---
license: mit
language:
- en
tags:
- math
- reasoning
- verl
- reinforcement-learning
- math-reasoning
size_categories:
- 10K<n<100K
task_categories:
- text-generation
- question-answering
dataset_info:
  features:
  - name: data_source
    dtype: string
  - name: prompt
    list:
    - name: role
      dtype: string
    - name: content
      dtype: string
  - name: ability
    dtype: string
  - name: reward_model
    struct:
    - name: style
      dtype: string
    - name: ground_truth
      dtype: string
  - name: extra_info
    struct:
    - name: index
      dtype: int64
    - name: original_dataset
      dtype: string
    - name: split
      dtype: string
  splits:
  - name: train
    num_bytes: 0
    num_examples: 95496
  download_size: 0
  dataset_size: 11000000
---

# DeepMath-103K VERL

<div align="center">

![Dataset](https://img.shields.io/badge/Dataset-95496_samples-blue)
![Size](https://img.shields.io/badge/Size-10.5_MB-green)
![Format](https://img.shields.io/badge/Format-VERL-orange)
![License](https://img.shields.io/badge/License-mit-red)

</div>

## 📊 Dataset Summary

This dataset contains 95,496 mathematical reasoning problems in VERL format, processed from zwhe99/DeepMath-103K.

**Key Features:**
- **95,496 high-quality math problems**
- Converted to VERL format for reward modeling
- Verified ground truth answers
- Ready for reinforcement learning training

---

## 🔗 Source Dataset

### Original Repository
- **Repository**: [zwhe99/DeepMath-103K](https://huggingface.co/datasets/zwhe99/DeepMath-103K)
- **License**: MIT

- **Paper**: [DeepMath: Deep Learning for Mathematical Reasoning](https://arxiv.org/abs/2504.11456)

- **Authors**: Zhenwen He et al.

### Dataset Description
DeepMath-103K is a curated collection of 103,000 mathematical problems covering various difficulty levels and mathematical domains. The dataset emphasizes step-by-step reasoning and verification.

---

## 🔄 Preprocessing Pipeline

This dataset has been preprocessed and converted to the VERL (Verification and Reinforcement Learning) format for use in mathematical reasoning tasks with reward modeling.

### Cleaning Methodology


**Standard Processing:**
- URL filtering (samples containing URLs removed)
- Format normalization to VERL schema
- Basic text cleaning and validation


### Deduplication


**Intra-dataset Deduplication:**
- **Method**: SHA-256 hash-based with text normalization
- **Before deduplication**: 103,000 samples
- **After deduplication**: 95,496 samples
- **Reduction**: 7.3%


**Inter-dataset Deduplication** (v3.0):
- **Priority level**: 4
- **Cross-dataset duplicates removed**: 5,000



---

## 💡 Preprocessing Examples

### Example 1: Standard Processing (No Cleaning Preset)

**Before Cleaning:**
```
Calculate the derivative of $f(x) = x^2 + 3x + 2$ with respect to $x$.
```

**After Cleaning:**
```
Calculate the derivative of $f(x) = x^2 + 3x + 2$ with respect to $x$.
```

**Changes Applied:**
- ✓ Format normalization to VERL schema
- ✓ URL filtering (samples with URLs removed)
- ✓ Basic text validation
- ✓ No artifact removal applied



---

## 📐 VERL Schema

This dataset follows the standardized VERL (Verification and Reinforcement Learning) format:

```json
{
  "data_source": "openai/gsm8k",
  "prompt": [
    {
      "content": "Calculate the sum of all odd numbers from 1 to 99.",
      "role": "user"
    }
  ],
  "ability": "math",
  "reward_model": {
    "style": "rule",
    "ground_truth": "\boxed{2500}",
    "hash": "sha256:abc123..."
  },
  "extra_info": {
    "split": "train"
  }
}
```

