SokoBench: Evaluating long-horizon planning and reasoning in large language models

Analysis

Why This Paper Matters

Long-horizon planning—the ability to reason through many sequential steps to achieve a goal—is a fundamental challenge for large language models (LLMs) and reasoning models (LRMs). While these models excel at short-term reasoning tasks like question answering or code generation, their performance often degrades when tasks require sustained logical consistency over dozens or hundreds of steps. This paper addresses this gap by introducing SokoBench, a benchmark derived from the classic Sokoban puzzle, but simplified to isolate planning ability from spatial complexity. By doing so, the authors provide a clean testbed for evaluating and comparing LRMs on long-horizon tasks.

The significance of this work lies in its focus on a core limitation of current AI systems. As LRMs are increasingly deployed in domains like robotics, autonomous driving, and strategic decision-making, the ability to plan over extended horizons becomes critical. SokoBench offers a controlled environment to measure this capability, enabling researchers to identify specific weaknesses and track improvements over time. The paper's approach of simplifying the puzzle to reduce confounding factors is methodologically sound, making it a valuable contribution to the evaluation landscape.

Technical Contributions

• SokoBench Benchmark: A new benchmark based on a simplified Sokoban variant, designed to test long-horizon planning in LRMs. The simplification reduces spatial complexity, focusing on sequential decision-making.
• Controlled Task Design: The benchmark includes tasks of varying horizon lengths, allowing systematic analysis of performance degradation as the number of steps increases.
• Comprehensive Evaluation: The authors evaluate multiple state-of-the-art LRMs, providing a comparative analysis of their planning capabilities.
• Failure Mode Analysis: The paper likely includes an analysis of common errors (e.g., deadlocks, suboptimal paths) to understand why models fail on long-horizon tasks.

Results

While the abstract does not provide specific numerical results, the paper reports that current LRMs exhibit significant difficulty with long-horizon planning. Performance drops as the required number of steps increases, with models often failing to complete tasks that require more than a certain number of moves. The benchmark likely reveals that even models with strong short-term reasoning capabilities struggle to maintain coherence over extended sequences. The paper may also compare different prompting strategies or model sizes, showing that larger models do not necessarily solve long-horizon problems better, highlighting a scaling limitation.

Significance

SokoBench fills a critical gap in AI evaluation by providing a focused benchmark for long-horizon planning. It challenges the assumption that scaling up models alone will solve reasoning problems, suggesting that architectural innovations or training methods may be needed. The benchmark can serve as a standard for future research, guiding the development of LRMs with improved planning abilities. Moreover, the insights from failure modes could inform the design of memory-augmented models or hierarchical planning frameworks. As AI systems move toward more autonomous and goal-directed behavior, benchmarks like SokoBench will be essential for measuring progress and ensuring reliability in real-world applications.