daVinci-LLM:Towards the Science of Pretraining
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arXiv
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| Autori principali: | , , , , , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866911550081597440 |
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| author | Qin, Yiwei Liu, Yixiu Mi, Tiantian Xie, Muhang Huang, Zhen Si, Weiye Lu, Pengrui Feng, Siyuan Wu, Xia Liu, Liming Luo, Ye Hou, Jinlong Guo, Qipeng Qiao, Yu Liu, Pengfei |
| author_facet | Qin, Yiwei Liu, Yixiu Mi, Tiantian Xie, Muhang Huang, Zhen Si, Weiye Lu, Pengrui Feng, Siyuan Wu, Xia Liu, Liming Luo, Ye Hou, Jinlong Guo, Qipeng Qiao, Yu Liu, Pengfei |
| contents | The foundational pretraining phase determines a model's capability ceiling, as post-training struggles to overcome capability foundations established during pretraining, yet it remains critically under-explored. This stems from a structural paradox: organizations with computational resources operate under commercial pressures that inhibit transparent disclosure, while academic institutions possess research freedom but lack pretraining-scale computational resources. daVinci-LLM occupies this unexplored intersection, combining industrial-scale resources with full research freedom to advance the science of pretraining. We adopt a fully-open paradigm that treats openness as scientific methodology, releasing complete data processing pipelines, full training processes, and systematic exploration results. Recognizing that the field lacks systematic methodology for data processing, we employ the Data Darwinism framework, a principled L0-L9 taxonomy from filtering to synthesis. We train a 3B-parameter model from random initialization across 8T tokens using a two-stage adaptive curriculum that progressively shifts from foundational capabilities to reasoning-intensive enhancement. Through 200+ controlled ablations, we establish that: processing depth systematically enhances capabilities, establishing it as a critical dimension alongside volume scaling; different domains exhibit distinct saturation dynamics, necessitating adaptive strategies from proportion adjustments to format shifts; compositional balance enables targeted intensification while preventing performance collapse; how evaluation protocol choices shape our understanding of pretraining progress. By releasing the complete exploration process, we enable the community to build upon our findings and systematic methodologies to form accumulative scientific knowledge in pretraining. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_27164 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | daVinci-LLM:Towards the Science of Pretraining Qin, Yiwei Liu, Yixiu Mi, Tiantian Xie, Muhang Huang, Zhen Si, Weiye Lu, Pengrui Feng, Siyuan Wu, Xia Liu, Liming Luo, Ye Hou, Jinlong Guo, Qipeng Qiao, Yu Liu, Pengfei Artificial Intelligence Computation and Language The foundational pretraining phase determines a model's capability ceiling, as post-training struggles to overcome capability foundations established during pretraining, yet it remains critically under-explored. This stems from a structural paradox: organizations with computational resources operate under commercial pressures that inhibit transparent disclosure, while academic institutions possess research freedom but lack pretraining-scale computational resources. daVinci-LLM occupies this unexplored intersection, combining industrial-scale resources with full research freedom to advance the science of pretraining. We adopt a fully-open paradigm that treats openness as scientific methodology, releasing complete data processing pipelines, full training processes, and systematic exploration results. Recognizing that the field lacks systematic methodology for data processing, we employ the Data Darwinism framework, a principled L0-L9 taxonomy from filtering to synthesis. We train a 3B-parameter model from random initialization across 8T tokens using a two-stage adaptive curriculum that progressively shifts from foundational capabilities to reasoning-intensive enhancement. Through 200+ controlled ablations, we establish that: processing depth systematically enhances capabilities, establishing it as a critical dimension alongside volume scaling; different domains exhibit distinct saturation dynamics, necessitating adaptive strategies from proportion adjustments to format shifts; compositional balance enables targeted intensification while preventing performance collapse; how evaluation protocol choices shape our understanding of pretraining progress. By releasing the complete exploration process, we enable the community to build upon our findings and systematic methodologies to form accumulative scientific knowledge in pretraining. |
| title | daVinci-LLM:Towards the Science of Pretraining |
| topic | Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2603.27164 |