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# Generating Training Data

Generate synthetic task data (user queries) for the {doc}`Workplace Assistant <real-world-environment>` environment using [NeMo Data Designer](https://github.com/NVIDIA-NeMo/DataDesigner). 

This pipeline focuses on generating tasks for use with the environment. It also simulates agent trajectories, but these are used for quality filtering and validation --- the environment itself produces the actual model responses during rollout collection. The Workplace Assistant uses 27 tools across 6 databases, and NeMo Data Designer can produce realistic multi-step user queries at scale.

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< Back to Workplace Assistant
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## Pipeline Overview

The data generation pipeline:

1. Load tool schemas for the Workplace Assistant environment
2. Use NeMo Data Designer to generate realistic multi-step user queries
3. Simulate agent trajectories (step-by-step tool-call solutions)
4. Apply dual-level LLM judge filtering to ensure data quality
5. Export task data in NeMo Gym JSONL format

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## Notebook

The tutorial is provided as a Jupyter notebook. See the [notebook README](https://github.com/NVIDIA-NeMo/Gym/blob/main/resources_servers/workplace_assistant/notebooks/synthetic-data-generation/) for prerequisites and setup instructions.

:::{button-link} https://github.com/NVIDIA-NeMo/Gym/blob/main/resources_servers/workplace_assistant/notebooks/synthetic-data-generation/multistep-toolcalling-sdg.ipynb
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View Notebook on GitHub
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## What's Next?

After generating your task data, use it with the Workplace Assistant resources server to {doc}`collect rollouts </get-started/rollout-collection>` (where the environment produces model responses) and then proceed to {doc}`GRPO training </training-tutorials/nemo-rl-grpo/index>`.

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Next: Resources Server Implementation >
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