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ziyan98
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E2K-loop-v0.2

<title>E2K-40 Internalization</title>
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<div class="wrap">
<header>
  <h1><span class="zh">E2K-40:把验真的可泛化知识内化进权重</span><span class="en">E2K-40: Internalizing verified, generalizable knowledge into weights</span></h1>
  <div class="sub"><span><span class="zh">进展 2026-09-12</span><span class="en">Progress 2026-09-12</span></span>
    <span class="lang" role="group" aria-label="Language"><button type="button" id="lang-zh" aria-pressed="true">中文</button> · <button type="button" id="lang-en" aria-pressed="false">EN</button></span></div>
</header>

<!-- ===== 0. Setup ===== -->
<section id="setup">
  <h2><span class="zh">设置</span><span class="en">Setup</span></h2>
  <div class="setup">
    <div><div class="k"><span class="zh">模型</span><span class="en">Model</span></div><div class="v">Qwen3.8-27B<br><span class="zh">(Qwen3.5 架构:Gated-DeltaNet + 注意力混合)</span><span class="en">(Qwen3.5 architecture: hybrid Gated-DeltaNet + attention)</span></div></div>
    <div><div class="k">Benchmark</div><div class="v">ARC-AGI-3<br><span class="zh">交互式网格游戏;本文一道题 <code>cd82-fb555c5d</code>(ARC3_004),6 个 level,64×64 棋盘,16 色</span><span class="en">interactive grid games; one game here, <code>cd82-fb555c5d</code> (ARC3_004): 6 levels, 64×64 board, 16 colours</span></div></div>
    <div><div class="k">Harness</div><div class="v"><span class="zh">Qwen Code 代理循环:模型按其后训练的方式用工具玩游戏(MCP 工具 <code>look / act / status</code>);知识以工作区 <code>QWEN.md</code> 进入上下文;每个条件 100 局</span><span class="en">Qwen Code agent loop: the model plays through tools the way it was post-trained (MCP tools <code>look / act / status</code>); knowledge enters as the workspace's <code>QWEN.md</code>; 100 episodes per condition</span></div></div>
    <div><div class="k"><span class="zh">RL 策略</span><span class="en">RL policy</span></div><div class="v"><span class="zh">同一模型,裸 prompt:每步收到棋盘,输出一个 JSON 动作;slime(Megatron 训练 + SGLang 采样),16×GH200</span><span class="en">Same model, bare prompt: receives the board each step, emits one JSON action; slime (Megatron training + SGLang sampling), 16×GH200</span></div></div>
  </div>

  <div class="hidden">
    <div class="h"><span class="zh">游戏机制 —— 对模型隐藏</span><span class="en">Game mechanics — hidden from the model</span></div>
    <div class="tablewrap"><table class="booktabs" style="margin-top:6px">
      <thead><tr><th><span class="zh">动作</span><span class="en">Action</span></th><th><span class="zh">真实效果(来自源码与引擎逐条核过)</span><span class="en">True effect (checked against the source and the live engine)</span></th></tr></thead>
      <tbody>
        <tr><td class="mono">ACTION1–4</td><td><span class="zh">把"涂色器"沿 3×3 环移动一格(上 / 下 / 左 / 右);移出环或移到中心 = 无效果,但仍计一步</span><span class="en">Move the applicator one place around a 3×3 ring (up / down / left / right); off the ring or onto the centre = no effect, still costs a step</span></td></tr>
        <tr><td class="mono">ACTION5</td><td><span class="zh">用当前颜色涂画布中面向涂色器的区域:在边上 = 一半,在角上 = 一个三角;覆盖写</span><span class="en">Paint the canvas region facing the applicator with the selected colour: a half from an edge, a triangle from a corner; overwrites</span></td></tr>
        <tr><td class="mono">ACTION6 x y</td><td><span class="zh">点击:点在色板框上 = 选色;点在涂色器旁的小方块上 = 涂该边中段(仅 level 3–6);其它位置 = 无效果</span><span class="en">Click: on a colour box = select that colour; on the small sprite beside the applicator = paint that edge's middle third (levels 3–6 only); elsewhere = nothing</span></td></tr>
        <tr><td class="mono">RESET</td><td><span class="zh">恢复本 level 起始状态,不计步</span><span class="en">Restore the level's starting state; free</span></td></tr>
        <tr><td><span class="zh">目标 / 预算</span><span class="en">Goal / budget</span></td><td><span class="zh">画布 = 参考图案(忽略两条对角线);每 level 99 步,第 100 步 = 输</span><span class="en">Canvas = reference pattern (two diagonals ignored); 99 actions per level, the 100th loses</span></td></tr>
      </tbody>
    </table></div>
    <p class="caption" style="margin-top:8px"><span class="zh">模型看到的只有 64×64 的整数网格和动作<i>名字</i>(下方灰框),没有任何说明;上表全部要靠它自己在游戏里发现。</span><span class="en">The model sees only the 64×64 integer grid and the action <i>names</i> (grey box below) — no description; everything in the table it has to discover by playing.</span></p>
  </div>

  <h3><span class="zh">一局的前八步(真实引擎)</span><span class="en">Eight steps of one episode (real engine)</span></h3>
  <div class="demo">
    <div>
      <img id="demo-board" class="board" alt="Board of cd82 level 1 after the current step" src="">
      <div class="ctl"><button type="button" id="demo-prev" aria-label="Previous step">‹</button><button type="button" id="demo-play" aria-pressed="true">❚❚</button><button type="button" id="demo-next" aria-label="Next step">›</button><span id="demo-label"></span></div>
    </div>
    <ol class="steps" id="demo-steps">
      <li data-i="0"><span class="n">0</span><span class="a">start</span><span class="m"><span class="zh">level 1 起始</span><span class="en">level 1 start</span></span><span class="d"></span></li>
      <li data-i="1"><span class="n">1</span><span class="a">ACTION6 37 4</span><span class="m"><span class="zh">点左侧色板框 → 选白色</span><span class="en">click the left colour box → select white</span></span><span class="d">Δ95</span></li>
      <li data-i="2"><span class="n">2</span><span class="a">ACTION5</span><span class="m"><span class="zh">画布已是白色 → 什么都没变</span><span class="en">canvas already white → nothing changes</span></span><span class="d">Δ0</span></li>
      <li data-i="3"><span class="n">3</span><span class="a">ACTION6 43 4</span><span class="m"><span class="zh">点右侧色板框 → 选紫色</span><span class="en">click the right colour box → select purple</span></span><span class="d">Δ95</span></li>
      <li data-i="4"><span class="n">4</span><span class="a">ACTION5</span><span class="m"><span class="zh">上半画布涂成紫色</span><span class="en">paint the top half of the canvas purple</span></span><span class="d">Δ51</span></li>
      <li data-i="5"><span class="n">5</span><span class="a">ACTION3</span><span class="m"><span class="zh">涂色器移到左上角</span><span class="en">applicator moves to the top-left corner</span></span><span class="d">Δ200</span></li>
      <li data-i="6"><span class="n">6</span><span class="a">ACTION5</span><span class="m"><span class="zh">左上三角涂成紫色</span><span class="en">paint the top-left triangle purple</span></span><span class="d">Δ16</span></li>
      <li data-i="7"><span class="n">7</span><span class="a">ACTION1</span><span class="m"><span class="zh">角上再"上" → 无效果</span><span class="en">"up" from a corner → no effect</span></span><span class="d">Δ0</span></li>
      <li data-i="8"><span class="n">8</span><span class="a">RESET</span><span class="m"><span class="zh">恢复起始</span><span class="en">back to the start</span></span><span class="d">Δ269</span></li>
    </ol>
  </div>
  <p class="caption"><b><span class="zh">图 1.</span><span class="en">Figure 1.</span></b> <span class="zh">Δ = 该步改变的格子数,是模型能观察到的全部反馈。</span><span class="en">Δ = cells changed by the step — the only feedback the model can observe.</span></p>

