GTAlign: Game-Theoretic Alignment of LLM Assistants for Mutual Welfare
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 .
68ec51cecd07fb414898c8b1
Siqi Zhu
claimed_verified
2025-10-13T10:06:14.231Z
65621fd68631d43d2baf33b2
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/1JemNCnPkS1mE3SNygsE2.png
siqi zhu
user
zsqzz
68ec51cecd07fb414898c8b2
David Zhang
68ec51cecd07fb414898c8b3
Pedro Cisneros-Velarde
68ec51cecd07fb414898c8b4
Jiaxuan You
GTAlign: Game-Theoretic Alignment of LLM Assistants for Mutual Welfare
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 .
Game-Theoretic Alignment (GTAlign) improves Large Language Model (LLM) performance by integrating game-theoretic decision making into reasoning and training, enhancing efficiency, answer quality, and mutual welfare.
Large Language Models Game-Theoretic Alignment GTAlign strategic game payoff matrices mutual welfare reward inference technique reasoning efficiency answer quality mutual welfare