Training AI agents to operate command-line interfaces safely using synthetic data generation and reinforcement learning with verifiable rewards (RLVR). The tutorial demonstrates fine-tuning NVIDIA Nemotron-Nano-9B-V2 to control the LangGraph CLI through a human-in-the-loop architecture. The approach combines NeMo Data Designer
•11m read time• From developer.nvidia.com
Table of contents
What you’ll build: a specialized agent to run a new CLI toolWhy use synthetic data generation and reinforcement learning to teach a new CLI?PrerequisitesStep 1: Design a synthetic dataset with NeMo Data DesignerStep 2: Fine-tune with RLVR (using GRPO)Step 3: Human-in-the-loop executionWhy RLVR + synthetic data work for customizing Agentic AIClosing thoughtsSort: