Learnable Gain Parameters Enhance Model Performance
SeunghyunSEO7 · x · 2026-07-16
This post discusses a model architecture experiment. Inspired by Alec's architecture, the author added head gain, activation gain, and embedding residual/skip connection, observing a performance boost.
Although the main post is quite brief, its core message is clear: these learnable gain parameters and embedding skip connections could be highly effective structural modifications for improving model performance.
Related event: Researchers Debate 1/d-Style Attention Scaling in Modern LLMs(6 posts)→
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