Distilled Pretraining: A modern lens of Data, In-Context Learning and Test-Time Scaling
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| Main Authors: | , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866908514597732352 |
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| author | Goyal, Sachin Lopez-Paz, David Ahuja, Kartik |
| author_facet | Goyal, Sachin Lopez-Paz, David Ahuja, Kartik |
| contents | In the past year, distillation has seen a renewed prominence in large language model (LLM) pretraining, exemplified by the Llama-3.2 and Gemma model families. While distillation has historically been shown to improve statistical modeling, its effects on new paradigms that are key to modern LLMs, such as test-time scaling and in-context learning, remain underexplored. In this work, we make three main contributions. First, we show that pretraining with distillation yields models that exhibit remarkably better test-time scaling. Second, we observe that this benefit comes with a trade-off: distillation impairs in-context learning capabilities, particularly the one modeled via induction heads. Third, to demystify these findings, we study distilled pretraining in a sandbox of a bigram model, which helps us isolate the common principal factor behind our observations. Finally, using these insights, we shed light on various design choices for pretraining that should help practitioners going forward. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2509_01649 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Distilled Pretraining: A modern lens of Data, In-Context Learning and Test-Time Scaling Goyal, Sachin Lopez-Paz, David Ahuja, Kartik Machine Learning In the past year, distillation has seen a renewed prominence in large language model (LLM) pretraining, exemplified by the Llama-3.2 and Gemma model families. While distillation has historically been shown to improve statistical modeling, its effects on new paradigms that are key to modern LLMs, such as test-time scaling and in-context learning, remain underexplored. In this work, we make three main contributions. First, we show that pretraining with distillation yields models that exhibit remarkably better test-time scaling. Second, we observe that this benefit comes with a trade-off: distillation impairs in-context learning capabilities, particularly the one modeled via induction heads. Third, to demystify these findings, we study distilled pretraining in a sandbox of a bigram model, which helps us isolate the common principal factor behind our observations. Finally, using these insights, we shed light on various design choices for pretraining that should help practitioners going forward. |
| title | Distilled Pretraining: A modern lens of Data, In-Context Learning and Test-Time Scaling |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2509.01649 |