Active Human Feedback Collection via Neural Contextual Dueling Bandits

Arun Verma, Xiaoqiang Lin, Zhongxiang Dai, Daniela Rus, Bryan Kian Hsiang Low

Keywords: Human feedback, Preference feedback, Neural dueling bandit, Contextual dueling bandit

TL;DR: This paper introduces an algorithm based on neural contextual dueling bandits, providing a principled and practical approach for efficiently collecting human preference feedback when the latent reward function is non-linear.

Abstract: Collecting human preference feedback is often expensive, leading recent works to develop principled algorithms to select them more efficiently. However, these works assume that the underlying reward function is linear, an assumption that does not hold in many real-life applications, such as online recommendation and LLM alignment. To address this limitation, we propose Neural-ADB, an algorithm based on the neural contextual dueling bandit framework that provides a principled and practical method for collecting human preference feedback when the underlying latent reward function is non-linear. We theoretically show that when preference feedback follows the Bradley-Terry-Luce model, the worst sub-optimality gap of the policy learned by Neural-ADB decreases at a sub-linear rate as the preference dataset increases. Our experimental results on preference datasets further corroborate the effectiveness of Neural-ADB.

Submission Type: Long Paper (9 Pages)

Archival Option: This is a non-archival submission

Presentation Venue Preference: ICLR 2025

Submission Number: 63