首个为具身智能而生的大规模强化学习框架RLinf！清华、北京中关村学院、无问芯穹等重磅开源

Core Viewpoint - The article discusses the launch of RLinf, a large-scale reinforcement learning framework designed for embodied intelligence, emphasizing its flexible and scalable architecture that integrates training, rendering, and inference processes [5][7]. Group 1: Development of RL Framework - The transition in artificial intelligence from "perception" to "action" highlights the importance of embodied intelligence, which is gaining attention in both academia and industry [2][4]. - RLinf is developed collaboratively by Tsinghua University, Beijing Zhongguancun College, and Wuwenchin, aiming to address the limitations of existing frameworks in supporting embodied intelligence [5][7]. Group 2: Features of RLinf - RLinf's architecture consists of six layers: user layer, task layer, execution layer, scheduling layer, communication layer, and hardware layer, allowing for a hybrid execution mode that achieves over 120% system speedup [7][12]. - The framework introduces a Macro-to-Micro Flow (M2Flow) mechanism, enabling flexible construction of training processes while maintaining high programming flexibility and debugging ease [14][15]. Group 3: Execution Modes - RLinf supports three execution modes: Collocated Mode, Disaggregated Mode, and Hybrid Mode, allowing users to configure components for optimal resource utilization [19][20]. - The framework integrates low-intrusion multi-backend solutions to cater to the diverse needs of researchers in the embodied intelligence field [16][20]. Group 4: Communication and Scheduling - RLinf features an adaptive communication library designed for reinforcement learning, optimizing data exchange between components to enhance system efficiency [22][28]. - An automated scheduling module minimizes resource idling by analyzing component performance and selecting the best execution mode, significantly improving training stability [24][25]. Group 5: Performance Metrics - RLinf demonstrates superior performance in embodied intelligence tasks, achieving over 120% efficiency improvement compared to existing frameworks in specific tests [27][33]. - The framework has shown significant success rate improvements in various tasks, with models achieving up to 97.3% success rates in specific scenarios [31][35]. Group 6: Future Development and Community Engagement - The RLinf team emphasizes open-source principles, providing comprehensive documentation and support to enhance user experience and facilitate collaboration [40][41]. - The team is actively recruiting for various positions to further develop and maintain the RLinf framework, inviting community engagement and feedback [42][43].