arXiv Machine Learning By Zhizhuo Kou, Sizai Hou, Long Chen, Jian Yang, Junyu Luo, Jingshu Peng, Wenkai Zhang, Sirui Han, Yike Guo

Inherited, Not Invented: Tracing the Provenance of Wait in Reasoning Models

Read the original on arXiv Machine Learning →

The paper investigates the origin of the word "wait" in reasoning models, arguing that it is inherited from training data rather than invented by reinforcement learning (RL). By analyzing training histories and using label‑free metrics, the authors show that RL improves accuracy without adding the marker, while the marker appears during mid‑training or fine‑tuning on reasoning traces. They also demonstrate that marker frequency alone does not prove RL‑induced reasoning and warn against relying on automated judges for evaluation.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

arXiv AI
Jul 31

Probing the Origins of Reasoning Performance: Representational Quality for Mathematical Problem-Solving in RL vs. SFT Fine-Tuned Models

arXiv:2607. 26119v1 Announce Type: new Abstract: Large reasoning models trained via reinforcement learning (RL) have been increasingly shown to outperform their supervised fine-tuned (SFT) counterparts on mathematical reasoning tasks; Yet the mechanistic basis for this advantage remains unclear.

By Antyabha Rahman, Akshaj Gurugubelli, Omar Ankit, Kevin Zhu, Aishwarya Balwani