KORGym: A Dynamic Game Platform for LLM Reasoning Evaluation
This provides a new benchmark for evaluating reasoning in LLMs, which is incremental as it builds on existing platforms like KOR-Bench and Gymnasium.
The authors tackled the need for better evaluation of LLM reasoning by introducing KORGym, a dynamic platform with over 50 games, and found that closed-source models performed best in tests on 19 LLMs and 8 VLMs.
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.