Closing the Inference to Post-Training Loop | CoreWeave Blog

Introduces CoreWeave Model Distillation to capture inference traffic and turn it into training data for relabeling and iterating on models. Details a programmable training API for Serverless Training, allowing users to write training loops in Python while CoreWeave manages the infrastructure, enabling mixed SFT and RL approaches. Enhances CoreWeave Inference with RL Rollouts built on NVIDIA Dynamo with vLLM support, allowing hot-loading of new weights onto live deployments without impacting in-flight requests, thus accelerating the inference-to-training loop for RL.