AREAL Asynchronous Reinforcement Learning System Breaks Large-Scale LLM Training Bottlenecks

Breaking the Large-Scale Language Model Training Bottleneck: The AREAL Asynchronous Reinforcement Learning System High-Performance AI Training Cluster Introduction: The Systemic Challenges in Reinforcement Learning In the field of large language model (LLM) training, 「reinforcement learning (RL)」 has become a critical technology for enhancing reasoning capabilities. Particularly in 「complex reasoning tasks」 like mathematical problem-solving and code generation, 「Large Reasoning Models (LRMs)」 trained with RL demonstrate significant advantages. However, existing synchronous RL systems face two fundamental bottlenecks: 「Low GPU Utilization」: 30-40% device idle time due to waiting for the longest output in a batch 「Scalability Limitations」: Inability to achieve linear throughput improvement …