Data for Agents
Image: Nemotron Post-Training v3 Prompt Atlas More Than Model Weights 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.
Key Takeaways
- 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.
- Part of why NVIDIA releases open datasets is to learn with the community to expand upon these various applications.
- Synthetic data is a key piece of the puzzle to making that possible.
Keep It Like a Secret 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.
- 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.
- 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.
Stats & Key Facts
- #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.
- #Exploring Agent Data 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.
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. 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. 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.
For more details please read the original article at Hugging Face.
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