We’ve written about how reinforcement learning (RL) is an infrastructure problem, and how the last five years of work we’ve done provides performant, available, and scalable infrastructure you can rely on to train your models. Even without the concerns of infrastructure, RL is still hard! There are many higher-level components in the system that control your outcome: you need appropriate base models, high-fidelity environments, clear and robust reward functions, and tuned training recipes that actually work.

But if you have an agent harness deployed in production, with evals and agent traces collected from that deployment, you’re already 80% of the way there. You just need an easy way to start training on your data.

That’s why we built Modal Dojo: a library that gives you the building blocks, tuned defaults, and observability you need for easy, production-grade LLM training.

It’s available now. All it takes is a prompt:

Use Modal Dojo to train [X model] on [Y proprietary dataset]
to replace [Z agent].

Read on to learn more about how we built it and why.