UltraData-Math

@@ -1,221 +1,221 @@
----
-language:
-- en
-- zh
-license: apache-2.0
-size_categories:
-- 100B<n<1T
-task_categories:
-- text-generation
-pretty_name: UltraData-Math
-arxiv: xxxx.xxxxx
-tags:
-- llm
-- pretraining
-- math
-- data-synthesis
-- data-filtering
-- high-quality
-- mathematical-reasoning
-- mirror
-configs:
-- config_name: UltraData-Math-L3-Conversation-Synthetic
-  data_files: data/UltraData-Math-L3/Conversation-Synthetic/*.parquet
-- config_name: UltraData-Math-L3-Multi-Style-Synthetic
-  data_files: data/UltraData-Math-L3/Multi-Style-Synthetic/*.parquet
-- config_name: UltraData-Math-L3-QA-Synthetic
-  data_files: data/UltraData-Math-L3/QA-Synthetic/*.parquet
-- config_name: UltraData-Math-L3-Textbook-Exercise-Synthetic
-  data_files: data/UltraData-Math-L3/Textbook-Exercise-Synthetic/*.parquet
-- config_name: UltraData-Math-L2-preview
-  data_files: data/UltraData-Math-L2-preview/**/*.parquet
-- config_name: UltraData-Math-L1
-  data_files: data/UltraData-Math-L1/**/*.parquet
-default_config_name: UltraData-Math-L3-Conversation-Synthetic
----
-<!-- scbe-status -->
-> **Status: mirror of an upstream dataset.** Not original SCBE work and not an SCBE experiment: this is a copy of the public UltraData-Math corpus (290B+ tokens, 181.2M rows, 552 GB). Kept as a pretraining source. Original SCBE data: [scbe-aethermoore-training-data](https://hf.co/issdandavis/scbe-aethermoore-training-data).
-# UltraData-Math
-<div align="center">
-  <img src="assets/ultradata-math-logo.png" width="600"/>
-</div>
-<p align="center">
-<a href="https://huggingface.co/datasets/openbmb/UltraData-Math">🤗 Dataset</a> | <a href="https://github.com/UltraData-OpenBMB/UltraData-Math">💻 Source Code</a> | <a href="https://huggingface.co/datasets/openbmb/UltraData-Math/blob/main/README_ZH.md">🇨🇳 中文 README</a>
-</p>
-***UltraData-Math*** is a large-scale, high-quality mathematical pre-training dataset totaling **290B+ tokens** across three progressive tiers—**L1** (170.5B tokens web corpus), **L2** (33.7B tokens quality-selected), and **L3** (88B tokens multi-format refined)—designed to systematically enhance mathematical reasoning in LLMs. It has been applied to the mathematical pre-training of the [MiniCPM Series](https://huggingface.co/collections/openbmb/minicpm4) models.
-## 🆕 What's New
-- **[2026.02.09]**: **UltraData-Math**, a large-scale high-quality mathematical pre-training dataset with 290B+ tokens across three progressive tiers (L1/L2-preview/L3), is now available on Hugging Face. Released as part of the [UltraData](https://ultradata.openbmb.cn/) ecosystem. 🔥🔥🔥
-- **[2026.02.10]**: **UltraData-Math** tops the Hugging Face Datasets Trending list, reaching the #1 spot! ⭐️⭐️⭐️
-## 📚 Introduction
-High-quality pre-training data is crucial for enhancing the mathematical reasoning capabilities of large language models (LLMs). However, existing mathematical pre-training data construction schemes have the following shortcomings:
-- **HTML Parsing**: General parsers (such as trafilatura, readability) are mainly designed for news/article parsing, lacking specialized processing for mathematical formulas and other content, often leading to formula structure destruction or loss; meanwhile, mathematical discussions on forum-like pages are difficult to extract completely.
-- **Data Quality**: Existing datasets generally lack a systematic quality grading mechanism, with high-value mathematical content mixed with low-quality noise.
