Stackelberg Equilibrium

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Multi-LLM Collaborative Alignment via Stackelberg Games
Sep 30, 2026Christina Hahn, Shangbin Feng, Dean Light +3Large Language Model Alignment Stackelberg Equilibrium
University of Washington · Amazon · University of British Columbia
Learning to Re-Draft: A Variational Stackelberg Game for Discrete Diffusion
Sep 28, 2026Dmitrii Moor, Federico Tomasi, Paul N. Bennett +2Masked Diffusion Language Models Diffusion Dynamics
Spotify, London, UK · Spotify, Boston, USA · Spotify, New York, USA
CoNav-UAV: Cooperative Dual-Altitude Aerial Navigation via Stackelberg Learning
Aug 3, 2026Junru Song, Wenhao Zhang, Yang Yang +7Aerial Vision-Language Navigation Multi-Uav
1Shanghai Jiao Tong University · 2Intelligent Game and Decision Laboratory · 3Renmin University of China +1
Entropy-Regularized Reinforcement Learning for Linear-Quadratic Stackelberg Differential Games in Regime-Switching Diffusion Models
Jun 27, 2026Congde Hu, Danping Li, Lin Xu +1Entropy Regularized Reinforcement Learning Hierarchical Reinforcement Learning
School of Mathematics and Statistics, Anhui Normal University, Wuhu, Anhui 241002, China · School of Statistics, Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE, Faculty of Economics and Management, East China Normal University, Shanghai, China · School of Mathematics, Southeast University, Nanjing, Jiangsu 211189, China
A Stackelberg Framework for Resource-Aware LLM Agents: Learning, Repair, and Conditional Guarantees
Jun 22, 2026Baoxun WangLarge Language Model Agents Stackelberg Equilibrium
Platform and Content Group, Tencent
Rethinking Neural Network Learning Rates: A Stackelberg Perspective
May 15, 2026Sihan Zeng, Sujay Bhatt, Sumitra GaneshBatch Gradient Descent
JPMorgan AI Research, United States.
Differentially Private Auditing Under Strategic Response
May 8, 2026Florian A. D. BurnatModel Auditing Bounded Adversary
University of Bath
Rethinking Priority Scheduling for Sequential Multi-Agent Decision Making in Stackelberg Games
May 8, 2026Xiangyu Liu, Liang Zhang, Bo Jin +1Stackelberg Equilibrium Prioritization
Dalian University of Technology
Evolutionarily Stable Stackelberg Equilibrium
Mar 19, 2026Sam GanzfriedStackelberg Equilibrium
Ganzfried Research, Cornell University
LLM-Empowered Agentic MAC Protocols: A Dynamic Stackelberg Game Approach
Oct 13, 2025Renxuan Tan, Rongpeng Li, Fei Wang +4Multi-Agent Reinforcement Learning Stackelberg Equilibrium
College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310027, China · Huawei Technologies Company Ltd., Shanghai 210026, China · Zhejiang Lab as well as Zhejiang University, Hangzhou 310027, China +1
Learning in Structured Stackelberg Games
Apr 11, 2025Maria-Florina Balcan, Kiriaki Fragkia, Keegan HarrisStackelberg Equilibrium Utility Maximization
Carnegie Mellon University, Pittsburgh, USA · University of California, Berkeley, USA
How Can Incentives and Cut Layer Selection Influence Data Contribution in Split Federated Learning?
Dec 10, 2024Joohyung Lee, Jungchan Cho, Wonjun Lee +2Stackelberg Equilibrium Incentives
School of Computing, Gachon University, Seongnam 13120, Rep. of Korea · School of Cybersecurity, Korea University, Seoul, Rep. of Korea · Department of Electrical and Computer Engineering, Princeton University, Princeton, NJ 08544 USA
Calibrated Stackelberg Games: Learning Optimal Commitments Against Calibrated Agents
Jun 5, 2023Nika Haghtalab, Chara Podimata, Kunhe YangStackelberg Equilibrium Game Theory
University of California, Berkeley · Massachusetts Institute of Technology

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Multi-LLM Collaborative Alignment via Stackelberg Games

Christina Hahn, Shangbin Feng, Dean Light, Swastik Roy+2

University of Washington · Amazon · University of British Columbia

Large Language Model Alignment Stackelberg Equilibrium Adaptive Curriculum Optimistic Follow-The-Regularized-Leader

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A pool of language models can collaborate and improve collectively by learning from one another's responses. These interactions depend on the instructions used during training. Existing methods typically sample instructions uniformly, even though their usefulness may change as the models improve: an instruction on which models' responses once differed in quality may later be answered equally well, while a previously difficult instruction may begin to provide a useful learning signal. We propose Stackelberg Alignment, a game-theory-inspired leader-follower framework that turns instruction selection into an adaptive curriculum. An EXP3 bandit acts as the leader, allocating a fixed sampling budget across instructions and updating its sampling distribution using a reward that combines instruction difficulty and response discriminability. The language models act as followers: they respond to the selected instructions, evaluate one another's responses, and learn from the resulting preference signals through DPO or GRPO. The framework uses Elo-style reputation-weighted peer judgment and reputation-based opponent matching to support reliable and competitive model interactions. Experiments across three heterogeneous model pools and 12 benchmarks spanning scientific discovery, reasoning, code, instruction following, and knowledge show that Stackelberg Alignment achieves the highest macro-average across three diverse model pools, outperforming the strongest training-time baseline by up to 7.4% and the best static inference baseline by 12-25%. Analysis confirms that the adaptive leader concentrates duels on the most informative instructions, and ablations show that both reputation-weighted judgment and reputation-based matching improve the effectiveness of multi-LLM evolution.

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