The text indicates that algorithm prompting does not lead to improved performance in Large Reasoning Models (LRMs). Even when provided with a complete algorithm for solving the Tower of Hanoi puzzle, models did not show improved performance, as their accuracy collapsed at similar complexity points. This suggests that their limitations lie not just in problem-solving and solution strategy discovery, but also in consistent logical verification and execution of steps throughout their reasoning processes[1].
The findings highlight a fundamental challenge: LRM performance does not significantly benefit from algorithm prompts, as they fail to leverage explicit guidance effectively[1].
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