Data Designer's execution engine now schedules work at the cell level rather than the column level. Instead of running each column to completion before starting the next, the async engine dispatches a cell as soon as its specific upstream dependencies complete. Multi-model pipelines keep every endpoint saturated, and single-model pipelines benefit from AIMD-based adaptive concurrency. The result is faster pipelines with no changes to your config.
Picture this: you're generating a million-record dataset. Thirty two concurrent requests per model, three models in the pipeline, two providers. Everything hums along for the first ten minutes — then one provider starts returning 429s, your retry logic kicks in, and suddenly you're in a feedback loop where retries cause more 429s. The run stalls. You restart with lower concurrency, waste throughput for hours, and wonder if there's a better way.
There is. This post is about the native model client layer we built with adaptive throttling (a system that discovers provider capacity at runtime) replacing our dependency on LiteLLM along the way.
Lessons from building an agent-first CLI and skill for Data Designer
We just published the data-designer skill, which leverages agent-focused CLI commands in Data Designer to efficiently generate datasets. Just describe the dataset you want and your agent will craft the Data Designer configuration for you — schema design, validation, preview, generation — interactively or on full autopilot (just tell the agent to "be opinionated" or "surprise me").
Training search agents requires trajectory data --- the full multi-turn interaction showing how a model searches, reads, reasons, and answers. We built a four-stage pipeline that generates synthetic search trajectories from Wikidata knowledge graph paths, converts them into BrowseComp-style riddles using NeMo Data Designer, generates multi-step search rollouts with live web search via Tavily, and post-processes the results into SFT-ready training data.
Using NeMo Data Designer, an orchestration framework for generating high-quality synthetic data at scale, we built an iterative pipeline that generates diverse, schema-constrained structured outputs across JSON, YAML, and XML. Through multiple rounds of prompt refinement, rejection sampling, and programmatic validation, we produced a 9,949-sample dataset of verified structured output training data.
Data Designer v0.5.0's MCP tool-use support lets you generate multi-turn research trajectories, the kind of data needed to train deep research agents that iteratively search, read, and synthesize evidence before answering a question.
Synthetic data generation is more than a single prompt to a large language model. In this post, we walk through the design principles behind NeMo Data Designer and explain why we built it as a composable orchestration framework - treating SDG as a system of specialized stages rather than a monolithic generation task.
Using NeMo Data Designer, we created the RQA (Reasoning Question-Answer) dataset: a massive collection of graduate-level, reasoning-heavy science samples designed to push the boundaries of model performance.