How to Run an Autoresearch Workflow with RL Agent Skills and NVIDIA NeMo | NVIDIA Technical Blog

This post details the practical application of agentic AI for automating reinforcement learning research workflows. It demonstrates how a coding agent (Codex with GPT 5.5) can autonomously set up the full software stack (NeMo RL, NeMo Gym), resolve dependencies, manage resources, launch experiments, monitor execution, analyze metrics, and summarize results. Key contributions include the use of specialized agent skills (Brev-etiquette, Session-memory, Autoresearch) for reproducibility and robust long-running ML workflows, enabling autonomous hypothesis generation, paper-to-code translation, and iterative model optimization. The post showcases a novel NeMo Gym visual counting environment creation and training of a Qwen3-VL-2B-Instruct model, increasing accuracy from 25% to 96.9%, and the implementation of an off-policy RL algorithm from a research paper.