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| <div class="wrap"> | |
| <div class="eyebrow">IGUANA · proposal fine-tuning for SMC · working notes, 2026-07-22</div> | |
| <h1>Three failure modes of IGUANA training — isolated, with their fixes</h1> | |
| <p class="sub">Every curve is a real training run on DS-1000 (Qwen2.5-Coder-3B, LoRA). | |
| <span class="faildot"></span>red = failure mode active | |
| <span class="fixdot"></span>blue = same run with the fix. Panel badges mark the training scale: | |
| 10-prompt probe vs full split (591 prompts).</p> | |
| <h2>The gradient, and where it breaks</h2> | |
| <p>IGUANA fine-tunes the proposal by descending the forward KL to the posterior, estimated with | |
| centered, self-normalized importance weights over <span class="math">M</span> sampled particles:</p> | |
| <div class="eq"><span class="eq-label">Objective & gradient</span> | |
| <math display="inline"><mrow> | |
| <msub><mo>∇</mo><mi>φ</mi></msub><mi>KL</mi> | |
| <mrow><mo stretchy="false">(</mo><mi>π</mi><mo>∥</mo><msub><mi>q</mi><mi>φ</mi></msub><mo stretchy="false">)</mo></mrow> | |
| <mo>≈</mo><mo>−</mo> | |
| <munderover><mo movablelimits="false">∑</mo><mrow><mi>m</mi><mo>=</mo><mn>1</mn></mrow><mi>M</mi></munderover> | |
| <msub><mi>w</mi><mi>m</mi></msub> | |
| <msub><mo>∇</mo><mi>φ</mi></msub><mi>log</mi><mo></mo> | |
| <msub><mi>q</mi><mi>φ</mi></msub> | |
| <mrow><mo stretchy="false">(</mo><msup><mi>y</mi><mrow><mo stretchy="false">(</mo><mi>m</mi><mo stretchy="false">)</mo></mrow></msup><mo stretchy="false">)</mo></mrow> | |
| </mrow></math> | |
| </div> | |
| <div class="eq"><span class="eq-label">IGUANA weight</span> | |
| <math display="inline"><mrow> | |
| <msub><mi>w</mi><mi>m</mi></msub><mo>=</mo> | |
| <mfrac> | |
| <mrow><mi>uw</mi><mrow><mo stretchy="false">(</mo><msup><mi>y</mi><mrow><mo stretchy="false">(</mo><mi>m</mi><mo stretchy="false">)</mo></mrow></msup><mo stretchy="false">)</mo></mrow> | |
| <mo>−</mo><mi>b</mi><mo stretchy="false">(</mo><mi>x</mi><mo stretchy="false">)</mo></mrow> | |
| <mrow><mi>M</mi><mo>·</mo><mi>Z</mi><mo stretchy="false">(</mo><mi>x</mi><mo stretchy="false">)</mo></mrow> | |
| </mfrac> | |
| <mo>,</mo><mspace width="1.6em"></mspace> | |
| <mi>b</mi><mo stretchy="false">(</mo><mi>x</mi><mo stretchy="false">)</mo><mo>=</mo><mi>Z</mi><mo stretchy="false">(</mo><mi>x</mi><mo stretchy="false">)</mo> | |
| </mrow></math> | |
| </div> | |
| <div class="eq"><span class="eq-label">Importance weight</span> | |
| <math display="inline"><mrow> | |
| <mi>uw</mi><mo stretchy="false">(</mo><mi>y</mi><mo stretchy="false">)</mo><mo>=</mo> | |
| <mfrac> | |
| <mrow><mi>r</mi><mo stretchy="false">(</mo><mi>y</mi><mo stretchy="false">)</mo><mo> </mo><msub><mi>p</mi><mi>T</mi></msub><mo stretchy="false">(</mo><mi>y</mi><mo stretchy="false">)</mo></mrow> | |
| <mrow><msub><mi>q</mi><mrow><mi>φ</mi><mo>,</mo><mi>T</mi></mrow></msub><mo stretchy="false">(</mo><mi>y</mi><mo stretchy="false">)</mo></mrow> | |
| </mfrac> | |
| <mspace width="1.6em"></mspace> | |
| <mo>∈</mo><mo stretchy="false">[</mo><mn>0</mn><mo>,</mo><mo>∞</mo><mo stretchy="false">)</mo><mo>,</mo> | |
| <mspace width="0.8em"></mspace> | |
| <mtext style="font-style:normal;font-size:0.82em;color:var(--muted)">= r(y) ∈ {0,1} at init, where q = p</mtext> | |
| </mrow></math> | |
| </div> | |
| <div class="eq"><span class="eq-label">Running normalizer</span> | |
| <math display="inline"><mrow> | |
| <mi>Z</mi><mo stretchy="false">(</mo><mi>x</mi><mo stretchy="false">)</mo><mo>=</mo> | |
| <mfrac><mn>1</mn><mi>K</mi></mfrac> | |
| <munderover><mo movablelimits="false">∑</mo><mrow><mi>k</mi><mo>=</mo><mn>1</mn></mrow><mi>K</mi></munderover> | |
| <msub><mover accent="true"><mi>Z</mi><mo>^</mo></mover><mi>k</mi></msub><mo stretchy="false">(</mo><mi>x</mi><mo stretchy="false">)</mo> | |
| <mo>,</mo><mspace width="1.6em"></mspace> | |
| <msub><mover accent="true"><mi>Z</mi><mo>^</mo></mover><mi>k</mi></msub><mo>=</mo> | |
| <mfrac><mn>1</mn><mi>M</mi></mfrac> | |
| <munder><mo movablelimits="false">∑</mo><mi>m</mi></munder> | |
| <mi>uw</mi><mrow><mo stretchy="false">(</mo><msup><mi>y</mi><mrow><mo stretchy="false">(</mo><mi>m</mi><mo stretchy="false">)</mo></mrow></msup><mo stretchy="false">)</mo></mrow> | |
| </mrow></math> | |
| </div> | |
| <div class="eq"><span class="eq-label">Offline estimate of Z(x)</span> | |
| <math display="inline"><mrow> | |
| <mi>Z</mi><mo stretchy="false">(</mo><mi>x</mi><mo stretchy="false">)</mo><mo>=</mo> | |
| <msub><mi mathvariant="normal">𝔼</mi><mrow><mi>y</mi><mo>∼</mo><msub><mi>p</mi><mi>T</mi></msub><mo stretchy="false">(</mo><mo>·</mo><mo>∣</mo><mi>x</mi><mo stretchy="false">)</mo></mrow></msub> | |
| <mo stretchy="false">[</mo><mi>r</mi><mo stretchy="false">(</mo><mi>y</mi><mo stretchy="false">)</mo><mo stretchy="false">]</mo> | |
| <mo>≈</mo> | |
| <mfrac><mn>1</mn><mi>N</mi></mfrac> | |
| <munderover><mo movablelimits="false">∑</mo><mrow><mi>i</mi><mo>=</mo><mn>1</mn></mrow><mi>N</mi></munderover> | |
| <mi>r</mi><mo stretchy="false">(</mo><msub><mi>y</mi><mi>i</mi></msub><mo stretchy="false">)</mo> | |
| <mo>=</mo> | |
| <mfrac><msub><mi>n</mi><mtext>valid</mtext></msub><mi>N</mi></mfrac> | |
| <mo>,</mo><mspace width="1em"></mspace> | |
| <msub><mi>y</mi><mi>i</mi></msub><mo>∼</mo><msub><mi>p</mi><mi>T</mi></msub><mo stretchy="false">(</mo><mo>·</mo><mo>∣</mo><mi>x</mi><mo stretchy="false">)</mo> | |
| <mtext style="font-style:normal;font-size:0.82em;color:var(--muted)"> (offline rollouts; ratio = 1, unbiased; proposal-independent → valid all run)</mtext> | |
| </mrow></math> | |
| </div> | |
| <p style="font-size:13.5px;color:var(--ink-2);max-width:80ch"><b style="color:var(--ink)">Unseeded vs seeded vs frozen Z</b> — one formula, three settings of <span class="math">n<sub>0</sub></span>. | |
| <b>Unseeded</b> (<span class="math">n<sub>0</sub> = 0</span>, the original setup and the red run in the Mode-1 panel): | |
| <span class="math">Z(x)</span> is built from online visits only — a prompt's first visit has no history | |
| (<span class="math">K=0</span>; the code falls back to the current batch's own <span class="math">Ẑ</span> as both denominator | |
| and baseline, a within-batch centering), and its second visit runs on <span class="math">K=1</span>, a single heavy-tailed | |
| sample — the Mode-1 trigger. | |
| <b>Seeded</b>: the offline estimate initializes the running mean as <span class="math">n<sub>0</sub> = N⁄M = 10</span> | |
| pseudo-visits (its information content in batches), and online estimates keep folding in — the prior dominates early | |
| and decays as real visits accumulate. | |
| <b>Frozen</b>: <span class="math">Z(x) = Ẑ<sub>off</sub>(x)</span> permanently, never updated — legitimate precisely because | |
| <span class="math">Z(x)</span> is proposal-independent. Seeded and frozen both eliminate the <span class="math">K=1</span> | |
| regime and behave equivalently in practice; frozen is the simpler/lower-variance choice.</p> | |
| <div class="eq"><span class="eq-label">Seeded running mean</span> | |
| <math display="inline"><mrow> | |
| <mi>Z</mi><mo stretchy="false">(</mo><mi>x</mi><mo stretchy="false">)</mo><mo>=</mo> | |
| <mfrac> | |
