Scaling Laws For Scalable Oversight
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Good quality. Reputable source with community review or editorial standards, but less rigorous than peer-reviewed venues.
Rating inherited from publication venue: arXiv
Proposes a quantitative framework for analyzing how scalable oversight—where weaker AI systems supervise stronger ones—scales with capability gaps, directly addressing a critical challenge in AI safety and governance of future superintelligent systems.
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Abstract
Scalable oversight, the process by which weaker AI systems supervise stronger ones, has been proposed as a key strategy to control future superintelligent systems. However, it is still unclear how scalable oversight itself scales. To address this gap, we propose a framework that quantifies the probability of successful oversight as a function of the capabilities of the overseer and the system being overseen. Specifically, our framework models oversight as a game between capability-mismatched players; the players have oversight-specific Elo scores that are a piecewise-linear function of their general intelligence, with two plateaus corresponding to task incompetence and task saturation. We validate our framework with a modified version of the game Nim and then apply it to four oversight games: Mafia, Debate, Backdoor Code and Wargames. For each game, we find scaling laws that approximate how domain performance depends on general AI system capability. We then build on our findings in a theoretical study of Nested Scalable Oversight (NSO), a process in which trusted models oversee untrusted stronger models, which then become the trusted models in the next step. We identify conditions under which NSO succeeds and derive numerically (and in some cases analytically) the optimal number of oversight levels to maximize the probability of oversight success. We also apply our theory to our four oversight games, where we find that NSO success rates at a general Elo gap of 400 are 13.5% for Mafia, 51.7% for Debate, 10.0% for Backdoor Code, and 9.4% for Wargames; these rates decline further when overseeing stronger systems.
Summary
This paper addresses a critical gap in AI safety by developing a framework to quantify how well scalable oversight—where weaker AI systems supervise stronger ones—actually scales. The authors model oversight as a game between capability-mismatched players with Elo-based scoring functions, validate their framework on Nim and four oversight games (Mafia, Debate, Backdoor Code, Wargames), and derive scaling laws for oversight success. They then analyze Nested Scalable Oversight (NSO), where trusted models progressively oversee stronger untrusted models, identifying conditions for success and optimal oversight levels. Their empirical results show NSO success rates ranging from 9.4% to 51.7% depending on the game, with performance degrading significantly when overseeing substantially stronger systems.
Cited by 1 page
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| AI Alignment | Approach | 91.0 |
a2f0c5f433869914 | Stable ID: OTQ3MWU3MW