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@@ -76,19 +76,17 @@ tok = AutoTokenizer.from_pretrained(p, trust_remote_code=True)
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  | `model_680m_6.4B` | 680m | 6.4B | 10 | 100–1000 |
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  | `model_680m_16B` | 680m | 16B | 4 | 1200–1900 |
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- ## Not included
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- These models exist in the study but had no usable weights available at upload
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- time:
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- - `model_200m_0.53B`
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- - `model_200m_11B`
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- - `model_200m_21B`
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- - `model_200m_40B`
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- - `model_200m_53B`
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- - `model_680m_0.64B`
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- - `model_680m_1.2B`
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- - `model_680m_12B`
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- - `model_680m_3.2B`
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- - `model_680m_32B`
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- - 2 32m models absent from the pretraining-token tables
 
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  | `model_680m_6.4B` | 680m | 6.4B | 10 | 100–1000 |
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  | `model_680m_16B` | 680m | 16B | 4 | 1200–1900 |
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+ ## Citation
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+ If you use these models, please cite it:
 
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+ ```bibtex
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+ @article{pre2post-chess,
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+ title = {Understanding Reasoning from Pretraining to Post-Training},
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+ author = {Shen, Jingyan and Li, Ang and Rahman, Salman and Sun, Yifan and
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+ Goldblum, Micah and Telgarsky, Matus and Izmailov, Pavel},
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+ journal = {arXiv preprint arXiv:2607.16097},
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+ year = {2026},
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+ url = {https://arxiv.org/pdf/2607.16097}
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+ }
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+ ```