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<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Eric X. Liu's Personal Page</title><link>/</link><description>Recent content on Eric X. Liu's Personal Page</description><generator>Hugo</generator><language>en</language><lastBuildDate>Sun, 03 Aug 2025 02:37:56 +0000</lastBuildDate><atom:link href="/index.xml" rel="self" type="application/rss+xml"/><item><title>A Deep Dive into PPO for Language Models</title><link>/posts/a-deep-dive-into-ppo-for-language-models/</link><pubDate>Sun, 03 Aug 2025 02:36:44 +0000</pubDate><guid>/posts/a-deep-dive-into-ppo-for-language-models/</guid><description><p>Large Language Models (LLMs) have demonstrated astonishing capabilities, but out-of-the-box, they are simply powerful text predictors. They don&rsquo;t inherently understand what makes a response helpful, harmless, or aligned with human values. The technique that has proven most effective at bridging this gap is Reinforcement Learning from Human Feedback (RLHF), and at its heart lies a powerful algorithm: Proximal Policy Optimization (PPO).</p>
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<p>You may have seen diagrams like the one below, which outlines the RLHF training process. It can look intimidating, with a web of interconnected models, losses, and data flows.</p></description></item><item><title>T5 - The Transformer That Zigged When Others Zagged - An Architectural Deep Dive</title><link>/posts/t5-the-transformer-that-zigged-when-others-zagged-an-architectural-deep-dive/</link><pubDate>Sun, 03 Aug 2025 02:36:44 +0000</pubDate><guid>/posts/t5-the-transformer-that-zigged-when-others-zagged-an-architectural-deep-dive/</guid><description><p>In the rapidly evolving landscape of Large Language Models, a few key architectures define the dominant paradigms. Today, the &ldquo;decoder-only&rdquo; model, popularized by the GPT series and its successors like LLaMA and Mistral, reigns supreme. These models are scaled to incredible sizes and excel at in-context learning.</p>
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<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Eric X. Liu's Personal Page</title><link>/</link><description>Recent content on Eric X. Liu's Personal Page</description><generator>Hugo</generator><language>en</language><lastBuildDate>Sun, 03 Aug 2025 02:45:50 +0000</lastBuildDate><atom:link href="/index.xml" rel="self" type="application/rss+xml"/><item><title>A Deep Dive into PPO for Language Models</title><link>/posts/a-deep-dive-into-ppo-for-language-models/</link><pubDate>Sun, 03 Aug 2025 02:45:11 +0000</pubDate><guid>/posts/a-deep-dive-into-ppo-for-language-models/</guid><description><p>Large Language Models (LLMs) have demonstrated astonishing capabilities, but out-of-the-box, they are simply powerful text predictors. They don&rsquo;t inherently understand what makes a response helpful, harmless, or aligned with human values. The technique that has proven most effective at bridging this gap is Reinforcement Learning from Human Feedback (RLHF), and at its heart lies a powerful algorithm: Proximal Policy Optimization (PPO).</p>
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<p>You may have seen diagrams like the one below, which outlines the RLHF training process. It can look intimidating, with a web of interconnected models, losses, and data flows.</p></description></item><item><title>T5 - The Transformer That Zigged When Others Zagged - An Architectural Deep Dive</title><link>/posts/t5-the-transformer-that-zigged-when-others-zagged-an-architectural-deep-dive/</link><pubDate>Sun, 03 Aug 2025 02:45:11 +0000</pubDate><guid>/posts/t5-the-transformer-that-zigged-when-others-zagged-an-architectural-deep-dive/</guid><description><p>In the rapidly evolving landscape of Large Language Models, a few key architectures define the dominant paradigms. Today, the &ldquo;decoder-only&rdquo; model, popularized by the GPT series and its successors like LLaMA and Mistral, reigns supreme. These models are scaled to incredible sizes and excel at in-context learning.</p>