  <pre class="obs">State: NOT_FINISHED
Levels completed: 0
Win levels: 6

Current frame (row-major numeric grid):
[5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,4,4,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3…
[5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,4,4,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3…
[5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,4,4,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,4,4,4,4,4,3,4,4,4,4,4,3,3,3,3,3,3,3,3,3,3,3,3,3,3…
[5,5,5,0,0,0,0,0,0,0,0,0,0,5,5,5,4,4,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,4,0,0,0,4,3,4,15,15,15,4,3,3,3,3,3,3,3,3,3,3,3,3,…
  …  (64 rows × 64 integers; 8,657 characters ≈ 8.6k tokens per observation)

Available actions:
- ACTION1
- ACTION2
- ACTION3
- ACTION4
- ACTION5
- ACTION6 x y (integers 0-63)</pre>
  <p class="caption"><span class="zh">模型每步收到的观测:状态、完成的 level 数、整数网格、可用动作名 —— 仅此而已。</span><span class="en">The observation the model receives each step: state, levels completed, the integer grid, the available action names — and nothing else.</span></p>
</section>

<!-- ===== 1. Motivation ===== -->
<section id="motivation">
  <h2><span class="zh">动机:从自蒸馏到显式、可验证、可组合的知识</span><span class="en">Motivation: from self-distillation to explicit, verifiable, composable knowledge</span></h2>
  <p><span class="zh"><b>SDPO(自蒸馏策略优化)怎么做。</b>同一个模型跑两遍:教师的 prompt 里放进 oracle 答案(或其它特权信息),学生只看原 prompt;在学生自己采出的 token 上最小化 KL(π<sub>学生</sub> ∥ π<sub>教师</sub>),可再加 RL reward。学生学会"像看到了答案那样"作答。</span><span class="en"><b>How SDPO (self-distillation policy optimization) works.</b> The same model is run twice: the teacher's prompt contains the oracle answer (or other privileged information), the student sees only the original prompt; on the student's own samples the KL(π<sub>student</sub> ∥ π<sub>teacher</sub>) is minimized, optionally with an RL reward. The student learns to answer "as if it had seen the answer".</span></p>
  <p><span class="zh"><b>问题。</b>信号全部来自模型对<i>那个答案</i>的自我反思:学到的东西隐含在答案里、绑定于具体题目,没有外部检验,也不会超出 oracle 给的范围 —— 它复制答案,不产生新知识。</span><span class="en"><b>The problem.</b> All signal comes from the model reflecting on <i>that answer</i>: what is learned stays implicit and answer-bound, nothing checks it externally, and it never grows beyond what the oracle handed over — it copies answers rather than producing knowledge.</span></p>
  <p><span class="zh"><b>我们的想法。</b>LLM 擅长组合创新,缺的是对"什么算知识"的约束。所以把知识做成显式的声明式规则,用两个 reward 引导模型自己去获得:<b>正确性</b>(在游戏里复现)和<b>泛化性</b>(在别的 level 上仍成立)。多轮迭代:第一轮拿到最底层的原子(ARC 里的动作映射),后面各轮把原子放在一起,组合出新的、更高层的知识;每轮的知识再通过 SFT ⊕ RL 内化。整个过程只需在现有 multi-step RL(harness)循环里加一个 SFT 项。</span><span class="en"><b>Our idea.</b> LLMs are good at combinatorial innovation; what is missing is a constraint on what counts as knowledge. So knowledge becomes explicit declarative rules that the model acquires itself under two rewards: <b>correctness</b> (reproduced in the game) and <b>generalization</b> (still holds on other levels). Iterated over rounds: round 1 yields the lowest-level atoms (ARC action mappings), later rounds put atoms together and compose new, higher-level knowledge; each round's set is internalized by SFT ⊕ RL. Only one SFT term is added to the existing multi-step RL (harness) loop.</span></p>

