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Reinforcement Learning Optimization for Large-Scale Learning: An Efficient and User-Friendly Scaling Library

2025-06-06 · Weixun Wang, Shaopan Xiong, Gengru Chen, Wei Gao, Sheng Guo, Yancheng He, Ju Huang, Jiaheng Liu, Zhendong Li, Xiaoyang Li, Zichen Liu, Haizhou Zhao, Dakai An, Lunxi Cao, Qiyang Cao, Wanxi Deng, Feilei Du, Yiliang Gu, Jiahe Li, Xiang Li, Mingjie Liu, Yijia Luo, Zihe Liu, Yadao Wang, Pei Wang, Tianyuan Wu, Yanan Wu, Yuheng Zhao, Shuaibing Zhao, Jin Yang, Siran Yang, Yingshui Tan, Huimin Yi, Yuchi Xu, Yujin Yuan, Xingyao Zhang, Lin Qu, Wenbo Su, Wei Wang, Jiamang Wang, Bo Zheng

We introduce ROLL, an efficient, scalable, and user-friendly library designed for Reinforcement Learning Optimization for Large-scale Learning. ROLL caters to three primary user groups: tech pioneers aiming for cost-effective, fault-tolerant large-scale training, developers requiring flexible control over training workflows, and researchers seeking agile experimentation. ROLL is built upon several key modules to serve these user groups effectively. First, a single-controller architecture combined with an abstraction of the parallel worker simplifies the development of the training pipeline. Second, the parallel strategy and data transfer modules enable efficient and scalable training. Third, the rollout scheduler offers fine-grained management of each sample's lifecycle during the rollout stage. Fourth, the environment worker and reward worker support rapid and flexible experimentation with agentic RL algorithms and reward designs. Finally, AutoDeviceMapping allows users to assign resources to different models flexibly across various stages.

📄 PDF Abstract BibTeX arXiv:2506.06122

Code (1)

alibaba/roll 공식 구현 pytorch

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Management

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NEORL: NeuroEvolution Optimization with Reinforcement Learning

2021-12-01 · Majdi I. Radaideh, Katelin Du, Paul Seurin, Devin Seyler 외

We present an open-source Python framework for NeuroEvolution Optimization with Reinforcement Learning (NEORL) developed at the Massachusetts Institute of Technology. NEORL offers a global optimization interface of state…

Benchmarking global-optimization reinforcement-learning Reinforcement Learning+1

Picasso: A Sparse Learning Library for High Dimensional Data Analysis in R and Python

2020-06-27 · Jason Ge, Xingguo Li, Haoming Jiang, Han Liu 외

We describe a new library named picasso, which implements a unified framework of pathwise coordinate optimization for a variety of sparse learning problems (e.g., sparse linear regression, sparse logistic regression, spa…

regression Sparse Learning

Reinforcement Learning from User Feedback

2025-05-20 · Eric Han, Jun Chen, Karthik Abinav Sankararaman, Xiaoliang Peng 외

As large language models (LLMs) are increasingly deployed in diverse user facing applications, aligning them with real user preferences becomes essential. Existing methods like Reinforcement Learning from Human Feedback …

reinforcement-learning Reinforcement Learning

Request-Only Optimization for Recommendation Systems

2025-07-24 · Liang Guo, Wei Li, Lucy Liao, Huihui Cheng 외 arxiv

Deep Learning Recommendation Models (DLRMs) represent one of the largest machine learning applications on the planet. Industry-scale DLRMs are trained with petabytes of recommendation data to serve billions of users ever…

Recommendation Systems

CityFlow: A Multi-Agent Reinforcement Learning Environment for Large Scale City Traffic Scenario

2019-05-13 · Huichu Zhang, Siyuan Feng, Chang Liu, Yaoyao Ding 외

Traffic signal control is an emerging application scenario for reinforcement learning. Besides being as an important problem that affects people's daily life in commuting, traffic signal control poses its unique challeng…

Multi-agent Reinforcement Learning reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)+1