Mastering Agentic Techniques: AI Agent Reinforcement Learning | NVIDIA Technical Blog

What this post added

This post details the application of Reinforcement Learning (RL), specifically Reinforcement Learning with Verifiable Rewards (RLVR) and Group Relative Policy Optimization (GRPO), for customizing language models and agents in domain-specific enterprise workflows. It introduces NVIDIA's Nemotron 3 Super and NeMo RL ecosystem (NeMo Gym, NeMo Data Designer) as tools for open model post-training, scalable environment-based evaluation, verifiable reward design, and synthetic data generation. The post outlines the minimal RL loop (policy model, task, action, environment, verifier, rollouts, policy update) and provides guidance on choosing between RAG, prompting, SFT, DPO, RLHF, and RLVR based on the available signal and desired behavior. It highlights GRPO as a practical default for RLVR tasks and discusses the importance of environment-based RL for agentic systems that fail across long-horizon workflows. The post also covers synthetic data generation using NeMo Data Designer and the use of NeMo Gym for generating scored trajectories.