README.md

---
base_model:
- Qwen/Qwen3-32B
library_name: transformers
pipeline_tag: text-generation
arxiv: 2602.11089
license: other
---

# DataChef-32B

[**HF Models**](https://huggingface.co/yichengchen24/DataChef-32B) | [**HF Demo**](https://huggingface.co/spaces/yichengchen24/DataChef) | [**Paper**](https://arxiv.org/abs/2602.11089) | [**GitHub**](https://github.com/yichengchen24/DataChef)

DataChef-32B is a specialized large language model designed for **automated data recipe generation**. It was introduced in the paper [DataChef: Cooking Up Optimal Data Recipes for LLM Adaptation via Reinforcement Learning](https://huggingface.co/papers/2602.11089).

DataChef-32B facilitates LLM adaptation by generating executable data processing pipelines (data recipes) that transform raw data sources into high-quality training corpora targeted at specific benchmarks.

## Model Description
DataChef-32B addresses the manual, labor-intensive process of designing data processing pipelines. It was trained using online reinforcement learning with a proxy reward system that predicts downstream performance for candidate recipes. Given a target benchmark and available data sources, the model outputs a complete data recipe to adapt a base LLM.

### Performance Highlights
Across diverse tasks, DataChef-32B produces practical recipes that reach performance comparable to those curated by human experts. Notably, a recipe generated by DataChef-32B was used to adapt Qwen3-1.7B-Base to the math domain, achieving a score of **66.7 on AIME'25**, surpassing the performance of the standard Qwen3-1.7B.

## Installation
To use the DataChef framework for generating your own data recipes, follow the installation steps from the [GitHub repository](https://github.com/yichengchen24/DataChef):

```bash
conda create -n datachef python=3.12
conda activate datachef
pip install -e .
```

## Citation
If you find this work helpful, please consider citing:

```bibtex
@article{chen2026datachef,
  title={DataChef: Cooking Up Optimal Data Recipes for LLM Adaptation via Reinforcement Learning},
  author={Chen, Yicheng and Ma, Zerun and Xie, Xinchen and Li, Yining and Chen, Kai},
  journal={arXiv preprint arXiv:2602.11089},
  year={2026}
}
```