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MegaMath

Tasks:
Text Generation
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English
Size:
100M - 1B
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math
code
pre-training
synthesis
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NP235 koalazf99 commited on
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Duplicate from LLM360/MegaMath

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Co-authored-by: Fan Zhou <koalazf99@users.noreply.huggingface.co>

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+ ---
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+ license: odc-by
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+ task_categories:
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+ - text-generation
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+ language:
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+ - en
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+ tags:
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+ - math
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+ - code
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+ - pre-training
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+ - synthesis
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+ size_categories:
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+ - 1B<n<10B
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+ ---
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+
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+ # MegaMath: Pushing the Limits of Open Math Copora
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+
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+ > Megamath is part of TxT360, curated by LLM360 Team.
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+ <center><img src="teasor.png" alt="MegaMath Collection" /></center>
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+
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+ We introduce MegaMath, an open math pretraining dataset curated from diverse, math-focused sources, with over 300B tokens.
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+ MegaMath is curated via the following three efforts:
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+ - **Revisiting web data**:
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+ We re-extracted mathematical documents from Common Crawl with math-oriented HTML optimizations, fasttext-based filtering and deduplication, all for acquiring higher-quality data on the Internet.
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+ - **Recalling Math-related code data**:
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+ We identified high quality math-related code from large code training corpus, Stack-V2, further enhancing data diversity.
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+ - **Exploring Synthetic data**:
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+ We synthesized QA-style text, math-related code, and interleaved text-code blocks from web data or code data.
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+
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+ ## MegaMath Compared to Existing Datasets
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+ MegaMath is the largest open math pre-training dataset to date, surpassing DeepSeekMath (120B) tokens.
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+ <div style="display: flex; justify-content: center; gap: 20px;">
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+ <img src="https://cdn-uploads.huggingface.co/production/uploads/628f6e5ab90dde28ef57d293/lFa_r4gSXhjwep7XAwwQj.png" width="75%" />
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+ </div>
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+
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+ ## MegaMath Delivers with High Quality
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+ During development, we use extensive experiments to find optimal practice for text extraction, deduplication, fasttext training, etc. Training MegaMath data shows better performance than existing open datasets.
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+
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+ <div style="display: flex; justify-content: center; gap: 20px;">
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+ <img src="https://cdn-uploads.huggingface.co/production/uploads/628f6e5ab90dde28ef57d293/-E1tZP-vbU1ZPzy56cl4s.png" width="30%" />
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+ <img src="https://cdn-uploads.huggingface.co/production/uploads/628f6e5ab90dde28ef57d293/XSBJ_wVexM-0rk9bcpU5Q.png" width="30%" />
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+ </div>
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+
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+
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+ ## Training MegaMath on Latest LMs
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+ We also release two proof-of-concept models which is based on [Llama-3.2-1B](https://huggingface.co/meta-llama/Llama-3.2-1B) and [LLama-3.2-3B](https://huggingface.co/meta-llama/Llama-3.2-3B).
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+ Training MegaMath on Llama-3.2-1B and LLama-3.2-3B brings about 15% ~ 20% performance boost on 10 downstream benchmarks, demonstrateing its high data quality.
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+ <div style="display: flex; justify-content: center; gap: 20px;">
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+ <img src="https://cdn-uploads.huggingface.co/production/uploads/628f6e5ab90dde28ef57d293/EIReQ8TIbyn1V3JfsEKiL.png" width="50%" />
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+ </div>
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+
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+ ## Detailed Statistics
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+ | **Category** | **# Sample(M)** | **# Toks(B)** | **Avg. (# Toks)** |
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+ |------------------------|----------------:|--------------:|------------------:|
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+ | **Web Domain** | **121.5** | **279.0** | **2296.9** |
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+ | Web | 106.5 | 263.9 | 2478.7 |
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+ | Web-Pro | 15.0 | 15.1 | 1006.0 |
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+ | **Code Domain** | **13.4** | **28.1** | **2102.7** |
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+ | **Synthetic Data** | **80.2** | **64.5** | **804.5** |
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+ | Translated Code | 7.4 | 7.2 | 979.5 |
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+ | Q&A | 22.6 | 7.0 | 308.3 |
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+ | Text&Code Block | 50.2 | 50.3 | 1002.1 |
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+ | **Total** | **215.1** | **371.6** | **1727.6** |
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+
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+
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+ ## Citation
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+ If you use our dataset or find our work useful, please cite
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+ ```bibtex
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+ @article{zhou2025megamath,
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+ title = {MegaMath: Pushing the Limits of Open Math Corpora},
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+ author = {Zhou, Fan and Wang, Zengzhi and Ranjan, Nikhil and Cheng, Zhoujun and Tang, Liping and He, Guowei and Liu, Zhengzhong and Xing, Eric P.},
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+ journal = {arXiv preprint arXiv:2504.02807},
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+ year = {2025},
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+ note = {Preprint}
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+ }
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+ ```
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