mlsanityData to 7 Oct 2026

GameTraversalBenchmark: Evaluating Planning Abilities Of Large Language Models Through Traversing 2D Game Maps

arXiv 2410.07765 ↗First posted 10 Oct 2024cs.CL cs.AIcited by 8

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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.

References · 32

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20 more
  1. 2022Visual Programming: Compositional visual reasoning without trainingcited by 507
  2. 2022PlanBench: An Extensible Benchmark for Evaluating Large Language Models on Planning and Reasoning about Changecited by 372
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 6; 2026: 2.

8 papers.

Similar, not cited

Close in wording, no citation either way

  1. 2024LogicGame: Benchmarking Rule-Based Reasoning Abilities of Large Language Models
  2. 2026Reasoning Capabilities of Large Language Models. Lessons Learned from General Game Playing
  3. 2024GameBench: Evaluating Strategic Reasoning Abilities of LLM Agents
  4. 2025Code World Models for General Game Playing
  5. 2024TMGBench: A Systematic Game Benchmark for Evaluating Strategic Reasoning Abilities of LLMs
  6. 2024Mastering Board Games by External and Internal Planning with Language Models
  7. 2025Measuring General Intelligence with Generated Games
  8. 2025Text2World: Benchmarking Large Language Models for Symbolic World Model Generation