Tailoring the Curriculum: Student-Centered Reasoning Distillation via Dynamic Data-Model Compatibility
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2026
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| _version_ | 1866918528871825408 |
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| author | Huang, Jiahao Cheng, Fei Jiang, Junfeng Aizawa, Akiko |
| author_facet | Huang, Jiahao Cheng, Fei Jiang, Junfeng Aizawa, Akiko |
| contents | Reasoning distillation transfers complex reasoning abilities from large language models (LLMs) to smaller ones, yet its success depends on how well the training data align with the student model. This paper introduces the Data-Model Compatibility (DMC) metric, which can be used to assess the suitability of a dataset for reasoning distillation on a student model. DMC provides an assessment by jointly considering data quality, relative difficulty, and student capability. We validated the effectiveness of DMC from two perspectives: (1) DMC exhibits a strong correlation with reasoning distillation performance; and (2) using DMC as the criterion for data selection leads to improved reasoning distillation performance. Both findings are consistently demonstrated across multiple student models and tasks. Moreover, since the DMC of each dataset dynamically changes during training, our experiments demonstrate that dynamically selecting datasets based on DMC can further enhance performance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_29229 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Tailoring the Curriculum: Student-Centered Reasoning Distillation via Dynamic Data-Model Compatibility Huang, Jiahao Cheng, Fei Jiang, Junfeng Aizawa, Akiko Artificial Intelligence Reasoning distillation transfers complex reasoning abilities from large language models (LLMs) to smaller ones, yet its success depends on how well the training data align with the student model. This paper introduces the Data-Model Compatibility (DMC) metric, which can be used to assess the suitability of a dataset for reasoning distillation on a student model. DMC provides an assessment by jointly considering data quality, relative difficulty, and student capability. We validated the effectiveness of DMC from two perspectives: (1) DMC exhibits a strong correlation with reasoning distillation performance; and (2) using DMC as the criterion for data selection leads to improved reasoning distillation performance. Both findings are consistently demonstrated across multiple student models and tasks. Moreover, since the DMC of each dataset dynamically changes during training, our experiments demonstrate that dynamically selecting datasets based on DMC can further enhance performance. |
| title | Tailoring the Curriculum: Student-Centered Reasoning Distillation via Dynamic Data-Model Compatibility |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2605.29229 |