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    <title>liqq</title>
    <link>https://liqq010.github.io/</link>
    <description>Recent content on liqq</description>
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    <lastBuildDate>Wed, 04 Nov 2026 00:00:00 +0000</lastBuildDate>
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      <title>Directional Ablation, and How Astra Fixed Qwen3.5-0.8B&#39;s Reasoning Loops</title>
      <link>https://liqq010.github.io/posts/weight-space-programming-qwen-loops/</link>
      <pubDate>Wed, 04 Nov 2026 00:00:00 +0000</pubDate>
      <guid>https://liqq010.github.io/posts/weight-space-programming-qwen-loops/</guid>
      <description>How an LLM edited 0.016% of Qwen3.5-0.8B&amp;rsquo;s weights to break its reasoning loops: directional ablation from the refusal-direction work, then pushed further with a strength α and 64 selected neurons per layer.</description>
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    <item>
      <title>Fixing Softmax and Cross-Entropy: Two Numerical Pitfalls</title>
      <link>https://liqq010.github.io/posts/fixing-softmax-cross-entropy/</link>
      <pubDate>Sun, 25 Oct 2026 00:00:00 +0000</pubDate>
      <guid>https://liqq010.github.io/posts/fixing-softmax-cross-entropy/</guid>
      <description>Softmax Collapse and Softmax Cancellation: two floating-point pitfalls in Softmax and Cross-Entropy, and mathematically equivalent fixes.</description>
    </item>
    <item>
      <title>Rotation Representations Part 1: Rotation Matrices, RoPE, and 6D/9D</title>
      <link>https://liqq010.github.io/posts/rotation-representations-part1/</link>
      <pubDate>Sat, 24 Oct 2026 00:00:00 +0000</pubDate>
      <guid>https://liqq010.github.io/posts/rotation-representations-part1/</guid>
      <description>Rotation matrices, complex numbers and RoPE, and the 6D and 9D rotation representations used in neural networks.</description>
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    <item>
      <title>Why Training Loss Sawtooths Across Epochs</title>
      <link>https://liqq010.github.io/posts/epochal-sawtooth/</link>
      <pubDate>Thu, 15 Oct 2026 00:00:00 +0000</pubDate>
      <guid>https://liqq010.github.io/posts/epochal-sawtooth/</guid>
      <description>Why does training loss creep up within each epoch and drop at the next? Not the optimizer, but what the loss measures: the expected gap since a sample was last trained on follows a sawtooth, and it explains every sampling ablation.</description>
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      <title>From 1000 Steps to 1: Building Diffusion and Flow Models from Scratch</title>
      <link>https://liqq010.github.io/posts/nano-diffusion/</link>
      <pubDate>Fri, 09 Oct 2026 00:00:00 +0000</pubDate>
      <guid>https://liqq010.github.io/posts/nano-diffusion/</guid>
      <description>Clean, minimal PyTorch implementations of diffusion and flow models, from 1000-step DDPM to 1-step generators, on one backbone with one set of conventions.</description>
    </item>
    <item>
      <title>About</title>
      <link>https://liqq010.github.io/about/</link>
      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
      <guid>https://liqq010.github.io/about/</guid>
      <description>&lt;!-- TODO: 姓名、职位/机构、研究方向，由作者补充 --&gt;
&lt;p&gt;I work on generative models. This blog is where I write up things I build to understand them.&lt;/p&gt;
&lt;ol class=&#34;timeline&#34;&gt;
  &lt;li&gt;
    &lt;time&gt;2022 – 2026&lt;/time&gt;
    &lt;div class=&#34;tl-name&#34;&gt;&lt;a href=&#34;https://viggle.ai/&#34;&gt;Viggle AI&lt;/a&gt;&lt;/div&gt;
    &lt;div class=&#34;tl-desc&#34;&gt;Generative Models&lt;/div&gt;
  &lt;/li&gt;
  &lt;li&gt;
    &lt;time&gt;2021 – 2022&lt;/time&gt;
    &lt;div class=&#34;tl-name&#34;&gt;University of Waterloo&lt;/div&gt;
    &lt;div class=&#34;tl-desc&#34;&gt;Master of Electrical and Computer Engineering&lt;/div&gt;
  &lt;/li&gt;
  &lt;li&gt;
    &lt;time&gt;2015 – 2021&lt;/time&gt;
    &lt;div class=&#34;tl-name&#34;&gt;SenseTime&lt;/div&gt;
    &lt;div class=&#34;tl-desc&#34;&gt;Computer Vision&lt;/div&gt;
  &lt;/li&gt;
  &lt;li&gt;
    &lt;time&gt;2010 – 2015&lt;/time&gt;
    &lt;div class=&#34;tl-name&#34;&gt;The Chinese University of Hong Kong&lt;/div&gt;
    &lt;div class=&#34;tl-desc&#34;&gt;Bachelor of Information Engineering&lt;/div&gt;
  &lt;/li&gt;
&lt;/ol&gt;</description>
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