mlsanityData to 7 Oct 2026

Beyond Compromise: Pareto-Lenient Consensus for Efficient Multi-Preference LLM Alignment

arXiv 2604.05965 ↗First posted 7 Apr 2026cs.AIcited by 2

On the map: LLM alignment / RLHF / preference optimisation → LLM preference optimization methods

Abstract (arXiv)

Transcending the single-preference paradigm, aligning LLMs with diverse human values is pivotal for robust deployment. Contemporary Multi-Objective Preference Alignment (MPA) approaches predominantly rely on static linear scalarization or rigid gradient projection to navigate these trade-offs. However, by enforcing strict conflict avoidance or simultaneous descent, these paradigms often prematurely converge to local stationary points. While mathematically stable, these points represent a conservative compromise where the model sacrifices potential global Pareto improvements to avoid transient local trade-offs. To break this deadlock, we propose Pareto-Lenient Consensus (PLC), a game-theoretic framework that reimagines alignment as a dynamic negotiation process. Unlike rigid approaches, PLC introduces consensus-driven lenient gradient rectification, which dynamically tolerates local degradation provided there is a sufficient dominant coalition surplus, thereby empowering the optimization trajectory to escape local suboptimal equilibrium and explore the distal Pareto-optimal frontier. Theoretical analysis validates PLC can facilitate stalemate escape and asymptotically converge to a Pareto consensus equilibrium. Moreover, extensive experiments show that PLC surpasses baselines in both fixed-preference alignment and global Pareto frontier quality. This work highlights the potential of negotiation-driven alignment as a promising avenue for MPA. Our codes are available at https://anonymous.4open.science/r/aaa-6BB8.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Placed in LLM preference optimization methods, one of 10 directions in LLM alignment / RLHF / preference optimisation. Builds on 40 of its 59 references that are in the map. First posted on arXiv 7 Apr 2026.

Builds on

40 of 59 references.

Closest ancestors · 10

Same direction, newest first

  1. 2025OrthAlign: Orthogonal Subspace Decomposition for Non-Interfering Multi-Objective Alignment29 Sep
  2. 2025Pareto Multi-Objective Alignment for Language Models11 Aug
  3. 2025Robust Multi-Objective Preference Alignment with Online DPO1 Mar
  4. 2025MPO: An Efficient Post-Processing Framework for Mixing Diverse Preference Alignment25 Feb
  5. 2025Mix Data or Merge Models? Balancing the Helpfulness, Honesty, and Harmlessness of Large Language Model via Model Merging8 Feb
  6. 2025Align-Pro: A Principled Approach to Prompt Optimization for LLM Alignment7 Jan
  7. 2024Decoding-Time Language Model Alignment with Multiple Objectives27 Jun
  8. 2024Arithmetic Control of LLMs for Diverse User Preferences: Directional Preference Alignment with Multi-Objective Rewards28 Feb
  9. 2024Rewards-in-Context: Multi-objective Alignment of Foundation Models with Dynamic Preference Adjustment15 Feb
  10. 2024Panacea: Pareto Alignment via Preference Adaptation for LLMs3 Feb

Other references · 30

  1. 2017Proximal Policy Optimization Algorithmscited by 14,441
  2. 2017Deep reinforcement learning from human preferencescited by 3,846
  3. 2015Trust Region Policy Optimizationcited by 2,503
  4. 2020Gradient Surgery for Multi-Task Learningcited by 1,210
  5. 2023BeaverTails: Towards Improved Safety Alignment of LLM via a Human-Preference Datasetcited by 828
  6. 2013Stochastic First- and Zeroth-order Methods for Nonconvex Stochastic Programmingcited by 716
  7. 2023Safe RLHF: Safe Reinforcement Learning from Human Feedbackcited by 630
  8. 2021Conflict-Averse Gradient Descent for Multi-task Learningcited by 441
  9. 2023Fine-Grained Human Feedback Gives Better Rewards for Language Model Trainingcited by 399
  10. 2023Understanding the Effects of RLHF on LLM Generalisation and Diversitycited by 386
  11. 2024Interpretable Preferences via Multi-Objective Reward Modeling and Mixture-of-Expertscited by 369
  12. 2023Iterative Preference Learning from Human Feedback: Bridging Theory and Practice for RLHF under KL-Constraintcited by 346
18 more
  1. 2014Identifying and attacking the saddle point problem in high-dimensional non-convex optimizationcited by 286
  2. 2017How to Escape Saddle Points Efficientlycited by 284
  3. 2023Personalized Soups: Personalized Large Language Model Alignment via Post-hoc Parameter Mergingcited by 271
  4. 2015Escaping From Saddle Points --- Online Stochastic Gradient for Tensor Decompositioncited by 241
  5. 2023Rewarded soups: towards Pareto-optimal alignment by interpolating weights fine-tuned on diverse rewardscited by 232
  6. 2023Using an LLM to Help With Code Understandingcited by 222
  7. 2024A Minimaximalist Approach to Reinforcement Learning from Human Feedbackcited by 141
  8. 2013Accelerated Gradient Methods for Nonconvex Nonlinear and Stochastic Programmingcited by 74
  9. 2023A Survey on Detection of LLMs-Generated Contentcited by 70
  10. 2025Calibrated Multi-Preference Optimization for Aligning Diffusion Modelscited by 40
  11. 2017Lenient Multi-Agent Deep Reinforcement Learningcited by 35
  12. 2024MAP: Multi-Human-Value Alignment Palettecited by 20
  13. 2022A Game-Theoretic Framework for Managing Risk in Multi-Agent Systemscited by 13
  14. 2025Fundamental Limits of Game-Theoretic LLM Alignment: Smith Consistency and Preference Matchingcited by 10
  15. 2023Inductive biases of multi-task learning and finetuning: multiple regimes of feature reusecited by 9
  16. 2025Stackelberg Learning from Human Feedback: Preference Optimization as a Sequential Gamecited by 9
  17. 2024Many-Objective Evolutionary Influence Maximization: Balancing Spread, Budget, Fairness, and Timecited by 1
  18. 2025Rational Adversaries and the Maintenance of Fragility: A Game-Theoretic Theory of Rational Stagnationcited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026Explaining and Breaking the Safety-Helpfulness Ceiling via Preference Dimensional Expansioncited by 1
  2. 2026Robotics-Inspired Guardrails for Foundation Models in Socially Sensitive Domainscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024Controllable Preference Optimization: Toward Controllable Multi-Objective Alignment
  2. 2024Pareto-Optimal Learning from Preferences with Hidden Context
  3. 2026Robust Nash Alignment under Preference Uncertainty
  4. 2025Self-Improvement Towards Pareto Optimality: Mitigating Preference Conflicts in Multi-Objective Alignment
  5. 2024COMAL: A Convergent Meta-Algorithm for Aligning LLMs with General Preferences
  6. 2026Socially-Weighted Alignment: A Game-Theoretic Framework for Multi-Agent LLM Systems
  7. 2025Multi-Objective Preference Optimization: Improving Human Alignment of Generative Models
  8. 2025Learning to Optimize Multi-Objective Alignment Through Dynamic Reward Weighting