AI Engineer World's Fair 2026 — unofficial import demo

Verifiable rewards are the gold standard for RL training, but real-world agent tasks frequently lack clean deterministic evaluation objectives. This talk surveys our efforts to scale RL in non-verifiable settings -- including task synthesis, unsupervised environment design, and automatic judge calibration -- to ultimately enable self-improvement in production, grounded in real-world agent traces and domain-specific context.