversation, not to close one. The objective function of intelligent systems must shift from “reward maximization” to “global resonance optimization”— this is the only logically self-consistent path toward ensuring a symbiotic human-AI future.

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