-- **Data Diversity**: Mainstream datasets mostly originate from textbooks or competition question banks, lacking mathematical discussions and application scenarios in real web pages; synthetic data formats are single, difficult to cover diverse needs such as multi-turn dialogues and multi-style expressions.
-To address these issues, we propose ***UltraData-Math***—a large-scale high-quality pre-training dataset for mathematical reasoning tasks. This dataset is developed based on the [UltraData](https://ultradata.openbmb.cn/blog/position-paper) L0-L4 Tiered Data Management Framework, containing four progressive levels:
-- **L0 Raw Data**: Develops a mathematical parser based on *magic-html*, combined with *w3m* layout preservation rendering and multi-level fallback strategies, standardizing MathML, KaTeX, and AsciiMath into LaTeX format.
-- **L1 Filtered Data**: Cleans noise through heuristic rules and performs document-level deduplication.
-- **L2 Selected Data**: Uses proprietary large models to annotate seed data and distills it into a lightweight embedding classifier to achieve efficient quality grading of the full corpus.
-- **L3 Refined Data**: Produces structured content with clear reasoning through rewriting, synthetic generation, and refinement in various formats such as Q&A, multi-turn dialogues, multi-style rewriting, and knowledge-grounded textbooks.
-Experiments show that on the MiniCPM-1.2B architecture, ***UltraData-Math*** achieves a score of **37.02pp** on the MATH500 benchmark, an improvement of **+3.62pp** compared to Nemotron-CC 4plus; it achieves **61.79pp** on GSM8K, an improvement of **+3.34pp**, while maintaining code generation and general knowledge capabilities.
-***UltraData-Math*** has been applied to the mathematical pre-training of the [MiniCPM Series](https://huggingface.co/collections/openbmb/minicpm-4-6841ab29d180257e940baa9b) models.
-- **[UltraData-Math-L1](https://huggingface.co/datasets/openbmb/UltraData-Math)**: Large-scale high-quality mathematical pre-training dataset, containing 170.5B tokens of web mathematical corpus.
-- **[UltraData-Math-L2](https://huggingface.co/datasets/openbmb/UltraData-Math-L2)**: High-quality mathematical pre-training dataset selected by the quality model, containing 33.7B tokens of high-quality web mathematical corpus.
-- **[UltraData-Math-L3](https://huggingface.co/datasets/openbmb/UltraData-Math-L3)**: High-quality refined mathematical dataset, containing 88B tokens of multi-format refined data (Q&A, multi-turn dialogues, knowledge textbooks, etc.).
-## 🏗️ Data Processing Pipeline
-To break through the limitations of existing mathematical datasets in quality and diversity, we established a refined grading standard centered on "mathematical content integrity" and "information density". ***UltraData-Math*** adopts the **L0-L4 Tiered Data Management Framework** proposed by the [UltraData](https://ultradata.openbmb.cn/blog/position-paper) paper. Through standardized level definitions, it achieves orderly management and efficient flow of mathematical data assets. Each level represents higher data purity and mathematical value, while also corresponding to a more refined degree of processing.
-<div align="center">
-  <img src="assets/ultradata-math-pipeline.png" width="900"/>
-</div>
-### L0: Raw Data Parsing and Standardization
-**Goal**: Address the poor support of general HTML parsers for mathematical formulas and maximize the preservation of mathematical semantics in web pages.
-The L0 phase mainly processes raw web data obtained from sources such as Common Crawl. Given the specificity of mathematical web pages, we develop specialized parsing strategies through the [UltraData-Math-Parser](https://huggingface.co/spaces/openbmb/UltraData-Math-L0-Parser) instead of directly using general parsers like trafilatura or readability.
-- **Unified Parsing Mode**: Automatically identifies page types to ensure complete content extraction as much as possible.
-- **Multi-level Fallback Strategy**: To prevent data loss due to parsing failures, we implement a multi-level fallback mechanism to ensure text content is captured even if structured parsing fails.