| <mrow><msub><mi>n</mi><mn>0</mn></msub><mo>·</mo><msub><mover accent="true"><mi>Z</mi><mo>^</mo></mover><mtext>off</mtext></msub><mo stretchy="false">(</mo><mi>x</mi><mo stretchy="false">)</mo> | |
| <mo>+</mo> | |
| <munderover><mo movablelimits="false">∑</mo><mrow><mi>k</mi><mo>=</mo><mn>1</mn></mrow><mi>K</mi></munderover> | |
| <msub><mover accent="true"><mi>Z</mi><mo>^</mo></mover><mi>k</mi></msub><mo stretchy="false">(</mo><mi>x</mi><mo stretchy="false">)</mo></mrow> | |
| <mrow><msub><mi>n</mi><mn>0</mn></msub><mo>+</mo><mi>K</mi></mrow> | |
| </mfrac> | |
| <mspace width="1.2em"></mspace> | |
| <mtext style="font-style:normal;font-size:0.82em;color:var(--muted)">(K = online visits so far; unseeded: n₀ = 0, frozen: n₀ → ∞)</mtext> | |
| </mrow></math> | |
| </div> | |
| <div class="notation"> | |
| <span><span class="math">π ∝ p<sub>T</sub>·r</span> — target posterior</span> | |
| <span><span class="math">p<sub>T</sub></span> — prior (base LM) at temp <span class="math">T</span>=0.6</span> | |
| <span><span class="math">r(y)</span> ∈ {0,1} — potential (code runs)</span> | |
| <span><span class="math">q<sub>φ</sub></span> — proposal (LoRA), sampled at <span class="math">T</span></span> | |
| <span><span class="math">y<sup>(m)</sup></span> — particle <span class="math">m</span> of <span class="math">M</span>=10</span> | |
| <span><span class="math">x</span> — prompt; <span class="math">K</span> — its past visits</span> | |
| <span><span class="math">Ẑ<sub>k</sub></span> — visit-<span class="math">k</span> batch estimate of <span class="math">Z(x)</span></span> | |
| <span><span class="math">w<sub>m</sub></span> > 0 reinforce · < 0 push away</span> | |
| </div> | |
| <p style="font-size:13.5px;color:var(--ink-2);max-width:80ch"><b style="color:var(--ink)">Clipping (common to every run shown)</b> — three layers, always on: | |
| the raw importance weight is clamped, <span class="math">uw ← min(uw, 1.0)</span> (truncated IS, applied both in | |
| <span class="math">Ẑ<sub>k</sub></span> and in the numerator); the centered weight is capped, | |
| <span class="math">w<sub>m</sub> ← clip(w<sub>m</sub>, ±0.5)</span>; and the summed gradient is norm-clipped to 1. | |
| Post-fix the uw clamp barely binds (valid particles sit at <span class="math">uw ≈ 1</span>). | |
| Note the failure modes below crater <em>through</em> all three layers — they are sign/direction pathologies, | |
| which magnitude clipping cannot correct; only the structural fixes do.</p> | |
| <p>A <em>positive</em> weight reinforces sample <span class="math">y<sup>(m)</sup></span>; a <em>negative</em> one pushes | |
| the model away from it. Each failure mode corrupts a different part of this expression:</p> | |
| <div class="modes"> | |
| <div class="mode"> | |
| <h3>Mode 1 · denominator degradation</h3> | |
| <div class="m-eq"><math display="inline"><mrow> | |
| <mi>K</mi><mo>=</mo><mn>1</mn><mspace width="0.5em"></mspace><mo>⇒</mo><mspace width="0.5em"></mspace> | |
| <mi>Z</mi><mo stretchy="false">(</mo><mi>x</mi><mo stretchy="false">)</mo><mo>=</mo> | |
| <msub><mover accent="true"><mi>Z</mi><mo>^</mo></mover><mn>1</mn></msub> | |
| <mspace width="0.5em"></mspace><mo>⇒</mo><mspace width="0.5em"></mspace> | |
| <mo stretchy="false">|</mo><msub><mi>w</mi><mi>m</mi></msub><mo stretchy="false">|</mo><mo>∝</mo> | |
| <mfrac><mn>1</mn><mrow><mi>Z</mi><mo stretchy="false">(</mo><mi>x</mi><mo stretchy="false">)</mo></mrow></mfrac> | |
| <mtext style="font-size:0.85em;color:var(--muted)"> (one heavy-tailed sample)</mtext> | |
| </mrow></math></div> | |
| <p>Under <i>exclude-current</i>, a prompt's running <span class="math">Z(x)</span> is the mean of its <em>past</em> visits' | |
| <span class="math">Ẑ<sub>k</sub></span>. From epoch 2, every prompt sits at <span class="math">K=1</span>: one unlucky | |
| low <span class="math">Ẑ</span> and <span class="math">1⁄Z</span> explodes. Scale-dependent — needs long epochs | |
| (fires on the full split at iter 591; the 10-prompt probe refreshes <span class="math">K</span> too fast).</p> | |
| <div class="fixline"><span class="fixdot"></span><b>Fix:</b> seed or freeze <span class="math">Z(x)</span> from | |
| offline rollouts (<span class="math">Ẑ<sub>off</sub> = n<sub>valid</sub>⁄100</span>; unbiased — | |
| <span class="math">Z</span> is proposal-independent).</div> | |
| </div> | |
| <div class="mode"> | |
| <h3>Mode 2 · sign-flip / push-away</h3> | |
| <div class="m-eq"><math display="inline"><mrow> | |
| <mtext style="font-size:0.85em;color:var(--muted)">resampling </mtext> | |
| <mo>⇒</mo><mspace width="0.4em"></mspace> | |
| <msup><mi>uw</mi><mrow><mo stretchy="false">(</mo><mi>m</mi><mo stretchy="false">)</mo></mrow></msup><mo>≈</mo> | |
| <munder><mo movablelimits="false">∏</mo><mi>t</mi></munder> | |
| <msub><mover accent="true"><mi>s</mi><mo>¯</mo></mover><mi>t</mi></msub> | |
| <mo>≪</mo><mi>Z</mi><mo stretchy="false">(</mo><mi>x</mi><mo stretchy="false">)</mo> | |
| <mspace width="0.5em"></mspace><mo>⇒</mo><mspace width="0.5em"></mspace> | |
| <msub><mi>w</mi><mi>m</mi></msub><mo><</mo><mn>0</mn> | |
| <mspace width="0.4em"></mspace><mo>∀</mo><mi>m</mi> | |
| </mrow></math></div> | |
| <p>SMC resampling resets each particle's weight to the population mean, so the final | |
| <span class="math">uw</span> carries the path-survival product (~0.01), not <span class="math">r·p<sub>T</sub>/q<sub>T</sub></span>. | |
| With <span class="math">b = Z ≈ 0.6</span> pinned above it, <em>every</em> weight is negative — the update pushes | |
| <span class="math">q</span> away from all of its own samples, valid ones included. Loss is negative from iteration 1.</p> | |
| <div class="fixline"><span class="fixdot"></span><b>Fix:</b> train with plain importance sampling | |
| (<span class="math">ess = 0</span>, no resampling) + tempered prior, so <span class="math">uw = r ≈ 1 > Z</span> at init. | |
| Resampling stays for inference/eval.</div> | |
| </div> | |
| <div class="mode"> | |
| <h3>Mode 3 · entropy collapse of the estimator</h3> | |
| <div class="m-eq"><math display="inline"><mrow> | |
| <munder><mo movablelimits="false">∑</mo><mi>m</mi></munder> | |
| <msub><mover accent="true"><mi>w</mi><mo>~</mo></mover><mi>m</mi></msub> | |
| <msub><mo>∇</mo><mi>φ</mi></msub><mi>log</mi><mo></mo> | |
| <msub><mi>q</mi><mi>φ</mi></msub> | |
| <mrow><mo stretchy="false">(</mo><msup><mi>y</mi><mrow><mo stretchy="false">(</mo><mi>m</mi><mo stretchy="false">)</mo></mrow></msup><mo stretchy="false">)</mo></mrow> | |
| <mo>,</mo><mspace width="0.6em"></mspace> | |
| <msup><mi>y</mi><mrow><mo stretchy="false">(</mo><mi>m</mi><mo stretchy="false">)</mo></mrow></msup><mo>∼</mo><msub><mi>q</mi><mi>φ</mi></msub> | |
| <mspace width="0.5em"></mspace><mo>⇒</mo><mspace width="0.5em"></mspace> | |
| <mtext style="font-size:0.85em;color:var(--muted)">blind where q ≈ 0</mtext> | |
| </mrow></math></div> | |
| <p>The <em>objective</em> (inclusive KL) is mean-seeking — its minimizer is <span class="math">q = π</span>, entropy | |
| included; it cannot prefer a collapsed <span class="math">q</span>. What collapses is the finite-M <em>estimator</em>: | |
| samples come only from <span class="math">q</span>, so the mass <span class="math">q</span> stops covering — where | |
