arXiv:2605.23033v1 Announce Type: new Abstract: Foundational Models pretrained on huge amount of data learn representations that evolve across depth, forming a hierarchy of embeddings with distinct semantic content and geometric structure. Contrary to the widespread practice of using only the final layer or shallow mixtures, we show that task-relevant information is distributed non-monotonically across layers and cannot be recovered by na\"ive aggregation. Through a geometric and empirical study across multiple modalities, we show that effective transfer depends on identifying which layers enc
This research builds on the increasing sophistication of foundational models and the constant drive to optimize their application and resource utilization.
Understanding how to best leverage intermediate representations in foundational models could significantly improve AI performance and efficiency across various tasks and modalities.
The conventional wisdom of using only final layers or simple aggregations for model transfer is challenged, pointing towards more complex and effective layer-selection strategies.
- · AI researchers
- · ML engineers
- · Foundational model developers
- · Companies utilizing advanced AI
- · Developers relying on naive layer aggregation
- · Less optimized AI applications
Improved performance and resource efficiency in AI model fine-tuning and transfer learning applications.
Development of new architectural patterns and tools specifically designed to extract and combine optimal intermediate representations.
Potentially leading to more versatile and generalizable AI systems that can adapt to new tasks with less training data.
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Read at arXiv cs.LG