How to Run Agent-Led Autoresearch with NVIDIA NeMo RL and NeMo Gym

Build an autonomous agent-led reinforcement learning research workflow using agent skills that leverage NVIDIA NeMo RL, NeMo Gym and Brev. This tutorial shows how Codex can configure a GPU environment, run experiments and track results using reusable agent skills for Brev etiquette, session memory, and autoresearch. This workflow covers two examples: 1. Visual counting with RL: Create a visual star-count environment for Qwen3-VL-2B-Instruct, scale training, and improve reported accuracy from 25.0% to 96.875%. 2. Paper-to-code validation: Demonstrate how an agent can help translate a research paper into code and start validation training, while the researcher remains responsible for goals, budgets, and final judgment. Tech blog: Brev Launchable: https://brev.nvidia.com/launchable/deploy?launchableID=env-3ECKNnhrpAkQVmcfgA88kFWW7v7 NeMo RL on GitHub: https://github.com/NVIDIA-NeMo/RL NeMo Gym on GitHub: https://github.com/NVIDIA-NeMo/Gym 0:00 - Agent-Led Coding and Research 0:21 - What Is Autoresearch? 0:47 - Three Capabilities in the Workflow 1:27 - NVIDIA NeMo RL, NeMo Gym and Brev 1:42 - Three Agent Skills for Codex 2:42 - Setting Up the Brev Environment 4:31 - Paper-to-Code Workflow 6:25 - Goal-Driven Autoresearch and Scaling 8:06 - Key Takeaways 8:39 - Learn More and Try It Yourself