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<p>But to truly understand the field, we must look at the pivotal models that explored different paths. Google&rsquo;s T5, or <strong>Text-to-Text Transfer Transformer</strong>, stands out as one of the most influential. It didn&rsquo;t just introduce a new model; it proposed a new philosophy. This article dives deep into the architecture of T5, how it fundamentally differs from modern LLMs, and the lasting legacy of its unique design choices.</p></description></item><item><title>Some useful files</title><link>/posts/useful/</link><pubDate>Mon, 26 Oct 2020 04:14:43 +0000</pubDate><guid>/posts/useful/</guid><description><ul>
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<li><a href="https://ericxliu.me/rootCA.pem" class="external-link" target="_blank" rel="noopener">rootCA.pem</a></li>
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<li><a href="https://ericxliu.me/vpnclient.ovpn" class="external-link" target="_blank" rel="noopener">vpnclient.ovpn</a></li>
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<!doctype html><html lang=en><head><title>A Deep Dive into PPO for Language Models · Eric X. Liu's Personal Page</title><meta charset=utf-8><meta name=viewport content="width=device-width,initial-scale=1"><meta name=color-scheme content="light dark"><meta name=author content="Eric X. Liu"><meta name=description content="Large Language Models (LLMs) have demonstrated astonishing capabilities, but out-of-the-box, they are simply powerful text predictors. They don’t inherently understand what makes a response helpful, harmless, or aligned with human values. The technique that has proven most effective at bridging this gap is Reinforcement Learning from Human Feedback (RLHF), and at its heart lies a powerful algorithm: Proximal Policy Optimization (PPO).
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You may have seen diagrams like the one below, which outlines the RLHF training process. It can look intimidating, with a web of interconnected models, losses, and data flows."><meta name=keywords content="software engineer,performance engineering,Google engineer,tech blog,software development,performance optimization,Eric Liu,engineering blog,mountain biking,Jeep enthusiast,overlanding,camping,outdoor adventures"><meta name=fediverse:creator content><meta name=twitter:card content="summary"><meta name=twitter:title content="A Deep Dive into PPO for Language Models"><meta name=twitter:description content="Large Language Models (LLMs) have demonstrated astonishing capabilities, but out-of-the-box, they are simply powerful text predictors. They don’t inherently understand what makes a response helpful, harmless, or aligned with human values. The technique that has proven most effective at bridging this gap is Reinforcement Learning from Human Feedback (RLHF), and at its heart lies a powerful algorithm: Proximal Policy Optimization (PPO).
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You may have seen diagrams like the one below, which outlines the RLHF training process. It can look intimidating, with a web of interconnected models, losses, and data flows."><meta property="og:url" content="/posts/a-deep-dive-into-ppo-for-language-models/"><meta property="og:site_name" content="Eric X. Liu's Personal Page"><meta property="og:title" content="A Deep Dive into PPO for Language Models"><meta property="og:description" content="Large Language Models (LLMs) have demonstrated astonishing capabilities, but out-of-the-box, they are simply powerful text predictors. They don’t inherently understand what makes a response helpful, harmless, or aligned with human values. The technique that has proven most effective at bridging this gap is Reinforcement Learning from Human Feedback (RLHF), and at its heart lies a powerful algorithm: Proximal Policy Optimization (PPO).