  <figure>
    <svg viewBox="0 0 960 250" role="img" aria-label="Left: SDPO runs the same model with and without the oracle answer in context and minimizes a KL between the two distributions. Right: our loop extracts claims from play, rewards them for correctness and generalization, keeps the verified set K, internalizes K by SFT and RL, and feeds K back so the next round composes atoms into higher-level knowledge.">
      <defs><marker id="arr" viewBox="0 0 10 10" refX="9" refY="5" markerWidth="8" markerHeight="8" orient="auto-start-reverse"><path d="M0 0L10 5L0 10z" fill="currentColor"/></marker></defs>
      <g font-family="Source Serif 4, Georgia, serif" font-size="13" fill="currentColor">
        <text x="8" y="18" font-weight="600">(a) SDPO</text>
        <rect x="8" y="40" width="150" height="46" rx="4" fill="none" stroke="currentColor" stroke-width="1.3"/>
        <text x="83" y="59" text-anchor="middle" class="zh">prompt + oracle 答案</text><text x="83" y="59" text-anchor="middle" class="en">prompt + oracle answer</text>
        <text x="83" y="77" text-anchor="middle" font-size="11.5" fill="var(--muted)" class="zh">教师(同一模型)</text><text x="83" y="77" text-anchor="middle" font-size="11.5" fill="var(--muted)" class="en">teacher (same model)</text>
        <rect x="8" y="150" width="150" height="46" rx="4" fill="none" stroke="currentColor" stroke-width="1.3"/>
        <text x="83" y="169" text-anchor="middle">prompt</text>
        <text x="83" y="187" text-anchor="middle" font-size="11.5" fill="var(--muted)" class="zh">学生 π<tspan baseline-shift="sub" font-size="9">θ</tspan></text><text x="83" y="187" text-anchor="middle" font-size="11.5" fill="var(--muted)" class="en">student π<tspan baseline-shift="sub" font-size="9">θ</tspan></text>
        <line x1="158" y1="63" x2="250" y2="63" stroke="currentColor" stroke-width="1.3" marker-end="url(#arr)"/>
        <line x1="158" y1="173" x2="250" y2="173" stroke="currentColor" stroke-width="1.3" marker-end="url(#arr)"/>
        <text x="270" y="67">π(y | x, a*)</text>
        <text x="270" y="177">π<tspan baseline-shift="sub" font-size="9">θ</tspan>(y | x)</text>
        <line x1="300" y1="80" x2="300" y2="156" stroke="var(--bad)" stroke-width="1.6" marker-end="url(#arr)" marker-start="url(#arr)"/>
        <text x="312" y="122" fill="var(--bad)" font-weight="600">min KL</text>
        <text x="8" y="232" font-size="12" fill="var(--muted)" class="zh">信号 = 对答案 a* 的自我反思;隐式、绑定题目、无外部检验</text>
        <text x="8" y="232" font-size="12" fill="var(--muted)" class="en">signal = self-reflection on the answer a*; implicit, answer-bound, unchecked</text>
        <line x1="420" y1="10" x2="420" y2="240" stroke="currentColor" stroke-opacity=".3" stroke-dasharray="3 4"/>
        <text x="440" y="18" font-weight="600" class="zh">(b) 本文:迭代获取显式知识</text><text x="440" y="18" font-weight="600" class="en">(b) Ours: iterated explicit knowledge</text>
        <rect x="440" y="40" width="118" height="46" rx="4" fill="var(--exp-soft)" stroke="var(--exp)" stroke-width="1.3"/>
        <text x="499" y="59" text-anchor="middle" class="zh">游玩 | 𝒦<tspan baseline-shift="sub" font-size="9">r−1</tspan></text><text x="499" y="59" text-anchor="middle" class="en">play | 𝒦<tspan baseline-shift="sub" font-size="9">r−1</tspan></text>
        <text x="499" y="77" text-anchor="middle" font-size="11.5" fill="var(--muted)" class="zh">经验 𝒟<tspan baseline-shift="sub" font-size="9">r</tspan></text><text x="499" y="77" text-anchor="middle" font-size="11.5" fill="var(--muted)" class="en">experience 𝒟<tspan baseline-shift="sub" font-size="9">r</tspan></text>
        <rect x="586" y="40" width="118" height="46" rx="4" fill="none" stroke="currentColor" stroke-width="1.3"/>
        <text x="645" y="59" text-anchor="middle" class="zh">提取 · 组合</text><text x="645" y="59" text-anchor="middle" class="en">extract · compose</text>
        <text x="645" y="77" text-anchor="middle" font-size="11.5" fill="var(--muted)" class="zh">候选 claims</text><text x="645" y="77" text-anchor="middle" font-size="11.5" fill="var(--muted)" class="en">candidate claims</text>
        <rect x="732" y="40" width="118" height="46" rx="4" fill="none" stroke="currentColor" stroke-width="1.3"/>
        <text x="791" y="59" text-anchor="middle" class="zh">验真(游戏)</text><text x="791" y="59" text-anchor="middle" class="en">verify (in game)</text>
        <text x="791" y="77" text-anchor="middle" font-size="11.5" fill="var(--ok)">R<tspan baseline-shift="sub" font-size="9">corr</tspan> · R<tspan baseline-shift="sub" font-size="9">gen</tspan></text>
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        <text x="916" y="67" text-anchor="middle" font-weight="600">𝒦<tspan baseline-shift="sub" font-size="9">r</tspan></text>
        <line x1="558" y1="63" x2="584" y2="63" stroke="currentColor" stroke-width="1.3" marker-end="url(#arr)"/>
        <line x1="704" y1="63" x2="730" y2="63" stroke="currentColor" stroke-width="1.3" marker-end="url(#arr)"/>
        <line x1="850" y1="63" x2="876" y2="63" stroke="currentColor" stroke-width="1.3" marker-end="url(#arr)"/>
        <rect x="586" y="150" width="264" height="46" rx="4" fill="none" stroke="var(--know)" stroke-width="1.8"/>
        <text x="718" y="169" text-anchor="middle" font-weight="600" class="zh">内化:SFT(𝒦<tspan baseline-shift="sub" font-size="9">r</tspan>)⊕ RL,同一个 multi-step 循环</text>
        <text x="718" y="169" text-anchor="middle" font-weight="600" class="en">internalize: SFT(𝒦<tspan baseline-shift="sub" font-size="9">r</tspan>) ⊕ RL, one multi-step loop</text>
        <text x="718" y="187" text-anchor="middle" font-size="11.5" fill="var(--muted)">θ ← θ − η∇[ℒ<tspan baseline-shift="sub" font-size="9">PG</tspan> + w·NLL(𝒦<tspan baseline-shift="sub" font-size="9">r</tspan>)]</text>
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        <line x1="916" y1="140" x2="852" y2="140" stroke="var(--know)" stroke-width="1.3"/>
        <line x1="852" y1="140" x2="852" y2="148" stroke="var(--know)" stroke-width="1.3" marker-end="url(#arr)"/>
        <path d="M916 86 L916 120 L470 120 L470 88" fill="none" stroke="var(--exp)" stroke-width="1.3" stroke-dasharray="4 3" marker-end="url(#arr)"/>
        <text x="600" y="115" font-size="11.5" fill="var(--exp)" class="zh">下一轮:𝒦<tspan baseline-shift="sub" font-size="9">r</tspan> 入上下文,原子被组合成更高层知识</text>
        <text x="600" y="115" font-size="11.5" fill="var(--exp)" class="en">next round: 𝒦<tspan baseline-shift="sub" font-size="9">r</tspan> in context, atoms compose into higher-level knowledge</text>
        <text x="440" y="232" font-size="12" fill="var(--muted)" class="zh">信号 = 游戏给的正确性 + 跨 level 泛化;显式、可组合、可迁移</text>
        <text x="440" y="232" font-size="12" fill="var(--muted)" class="en">signal = correctness from the game + cross-level generalization; explicit, composable, transferable</text>
      </g>
    </svg>
    <figcaption><b><span class="zh">图 2.</span><span class="en">Figure 2.</span></b> <span class="zh">(a) SDPO 让模型对着 oracle 答案反思自己;(b) 本文让模型在正确性与泛化性两个 reward 下多轮获取显式知识,并在同一 RL 循环里内化。</span><span class="en">(a) SDPO has the model reflect on itself against an oracle answer; (b) ours has the model acquire explicit knowledge over rounds under correctness and generalization rewards, internalized inside the same RL loop.</span></figcaption>
  </figure>
</section>

