Complexity-aware fine-tuning
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866917357254868992 |
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| author | Goncharov, Andrey Vyazhev, Daniil Sychev, Petr Khalafyan, Edvard Zaytsev, Alexey |
| author_facet | Goncharov, Andrey Vyazhev, Daniil Sychev, Petr Khalafyan, Edvard Zaytsev, Alexey |
| contents | General-purpose Large Language Models (LLMs) are frequently fine-tuned through supervised fine-tuning (SFT) to enhance performance in specific domains. Better results can be achieved by distilling the chain-of-thought of a larger model at the cost of numerous expensive calls and a much greater amount of data. We propose a novel blueprint for efficient fine-tuning that uses reasoning only for complex data identified by entropy. Specifically, across three small open models ($\approx 3B$) we split the training data into complexity categories by a single token answer entropy (ROC AUC $0.73$), fine-tune large language models (LLMs) via SFT and distillation, and show that our pipeline significantly outperforms the standard SFT approach ($0.58$ vs $0.45$ average accuracy) and outperforms the distillation approach ($0.58$ vs $0.56$ average accuracy) while using $81\%$ less data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_21220 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Complexity-aware fine-tuning Goncharov, Andrey Vyazhev, Daniil Sychev, Petr Khalafyan, Edvard Zaytsev, Alexey Machine Learning Computation and Language General-purpose Large Language Models (LLMs) are frequently fine-tuned through supervised fine-tuning (SFT) to enhance performance in specific domains. Better results can be achieved by distilling the chain-of-thought of a larger model at the cost of numerous expensive calls and a much greater amount of data. We propose a novel blueprint for efficient fine-tuning that uses reasoning only for complex data identified by entropy. Specifically, across three small open models ($\approx 3B$) we split the training data into complexity categories by a single token answer entropy (ROC AUC $0.73$), fine-tune large language models (LLMs) via SFT and distillation, and show that our pipeline significantly outperforms the standard SFT approach ($0.58$ vs $0.45$ average accuracy) and outperforms the distillation approach ($0.58$ vs $0.56$ average accuracy) while using $81\%$ less data. |
| title | Complexity-aware fine-tuning |
| topic | Machine Learning Computation and Language |
| url | https://arxiv.org/abs/2506.21220 |