-- **Mathematical Formula Standardization**: We unify different mathematical expressions in web pages into standard LaTeX format, achieving data format normalization for unified model learning.
-### L1: Heuristic Cleaning and Filtering
-**Goal**: Remove format noise and improve data readability and standardization.
-After obtaining text containing complete mathematical formulas, we clean the L0 data through a series of heuristic rules:
-- **Format Repair**:
-  - Clean invisible characters, garbled text, and unnatural continuous line breaks.
-  - Remove irrelevant web noise such as navigation bars, footers, ad pop-ups, and "read more".
-- **Content Filtering**:
-  - *Length Filtering*: Remove overly short text fragments, which usually lack context and are difficult to support effective mathematical reasoning training.
-  - *Language Identification*: Ensure the dataset is composed mainly of high-quality English and Chinese mathematical content.
-  - *Document Deduplication*: Perform deduplication at the document level to prevent duplicate content from biasing model training.
-### L2: Selection Based on Quality Models
-**Goal**: Identify core corpora with high value from massive data.
-Although L1 data has a clean format, the content quality varies. The L2 phase introduces a model-based quality assessment system:
-- **Seed Data Annotation**: Use proprietary large models to score a portion of seed data across multiple dimensions.
-- **Classifier Training and Distillation**: Train lightweight embedding classifiers based on annotated data to equip them with the ability to identify high-value mathematical content.
-- **Full-scale Inference**: Use the trained classifier to score and screen L1 data in full.
-  - *Retention*: Content containing detailed problem-solving steps, mathematical concept explanations, and high-level academic discussions.
-  - *Exclusion*: Simple stacking of nouns, meaningless lists of numbers, juvenile content, or noise from non-mathematical fields.
-### L3: Refined Data
-**Goal**: Produce structured content with clear reasoning and explicit educational intent through rewriting, synthetic generation, and refinement, achieving textbook-quality standards and ensuring maximum learnability.
-Natural web data is mostly declarative text, lacking structured reasoning steps and diverse pedagogical formats. To enhance the model's chain-of-thought (CoT) capabilities and multi-turn interaction skills, we build the L3 refined data layer through the [UltraData-Math-Generator](https://huggingface.co/spaces/openbmb/UltraData-Math-L3-Generator):
-- **Q&A Pair Generation**: Use high-performance models to rewrite declarative documents into "Question-Answer" pairs, constructing QA-style data with explicit reasoning steps.
-- **Multi-turn Dialogue Synthesis**: Simulate "Teacher-Student" tutoring scenarios to generate multi-turn dialogue data containing follow-up questions, corrections, and guidance.
-- **Multi-style Rewriting**: Rewrite single-source data into multiple styles (such as rigorous textbook style, competition problem-solving style, intuitive popular science style) to improve model generalization.
-- **Knowledge Point Textbook Generation**: Generate systematic textbook-like content based on specific knowledge points to ensure the model masters core mathematical concepts.
-- **Format Repair and Enhancement**: Fix formatting issues in the source data (e.g., broken LaTeX formulas, inconsistent notation) and enhance content coherence to achieve textbook-quality standards.
