Mixture of Reasonings: Teach Large Language Models to Reason with
  Adaptive Strategies
Authors: name: Tao Xiong; name: Xavier Hu; name: Wenyan Fan; name: Shengyu Zhang
Published: 2025-07-01 09:39:04

Abstract: Large language models (LLMs) excel in complex tasks through advanced
prompting techniques like Chain-of-Thought (CoT) and Tree-of-Thought (ToT), but
their reliance on manually crafted, task-specific prompts limits adaptability
and efficiency. We introduce Mixture of Reasoning (MoR), a training framework
that embeds diverse reasoning strategies into LLMs for autonomous,
task-adaptive reasoning without external prompt engineering. MoR has two
phases: Thought Generation, creating reasoning chain templates with models like
GPT-4o, and SFT Dataset Construction, pairing templates with benchmark datasets
for supervised fine-tuning.Our experiments show that MoR significantly enhances
performance, with MoR150 achieving 0.730 (2.2% improvement) using CoT prompting
and 0.734 (13.5% improvement) compared to baselines. MoR eliminates the need
for task-specific prompts, offering a generalizable solution for robust
reasoning across diverse tasks.