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You may have seen diagrams like the one below, which outlines the RLHF training process. It can look intimidating, with a web of interconnected models, losses, and data flows."><meta property="og:locale" content="en"><meta property="og:type" content="article"><meta property="article:section" content="posts"><meta property="article:published_time" content="2025-08-03T02:36:44+00:00"><meta property="article:modified_time" content="2025-08-03T02:37:56+00:00"><link rel=canonical href=/posts/a-deep-dive-into-ppo-for-language-models/><link rel=preload href=/fonts/fa-brands-400.woff2 as=font type=font/woff2 crossorigin><link rel=preload href=/fonts/fa-regular-400.woff2 as=font type=font/woff2 crossorigin><link rel=preload href=/fonts/fa-solid-900.woff2 as=font type=font/woff2 crossorigin><link rel=stylesheet href=/css/coder.min.60f552a2c0452fcc0254c54f21c3e0728460c1ae85f97a9c35833a222ef8b884.css integrity="sha256-YPVSosBFL8wCVMVPIcPgcoRgwa6F+XqcNYM6Ii74uIQ=" crossorigin=anonymous media=screen><link rel=stylesheet href=/css/coder-dark.min.a00e6364bacbc8266ad1cc81230774a1397198f8cfb7bcba29b7d6fcb54ce57f.css integrity="sha256-oA5jZLrLyCZq0cyBIwd0oTlxmPjPt7y6KbfW/LVM5X8=" crossorigin=anonymous media=screen><link rel=icon type=image/svg+xml href=/images/favicon.svg sizes=any><link rel=icon type=image/png href=/images/favicon-32x32.png sizes=32x32><link rel=icon type=image/png href=/images/favicon-16x16.png sizes=16x16><link rel=apple-touch-icon href=/images/apple-touch-icon.png><link rel=apple-touch-icon sizes=180x180 href=/images/apple-touch-icon.png><link rel=manifest href=/site.webmanifest><link rel=mask-icon href=/images/safari-pinned-tab.svg color=#5bbad5></head><body class="preload-transitions colorscheme-auto"><div class=float-container><a id=dark-mode-toggle class=colorscheme-toggle><i class="fa-solid fa-adjust fa-fw" aria-hidden=true></i></a></div><main class=wrapper><nav class=navigation><section class=container><a class=navigation-title href=/>Eric X. Liu's Personal Page
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You may have seen diagrams like the one below, which outlines the RLHF training process. It can look intimidating, with a web of interconnected models, losses, and data flows."><meta property="og:locale" content="en"><meta property="og:type" content="article"><meta property="article:section" content="posts"><meta property="article:published_time" content="2025-08-03T02:45:11+00:00"><meta property="article:modified_time" content="2025-08-03T02:45:50+00:00"><link rel=canonical href=/posts/a-deep-dive-into-ppo-for-language-models/><link rel=preload href=/fonts/fa-brands-400.woff2 as=font type=font/woff2 crossorigin><link rel=preload href=/fonts/fa-regular-400.woff2 as=font type=font/woff2 crossorigin><link rel=preload href=/fonts/fa-solid-900.woff2 as=font type=font/woff2 crossorigin><link rel=stylesheet href=/css/coder.min.60f552a2c0452fcc0254c54f21c3e0728460c1ae85f97a9c35833a222ef8b884.css integrity="sha256-YPVSosBFL8wCVMVPIcPgcoRgwa6F+XqcNYM6Ii74uIQ=" crossorigin=anonymous media=screen><link rel=stylesheet href=/css/coder-dark.min.a00e6364bacbc8266ad1cc81230774a1397198f8cfb7bcba29b7d6fcb54ce57f.css integrity="sha256-oA5jZLrLyCZq0cyBIwd0oTlxmPjPt7y6KbfW/LVM5X8=" crossorigin=anonymous media=screen><link rel=icon type=image/svg+xml href=/images/favicon.svg sizes=any><link rel=icon type=image/png href=/images/favicon-32x32.png sizes=32x32><link rel=icon type=image/png href=/images/favicon-16x16.png sizes=16x16><link rel=apple-touch-icon href=/images/apple-touch-icon.png><link rel=apple-touch-icon sizes=180x180 href=/images/apple-touch-icon.png><link rel=manifest href=/site.webmanifest><link rel=mask-icon href=/images/safari-pinned-tab.svg color=#5bbad5></head><body class="preload-transitions colorscheme-auto"><div class=float-container><a id=dark-mode-toggle class=colorscheme-toggle><i class="fa-solid fa-adjust fa-fw" aria-hidden=true></i></a></div><main class=wrapper><nav class=navigation><section class=container><a class=navigation-title href=/>Eric X. Liu's Personal Page
