AINeutralarXiv – CS AI · Apr 136/10
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Temperature-Dependent Performance of Prompting Strategies in Extended Reasoning Large Language Models
Researchers systematically evaluated how sampling temperature and prompting strategies affect extended reasoning performance in large language models, finding that zero-shot prompting peaks at moderate temperatures (T=0.4-0.7) while chain-of-thought performs better at extremes. The study reveals that extended reasoning benefits grow substantially with higher temperatures, suggesting that T=0 is suboptimal for reasoning tasks.
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