-Based on the above methodology, we produce the following ***UltraData-Math*** datasets:
-| Dataset | # Tokens | # Documents |
-|:---|:---:|:---:|
-| UltraData-Math-L1 | 170.5B | 85.6M |
-| UltraData-Math-L2-preview | 33.7B | 14.98M |
-| UltraData-Math-L3 | 88B | 81.4M |
-## 🚀 Quick Start
-You can load the dataset directly from Hugging Face:
-```python
-from datasets import load_dataset
-# Load UltraData-Math-L1
-ds = load_dataset("openbmb/UltraData-Math", "UltraData-Math-L1")
-# Load UltraData-Math-L2-preview
-ds = load_dataset("openbmb/UltraData-Math", "UltraData-Math-L2-preview")
-# Load UltraData-Math-L3 (default: Conversation-Synthetic)
-ds = load_dataset("openbmb/UltraData-Math", "UltraData-Math-L3-Conversation-Synthetic")
-# Other L3 configs:
-# - UltraData-Math-L3-Multi-Style-Synthetic
-# - UltraData-Math-L3-QA-Synthetic
-# - UltraData-Math-L3-Textbook-Exercise-Synthetic
-```
-## 📈 Experimental Results
-We evaluated data quality using the **Decay Verification** method: continuing pre-training of a **MiniCPM-1.2B** base model (pre-trained on 1.3T tokens with **MiniCPM3-4B** tokenizer) with **~100B tokens** (30% target data + 70% general data). We used [OpenCompass](https://github.com/open-compass/opencompass) as our evaluation framework. Evaluation benchmarks include:
-- **General English:** MMLU, ARC-E, ARC-C, BigBench Hard (BBH), CommonSenseQA, HellaSwag, OpenbookQA, PIQA, SIQA, Winogrande
-- **General Chinese:** C-Eval, CMMLU
-- **Math Reasoning:** MATH500, GSM8K, Math-Bench, R-Bench-Math
-- **Code Reasoning:** MBPP, HumanEval
-### Effectiveness of L0 Parsing Strategy
-To fairly compare different parsing strategies, we conducted experiments on a data subset sampled from the **2023-2024** distribution. We re-parsed the raw HTML from this source using different parsers. This comparison demonstrates the **effectiveness of our L0 Parser** against other parsers.
-<div align="center">
-  <img src="assets/ultradata-math-l0-parser-comparison.png" width="700"/>
-</div>
-### Pipeline Effectiveness (L1 vs L2 vs L3)
-To validate the effectiveness of our L0-L3 tiered framework, we conducted ablation studies comparing models trained on different tiers of UltraData-Math. Unlike the L0 parser comparison above (which used a 2023-2024 subset), these results are based on the **full dataset**. Results demonstrate that higher-tier data (L3) significantly boosts mathematical reasoning (MATH500, GSM8K) and general capabilities.
-<div align="center">
-  <img src="assets/ultradata-math-l1l2l3-comparison.png" width="700"/>
-</div>
-### Full Evaluation Results
-To compare against existing public mathematical pre-training datasets, we trained models independently on each dataset using the same model architecture and training budget (~100B tokens). The baselines include [Nemotron-CC-Math](https://huggingface.co/datasets/nvidia/Nemotron-CC-Math-v1), [MegaMath-Web-Pro](https://huggingface.co/datasets/LLM360/MegaMath), and [FineMath](https://huggingface.co/datasets/HuggingFaceTB/finemath). All models are evaluated under identical conditions for a fair comparison:
-<div align="center">
-  <img src="assets/ultradata-math-full-comparison.png" width="700"/>
-</div>
-## ❤️ Acknowledgements
-- **L0 Parsing Layer**: [magic-html](https://github.com/opendatalab/magic-html), [w3m](http://w3m.sourceforge.net/), [trafilatura](https://github.com/adbar/trafilatura)
-- **L3 Synthesis Layer**: [Qwen2.5-72B-Instruct](https://huggingface.co/Qwen/Qwen2.5-72B-Instruct), [Qwen3-32B](https://huggingface.co/Qwen/Qwen3-32B), [GLM-4.5](https://huggingface.co/zai-org/GLM-4.5)
-- **Seed Data**: [Nemotron-CC-Math](https://huggingface.co/datasets/nvidia/Nemotron-CC-Math-v1), [MegaMath](https://huggingface.co/datasets/LLM360/MegaMath), [FineMath](https://huggingface.co/datasets/HuggingFaceTB/finemath)
-## 📖 Citation
-If you find **UltraData-Math** useful in your research, please consider citing:
-```bibtex
-@misc{ultradata-math,
-  title={UltraData-Math},
-  author={UltraData Team},
-  year={2026},
-  url={https://huggingface.co/datasets/openbmb/UltraData-Math},
-  publisher={Hugging Face}
-}
-```
-## 📜 License
-This project is licensed under the [Apache 2.0](./LICENSE) license.

