mlsanityData to 7 Oct 2026

Personalized Alignment Revisited: The Necessity and Sufficiency of User Diversity

arXiv 2605.09119 ↗First posted 9 May 2026cs.LG cs.AI cs.CLcited by 0

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Abstract (arXiv)

Personalized alignment aims to adapt large language models to heterogeneous user preferences, yet the precise theoretical conditions for its statistical efficiency have not been formally established. This paper characterizes the conditions under which personalized alignment achieves O(1) online regret and log(1/epsilon) offline sample complexity. We show that these optimal rates depend on a specific user-diversity condition: the population of user-specific heads must span the latent reward directions that can alter the optimal response. We prove that this condition is both necessary and sufficient. When it holds, simple greedy algorithms achieve benchmark efficiency; when it fails, every learner in a natural admissible class incurs at least logarithmic regret. Our results identify user diversity as the fundamental driver of personalized identifiability.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 43 of its 48 references that are in the map. First posted on arXiv 9 May 2026.

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43 of 48 references.

References · 43

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31 more
  1. 2022Fine-tuning language models to find agreement among humans with diverse preferencescited by 189
  2. 2020On the Theory of Transfer Learning: The Importance of Task Diversitycited by 186
  3. 2024A Roadmap to Pluralistic Alignmentcited by 175
  4. 2024Rewards-in-Context: Multi-objective Alignment of Foundation Models with Dynamic Preference Adjustmentcited by 171
  5. 2024Secrets of RLHF in Large Language Models Part II: Reward Modelingcited by 170
  6. 2020Provable Meta-Learning of Linear Representationscited by 161
  7. 2024Personalizing Reinforcement Learning from Human Feedback with Variational Preference Learningcited by 147
  8. 2024The PRISM Alignment Dataset: What Participatory, Representative and Individualised Human Feedback Reveals About the Subjective and Multicultural Alignment of Large Language Modelscited by 136
  9. 2023Beyond One-Preference-Fits-All Alignment: Multi-Objective Direct Preference Optimizationcited by 121
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  12. 2023Group Preference Optimization: Few-Shot Alignment of Large Language Modelscited by 61
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  21. 2021Representation Learning Beyond Linear Prediction Functionscited by 21
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  27. 2024Towards Understanding the Influence of Reward Margin on Preference Model Performancecited by 9
  28. 2024Local Anti-Concentration Class: Logarithmic Regret for Greedy Linear Contextual Banditcited by 8
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  30. 2025The Reward Model Selection Crisis in Personalized Alignmentcited by 3
  31. 2022On the Complexity of Representation Learning in Contextual Linear Banditscited by 2

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