What if your computer-use agent could learn a new command-line tool—operating it safely, without writing files or free-typing shell commands? With NVIDIA Nemotron open models, you can quickly train specialized agents for any CLI, using synthetic data and reinforcement learning—no usage logs required. Curious how it can be adapted for your own proprietary CLI environment? Join us live for a developer-focused livestream. In this interactive livestream, we’ll cover: - A live demo: Generating synthetic training data using NeMo Data Designer, validating examples, and fine-tuning with techniques like LoRA and reinforcement learning with verifiable rewards (RLVR) using NeMo RL. - Showcase how safety is built into every layer, from human-in-the-loop command approval to runtime isolation. - Explain why synthetic data generation and RL accelerate agent specialization without compromising accuracy or trust. Bring your questions about the workflow, model customization, and synthetic data generation, we’ll be answering them live throughout the livestream.
Train AI Agents for CLI Tasks with Synthetic Data & RL | Nemotron Labs
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Thanks for posting.
We’re slowly moving from ‘AI that executes commands’ to ‘AI that understands workflows and optimizes them. Synthetic data + RL may be the key step that closes the gap between automation and autonomy.
Great initiative! The combination of synthetic data generation and RLVR makes agent specialization for CLI both safe and scalable. Looking forward to the livestream demo.👍
Gunna be a fun one!
Interesting. Curious to see how RLVR performs in the live demo.
Could AI agents lower the cost of my 87 year old mothers care at Aegis Living Laurelhurst? It’s 12000 dollars a month. Here’s an invoice.
This demonstrates a new paradigm in specialised agent training: leveraging synthetic data and reinforcement learning to enable models to adapt safely and efficiently to entirely new CLI environments, thereby truly advancing intelligent operational capabilities towards practical application.
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8hFascinating direction. Synthetic data + verifiable RL is exactly what makes agent behavior predictable instead of risky. When every action is grounded in validated examples, real trust becomes scalable.