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arXiv · 2509.03672

SharedRep-RLHF: A Shared Representation Approach to RLHF with Diverse Preferences

Arpan Mukherjee·Marcello Bullo·Deniz Gündüz
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Abstract

Uniform-reward reinforcement learning from human feedback (RLHF), which trains a single reward model to represent the preferences of all annotators, fails to capture the diversity of opinions across sub-populations, inadvertently favoring dominant groups. The state-of-the-art, MaxMin-RLHF, addresses this by learning group-specific reward models, and by optimizing for the group receiving the minimum reward, thereby promoting fairness. However, we identify that a key limitation of MaxMin-RLHF is its poor performance when the minimum-reward group is a minority. To mitigate this drawback, we introduce a novel framework, termed {\em SharedRep-RLHF}. At its core, SharedRep-RLHF learns and leverages {\em shared traits} in annotations among various groups, in contrast to learning separate reward models across groups. We first show that MaxMin-RLHF is provably suboptimal in learning shared traits, and then quantify the sample complexity of SharedRep-RLHF. Experiments across diverse natural language tasks showcase the effectiveness of SharedRep-RLHF compared to MaxMin-RLHF with a gain of up to 20% in win rate.

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Arpan Mukherjee, Marcello Bullo, Deniz Gündüz. 2025-09-03. SharedRep-RLHF: A Shared Representation Approach to RLHF with Diverse Preferences. https://arxiv.org/abs/2509.03672

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