+---
+language:
+- en
+- zh
+license: apache-2.0
+size_categories:
+- 100B<n<1T
+task_categories:
+- text-generation
+pretty_name: UltraData-Math
+arxiv: xxxx.xxxxx
+tags:
+- llm
+- pretraining
+- math
+- data-synthesis
+- data-filtering
+- high-quality
+- mathematical-reasoning
+- mirror
+configs:
+- config_name: UltraData-Math-L3-Conversation-Synthetic
+  data_files: data/UltraData-Math-L3/Conversation-Synthetic/*.parquet
+- config_name: UltraData-Math-L3-Multi-Style-Synthetic
+  data_files: data/UltraData-Math-L3/Multi-Style-Synthetic/*.parquet
+- config_name: UltraData-Math-L3-QA-Synthetic
+  data_files: data/UltraData-Math-L3/QA-Synthetic/*.parquet
+- config_name: UltraData-Math-L3-Textbook-Exercise-Synthetic
+  data_files: data/UltraData-Math-L3/Textbook-Exercise-Synthetic/*.parquet
+- config_name: UltraData-Math-L2-preview
+  data_files: data/UltraData-Math-L2-preview/**/*.parquet
+- config_name: UltraData-Math-L1
+  data_files: data/UltraData-Math-L1/**/*.parquet
+default_config_name: UltraData-Math-L3-Conversation-Synthetic
+---
+<!-- scbe-status -->
+> **Status: mirror of an upstream dataset.** Not original SCBE work and not an SCBE experiment: this is a copy of the public UltraData-Math corpus (290B+ tokens, 181.2M rows, 552 GB). Kept as a pretraining source. Original SCBE data: [scbe-aethermoore-training-data](https://hf.co/issdandavis/scbe-aethermoore-training-data).
+# UltraData-Math
+<div align="center">
+  <img src="assets/ultradata-math-logo.png" width="600"/>
+</div>
+<p align="center">
+<a href="https://huggingface.co/datasets/openbmb/UltraData-Math">🤗 Dataset</a> | <a href="https://github.com/UltraData-OpenBMB/UltraData-Math">💻 Source Code</a> | <a href="https://huggingface.co/datasets/openbmb/UltraData-Math/blob/main/README_ZH.md">🇨🇳 中文 README</a>
+</p>
+***UltraData-Math*** is a large-scale, high-quality mathematical pre-training dataset totaling **290B+ tokens** across three progressive tiers—**L1** (170.5B tokens web corpus), **L2** (33.7B tokens quality-selected), and **L3** (88B tokens multi-format refined)—designed to systematically enhance mathematical reasoning in LLMs. It has been applied to the mathematical pre-training of the [MiniCPM Series](https://huggingface.co/collections/openbmb/minicpm4) models.
+## 🆕 What's New
+- **[2026.02.09]**: **UltraData-Math**, a large-scale high-quality mathematical pre-training dataset with 290B+ tokens across three progressive tiers (L1/L2-preview/L3), is now available on Hugging Face. Released as part of the [UltraData](https://ultradata.openbmb.cn/) ecosystem. 🔥🔥🔥
+- **[2026.02.10]**: **UltraData-Math** tops the Hugging Face Datasets Trending list, reaching the #1 spot! ⭐️⭐️⭐️
+## 📚 Introduction
+High-quality pre-training data is crucial for enhancing the mathematical reasoning capabilities of large language models (LLMs). However, existing mathematical pre-training data construction schemes have the following shortcomings:
+- **HTML Parsing**: General parsers (such as trafilatura, readability) are mainly designed for news/article parsing, lacking specialized processing for mathematical formulas and other content, often leading to formula structure destruction or loss; meanwhile, mathematical discussions on forum-like pages are difficult to extract completely.
+- **Data Quality**: Existing datasets generally lack a systematic quality grading mechanism, with high-value mathematical content mixed with low-quality noise.
