Bounded Hyperbolic Tangent: A Stable and Efficient Alternative to Pre-Layer Normalization in Large Language Models
arXiv:2601.09719v3 Announce Type: replace-cross Abstract: Pre-Layer Normalization (Pre-LN) is the de facto choice for large language models (LLMs) and is crucial for stable pretraining and effective transfer learning. However, Pre-LN incurs repeated statistical-computation overhead and remains vulnerable to the curse of depth, where hidden-state magnitudes and variances grow as the number of layers increases, destabilizing training. Efficiency-oriented normalization-free methods such as Dynamic Tanh (DyT) improve throughput but remain fragile at depth. To jointly address stability and efficien
This research addresses fundamental stability and efficiency challenges in large language models, a critical area of active development as LLM scale and complexity increase.
Improved stability and efficiency in LLM training directly impacts the cost, speed, and ultimate performance ceiling of AI development, making more complex models feasible.
The proposed 'Bounded Hyperbolic Tangent' offers a potential alternative to current normalization techniques, promising more stable and efficient LLM growth without incurring prior computational overheads.
- · AI model developers
- · Cloud infrastructure providers
- · AI research institutions
This research could lead to new architectures or training paradigms for LLMs that are more computationally efficient and stable at extreme scales.
Reduced training costs and improved model stability could accelerate the development of more sophisticated AI applications and services.
Easier scaling of LLMs might lead to broader deployment of powerful AI, potentially exacerbating the existing 'compute supply chain' constraints if hardware cannot keep pace.
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Read at arXiv cs.AI