GameLab’s Training Infrastructure provides the systems and pipelines required to train models at scale using gameplay data and interactive environments. This includes data ingestion, alignment of visual and action-based signals, and integration with reinforcement learning workflows, enabling large-scale experimentation across different model architectures and training strategies.

The infrastructure is designed for continuous iteration. As new gameplay data is generated and environments evolve, the system supports ongoing training, evaluation, and refinement. This creates a feedback loop where models can be trained on human data, improved through reinforcement learning, and validated through evaluation and benchmarks—all within a unified platform.