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<label class="menu-button float-right" for=menu-toggle><i class="fa-solid fa-bars fa-fw" aria-hidden=true></i></label><ul class=navigation-list><li class=navigation-item><a class=navigation-link href=/posts/>Posts</a></li><li class=navigation-item><a class=navigation-link href=https://chat.ericxliu.me>Chat</a></li><li class=navigation-item><a class=navigation-link href=https://git.ericxliu.me/user/oauth2/Authenitk>Git</a></li><li class=navigation-item><a class=navigation-link href=https://coder.ericxliu.me/api/v2/users/oidc/callback>Coder</a></li><li class=navigation-item><a class=navigation-link href=https://rss.ericxliu.me/oauth2/oidc/redirect>RSS</a></li><li class=navigation-item><a class=navigation-link href=/>|</a></li><li class=navigation-item><a class=navigation-link href=https://sso.ericxliu.me>Sign in</a></li></ul></section></nav><div class=content><section class="container post"><article><header><div class=post-title><h1 class=title><a class=title-link href=/posts/a-deep-dive-into-ppo-for-language-models/>A Deep Dive into PPO for Language Models</a></h1></div><div class=post-meta><div class=date><span class=posted-on><i class="fa-solid fa-calendar" aria-hidden=true></i>
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<time datetime=2025-08-03T02:36:44Z>August 3, 2025
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<time datetime=2025-08-03T02:45:11Z>August 3, 2025
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</time></span><span class=reading-time><i class="fa-solid fa-clock" aria-hidden=true></i>
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7-minute read</span></div></div></header><div class=post-content><p>Large Language Models (LLMs) have demonstrated astonishing capabilities, but out-of-the-box, they are simply powerful text predictors. They don’t inherently understand what makes a response helpful, harmless, or aligned with human values. The technique that has proven most effective at bridging this gap is Reinforcement Learning from Human Feedback (RLHF), and at its heart lies a powerful algorithm: Proximal Policy Optimization (PPO).</p><p>You may have seen diagrams like the one below, which outlines the RLHF training process. It can look intimidating, with a web of interconnected models, losses, and data flows.</p><p>![[Pasted image 20250730232756.png]]</p><p>This post will decode that diagram, piece by piece. We’ll explore the “why” behind each component, moving from high-level concepts to the deep technical reasoning that makes this process work.</p><h3 id=translating-rl-to-a-conversation>Translating RL to a Conversation
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7-minute read</span></div></div></header><div class=post-content><p>Large Language Models (LLMs) have demonstrated astonishing capabilities, but out-of-the-box, they are simply powerful text predictors. They don’t inherently understand what makes a response helpful, harmless, or aligned with human values. The technique that has proven most effective at bridging this gap is Reinforcement Learning from Human Feedback (RLHF), and at its heart lies a powerful algorithm: Proximal Policy Optimization (PPO).</p><p>You may have seen diagrams like the one below, which outlines the RLHF training process. It can look intimidating, with a web of interconnected models, losses, and data flows.</p><p><img src=/images/a-deep-dive-into-ppo-for-language-models/.png alt></p><p>This post will decode that diagram, piece by piece. We’ll explore the “why” behind each component, moving from high-level concepts to the deep technical reasoning that makes this process work.</p><h3 id=translating-rl-to-a-conversation>Translating RL to a Conversation
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<span class=sr-only>Link to heading</span></a></h3><p>The first step is to understand how the traditional language of reinforcement learning maps to the world of text generation.</p><ul><li><strong>State (<code>s_t</code>)</strong>: In a chat setting, the “state” is the context of the conversation so far. It’s the initial prompt (<code>x</code>) plus all the text the model has generated up to the current moment (<code>y₁, ..., y_{t-1}</code>).</li><li><strong>Action (<code>a_t</code>)</strong>: The “action” is the model’s decision at each step. For an LLM, this means generating the very next token (<code>y_t</code>). A full response is a sequence of these actions.blob:https://aistudio.google.com/872e746f-88c1-40ec-8e45-fa0efce97299</li><li><strong>Reward (<code>r</code>)</strong>: The “reward” is a numeric score that tells the model how good its full response (<code>y</code>) was. This score comes from a separate <strong>Reward Model</strong>, which has been trained on a large dataset of human preference comparisons (e.g., humans rating which of two responses is better). This reward is often only awarded at the end of the entire generated sequence.