Large Reasoning Models (LRMs) outpace standard Large Language Models (LLMs) at medium complexity tasks. In these scenarios, LRMs demonstrate an advantage as their additional reasoning capabilities allow them to perform better than their non-thinking counterparts. Specifically, they begin to show their strengths as the complexity of the problems increases beyond the initial low-complexity tasks where standard LLMs often outperform them.
However, both model types eventually experience a collapse in accuracy at high complexity tasks, highlighting a fundamental limitation in LRMs despite their advanced reasoning models. This pattern reveals three distinct reasoning regimes based on problem complexity[1].
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