<!-- ===== 2. Framework ===== -->
<section id="framework">
  <h2><span class="zh">框架</span><span class="en">Framework</span></h2>
  <div class="algo">
    <div class="title"><span class="zh">算法 1  迭代式知识获取与内化</span><span class="en">Algorithm 1  Iterated knowledge acquisition and internalization</span></div>
    <ol>
      <li>𝒦<sub>0</sub> ← ∅;  <span class="kw">for</span> r = 1 … R <span class="kw">do</span></li>
      <li>&nbsp;&nbsp;𝒟<sub>r</sub> ← Play(π<sub>θ</sub>, levels 1–4 | 𝒦<sub>r−1</sub> in context) <span class="cm">▷ <span class="zh">带当前知识的经验</span><span class="en">experience with current knowledge</span></span></li>
      <li>&nbsp;&nbsp;𝒞 ← Extract(𝒟<sub>r</sub>, 𝒦<sub>r−1</sub>) <span class="cm">▷ <span class="zh">原子 → 组合</span><span class="en">atoms → compositions</span></span></li>
      <li>&nbsp;&nbsp;𝒦<sub>r</sub> ← 𝒦<sub>r−1</sub> ∪ { c ∈ 𝒞 : R<sub>corr</sub>(c) = 1, R<sub>gen</sub>(c) ≥ γ } <span class="cm">▷ <span class="zh">在游戏里验真;答案型拒绝</span><span class="en">verified by play; answers rejected</span></span></li>
      <li>&nbsp;&nbsp;θ ← θ − η ∇<sub>θ</sub> [ ℒ<sub>PG</sub>(τ ∼ π<sub>θ</sub>) + w · NLL<sub>θ</sub>(𝒦<sub>r</sub>) ] <span class="cm">▷ <span class="zh">同一 multi-step RL 循环</span><span class="en">same multi-step RL loop</span></span></li>
      <li><span class="kw">return</span> Eval(π<sub>θ</sub>, levels 5–6 | 𝒦 ∉ context)</li>
    </ol>
  </div>
  <p class="tabcap"><span class="zh">当前实现的是 r = 1;NLL 项只作用于声明式陈述,从不作用于轨迹。</span><span class="en">Currently r = 1; the NLL term applies to declarative statements only, never to trajectories.</span></p>
  <div class="tablewrap"><table class="booktabs">
    <thead><tr><th><span class="zh">实验组</span><span class="en">Arm</span></th><th><span class="zh">SFT 何时进入</span><span class="en">When SFT enters</span></th><th><span class="zh">每步目标</span><span class="en">Per-step objective</span></th></tr></thead>
    <tbody>
      <tr><td>baseline</td><td>—</td><td>ℒ<sub>PG</sub></td></tr>
      <tr><td>sft_first</td><td><span class="zh">RL 前 4 轮纯 SFT</span><span class="en">4 SFT-only rollouts before RL</span></td><td>NLL → ℒ<sub>PG</sub></td></tr>
      <tr><td>sft_interleaved</td><td><span class="zh">每轮 RL 后 2 轮 SFT</span><span class="en">2 SFT rollouts after every RL rollout</span></td><td>ℒ<sub>PG</sub> ⇄ NLL</td></tr>
      <tr><td>sft_joint</td><td><span class="zh">每一步</span><span class="en">every step</span></td><td>ℒ<sub>PG</sub> + w·NLL</td></tr>
      <tr><td>oracle</td><td><span class="zh">同 sft_first,𝒦 = 手写规则(14 条)</span><span class="en">as sft_first, 𝒦 = hand-written rules (14)</span></td><td>NLL → ℒ<sub>PG</sub></td></tr>
    </tbody>
  </table></div>
  <p class="tabcap"><b><span class="zh">表 1.</span><span class="en">Table 1.</span></b> <span class="zh">消融:同一 𝒦、同一 RL(Dr.GRPO,每轮 32 局 × ≤80 步)、同一评测(level 5–6,prompt 中无 𝒦);只改 SFT 的调度。</span><span class="en">Ablation: same 𝒦, same RL (Dr.GRPO, 32 episodes × ≤80 decisions per rollout), same evaluation (levels 5–6, no 𝒦 in the prompt); only the SFT schedule changes.</span></p>
</section>

<!-- ===== 3. Verification ===== -->
<section id="verify">
  <h2><span class="zh">怎么验真:让代码替模型检验</span><span class="en">How a claim is verified: code checks the model</span></h2>
  <p><span class="zh">验真者是同一个模型的另一个会话,拿到一条 claim 和过去的对局记录。它<b>不判断</b> claim 对不对,而是<b>写代码</b>去复现:把 claim 拆成子句,对每个子句设计检查 —— 在对局记录里数、或开一局新游戏用 <code>act</code> 去试;计数 <code>reproduced / total</code>,声明极性(数成功还是数违例:"从不发生"的子句 0/N 才算通过);再用 <code>set_level(k)</code> 在至少两个别的 level 上重跑同样的检查。产出一份结构化 ledger。过滤器只读 ledger:任一子句被驳回 → 删掉该子句;本 level 检查成立 → 正确;别的 level 的结果 → 泛化分 0–3;点击配方 / 解法型 → 拒绝。</span><span class="en">The verifier is another session of the same model, given one claim and the past episode journals. It does <b>not judge</b> the claim; it <b>writes code</b> that reproduces it: the claim is split into clauses, each gets a check — counted in the journals, or tried in a fresh episode through <code>act</code>; it records <code>reproduced / total</code> with a declared polarity (successes or violations: a "never happens" clause passes at 0/N); then the same checks are re-run on at least two other levels via <code>set_level(k)</code>. The output is a structured ledger. The filter reads only the ledger: any refuted clause is cut; home-level checks hold → correct; the other-level results → a generalization score 0–3; click recipes / solutions → rejected.</span></p>
  <figure>
    <svg viewBox="0 0 960 200" role="img" aria-label="Verification loop: a claim goes to the verifier model, which writes code; the code drives fresh episodes through the game tools look, act, status and set_level and reads the journals; it produces per-clause counts reproduced over total with a polarity and per-level results; a ledger is written; the filter gates correctness, scores generalization 0 to 3 and rejects answers; surviving clauses enter the knowledge set.">
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      <g font-family="Source Serif 4, Georgia, serif" font-size="13" fill="currentColor">
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        <text x="62" y="67" text-anchor="middle" font-weight="600">claim c</text>
        <text x="62" y="85" text-anchor="middle" font-size="11.5" fill="var(--muted)" class="zh">子句 c1 … cN</text><text x="62" y="85" text-anchor="middle" font-size="11.5" fill="var(--muted)" class="en">clauses c1 … cN</text>

