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

---
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