Closing the Loop Between Inference and Post-Training
CoreWeave, Monday, October 5th, 2026
CoreWeave explains how Model Distillation, RL Rollouts and a training API in CoreWeave Forge turn production inference signal into better models.
CoreWeave argues that post-training should begin once a model ships, using production traces to improve model quality and economics.
At Fully Connected 2026 it introduced CoreWeave Model Distillation, CoreWeave RL Rollouts on CoreWeave Inference and a programmable training API in preview, all brought together in CoreWeave Forge.
The post advises choosing distillation, supervised fine-tuning or reinforcement learning based on the failure mode and available signal, citing a Method example where a retrained Llama 3.1 8B model won 57.5% of head-to-head evaluations against the production version.