| forward KL's corrective signal lives — is never observed, and self-normalization hides the loss. Support shrinkage | |
| is a one-way ratchet, the negative weights on failed particles push mass onto the modes <span class="math">q</span> | |
| already samples, and gradient noise drifts it into the absorbing near-δ state (all particles identical → degenerate).</p> | |
| <div class="fixline"><span class="fixdot"></span><b>Guard used here:</b> the anchor | |
| <span class="math">β ∇<sub>φ</sub>KL(q<sub>φ</sub> ‖ p)</span>, <span class="math">β = 0.1</span> — bounds the | |
| support ratchet (pragmatic, biases the objective). <b>Principled fixes</b> target the estimator: defensive sampling | |
| (a fraction of particles from <span class="math">p<sub>T</sub></span>, keeping abandoned regions observable) or | |
| MSC/CSMC — a consistent inclusive-KL gradient, which ran single-prompt with <em>no collapse and no anchor</em>.</div> | |
| </div> | |
| </div> | |
| <p class="note"><b>Working recipe</b> (all three fixes): seed-or-freeze <span class="math">Z(x)</span> + | |
| <span class="math">ess=0</span> + tempered weights + KL anchor. Reference run: 7,538 iterations, no collapse, | |
| grad-norm flat ~50; held-out WACC 0.178 → ~0.23.</p> | |
| <h2>Training trajectories</h2> | |
| <div class="controls"> | |
| <label>Smoothing window | |
| <input type="range" id="smooth" min="1" max="51" step="2" value="15"> | |
| <span id="smoothval" style="font-variant-numeric:tabular-nums">15</span> | |
| </label> | |
| <label>Metric | |
| <span class="seg" id="metricseg" role="group" aria-label="Metric"> | |
| <button aria-pressed="true" data-m="valid">Validity</button> | |
| <button aria-pressed="false" data-m="gnorm">Grad norm (log)</button> | |
| <button aria-pressed="false" data-m="loss">Loss</button> | |
| </span> | |
| </label> | |
| </div> | |
| <div class="panel"> | |
| <div class="panel-head"><h3>Mode 1 — denominator degradation at the epoch boundary</h3> | |
| <span class="badge full">full split · 591 prompts</span></div> | |
| <p class="expl">Dotted line = iter 591 (epoch 2 = every prompt's second visit, <span class="math">K=1</span>). | |
| The unseeded run breaks its climb and runs with ~3× the gradient noise from there on; the seeded run | |
| sails through. Contained (not catastrophic) because the other two fixes are on.</p> | |
| <div class="chart" id="c-mode1"></div> | |
| </div> | |
| <div class="panel"> | |
| <div class="panel-head"><h3>Mode 2 — sign-flip / push-away under resampling</h3> | |
| <span class="badge">probe · 10 prompts</span></div> | |
| <p class="expl">Red validity starts near 1.0 only because it counts post-resampling survivors (inflated); | |
| the model beneath is being pushed away from every sample — no climb phase, monotone erosion, dead by | |
| iter ~180. Switch the metric to Loss: negative from iteration 1 (the signature).</p> | |
| <div class="chart" id="c-mode2"></div> | |
| </div> | |
| <div class="panel"> | |
| <div class="panel-head"><h3>Mode 3 — entropy collapse of the estimator</h3> | |
| <span class="badge">probe · 10 prompts</span></div> | |
| <p class="expl">Climb-then-crater: concentrating on covered good sequences first <em>helps</em> (validity 0.1 → 0.85 | |
| by ~iter 100) — but the estimator never sees the mass q stops covering, so the support ratchet runs one way, into a | |
| near-deterministic degenerate proposal (0 by ~iter 160) with the gradient norm running away. The anchored reference | |
| stays flat indefinitely. (The objective itself is mean-seeking — this is estimator failure, not objective preference; | |
| see the Mode-3 card above.)</p> | |
| <div class="chart" id="c-mode3"></div> | |
| </div> | |
| <h2>Stable training — all three fixes on</h2> | |
| <p class="sub" style="font-size:13.5px">Same controls apply. Seeded vs frozen Z(x) — the two variants of the | |
| stable recipe (both: ess=0, tempered, KL anchor 0.1) — track each other at both scales; no collapse anywhere.</p> | |
| <div class="panel"> | |
| <div class="panel-head"><h3>10-prompt runs, full length</h3><span class="badge">probe · 10 prompts</span></div> | |
| <div class="chart" id="c-stable1"></div> | |
| </div> | |
| <div class="panel"> | |
| <div class="panel-head"><h3>Full-split runs (in progress)</h3><span class="badge full">full split · 591 prompts</span></div> | |
| <div class="chart" id="c-stable2"></div> | |
| </div> | |
| <h2>DS-1000 results — held-out quality (weighted correctness)</h2> | |
| <p>WACC = weight-averaged <em>test-passing</em> rate — the metric we report (the training potential is only | |
| “runs without error”). Held-out 12 prompts from the trained libraries; ±≈0.03–0.08 noise, read directionally.</p> | |
| <div class="grid2"> | |
| <div class="panel"> | |
| <div class="panel-head"><h3>10-prompt arms</h3><span class="badge">probe · 10 prompts</span></div> | |
| <p class="expl">Blue vs green = the <b>same reference run</b> (all fixes) under two eval regimes: training helps | |
| under both, the SMC-vs-plain gap is small, and the plain-sampling eval gains <em>more</em> — the fine-tuned | |
| proposal needs less of SMC's help. Magenta = a <b>different training run</b>: the ess=0.9 <em>ablation</em> | |
| (resampling ON during training, i.e. corrupted weights, held stable only by the KL anchor), evaluated with SMC — | |
| it reaches similar WACC here, the honest null: at this scale, anchored training survives resampling's | |
| weight corruption without measurable quality loss.</p> | |
| <div class="chart" id="c-eval1"></div> | |
| </div> | |
| <div class="panel"> | |
| <div class="panel-head"><h3>Offline SFT (tempered) — WACC vs particle budget</h3><span class="badge full">full 254 eval prompts</span></div> | |
| <p class="expl">The efficiency curve on the complete eval set (R=3, clean weights): single-sample WACC jumps | |
| 0.184 → 0.278 after SFT (+51% relative), and SFT at M=1 nearly matches the base model at M=5 — the | |
| fine-tuned proposal replaces most of a 5× particle budget. At large M the two converge: SMC with enough | |
| particles saturates what the base already knows. All 254 prompts, complete grid; the final checkpoint | |
| (iter 10400) is indistinguishable from 7500 — the SFT is converged.</p> | |
| <div class="chart" id="c-eval2"></div> | |
| </div> | |
| </div> | |
| <div class="panel"> | |
| <div class="panel-head"><h3>Full-split TRAINING (591 prompts) — quick-eval on 18 held-out prompts</h3> | |
| <span class="badge full">eval set: 18 held-out (3 × 6 libraries) · M=10 — the cheap monitoring set; full-254 results in the panel below</span></div> | |
| <p class="expl">Plain-sampling eval (ess=0) — the proposal-quality metric. Both stable arms improve | |
| (validity +30% rel., pass@1 +0.05–0.06 behind these numbers); the SMC-eval (ess=0.9) curve is flat at the | |
| posterior value as expected (shown dashed where complete). Early: iter 2000 ≈ 3.4 epochs of 591 prompts — | |
| the 10-prompt probes needed ~100 epochs to peak. Frozen ≈ seeded, within 18-prompt noise. | |
| Red dash: the skyline on these 18 prompts — <span class="math">E<sub>σ</sub></span>[correct] = 0.244 [0.148, 0.347].</p> | |
| <div class="chart" id="c-eval3"></div> | |
