The Effect of Layer Ordering in Hybrid Architectures
Empirically, we observe that for standard training, learned hybrid architectures exhibit separations between layer orderings that match our theoretical findings.
ProofPaper ↗
Key points
- Hybrid attention is rapidly becoming the dominant architectural choice for frontier models.
- Despite their successes, such architectures complicate model design by introducing additional choices, including the types and locations of layers.
- In addition, we investigate the mechanisms learned by hybrids, observing that Transformer layers contribute the largest share of performance on associative recall across architectures.
- This work establishes insights into task-dependent hybrid architecture design relevant to the training and scaling of frontier-scale hybrids.
Sources (1)
- [1]The Effect of Layer Ordering in Hybrid ArchitecturesarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 13, 10:20 AM
Empirically, we observe that for standard training, learned hybrid architectures exhibit separations between layer orderings that match our theoretical findings.
Hybrid attention is rapidly becoming the dominant architectural choice for frontier models.
Extractive summary: sentences quoted from the sources.
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