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MinT: RL Infrastructure for Experiential Intelligence
How We Build Trillion Parameter Reasoning RL with 10% GPUs
Introducing Mind Lab: Building AI that Learns from Real Experience
You're all set!MinT - Mind Lab Toolkit
Read docsMinT (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.
Congratulations to Mind Lab on the release of MinT. This innovative training abstraction has the potential to become a mainstream approach, offering academic teams a practical pathway to test and validate their ideas on significantly larger models.
Tsinghua University, Prof. Gao Huang
MinT streamlines VLM training by resolving infrastructure and compatibility issues. We can focus solely on algorithmic innovation itself. Most importantly, it saves us a tremendous amount of energy and empowers us to run more experiments simultaneously, enabling us to deliver better product models.
OdyssLife, CTO, Scott Liu
MinT let us focus entirely on our data and RL objectives, without getting bogged down in framework adaptation or infrastructure complexity. It turned RL post-training into a practical, repeatable workflow rather than an engineering project.
Mindical Health, CEO, Zhiting Huang
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