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        <text x="226" y="67" text-anchor="middle" font-weight="600" class="zh">验真者写代码</text><text x="226" y="67" text-anchor="middle" font-weight="600" class="en">verifier writes code</text>
        <text x="226" y="85" text-anchor="middle" font-size="11.5" fill="var(--muted)" class="zh">同一模型,另一会话</text><text x="226" y="85" text-anchor="middle" font-size="11.5" fill="var(--muted)" class="en">same model, own session</text>

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        <text x="422" y="52" text-anchor="middle" font-weight="600" class="zh">代码跑游戏</text><text x="422" y="52" text-anchor="middle" font-weight="600" class="en">code runs the game</text>
        <text x="422" y="70" text-anchor="middle" font-size="11.5">look · act · status</text>
        <text x="422" y="86" text-anchor="middle" font-size="11.5">set_level(k)</text>
        <text x="422" y="103" text-anchor="middle" font-size="11.5" fill="var(--muted)" class="zh">+ 读对局记录</text><text x="422" y="103" text-anchor="middle" font-size="11.5" fill="var(--muted)" class="en">+ reads journals</text>

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        <text x="640" y="52" text-anchor="middle" font-weight="600">ledger</text>
        <text x="640" y="70" text-anchor="middle" font-size="11.5" class="zh">每子句 reproduced/total,极性</text><text x="640" y="70" text-anchor="middle" font-size="11.5" class="en">per clause reproduced/total, polarity</text>
        <text x="640" y="86" text-anchor="middle" font-size="11.5" class="zh">每个别的 level:held / failed</text><text x="640" y="86" text-anchor="middle" font-size="11.5" class="en">per other level: held / failed</text>
        <text x="640" y="103" text-anchor="middle" font-size="11.5" fill="var(--muted)" class="zh">被驳回的子句 + 观察证据</text><text x="640" y="103" text-anchor="middle" font-size="11.5" fill="var(--muted)" class="en">refuted clauses + the observation</text>

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        <text x="862" y="52" text-anchor="middle" font-weight="600" class="zh">过滤 → 𝒦</text><text x="862" y="52" text-anchor="middle" font-weight="600" class="en">filter → 𝒦</text>
        <text x="862" y="70" text-anchor="middle" font-size="11.5" class="zh">正确:硬门槛</text><text x="862" y="70" text-anchor="middle" font-size="11.5" class="en">correctness: gate</text>
        <text x="862" y="86" text-anchor="middle" font-size="11.5" class="zh">泛化:打分 0–3</text><text x="862" y="86" text-anchor="middle" font-size="11.5" class="en">generalization: score 0–3</text>
        <text x="862" y="103" text-anchor="middle" font-size="11.5" fill="var(--bad)" class="zh">答案型:拒绝</text><text x="862" y="103" text-anchor="middle" font-size="11.5" fill="var(--bad)" class="en">answer-like: rejected</text>

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        <!-- the verifier never sees the source -->
        <text x="6" y="150" font-size="12" fill="var(--muted)" class="zh">验真者拿不到游戏源码,只能通过 look / act / status / set_level 操作;源码只用于人工审计。</text>
        <text x="6" y="150" font-size="12" fill="var(--muted)" class="en">The verifier never sees the game source; it can only drive the game via look / act / status / set_level. The source is used for human audit only.</text>
        <text x="6" y="172" font-size="12" fill="var(--muted)" class="zh">检查种类是固定词表(home_level_fresh, other_level:k, negative_expectation, …),写的是"测了什么条件",不是"希望说明什么"。</text>
        <text x="6" y="172" font-size="12" fill="var(--muted)" class="en">Check kinds are a fixed vocabulary (home_level_fresh, other_level:k, negative_expectation, …) naming the condition tested, not the hoped-for conclusion.</text>
      </g>
    </svg>
    <figcaption><b><span class="zh">图 3.</span><span class="en">Figure 3.</span></b> <span class="zh">验真回路。正确性和泛化性都由游戏本身给出,不由模型的自我评价给出。</span><span class="en">The verification loop. Correctness and generalization come from the game itself, never from the model's self-assessment.</span></figcaption>
  </figure>

  <div class="ledger">
    <div class="box"><span class="tag"><span class="zh">子句 c1 · holds · home_level_fresh 4 次检查</span><span class="en">clause c1 · holds · home_level_fresh, 4 checks</span></span>Clicking any cell of a framed legend box, frame included, sets the applicator's fill colour to that box's colour.<span class="obs2"><span class="zh">别的 level:level 2 held(7 个色框、坐标不同,机制相同)· level 3 held → 泛化 3</span><span class="en">other levels: level 2 held (7 boxes at other coordinates, same mechanism) · level 3 held → generalization 3</span></span></div>
    <div class="box no"><span class="tag"><span class="zh">子句 c2 · refuted · 被删</span><span class="en">clause c2 · refuted · cut</span></span>Rows 3 and 6 of the frame are inert targets: clicking them changes nothing.<span class="obs2"><span class="zh">观察:在 (39,3)、(37,6)、(43,6) 点击各改变 94–95 格;记录里 18 次中 13 次"无变化"都是涂色器已持有该色的情形。</span><span class="en">observation: clicks at (39,3), (37,6), (43,6) changed 94–95 cells; the 13 of 18 journal instances that changed nothing were all cases where the applicator already held that colour.</span></span></div>
  </div>
  <p class="caption"><span class="zh">一份真实 ledger 的两条子句(verifier v2 示例):一条 claim 里对的部分进入 𝒦,错的子句连同证据被去掉 —— 不是整条接受或整条拒绝。</span><span class="en">Two clauses of a real ledger (verifier v2 example): the correct part of a claim enters 𝒦, the wrong clause is removed with its evidence — not accept-or-reject the whole claim.</span></p>
</section>

