---
title: "Simplifying transformer blocks"
url: https://daily.dev/posts/simplifying-transformer-blocks-emcyppjxu
source_url: https://notes.aimodels.fyi/simplifyig-transformer-blocks/
type: article
source: "AIModels.fyi"
published: 2026-04-06T20:02:30.781Z
updated: 2026-04-06T20:02:51.944Z
tags: ["nlp", "neural-networks"]
reading_time: 6
upvotes: 0
comments: 0
language: en
---

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# Simplifying transformer blocks

**[AIModels.fyi](https://daily.dev/sources/aimodelsfyi)** · 6 min read · 0 upvotes · 0 comments

## Summary

Researchers from ETH Zurich systematically investigated which components of the standard transformer block architecture can be removed without degrading performance or training efficiency. Key findings include: residual connections can be eliminated by constraining self-attention initialization, value and projection matrices can be fixed as identity matrices without performance loss, and switching from sequential to parallel attention/feedforward sub-blocks allows dropping remaining residual connections. However, removing normalization layers entirely hurts fine-tuning performance. The resulting simplified architecture matches standard transformer training efficiency on language modeling tasks, offering a path toward cheaper and more accessible NLP model training.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://notes.aimodels.fyi/simplifyig-transformer-blocks/>

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---

Tags: [#nlp](https://daily.dev/tags/nlp), [#neural-networks](https://daily.dev/tags/neural-networks)

[View this post on daily.dev](https://daily.dev/posts/simplifying-transformer-blocks-emcyppjxu)