Beyond Compromise: Pareto-Lenient Consensus for Efficient Multi-Preference LLM Alignment
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-theor...
| Authors | Renxuan Tan, Rongpeng Li, Zhifeng Zhao et al. |
|---|---|
| Primary category | cs.AI |
| Published | 7 April 2026 |
| Last updated at source | 7 April 2026 |
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