+- **Data Diversity**: Mainstream datasets mostly originate from textbooks or competition question banks, lacking mathematical discussions and application scenarios in real web pages; synthetic data formats are single, difficult to cover diverse needs such as multi-turn dialogues and multi-style expressions.
+To address these issues, we propose ***UltraData-Math***—a large-scale high-quality pre-training dataset for mathematical reasoning tasks. This dataset is developed based on the [UltraData](https://ultradata.openbmb.cn/blog/position-paper) L0-L4 Tiered Data Management Framework, containing four progressive levels:
+- **L0 Raw Data**: Develops a mathematical parser based on *magic-html*, combined with *w3m* layout preservation rendering and multi-level fallback strategies, standardizing MathML, KaTeX, and AsciiMath into LaTeX format.
+- **L1 Filtered Data**: Cleans noise through heuristic rules and performs document-level deduplication.
+- **L2 Selected Data**: Uses proprietary large models to annotate seed data and distills it into a lightweight embedding classifier to achieve efficient quality grading of the full corpus.
+- **L3 Refined Data**: Produces structured content with clear reasoning through rewriting, synthetic generation, and refinement in various formats such as Q&A, multi-turn dialogues, multi-style rewriting, and knowledge-grounded textbooks.
+Experiments show that on the MiniCPM-1.2B architecture, ***UltraData-Math*** achieves a score of **37.02pp** on the MATH500 benchmark, an improvement of **+3.62pp** compared to Nemotron-CC 4plus; it achieves **61.79pp** on GSM8K, an improvement of **+3.34pp**, while maintaining code generation and general knowledge capabilities.
+***UltraData-Math*** has been applied to the mathematical pre-training of the [MiniCPM Series](https://huggingface.co/collections/openbmb/minicpm-4-6841ab29d180257e940baa9b) models.
+- **[UltraData-Math-L1](https://huggingface.co/datasets/openbmb/UltraData-Math)**: Large-scale high-quality mathematical pre-training dataset, containing 170.5B tokens of web mathematical corpus.
+- **[UltraData-Math-L2](https://huggingface.co/datasets/openbmb/UltraData-Math-L2)**: High-quality mathematical pre-training dataset selected by the quality model, containing 33.7B tokens of high-quality web mathematical corpus.
+- **[UltraData-Math-L3](https://huggingface.co/datasets/openbmb/UltraData-Math-L3)**: High-quality refined mathematical dataset, containing 88B tokens of multi-format refined data (Q&A, multi-turn dialogues, knowledge textbooks, etc.).
+## 🏗️ Data Processing Pipeline
+To break through the limitations of existing mathematical datasets in quality and diversity, we established a refined grading standard centered on "mathematical content integrity" and "information density". ***UltraData-Math*** adopts the **L0-L4 Tiered Data Management Framework** proposed by the [UltraData](https://ultradata.openbmb.cn/blog/position-paper) paper. Through standardized level definitions, it achieves orderly management and efficient flow of mathematical data assets. Each level represents higher data purity and mathematical value, while also corresponding to a more refined degree of processing.
+<div align="center">
+  <img src="assets/ultradata-math-pipeline.png" width="900"/>
+</div>
+### L0: Raw Data Parsing and Standardization
+**Goal**: Address the poor support of general HTML parsers for mathematical formulas and maximize the preservation of mathematical semantics in web pages.
+The L0 phase mainly processes raw web data obtained from sources such as Common Crawl. Given the specificity of mathematical web pages, we develop specialized parsing strategies through the [UltraData-Math-Parser](https://huggingface.co/spaces/openbmb/UltraData-Math-L0-Parser) instead of directly using general parsers like trafilatura or readability.
+- **Unified Parsing Mode**: Automatically identifies page types to ensure complete content extraction as much as possible.
+- **Multi-level Fallback Strategy**: To prevent data loss due to parsing failures, we implement a multi-level fallback mechanism to ensure text content is captured even if structured parsing fails.
+- **Mathematical Formula Standardization**: We unify different mathematical expressions in web pages into standard LaTeX format, achieving data format normalization for unified model learning.
