MinT (Mind Lab Toolkit) is an RL infrastructure that helps agents and models learn from real experience. It abstracts away compute scheduling, distributed rollout, and training orchestration so teams can iterate learning loops inside real tasks with real feedback under real product constraints.

MinT provides a unified and reproducible way to run reinforcement learning across multiple models and tasks, with a strong focus on making LoRA RL simple, stable, and efficient for both mainstream and frontier scale models. You define what to train, what data to learn from, how to optimize, and how to evaluate, and MinT handles the rest.