Data for Agents / 智能体数据

Why agentic AI needs open data, and why synthetic data is how we scale it. 为什么智能体 AI 需要开放数据，以及为什么合成数据是我们实现规模化的关键。

More Than Model Weights 不仅仅是模型权重

Building AI agents is hard, because the real world does not behave like a benchmark. An agent that can’t recover from a broken API call, or a workflow it has never seen, is not really an agent. It is an autocompleter with tools. Getting from one to the other is a data problem: software engineering traces, tool-use failures, multi-step reasoning, retrieval, safety, user simulation, workflow execution, and eventually physical world interaction. That is where NVIDIA Nemotron’s open data products live. 构建 AI 智能体非常困难，因为现实世界并不像基准测试那样运行。一个无法从损坏的 API 调用或从未见过的流程中恢复的智能体，并不是真正的智能体，它只是一个带有工具的自动补全程序。从前者跨越到后者是一个数据问题：涉及软件工程轨迹、工具使用失败、多步推理、检索、安全性、用户模拟、工作流执行，以及最终的物理世界交互。这正是 NVIDIA Nemotron 开放数据产品的用武之地。

NVIDIA recently highlighted how open models are driving AI research and showing up across the popular International Conference on Machine Learning (ICML), with nearly 145 papers citing Nemotron models and datasets. Synthetic data plays an important role across that ecosystem: Nemotron-CC used synthetics to enhance the popular Common Crawl dataset for pretraining. Nemotron-CC-MATH leverages synthetic math questions to improve reasoning. Nemotron Pretraining is a broad collection spanning general, code, math, and synthetic data across trillions of tokens. Part of why NVIDIA releases open datasets is to learn with the community to expand upon these various applications. NVIDIA 最近强调了开放模型如何推动 AI 研究，并出现在备受瞩目的国际机器学习会议 (ICML) 上，有近 145 篇论文引用了 Nemotron 模型和数据集。合成数据在该生态系统中发挥着重要作用：Nemotron-CC 使用合成数据增强了流行的 Common Crawl 数据集以进行预训练；Nemotron-CC-MATH 利用合成数学题来提高推理能力。Nemotron 预训练数据集是一个涵盖通用、代码、数学和合成数据的庞大集合，包含数万亿个 token。NVIDIA 发布开放数据集的部分原因，是为了与社区共同学习，从而扩展这些应用场景。

Open weights matter. But for agents, weights are only part of the story. Reproducibility also depends on the datasets, curation choices, training recipes, and evaluation methods behind the model. Agent behavior needs to be inspectable. If a model calls tools, executes workflows, retrieves information, and acts across systems, developers need to understand the data that shaped those behaviors. Open data makes agent behavior inspectable and explainable. Synthetic data is a key piece of the puzzle to making that possible. 开放权重固然重要，但对于智能体而言，权重只是故事的一部分。可重复性还取决于模型背后的数据集、策展选择、训练配方和评估方法。智能体的行为需要是可检查的。如果模型调用工具、执行工作流、检索信息并在系统间采取行动，开发者就需要理解塑造这些行为的数据。开放数据使智能体的行为变得可检查且可解释，而合成数据是实现这一目标的关键拼图。

Keep It Like a Secret 保守秘密

NVIDIA’s VP of Applied Deep Learning Research Bryan Catanzaro recently noted: “every company is built around a secret” — a workflow, corpus, or customer pattern competitors don’t have. Those secrets make AI useful, but companies shouldn’t casually expose them. Synthetic data gives teams a way to preserve useful signals without exposing the underlying sources. NVIDIA 应用深度学习研究副总裁 Bryan Catanzaro 最近指出：“每家公司都建立在一个秘密之上”——即竞争对手所不具备的工作流、语料库或客户模式。这些秘密使 AI 变得有用，但公司不应随意泄露它们。合成数据为团队提供了一种在不暴露底层来源的情况下保留有用信号的方法。

Bryan also talks about cultivating a diverse and participatory AI ecosystem where many kinds of companies, researchers, governments, and communities can contribute. That is not just a value claim. It is a data claim. If every model learns from the same narrow pool of data, we should not be surprised when the models start to feel the same. The hard part is that the most useful data often sits inside organizations that cannot or will not publish it directly. Everyone benefits from a richer shared data layer. No one wants to be the first to give away the thing that makes them special. Synthetic data, released openly, is one way to change that math. Bryan 还谈到了培育一个多元化且具有参与性的 AI 生态系统，让各类公司、研究人员、政府和社区都能做出贡献。这不仅仅是一个价值主张，更是一个数据主张。如果每个模型都从同一个狭窄的数据池中学习，那么当模型开始变得千篇一律时，我们不应感到惊讶。困难在于，最有用的数据往往存在于无法或不愿直接发布数据的组织内部。每个人都能从更丰富的数据共享层中受益，但没有人愿意第一个交出让自己与众不同的东西。而公开发布的合成数据，正是改变这一现状的一种方式。

