开启RL Scaling新纪元，siiRL开源：完全分布式强化学习框架，支持超千卡规模高效训练

Core Insights - The article emphasizes the importance of overcoming scalability bottlenecks in Reinforcement Learning (RL) frameworks as a key to unlocking advanced AI reasoning capabilities and achieving stronger general intelligence [2][31] - The introduction of the siiRL framework by the Shanghai Institute of Intelligent Technology is highlighted as a significant advancement in supporting large-scale and efficient RL training [3][31] Group 1: Scalability Challenges - Traditional RL frameworks often rely on a centralized controller architecture, which leads to performance bottlenecks and memory overflow when scaled to hundreds or thousands of GPUs [8][9] - The centralized design is manageable at smaller scales but becomes a critical limitation as the system expands, resulting in high I/O and communication overhead [9][10] Group 2: siiRL Framework Features - siiRL employs an innovative multi-controller paradigm and fully distributed architecture, effectively removing the central node and distributing tasks across all worker nodes [11][31] - The framework demonstrates near-linear scalability, achieving a 7-fold increase in end-to-end training throughput and maintaining performance even at 1024 GPU scales [21][31] - The architecture includes three core components: DAG Planner for workflow definition, DAG Workers for task execution, and Data Coordinators for managing data flow [13][14][15] Group 3: Performance Validation - Experimental results show that siiRL outperforms baseline frameworks, achieving up to 2.62 times higher throughput in data-intensive scenarios [19][26] - In long-context tasks, the performance advantage of siiRL increases significantly as context length grows, demonstrating its efficiency in handling larger data communication volumes [26][27] - Convergence tests indicate that performance improvements do not compromise model accuracy, with reward and entropy curves closely aligning with baseline frameworks [28][31] Group 4: Future Plans - The framework is designed to support complex multi-agent systems, with plans to enhance compatibility with multi-agent reinforcement learning (MARL) algorithms and improve agent-environment interaction mechanisms [29][31]