Core Viewpoint - The article discusses the launch of RLinf, a large-scale reinforcement learning framework aimed at embodied intelligence, highlighting its innovative design and capabilities in enhancing AI's transition from perception to action [2][5]. Group 1: Framework Overview - RLinf is a flexible and scalable framework designed for embodied intelligence, integrating various components to optimize performance [5]. - The framework's name "inf" signifies both "infrastructure" and "infinite" scaling, emphasizing its adaptable system design [7]. - RLinf features a hybrid execution model that achieves over 120% system speedup compared to traditional frameworks, with VLA model performance improvements of 40%-60% [7][12]. Group 2: Execution Modes - RLinf supports three execution modes: Collocated, Disaggregated, and Hybrid, allowing users to configure components based on their needs [17][15]. - The hybrid mode combines the advantages of both shared and separated execution, minimizing system idle time and enhancing efficiency [12][15]. Group 3: Communication and Scheduling - The framework includes an adaptive communication library designed for reinforcement learning, optimizing data exchange between components [19][22]. - RLinf features an automated scheduling module that minimizes resource idleness and dynamically adjusts to user training flows, achieving rapid scaling capabilities [23][24]. Group 4: Performance Metrics - RLinf has demonstrated significant performance improvements in embodied intelligence tasks, achieving success rates of 80%-90% in specific scenarios, compared to 30%-50% in previous models [24][26]. - The framework has also achieved state-of-the-art (SOTA) performance in mathematical reasoning tasks across multiple datasets, showcasing its versatility [29][30]. Group 5: Documentation and Community Engagement - Comprehensive documentation and API support are provided to enhance user experience and facilitate understanding of the framework [32][34]. - The RLinf team encourages collaboration and invites users to explore the framework, highlighting ongoing recruitment for various research and engineering positions [33][34].