| </div> | |
| <div class="panel"> | |
| <div class="panel-head"><h3>FULL EVAL SET (254 prompts) — WACC and pass@K</h3> | |
| <span class="badge full">full 254 · plain IS · M=10 · R=5 (50 iid draws/prompt)</span></div> | |
| <p class="expl">The main-table numbers. <b>Left/top:</b> WACC (macro mean, prompt-bootstrap CIs) against the | |
| skyline — the posterior ceiling <span class="math">E<sub>σ</sub></span>[correct] = 0.323 [0.284, 0.363] on these | |
| exact 254 prompts (macro; pooled ratio-of-sums variant: 0.377 [0.331, 0.422]; 59/254 prompts have zero | |
| correct-given-valid base samples). Base already sits at 0.302: the no_error target leaves ~2 points of total | |
| headroom, and both trained arms land <em>inside the skyline CI</em> — the target, not the optimizer, binds. | |
| <b>Bottom:</b> pass@K of the proposal itself (ess=0 particles are iid draws; unbiased estimator, identical | |
| prompts for all arms). pass@1 rises 0.192 → 0.266 (+39% rel.) at ~3.4 epochs, β=0.02 > β=0.1 at every K, | |
| The deep-k picture (two independently-seeded waves pooled, 150 draws/prompt, 222 common prompts — one shard | |
| refilling): EARLY checkpoints (@2000) dominate at small k (pass@1 +43% rel.) but converge to a statistical tie | |
| with base by k≈100–150. The PEAK checkpoint changes the story: β=0.1 @10000 dominates base at EVERY k — | |
| pass@50 0.715 vs 0.687, pass@100 0.766 vs 0.739, pass@150 0.793 vs 0.770 (+2.3 pts ≈ 5 prompts). Contrast | |
| with RLVR (Yue et al. 2504.13837, significant cross-under at large k on code): at its peak, stabilized | |
| inclusive-KL training shows coverage GAIN through the deepest k measured — concentration and coverage, | |
| not a trade. NEW: the CORRECTNESS-potential arm peaks at WACC 0.349 / pass@1 0.323 at iter 3000 — the FIRST | |
| arm above the no_error skyline, and the best pass@1 measured (+68% rel. over base) — then declines | |
| (0.319/0.247 @7000) as the training-batch solve rate collapses; see the per-epoch chart below. | |
| Recipe: identical to the β=0.02 no_error arm — KL anchor β=0.02 to the base model, ess=0, tempered, | |
| frozen Z — plus the terminal reward (real DS-1000 test execution lands multiplicatively on the weights, | |
| so the target is p·φ_noerr·correct) and Z re-measured for THIS target (frozen at P(valid AND correct), | |
| mean 0.195 — freezing the no_error rates would miscalibrate the baseline b(x)=Z(x) by ~2.5×). The anchor | |
| matters twice here: correctness is a much sparser reward (Ẑ≈0.2 vs ~0.5), and sparser targets have higher | |
| stability thresholds (the LCB lesson) — β=0.02 holds. Its own skyline is 1.0, not 0.323: the richer target | |
| raises the ceiling, and the probe-scale degradation was the 10-prompt memorization artifact. | |
| Trajectory: complete 254-prompt cells at iters 1000–7000 are plotted; @4000/@6000 cells still partial. The red dash on the pass@1 | |
| group marks the skyline: the pass@1 of a proposal that exactly matches the posterior; both trained arms are | |
| below it here (0.266 vs 0.323), i.e. concentration has not yet reached posterior sharpness at 3.4 epochs.</p> | |
| <div class="chart" id="c-eval6a"></div> | |
| <div class="chart" id="c-eval6b"></div> | |
| </div> | |
| <div class="panel"> | |
| <div class="panel-head"><h3>Full-split trajectories — the anchor trades speed for peak</h3> | |
| <span class="badge full">full 254 · plain IS · one checkpoint per point</span></div> | |
| <p class="expl">Both anchor strengths show the same rise–peak–decline shape; β sets the timing and the height. | |
| β=0.02 peaks at ~iter 2000 (pass@1 0.266) then recedes; β=0.1 needs 10,000 iterations to reach the same pass@1 | |
| but at a higher peak everywhere else — WACC 0.324 (touching the skyline 0.323) and pass@50 0.717 (+4.8 over base, | |
| a genuine support gain) — before receding by 19,500. Practical rule: weak anchor if compute-limited, strong | |
| anchor + checkpoint selection if not. Frozen-Z at iter 2000 posts the highest pass@1 of any measured cell (0.283, on 222/254 prompts — one shard | |
| pending) before declining by 3500 — same shape, possibly a slightly higher/earlier peak than seeded. | |
| Curves now show the full peak-bracketing resolution (β=0.02 every 500 iters around its peak; β=0.1 every | |
| 2000–3000 across its rise and decline).</p> | |
| <div class="chart" id="c-eval7a"></div> | |
| <div class="chart" id="c-eval7b"></div> | |
| </div> | |
| <div class="panel"> | |
| <div class="panel-head"><h3>Correctness potential at probe scale — richer target ≠ free lunch</h3> | |
| <span class="badge">probe · 10 prompts · held-out 12 · plain IS</span></div> | |
| <p class="expl">Training against the CORRECTNESS potential (terminal test execution; skyline = 1 by construction) | |
| at 10-prompt scale briefly matches the no_error arm (0.300 @500) then <em>degrades</em> to the held-out asymptote | |
| (~0.24) and stays there — while the no_error arm (β=0.02) holds its 0.30 plateau to 12,500. Interpretation: | |
| with 10 prompts and a sparse reward (Ẑ≈0.2), the correctness signal teaches the solutions to those ten problems | |
| (memorization) instead of a transferable skill. The full-split correctness arm (591 prompts, running) is the | |
| decisive test — the memorization shortcut doesn't exist at that scale.</p> | |
| <div class="chart" id="c-eval8"></div> | |
| <p class="expl">Below: the FULL-SPLIT correctness arm's <b>training-batch solve rate</b> (windowed mean of | |
| #particles passing valid∧correct per batch; dashed line = the frozen-Z prior rate 0.195, i.e. the base | |
| model's solve rate on the training prompts). Because the terminal reward measures correctness directly, | |
| this curve aggregates each FULL EPOCH (591 iterations = every training prompt exactly once), so points are | |
| directly comparable — no prompt-composition noise. The full β=0.02 arc is now a rise–collapse–RECOVERY: 0.161 → 0.188 (epoch 7, iters ~3500–4100, | |
| matching the held-out peak WACC 0.349 @3000), then a full collapse to 0.000 for four consecutive epochs | |
| (10–13, iters ~5300–7700 — the held-out decline to 0.319 @7000 is this collapse seen from outside), then a | |
| spontaneous recovery back to 0.169 by epoch 17. The recovery is the anchor doing its job: β=0.02 keeps the | |
| collapsed proposal within reach of the prior, so coverage regrows — unanchored arms never come back. | |
| The β=0.1 twin runs lower and slower, had a shallow dip at epochs 3–4, and is climbing steadily: | |
| 0.079 → 0.105 at epoch 10, still rising. (A prior-rate | |
| reference line is omitted for now: the rollout-measured prior (0.195) used 400-token generations vs the | |
| run\u2019s 200, so it is not directly comparable — a truncation-matched prior can be added later.)</p> | |
| <div class="chart" id="c-eval8b"></div> | |
| </div> | |
| <div class="panel"> | |
| <div class="panel-head"><h3>Improvement probes — best held-out WACC per arm</h3> | |
| <span class="badge">probe · 10 prompts · held-out 12</span></div> | |
| <p class="expl">Single-knob variants of the reference recipe, best checkpoint each (iters ≤ 2000, except the frozen-Z twin | |
| whose checkpoint cadence is 2000/4000/6000 — its best is iter 4000). Whiskers: 95% CI from the macro-mean prompt | |
