A Descriptive and Normative Theory of Human Beliefs in RLHF

TL;DR

This paper explores how human beliefs about agent capabilities influence preferences in RLHF, proposing a new model and normative bounds to improve alignment and performance.

Contribution

It introduces a preference model incorporating human beliefs, provides a normative theory bounding policy error based on belief mismatch, and empirically demonstrates the impact of beliefs on preferences.

Findings

Beliefs about agent capabilities significantly affect human preferences.

Simple interventions can influence human beliefs about agent capabilities.

Reducing belief mismatch improves RLHF performance.

Abstract

Human preferences in RLHF are typically modeled as a function of the human's reward function or corresponding optimal state-action values. In this work, we propose that human beliefs about the capabilities of the agent being trained also play a key role in preference generation. We examine two questions related to this hypothesis, one descriptive and one normative, respectively: Do human labelers' beliefs about agent capabilities affect the preferences that they provide? And what is the ideal set of beliefs about an agent -- and resulting preferences -- for humans to have? We propose a new preference model that incorporates human beliefs and provide a normative theory that bounds the error on the final learned policy based on the \textit{mismatch} between the human's beliefs and an idealized set of beliefs. We then confirm via a human study that beliefs about agent capabilities do, in fact, significantly affect preferences and can be influenced through simple interventions. Additionally, we empirically show through synthetic experiments that it is often suboptimal for human preference labelers to assume agent optimality. Collectively, these results theoretically and empirically demonstrate how reducing the mismatch between human beliefs and agent capabilities can lead to more performant RLHF and point toward new best practices for RLHF practitioners.