Applied Compute Training

Applied Compute Training is a platform for customizing AI models. It lets you take an open-source model, feed it your own dataset, and train it to perform better on your specific tasks. The page shows a live training run of a Qwen model fine-tuned for code quality grading, with metrics like token counts and evaluation scores tracked step by step. The core promise is turning your data into models you own that are cheaper and faster than frontier alternatives.

The platform launches training with a single command. You pick a model, a dataset, an environment, and a grader — then watch the training happen live. Applied Compute Training offers fully asynchronous reinforcement learning, full parameter fine-tuning at multi-trillion parameter scale, and continual learning via self-distillation from production traces. It also includes an AI research agent named Ari that monitors runs, spots regressions, and writes reports automatically. The system optimizes toward your own evaluation metrics, not generic benchmarks.

This tool is best for AI teams who need to fine-tune large models for domain-specific tasks — like legal document review, code review, or merchant onboarding. Companies like Harvey, Cognition, DoorDash, and Mercor are listed as customers, each achieving higher performance at a fraction of the cost of GPT-class models. If you're a machine learning engineer or product team that wants to own your model and iterate fast, Applied Compute Training gives you the infrastructure without the cluster management headache.