</li></ul><p>Let’s make this concrete. If a user provides the prompt <strong>(x)</strong>: <em>“The best thing about AI is”</em>, and the model generates the response <strong>(y)</strong>: <em>“its potential to solve problems.”</em>, here is how it’s broken down for training:</p><ul><li><strong>State 1</strong>: “The best thing about AI is”<ul><li><strong>Action 1</strong>: “its”</li></ul></li><li><strong>State 2</strong>: “The best thing about AI is its”<ul><li><strong>Action 2</strong>: " potential"</li></ul></li><li><strong>State 3</strong>: “The best thing about AI is its potential”<ul><li><strong>Action 3</strong>: " to"</li></ul></li><li>…and so on for every generated token.</li></ul><p>This breakdown transforms a single prompt-response pair into a rich trajectory of state-action pairs, which becomes the raw data for our learning algorithm.</p><h3 id=the-cast-of-models-an-actor-critic-ensemble>The Cast of Models: An Actor-Critic Ensemble
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@@ -23,4 +23,4 @@ where <code>δ_t = r_t + γV(s_{t+1}) - V(s_t)</code></p><ul><li><strong>γ (gam
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<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Posts on Eric X. Liu's Personal Page</title><link>/posts/</link><description>Recent content in Posts on Eric X. Liu's Personal Page</description><generator>Hugo</generator><language>en</language><lastBuildDate>Sun, 03 Aug 2025 02:37:56 +0000</lastBuildDate><atom:link href="/posts/index.xml" rel="self" type="application/rss+xml"/><item><title>A Deep Dive into PPO for Language Models</title><link>/posts/a-deep-dive-into-ppo-for-language-models/</link><pubDate>Sun, 03 Aug 2025 02:36:44 +0000</pubDate><guid>/posts/a-deep-dive-into-ppo-for-language-models/</guid><description><p>Large Language Models (LLMs) have demonstrated astonishing capabilities, but out-of-the-box, they are simply powerful text predictors. They don&rsquo;t inherently understand what makes a response helpful, harmless, or aligned with human values. The technique that has proven most effective at bridging this gap is Reinforcement Learning from Human Feedback (RLHF), and at its heart lies a powerful algorithm: Proximal Policy Optimization (PPO).</p>
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<p>You may have seen diagrams like the one below, which outlines the RLHF training process. It can look intimidating, with a web of interconnected models, losses, and data flows.</p></description></item><item><title>T5 - The Transformer That Zigged When Others Zagged - An Architectural Deep Dive</title><link>/posts/t5-the-transformer-that-zigged-when-others-zagged-an-architectural-deep-dive/</link><pubDate>Sun, 03 Aug 2025 02:36:44 +0000</pubDate><guid>/posts/t5-the-transformer-that-zigged-when-others-zagged-an-architectural-deep-dive/</guid><description><p>In the rapidly evolving landscape of Large Language Models, a few key architectures define the dominant paradigms. Today, the &ldquo;decoder-only&rdquo; model, popularized by the GPT series and its successors like LLaMA and Mistral, reigns supreme. These models are scaled to incredible sizes and excel at in-context learning.</p>
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<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Posts on Eric X. Liu's Personal Page</title><link>/posts/</link><description>Recent content in Posts on Eric X. Liu's Personal Page</description><generator>Hugo</generator><language>en</language><lastBuildDate>Sun, 03 Aug 2025 02:45:50 +0000</lastBuildDate><atom:link href="/posts/index.xml" rel="self" type="application/rss+xml"/><item><title>A Deep Dive into PPO for Language Models</title><link>/posts/a-deep-dive-into-ppo-for-language-models/</link><pubDate>Sun, 03 Aug 2025 02:45:11 +0000</pubDate><guid>/posts/a-deep-dive-into-ppo-for-language-models/</guid><description><p>Large Language Models (LLMs) have demonstrated astonishing capabilities, but out-of-the-box, they are simply powerful text predictors. They don&rsquo;t inherently understand what makes a response helpful, harmless, or aligned with human values. The technique that has proven most effective at bridging this gap is Reinforcement Learning from Human Feedback (RLHF), and at its heart lies a powerful algorithm: Proximal Policy Optimization (PPO).</p>
