agent/papers/items/2026-2603-03680-mage-meta-reinforcement-learning-for-language-agents-toward-strategic-exploratio.md

Paper: MAGE: Meta-Reinforcement Learning for Language Agents toward Strategic Exploration and Exploitation

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type: paper title: "MAGE: Meta-Reinforcement Learning for Language Agents toward Strategic Exploration and Exploitation" authors: Lu Yang, Zelai Xu, Minyang Xie, Jiaxuan Gao, Zhao Shok, Yu Wang, Yi Wu year: 2026 venue: arXiv url: https://arxiv.org/abs/2603.03680 code_url: source: arxiv collected_at: 2026-07-08 published_at: 2026-03-04 updated_at: 2026-03-04 status: queued relevance: high topics:

• memory
• multi-agent
• reasoning
• tool-use methods:

benchmarks:

models:

datasets:

• cs.AI related_concepts:

related_jobs:

related_experiments:

related_projects:

collection_score: 17 collection_queries: language-agent

One-line Takeaway

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

Why Collected

• matched queries: language-agent
• inferred topics: memory, multi-agent, reasoning, tool-use
• arXiv categories: cs.AI
• collection score: 17

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/2603.03680