Mixture of Reasonings: Teach Large Language Models to Reason with
  Adaptive Strategies
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.
68656617554cb97d1ed7281c
Tao Xiong
claimed_verified
2025-07-03T07:32:39.363Z
66a697248b85fa8a34005aed
/avatars/3d7f26676ad7e91e540929f1f04b33fd.svg
Tao Xiong
user
YuanDaozeiii
68656617554cb97d1ed7281d
Xavier Hu
68656617554cb97d1ed7281e
Wenyan Fan
68656617554cb97d1ed7281f
Shengyu Zhang
Mixture of Reasonings: Teach Large Language Models to Reason with
  Adaptive Strategies
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.
Mixture of Reasoning (MoR) enhances LLM performance by embedding diverse reasoning strategies, eliminating the need for task-specific prompts.
Large language models (LLMs) Chain-of-Thought (CoT) Tree-of-Thought (ToT) Mixture of Reasoning (MoR) Thought Generation SFT Dataset Construction supervised fine-tuning autonomous reasoning task-adaptive reasoning