Polymath

Polymath is building world generation models and systems to automate the creation of reinforcement learning environments. As the demand for AI shifts from question-answering to autonomous agents that can operate over long horizons, models must be trained in environments that reflect the real world. Today, RL environment generation is bottlenecked by human labor. Companies hire contractors to hand-build artifacts one-by-one. This approach is expensive and doesn’t scale. Moreover, human data alone will never lead to superintelligence. We’re building the core technology to enable automated environment generation using far less human effort than traditionally required, and eventually none. This allows for more complex and realistic worlds, and higher quality, scale, and diversity of tasks. This will be essential to unlock RL scaling. Our end goal is to create realistic, long-horizon environments from a text description alone. This will enable the creation of worlds of arbitrary complexity and scale, which is foundational for training & evaluating autonomous, superintelligent AI agents. We’re a team of researchers and engineers from UC Berkeley, Hume AI, Plaid, and Amazon. We have years of experience post-training frontier models in industry, and building large-scale data systems.