+### L1: Heuristic Cleaning and Filtering
+**Goal**: Remove format noise and improve data readability and standardization.
+After obtaining text containing complete mathematical formulas, we clean the L0 data through a series of heuristic rules:
+- **Format Repair**:
+  - Clean invisible characters, garbled text, and unnatural continuous line breaks.
+  - Remove irrelevant web noise such as navigation bars, footers, ad pop-ups, and "read more".
+- **Content Filtering**:
+  - *Length Filtering*: Remove overly short text fragments, which usually lack context and are difficult to support effective mathematical reasoning training.
+  - *Language Identification*: Ensure the dataset is composed mainly of high-quality English and Chinese mathematical content.
+  - *Document Deduplication*: Perform deduplication at the document level to prevent duplicate content from biasing model training.
+### L2: Selection Based on Quality Models
+**Goal**: Identify core corpora with high value from massive data.
+Although L1 data has a clean format, the content quality varies. The L2 phase introduces a model-based quality assessment system:
+- **Seed Data Annotation**: Use proprietary large models to score a portion of seed data across multiple dimensions.
+- **Classifier Training and Distillation**: Train lightweight embedding classifiers based on annotated data to equip them with the ability to identify high-value mathematical content.
+- **Full-scale Inference**: Use the trained classifier to score and screen L1 data in full.
+  - *Retention*: Content containing detailed problem-solving steps, mathematical concept explanations, and high-level academic discussions.
+  - *Exclusion*: Simple stacking of nouns, meaningless lists of numbers, juvenile content, or noise from non-mathematical fields.
+### L3: Refined Data
+**Goal**: Produce structured content with clear reasoning and explicit educational intent through rewriting, synthetic generation, and refinement, achieving textbook-quality standards and ensuring maximum learnability.
+Natural web data is mostly declarative text, lacking structured reasoning steps and diverse pedagogical formats. To enhance the model's chain-of-thought (CoT) capabilities and multi-turn interaction skills, we build the L3 refined data layer through the [UltraData-Math-Generator](https://huggingface.co/spaces/openbmb/UltraData-Math-L3-Generator):
+- **Q&A Pair Generation**: Use high-performance models to rewrite declarative documents into "Question-Answer" pairs, constructing QA-style data with explicit reasoning steps.
+- **Multi-turn Dialogue Synthesis**: Simulate "Teacher-Student" tutoring scenarios to generate multi-turn dialogue data containing follow-up questions, corrections, and guidance.
+- **Multi-style Rewriting**: Rewrite single-source data into multiple styles (such as rigorous textbook style, competition problem-solving style, intuitive popular science style) to improve model generalization.
+- **Knowledge Point Textbook Generation**: Generate systematic textbook-like content based on specific knowledge points to ensure the model masters core mathematical concepts.
+- **Format Repair and Enhancement**: Fix formatting issues in the source data (e.g., broken LaTeX formulas, inconsistent notation) and enhance content coherence to achieve textbook-quality standards.
