arXiv:2605.25704v1 Announce Type: new Abstract: In contemporary large language models (LLMs), the swish-gated linear unit (SwiGLU) activation function is widely adopted to regulate the information flow and introduce non-linearity. For large positive inputs, SwiGLU approximates the quadratic function $x^2$, providing strong nonlinearity and expressive capacity. However, this property also causes numerical instability as the input or model scale increases, particularly in low-precision LLM training. The main reason is its approximate quadratic amplification, which enlarges the output range and e
The continuous push for larger and more complex LLMs, coupled with the need for efficient low-precision training, necessitates advancements in fundamental architectural components like activation functions.
Improved stability in LLM pre-training, especially with low-precision arithmetic, could significantly reduce computational costs and accelerate AI development, making advanced models more accessible.
A more stable activation function could lead to more efficient and robust training of large language models, potentially enabling the use of lower precision hardware without compromising performance.
- · AI researchers
- · LLM developers
- · Cloud computing providers
- · Hardware manufacturers
- · Less efficient LLM training methods
More stable and faster training of large language models becomes possible.
Reduced compute costs could lead to a proliferation of more specialized and powerful AI models across various industries.
Increased accessibility to advanced AI models might accelerate broader AI adoption and innovation, potentially shifting global AI leadership dynamics.
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