<!-- ===== 4. Evidence ===== -->
<section id="results">
  <h2><span class="zh">上下文中的知识有效;RL 起点无信号</span><span class="en">Knowledge in context works; the RL start has no signal</span></h2>
  <figure>
    <div class="fig-panel"><img 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" alt="Bar chart of level-1 clear rate: agent harness with no knowledge 9/100, mechanism rules in context 33/91, full knowledge set in context 100/100; RL starting policy with no knowledge 0/320. Error bars are 95% Wilson intervals."></div>
    <figcaption><b><span class="zh">图 4.</span><span class="en">Figure 4.</span></b> <span class="zh">同一策略,只改上下文:level 1 通关率 9/100 → 33/91 → 100/100(agent harness)。RL 起点 π₀(裸 prompt,无知识)为 0/320,纯 RL 基线梯度恒为 0 —— 内化只能从 SFT 组开始。误差棒:95% Wilson 区间。</span><span class="en">Same policy, only the context changes: level-1 clear rate 9/100 → 33/91 → 100/100 (agent harness). The RL start π₀ (bare prompt, no knowledge) is 0/320, so the pure-RL baseline's gradient is identically zero — internalization has to start from the SFT arms. Error bars: 95% Wilson intervals.</span></figcaption>
  </figure>
</section>

