Coding AI agents are becoming practical operators for long-running machine learning (ML) workflows. They can inspect repositories, set up runtimes, resolve build issues, launch experiments, monitor execution, analyze metrics, and summarize results.
For reinforcement learning (RL) research, this matters because meaningful metrics often appear only after the essential experiment infrastructure is in place. Autoresearch is an open source Python project by Andrej Karpathy for automating AI and ML model training.
With this approach, autonomous AI agents translate high-level goals into hypotheses, edit and test a real codebase, keep metric-improving ideas, and hand results back to the human researcher. This enables teams to build specialized domain agents starting from a performant open model, such as NVIDIA Nemotron, then improve it with RL against measurable domain tasks while maintaining control over data, IP, deployment, and the training workflow.
This post walks through how to run a lightweight, no-code-from-the-user-side, skill-based autoresearch workflow. It was tested using a frontier coding agent (Codex with GPT 5.5) on NVIDIA NeMo RL and NVIDIA NeMo Gym, using an NVIDIA Brev GPU instance.
