Orak: A Foundational Benchmark for Training and Evaluating LLM Agents on Diverse Video Games
KRAFTON · KRAFTON AI · KRAFTON, Inc. · Amazon · KAIST · Seoul National University · NVIDIA · University of Minnesota, Minneapolis · Columbia University · University of Wisconsin-Madison, KRAFTON AI · Korea Advanced Institute of Science and Technology
PDF 由论文原始站点提供，PaperCompass 不保存论文文件。
摘要
Large Language Model (LLM) agents are reshaping the game industry, by enabling more intelligent and human-preferable characters. Yet, current game benchmarks fall short of practical needs: they lack evaluations of diverse LLM capabilities across various game genres, studies of agentic modules crucial for complex gameplay, and fine-tuning datasets to adapt pre-trained LLMs into gaming agents. To fill these gaps, we present Orak, a benchmark for training and evaluating LLM agents across 12 popular video games spanning all major genres. Using a plug-and-play interface built on Model Context Protocol (MCP), Orak supports systematic and reproducible studies of agentic modules in varied game scenarios. We further release a fine-tuning dataset of expert LLM gameplay trajectories spanning multiple genres, turning general LLMs into effective game agents. Orak offers a comprehensive evaluation framework, including game leaderboards, LLM battle arenas, and in-depth analyses of input modality, agentic strategies, and fine-tuning effects, establishing a foundation towards versatile gaming agents. Code and datasets are available at https://github.com/krafton-ai/Orak and https://huggingface.co/datasets/KRAFTON/Orak.