<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>How LLMs Learn and Acquire Capabilities on Signal &amp; Syntax</title><link>https://tomarcher.io/learningpaths/how-llms-learn-and-acquire-capabilities/</link><description>Recent content in How LLMs Learn and Acquire Capabilities on Signal &amp; Syntax</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Wed, 08 Oct 2025 06:00:00 -0700</lastBuildDate><atom:link href="https://tomarcher.io/learningpaths/how-llms-learn-and-acquire-capabilities/index.xml" rel="self" type="application/rss+xml"/><item><title>How Large Language Models (LLMs) Learn: Calculus and the Search for Understanding</title><link>https://tomarcher.io/posts/how-large-language-models-learn/</link><pubDate>Wed, 08 Oct 2025 06:00:00 -0700</pubDate><guid>https://tomarcher.io/posts/how-large-language-models-learn/</guid><description>When you interact with a large language model (LLM) such as ChatGPT or Claude , the model seems to respond instantly relative to the question&amp;rsquo;s degree of difficulty. What&amp;rsquo;s easy to forget is that every word it predicts comes from a long history of learning where billions of gradient steps have slowly sculpted its understanding of language.
Large language models don&amp;rsquo;t memorize text. They optimize it. Behind that optimization lies calculus.</description></item></channel></rss>