agent/papers/items/2026-2607-15660-toolverse-unlocking-massive-environments-and-long-horizon-tasks-for-agentic-rein.md

Paper: ToolVerse: Unlocking Massive Environments and Long-Horizon Tasks for Agentic Reinforcement Learning

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type: paper title: "ToolVerse: Unlocking Massive Environments and Long-Horizon Tasks for Agentic Reinforcement Learning" authors: Shuaiyu Zhou, Fengpeng Yue, Zengjie Hu, Yuanzhe Shen, Chenyang Zhang, feng hong, Cao Liu, Ke Zeng year: 2026 venue: arXiv url: https://arxiv.org/abs/2607.15660 code_url: source: arxiv collected_at: 2026-07-27 published_at: 2026-07-17 updated_at: 2026-07-17 status: queued relevance: high topics:

• agent-evaluation
• planning
• reasoning
• tool-use
• world-model methods:

benchmarks:

models:

datasets:

• cs.AI related_concepts:

related_jobs:

related_experiments:

related_projects:

collection_score: 20 collection_queries: agent-evaluation, llm-agent, tool-use

One-line Takeaway

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

Why Collected

• matched queries: agent-evaluation, llm-agent, tool-use
• inferred topics: agent-evaluation, planning, reasoning, tool-use, world-model
• arXiv categories: cs.AI
• collection score: 20

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/2607.15660