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<p>You may have seen diagrams like the one below, which outlines the RLHF training process. It can look intimidating, with a web of interconnected models, losses, and data flows.</p></description></item><item><title>T5 - The Transformer That Zigged When Others Zagged - An Architectural Deep Dive</title><link>/posts/t5-the-transformer-that-zigged-when-others-zagged-an-architectural-deep-dive/</link><pubDate>Sun, 03 Aug 2025 02:45:11 +0000</pubDate><guid>/posts/t5-the-transformer-that-zigged-when-others-zagged-an-architectural-deep-dive/</guid><description><p>In the rapidly evolving landscape of Large Language Models, a few key architectures define the dominant paradigms. Today, the &ldquo;decoder-only&rdquo; model, popularized by the GPT series and its successors like LLaMA and Mistral, reigns supreme. These models are scaled to incredible sizes and excel at in-context learning.</p>
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<p>But to truly understand the field, we must look at the pivotal models that explored different paths. Google&rsquo;s T5, or <strong>Text-to-Text Transfer Transformer</strong>, stands out as one of the most influential. It didn&rsquo;t just introduce a new model; it proposed a new philosophy. This article dives deep into the architecture of T5, how it fundamentally differs from modern LLMs, and the lasting legacy of its unique design choices.</p></description></item><item><title>Some useful files</title><link>/posts/useful/</link><pubDate>Mon, 26 Oct 2020 04:14:43 +0000</pubDate><guid>/posts/useful/</guid><description><ul>
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<li><a href="https://ericxliu.me/rootCA.pem" class="external-link" target="_blank" rel="noopener">rootCA.pem</a></li>
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<li><a href="https://ericxliu.me/vpnclient.ovpn" class="external-link" target="_blank" rel="noopener">vpnclient.ovpn</a></li>
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But to truly understand the field, we must look at the pivotal models that explored different paths. Google’s T5, or Text-to-Text Transfer Transformer, stands out as one of the most influential. It didn’t just introduce a new model; it proposed a new philosophy. This article dives deep into the architecture of T5, how it fundamentally differs from modern LLMs, and the lasting legacy of its unique design choices."><meta name=keywords content="software engineer,performance engineering,Google engineer,tech blog,software development,performance optimization,Eric Liu,engineering blog,mountain biking,Jeep enthusiast,overlanding,camping,outdoor adventures"><meta name=fediverse:creator content><meta name=twitter:card content="summary"><meta name=twitter:title content="T5 - The Transformer That Zigged When Others Zagged - An Architectural Deep Dive"><meta name=twitter:description content="In the rapidly evolving landscape of Large Language Models, a few key architectures define the dominant paradigms. Today, the “decoder-only” model, popularized by the GPT series and its successors like LLaMA and Mistral, reigns supreme. These models are scaled to incredible sizes and excel at in-context learning.
|
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But to truly understand the field, we must look at the pivotal models that explored different paths. Google’s T5, or Text-to-Text Transfer Transformer, stands out as one of the most influential. It didn’t just introduce a new model; it proposed a new philosophy. This article dives deep into the architecture of T5, how it fundamentally differs from modern LLMs, and the lasting legacy of its unique design choices."><meta property="og:url" content="/posts/t5-the-transformer-that-zigged-when-others-zagged-an-architectural-deep-dive/"><meta property="og:site_name" content="Eric X. Liu's Personal Page"><meta property="og:title" content="T5 - The Transformer That Zigged When Others Zagged - An Architectural Deep Dive"><meta property="og:description" content="In the rapidly evolving landscape of Large Language Models, a few key architectures define the dominant paradigms. Today, the “decoder-only” model, popularized by the GPT series and its successors like LLaMA and Mistral, reigns supreme. These models are scaled to incredible sizes and excel at in-context learning.
