<?xml version="1.0" encoding="utf-8" standalone="yes"?>
<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:content="http://purl.org/rss/1.0/modules/content/">
  <channel>
    <title>Pre-Training on Tenu Tech Brief</title>
    <link>https://cluster-site.onrender.com/tags/pre-training/</link>
    <description>Recent content in Pre-Training on Tenu Tech Brief</description>
    <generator>Hugo -- 0.146.0</generator>
    <language>en-us</language>
    <lastBuildDate>Wed, 25 Feb 2026 21:40:10 +0000</lastBuildDate>
    <atom:link href="https://cluster-site.onrender.com/tags/pre-training/index.xml" rel="self" type="application/rss+xml" />
    <item>
      <title>Reusing Pre-Training Data at Test Time is a Compute Multiplier</title>
      <link>https://cluster-site.onrender.com/posts/reusing-pre-training-data-at-test-time-is-a-compute-multiplier/</link>
      <pubDate>Wed, 25 Feb 2026 00:00:00 +0000</pubDate>
      <guid>https://cluster-site.onrender.com/posts/reusing-pre-training-data-at-test-time-is-a-compute-multiplier/</guid>
      <description>• Reusing Pre-Training Data at Test Time is a Compute Multiplier Reusing Pre-Training Data at Test Time is a Compute Multiplier AuthorsAlex Fangâ &lt;strong&gt;, Thomas Voice, Ruoming Pang&lt;/strong&gt;,</description>
    </item>
    <item>
      <title>Robust Pre-Training of Medical Vision-and-Language Models with Domain-Invariant Multi-Modal Masked Reconstruction</title>
      <link>https://cluster-site.onrender.com/posts/robust-pre-training-of-medical-vision-and-language-models-with-domain-invariant-multi-modal-masked-reconstruction/</link>
      <pubDate>Mon, 23 Feb 2026 05:00:00 +0000</pubDate>
      <guid>https://cluster-site.onrender.com/posts/robust-pre-training-of-medical-vision-and-language-models-with-domain-invariant-multi-modal-masked-reconstruction/</guid>
      <description>• Computer Science &amp;gt; Machine Learning [Submitted on 6 Feb 2026] Title:Robust Pre-Training of Medical Vision-and-Language Models with Domain-Invariant Multi-Modal Masked Reconstruct</description>
    </item>
  </channel>
</rss>