Exploring Agent Data 探索智能体数据

As part of Nemotron open data, we’ve released over 10 trillion pre-training tokens and millions of post-training samples spanning many domains and data shapes. That’s a lot to make sense of — and raw dataset tables don’t help much. To make it easier to explore what’s actually in Nemotron post-training data, we built the Nemotron Post-Training v3 Prompt Atlas: an interactive visual map where each point is a prompt sample, drawn from the Nemotron v3 post-training collection and volume-sampled to reflect the honest proportions of the data mixture. 作为 Nemotron 开放数据的一部分，我们发布了超过 10 万亿个预训练 token 和数百万个涵盖多个领域和数据形态的后训练样本。这些数据量巨大，难以理解，而原始数据集表格作用有限。为了更轻松地探索 Nemotron 后训练数据中的实际内容，我们构建了 Nemotron Post-Training v3 Prompt Atlas：这是一个交互式视觉地图，其中每个点都是一个提示词样本，取自 Nemotron v3 后训练集合，并通过容量采样来真实反映数据混合的比例。

Color overlays and filters let you reorganize the map by dataset, pipeline stage, domain, or tool use. Since semantically similar prompts cluster together, you can zoom into a region — coding algorithms, safety, math, agentic behavior — inspect representative examples, and use that signal to curate data, build evals, or understand why a model behaves the way it does. 颜色叠加和过滤器允许你按数据集、流水线阶段、领域或工具使用情况重新组织地图。由于语义相似的提示词会聚集在一起，你可以放大某个区域（如编码算法、安全性、数学、智能体行为），检查代表性示例，并利用这些信号来策展数据、构建评估体系，或理解模型为何表现出特定的行为。

Viva La Persona 万岁，角色（Persona）

Agents also need to understand people they are built to support, and this is where “data quality” becomes local, not universal. A toxicity classifier trained on English internet data can miss hostile messages in Korean or Japanese, where aggression is often encoded in politeness levels rather than obvious vocabulary. Same signal, different context. 智能体还需要理解它们所服务的对象，这就是“数据质量”变得本地化而非普适性的地方。一个基于英语互联网数据训练的毒性分类器，可能会漏掉韩语或日语中的敌意信息，因为在这些语言中，攻击性往往隐藏在礼貌用语的层级中，而非明显的词汇里。同样的信号，不同的语境。

Teams are already grounding agents this way. Nemotron-Personas is one attempt at addressing that: locally grounded synthetic personas capturing the diversity and complexity of populations. Built using NeMo Data Designer, NVIDIA’s state-of-the-art compound-AI tooling for synthetic data generation, Nemotron-Personas mirrors official regional demographic and geographic statistics. The goal is not to recreate real people. In a way, it’s to help developers test whether their systems reflect the users, languages, regions, and occupations they claim to serve. 团队已经在以这种方式为智能体提供基础。Nemotron-Personas 是解决这一问题的一次尝试：通过本地化的合成角色来捕捉人口的多样性和复杂性。Nemotron-Personas 使用 NVIDIA 用于合成数据生成的尖端复合 AI 工具 NeMo Data Designer 构建，反映了官方的区域人口统计和地理统计数据。其目标并非重现真实的人，而是帮助开发者测试他们的系统是否真正反映了他们所声称服务的用户、语言、地区和职业。

Last month at VivaTech in Paris, we launched our tenth country in the collection, which now represents more than 2.4B people. When quality is local, only people who know that locality can build it — regional researchers, native speakers, subject-matter experts, stakeholders who can inspect and correct alongside you. That’s learning in public: not releasing data in isolation, but building it collaboratively. 上个月在巴黎的 VivaTech 大会上，我们发布了该集合中的第十个国家，目前已覆盖超过 24 亿人口。当质量是本地化的时候，只有了解该地区的人才能构建它——包括区域研究人员、母语使用者、主题专家以及能与你共同检查和纠正的利益相关者。这就是“公开学习”：不是孤立地发布数据，而是协作构建数据。

Ground Truths 基本事实（Ground Truths）

Synthetic data needs to be integrated as part of a system of data sources. There are tradeoffs. It can reduce risk, but it does not remove the need for grounding, lineage, curation, evaluation, and human judgment. One useful way to think about this is with “synthetic thresholds”: points where data can no longer be treated as purely real. That line is not always obvious. 合成数据需要作为数据源系统的一部分进行整合。这其中存在权衡。它虽然可以降低风险，但并不能消除对基础（grounding）、血缘（lineage）、策展、评估和人类判断的需求。思考这个问题的一个有效方式是使用“合成阈值”：即数据不再能被视为纯粹真实数据的临界点。这条界限并不总是显而易见的。