mlsanityData to 14 Oct 2026

Rethinking Diverse Human Preference Learning through Principal Component Analysis

arXiv 2502.13131 ↗First posted 18 Feb 2025cs.AI cs.CLcited by 10

On the map: LLM reasoning → Reinforcement learning for reasoning

also in 1 more
Abstract (arXiv)

Understanding human preferences is crucial for improving foundation models and building personalized AI systems. However, preferences are inherently diverse and complex, making it difficult for traditional reward models to capture their full range. While fine-grained preference data can help, collecting it is expensive and hard to scale. In this paper, we introduce Decomposed Reward Models (DRMs), a novel approach that extracts diverse human preferences from binary comparisons without requiring fine-grained annotations. Our key insight is to represent human preferences as vectors and analyze them using Principal Component Analysis (PCA). By constructing a dataset of embedding differences between preferred and rejected responses, DRMs identify orthogonal basis vectors that capture distinct aspects of preference. These decomposed rewards can be flexibly combined to align with different user needs, offering an interpretable and scalable alternative to traditional reward models. We demonstrate that DRMs effectively extract meaningful preference dimensions (e.g., helpfulness, safety, humor) and adapt to new users without additional training. Our results highlight DRMs as a powerful framework for personalized and interpretable LLM alignment. Our code is available at https://github.com/amandaluof/DRMs.

Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). Placed in Reinforcement learning for reasoning, one of 17 directions in LLM reasoning. Builds on 39 of its 59 references that are in the map. First posted on arXiv 18 Feb 2025.

Builds on

39 of 59 references.

Closest ancestors · 4

Same direction, newest first

  1. 2025Reusing Embeddings: Reproducible Reward Model Research in Large Language Model Alignment without GPUs4 Feb
  2. 2024Rethinking Bradley-Terry Models in Preference-Based Reward Modeling: Foundations, Theory, and Alternatives7 Nov
  3. 2024Skywork-Reward: Bag of Tricks for Reward Modeling in LLMs24 Oct
  4. 2024RLHF Workflow: From Reward Modeling to Online RLHF13 May

Other references · 35

  1. 2023GPT-4 Technical Reportcited by 18,396
  2. 2024The Llama 3 Herd of Modelscited by 14,715
  3. 2022Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedbackcited by 3,720
  4. 2021WebGPT: Browser-assisted question-answering with human feedbackcited by 1,745
  5. 2024Gemma: Open Models Based on Gemini Research and Technologycited by 916
  6. 2023Gemini: A Family of Highly Capable Multimodal Modelscited by 872
  7. 2024DeepSeek-V3 Technical Reportcited by 811
  8. 2023LLM-Blender: Ensembling Large Language Models with Pairwise Ranking and Generative Fusioncited by 564
  9. 2024RewardBench: Evaluating Reward Models for Language Modelingcited by 499
  10. 2023UltraFeedback: Boosting Language Models with Scaled AI Feedbackcited by 406
  11. 2024Interpretable Preferences via Multi-Objective Reward Modeling and Mixture-of-Expertscited by 376
  12. 2023Personalized Soups: Personalized Large Language Model Alignment via Post-hoc Parameter Mergingcited by 280
23 more
  1. 2025EmbodiedBench: Comprehensive Benchmarking Multi-modal Large Language Models for Vision-Driven Embodied Agentscited by 221
  2. 2024Rewards-in-Context: Multi-objective Alignment of Foundation Models with Dynamic Preference Adjustmentcited by 175
  3. 2023HelpSteer: Multi-attribute Helpfulness Dataset for SteerLMcited by 142
  4. 2024Regularizing Hidden States Enables Learning Generalizable Reward Model for LLMscited by 142
  5. 2024Personalization of Large Language Models: A Surveycited by 104
  6. 2024MetaAligner: Towards Generalizable Multi-Objective Alignment of Language Modelscited by 44
  7. 2023ChiMed-GPT: A Chinese Medical Large Language Model with Full Training Regime and Better Alignment to Human Preferencescited by 43
  8. 2024Conditional Language Policy: A General Framework for Steerable Multi-Objective Finetuningcited by 43
  9. 2024Reward Guided Latent Consistency Distillationcited by 35
  10. 2024Beyond Bradley-Terry Models: A General Preference Model for Language Model Alignmentcited by 27
  11. 2024Direct Preference Optimization With Unobserved Preference Heterogeneity: The Necessity of Ternary Preferencescited by 23
  12. 2024Improving Context-Aware Preference Modeling for Language Modelscited by 22
  13. 2025PILAF: Optimal Human Preference Sampling for Reward Modelingcited by 20
  14. 2024Assessing and Enhancing Large Language Models in Rare Disease Question-answeringcited by 16
  15. 2024Q-Probe: A Lightweight Approach to Reward Maximization for Language Modelscited by 15
  16. 2024DMoERM: Recipes of Mixture-of-Experts for Effective Reward Modelingcited by 15
  17. 2024Traversing Pareto Optimal Policies: Provably Efficient Multi-Objective Reinforcement Learningcited by 15
  18. 2024Scalable Ensembling For Mitigating Reward Overoptimisationcited by 14
  19. 2024How to Leverage Demonstration Data in Alignment for Large Language Model? A Self-Imitation Learning Perspectivecited by 14
  20. 2024Test-Time Alignment via Hypothesis Reweightingcited by 11
  21. 2024Embedding-Aligned Language Modelscited by 9
  22. 2025Reviving The Classics: Active Reward Modeling in Large Language Model Alignmentcited by 8
  23. 2024Uncovering Latent Human Wellbeing in Language Model Embeddingscited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 6; 2026: 4.

10 papers.

  1. 2025Reinforcement Learning from Human Feedbackcited by 58
  2. 2026Reward Hacking in the Era of Large Models: Mechanisms, Emergent Misalignment, Challengescited by 33
  3. 2026Adversarial Reward Auditing for Active Detection and Mitigation of Reward Hackingcited by 11
  4. 2025ERA: Transforming VLMs into Embodied Agents via Embodied Prior Learning and Online Reinforcement Learningcited by 10
  5. 2025Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunitiescited by 7
  6. 2025OpenReview Should be Protected and Leveraged as a Community Asset for Research in the Era of Large Language Modelscited by 5
  7. 2026Reward Models Inherit Value Biases from Pretrainingcited by 3
  8. 2025Steering Information Utility in Key-Value Memory for Language Model Post-Trainingcited by 2
  9. 2025Limited Preference Data? Learning Better Reward Model with Latent Space Synthesiscited by 2
  10. 2026Beyond Routing Weights: Faithful Response-Level Interpretation of Mixture-of-Experts Reward Models via Contribution Contrastcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Learning a Canonical Basis of Human Preferences from Binary Ratings
  2. 2026VRM: Teaching Reward Models to Understand Authentic Human Preferences
  3. 2026Preference learning in shades of gray: Interpretable and bias-aware reward modeling for human preferences
  4. 2026Personalized RewardBench: Evaluating Reward Models with Human Aligned Personalization
  5. 2024LRHP: Learning Representations for Human Preferences via Preference Pairs
  6. 2025Skywork-Reward-V2: Scaling Preference Data Curation via Human-AI Synergy
  7. 2026Beyond Binary Preferences: A Principled Framework for Reward Modeling with Ordinal Feedback
  8. 2025Direct Preference Optimization with Unobserved Preference Heterogeneity: The Necessity of Ternary Preferences