<!-- ===== 5. Knowledge demo ===== -->
<section id="knowledge">
  <h2><span class="zh">知识长什么样</span><span class="en">What the knowledge looks like</span></h2>
  <div class="boards">
    <figure class="train"><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAQAAAAEACAYAAABccqhmAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjExLjEsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvctoD+AAAAAlwSFlzAAAYmwAAGJsBSXWDlAAABAtJREFUeJzt3SFyFFEUQNHf1Ci2gcN2LCqLQEWPZSnYiQWVPaCwiUaxDexgCSKprsnwJ9xzbLq6nrr18uuns4wxjiNsXdeTfg6v2ZvZAwDzCACECQCECQCECQCECQCECQCE7WYPwOvy8PDwou/7+57Fud/PYzYACBMACBMACNt8BnA8XvafDizLMnuElPv7+03PX11dbXr+07svm57//PNm0/N1NgAIEwAIEwAIEwAIEwAIEwAIEwAIEwAIEwAIEwAIEwAI8z0ATrL1bv9W7vaflw0AwgQAwgQAwpwBsMm5v7G39fsCz9nv9y/6vv+NDQDCBADCBADCNp8BfP344xxzwBjD7+z/mg0AwgQAwgQAwtwDeMbt7e3sEeBsbAAQJgAQJgAQJgAQJgAQJgAQJgAQJgAQJgAQJgAQJgAQJgAQJgAQJgAQJgAQtvl7ADd3788xBzCBDQDCBADCBADCBADCBADCBADCBADCBADCBADCBADCBADCduu6zp4BmMQGAGECAGECAGECAGECAGECAGECAGHLGOM4ewhgDhsAhAkAhAkAhAkAhAkAhAkAhAkAhAkAhAkAhAkAhAkAhAkAhAkAhAkAhAkAhAkAhAkAhAkAhAkAhAkAhAkAhAkAhAkAhO1mD8Bjh8Nh9ghntd/vZ4/AH2wAECYAECYAEOYM4MK9/fZh9ggn+XX9ffYIPMEGAGECAGECAGECAGECAGECAGECAGECAGECAGECAGECAGECAGECAGECAGECAGECAGECAGECAGECAGECAGECAGECAGECAGECAGECAGECAGECAGH+N+CFu7l7P3uEkxyuD7NH4Ak2AAgTAAgTAAhbxhjH2UMAc9gAIEwAIEwAIMw9gFfmeLzsI5tlWWaPwAY2AAgTAAgTAAgTAAgTAAgTAAgTAAgTAAgTAAgTAAgTAAgTAAgTAAgTAAgTAAgTAAgTAAgTAAgTAAgTAAgTAAgTAAgTAAgTAAgTAAgTAAgTAAgTAAgTAAgTAAgTAAgTAAgTAAgTAAgTAAgTAAgTAAgTAAgTAAhbxhjH2UMAc9gAIEwAIEwAIEwAIEwAIEwAIEwAIEwAIEwAIEwAIEwAIEwAIEwAIEwAIEwAIEwAIEwAIEwAIEwAIEwAIEwAIEwAIEwAIEwAIEwAIEwAIEwAIEwAIEwAIEwAIEwAIEwAIEwAIEwAIEwAIEwAIEwAIEwAIEwAIEwAIEwAIEwAIEwAIEwAIEwAIEwAIEwAIEwAIEwAIEwAIEwAIEwAIEwAIEwAIEwAIEwAIEwAIEwAIEwAIEwAIEwAIEwAIEwAIEwAIEwAIEwAIEwAIEwAIEwAIEwAIEwAIEwAIEwAIEwAIEwAIGy3ruvsGYBJbAAQJgAQJgAQ9huFiy+7lz3MvQAAAABJRU5ErkJggg==" alt="Level 1 starting board"><figcaption>L1 · train</figcaption></figure>
    <figure class="train"><img src="data:image/png;base64,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" alt="Level 2 starting board"><figcaption>L2 · train</figcaption></figure>
    <figure class="train"><img src="data:image/png;base64,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" alt="Level 3 starting board"><figcaption>L3 · train</figcaption></figure>
    <figure class="train"><img src="data:image/png;base64,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" alt="Level 4 starting board"><figcaption>L4 · train</figcaption></figure>
    <figure class="test"><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAQAAAAEACAYAAABccqhmAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjExLjEsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvctoD+AAAAAlwSFlzAAAYmwAAGJsBSXWDlAAABRFJREFUeJzt3SFvJGUcwOFZqAFPgjhHMCiOrYEEdR+ABMGpS1Bdi+dLnN0qEjBFECziFAmYlpMIgiCBhOQcAhQUvRXbvOyW2e3vedxm56avaH73nzfTmcU0TddT2HK53Ol7OGYvzb0AYD4CAGECAGECAGECAGECAGECAGEncy/gvrm6utrr+W7eh+D8zr/L+W8yAUCYAECYAEDY8B7AB5dvb3x+/v3zrcf/dPHO6I/Y8ObjH7Z+/8sfDzY+Lz79baeft2+Xl5dDx5+eng4d/8kbnw8d//TnJ0PHX374+9Dxp1+9Pnb+i7Fr3tPHY3+b8WD999Dxv65eHjr+u1f+GTr+vb/G/s998fHY789rn439/pgAIEwAIEwAIGzn+wAevvtw4/NtewK7unnND/x3JgAIEwAIEwAI2/vfAtzcE5guFjudzzU/3B0TAIQJAIQJAITd+fMAPnq6+dqB2+4TuG/X/KP39o8avbd/1Oi9/cPnH7y3f9Tovf2jRu/tHzV6b/8oEwCECQCECQCELabBdwPefB7Arr7+5sVezzf6PIBjezfger3e6/lWq9Vez8dxMQFAmABAmABA2P/+XoB9X/PXuGZnn0wAECYAECYAEHbnewDHfs1/fn4+9xLgzpgAIEwAIEwAIGzvewDHfs0PJSYACBMACBMACNt5D8A1PxwvEwCECQCECQCEDe8BuOaH+8MEAGECAGECAGHDewCjz90HDpcJAMIEAMIEAMIEAMIEAMIEAMIEAMIEAMIEAMIEAMIEAMJOlsvl3GsAZmICgDABgDABgDABgDABgDABgDABgLDFNE3Xcy+Cu7Ner3f696vVak8r4RCZACBMACBMACDMHsA9c9s1/9nZ2dbvz8/Pt35vT+B+MQFAmABAmABA2PC7ATkurz57f+PzF89+3H78tHn8n4++3fuaOBwmAAgTAAgTAAgTAAgTAAgTAAgTAAgTAAgTAAgTAAgTAAjzPAAIMwFAmABAmABAmOcBHJhdn+N/6DxT8LCYACBMACBMACDMHsCBu/lMv2PjmYKHzQQAYQIAYQIAYQIAYQIAYQIAYQIAYQIAYQIAYQIAYQIAYQIAYQIAYQIAYQIAYQIAYQIAYQIAYQIAYQIAYQIAYQIAYQIAYQIAYQIAYQIAYQIAYd4NeOCefPnW3EvYyfrReu4lsIUJAMIEAMIEAMIW0zRdz70IYB4mAAgTAAgTAAhzH8CRub4+7C2bxWIx9xIYYAKAMAGAMAGAMAGAMAGAMAGAMAGAMAGAMAGAMAGAMAGAMAGAMAGAMAGAMAGAMAGAMAGAMAGAMAGAMAGAMAGAMAGAMAGAMAGAMAGAMAGAMAGAMAGAMAGAMAGAMAGAMAGAMAGAMAGAMAGAMAGAMAGAMAGAMAGAsMU0TddzLwKYhwkAwgQAwgQAwgQAwgQAwgQAwgQAwgQAwgQAwgQAwgQAwgQAwgQAwgQAwgQAwgQAwgQAwgQAwgQAwgQAwgQAwgQAwgQAwgQAwgQAwgQAwgQAwgQAwgQAwgQAwgQAwgQAwgQAwgQAwgQAwgQAwgQAwgQAwgQAwgQAwgQAwgQAwgQAwgQAwgQAwgQAwgQAwgQAwgQAwgQAwgQAwgQAwgQAwgQAwgQAwgQAwgQAwgQAwgQAwgQAwgQAwgQAwgQAwgQAwgQAwgQAwgQAwgQAwgQAwgQAwgQAwgQAwgQAwgQAwgQAwk6Wy+XcawBmYgKAMAGAMAGAsH8BvUSIvt48sYAAAAAASUVORK5CYII=" alt="Level 5 starting board"><figcaption>L5 · test</figcaption></figure>
    <figure class="test"><img src="data:image/png;base64,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" alt="Level 6 starting board"><figcaption>L6 · test</figcaption></figure>
  </div>
  <p class="caption"><b><span class="zh">图 5.</span><span class="en">Figure 5.</span></b> <span class="zh">六个 level 的起始棋盘。同一套机制、不同布局:知识若可泛化,应在 L5–L6 上仍成立。</span><span class="en">Starting boards of the six levels. Same mechanics, different layouts: knowledge that generalizes should still hold on L5–L6.</span></p>
  <div class="atoms">
    <div class="atom"><span class="tag"><span class="zh">已验真 · level 1 · 泛化 3/3</span><span class="en">verified · level 1 · generalization 3/3</span></span>Clicking the colour box whose colour the applicator already holds changes no board cell.</div>
    <div class="atom"><span class="tag"><span class="zh">已验真 · level 1 · 泛化 3/3</span><span class="en">verified · level 1 · generalization 3/3</span></span>An ACTION5 that changes no cell means that region already holds that colour.</div>
    <div class="atom"><span class="tag"><span class="zh">已验真 · level 1 · 泛化 3/3</span><span class="en">verified · level 1 · generalization 3/3</span></span>The canvas is written only by a painting action or by a level (re)load.</div>
    <div class="atom"><span class="tag"><span class="zh">已验真 · level 1 · 泛化 3/3</span><span class="en">verified · level 1 · generalization 3/3</span></span>ACTION6 aimed at a cell that belongs to no interactive sprite changes no board cell.</div>
    <div class="atom new"><span class="tag"><span class="zh">新 · L2+L3 合并 · L1–L4 成立 · 泛化 3/3</span><span class="en">new · merged from L2+L3 · holds on L1–L4 · generalization 3/3</span></span>Clicking a swatch whose color differs from the interior recolors the interior to that swatch's color.<span class="cond"><span class="zh">条件:内部颜色与所点色块不同</span><span class="en">condition: the interior's color differs from the tapped swatch's color</span></span></div>
    <div class="atom new"><span class="tag"><span class="zh">新 · L3+L4 合并 · L1–L4 成立 · 泛化 3/3</span><span class="en">new · merged from L3+L4 · holds on L1–L4 · generalization 3/3</span></span>Clicking a palette slot moves the marker bar to sit directly under the clicked slot.</div>
    <div class="atom new"><span class="tag"><span class="zh">新 · L4 · L2–L4 成立 · 泛化 3/3</span><span class="en">new · L4 · holds on L2–L4 · generalization 3/3</span></span>Clicking a spout cell pours 6–13 cells of liquid into the pit.</div>
    <div class="atom new"><span class="tag"><span class="zh">新 · L2 · L1–L3 成立 · 泛化 3/3</span><span class="en">new · L2 · holds on L1–L3 · generalization 3/3</span></span>Each act call drains at most one cell from the rightmost end of the timer bar.</div>
  </div>
  <p class="caption"><span class="zh">蓝色:旧 level-1 集深审保留的原子(36 条中的 4 条);橙色:最终 𝒦₁ 中来自 level 2–4 的新原子(888 条中的 4 条),已由代码验真,标注其成立的 level。每条 = 一个机制 + 条件,不含解法。</span><span class="en">Blue: level-1 atoms kept after the claim-by-claim review (4 of 36); amber: new atoms of the final 𝒦₁ from levels 2–4 (4 of 888), verified by code and tagged with the levels they hold on. Each = one mechanism + its condition, no solution.</span></p>
</section>

