Modal launches Modal Dojo, a library for production-grade LLM training
Modal announced Modal Dojo, a library it says provides the building blocks, tuned defaults and observability needed for production-grade LLM training, and said it is available now. The library builds on open-source frameworks Miles and Slime, to which Modal says it has contributed upstream patches, and ships an out-of-box observability dashboard covering loss and reward curves, rollout trajectories and substep profiling. Modal also says training can be started from a single prompt describing the model, dataset and target agent, and cites customer testimonials from Axiom Bio and Afterquery about long-context reinforcement-learning runs.
Modal is pitching Dojo as a way for teams with deployed agent harnesses and their own traces to move into post-training, extending its platform from inference to training.
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.
#Modal#Modal Dojo#Miles#Slime#Hugging Face#Axiom Bio#Afterquery#DeepSeek V4.1 Flash#Kimi