The official repository for the paper ["CoDiQ: Test-Time Scaling for Controllable Difficult Question Generation"](https://arxiv.org/pdf/2602.01660)

## 💡 Introduction

Large Reasoning Models (LRMs) benefit substantially from training on challenging, competition-level questions. However, existing automated synthesis methods struggle with **"fake hard"** questions—problems that are complex but unsolvable or ill-defined.

**CoDiQ (Controllable Difficult Question Generation)** is a novel framework that enables fine-grained difficulty control via **test-time scaling** while ensuring solvability.

Key innovations include:
1.  **Test-Time Scaling Tendency**: We identify that extending the reasoning token budget boosts difficulty but can reduce solvability.
2.  **CoDiQ-Generator**: A specialized model (finetuned from Qwen3-8B) that improves the upper bound of valid, high-difficulty question generation.
3.  **CoDiQ-Corpus**: A dataset of **44K** competition-grade math and coding question sequences, which is significantly more challenging than LiveCodeBench and AIME.

Training LRMs on CoDiQ-Corpus substantially enhances downstream reasoning performance. The [CoDiQ-Generator](https://huggingface.co/AleXGroup/CoDiQ-Gen-8B) and [CoDiQ-Corpus](https://huggingface.co/datasets/AleXGroup/CoDiQ-Corpus) are released.


## 📖 Citation

If you find **CoDiQ** useful for your research, please consider citing our paper:

```bibtex
@article{codiq2026,
  title={CoDiQ: Test-Time Scaling for Controllable Difficult Question Generation},
  author={Zhongyuan Peng, Caijun Xu, Changyi Xiao, Shibo Hong, Eli Zhang, Stephen Huang, Yixin Cao},
  journal={arXiv preprint arXiv:2602.01660},
  year={2026}
}
```