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

来源摘要

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 reso…

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