| bootstrap over the 12 held-out prompts (base model: plain [0.093, 0.312], SMC [0.077, 0.296]). With only 12 prompts | |
| the intervals are wide and all arms overlap each other and the base model — this panel ranks the knobs; the | |
| full-split runs are the confirmatory eval. | |
| Dashed lines mark the base model (iteration 0) on the same held-out 12 (plain 0.195, SMC-eval 0.178 — every arm clears both) | |
| and the skyline: the posterior ceiling <span class="math">E<sub>σ</sub></span>[correct] on these exact 12 prompts, 139/594 = 0.234 | |
| (pooled ratio-of-sums; bootstrap 95% CI [0.116, 0.346]; 4 of 12 prompts have zero correct-given-valid base samples). | |
| β=0.02's plain 0.304 sits ABOVE the skyline — legitimate at finite M: plain-eval WACC at M=10 is proposal-dependent, | |
| and a proposal with more correct mass than the posterior beats the asymptote. The no_error target is exhausted here. | |
| Weakening the anchor to β=0.02 is the biggest single win — its plain-sampling WACC (0.304) sits essentially at the | |
| no_error target's posterior ceiling (≈0.31 measured on the full 254), i.e. this target is nearly exhausted; further | |
| gains need the correctness potential. Warm-starting from the offline SFT (anchored to the init, not the base) gives | |
| the best SMC-eval number and composes offline + online training.</p> | |
| <div class="chart" id="c-eval4"></div> | |
| </div> | |
| <div class="panel"> | |
| <div class="panel-head"><h3>β=0.02 — per-prompt held-out improvement</h3> | |
| <span class="badge">best ckpt vs base · held-out 12 · mean of R=10, M=10</span></div> | |
| <p class="expl">Where the gain actually lives. Red dashes: each prompt's own skyline — | |
| <span class="math">E<sub>σ</sub></span>[correct] measured on its base rollouts (correct-given-valid, N=100). | |
| The four zero-skyline prompts (319, 388, 42, 799) stay at ≈0 after fine-tuning: training toward the posterior | |
| cannot manufacture correctness the posterior does not contain (799's +0.012 is the lone exception — the tuned | |
| proposal occasionally emits a correct program the base produced 0/100 times). All macro improvement comes from | |
| the 8 solvable prompts, and on 5 of them the tuned proposal ends <em>above its own skyline</em> in plain eval | |
| (909: 0.771 vs 0.667; 770: 0.624 vs 0.297) — legitimate at finite M, where WACC is proposal-dependent. | |
| Regressions are mild and mode-specific: 728 in plain (started far above its skyline), 875 in SMC eval.</p> | |
| <div class="chart" id="c-eval5a"></div> | |
| <div class="chart" id="c-eval5b"></div> | |
| </div> | |
| <div class="panel"> | |
| <div class="panel-head"><h3>Sample generations — what the models actually write</h3> | |
| <span class="badge">verbatim particles from training logs (per_particle.jsonl)</span></div> | |
| <p class="expl">One representative particle per regime. The collapse row is not an illustration — | |
| it is the verbatim output of the unanchored run after the entropy ratchet completes.</p> | |
| <div class="tblwrap"><table> | |
| <tr><th>model state</th><th>verdict</th><th>sample</th><th>reading</th></tr> | |
| <tr><td style="white-space:nowrap">base model (iter 50)</td><td>invalid (KeyError at run time)</td><td><pre style="margin:0;font-size:11.5px;white-space:pre-wrap;max-width:52ch;overflow-x:auto">df = df.set_index('dt') | |
| all_dates = pd.date_range(df.index.min(), df.index.max()) | |
| result = df.groupby('user').val.reindex(all_dates, fill_value=233) | |
| result = result.to_frame(name='val').reset_index() | |
| df = df.set_index('user') | |
| result = pd.concat([df,result])</pre></td><td style="max-width:34ch">plausible-looking pandas that crashes — the typical base failure the no_error target trains away</td></tr> | |
| <tr><td style="white-space:nowrap">IGUANA β=0.02 (iter 7200)</td><td>valid + correct</td><td><pre style="margin:0;font-size:11.5px;white-space:pre-wrap;max-width:52ch;overflow-x:auto">result = [tf.compat.as_text(x_) for x_ in x]</code></pre></td><td style="max-width:34ch">terse, well-formed, terminates properly — what a good proposal sample looks like</td></tr> | |
| <tr><td style="white-space:nowrap">IGUANA β=0.02 (iter 7350)</td><td>valid but wrong</td><td><pre style="margin:0;font-size:11.5px;white-space:pre-wrap;max-width:52ch;overflow-x:auto">result = (df | |
| .apply(pd.Series.value_counts) | |
| .unstack() | |
| .astype(float) | |
| )</pre></td><td style="max-width:34ch">clean, runnable, wrong semantics — the gap no_error cannot see (motivates the correctness potential)</td></tr> | |
| <tr><td style="white-space:nowrap">NO ANCHOR, post-collapse (iter 950)</td><td>degenerate</td><td><pre style="margin:0;font-size:11.5px;white-space:pre-wrap;max-width:52ch;overflow-x:auto">!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!</pre></td><td style="max-width:34ch">every particle, every prompt: the proposal collapsed to a delta on a single token — q→δ, literally</td></tr> | |
| </table></div> | |
| </div> | |
| <h2>LiveCodeBench — the anchor A/B at full scale</h2> | |
| <div class="panel"> | |
| <div class="panel-head"><h3>Online IGUANA on LCB: the anchor delays but β=0.02 does not prevent collapse</h3> | |
| <span class="badge full">full 500-prompt split · Qwen3-4B nothink · strict-public × noerror target · frozen measured Z</span></div> | |
| <p class="expl">A controlled simultaneous A/B on the binding LCB target (strict public tests; prior rate <span class="math">Z̄</span>=0.51): identical recipes, hardware, and data — the ONLY difference is the KL anchor. The unanchored arm collapses by iter ~300; the β=0.02 arm survives to ~600 and then collapses too, sitting degenerate through iter 2800+ with no recovery — support abandonment is absorbing in both. UPDATED CONCLUSION: the anchor <em>delays</em> collapse, but the required dose is TARGET-DEPENDENT — on DS-1000 (denser reward, 200-token sequences) the stability boundary is β ∈ (0.005, 0.01]; on LCB-strict (many Ẑ≈0.03 prompts, 1024-unit sequences → faster KL drift) it lies above 0.02. This matches the support-floor scaling (floor ∝ e<sup>−1/β</sup> against a domain-dependent leak rate). RESOLVED: the β=0.1 arm is STABLE AND LEARNING — validity climbs from the prior rate 0.51 to ~0.55 by iter 1000, through both predecessors’ death windows, with calm gradients. The LCB stability threshold sits in (0.02, 0.1]; held-out evals of its checkpoints are running. (Earlier LCB probes: bare arm died @~700, frozen-Z-only @~300.)</p> | |
| <div class="chart" id="c-lcb1"></div> | |
| <div class="chart" id="c-lcb2"></div> | |
| </div> | |
| <h2>Run inventory (DS-1000)</h2> | |
| <p class="expl" style="max-width:90ch">Shared config across all runs: Qwen2.5-Coder-3B, LoRA (r=8, all layers), | |
| ipi=1, M=10, temp 0.6, lr 1e-5, max_tokens 200 — and the three clipping layers: | |
| <span class="math">uw</span>-clip 1.0 (truncated IS), weight cap <span class="math">w<sub>m</sub></span> ∈ ±0.5, | |
| gradient norm-clip 1.0. Rows differ only by the knob listed.</p> | |
| <div class="tblwrap"><table> | |
| <tr><th>panel</th><th>run (red = mode active)</th><th>scale</th><th>differs from fixed run by</th><th>outcome</th></tr> | |
