CoreWeave Forge

Tracking, evaluation, and versioning stay wired to the work as it moves from experiment to production — the record travels with the work instead of living in a system you reconcile by hand.

Supervised fine-tuning with nothing to provision — turn your curated datasets into a sharper model without managing training infrastructure.

Train agents against reward signals with nothing to provision. Built on ART, the Agent Reinforcement Trainer, with RULER providing automatic reward scoring — no hand-labeled data or hand-crafted reward functions required.

Carry what a larger model learned into a smaller, faster one — so quality you’ve already proven can ship at lower cost and latency.

ARIA analyzes your experiment history, surfaces what drove a change, and proposes what’s worth trying next — your judgment stays in charge.

Every model checkpoint and agent configuration versioned in open formats, so your next attempt starts from any point in your history, on any stack.

Notebooks live in the environment your work runs in, so what you prototype carries straight into training, evaluation, and production.

Serve models from autoscaling endpoints with nothing to provision — pick from the Model Catalog and pay for what you use.

Serve your models on dedicated capacity reserved for you — predictable performance and isolation for production workloads that can’t share.

See every step your agent took, score it against your own production traces, and catch drift while the fix is still a small one.

A clean, isolated environment for every run, so the safe place to test is there the moment you need it.