[2606.02113] A Primer in Post-Training Reasoning Data: What We Know About How It Works

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Computer Science > Computation and Language

arXiv:2606.02113 (cs)

[Submitted on 1 Jun 2026]

Title:A Primer in Post-Training Reasoning Data: What We Know About How It Works

Authors:Yaoming Li, Guangxiang Zhao, Qilong Shi, Lin Sun, Xiangzheng Zhang, Tong Yang<br>View a PDF of the paper titled A Primer in Post-Training Reasoning Data: What We Know About How It Works, by Yaoming Li and 5 other authors

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Abstract:Post-training has become a primary driver of recent progress in large reasoning models, and reasoning data are often the key variable determining whether this stage succeeds. Work on post-training reasoning data has grown rapidly, yet this literature remains scattered across dataset papers, reinforcement-learning recipes, reward-model studies, benchmarks, and frontier system reports. This paper is the first primer to synthesize over 150 key public studies and system reports on post-training reasoning data. We organize the field around four questions: what data objects exist, what makes them useful, how they are constructed, and how they scale. Together, this organization provides an attribution framework for future reasoning-data releases and post-training recipes.

Comments:<br>22 pages. Project Repository: this https URL

Subjects:

Computation and Language (cs.CL); Artificial Intelligence (cs.AI)

Cite as:<br>arXiv:2606.02113 [cs.CL]

(or<br>arXiv:2606.02113v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2606.02113

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arXiv-issued DOI via DataCite

Submission history<br>From: Yaoming Li [view email]<br>[v1]<br>Mon, 1 Jun 2026 11:45:50 UTC (19,442 KB)

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