GameTraversalBenchmark: Evaluating Planning Abilities Of Large Language Models Through Traversing 2D Game Maps
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Abstract (arXiv)
Large language models (LLMs) have recently demonstrated great success in generating and understanding natural language. While they have also shown potential beyond the domain of natural language, it remains an open question as to what extent and in which way these LLMs can plan. We investigate their planning capabilities by proposing GameTraversalBenchmark (GTB), a benchmark consisting of diverse 2D grid-based game maps. An LLM succeeds if it can traverse through given objectives, with a minimum number of steps and a minimum number of generation errors. We evaluate a number of LLMs on GTB and found that GPT-4-Turbo achieved the highest score of 44.97% on GTBScore (GTBS), a composite score that combines the three above criteria. Furthermore, we preliminarily test large reasoning models, namely o1, which scores 67.84% on GTBS, indicating that the benchmark remains challenging for current models. Code, data, and documentation are available at https://github.com/umair-nasir14/Game-Traversal-Benchmark.
Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 32 of its 39 references that are in the map. First posted on arXiv 10 Oct 2024.
Builds on
32 of 39 references.
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20 more
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Built on by
Citing papers per year: 2025: 6; 2026: 2.
8 papers.
- 2025Measuring what Matters: Construct Validity in Large Language Model Benchmarkscited by 102
- 2025lmgame-Bench: How Good are LLMs at Playing Games?cited by 54
- 2025VideoGameBench: Can Vision-Language Models complete popular video games?cited by 29
- 2025Agents of Change: Self-Evolving LLM Agents for Strategic Planningcited by 20
- 2025GVGAI-LLM: Evaluating Large Language Model Agents with Infinite Gamescited by 8
- 2026Agentic Environment Engineering for Large Language Models: A Survey of Environment Modeling, Synthesis, Evaluation, and Applicationcited by 8
- 2025PuzzleJAX: A Benchmark for Reasoning and Learningcited by 4
- 2026Towards Generalist Game Players: An Investigation of Foundation Models in the Game Multiversecited by 2
- 2026Agentic Environment Engineering for Large Language Models: A Survey of Environment Modeling, Synthesis, Evaluation, and Application10 Jun 2026
- 2026Towards Generalist Game Players: An Investigation of Foundation Models in the Game Multiverse11 May 2026
- 2025Measuring what Matters: Construct Validity in Large Language Model Benchmarks3 Nov 2025
- 2025PuzzleJAX: A Benchmark for Reasoning and Learning22 Aug 2025
- 2025GVGAI-LLM: Evaluating Large Language Model Agents with Infinite Games11 Aug 2025
- 2025Agents of Change: Self-Evolving LLM Agents for Strategic Planning5 Jun 2025
- 2025VideoGameBench: Can Vision-Language Models complete popular video games?23 May 2025
- 2025lmgame-Bench: How Good are LLMs at Playing Games?21 May 2025
Similar, not cited
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- 2026Reasoning Capabilities of Large Language Models. Lessons Learned from General Game Playing
- 2024GameBench: Evaluating Strategic Reasoning Abilities of LLM Agents
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- 2024TMGBench: A Systematic Game Benchmark for Evaluating Strategic Reasoning Abilities of LLMs
- 2024Mastering Board Games by External and Internal Planning with Language Models
- 2025Measuring General Intelligence with Generated Games
- 2025Text2World: Benchmarking Large Language Models for Symbolic World Model Generation