Products
Human Gameplay Data
Large-scale datasets capturing real human decisions across hundreds of games. These structured logs provide high-quality signals for studying strategic reasoning, long-horizon planning, and decision-making under uncertainty at scale.
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RL Environments
Interactive game environments purpose-built for reinforcement learning research. Agents can train, simulate, and test strategies in controlled settings with standardized rules, reward structures, and reproducible outcomes.
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Bespoke Datasets
GameLab creates new game experiences for our partners tailored to capture specific behaviors and signals, enabling rapid collection of high-quality, large-scale datasets from real players.
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EVALS
A systematic framework for evaluating AI models on complex game tasks. Models are tested against human gameplay patterns and measurable objectives to track improvements in reasoning, strategy, and generalization.
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Benchmarks
Standardized challenge suites built from real-world games that measure model performance over time. These benchmarks create consistent comparisons across systems in areas such as planning, imperfect information, and strategic decision-making.
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Training Infrastructure
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.
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SFT (Supervised Fine-Tuning)
Human gameplay examples are used to fine-tune models through supervised learning, enabling models to learn strong baseline behaviors and replicate effective human decision-making patterns.
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World Models
Simulation models that learn the underlying dynamics of games and environments, enabling AI systems to predict future states, reason about consequences, and plan actions over long horizons.
Continue reading >FAQ: High-Fidelity AI Data Solutions
Our AI training data services are built to address the specific challenges of frontier AI development, from benchmark saturation to data contamination. Explore the questions below to understand how GameLab’s unique methodology ensures data integrity, strategic depth, and high-performance results for your models.
We produce structured, sequential records of human decision-making across a “Multiverse” of games. Our AI training data services include full-state game records, move-by-move decision trees, and outcome-based trajectories. This data is delivered in machine-readable formats (JSON/CSV) optimized for rapid integration into existing training pipelines.
- Strategic Planning Sets: Data from long-term horizon games (e.g., Chess, Strategy puzzles).
- Probabilistic Inference Sets: Data from non-perfect information card games (e.g., Gin Rummy).
- Adversarial Modeling Sets: Data focused on competitive multi-agent interactions.
- The “Human Game Multiverse” Corpus: A diverse, multi-genre dataset for general-purpose reasoning fine-tuning.
Most training data for AI is currently scraped from the public web, which is increasingly contaminated with AI-generated text. GameLab data is proprietary and generated in “cleanroom” human environments. Because games involve non-deterministic outcomes, our datasets provide a more rigorous test of a model’s ability to handle uncertainty than any static text corpus can.
- Reinforcement Learning from Human Feedback (RLHF): Aligning models with expert human strategic logic.
- Fine-Tuning: Teaching models spatial reasoning, deductive logic, and long-term planning.
- Agentic Evaluation: Verifying how autonomous agents handle multi-step tasks in non-perfect information environments.
Absolutely. Beyond our standard library, we offer bespoke AI training data services. We can design custom game environments or curate specific gameplay distributions, such as “edge case” scenarios or high-level expert play, to meet your specific research objectives.
Every data point is sourced from our private network of human players, ensuring it has never touched the open internet. We use automated verification to ensure sequential logic is preserved, providing a “clean” environment where models must solve problems from first principles rather than retrieved memory.
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