Nice primer on post-training reasoning data.

(bookmark it)

This is one of the first primers to pull the scattered post-training reasoning-data literature into one place, synthesizing over 150 public studies and system reports that previously lived across dataset papers, RL recipes, reward-model studies, benchmarks, and frontier reports.

It organizes everything around four questions. What data objects exist, what makes them useful, how they are constructed, and how they scale.

Paper: https://arxiv.org/abs/2606.02113

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

Source: https://arxiv.org/abs/2606.02113 View PDF

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.

Submission history

From: Yaoming Li [view email] **[v1]**Mon, 1 Jun 2026 11:45:50 UTC (19,442 KB)