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

Niloofar Gholipour; Marcos Assuncao; Gursimran Singh; Timothy Yu; Rajkumar Buyya; Julien Gascon-Samson; Zhenan Fan; Yong Zhang; Xiaojie Xu; Yaqiang Yao; Xiaolong Bai

arXiv · 2609.25463

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

Niloofar Gholipour·Marcos Assuncao·Gursimran Singh·Timothy Yu·Rajkumar Buyya·Julien Gascon-Samson·Zhenan Fan·Yong Zhang·Xiaojie Xu·Yaqiang Yao·Xiaolong Bai

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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 for policy updates. Efficient rollout mechanisms are therefore essential to reduce this cost while maintaining the freshness, consistency, and statistical validity of training data. This survey provides a systematic taxonomy of recent research on rollout efficiency for reasoning-oriented reinforcement learning, classifying existing approaches from both mechanism and bottleneck perspectives. Based on this taxonomy, we analyze how different technique families address distinct sources of rollout inefficiency, examine opportunities and potential conflicts for combining them, identify gaps in the evaluation and reporting of efficiency gains, and discuss open challenges and future research directions.

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Niloofar Gholipour, Marcos Assuncao, Gursimran Singh, Timothy Yu, Rajkumar Buyya, Julien Gascon-Samson, Zhenan Fan, Yong Zhang, Xiaojie Xu, Yaqiang Yao, Xiaolong Bai. 2026-09-21. Rollout Efficiency in Reinforcement Learning for Reasoning Large Language Models: A Taxonomy and Future Directions. https://arxiv.org/abs/2609.25463

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