A Short Survey on Small Reasoning Models: Training, Inference, Applications and Research Directions
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866915238164561920 |
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| author | Wang, Chengyu Zhang, Taolin Hong, Richang Huang, Jun |
| author_facet | Wang, Chengyu Zhang, Taolin Hong, Richang Huang, Jun |
| contents | Recently, the reasoning capabilities of large reasoning models (LRMs), such as DeepSeek-R1, have seen significant advancements through the slow thinking process. Despite these achievements, the substantial computational demands of LRMs present considerable challenges. In contrast, small reasoning models (SRMs), often distilled from larger ones, offer greater efficiency and can exhibit distinct capabilities and cognitive trajectories compared to LRMs. This work surveys around 170 recently published papers on SRMs for tackling various complex reasoning tasks. We review the current landscape of SRMs and analyze diverse training and inference techniques related to SRMs. Furthermore, we provide a comprehensive review of SRMs for domain-specific applications and discuss possible future research directions. This survey serves as an essential reference for researchers to leverage or develop SRMs for advanced reasoning functionalities with high efficiency. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_09100 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Short Survey on Small Reasoning Models: Training, Inference, Applications and Research Directions Wang, Chengyu Zhang, Taolin Hong, Richang Huang, Jun Artificial Intelligence Computation and Language Recently, the reasoning capabilities of large reasoning models (LRMs), such as DeepSeek-R1, have seen significant advancements through the slow thinking process. Despite these achievements, the substantial computational demands of LRMs present considerable challenges. In contrast, small reasoning models (SRMs), often distilled from larger ones, offer greater efficiency and can exhibit distinct capabilities and cognitive trajectories compared to LRMs. This work surveys around 170 recently published papers on SRMs for tackling various complex reasoning tasks. We review the current landscape of SRMs and analyze diverse training and inference techniques related to SRMs. Furthermore, we provide a comprehensive review of SRMs for domain-specific applications and discuss possible future research directions. This survey serves as an essential reference for researchers to leverage or develop SRMs for advanced reasoning functionalities with high efficiency. |
| title | A Short Survey on Small Reasoning Models: Training, Inference, Applications and Research Directions |
| topic | Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2504.09100 |