RL & Training

Infrastructure and scalable pipelines designed to train AI models using gameplay data and interactive environments. This platform supports large-scale experimentation across architectures, learning strategies, and reinforcement learning workflows.

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.

Supervised Fine-Tuning (SFT) uses GameLab’s human gameplay data to train models on high-quality examples of real decision-making. By learning directly from human actions in well-defined contexts, models develop strong baseline behaviors that reflect effective strategies and practical reasoning patterns.

This step plays a critical role in bootstrapping model performance before reinforcement learning. It provides a stable starting point that reduces exploration inefficiencies and improves convergence, enabling models to build on human-like behaviors as they transition into more advanced learning phases such as RL and self-play.