Compositional learning, inspired by the human ability to derive complex ideas from simpler constituents, seeks to equip machines with analogous capabilities for understanding, reasoning, and adaptive learning. This methodology bolsters machines' ability to generalize to out-of-distribution samples through the recombination of learned components, proving effective across diverse tasks such as machine translation, visual reasoning, image generation, reinforcement learning, and more. Despite notable advancements, persistent challenges remain in achieving robust compositional generalization and reasoning within even the most advanced foundation models. Our workshop aims to discuss these challenges as well as untapped opportunities ahead from the following four aspects: exploring the capacity for compositionality in foundation models and dissecting the underlying mechanisms of their compositional learning; devising reliable and model-agnostic strategies for constructing compositional systems; establishing theoretical and empirical connections between modular architectures and compositional generalization; and extending compositional learning principles to continual learning contexts. By confronting these themes, we aim to foster a collaborative exploration of theoretical and empirical dimensions of compositional learning, thus advancing understanding and practical applications of compositional learning.