ARL-Tangram: Unleash the Resource Efficiency in Agentic Reinforcement Learning

ArXi:2603.13019v1 Announce Type: cross Agentic reinforcement learning (RL) has emerged as a transformative workload in cloud clusters, enabling large language models (LLMs) to solve complex problems through interactions with real world. However, unlike traditional RL, agentic RL demands substantial external cloud resources, e.g., CPUs for code execution and GPUs for reward models, that exist outside the primary