We are at the cusp of a transition from "learning from data" to "learning
what data to learn from" as a central focus of artificial intelligence (AI)
research. While the first-order learning problem is not completely solved,
large models under unified architectures, such as transformers, have shifted
the learning bottleneck from how to effectively train our models to how to
effectively acquire and use task-relevant data. This problem, which we frame as
exploration, is a universal aspect of learning in open-ended domains, such as
the real world. Although the study of exploration in AI is largely limited to
the field of reinforcement learning, we argue that exploration is essential to
all learning systems, including supervised learning. We propose the problem of
generalized exploration to conceptually unify exploration-driven learning
between supervised learning and reinforcement learning, allowing us to
highlight key similarities across learning settings and open research
challenges. Importantly, generalized exploration serves as a necessary
objective for maintaining open-ended learning processes, which in continually
learning to discover and solve new problems, provides a promising path to more
general intelligence.

通过将探索驱动的学习概念性地统一监督学习和强化学习之间的探索驱动学习，我们提出了广义探索问题，以突出不同学习设置之间的关键相似之处和开放研究挑战，广义探索是用于维护开放式学习过程的必要目标。