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    <title>Pre-Training on Frontier LLM Notes</title>
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    <description>Recent content in Pre-Training on Frontier LLM Notes</description>
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      <title>PreTraining Basics</title>
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      <description>Pre-training math basics (autoregressive token modeling, MLE / NLL training objectives, training gradients, KL divergence)</description>
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      <title>PreTraining Data Selection</title>
      <link>https://xiyuanyang-code.github.io/llm/posts/pre-training/pretraining-data-selection/</link>
      <pubDate>Mon, 02 Nov 2026 20:39:37 +0800</pubDate>
      <guid>https://xiyuanyang-code.github.io/llm/posts/pre-training/pretraining-data-selection/</guid>
      <description>Pre-training data recipes &amp;amp; paper list (data sampling selection, data filtering / de-duplication, model-based data scoring)</description>
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    <item>
      <title>Distributed Parallel Systems for LLM Pre-Training</title>
      <link>https://xiyuanyang-code.github.io/llm/posts/pre-training/distributed-parallel-systems/</link>
      <pubDate>Wed, 14 Oct 2026 17:00:13 +0800</pubDate>
      <guid>https://xiyuanyang-code.github.io/llm/posts/pre-training/distributed-parallel-systems/</guid>
      <description>LLM Pretraining Parallelism (GPU Communication, DP/DDP, ZeRO, FSDP, TP, SP, CP, PP, EP, Mixed Precision)</description>
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      <title>Optimizers</title>
      <link>https://xiyuanyang-code.github.io/llm/posts/pre-training/optimizers/</link>
      <pubDate>Wed, 30 Sep 2026 23:28:50 +0800</pubDate>
      <guid>https://xiyuanyang-code.github.io/llm/posts/pre-training/optimizers/</guid>
      <description>Classical optimizers for LLM pretraining. (SGD, Adam, MUON)</description>
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