How to Train Open Models with RL on Prime Intellect | Nemotron Labs

Customizing open models doesn't have to mean managing your own GPU cluster. In this livestream, we walk through a complete hosted reinforcement learning run on Nemotron 3 Nano — from a cold start to a downloadable LoRA adapter — using Prime Intellect Lab. Local setup takes about five minutes. Prime Intellect handles the rest. The session follows the same three-step loop: get a baseline, train with RLVR, and reevaluate under identical conditions. You'll see how to configure and launch a LoRA RL job, read reward curves and rollouts to understand what the model actually learned, and deploy the adapter for inference. You can also apply the same workflow to Nemotron 3 Super and Ultra, and extend it to real software engineering tasks. What you'll learn: - How to install the Prime CLI, set up a Lab workspace, and run a baseline evaluation in minutes - How to configure and launch a hosted LoRA RL training job on Nemotron 3 Nano - How to read reward curves and rollout traces to distinguish learning from reward hacking - How to deploy a LoRA adapter and rerun evaluation to measure actual improvement How to apply this workflow to Nemotron 3 Super and Ultra, and scale to harder tasks Ready to start training your own open models? Bring your questions live.