ARL-Tangram: Unleash the Resource Efficiency in Agentic Reinforcement Learning
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 training cluster. Existing agentic RL framework typically rely on static over-provisioning, i.e., resources are often tied to long-lived trajectories or isolated by tasks, which leads to severe resource inefficiency. We propose the action-level orchestration, and incorporate it into ARL-Tangram, a unified resource manag...
| Authors | Bangjun Xiao, Yihao Zhao, Xiangwei Deng et al. |
|---|---|
| Primary category | cs.DC |
| Published | 13 March 2026 |
| Last updated at source | 13 March 2026 |
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