The Illusion of Thinking: Understanding the Strengths and Limitations of Reasoning Models via the Lens of Problem Complexity
Overview
Paper Summary
Large Reasoning Models (LRMs) fail to develop generalizable problem-solving capabilities in complex puzzle environments, eventually reaching zero accuracy beyond certain complexity thresholds. They also exhibit a counterintuitive behavior, reducing their reasoning effort (thinking tokens) as problem complexity increases despite having available compute budget, suggesting inherent scaling limitations.
Explain Like I'm Five
Scientists found that even very smart computer brains are like kids doing puzzles: they're great at easy ones. But when puzzles get too hard, they just give up and can't solve them at all, even trying less hard as it gets tougher.
Possible Conflicts of Interest
The authors work at Apple, which may have interests in LLM development, but the study does not directly evaluate Apple's models, minimizing the direct COI.
Identified Limitations
Rating Explanation
This is a well-designed study that provides interesting insights into LRM limitations. The controlled experiments and detailed trace analysis offer valuable data. While the scope is limited to puzzle environments, the findings on scaling limitations and reasoning patterns have broader relevance. Minor limitations related to reliance on closed-source LLMs and limited evaluation metrics prevent a top rating.
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