|
||||
But to truly understand the field, we must look at the pivotal models that explored different paths. Google’s T5, or Text-to-Text Transfer Transformer, stands out as one of the most influential. It didn’t just introduce a new model; it proposed a new philosophy. This article dives deep into the architecture of T5, how it fundamentally differs from modern LLMs, and the lasting legacy of its unique design choices."><meta property="og:locale" content="en"><meta property="og:type" content="article"><meta property="article:section" content="posts"><meta property="article:published_time" content="2025-08-03T02:36:44+00:00"><meta property="article:modified_time" content="2025-08-03T02:37:56+00:00"><link rel=canonical href=/posts/t5-the-transformer-that-zigged-when-others-zagged-an-architectural-deep-dive/><link rel=preload href=/fonts/fa-brands-400.woff2 as=font type=font/woff2 crossorigin><link rel=preload href=/fonts/fa-regular-400.woff2 as=font type=font/woff2 crossorigin><link rel=preload href=/fonts/fa-solid-900.woff2 as=font type=font/woff2 crossorigin><link rel=stylesheet href=/css/coder.min.60f552a2c0452fcc0254c54f21c3e0728460c1ae85f97a9c35833a222ef8b884.css integrity="sha256-YPVSosBFL8wCVMVPIcPgcoRgwa6F+XqcNYM6Ii74uIQ=" crossorigin=anonymous media=screen><link rel=stylesheet href=/css/coder-dark.min.a00e6364bacbc8266ad1cc81230774a1397198f8cfb7bcba29b7d6fcb54ce57f.css integrity="sha256-oA5jZLrLyCZq0cyBIwd0oTlxmPjPt7y6KbfW/LVM5X8=" crossorigin=anonymous media=screen><link rel=icon type=image/svg+xml href=/images/favicon.svg sizes=any><link rel=icon type=image/png href=/images/favicon-32x32.png sizes=32x32><link rel=icon type=image/png href=/images/favicon-16x16.png sizes=16x16><link rel=apple-touch-icon href=/images/apple-touch-icon.png><link rel=apple-touch-icon sizes=180x180 href=/images/apple-touch-icon.png><link rel=manifest href=/site.webmanifest><link rel=mask-icon href=/images/safari-pinned-tab.svg color=#5bbad5></head><body class="preload-transitions colorscheme-auto"><div class=float-container><a id=dark-mode-toggle class=colorscheme-toggle><i class="fa-solid fa-adjust fa-fw" aria-hidden=true></i></a></div><main class=wrapper><nav class=navigation><section class=container><a class=navigation-title href=/>Eric X. Liu's Personal Page
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But to truly understand the field, we must look at the pivotal models that explored different paths. Google’s T5, or Text-to-Text Transfer Transformer, stands out as one of the most influential. It didn’t just introduce a new model; it proposed a new philosophy. This article dives deep into the architecture of T5, how it fundamentally differs from modern LLMs, and the lasting legacy of its unique design choices."><meta property="og:locale" content="en"><meta property="og:type" content="article"><meta property="article:section" content="posts"><meta property="article:published_time" content="2025-08-03T02:45:11+00:00"><meta property="article:modified_time" content="2025-08-03T02:45:50+00:00"><link rel=canonical href=/posts/t5-the-transformer-that-zigged-when-others-zagged-an-architectural-deep-dive/><link rel=preload href=/fonts/fa-brands-400.woff2 as=font type=font/woff2 crossorigin><link rel=preload href=/fonts/fa-regular-400.woff2 as=font type=font/woff2 crossorigin><link rel=preload href=/fonts/fa-solid-900.woff2 as=font type=font/woff2 crossorigin><link rel=stylesheet href=/css/coder.min.60f552a2c0452fcc0254c54f21c3e0728460c1ae85f97a9c35833a222ef8b884.css integrity="sha256-YPVSosBFL8wCVMVPIcPgcoRgwa6F+XqcNYM6Ii74uIQ=" crossorigin=anonymous media=screen><link rel=stylesheet href=/css/coder-dark.min.a00e6364bacbc8266ad1cc81230774a1397198f8cfb7bcba29b7d6fcb54ce57f.css