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AI CURATED HOT STORY · 11/7/2026

NeurIPS 2026 Paper Proposes Explore-First Pipeline for Verified Tool-Calling Agent Data

1 reports archived1 independent sourcesupdated 11/7/2026, 12:04:00
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Tool-calling agents need thousands of verifiable tasks for RL training, yet benchmarks offer only dozens to hundreds and LLM-synthesized tasks often lack reliable ground truth. This NeurIPS 2026 work inverts the pipeline: explore real MCP servers first, then derive tasks from observed outcomes, yielding 5,144 verified tasks across 240 servers and 993 tools, plus an offline-reproducible retrieval-augmented tool-call simulator.

LATEST/Tool-calling agents need thousands of verifiable tasks for RL training, yet benchmarks offer only dozens to hundreds and LLM-synthesized tasks often lack reliable ground truth. This NeurIPS 2026 work inverts the pipeline: explore real MCP servers first, then derive tasks from observed outcomes, yielding 5,144 verified tasks across 240 servers and 993 tools, plus an offline-reproducible retrieval-augmented tool-call simulator.

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  1. 机器之心 (微信公众号)T2·74 pts

    NeurIPS 2026 Paper Proposes Explore-First Pipeline for Verified Tool-Calling Agent Data

    • Key insight is ordering: explore real APIs and record actual returns first, then derive tasks so ground truth is verifiable by construction
    • A retrieval-augmented tool-call simulator enables offline reproduction of the same environment, mitigating live API drift
    • Resulting dataset spans 240 MCP servers, 993 tools and 5,144 verified tasks, far exceeding existing agent benchmarks