EARL: Efficient Agentic Reinforcement Learning Systems for Large Language Models

• URL: http://arxiv.org/abs/2510.05943v1
• Date: Tue, 07 Oct 2025 13:52:51 GMT
• Title: EARL: Efficient Agentic Reinforcement Learning Systems for Large Language Models
• Authors: Zheyue Tan, Mustapha Abdullahi, Tuo Shi, Huining Yuan, Zelai Xu, Chao Yu, Boxun Li, Bo Zhao,
• Abstract summary: Reinforcement learning (RL) has become a pivotal component of large language model (LLM) post-training.<n>We present EARL, a scalable system for efficient agentic RL.
• Score: 10.372430331898608
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Reinforcement learning (RL) has become a pivotal component of large language model (LLM) post-training, and agentic RL extends this paradigm to operate as agents through multi-turn interaction and tool use. Scaling such systems exposes two practical bottlenecks: (1) context length grows rapidly during training, inflating memory usage and latency, and triggering out-of-memory (OOM) failures; and (2) intermediate tensors accumulate with context length, making cross-device data movement a major system bottleneck. We present EARL, a scalable system for efficient agentic RL. EARL designs a parallelism selector that dynamically adapts model and training parallelism across RL stages based on sequence length and system load, and a data dispatcher that performs layout-aware, decentralized exchange of intermediate data batches. Together, these components increase throughput, reduce long-context failures, and enable stable large-scale training of agentic LLMs without relying on hard limits or penalties of context length.

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