Capturing Individual Human Preferences with Reward Features

• URL: http://arxiv.org/abs/2503.17338v1
• Date: Fri, 21 Mar 2025 17:39:33 GMT
• Title: Capturing Individual Human Preferences with Reward Features
• Authors: André Barreto, Vincent Dumoulin, Yiran Mao, Nicolas Perez-Nieves, Bobak Shahriari, Yann Dauphin, Doina Precup, Hugo Larochelle,
• Abstract summary: We show that individual preferences can be captured as a linear combination of a set of general reward features.<n>We show how to learn such features and subsequently use them to quickly adapt the reward model to a specific individual.<n>We present experiments with large language models comparing the proposed architecture with a non-adaptive reward model and also adaptive counterparts.
• Score: 47.43999785878563
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Reinforcement learning from human feedback usually models preferences using a reward model that does not distinguish between people. We argue that this is unlikely to be a good design choice in contexts with high potential for disagreement, like in the training of large language models. We propose a method to specialise a reward model to a person or group of people. Our approach builds on the observation that individual preferences can be captured as a linear combination of a set of general reward features. We show how to learn such features and subsequently use them to quickly adapt the reward model to a specific individual, even if their preferences are not reflected in the training data. We present experiments with large language models comparing the proposed architecture with a non-adaptive reward model and also adaptive counterparts, including models that do in-context personalisation. Depending on how much disagreement there is in the training data, our model either significantly outperforms the baselines or matches their performance with a simpler architecture and more stable training.

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