Morning Edition · Friday, July 31, 2026Published at 1:48 AM EDT · New York

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.

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…