| <tr><td>Mode 1</td><td class="math">ftnoseed</td><td>full split</td><td>no Z-seeding (running Z, exclude-current)</td><td>boundary break @591, degraded plateau</td></tr> | |
| <tr><td>Mode 1 fix</td><td class="math">ftfix</td><td>full split</td><td>—</td><td>stable through boundary, gnorm ~29</td></tr> | |
| <tr><td>Mode 2</td><td class="math">mode2</td><td>10 prompts</td><td>resampling on (ess=0.9), no anchor, frozen Z</td><td>negative loss from iter 1, dead @~180</td></tr> | |
| <tr><td>Mode 3</td><td class="math">zfrozen_nokl</td><td>10 prompts</td><td>no KL anchor</td><td>climb 0.85 → crater @~160</td></tr> | |
| <tr><td>Modes 2–3 fix</td><td class="math">reference</td><td>10 prompts</td><td>—</td><td>7,538 iters flat, +0.05 held-out WACC</td></tr> | |
| </table></div> | |
| <h2>Training status — all arms stopped (2026-08-04): analysis & eval phase</h2> | |
| <p class="expl" style="max-width:90ch">Every full-scale training is now concluded or deliberately paused; the GPU | |
| allocation goes entirely to draining the eval backlog. All checkpoints and resume bundles are intact — any | |
| "paused" arm can be resumed. A new <b>plain-IS fast runner</b> (same command line, <span class="math">--estep is</span>) | |
| replaces the genlm SMC E-step with one direct vLLM generate + two scoring forwards | |
| (<span class="math">uw = r·p_T/q_T</span> on the training model, adapter on/off): since ess=0 runs never resample, | |
| the SMC machinery was pure overhead. The IGUANA weight math is the same shared code, so resumed/re-run arms stay | |
| comparable; expected ~3–4× per iteration (smoke run validating).</p> | |
| <div class="tblwrap"><table> | |
| <tr><th>arm</th><th>target · β</th><th>final iter</th><th>state</th><th>why stopped</th></tr> | |
| <tr><td class="math">ftb002</td><td>no_error · 0.02</td><td class="num">3,700</td><td>retired</td><td>peak @~1500–2000 mapped, declining after</td></tr> | |
| <tr><td class="math">ftzfb002</td><td>no_error · 0.02, frozen-Z</td><td class="num">3,500</td><td>retired</td><td>frozen ≈ seeded confirmed; best pass@1 @2000 then decline</td></tr> | |
| <tr><td class="math">ftb01</td><td>no_error · 0.1</td><td class="num">19,500</td><td>complete</td><td>full rise–peak–decline mapped; peak @10000 touches the skyline</td></tr> | |
| <tr><td class="math">ftb001 / ftb0005</td><td>no_error · 0.01 / 0.005</td><td class="num">—</td><td>retired</td><td>dose-response probes: stability boundary β ∈ (0.005, 0.01]</td></tr> | |
| <tr><td class="math">ftcor</td><td>correctness · 0.02</td><td class="num">10,164</td><td>complete (ALLDONE)</td><td>full rise–collapse–recovery arc captured (per-epoch chart above)</td></tr> | |
| <tr><td class="math">ftcor01</td><td>correctness · 0.1</td><td class="num">5,934</td><td><b>paused</b></td><td>training solve rate still climbing @epoch 10 — resumable (on the fast runner) if the pending @2000/@4000 evals warrant</td></tr> | |
| <tr><td class="math">lcb offline</td><td>LCB both-filter BC</td><td class="num">9,609</td><td>complete</td><td>3 epochs over 25,624 rejection-sampled rollouts</td></tr> | |
| <tr><td class="math">lcb noanchor / kl002</td><td>LCB strict · 0 / 0.02</td><td class="num">3,325 / 2,825</td><td>complete</td><td>both collapsed (@~600) — the LCB threshold sits ABOVE β=0.02</td></tr> | |
| <tr><td class="math">lcb kl01</td><td>LCB strict · 0.1</td><td class="num">5,365</td><td>stopped</td><td>stable & learning (validity 0.51→0.56 @2000); trajectory sufficient for the ladder, checkpointed through 5365</td></tr> | |
| </table></div> | |
| <footer> | |
| Sources: training logs on Euler (per-iteration validity / grad norm / loss), eval sweeps on held-out prompts. Grad norm is the PRE-clip raw norm (running mean, window 20): every applied update is clipped to norm 1, so large values show what the loss demanded, not the step taken — clipping bounds magnitude but keeps the (pathological) direction, which is why it cannot prevent these collapses. | |
| (R=10, M=10, temp 0.6 unless noted). Mode-2 validity is post-resampling (inflated by construction). | |
| Old dashboard offline numbers predate the eval-stack fix; the offline numbers here are regenerated. | |
| Static figure: plots/mode_collapse_figure.png in the repo. | |
| </footer> | |
| </div> | |
| <script> | |
| const TRAJ = 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| |
| const EVALS = {"tenprompt":{"iters":[0,1000,2000,3000],"series":[{"name":"reference \u2014 SMC eval (ess=0.9)","wacc":[0.178,0.23,0.206,0.22]},{"name":"reference \u2014 plain eval (ess=0)","wacc":[0.195,0.246,0.239,0.234]}],"extra":{"name":"trained WITH resampling (ess=0.9)","iters":[0,500,1000,1500],"wacc":[0.178,0.22,0.214,0.228]}},"fullsplit18":{"iters":[0,1000,2000],"ftfix_e0":[0.192,0.191,0.209],"ftzf_e0":[0.189,0.169,0.199],"ftfix_e09":{"iters":[0,2000],"wacc":[0.204,0.193]}},"probes":{"arms":["reference (\u03b2=0.1)","\u03b2=0.02","warm-start SFT","lr 3e-5","frozen-Z twin"],"plain":[0.246,0.304,0.277,0.268,0.229],"smc":[0.23,0.25,0.282,0.22,0.244],"plain_ci":[[0.114,0.391],[0.156,0.453],[0.133,0.426],[0.113,0.437],[0.1,0.367]],"smc_ci":[[0.103,0.37],[0.115,0.392],[0.133,0.442],[0.08,0.379],[0.097,0.413]],"base_plain":0.195,"base_smc":0.178,"skyline":0.234,"skyline_ci":[0.116,0.346]},"probespp":{"ids":["319","388","476","42","137","248","838","875","909","728","770","799"],"libs":["Numpy","Numpy","Numpy","Pandas","Pandas","Pandas","Sklearn","Sklearn","Sklearn","Scipy","Scipy","Scipy"],"skyline":[0.0,0.0,0.44,0.0,0.397,0.181,0.5,0.143,0.667,0.471,0.297,0.0],"plain_base":[0.0,0.0,0.1,0.0,0.276,0.17,0.293,0.158,0.4,0.665,0.284,0.0],"plain_best":[0.0,0.0,0.208,0.0,0.478,0.222,0.5,0.309,0.771,0.528,0.624,0.012],"smc_base":[0.0,0.0,0.23,0.0,0.196,0.12,0.189,0.08,0.53,0.61,0.18,0.0],"smc_best":[0.0,0.0,0.332,0.0,0.365,0.176,0.36,0.0,0.69,0.656,0.425,0.0]},"offline254":{"M":[1,5,10],"base":[0.184,0.297,0.306],"sft7500":[0.278,0.295,0.305]},"full254":{"arms":["base model","IGUANA \u03b2=0.1 @2000","IGUANA \u03b2=0.02 @2000","IGUANA \u03b2=0.1 @10000","IGUANA CORRECTNESS @2000"],"wacc":[0.302,0.314,0.315,0.324,0.34],"wacc_ci":[[0.262,0.343],[0.272,0.357],[0.275,0.356],[0.284,0.365],[0.297,0.384]],"skyline":0.323,"skyline_ci":[0.284,0.363],"ks":[1,5,10,50],"passk":{"base model":[0.192,0.427,0.52,0.669],"IGUANA \u03b2=0.1 @2000":[0.248,0.476,0.552,0.677],"IGUANA \u03b2=0.02 @2000":[0.266,0.493,0.567,0.681],"IGUANA \u03b2=0.1 @10000":[0.266,0.502,0.582,0.717],"IGUANA CORRECTNESS @2000":[0.297,0.513,0.581,0.681]}},"sky18":{"v":0.244,"ci":[0.148,0.347]},"traj254":{"b01":{"it":[0,2000,4000,6000,8000,10000,12000,15000,19500],"p1":[0.192,0.248,0.241,0.248,0.253,0.266,0.261,0.259,0.257],"wacc":[0.302,0.314,0.302,0.306,0.302,0.324,0.309,0.311,0.313]},"b002":{"it":[0,1000,1500,2000,2500,3000,3700],"p1":[0.192,0.253,0.269,0.266,0.264,0.258,0.257],"wacc":[0.302,0.314,0.319,0.315,0.31,0.304,0.299]},"b002zf":{"it":[2000,3500],"p1":[0.283,0.24],"wacc":[0.316,0.292]},"cor":{"it":[0,1000,2000,3000,5000,7000],"p1":[0.192,0.287,0.297,0.323,0.292,0.247],"wacc":[0.302,0.325,0.34,0.349,0.34,0.319]}},"corprobe":{"cor":{"it":[500,1000,2000,5000,10000],"wacc":[0.3,0.239,0.234,0.236,0.242]},"kl002":{"it":[0,500,1000,1500,3000,4500,6000,8000,10000,12500],"wacc":[0.195,0.304,0.304,0.27,0.266,0.281,0.3,0.279,0.278,0.302]},"sky12":0.234},"offline":{"iters":[0,2500,5000,7500,10000],"M":{"1":{"tempered":[0.075,0.15,0.15,0.167,0.183]},"5":{"tempered":[0.177,0.271,0.196,0.216,0.226]},"10":{"tempered":[0.178,0.203,0.196,0.233,0.231]}}},"lcbrace":{"noanchor":{"it":[25,50,75,100,125,150,175,200,225,250,275,300,325,350,375,400,425,450,475,500,525,550,575,600,625,650,675,700,725,750,775,800,825,850,875,900,925,950,975,1000,1025,1050,1075,1100,1125,1150,1175,1200,1225,1250,1275,1300,1325,1350,1375,1400,1425,1450,1475,1500,1525,1550,1575,1600,1625,1650,1675,1700,1725,1750,1775,1800,1825,1850,1875,1900,1925,1950,1975,2000,2025,2050,2075,2100,2125,2150,2175,2200,2225,2250,2275,2300,2325,2350,2375,2400,2425,2450,2475,2500,2525,2550,2575,2600,2625,2650,2675,2700,2725,2750,2775,2800,2825,2850,2875,2900,2925,2950,2975,3000,3025,3050,3075,3100,3125,3150,3175,3200,3225,3250,3275,3300,3325],"valid":[0.492,0.46,0.372,0.62,0.424,0.416,0.18,0.0,0.0,0.0,0.0,0.02,0.008,0.024,0.28,0.22,0.128,0.116,0.048,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0],"gnorm":[190.9,173.9,260.6,249.0,348.3,578.1,466.8,184.2,682.8,1557.9,855.4,1373.6,362.6,371.0,770.0,819.9,450.7,1187.9,821.0,2517.6,1233.7,585.6,368.2,353.1,440.7,246.3,302.7,371.3,196.8,130.5,124.6,144.2,144.1,76.1,117.4,90.5,77.1,101.9,79.5,82.5,61.6,64.9,75.6,41.2,48.0,43.8,45.6,42.3,47.5,55.6,39.6,31.1,31.2,36.4,43.3,28.8,50.0,31.0,43.3,60.5,52.8,50.8,31.4,31.1,24.8,32.0,30.4,29.2,34.9,29.5,22.4,24.4,27.0,35.3,36.5,26.2,28.0,20.4,25.5,23.2,22.4,14.8,34.0,38.2,28.4,29.4,31.5,16.9,20.8,23.7,24.2,24.6,22.3,19.8,23.1,26.1,18.9,25.2,27.2,26.4,35.7,20.2,21.7,26.2,17.9,18.3,16.8,17.3,14.8,15.3,13.9,18.9,18.4,20.0,16.9,16.8,16.2,19.5,20.5,16.8,18.5,15.2,18.9,24.3,19.2,20.5,22.5,23.0,12.4,15.6,23.5,21.0,13.9]},"kl002":{"it":[25,50,75,100,125,150,175,200,225,250,275,300,325,350,375,400,425,450,475,500,525,550,575,600,625,650,675,700,725,750,775,800,825,850,875,900,925,950,975,1000,1025,1050,1075,1100,1125,1150,1175,1200,1225,1250,1275,1300,1325,1350,1375,1400,1425,1450,1475,1500,1525,1550,1575,1600,1625,1650,1675,1700,1725,1750,1775,1800,1825,1850,1875,1900,1925,1950,1975,2000,2025,2050,2075,2100,2125,2150,2175,2200,2225,2250,2275,2300,2325,2350,2375,2400,2425,2450,2475,2500,2525,2550,2575,2600,2625,2650,2675,2700,2725,2750,2775,2800,2825],"valid":[0.512,0.488,0.356,0.652,0.468,0.508,0.516,0.468,0.5,0.56,0.528,0.084,0.08,0.092,0.0,0.036,0.072,0.016,0.0,0.0,0.0,0.0,0.0,0.012,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0],"gnorm":[184.1,169.3,118.7,100.0,158.8,171.9,265.9,414.0,551.1,908.7,8570.8,23960.0,29142.4,16865.5,16849.0,13416.9,24575.5,22609.0,14274.6,14167.4,11068.4,7915.3,8620.8,6761.2,3041.9,15474.7,14930.1,30342.9,3926.4,1264.2,116.7,115.7,59.7,29.3,60.3,57.5,49.2,37.1,32.0,39.6,31.7,47.1,39.7,27.1,16.9,38.4,30.9,36.2,19.3,32.4,49.0,12.7,16.8,29.7,31.4,16.7,27.4,18.9,16.1,19.9,20.6,18.8,13.9,14.9,15.1,17.0,17.0,16.6,9.3,26.7,14.9,17.8,18.2,15.9,12.1,11.6,12.0,13.8,11.9,13.2,10.3,10.0,13.7,10.1,8.4,9.6,9.3,7.7,8.8,6.2,12.9,15.8,6.3,10.9,11.3,8.4,10.3,9.8,16.9,10.5,11.7,9.6,10.3,13.1,8.6,8.7,7.4,6.6,9.4,15.6,13.7,16.6,3.7]},"kl01":{"it":[25,50,75,100,125,150,175,200,225,250,275,300,325,350,375,400,425,450,475,500,525,550,575,600,625,650,675,700,725,750,775,800,825,850,875,900,925,950,975,1000,1025,1050,1075],"valid":[0.52,0.468,0.36,0.66,0.448,0.488,0.52,0.5,0.468,0.576,0.564,0.408,0.564,0.516,0.52,0.468,0.484,0.724,0.592,0.38,0.46,0.516,0.436,0.412,0.532,0.48,0.516,0.548,0.58,0.4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| |
| const css = v => getComputedStyle(document.documentElement).getPropertyValue(v).trim(); | |
| function roll(arr, w) { | |
| if (w <= 1) return arr.slice(); | |
| const n = arr.length, half = Math.floor(w / 2), out = new Array(n); | |
| let sum = 0, cnt = 0; | |
| // prefix sums with null-safety | |
| const vals = arr.map(v => (v == null || isNaN(v)) ? null : v); | |
| for (let i = 0; i < n; i++) { | |
| let s = 0, c = 0; | |
| for (let j = Math.max(0, i - half); j <= Math.min(n - 1, i + half); j++) { | |
| if (vals[j] != null) { s += vals[j]; c++; } | |
| } | |
| out[i] = c ? s / c : null; | |
| } | |
| return out; | |
| } | |
| function makeChart(el, seriesList, opts) { | |
| // seriesList: [{x, y, color, label}] ; opts: {log, boundary, ylab, y0, xmax} | |
| const W = 860, H = 300, ML = 56, MR = 120, MT = 12, MB = 30; | |
| const iw = W - ML - MR, ih = H - MT - MB; | |
| const xs = seriesList.flatMap(s => s.x); | |
| const xmax = opts.xmax || Math.max(...xs), xmin = opts.xstart || 0; | |
| let ys = seriesList.flatMap(s => s.y).filter(v => v != null && isFinite(v)); | |
| if (opts.log) ys = ys.filter(v => v > 0); | |
| let ymin = opts.y0 != null ? opts.y0 : Math.min(...ys), ymax = Math.max(...ys); | |
| if (opts.log) { ymin = Math.max(1, Math.min(...ys)); } | |
| if (ymax === ymin) ymax = ymin + 1; | |
| const X = v => ML + (v - xmin) / (xmax - xmin) * iw; | |
| const Y = v => { | |
| if (opts.log) return MT + ih - (Math.log10(Math.max(v, ymin)) - Math.log10(ymin)) / | |
| (Math.log10(ymax) - Math.log10(ymin)) * ih; | |
| return MT + ih - (v - ymin) / (ymax - ymin) * ih; | |
| }; | |
| const svgns = "http://www.w3.org/2000/svg"; | |
| const svg = document.createElementNS(svgns, "svg"); | |
| svg.setAttribute("viewBox", `0 0 ${W} ${H}`); | |
| // grid + y ticks | |
| const ticks = opts.log | |
| ? [...Array(9)].map((_, i) => Math.pow(10, Math.ceil(Math.log10(ymin)) + i)).filter(t => t >= ymin && t <= ymax) | |
| : [...Array(5)].map((_, i) => ymin + (ymax - ymin) * i / 4); | |
| for (const t of ticks) { | |
| const g = document.createElementNS(svgns, "line"); | |
| g.setAttribute("x1", ML); g.setAttribute("x2", ML + iw); | |
| g.setAttribute("y1", Y(t)); g.setAttribute("y2", Y(t)); | |
| g.setAttribute("stroke", css("--grid")); g.setAttribute("stroke-width", "1"); | |
| svg.appendChild(g); | |
| const lb = document.createElementNS(svgns, "text"); | |
| lb.setAttribute("x", ML - 8); lb.setAttribute("y", Y(t) + 4); | |
| lb.setAttribute("text-anchor", "end"); | |
| lb.setAttribute("fill", css("--muted")); lb.setAttribute("font-size", "11"); | |
| lb.textContent = opts.log ? t.toLocaleString() : (Math.abs(t) >= 100 ? t.toFixed(0) : t.toFixed(2)); | |
| svg.appendChild(lb); | |
| } | |
| // x axis | |
| const ax = document.createElementNS(svgns, "line"); | |
| ax.setAttribute("x1", ML); ax.setAttribute("x2", ML + iw); | |
| ax.setAttribute("y1", MT + ih); ax.setAttribute("y2", MT + ih); | |
| ax.setAttribute("stroke", css("--axis")); svg.appendChild(ax); | |
| const xtickvals = opts.xticks || [...Array(7)].map((_, i) => xmin + (xmax - xmin) * i / 6); | |
| for (const v of xtickvals) { | |
| const lb = document.createElementNS(svgns, "text"); | |
| lb.setAttribute("x", X(v)); lb.setAttribute("y", MT + ih + 18); | |
| lb.setAttribute("text-anchor", "middle"); | |
| lb.setAttribute("fill", css("--muted")); lb.setAttribute("font-size", "11"); | |
| lb.textContent = Math.round(v); | |
| svg.appendChild(lb); | |
| } | |
| // boundary marker | |
| if (opts.boundary && opts.boundary < xmax) { | |
| const b = document.createElementNS(svgns, "line"); | |
| b.setAttribute("x1", X(opts.boundary)); b.setAttribute("x2", X(opts.boundary)); | |
