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Bibliographic Details
Main Authors:	Shi, Jiajun, Yang, Jian, Liu, Jiaheng, Bu, Xingyuan, Chen, Jiangjie, Zhou, Junting, Ma, Kaijing, Wen, Zhoufutu, Wang, Bingli, He, Yancheng, Song, Liang, Zhu, Hualei, Li, Shilong, Wang, Xingjian, Zhang, Wei, Yuan, Ruibin, Yao, Yifan, Yang, Wenjun, Wang, Yunli, Fang, Siyuan, Yuan, Siyu, He, Qianyu, Tang, Xiangru, Tan, Yingshui, Zhou, Wangchunshu, Zhang, Zhaoxiang, Li, Zhoujun, Huang, Wenhao, Zhang, Ge
Format:	Preprint
Published:	2025
Subjects:	Computation and Language Artificial Intelligence Machine Learning
Online Access:	https://arxiv.org/abs/2505.14552
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Table of Contents:

Recent advancements in large language models (LLMs) underscore the need for more comprehensive evaluation methods to accurately assess their reasoning capabilities. Existing benchmarks are often domain-specific and thus cannot fully capture an LLM's general reasoning potential. To address this limitation, we introduce the Knowledge Orthogonal Reasoning Gymnasium (KORGym), a dynamic evaluation platform inspired by KOR-Bench and Gymnasium. KORGym offers over fifty games in either textual or visual formats and supports interactive, multi-turn assessments with reinforcement learning scenarios. Using KORGym, we conduct extensive experiments on 19 LLMs and 8 VLMs, revealing consistent reasoning patterns within model families and demonstrating the superior performance of closed-source models. Further analysis examines the effects of modality, reasoning strategies, reinforcement learning techniques, and response length on model performance. We expect KORGym to become a valuable resource for advancing LLM reasoning research and developing evaluation methodologies suited to complex, interactive environments.

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