Build frontier models and agents that learn from experience.
Model training6 mins read
Introduction
General-purpose models are remarkable, but they don't know your contracts, your claims rules or the shorthand your engineers use. The gap between a demo and a dependable system is almost always domain knowledge.
Learning from experience closes that gap. Instead of prompting harder, you let a model practise on your tasks, score the results against what good looks like, and keep the improvements.
The learning loop
Rollouts, rewards and updated weights
Inference workers run the current model on real tasks and collect rollouts. A reward signal, from tests, graders or expert review, scores each attempt. Training workers turn those scores into updated weights, and the loop starts again.
Running training and inference on one shared GPU fleet keeps the loop tight: a checkpoint never leaves the system that made it, and you can compare runs without reconciling two stacks.
Keeping it safe
Guardrails for long runs
Long reinforcement learning runs fail quietly. A small mismatch between what the trainer expects and what inference produced won't crash anything; it just bends the learning. We check for that mismatch and for numerical instability on every run.
Every checkpoint is versioned and evaluated before it can be promoted, so a regression is a rollback, not an incident.
What to do next
Start with a narrow, valuable task where you already have examples of good work. Measure the base model, run a first fine-tune, and compare it against that baseline before scaling up.
