Mixture of Reasonings: Teach Large Language Models to Reason with Adaptive Strategies
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| Main Authors: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866908431209725952 |
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| author | Xiong, Tao Hu, Xavier Fan, Wenyan Zhang, Shengyu |
| author_facet | Xiong, Tao Hu, Xavier Fan, Wenyan Zhang, Shengyu |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_00606 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Mixture of Reasonings: Teach Large Language Models to Reason with Adaptive Strategies Xiong, Tao Hu, Xavier Fan, Wenyan Zhang, Shengyu Computation and Language Artificial Intelligence 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. |
| title | Mixture of Reasonings: Teach Large Language Models to Reason with Adaptive Strategies |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2507.00606 |