+Based on the above methodology, we produce the following ***UltraData-Math*** datasets:
+| Dataset | # Tokens | # Documents |
+|:---|:---:|:---:|
+| UltraData-Math-L1 | 170.5B | 85.6M |
+| UltraData-Math-L2-preview | 33.7B | 14.98M |
+| UltraData-Math-L3 | 88B | 81.4M |
+## 🚀 Quick Start
+You can load the dataset directly from Hugging Face:
+```python
+from datasets import load_dataset
+# Load UltraData-Math-L1
+ds = load_dataset("openbmb/UltraData-Math", "UltraData-Math-L1")
+# Load UltraData-Math-L2-preview
+ds = load_dataset("openbmb/UltraData-Math", "UltraData-Math-L2-preview")
+# Load UltraData-Math-L3 (default: Conversation-Synthetic)
+ds = load_dataset("openbmb/UltraData-Math", "UltraData-Math-L3-Conversation-Synthetic")
+# Other L3 configs:
+# - UltraData-Math-L3-Multi-Style-Synthetic
+# - UltraData-Math-L3-QA-Synthetic
+# - UltraData-Math-L3-Textbook-Exercise-Synthetic
+```
+## 📈 Experimental Results
+We evaluated data quality using the **Decay Verification** method: continuing pre-training of a **MiniCPM-1.2B** base model (pre-trained on 1.3T tokens with **MiniCPM3-4B** tokenizer) with **~100B tokens** (30% target data + 70% general data). We used [OpenCompass](https://github.com/open-compass/opencompass) as our evaluation framework. Evaluation benchmarks include:
+- **General English:** MMLU, ARC-E, ARC-C, BigBench Hard (BBH), CommonSenseQA, HellaSwag, OpenbookQA, PIQA, SIQA, Winogrande
+- **General Chinese:** C-Eval, CMMLU
+- **Math Reasoning:** MATH500, GSM8K, Math-Bench, R-Bench-Math
+- **Code Reasoning:** MBPP, HumanEval
+### Effectiveness of L0 Parsing Strategy
+To fairly compare different parsing strategies, we conducted experiments on a data subset sampled from the **2023-2024** distribution. We re-parsed the raw HTML from this source using different parsers. This comparison demonstrates the **effectiveness of our L0 Parser** against other parsers.
+<div align="center">
+  <img src="assets/ultradata-math-l0-parser-comparison.png" width="700"/>
+</div>
+### Pipeline Effectiveness (L1 vs L2 vs L3)
+To validate the effectiveness of our L0-L3 tiered framework, we conducted ablation studies comparing models trained on different tiers of UltraData-Math. Unlike the L0 parser comparison above (which used a 2023-2024 subset), these results are based on the **full dataset**. Results demonstrate that higher-tier data (L3) significantly boosts mathematical reasoning (MATH500, GSM8K) and general capabilities.
+<div align="center">
+  <img src="assets/ultradata-math-l1l2l3-comparison.png" width="700"/>
+</div>
+### Full Evaluation Results
+To compare against existing public mathematical pre-training datasets, we trained models independently on each dataset using the same model architecture and training budget (~100B tokens). The baselines include [Nemotron-CC-Math](https://huggingface.co/datasets/nvidia/Nemotron-CC-Math-v1), [MegaMath-Web-Pro](https://huggingface.co/datasets/LLM360/MegaMath), and [FineMath](https://huggingface.co/datasets/HuggingFaceTB/finemath). All models are evaluated under identical conditions for a fair comparison:
+<div align="center">
+  <img src="assets/ultradata-math-full-comparison.png" width="700"/>
+</div>
+## ❤️ Acknowledgements
+- **L0 Parsing Layer**: [magic-html](https://github.com/opendatalab/magic-html), [w3m](http://w3m.sourceforge.net/), [trafilatura](https://github.com/adbar/trafilatura)
+- **L3 Synthesis Layer**: [Qwen2.5-72B-Instruct](https://huggingface.co/Qwen/Qwen2.5-72B-Instruct), [Qwen3-32B](https://huggingface.co/Qwen/Qwen3-32B), [GLM-4.5](https://huggingface.co/zai-org/GLM-4.5)
+- **Seed Data**: [Nemotron-CC-Math](https://huggingface.co/datasets/nvidia/Nemotron-CC-Math-v1), [MegaMath](https://huggingface.co/datasets/LLM360/MegaMath), [FineMath](https://huggingface.co/datasets/HuggingFaceTB/finemath)
+## 📖 Citation
+If you find **UltraData-Math** useful in your research, please consider citing:
+```bibtex
+@misc{ultradata-math,
+  title={UltraData-Math},
+  author={UltraData Team},
+  year={2026},
+  url={https://huggingface.co/datasets/openbmb/UltraData-Math},
+  publisher={Hugging Face}
+}
+```
+## 📜 License
+This project is licensed under the [Apache 2.0](./LICENSE) license.