agent/papers/items/2025-2510-04206-agentrl-scaling-agentic-reinforcement-learning-with-a-multi-turn-multi-task-fram.md

Paper: AgentRL: Scaling Agentic Reinforcement Learning with a Multi-Turn, Multi-Task Framework

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type: paper title: "AgentRL: Scaling Agentic Reinforcement Learning with a Multi-Turn, Multi-Task Framework" authors: Hanchen Zhang, Xiao Liu, Bowen Lv, Xueqiao Sun, Bohao Jing, Iat Long Iong, Zhenyu Hou, Zehan Qi, et al. year: 2025 venue: arXiv url: https://arxiv.org/abs/2510.04206 code_url: source: arxiv collected_at: 2026-07-08 published_at: 2025-10-05 updated_at: 2025-10-05 status: queued relevance: high topics:

• rag
• tool-use methods:

benchmarks:

models:

datasets:

• cs.AI related_concepts:

related_jobs:

related_experiments:

related_projects:

collection_score: 13 collection_queries: function-calling

One-line Takeaway

Auto-collected from arXiv because it matched the Agent collection queries. Needs human skim.

Why Collected

• matched queries: function-calling
• inferred topics: rag, tool-use
• arXiv categories: cs.AI
• collection score: 13

Review Checklist

• Does this paper directly inform Agent architecture, evaluation, memory, tools, safety, coding agents, GUI/browser agents, or multi-agent workflows?
• Does it include a benchmark, dataset, code, or reproducible experimental setup?
• Should it be promoted from queued to skimmed or summarized?

Links

• arXiv: https://arxiv.org/abs/2510.04206