--- title: The Problem with Long Sequences: Why Transformers Needed... description: Explains why the Transformer architecture was needed by walking through the limitations of RNNs and LSTMs. RNNs process tokens sequentially, compressing all... canonical: https://daily.dev/posts/the-problem-with-long-sequences-why-transformers-needed-attention-etumffuqu twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: The Problem with Long Sequences: Why Transformers Needed Attention | daily.dev og:description: Explains why the Transformer architecture was needed by walking through the limitations of RNNs and LSTMs. RNNs process tokens sequentially, compressing all... og:url: https://daily.dev/posts/the-problem-with-long-sequences-why-transformers-needed-attention-etumffuqu og:image: https://api.daily.dev/og/posts/ETuMFfuQU.png og:image:alt: The Problem with Long Sequences: Why Transformers Needed Attention og:image:width: 1200 og:image:height: 630 og:locale: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # The Problem with Long Sequences: Why Transformers Needed Attention **[Medium](https://daily.dev/sources/medium_js)** · 6 min read · 0 upvotes · 0 comments ## Summary Explains why the Transformer architecture was needed by walking through the limitations of RNNs and LSTMs. RNNs process tokens sequentially, compressing all past information into a fixed-size hidden state, which creates an information bottleneck and vanishing gradient problems for long sequences. LSTMs improved memory retention but still required sequential processing, limiting parallelism. Attention solves this by allowing every token to directly interact with every other token, eliminating the need to compress history into a single vector and enabling parallel computation. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Questions this post answers ### Why do RNNs struggle with long-range dependencies in text? Recurrent neural networks compress all previous information into a single fixed-size hidden state, so as sequences grow longer, earlier details get overwritten and effectively forgotten. Training also suffers from vanishing gradients, where the backpropagated signal shrinks toward zero as it travels through many time steps, making it hard for the model to learn relationships between distant words. _Developers building sequence models can dig deeper into RNN limitations and attention alternatives via daily.dev._ ### How does the attention mechanism in transformers differ from how RNNs process sequences? Instead of compressing the past into one hidden state updated sequentially, attention lets every token directly look back at any other token in the sequence to pull relevant information. This removes the need to carry information through every intermediate step, better preserves long-range dependencies, and allows all tokens to be processed in parallel rather than one after another. _Those comparing sequence architectures can track transformer explainers like this on daily.dev._ ### Why is parallel processing in transformers faster than sequential processing in RNNs or LSTMs? RNNs and LSTMs must process tokens one at a time, so a sentence of one thousand tokens requires one thousand sequential computations even on powerful GPUs, since later tokens depend on earlier ones finishing. Transformers process every token simultaneously through attention, removing this dependency chain and dramatically accelerating training. _Engineers optimizing model training speed can follow explainers on transformer parallelism via daily.dev._ ## Similar posts on daily.dev - [Capturing Long-Range Dependencies with Attention: The Idea That Changed Deep Learning](https://daily.dev/posts/capturing-long-range-dependencies-with-attention-the-idea-that-changed-deep-learning-vkrgcdv0z) · Medium · 0 upvotes · 0 comments - [Transformers in Machine Learning: The Architecture That Changed AI Forever](https://daily.dev/posts/transformers-in-machine-learning-the-architecture-that-changed-ai-forever-j8te3edwb) · Medium · 1 upvotes · 0 comments - [Google solved an Old RNN Problem](https://daily.dev/posts/google-solved-an-old-rnn-problem-xsfjvxaga) · Daily Dose of Data Science \| Avi Chawla \| Substack · 44 upvotes · 3 comments - [Fundamentals of AI: Inside the transformer](https://daily.dev/posts/fundamentals-of-ai-inside-the-transformer-szwloqlw1) · Cisco · 0 upvotes · 0 comments --- Tags: [#deep-learning](https://daily.dev/tags/deep-learning), [#nlp](https://daily.dev/tags/nlp), [#transformers](https://daily.dev/tags/transformers) [View this post on daily.dev](https://daily.dev/posts/the-problem-with-long-sequences-why-transformers-needed-attention-etumffuqu) ```json {"@context":"https://schema.org","@graph":[{"@type":"Organization","@id":"https://daily.dev/#organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180},"sameAs":["https://twitter.com/dailydotdev","https://github.com/dailydotdev","https://www.linkedin.com/company/daily-dev-ltd"]},{"@type":"WebSite","@id":"https://daily.dev/#website","url":"https://daily.dev","name":"daily.dev","publisher":{"@id":"https://daily.dev/#organization"},"potentialAction":{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https://daily.dev/search?q={search_term_string}"},"query-input":"required name=search_term_string"}}]} {"@context":"https://schema.org","@type":"TechArticle","headline":"The Problem with Long Sequences: Why Transformers Needed Attention","url":"https://daily.dev/posts/the-problem-with-long-sequences-why-transformers-needed-attention-etumffuqu","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/the-problem-with-long-sequences-why-transformers-needed-attention-etumffuqu"},"datePublished":"2026-07-23T07:09:14.595Z","dateModified":"2026-09-14T06:37:43.792Z","description":"Explains why the Transformer architecture was needed by walking through the limitations of RNNs and LSTMs. RNNs process tokens sequentially, compressing all...","image":"https://media.daily.dev/image/upload/s--HRgLpUt6--/f_auto/v1722860399/public/Placeholder%2003","thumbnailUrl":"https://media.daily.dev/image/upload/s--HRgLpUt6--/f_auto/v1722860399/public/Placeholder%2003","isAccessibleForFree":true,"articleSection":"Medium","inLanguage":"en","publisher":{"@type":"Organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180}},"author":{"@type":"Organization","name":"Medium","logo":"https://media.daily.dev/image/upload/t_logo,f_auto/v1/logos/medium","url":"https://daily.dev/sources/medium_js"},"commentCount":0,"discussionUrl":"https://daily.dev/posts/the-problem-with-long-sequences-why-transformers-needed-attention-etumffuqu","interactionStatistic":[{"@type":"InteractionCounter","interactionType":{"@type":"LikeAction"},"userInteractionCount":0},{"@type":"InteractionCounter","interactionType":{"@type":"CommentAction"},"userInteractionCount":0}],"keywords":"deep-learning,nlp,transformers","timeRequired":"PT6M"} {"@context":"https://schema.org","@type":"BreadcrumbList","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https://daily.dev"},{"@type":"ListItem","position":2,"name":"Medium","item":"https://daily.dev/sources/medium_js"},{"@type":"ListItem","position":3,"name":"The Problem with Long Sequences: Why Transformers Needed Attention"}]} {"@context":"https://schema.org","@type":"FAQPage","@id":"https://daily.dev/posts/the-problem-with-long-sequences-why-transformers-needed-attention-etumffuqu#faq","mainEntity":[{"@type":"Question","name":"Why do RNNs struggle with long-range dependencies in text?","acceptedAnswer":{"@type":"Answer","text":"Recurrent neural networks compress all previous information into a single fixed-size hidden state, so as sequences grow longer, earlier details get overwritten and effectively forgotten. 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