LLMs: Why Simple Puzzles Stump, Hard Ones Fail

Large Language Models (LLMs) and Large Reasoning Models (LRMs) show surprising behavior when solving puzzles. A recent Apple study reveals that LLMs excel at simple tasks but struggle with complex ones, while LRMs overthink simple puzzles and give up on hard ones. The study utilized classic puzzles like the Tower of Hanoi with varying complexity to evaluate performance. At low complexity, standard LLMs outperformed LRMs, which over-reasoned. For medium complexity, LRMs excelled due to their Chain-of-Thought (CoT) prompting, breaking problems into steps. However, for high complexity, both failed, with LRMs surprisingly reducing effort despite resources, showcasing a limitation in scaling reasoning capabilities. This “overthinking” on simple tasks may stem from training data containing detailed explanations, even when concise answers suffice. Failure on complex puzzles highlights an inability to generalize logical rules and consistently apply algorithms, unlike human reasoning. The research sparked discussions on AI reasoning definitions and evaluation methods. The study emphasizes the need for AI to adapt reasoning effort to problem complexity, and for new evaluation metrics focusing on reasoning quality and adaptability beyond just final accuracy. Future research should focus on improving generalization of logical rules and developing benchmarks reflecting real-world reasoning tasks for more meaningful AI evaluation. The study exposes a significant gap between simulated and genuine reasoning in current LLMs and LRMs.

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(Source: https://www.unite.ai/why-llms-overthink-easy-puzzles-but-give-up-on-hard-ones/)