RLHF typically ignores user differences, potentially harming personalization.
Authors derive a PAC bound linking error to example count and rater count.
Adaptive reward models are suggested to benefit scenarios with high user disagreement.
They propose an architecture that learns linear combinations of general reward features.
The architecture allows fast adaptation to a user even without their data.
Experiments on large language models confirm theoretical predictions.
Adaptive models outperform non‑adaptive baselines and in‑context personalization.
📖 Full Retelling
The paper "Capturing Individual Human Preferences with Reward Features" was authored by André Barreto, Vincent Dumoulin, Yiran Mao, Mark Rowland, Nicolas Perez‑Nieves, Bobak Shahriari, Yann Dauphin, Doina Precup, and Hugo Larochelle. It was submitted to the arXiv preprint server on 21 March 2025 (version v1) and revised on 19 February 2026 (version v2). The authors argue that conventional reinforcement learning from human feedback (RLHF) models, which use a single reward function for all users, fail to capture the high degree of disagreement that can arise in settings such as large‑language‑model training. To address this, they formalise the problem of learning a reward model that can be specialised to individual users, derive a probably approximately correct (PAC) bound that shows how approximation error depends on both the number of examples and the number of raters, and propose an adaptive reward‑feature architecture that learns a linear combination of general reward features to quickly personalise the model. Empirical tests on large language models show that the adaptive approach outperforms non‑adaptive baselines, especially as the number of raters and preference heterogeneity increase.
🏷️ Themes
Artificial Intelligence, Reinforcement Learning from Human Feedback, Personalization, Large Language Models, Reward Modeling, Statistical Machine Learning, NeurIPS 2025
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Deep Analysis
Why It Matters
The paper shows how to model human preferences individually in reinforcement learning, which is crucial for building AI that aligns with diverse users. By learning reward features that can be adapted to each user, the approach can reduce bias and improve user satisfaction in large language models.
Context & Background
Traditional RLHF uses a single reward model that treats all users the same
The authors propose a linear combination of general reward features to capture individual differences
They provide theoretical bounds and empirical evidence that adaptive models outperform non-adaptive baselines when user preferences differ
What Happens Next
Future work may integrate these adaptive reward models into commercial LLMs, enabling on‑the‑fly personalization. Researchers will likely explore richer feature sets and more efficient data collection strategies to scale the approach.
Frequently Asked Questions
What is the main contribution of the paper?
It introduces a method to learn reward features that can be linearly combined to adapt a reward model to individual users, supported by theory and experiments.
How does the approach handle users whose preferences were not in the training data?
By learning general reward features, the model can quickly adapt to a new user using only a few preference examples, even if those preferences were not seen before.
What evidence shows the benefit of adaptive models?
Experiments with large language models demonstrate that the adaptive architecture outperforms a non‑adaptive baseline, especially when the number of raters and preference heterogeneity increase.
Original Source
--> Computer Science > Artificial Intelligence arXiv:2503.17338 [Submitted on 21 Mar 2025 ( v1 ), last revised 19 Feb 2026 (this version, v2)] Title: Capturing Individual Human Preferences with Reward Features Authors: André Barreto , Vincent Dumoulin , Yiran Mao , Mark Rowland , Nicolas Perez-Nieves , Bobak Shahriari , Yann Dauphin , Doina Precup , Hugo Larochelle View a PDF of the paper titled Capturing Individual Human Preferences with Reward Features, by Andr\'e Barreto and 8 other authors View PDF HTML Abstract: Reinforcement learning from human feedback usually models preferences using a reward function 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 formalise and analyse the problem of learning a reward model that can be specialised to a user. Using the principle of empirical risk minimisation, we derive a probably approximately correct bound showing the dependency of the approximation error on the number of training examples, as usual, and also on the number of human raters who provided feedback on them. Based on our theoretical findings, we discuss how to best collect pairwise preference data and argue that adaptive reward models should be beneficial when there is considerable disagreement among users. We also propose a concrete architecture for an adaptive reward model. Our approach leverages 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 illustrating our theoretical results and comparing the proposed architecture with a non-adaptive baseline. Consistent with our analysis, the benefits provided by our ...
