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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A Benchmark for Scalable Oversight Mechanisms
A Benchmark for Scalable Oversight Mechanisms
Abhimanyu Pallavi Sudhir
University of Warwick
abhimanyu.pallavi-sudhir@warwick.ac.uk
&Jackson Kaunismaa
MATS
jackkaunis@protonmail.com
&Arjun Panickssery
ZemblaAI
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.
1 Introduction
One way to frame the limitations of currently widely-used alignment techniques such as reinforcement learning from human feedback (Christiano et al., 2017 ) , 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 ) . In other words, the AI model is trained on the human supervisor’s immediate, superficial volition, rather than on her extrapolated volition (Yudkowsky, 2004 ) .
The problem of developing a human feedback mechanism that scales to superhuman intelligences is known as scalable oversight (Bowman et al., 2022 ) . Broadly speaking, there are two ways to think about the scalable oversight problem:
1.
The problem of developing a training method that makes honesty (or more generally “alignment”) the best policy for the model; i.e. something to replace or extend RLHF to the superhuman realm.
2.
An inference-time oversight mechanism to catch a model when it says something false or does something bad; i.e. a mechanism design problem to get AIs to be truthful or useful.
For example, in Debate , the most widely-known scalable oversight protocol introduced in Irving et al. ( 2018 ) , the model is incentivized to tell the truth if it knows that a lie can be caught and convincingly refuted by its opponent. A list of other competing proposals is given in Section 1.1 .
While there is mathematical and intuitive elegance underlying each of these protocols, their diversity and theoretical claims to superiority beg the question: how can we evaluate and compare scalable oversight protocols themselves?
One approach, taken by recent
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