<!-- ===== 6. Status ===== -->
<section id="status">
  <h2><span class="zh">进展</span><span class="en">Progress</span></h2>
  <div class="tablewrap"><table class="booktabs">
    <thead><tr><th>Level</th><th class="num"><span class="zh">局</span><span class="en">Episodes</span></th><th class="num"><span class="zh">清关</span><span class="en">Cleared</span></th><th class="num"><span class="zh">claims (v3)</span><span class="en">Claims (v3)</span></th><th class="num"><span class="zh">已验真</span><span class="en">Verified</span></th><th class="num"><span class="zh">子句通过</span><span class="en">Clauses kept</span></th><th class="num"><span class="zh">原子</span><span class="en">Atoms</span></th></tr></thead>
    <tbody>
      <tr><td>1</td><td class="num">100</td><td class="num">9</td><td class="num">101</td><td class="num">96</td><td class="num">167</td><td class="num">198</td></tr>
      <tr><td>2</td><td class="num">100</td><td class="num">0</td><td class="num">121</td><td class="num">100</td><td class="num">167</td><td class="num">194</td></tr>
      <tr><td>3</td><td class="num">100</td><td class="num">0</td><td class="num">128</td><td class="num">123</td><td class="num">168</td><td class="num">244</td></tr>
      <tr><td>4</td><td class="num">100</td><td class="num">0</td><td class="num">124</td><td class="num">117</td><td class="num">172</td><td class="num">228</td></tr>
      <tr><td><b>Σ</b></td><td class="num"><b>400</b></td><td class="num"><b>9</b></td><td class="num"><b>474</b></td><td class="num"><b>436</b></td><td class="num"><b>674</b></td><td class="num"><b>888</b></td></tr>
    </tbody>
  </table></div>
  <p class="tabcap"><b><span class="zh">表 2.</span><span class="en">Table 2.</span></b> <span class="zh">知识流水线(四个 level 同一流程)。已验真 = 有账本的 claim(38 条验真会话超预算未出账本);子句通过 = 过 v2 门控的子句;原子 = 两遍 Qwen(原子化、同事实合并)+ 门控后的最终 𝒦₁:888 条,其中 24 条跨 level 合并、269 条泛化 3/3。</span><span class="en">Knowledge pipeline (one procedure for all four levels). Verified = claims with a ledger (38 verifier sessions ran out of budget before writing one); clauses kept = clauses passing the v2 gates; atoms = the final 𝒦₁ after two Qwen passes (atomize, merge same-fact) and the gates: 888 atoms, 24 merged across levels, 269 with generalization 3/3.</span></p>
  <ul class="tight">
    <li><span class="zh">𝒦₁ 已冻结(888 原子 → 1066 SFT 行,sha256 固定)。三组实验 sft_first / sft_interleaved / sft_joint 于 2026-09-12 起跑:lr 5e-6,每轮 8 步优化;sft_first 先做 12 轮纯 SFT(每轮过一遍 𝒦₁)再 12 轮 RL;每轮 ≈ 1.6 h,曲线随后补入。</span><span class="en">𝒦₁ frozen (888 atoms → 1066 SFT rows, sha256 pinned). The three arms sft_first / sft_interleaved / sft_joint started on 2026-09-12: lr 5e-6, 8 optimizer steps per rollout; sft_first runs 12 SFT-only rollouts (one pass over 𝒦₁ each) before 12 RL rollouts; ≈ 1.6 h per rollout, curves to follow.</span></li>
    <li><span class="zh">纯 RL 基线需要 π₀ 非零的设定(xhigh 推理或更多局)才有信息量。</span><span class="en">The pure-RL baseline needs a setting with non-zero π₀ (xhigh reasoning or more episodes) to be informative.</span></li>
    <li><span class="zh">第 2 轮(r = 2):把 𝒦₁ 放回上下文再游玩、再提取,看模型能否组合出更高层的知识。</span><span class="en">Round 2 (r = 2): play and extract again with 𝒦₁ in context, to see whether the model composes higher-level knowledge.</span></li>
  </ul>
</section>

<footer>
  <span class="mono">~/code/knowledge_learning</span> · <span class="mono">/projects/u6uv/ziyan/outputs/{rl,harness,review,report}</span> · wandb <span class="mono">wangziyan1998/e2k-internalize</span>
</footer>
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  var img=document.getElementById('demo-board'), label=document.getElementById('demo-label'), steps=document.getElementById('demo-steps');
  var items=steps.querySelectorAll('li'); var i=0, timer=null;
  var reduce=window.matchMedia&&window.matchMedia('(prefers-reduced-motion: reduce)').matches;
  function show(k){ i=(k+frames.length)%frames.length; img.src='data:image/png;base64,'+frames[i].png; label.textContent=(i)+' / '+(frames.length-1);
    for(var j=0;j<items.length;j++){ items[j].classList.toggle('cur', j===i); } }
  function play(){ if(timer) return; timer=setInterval(function(){show(i+1);},1700); document.getElementById('demo-play').textContent='❚❚'; document.getElementById('demo-play').setAttribute('aria-pressed','true'); }
  function pause(){ if(timer){clearInterval(timer); timer=null;} document.getElementById('demo-play').textContent='▶'; document.getElementById('demo-play').setAttribute('aria-pressed','false'); }
  document.getElementById('demo-prev').addEventListener('click',function(){pause(); show(i-1);});
  document.getElementById('demo-next').addEventListener('click',function(){pause(); show(i+1);});
  document.getElementById('demo-play').addEventListener('click',function(){ timer?pause():play(); });
  for(var j=0;j<items.length;j++){ (function(k){ items[k].addEventListener('click',function(){pause(); show(k);}); })(j); }
  show(0); if(!reduce){ play(); } else { pause(); }
})();
</script>