diff --git a/404.html b/404.html index 21cfb31..7115a35 100644 --- a/404.html +++ b/404.html @@ -4,4 +4,4 @@ 2016 - 2025 Eric X. Liu -[9c5d4a2] \ No newline at end of file +[f90b459] \ No newline at end of file diff --git a/about/index.html b/about/index.html index a04d393..39a7e83 100644 --- a/about/index.html +++ b/about/index.html @@ -4,4 +4,4 @@ 2016 - 2025 Eric X. Liu -[9c5d4a2] \ No newline at end of file +[f90b459] \ No newline at end of file diff --git a/categories/index.html b/categories/index.html index a4e6ef5..a7aa821 100644 --- a/categories/index.html +++ b/categories/index.html @@ -4,4 +4,4 @@ 2016 - 2025 Eric X. Liu -[9c5d4a2] \ No newline at end of file +[f90b459] \ No newline at end of file diff --git a/index.html b/index.html index da1cff5..cc71cd3 100644 --- a/index.html +++ b/index.html @@ -4,4 +4,4 @@ 2016 - 2025 Eric X. Liu -[9c5d4a2] \ No newline at end of file +[f90b459] \ No newline at end of file diff --git a/index.xml b/index.xml index 6fdfcbd..116d8b2 100644 --- a/index.xml +++ b/index.xml @@ -1,5 +1,4 @@ -Eric X. Liu's Personal Page/Recent content on Eric X. Liu's Personal PageHugoenSun, 03 Aug 2025 03:29:23 +0000T5 - The Transformer That Zigged When Others Zagged - An Architectural Deep Dive/posts/t5-the-transformer-that-zigged-when-others-zagged-an-architectural-deep-dive/Sun, 03 Aug 2025 03:29:14 +0000/posts/t5-the-transformer-that-zigged-when-others-zagged-an-architectural-deep-dive/<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> -<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>A Deep Dive into PPO for Language Models/posts/a-deep-dive-into-ppo-for-language-models/Sat, 02 Aug 2025 00:00:00 +0000/posts/a-deep-dive-into-ppo-for-language-models/<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> +Eric X. Liu's Personal Page/Recent content on Eric X. Liu's Personal PageHugoenSun, 03 Aug 2025 03:41:10 +0000A Deep Dive into PPO for Language Models/posts/a-deep-dive-into-ppo-for-language-models/Sat, 02 Aug 2025 00:00:00 +0000/posts/a-deep-dive-into-ppo-for-language-models/<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> <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>Mixture-of-Experts (MoE) Models Challenges & Solutions in Practice/posts/mixture-of-experts-moe-models-challenges-solutions-in-practice/Wed, 02 Jul 2025 00:00:00 +0000/posts/mixture-of-experts-moe-models-challenges-solutions-in-practice/<p>Mixture-of-Experts (MoEs) are neural network architectures that allow different parts of the model (called &ldquo;experts&rdquo;) to specialize in different types of inputs. A &ldquo;gating network&rdquo; or &ldquo;router&rdquo; learns to dispatch each input (or &ldquo;token&rdquo;) to a subset of these experts. While powerful for scaling models, MoEs introduce several practical challenges.</p> <h3 id="1-challenge-non-differentiability-of-routing-functions"> 1. Challenge: Non-Differentiability of Routing Functions @@ -9,7 +8,8 @@ </a> </h3> <p><strong>The Problem:</strong> -Many routing mechanisms, especially &ldquo;Top-K routing,&rdquo; involve a discrete, hard selection process. A common function is <code>KeepTopK(v, k)</code>, which selects the top <code>k</code> scoring elements from a vector <code>v</code> and sets others to $-\infty$ or $0$.</p>Some useful files/posts/useful/Mon, 26 Oct 2020 04:14:43 +0000/posts/useful/<ul> +Many routing mechanisms, especially &ldquo;Top-K routing,&rdquo; involve a discrete, hard selection process. A common function is <code>KeepTopK(v, k)</code>, which selects the top <code>k</code> scoring elements from a vector <code>v</code> and sets others to $-\infty$ or $0$.</p>An Architectural Deep Dive of T5/posts/t5-the-transformer-that-zigged-when-others-zagged-an-architectural-deep-dive/Sun, 01 Jun 2025 00:00:00 +0000/posts/t5-the-transformer-that-zigged-when-others-zagged-an-architectural-deep-dive/<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> +<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>Some useful files/posts/useful/Mon, 26 Oct 2020 04:14:43 +0000/posts/useful/<ul> <li><a href="https://ericxliu.me/rootCA.pem" class="external-link" target="_blank" rel="noopener">rootCA.pem</a></li> <li><a href="https://ericxliu.me/vpnclient.ovpn" class="external-link" target="_blank" rel="noopener">vpnclient.ovpn</a></li> </ul>About/about/Fri, 01 Jun 2018 07:13:52 +0000/about/ \ No newline at end of file diff --git a/posts/a-deep-dive-into-ppo-for-language-models/index.html b/posts/a-deep-dive-into-ppo-for-language-models/index.html index 6475cd9..4e5ff5b 100644 --- a/posts/a-deep-dive-into-ppo-for-language-models/index.html +++ b/posts/a-deep-dive-into-ppo-for-language-models/index.html @@ -23,4 +23,4 @@ where δ_t = r_t + γV(s_{t+1}) - V(s_t)

