PAPERS

2024

1. AAMAS

   Facility Location Games with Scaling Effects

   Yu He, Alexander Lam, and Minming Li

   In Proceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2024), May 2024

   arXiv Bib

   @inproceedings{Yu2024aamas,
     author = {He, Yu and Lam, Alexander and Li, Minming},
     title = {Facility Location Games with Scaling Effects},
     booktitle = {Proceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2024)},
     editor = {Alechina, Natasha and Dignum, Virginia and Dastani, Mehdi and Sichman, J. S.},
     address = {Auckland, New Zealand},
     month = may,
     year = {2024},
     pages = {816--824},
     publisher = {International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS)},
     howpublished = {conference}
   }

2026

1. Arxiv

   How Sampling Shapes LLM Alignment: From One-Shot Optima to Iterative Dynamics

   (αβ) Yurong Chen, Yu He, Michael I. Jordan, and Fan Yao

   2026

   Abs arXiv Bib

   Standard methods for aligning large language models with human preferences learn from pairwise comparisons among sampled candidate responses and regularize toward a reference policy. Despite their effectiveness, the effects of sampling and reference choices are poorly understood theoretically. We investigate these effects through Identity Preference Optimization, a widely used preference alignment framework, and show that proper instance-dependent sampling can yield stronger ranking guarantees, while skewed on-policy sampling can induce excessive concentration under structured preferences. We then analyze iterative alignment dynamics in which the learned policy feeds back into future sampling and reference policies, reflecting a common practice of model-generated preference data. We prove that these dynamics can exhibit persistent oscillations or entropy collapse for certain parameter choices, and characterize regimes that guarantee stability. Our theoretical insights extend to Direct Preference Optimization, indicating the phenomena we captured are common to a broader class of preference-alignment methods. Experiments on real-world preference data validate our findings.

   @misc{chen2026sampling,
     title = {How Sampling Shapes LLM Alignment: From One-Shot Optima to Iterative Dynamics},
     author = {Chen, Yurong and He, Yu and Jordan, Michael I. and Yao, Fan},
     year = {2026},
     eprint = {2602.12180},
     archiveprefix = {arXiv},
     primaryclass = {cs.LG},
     howpublished = {preprint},
     ab = {true}
   }

2025

1. Arxiv

   The Complexity of Tullock Contests

   Yu He, Fan Yao, Yang Yu, Xiaoyun Qiu, Minming Li, and Haifeng Xu

   Apr 2025

   arXiv:2412.06444v2

   arXiv Bib

   @misc{he2025complexity,
     title = {The Complexity of Tullock Contests},
     author = {He, Yu and Yao, Fan and Yu, Yang and Qiu, Xiaoyun and Li, Minming and Xu, Haifeng},
     year = {2025},
     month = apr,
     eprint = {2412.06444},
     archiveprefix = {arXiv},
     primaryclass = {cs.GT},
     note = {arXiv:2412.06444v2},
     howpublished = {preprint}
   }