We teach AI how to Really think.

Gamelab data is built on the foundation of billions of annual gameplays. Unlike web-scraped text, this is game data for AI training generated by real humans making real decisions in realtime. We capture every action, state change, and strategic pivot as a structured, sequential record.

Most LLM training data is static, consisting of snapshots of internet text that models can easily memorize. Games represent a "Multiverse of Human Games" refined abstractions of real-world activities like strategic planning and social dynamics. Training in these stochastic environments forces a model to move beyond pattern recognition toward genuine probabilistic reasoning.

Models can be integrated into the GameLab environment to perform in non-deterministic settings. This data is particularly valuable for reinforcement learning from human feedback (RLHF) and fine-tuning agents to handle "long-term horizon planning." By analyzing how an AI in games manages incomplete information, developers can identify reasoning gaps that traditional text-based benchmarks miss.

We benchmark a wide spectrum of frontier models, including the latest iterations of GPT, Claude, and Gemini. Our AI Model Leaderboard provides a live comparison of how these models perform across different game genres, from deterministic puzzles to complex, non-perfect information card games.

Standard benchmarks often saturate quickly because they are static. GameLab is dynamic and open-ended. Because our games are designed by humans for humans, they test for "human-like" general intelligence. We focus on the "Multiverse" approach evaluating how well systems learn and adapt to all conceivable human games.

Our datasets are derived from a vast network that generates billions of gameplays annually. This scale allows us to provide AI training data services that include high-fidelity, non-contaminated datasets. These records are uniquely suited for researchers who need to verify model performance in environments the model has not previously encountered.

We prioritize games that feature "non-perfect information" and stochasticity. In these environments, the full state of the game is not visible to the player. This requires the AI to engage in opponent modeling and probabilistic inference, which are essential markers of machine general intelligence.

Yes. Traditional text benchmarks are often part of the internet-scale data used to train LLMs, leading to artificially high scores through memorization. Because GameLab generates original, proprietary gameplay sequences, we provide a "clean" environment where models must solve problems from first principles.

Absolutely. Our structured records include human decision-making across a variety of genres. This is highly effective for "superfine tuning" and RLHF, helping models mimic expert human strategic planning, spatial reasoning, and deductive logic in complex, multi-step environments.

Beyond simple wins, we measure "Model Median Normalized Scores" and geometric means across diverse game types. We evaluate how efficiently a model learns a new game and its ability to generalize strategies across different branches of the human game multiverse