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    <abstract>Large Reasoning Models (LRMs) benefit substantially from training on challenging competition-level questions. However, existing automated question synthesis methods lack precise difficulty control, incur high computational costs, and struggle to generate competition-level questions at scale. In this paper, we propose CoDiQ (Controllable Difficult Question Generation), a novel framework enabling fine-grained difficulty control via test-time scaling while ensuring question solvability. Specifically, first, we identify a test-time scaling tendency (extended reasoning token budget boosts difficulty but reduces solvability) and the intrinsic properties defining the upper bound of a model’s ability to generate valid, high-difficulty questions. Then, we develop CoDiQ-Generator from Qwen3-8B, which improves the upper bound of difficult question generation, making it particularly well-suited for challenging question construction. Building on the CoDiQ framework, we build CoDiQ-Corpus (44K competition-grade question sequences). Human evaluations show these questions are significantly more challenging than LiveCodeBench/AIME with over 82% solvability. Training LRMs on CoDiQ-Corpus substantially improves reasoning performance, verifying that scaling controlled-difficulty training questions enhances reasoning capabilities. We open-source CoDiQ-Corpus, CoDiQ-Generator, and implementations to support related research.</abstract>
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%0 Conference Proceedings
%T CoDiQ: Test-Time Scaling for Controllable Difficult Question Generation
%A Peng, Zhongyuan
%A Xu, Caijun
%A Xiao, Changyi
%A Hong, Shibo
%A Zhang, Ge
%A Huang, Wenhao
%A Cao, Yixin
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F peng-etal-2026-codiq
%X Large Reasoning Models (LRMs) benefit substantially from training on challenging competition-level questions. However, existing automated question synthesis methods lack precise difficulty control, incur high computational costs, and struggle to generate competition-level questions at scale. In this paper, we propose CoDiQ (Controllable Difficult Question Generation), a novel framework enabling fine-grained difficulty control via test-time scaling while ensuring question solvability. Specifically, first, we identify a test-time scaling tendency (extended reasoning token budget boosts difficulty but reduces solvability) and the intrinsic properties defining the upper bound of a model’s ability to generate valid, high-difficulty questions. Then, we develop CoDiQ-Generator from Qwen3-8B, which improves the upper bound of difficult question generation, making it particularly well-suited for challenging question construction. Building on the CoDiQ framework, we build CoDiQ-Corpus (44K competition-grade question sequences). Human evaluations show these questions are significantly more challenging than LiveCodeBench/AIME with over 82% solvability. Training LRMs on CoDiQ-Corpus substantially improves reasoning performance, verifying that scaling controlled-difficulty training questions enhances reasoning capabilities. We open-source CoDiQ-Corpus, CoDiQ-Generator, and implementations to support related research.
%U https://aclanthology.org/2026.emnlp-main.219/
%P 5037-5072

Markdown (Informal)

[CoDiQ: Test-Time Scaling for Controllable Difficult Question Generation](https://aclanthology.org/2026.emnlp-main.219/) (Peng et al., EMNLP 2026)

• CoDiQ: Test-Time Scaling for Controllable Difficult Question Generation (Peng et al., EMNLP 2026)

ACL

• Zhongyuan Peng, Caijun Xu, Changyi Xiao, Shibo Hong, Ge Zhang, Wenhao Huang, and Yixin Cao. 2026. CoDiQ: Test-Time Scaling for Controllable Difficult Question Generation. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 5037–5072, Budapest, Hungary. Association for Computational Linguistics.