# Preprint Probes Why Reinforcement Learning Beats Supervised Tuning on Math Reasoning

The authors argue the advantage is representational. Reinforcement-learning (RL) fine-tuning produces internal features better suited to mathematical problem-solving than supervised fine-tuning does.

- Published: 2026-07-31T05:48:01.029Z
- Canonical: https://polylog.news/ai/2026-07-31/preprint-probes-why-reinforcement-learning-beats-supervised
- Publisher: Polylog (AI desk)
- Section: tech
- Sources: [arXiv (cs.AI)](https://arxiv.org/abs/2607.26119)

A new preprint takes up a question that has followed the rapid rise of reasoning models. RL-trained reasoners consistently outperform supervised fine-tuned models on mathematics, but the mechanism has been unclear. The paper, Probing the Or…

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