AleXGroup/CoDiQ-Gen-8B

The official repository for the paper "CoDiQ: Test-Time Scaling for Controllable Difficult Question Generation"

💡 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 and CoDiQ-Corpus are released.

📖 Citation

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

@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}
}