agent/papers/items/2026-2604-27859-rethinking-agentic-reinforcement-learning-in-large-language-models.md

Paper: Rethinking Agentic Reinforcement Learning In Large Language Models

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type: paper title: Rethinking Agentic Reinforcement Learning In Large Language Models authors: Fangming Cui, Ruixiao Zhu, Cheng Fang, Sunan Li, Jiahong Li year: 2026 venue: arXiv url: https://arxiv.org/abs/2604.27859 code_url: source: arxiv collected_at: 2026-07-08 published_at: 2026-04-30 updated_at: 2026-05-15 status: queued relevance: high topics:

• memory
• planning
• reasoning
• tool-use methods:

benchmarks:

models:

datasets:

• cs.AI
• cs.ET related_concepts:

related_jobs:

related_experiments:

related_projects:

collection_score: 14 collection_queries: autonomous-agent-llm

One-line Takeaway

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

Why Collected

• matched queries: autonomous-agent-llm
• inferred topics: memory, planning, reasoning, tool-use
• arXiv categories: cs.AI, cs.ET
• collection score: 14

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/2604.27859