mlsanityData to 7 Oct 2026

Provable Pluralistic Alignment: Multi-Party RLHF under Offline Human Feedback

arXiv 2403.05006 ↗First posted 8 Mar 2024cs.LG cs.AI stat.MEcited by 31

On the map: LLM alignment / RLHF / preference optimisation → Reward modeling and RLHF

Abstract (arXiv)

Pluralistic alignment requires learning from feedback that reflects persistent and potentially conflicting stakeholder preferences while ultimately selecting a single collective policy. We study this problem in offline reinforcement learning from human feedback (RLHF), where the party associated with each comparison is observed. Under a shared low-rank linear reward model, we jointly estimate party-specific rewards and perform pessimistic policy optimization under Nash, Utilitarian, and Egalitarian social-welfare objectives. We establish nonasymptotic bounds for party-specific reward estimation and the resulting policy suboptimality under offline coverage conditions. We further consider general pairwise preferences that need not admit a scalar reward representation and may exhibit cycles. In this setting, we construct a pessimistic von Neumann winner policy and derive corresponding performance guarantees. Under these models, our results provide a unified finite-sample solution to a central challenge in pluralistic alignment: learning from limited, heterogeneous, and potentially cyclic feedback, and producing a single policy with explicit collective-welfare guarantees. Our framework thereby makes preference aggregation an explicit and statistically analyzable design choice rather than an implicit consequence of pooling human feedback.

Key facts. Cited by 31 papers in the map (data through 7 Oct 2026). Placed in Reward modeling and RLHF, one of 10 directions in LLM alignment / RLHF / preference optimisation. Builds on 8 of its 62 references that are in the map. First posted on arXiv 8 Mar 2024.

Builds on

8 of 62 references.

Closest ancestors · 2

Same direction, newest first

  1. 2023Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback27 Jul
  2. 2019Fine-Tuning Language Models from Human Preferences18 Sep

Other references · 6

  1. 2024Arithmetic Control of LLMs for Diverse User Preferences: Directional Preference Alignment with Multi-Objective Rewardscited by 145
  2. 2023Beyond One-Preference-Fits-All Alignment: Multi-Objective Direct Preference Optimizationcited by 121
  3. 2022Proportional Fairness in Federated Learningcited by 20
  4. 2022Socially Fair Reinforcement Learningcited by 13
  5. 2024Offline Multi-task Transfer RL with Representational Penalizationcited by 13
  6. 2023Multi-dimensional domain generalization with low-rank structurescited by 2

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 8; 2025: 19; 2026: 4.

31 papers.

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  1. 2024On the Algorithmic Bias of Aligning Large Language Models with RLHF: Preference Collapse and Matching Regularizationcited by 72
  2. 2024RLHF from Heterogeneous Feedback via Personalization and Preference Aggregationcited by 71
  3. 2024Axioms for AI Alignment from Human Feedbackcited by 50
  4. 2024Direct Preference Optimization With Unobserved Preference Heterogeneity: The Necessity of Ternary Preferencescited by 23
  5. 2025Distortion of AI Alignment: Does Preference Optimization Optimize for Preferences?cited by 20
  6. 2024Dual Active Learning for Reinforcement Learning from Human Feedbackcited by 19
  7. 2025Direct Alignment with Heterogeneous Preferencescited by 19
  8. 2026Demystifying Group Relative Policy Optimization: Its Policy Gradient is a U-Statisticcited by 19
  9. 2025Game Theory Meets Large Language Models: A Systematic Survey with Taxonomy and New Frontierscited by 14
  10. 2025Robust Reinforcement Learning from Human Feedback for Large Language Models Fine-Tuningcited by 13
  11. 2025Doubly Robust Alignment for Large Language Modelscited by 11
  12. 2024Low-Rank Contextual Reinforcement Learning from Heterogeneous Human Feedbackcited by 10
19 more
  1. 2025MPO: An Efficient Post-Processing Framework for Mixing Diverse Preference Alignmentcited by 10
  2. 2024Fairness in Reinforcement Learning: A Surveycited by 9
  3. 2025Projection Optimization: A General Framework for Multi-Objective and Multi-Group RLHFcited by 9
  4. 2024Policy Aggregationcited by 8
  5. 2025Capturing Individual Human Preferences with Reward Featurescited by 8
  6. 2025Clone-Robust AI Alignmentcited by 7
  7. 2025Contextual Online Uncertainty-Aware Preference Learning for Human Feedbackcited by 7
  8. 2025Uncertainty Quantification for Large Language Model Reward Learning under Heterogeneous Human Feedbackcited by 7
  9. 2025Theoretical Tensions in RLHF: Reconciling Empirical Success with Inconsistencies in Social Choice Theorycited by 6
  10. 2025Strategyproof Reinforcement Learning from Human Feedbackcited by 5
  11. 2025A Shared Low-Rank Adaptation Approach to Personalized RLHFcited by 4
  12. 2026Reinforcement Learning from Human Feedback: A Statistical Perspectivecited by 4
  13. 2025Beyond RLHF and NLHF: Population-Proportional Alignment under an Axiomatic Frameworkcited by 3
  14. 2026Adaptive Pluralistic Alignment: A pipeline for dynamic artificial democracycited by 2
  15. 2025Is Active Persona Inference Necessary for Aligning Small Models to Personal Preferences?cited by 1
  16. 2025Direct Preference Optimization with Unobserved Preference Heterogeneity: The Necessity of Ternary Preferencescited by 1
  17. 2025Learning Guarantee of Reward Modeling Using Deep Neural Networkscited by 0
  18. 2025Preference-Based Dynamic Ranking Structure Recognitioncited by 0
  19. 2026AI Alignment through a Game-theoretic Lens: A Surveycited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026Groupwise Distortion Guarantees for Preference-Based Alignment
  2. 2023Iterative Preference Learning from Human Feedback: Bridging Theory and Practice for RLHF under KL-Constraint
  3. 2024MaxMin-RLHF: Alignment with Diverse Human Preferences
  4. 2026Procedural Fairness Failures in RLHF from Preference Averaging
  5. 2026A Markov Chain Approach to Preference Alignment
  6. 2025Stackelberg Learning from Human Feedback: Preference Optimization as a Sequential Game
  7. 2025SharedRep-RLHF: A Shared Representation Approach to RLHF with Diverse Preferences
  8. 2024COMAL: A Convergent Meta-Algorithm for Aligning LLMs with General Preferences