### Field Descriptions

| Field | Type | Description |
|-------|------|-------------|
| `data_source` | `string` | Original dataset identifier (e.g., `openai/gsm8k`, `numina_aime`) |
| `prompt` | `list[dict]` | User query in chat format with role and content |
| `ability` | `string` | Task type (always `"math"` for this dataset) |
| `reward_model.style` | `string` | Reward computation method (`"rule"` for rule-based verification) |
| `reward_model.ground_truth` | `string` | Expected answer for verification (often in `\boxed{}` format) |
| `reward_model.hash` | `string` | SHA-256 hash of prompt content for deduplication |
| `extra_info.split` | `string` | Original split identifier (`"train"`, `"test"`, etc.) |

---

## 📈 Dataset Statistics

### Sample Distribution

- **Total Samples**: 95,496
- **Dataset Size**: 10.5 MB
- **Average Problem Length**: N/A characters

### Data Sources

Distribution of problems by original data source:

| Source | Count | Percentage |
|--------|-------|------------|
| Mixed Sources | 95,496 | 100% |

*Note: Detailed source distribution statistics will be added in future updates.*


---

## 🚀 Usage

### Loading the Dataset

```python
from datasets import load_dataset

# Load the full dataset
dataset = load_dataset("sungyub/deepmath-103k-verl")

# Load with streaming (recommended for large datasets)
dataset = load_dataset("sungyub/deepmath-103k-verl", streaming=True)

# Preview first few examples
for example in dataset['train'].take(5):
    print(example['prompt'][0]['content'])  # User question
    print(example['reward_model']['ground_truth'])  # Answer
    print("---")
```

### Using with VERL

```python
from datatrove.utils.reward_score import compute_score

# Compute reward score for a generated solution
score = compute_score(
    data_source=example['data_source'],
    solution_str=generated_solution,
    ground_truth=example['reward_model']['ground_truth'],
    format_type="auto"  # Auto-detect XML or GPT OSS format
)

print(f"Reward score: {score}")
```

### Integration with DataTrove

```python
from datatrove.pipeline.readers import ParquetReader
from datatrove.pipeline.filters import LambdaFilter
from datatrove.executor import LocalPipelineExecutor

pipeline = [
    ParquetReader("sungyub/deepmath-103k-verl", text_key="prompt"),
    LambdaFilter(lambda doc: len(doc.text) > 100),  # Filter short problems
    # Add more processing steps...
]

executor = LocalPipelineExecutor(pipeline=pipeline, tasks=4)
executor.run()
```

---

## 📚 Citation

### Original Dataset

```bibtex
@article{he2025deepmath,
  title = {DeepMath: Deep Learning for Mathematical Reasoning},
  author = {He, Zhenwen and others},
  journal = {arXiv preprint arXiv:2504.11456},
  year = {2025},
  url = {https://arxiv.org/abs/2504.11456}
}
```

### This Processed Version

```bibtex
@dataset{sungyub_math_verl_deepmath-103k-verl,
  author = {Sungyub Kim},
  title = DeepMath-103K VERL,
  year = {2025},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/datasets/sungyub/deepmath-103k-verl}}
}
```

---

## ⚖️ License

- **This processed dataset**: mit
- **Original dataset**: MIT



---

## 🙏 Acknowledgments

This dataset was processed using the [DataTrove](https://github.com/huggingface/datatrove) library.

**Credits:**
- Original dataset authors: Zhenwen He et al.
- Processing and VERL conversion: Sungyub Kim
- MathDatasetCleaner implementation: DataTrove contributors

**Special thanks to:**
Zhenwen He and collaborators for the DeepMath dataset

---

## 📝 Version History

### v1.0.0 (Initial Release)
- Processed 95,496 samples from zwhe99/DeepMath-103K
- Converted to VERL format
- Standard processing applied
- Ready for reinforcement learning training


---

## 🔗 Related Resources

- **Unified Collection**: [sungyub/math-verl-unified](https://huggingface.co/datasets/sungyub/math-verl-unified) - All 9 math datasets with inter-dataset deduplication
- **DataTrove Documentation**: [https://github.com/huggingface/datatrove](https://github.com/huggingface/datatrove)
- **VERL Format Specification**: See VERL Schema section above

---

<div align="center">

**Questions or issues?** Open an issue on the [DataTrove GitHub repository](https://github.com/huggingface/datatrove/issues)

</div>