Learning Meaningful Representations of Life 2025 (LMRL 2025) aims to address the growing interest in large-scale representation learning for biological data, driven by the availability of large biological datasets, such as DNA and RNA sequences, protein structures, and cell imaging. There have been many recent papers proposing “foundation models” for biological data, but the performance of these models varies dramatically across domains: in some settings, large-scale pre-training has significantly expanded the range of solvable tasks, while in others, foundation models are often outperformed by simple baselines. This workshop will encourage work that explains this gap by focusing on two key issues: first, identifying the data, models, and algorithms necessary to extract meaningful representations that generalize well to downstream tasks, and second, establishing appropriate methods to evaluate the quality and utility of these learned representations. By bringing together researchers from AI and biology, the workshop aims to foster collaboration, promote standardization of datasets and evaluation metrics, and explore real-world applications that can benefit from improved strategies in representation learning.