[2604.08563] Temperature-Dependent Performance of Prompting Strategies in Extended Reasoning Large Language Models

Computer Science > Computation and Language arXiv:2604.08563 (cs) [Submitted on 18 Mar 2026] Title:Temperature-Dependent Performance of Prompting Strategies in Extended Reasoning Large Language Models Authors:Mousa Salah, Amgad Muneer View a PDF of the paper titled Temperature-Dependent Performance of Prompting Strategies in Extended Reasoning Large Language Models, by Mousa Salah and Amgad Muneer View PDF HTML (experimental) Abstract:Extended reasoning models represent a transformative shift in Large Language Model (LLM) capabilities by enabling explicit test-time computation for complex problem solving. However, the optimal configuration of sampling temperature and prompting strategy for these systems remains largely underexplored. We systematically evaluate chain-of-thought and zero-shot prompting across four temperature settings (0.0, 0.4, 0.7, and 1.0) using Grok-4.1 with extended reasoning on 39 mathematical problems from AMO-Bench, a challenging International Mathematical Olympiad-level benchmark. We find that zero-shot prompting achieves peak performance at moderate temperatures, reaching 59% accuracy at T=0.4 and T=0.7, while chain-of-thought prompting performs best at the temperature extremes. Most notably, the benefit of extended reasoning increases from 6x at T=0.0 to 14.3x at T=1.0. These results suggest that temperature should be optimized jointly with prompting strategy, challenging the common practice of using T=0 for reasoning tasks. Comments: Subjects: Co...