RLAX: Large-Scale, Distributed Reinforcement Learning for Large Language Models on TPUs

Runlong Zhou; Lefan Zhang; Shang-Chen Wu; Kelvin Zou; Hanzhi Zhou; Ke Ye; Yihao Feng; Dong Yin; Alex Guillen Garcia; Dmytro Babych; Rohit Chatterjee; Matthew Hopkins; Xiang Kong; Chang Lan; Lezhi Li; Yiping Ma; Daniele Molinari; Senyu Tong; Yanchao Sun; Thomas Voice; Jianyu Wang; Chong Wang; Simon Wang; Floris Weers; Yechen Xu; Guolin Yin; Muyang Yu; Yi Zhang; Zheng Zhou; Danyang Zhuo; Ruoming Pang; Cheng Leong

arXiv · 2512.06392

RLAX: Large-Scale, Distributed Reinforcement Learning for Large Language Models on TPUs

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Abstract

Reinforcement learning (RL) has emerged as the de-facto paradigm for improving the reasoning capabilities of large language models (LLMs). We have developed RLAX, a scalable RL framework on TPUs. RLAX employs a parameter-server architecture. A master trainer periodically pushes updated model weights to the parameter server while a fleet of inference workers pull the latest weights and generates new rollouts. We introduce a suite of system techniques to enable scalable and preemptible RL for a diverse set of state-of-art RL algorithms. To accelerate convergence and improve model quality, we have devised new dataset curation and alignment techniques. Large-scale evaluations show that RLAX improves QwQ-32B's pass@8 accuracy by 12.8% in just 12 hours 48 minutes on 1024 v5p TPUs, while remaining robust to preemptions during training.

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Runlong Zhou, Lefan Zhang, Shang-Chen Wu, Kelvin Zou, Hanzhi Zhou, Ke Ye, Yihao Feng, Dong Yin, Alex Guillen Garcia, Dmytro Babych, Rohit Chatterjee, Matthew Hopkins, Xiang Kong, Chang Lan, Lezhi Li, Yiping Ma, Daniele Molinari, Senyu Tong, Yanchao Sun, Thomas Voice, Jianyu Wang, Chong Wang, Simon Wang, Floris Weers, Yechen Xu, Guolin Yin, Muyang Yu, Yi Zhang, Zheng Zhou, Danyang Zhuo, Ruoming Pang, Cheng Leong. 2025-12-06. RLAX: Large-Scale, Distributed Reinforcement Learning for Large Language Models on TPUs. https://arxiv.org/abs/2512.06392

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