# Rollout Efficiency in Reinforcement Learning for Reasoning Large Language Models: A Taxonomy and Future Directions

**Published:** September 23, 2026
**Author:** Niloofar Gholipour, Marcos Assuncao, Gursimran Singh, Timothy Yu, Rajkumar Buyya, Julien Gascon-Samson, Zhenan Fan, Yong Zhang, Xiaojie Xu, Yaqiang Yao, Xiaolong Bai
**Source:** arXiv — cs.AI
**Language:** en-US
**HTML:** https://aiqazaqstan.com/en/articles/rollout-efficiency-in-reinforcement-learning-for-reasoning-large-languag

arXiv:2609.25463v1 Announce Type: new Abstract: Reasoning-oriented reinforcement learning enables large language models to solve mathematical, coding, and other multi-step tasks, but shifts a substantial portion of the training cost to rollout, where trajectories are generated…

> From the original source: **arXiv — cs.AI**
> [Read full article at arXiv — cs.AI](https://arxiv.org/abs/2609.25463)

**Tags:** #arXiv

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