  • γ (gam 2016 - 2025 Eric X. Liu -[9c5d4a2] \ No newline at end of file +[f90b459] \ No newline at end of file diff --git a/posts/index.html b/posts/index.html index 940f3bd..40f0471 100644 --- a/posts/index.html +++ b/posts/index.html @@ -1,11 +1,11 @@ Posts · Eric X. Liu's Personal Page

    Posts

    Posts

    \ No newline at end of file +[f90b459] \ No newline at end of file diff --git a/posts/index.xml b/posts/index.xml index 7facfb4..143dc20 100644 --- a/posts/index.xml +++ b/posts/index.xml @@ -1,5 +1,4 @@ -Posts on Eric X. Liu's Personal Page/posts/Recent content in Posts on Eric X. Liu's Personal PageHugoenSun, 03 Aug 2025 03:29:23 +0000T5 - The Transformer That Zigged When Others Zagged - An Architectural Deep Dive/posts/t5-the-transformer-that-zigged-when-others-zagged-an-architectural-deep-dive/Sun, 03 Aug 2025 03:29:14 +0000/posts/t5-the-transformer-that-zigged-when-others-zagged-an-architectural-deep-dive/<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> -<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>A Deep Dive into PPO for Language Models/posts/a-deep-dive-into-ppo-for-language-models/Sat, 02 Aug 2025 00:00:00 +0000/posts/a-deep-dive-into-ppo-for-language-models/<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> +Posts on Eric X. Liu's Personal Page/posts/Recent content in Posts on Eric X. Liu's Personal PageHugoenSun, 03 Aug 2025 03:41:10 +0000A Deep Dive into PPO for Language Models/posts/a-deep-dive-into-ppo-for-language-models/Sat, 02 Aug 2025 00:00:00 +0000/posts/a-deep-dive-into-ppo-for-language-models/<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> <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>Mixture-of-Experts (MoE) Models Challenges & Solutions in Practice/posts/mixture-of-experts-moe-models-challenges-solutions-in-practice/Wed, 02 Jul 2025 00:00:00 +0000/posts/mixture-of-experts-moe-models-challenges-solutions-in-practice/<p>Mixture-of-Experts (MoEs) are neural network architectures that allow different parts of the model (called &ldquo;experts&rdquo;) to specialize in different types of inputs. A &ldquo;gating network&rdquo; or &ldquo;router&rdquo; learns to dispatch each input (or &ldquo;token&rdquo;) to a subset of these experts. While powerful for scaling models, MoEs introduce several practical challenges.</p> <h3 id="1-challenge-non-differentiability-of-routing-functions"> 1. Challenge: Non-Differentiability of Routing Functions @@ -9,7 +8,8 @@ </a> </h3> <p><strong>The Problem:</strong> -Many routing mechanisms, especially &ldquo;Top-K routing,&rdquo; involve a discrete, hard selection process. A common function is <code>KeepTopK(v, k)</code>, which selects the top <code>k</code> scoring elements from a vector <code>v</code> and sets others to $-\infty$ or $0$.</p>Some useful files/posts/useful/Mon, 26 Oct 2020 04:14:43 +0000/posts/useful/<ul> +Many routing mechanisms, especially &ldquo;Top-K routing,&rdquo; involve a discrete, hard selection process. A common function is <code>KeepTopK(v, k)</code>, which selects the top <code>k</code> scoring elements from a vector <code>v</code> and sets others to $-\infty$ or $0$.</p>An Architectural Deep Dive of T5/posts/t5-the-transformer-that-zigged-when-others-zagged-an-architectural-deep-dive/Sun, 01 Jun 2025 00:00:00 +0000/posts/t5-the-transformer-that-zigged-when-others-zagged-an-architectural-deep-dive/<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> +<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>Some useful files/posts/useful/Mon, 26 Oct 2020 04:14:43 +0000/posts/useful/<ul> <li><a href="https://ericxliu.me/rootCA.pem" class="external-link" target="_blank" rel="noopener">rootCA.pem</a></li> <li><a href="https://ericxliu.me/vpnclient.ovpn" class="external-link" target="_blank" rel="noopener">vpnclient.ovpn</a></li> </ul> \ No newline at end of file diff --git a/posts/mixture-of-experts-moe-models-challenges-solutions-in-practice/index.html b/posts/mixture-of-experts-moe-models-challenges-solutions-in-practice/index.html index bd1122b..7d9c9ad 100644 --- a/posts/mixture-of-experts-moe-models-challenges-solutions-in-practice/index.html +++ b/posts/mixture-of-experts-moe-models-challenges-solutions-in-practice/index.html @@ -44,4 +44,4 @@ The Top-K routing mechanism, as illustrated in the provided ima 2016 - 2025 Eric X. Liu -[9c5d4a2] \ No newline at end of file +[f90b459] \ No newline at end of file diff --git a/posts/t5-the-transformer-that-zigged-when-others-zagged-an-architectural-deep-dive/index.html b/posts/t5-the-transformer-that-zigged-when-others-zagged-an-architectural-deep-dive/index.html index 640db24..e33b66a 100644 --- a/posts/t5-the-transformer-that-zigged-when-others-zagged-an-architectural-deep-dive/index.html +++ b/posts/t5-the-transformer-that-zigged-when-others-zagged-an-architectural-deep-dive/index.html @@ -1,10 +1,10 @@ -T5 - The Transformer That Zigged When Others Zagged - An Architectural Deep Dive · Eric X. Liu's Personal Page

