For artificial intelligence to thrive in a complex, constantly evolving world, it must overcome significant challenges: limited data quality and scale, and a lag in new, relevant information creation. An intriguing question emerges—can language models autonomously generate new tasks to practice on, enabling them to self-improve and better align with human preferences? In a new paper Evolving Alignment via Asymmetric Self-Play, a research team from Google D