Qirun Dai

Qirun Dai

CS PhD student at UChicago working on data and algorithms for LLM post-training.

About Me

I am a first-year CS PhD student at the University of Chicago, advised by Prof. Chenhao Tan. I am a member of the Chicago Human+AI (CHAI) lab, and affiliated with the broader Communication and Intelligence (C&I) group. I am also working closely with Prof. Hao Peng at the University of Illinois at Urbana-Champaign (UIUC).

Previously, I received my Bachelor’s degree in Artificial Intelligence from Fudan University in 2025. During my undergraduate study, I was fortunate to intern at UIUC with Prof. Hao Peng and Prof. Jiaqi W. Ma, and at Shanghai Jiao Tong University with Prof. Dequan Wang.

Research Interests

I am broadly interested in post-training data and algorithms for large language models (LLMs). My current research focuses on the following specific topics:

  • Extending the generalization scope of reasoning and thinking behaviors of LLMs. I believe that genuine thoughtful reasoning should be a robust behavior that can transfer to versatile domains (e.g., philosophical and social science writing) and formalisms (e.g., Bayesian and causal reasoning), but the current math-centered post-training paradigm struggles towards this goal.

  • Evaluating and advancing complex and composite real-world capabilities of LLMs. I am especially interested in training LLMs to (1) proactively explore and discover, (2) leverage ambiguity in strategic communications, and (3) balance accurate reasoning with controllable creativity.

In the past, I have mainly worked on data-centric topics of post-training, including data selection, data attribution, and data-efficient supervision paradigms. I still enjoy following the latest advances of these data-centric techniques, and am always excited about applying them to achieve my dynamic research goals.

Selected Publications

  • Executable Counterfactuals: Improving LLMs’ Causal Reasoning Through Code
    Aniket Vashishtha*, Qirun Dai*, Hongyuan Mei, Amit Sharma, Chenhao Tan, Hao Peng
    NeurIPS 2025 Workshop on Foundations of Reasoning in Language Models
    [paper] [code]
  • The Best Instruction-Tuning Data are Those That Fit
    Dylan Zhang, Qirun Dai, Hao Peng
    NeurIPS 2025 (Spotlight)
    [paper]
  • Improving Influence-based Instruction Tuning Data Selection for Balanced Learning of Diverse Capabilities
    Qirun Dai, Dylan Zhang, Jiaqi W. Ma, Hao Peng
    Findings of EMNLP 2025; ICLR 2025 Workshop on DATA-FM
    [paper]

News

[12/2025] Attending NeurIPS 2025 and will present my work, GRAPE (Spotlight) and Executable Counterfactuals (FoRLM Workshop).

[09/2025] Officially started my CS PhD study as an honored member of the CHAI lab.

[09/2025] One paper accepted by EMNLP 2025 (Findings), and two papers by NeurIPS 2025 (Spotlight & Workshop)!

[06/2025] Officially graduated from Fudan University and received my Bachelor’s degree.

[04/2025] Attending ICLR 2025 and will present my work, Balanced and Influential Data Selection (BIDS), at the 2nd DATA-FM Workshop.