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

This post details the practical application of reinforcement learning (RL), specifically RLVR and GRPO, for customizing language models and agents in domain-specific enterprise workflows. It introduces NVIDIA's Nemotron 3 Super and the 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 a minimal RL loop, emphasizing the importance of starting with evaluation, defining clear task requirements, designing trustworthy reward functions, and conducting iterative, small-scale experiments with continuous logging. It also contrasts RL with other techniques like RAG, prompting, and SFT, providing guidance on when to use each based on the available signal and desired behavior. The core contribution is a practical guide to implementing verifiable RL training for agents, highlighting the role of environments and verifiers in scoring success and updating model weights.