חלומות בקוד ללמידת תוכנית בעולמות פתוחים

תקציר מקורי באנגליתarXiv:2602.08194v2 Announce Type: replace-cross Abstract: Open-ended learning frames intelligence as emerging from continual interaction with an ever-expanding space of environments. While recent advances have utilized foundation models to programmatically generate diverse environments, these approaches often focus on discovering isolated behaviors rather than orchestrating sustained progression. In complex open-ended worlds, the large combinatorial space of possible challenges makes it difficult for agents to discover sequences of experiences that remain consistently learnable. To address this, we propose Dreaming in Code (DiCode), an unsupervised environment design (UED) framework in which foundation models (large language models) synthesize executable environment code to scaffold learni