Builds scalable, increasingly difficult reinforcement-learning environments from financial market data to train and evaluate AI agents on iterative research tasks
Builds scalable, increasingly difficult reinforcement-learning environments from financial market data to train and evaluate AI agents on iterative research tasks. Works with frontier AI labs and academic groups on research harnesses, evaluation benchmarks, and post-training environments, with a long-term mission focused on recursive self-improvement.