lmgame-Bench: How Good are LLMs at Playing Games?
Authors: name: Lanxiang Hu, name: Mingjia Huo, name: Yuxuan Zhang, name: Haoyang Yu, name: Eric P. Xing, name: Ion Stoica, name: Tajana Rosing, name: Haojian Jin, name: Hao Zhang

Playing video games requires perception, memory, and planning, exactly the
faculties modern large language model (LLM) agents are expected to master. We
study the major challenges in using popular video games to evaluate modern LLMs
and find that directly dropping LLMs into games cannot make an effective
evaluation, for three reasons -- brittle vision perception, prompt sensitivity,
and potential data contamination. We introduce lmgame-Bench to turn games into
reliable evaluations. lmgame-Bench features a suite of platformer, puzzle, and
narrative games delivered through a unified Gym-style API and paired with
lightweight perception and memory scaffolds, and is designed to stabilize
prompt variance and remove contamination. Across 13 leading models, we show
lmgame-Bench is challenging while still separating models well. Correlation
analysis shows that every game probes a unique blend of capabilities often
tested in isolation elsewhere. More interestingly, performing reinforcement
learning on a single game from lmgame-Bench transfers both to unseen games and
to external planning tasks. Our evaluation code is available at
https://github.com/lmgame-org/GamingAgent/lmgame-bench.