    An Architectural Deep Dive of T5

    + 6-minute read

    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.

    The Core Philosophy: Everything is a Text-to-Text Problem @@ -30,4 +30,4 @@ But to truly understand the field, we must look at the pivotal models that explo 2016 - 2025 Eric X. Liu -[9c5d4a2]

    \ No newline at end of file +[f90b459] \ No newline at end of file diff --git a/posts/useful/index.html b/posts/useful/index.html index ac0965e..f04864e 100644 --- a/posts/useful/index.html +++ b/posts/useful/index.html @@ -10,4 +10,4 @@ One-minute read
    • [9c5d4a2] \ No newline at end of file +[f90b459] \ No newline at end of file diff --git a/sitemap.xml b/sitemap.xml index aef9a75..60dfc1b 100644 --- a/sitemap.xml +++ b/sitemap.xml @@ -1 +1 @@ -/2025-08-03T03:29:23+00:00weekly0.5/posts/2025-08-03T03:29:23+00:00weekly0.5/posts/t5-the-transformer-that-zigged-when-others-zagged-an-architectural-deep-dive/2025-08-03T03:29:23+00:00weekly0.5/posts/a-deep-dive-into-ppo-for-language-models/2025-08-03T03:28:39+00:00weekly0.5/posts/mixture-of-experts-moe-models-challenges-solutions-in-practice/2025-08-03T03:28:39+00:00weekly0.5/posts/useful/2020-10-26T04:47:36+00:00weekly0.5/about/2020-06-16T23:30:17-07:00weekly0.5/categories/weekly0.5/tags/weekly0.5 \ No newline at end of file +/posts/a-deep-dive-into-ppo-for-language-models/2025-08-03T03:28:39+00:00weekly0.5/2025-08-03T03:41:10+00:00weekly0.5/posts/2025-08-03T03:41:10+00:00weekly0.5/posts/mixture-of-experts-moe-models-challenges-solutions-in-practice/2025-08-03T03:28:39+00:00weekly0.5/posts/t5-the-transformer-that-zigged-when-others-zagged-an-architectural-deep-dive/2025-08-03T03:41:10+00:00weekly0.5/posts/useful/2020-10-26T04:47:36+00:00weekly0.5/about/2020-06-16T23:30:17-07:00weekly0.5/categories/weekly0.5/tags/weekly0.5 \ No newline at end of file diff --git a/tags/index.html b/tags/index.html index d6a8dcd..eadebbc 100644 --- a/tags/index.html +++ b/tags/index.html @@ -4,4 +4,4 @@ 2016 - 2025 Eric X. Liu -[9c5d4a2] \ No newline at end of file +[f90b459] \ No newline at end of file