How to Run an Autoresearch Workflow with RL Agent Skills and NVIDIA NeMo
Coding AI agents are becoming practical operators for long-running machine learning workflows, enabling tasks like repository inspection, runtime setup, issue resolution, experiment launching, monitoring, and result summarization, which is crucial for reinforcement learning research.
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 established. This article discusses how to run an autoresearch workflow using RL agent skills combined with NVIDIA NeMo to automate and streamline these processes.