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Research: "Wait" marker in reasoning models inherited from data, not RL

By PulseAugur Editorial · [2 sources] ·

A new research paper argues that the "wait" marker used in reasoning models, often attributed to reinforcement learning (RL), is primarily inherited from training data rather than invented by RL itself. The study traces this marker's provenance across various open models, finding that RL significantly improves accuracy on benchmarks like Math-500 even when the "wait" word is absent. The research also cautions against relying solely on LLM-as-a-judge metrics, as they can misrepresent reasoning trends observed in human evaluations. AI

IMPACT Challenges the perceived role of RL in developing reasoning markers, suggesting a greater reliance on training data and cautioning against automated evaluation metrics.

RANK_REASON The cluster contains a research paper detailing findings about the origin of a specific marker in reasoning models.

Read on Hugging Face Daily Papers →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

Research: "Wait" marker in reasoning models inherited from data, not RL

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COVERAGE [2]

  1. arXiv cs.CL TIER_1 English(EN) · 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

    arXiv:2610.30882v1 Announce Type: cross Abstract: Reinforcement learning (RL) is credited with teaching reasoning models to write {wait} before reconsidering a step, yet large RL accuracy gains do not require the word. This view can confuse what RL discovers with what a model inh…

  2. Hugging Face Daily Papers TIER_1 English(EN) ·

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

    Reinforcement learning improves reasoning accuracy without requiring a specific wait marker, which originates from training data rather than RL, and automated evaluation metrics can misrepresent reasoning trends.

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