用棋类游戏评估大模型决策力，支持多类型智能体对比。
Game Reasoning Arena: A Framework and Benchmark for Assessing Reasoning Capabilities of Large Language Models via Game Play
- 通过谷歌开源棋类框架封装多种策略游戏
- 支持大模型与随机/强化学习等智能体对比测试
- 适合研究大模型推理与博弈行为的学者使用
Game Reasoning Arena 库提供了一个基于 Google OpenSpiel 框架的评估体系，通过战略棋类游戏系统性地评测大型语言模型（LLM）的决策能力。该框架封装了多种棋类和矩阵博弈游戏，支持不同类型的智能体（如随机、启发式、强化学习等）进行对比实验。通过 liteLLM 提供模型 API 接入，支持 vLLM 本地部署，并利用 Ray 实现分布式执行。本文总结了该库的结构、核心特性与设计动机，强调其在实证评估大模型推理能力和博弈行为方面的贡献。
原文摘要 · Abstract (English)
The Game Reasoning Arena library provides a framework for evaluating the decision making abilities of large language models (LLMs) through strategic board games implemented in Google OpenSpiel library. The framework enables systematic comparisons between LLM based agents and other agents (random, heuristic, reinforcement learning agents, etc.) in various game scenarios by wrapping multiple board and matrix games and supporting different agent types. It integrates API access to models via liteLLM, local model deployment via vLLM, and offers distributed execution through Ray. This paper summarises the library structure, key characteristics, and motivation of the repository, highlighting how it contributes to the empirical evaluation of the reasoning of LLM and game theoretic behaviour.
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