Towards Robust Generalization in Agentic AI via Environment Scaling

This project addresses the challenge of enabling AI agents to operate effectively in complex, realistic environments such as web navigation, computer use, and mobile interfaces. While current models excel in structured domains like mathematics and coding, they struggle with tasks requiring long-horizon planning, exploration, and error recovery. The research focuses on building a scalable infrastructure capable of hosting hundreds of virtual environments and advancing reinforcement learning with world-model techniques to improve agents’ understanding and decision-making. The outcome will include an extensible training platform, enhanced RL frameworks, and AI agents that outperform state-of-the-art models on benchmarks for browser, computer, and phone use—laying the foundation for robust, general-purpose agentic AI.