Once you have a strong base model, adapt it using the training strategy that best fits your task. Cortex Training supports state-of-the-art open-weight models — including Qwen, Nemotron, Gemma, DeepSeek and others — across a range of parameter sizes. Teams can use LoRA or full-parameter training for SFT, RL, direct preference optimization, distillation, continued pretraining and mid-training.

Cortex Training integrates with popular post-training frameworks such as Hugging Face TRL, PrimeRL, veRL and SkyRL, so teams that are already using these frameworks can onboard quickly. It also integrates with agent platforms such as OpenHands to train models and entire agent harnesses. Beyond these integrations, the serverless APIs enable agent-driven model customization for enterprise workloads, reducing training complexity and making customization accessible without specialized AI expertise.

For greater control, advanced ML teams can use lower-level APIs to define custom loss functions, model architectures and training loops. For example, Cortex Training provides APIs for generating rollouts — model responses or agent interactions — updating model weights and synchronizing those weights across the system. These building blocks enable RL workflows that repeatedly generate behavior, evaluate outcomes and use the results to improve the model over time.

This flexibility extends to the underlying infrastructure. Cortex Training is powered by Arctic Platform, an open source backend that gives users full visibility into the code running on the GPUs. Advanced teams can contribute PRs and incorporate emerging techniques and innovations from the broader AI/ML community into their training workloads.

Check out our Cortex Training examples for the complete set of recipes, integrations and skills.