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  <title>Hanze Dong&apos;s Blogs</title>
  <subtitle>Research notes and essays by Hanze Dong on reinforcement learning, agents, and AI systems.</subtitle>
  <link href="https://hendrydong.github.io/blogs/feed.xml" rel="self" type="application/atom+xml"/>
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  <id>https://hendrydong.github.io/blogs/</id>
  <updated>2026-03-13T00:00:00+00:00</updated>
  <author>
    <name>Hanze Dong</name>
    <uri>https://hendrydong.github.io/</uri>
  </author>
  <entry>
    <title>When Execution Is No Longer Human</title>
    <link href="https://hendrydong.github.io/blogs/pages/agentgov.html" rel="alternate" type="text/html"/>
    <id>https://hendrydong.github.io/blogs/pages/agentgov.html</id>
    <published>2026-03-13T00:00:00+00:00</published>
    <updated>2026-03-13T00:00:00+00:00</updated>
    <summary>AI agents exercise execution without legitimacy. Beyond formal authority and operational power, agent-mediated organizations create a third kind of power: control over the architecture through which work gets done.</summary>
  </entry>
  <entry>
    <title>Curate the Learning Signal for Reinforcement Learning</title>
    <link href="https://hendrydong.github.io/blogs/pages/rl-ada.html" rel="alternate" type="text/html"/>
    <id>https://hendrydong.github.io/blogs/pages/rl-ada.html</id>
    <published>2026-02-25T00:00:00+00:00</published>
    <updated>2026-02-25T00:00:00+00:00</updated>
    <summary>One gradient-first story for LLM RL: variance-minimizing sample allocation (GVM-RAFT), non-linear pass-rate objectives with adaptive sampling (Reinforce-Ada), and self-hinting to open the GRPO gate (SAGE).</summary>
  </entry>
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