integrity="sha256-oA5jZLrLyCZq0cyBIwd0oTlxmPjPt7y6KbfW/LVM5X8=" crossorigin=anonymous media=screen><link rel=icon type=image/svg+xml href=/images/favicon.svg sizes=any><link rel=icon type=image/png href=/images/favicon-32x32.png sizes=32x32><link rel=icon type=image/png href=/images/favicon-16x16.png sizes=16x16><link rel=apple-touch-icon href=/images/apple-touch-icon.png><link rel=apple-touch-icon sizes=180x180 href=/images/apple-touch-icon.png><link rel=manifest href=/site.webmanifest><link rel=mask-icon href=/images/safari-pinned-tab.svg color=#5bbad5></head><body class="preload-transitions colorscheme-auto"><div class=float-container><a id=dark-mode-toggle class=colorscheme-toggle><i class="fa-solid fa-adjust fa-fw" aria-hidden=true></i></a></div><main class=wrapper><nav class=navigation><section class=container><a class=navigation-title href=/>Eric X. Liu's Personal Page
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<label class="menu-button float-right" for=menu-toggle><i class="fa-solid fa-bars fa-fw" aria-hidden=true></i></label><ul class=navigation-list><li class=navigation-item><a class=navigation-link href=/posts/>Posts</a></li><li class=navigation-item><a class=navigation-link href=https://chat.ericxliu.me>Chat</a></li><li class=navigation-item><a class=navigation-link href=https://git.ericxliu.me/user/oauth2/Authenitk>Git</a></li><li class=navigation-item><a class=navigation-link href=https://coder.ericxliu.me/api/v2/users/oidc/callback>Coder</a></li><li class=navigation-item><a class=navigation-link href=https://rss.ericxliu.me/oauth2/oidc/redirect>RSS</a></li><li class=navigation-item><a class=navigation-link href=/>|</a></li><li class=navigation-item><a class=navigation-link href=https://sso.ericxliu.me>Sign in</a></li></ul></section></nav><div class=content><section class="container post"><article><header><div class=post-title><h1 class=title><a class=title-link href=/posts/t5-the-transformer-that-zigged-when-others-zagged-an-architectural-deep-dive/>T5 - The Transformer That Zigged When Others Zagged - An Architectural Deep Dive</a></h1></div><div class=post-meta><div class=date><span class=posted-on><i class="fa-solid fa-calendar" aria-hidden=true></i>
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<time datetime=2025-08-03T02:36:44Z>August 3, 2025
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<time datetime=2025-08-03T02:45:11Z>August 3, 2025
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</time></span><span class=reading-time><i class="fa-solid fa-clock" aria-hidden=true></i>
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6-minute read</span></div></div></header><div class=post-content><p>In the rapidly evolving landscape of Large Language Models, a few key architectures define the dominant paradigms. Today, the “decoder-only” model, popularized by the GPT series and its successors like LLaMA and Mistral, reigns supreme. These models are scaled to incredible sizes and excel at in-context learning.</p><p>But to truly understand the field, we must look at the pivotal models that explored different paths. Google’s T5, or <strong>Text-to-Text Transfer Transformer</strong>, stands out as one of the most influential. It didn’t just introduce a new model; it proposed a new philosophy. This article dives deep into the architecture of T5, how it fundamentally differs from modern LLMs, and the lasting legacy of its unique design choices.</p><h3 id=the-core-philosophy-everything-is-a-text-to-text-problem>The Core Philosophy: Everything is a Text-to-Text Problem
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@@ -30,4 +30,4 @@ But to truly understand the field, we must look at the pivotal models that explo
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