GTAlign: Game-Theoretic Alignment of LLM Assistants for Mutual Welfare
Authors: name: Siqi Zhu, name: David Zhang, name: Pedro Cisneros-Velarde, name: Jiaxuan You
Organization: University of Illinois at Urbana-Champaign

Large Language Models (LLMs) have achieved remarkable progress in reasoning,
yet sometimes produce responses that are suboptimal for users in tasks such as
writing, information seeking, or providing practical guidance. Conventional
alignment practices typically assume that maximizing model reward also
maximizes user welfare, but this assumption frequently fails in practice:
models may over-clarify or generate overly verbose reasoning when users prefer
concise answers. Such behaviors resemble the prisoner's dilemma, where
individually rational choices lead to socially suboptimal outcomes. The
fundamental challenge is the lack of a principled decision making mechanism
that mutually benefits both the LLM and the user. We propose Game-Theoretic
Alignment (GTAlign), an alignment framework that integrates game-theoretic
decision making into both reasoning and training. During reasoning, the model
explicitly treats user-LLM interaction as a strategic game: it constructs
payoff matrices within its reasoning chain to estimate welfare for both itself
and the user, and then selects actions that are mutually beneficial. During
training, we introduce a mutual welfare reward that reinforces cooperative
responses, aligning model behavior with socially efficient outcomes. In
addition, we introduce an inference technique that leverages game-theoretic
reasoning to dynamically adapt LLM's response when pricing policies of LLM
service change. Extensive experiments demonstrate that GTAlign substantially
improves reasoning efficiency, answer quality, and mutual welfare compared to
baselines across diverse tasks. The code is available at
https://github.com/ulab-uiuc/GTAlign .