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

ToolVerse 面向 Tool-Integrated Reasoning，把近 400 个真实 MCP 自动转成约 4,500 个工具的可执行训练环境。任务生成依赖 tool dependency graph 与 Dynamic Unlocking Sampling，产出 GUST 数据集；训练算法则用 Turn-Aware Relative Advantage 细化长程 credit assignment。它的价值在于把 agentic RL 从小型静态工具集推向动态、多工具、长 horizon 环境。