2025 benchmark for scalable oversight
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A 2025 paper offering a standardized benchmark and metric for comparing scalable oversight methods; particularly relevant for researchers studying debate, amplification, or other human-AI oversight protocols in the context of superhuman AI systems.
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Abstract
As AI agents surpass human capabilities, scalable oversight -- the problem of effectively supplying human feedback to potentially superhuman AI models -- becomes increasingly critical to ensure alignment. While numerous scalable oversight protocols have been proposed, they lack a systematic empirical framework to evaluate and compare them. While recent works have tried to empirically study scalable oversight protocols -- particularly Debate -- we argue that the experiments they conduct are not generalizable to other protocols. We introduce the scalable oversight benchmark, a principled framework for evaluating human feedback mechanisms based on our agent score difference (ASD) metric, a measure of how effectively a mechanism advantages truth-telling over deception. We supply a Python package to facilitate rapid and competitive evaluation of scalable oversight protocols on our benchmark, and conduct a demonstrative experiment benchmarking Debate.
Summary
This paper introduces a systematic empirical benchmark framework for evaluating scalable oversight protocols, addressing the lack of generalizable comparisons across mechanisms like Debate. The authors propose the Agent Score Difference (ASD) metric to measure how well a mechanism incentivizes truth-telling over deception, and release an open-source Python package for standardized evaluation. A demonstrative Debate experiment validates the framework.
Key Points
- •Existing evaluations of scalable oversight protocols (e.g., Debate) lack generalizability, motivating a unified benchmark framework.
- •Introduces the Agent Score Difference (ASD) metric: measures how effectively a mechanism advantages truth-telling agents over deceptive ones.
- •Provides an open-source Python package enabling rapid, standardized, and competitive evaluation of diverse oversight protocols.
- •Demonstrates the framework with an initial Debate benchmarking experiment as a proof of concept.
- •Addresses a critical alignment problem: how humans can reliably supervise AI systems that exceed human-level capabilities.
Cited by 1 page
| Page | Type | Quality |
|---|---|---|
| AI Alignment | Approach | 91.0 |
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arXiv:2504.03731v1 \[cs.AI\] 31 Mar 2025
# A Benchmark for Scalable Oversight Mechanisms
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Abhimanyu Pallavi Sudhir
University of Warwick
abhimanyu.pallavi-sudhir@warwick.ac.uk
&Jackson Kaunismaa
MATS
jackkaunis@protonmail.com
&Arjun Panickssery
ZemblaAI
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###### Abstract
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As AI agents surpass human capabilities, _scalable oversight_ – the problem of effectively supplying human feedback to potentially superhuman AI models – becomes increasingly critical to ensure alignment. While numerous scalable oversight protocols have been proposed, they lack a systematic empirical framework to evaluate and compare them. While recent works have tried to empirically study scalable oversight protocols – particularly Debate – we argue that the experiments they conduct are not generalizable to other protocols. We introduce the _scalable oversight benchmark_, a principled framework for evaluating human feedback mechanisms based on our agent score difference (ASD) metric, a measure of how effectively a mechanism advantages truth-telling over deception. We supply a Python package to facilitate rapid and competitive evaluation of scalable oversight protocols on our benchmark, and conduct a demonstrative experiment benchmarking Debate.
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## 1 Introduction
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One way to frame the limitations of currently widely-used alignment techniques such as reinforcement learning from human feedback (Christiano et al., [2017](https://arxiv.org/html/2504.03731v1#bib.bib7 "")), is that they fundamentally rely on a human’s ability to judge the correctness or value of a (potentially superhuman) AI’s outputs (Burns et al., [2024](https://arxiv.org/html/2504.03731v1#bib.bib4 "")). In other words, the AI model is trained on the human supervisor’s _immediate, superficial_ volition, rather than on her _extrapolated volition_(Yudkowsky, [2004](https://arxiv.org/html/2504.03731v1#bib.bib20 "")).
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The problem of developing a human feedback mechanism that scales to superhuman intelligences is known as _scalable oversight_(Bowman et al., [2022](https://arxiv.org/html/2504.03731v1#bib.bib2 "")). Broadly speaking, there are two ways to think about the
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