| b.setAttribute("y1", MT); b.setAttribute("y2", MT + ih); | |
| b.setAttribute("stroke", css("--muted")); b.setAttribute("stroke-dasharray", "3,4"); | |
| svg.appendChild(b); | |
| const bl = document.createElementNS(svgns, "text"); | |
| bl.setAttribute("x", X(opts.boundary) + 5); bl.setAttribute("y", MT + 12); | |
| bl.setAttribute("fill", css("--muted")); bl.setAttribute("font-size", "11"); | |
| bl.textContent = "epoch 2"; | |
| svg.appendChild(bl); | |
| } | |
| // series | |
| const labelYs = []; | |
| for (const s of seriesList) { | |
| let d = "", pen = false; | |
| for (let i = 0; i < s.x.length; i++) { | |
| if (s.x[i] > xmax) break; | |
| let v = s.y[i]; | |
| if (v == null || !isFinite(v) || (opts.log && v <= 0)) { pen = false; continue; } | |
| d += (pen ? "L" : "M") + X(s.x[i]).toFixed(1) + " " + Y(v).toFixed(1); | |
| pen = true; | |
| } | |
| const p = document.createElementNS(svgns, "path"); | |
| p.setAttribute("d", d); p.setAttribute("fill", "none"); | |
| p.setAttribute("stroke", s.color); p.setAttribute("stroke-width", "2"); | |
| p.setAttribute("stroke-linejoin", "round"); | |
| if (s.dash) p.setAttribute("stroke-dasharray", "6 4"); | |
| svg.appendChild(p); | |
| if (s.dots) { // point markers: sparse checkpoint series + single-point series stay visible | |
| for (let i = 0; i < s.x.length; i++) { | |
| if (s.x[i] > xmax) break; | |
| const v = s.y[i]; | |
| if (v == null || !isFinite(v) || (opts.log && v <= 0)) continue; | |
| const c = document.createElementNS(svgns, "circle"); | |
| c.setAttribute("cx", X(s.x[i])); c.setAttribute("cy", Y(v)); | |
| c.setAttribute("r", "4"); c.setAttribute("fill", s.color); | |
| svg.appendChild(c); | |
| } | |
| } | |
| // direct label at line end | |
| let li = s.x.findLastIndex((x, i) => x <= xmax && s.y[i] != null && isFinite(s.y[i]) && (!opts.log || s.y[i] > 0)); | |
| if (li >= 0) { | |
| const lb = document.createElementNS(svgns, "text"); | |
| lb.setAttribute("x", X(Math.min(s.x[li], xmax)) + 6); | |
| let ly = Y(s.y[li]) + 4; | |
| while (labelYs.some(v => Math.abs(v - ly) < 13)) ly += 13; // avoid overlap | |
| labelYs.push(ly); | |
| lb.setAttribute("y", ly); | |
| lb.setAttribute("fill", s.color); lb.setAttribute("font-size", "11.5"); | |
| lb.setAttribute("font-weight", "600"); | |
| lb.textContent = s.label; | |
| svg.appendChild(lb); | |
| } | |
| } | |
| // ylabel | |
| const yl = document.createElementNS(svgns, "text"); | |
| yl.setAttribute("x", 14); yl.setAttribute("y", MT + ih / 2); | |
| yl.setAttribute("transform", `rotate(-90 14 ${MT + ih / 2})`); | |
| yl.setAttribute("text-anchor", "middle"); yl.setAttribute("fill", css("--ink-2")); | |
| yl.setAttribute("font-size", "12"); | |
| yl.textContent = opts.ylab; | |
| svg.appendChild(yl); | |
| // crosshair + tooltip | |
| const cross = document.createElementNS(svgns, "line"); | |
| cross.setAttribute("y1", MT); cross.setAttribute("y2", MT + ih); | |
| cross.setAttribute("stroke", css("--axis")); cross.setAttribute("visibility", "hidden"); | |
| svg.appendChild(cross); | |
| el.innerHTML = ""; | |
| el.appendChild(svg); | |
| const tip = document.createElement("div"); | |
| tip.className = "tip"; | |
| el.appendChild(tip); | |
| svg.addEventListener("mousemove", ev => { | |
| const r = svg.getBoundingClientRect(); | |
| const fx = (ev.clientX - r.left) / r.width * W; | |
| if (fx < ML || fx > ML + iw) { tip.style.display = "none"; cross.setAttribute("visibility", "hidden"); return; } | |
| const xv = xmin + (fx - ML) / iw * (xmax - xmin); | |
| cross.setAttribute("x1", X(xv)); cross.setAttribute("x2", X(xv)); | |
| cross.setAttribute("visibility", "visible"); | |
| let html = `<div class="t-it">iter ${Math.round(xv)}</div>`; | |
| for (const s of seriesList) { | |
| // nearest index | |
| let lo = 0, hi = s.x.length - 1; | |
| while (hi - lo > 1) { const mid = (hi + lo) >> 1; (s.x[mid] < xv) ? lo = mid : hi = mid; } | |
| const i = (Math.abs(s.x[lo] - xv) < Math.abs(s.x[hi] - xv)) ? lo : hi; | |
| const v = s.y[i]; | |
| if (s.x[i] > xmax || v == null || !isFinite(v)) continue; | |
| html += `<div class="row"><span style="width:9px;height:9px;border-radius:50%;background:${s.color};display:inline-block"></span>` + | |
| `<span>${s.label}</span><span class="val">${Math.abs(v) >= 100 ? v.toFixed(0) : v.toFixed(3)}</span></div>`; | |
| } | |
| tip.innerHTML = html; | |
| tip.style.display = "block"; | |
| const px = (ev.clientX - r.left), py = (ev.clientY - r.top); | |
| tip.style.left = Math.min(px + 14, r.width - 190) + "px"; | |
| tip.style.top = Math.max(py - 20, 4) + "px"; | |
| }); | |
| svg.addEventListener("mouseleave", () => { tip.style.display = "none"; cross.setAttribute("visibility", "hidden"); }); | |
| } | |
| let smoothW = 15, metric = "valid"; | |
| const YLAB = { valid: "validity (no-error rate)", gnorm: "grad norm (pre-clip)", loss: "loss" }; | |
| function drawModes() { | |
| const specs = [ | |
| { el: "c-mode1", fail: "mode1_fail_ftnoseed", failLab: "no Z-seeding", | |
| fix: "mode1_fix_ftfix", fixLab: "seeded Z", xmax: 1400, boundary: 591 }, | |
| { el: "c-mode2", fail: "mode2_fail", failLab: "ess=0.9, no anchor", | |
| fix: "fix_reference", fixLab: "ess=0 reference", xmax: 320 }, | |
| { el: "c-mode3", fail: "mode3_fail_zfnokl", failLab: "no KL anchor", | |
| fix: "fix_reference", fixLab: "KL anchor (ref)", xmax: 320 }, | |
| ]; | |
| const stables = [ | |
| { el: "c-stable1", a: "stable_p10_seeded", aLab: "seeded Z (reference)", | |
| b: "stable_p10_frozen", bLab: "frozen Z" }, | |
| { el: "c-stable2", a: "stable_full_seeded", aLab: "seeded Z", | |
| b: "stable_full_frozen", bLab: "frozen Z" }, | |
| ]; | |
| for (const sp of stables) { | |
| const A = TRAJ[sp.a], B = TRAJ[sp.b]; | |
| const w = Math.max(1, smoothW); | |
| makeChart(document.getElementById(sp.el), [ | |
| { x: A.it, y: roll(A[metric], w), color: css("--fix"), label: sp.aLab }, | |
| { x: B.it, y: roll(B[metric], w), color: css("--s2"), label: sp.bLab }, | |
| ], { log: metric === "gnorm", ylab: YLAB[metric], y0: metric === "valid" ? 0 : null }); | |
| } | |
| for (const sp of specs) { | |
| const f = TRAJ[sp.fail], x = TRAJ[sp.fix]; | |
| const w = (sp.xmax > 1000) ? Math.max(1, smoothW) : Math.max(1, Math.min(smoothW, 21)); | |
| makeChart(document.getElementById(sp.el), [ | |
| { x: x.it, y: roll(x[metric], w), color: css("--fix"), label: sp.fixLab }, | |
| { x: f.it, y: roll(f[metric], w), color: css("--fail"), label: sp.failLab }, | |
| ], { log: metric === "gnorm", boundary: sp.boundary, ylab: YLAB[metric], | |
| y0: metric === "valid" ? 0 : null, xmax: sp.xmax }); | |
| } | |
| } | |
| function drawEval1() { | |
| const e = EVALS.tenprompt; | |
| makeChart(document.getElementById("c-eval1"), [ | |
| { x: e.iters, y: e.series[0].wacc, color: css("--fix"), label: "ref · SMC eval" }, | |
| { x: e.iters, y: e.series[1].wacc, color: css("--s2"), label: "ref · plain eval" }, | |
| { x: e.extra.iters, y: e.extra.wacc, color: css("--s3"), label: "ablation: trained w/ resampling (ess=0.9)" }, | |
| ], { ylab: "held-out WACC", y0: 0, xmax: 3000 }); | |
| } | |
| function makeBarChart(el, groups, seriesList, opts) { | |
| // groups: ["M=1",...]; seriesList: [{label, color, values[]}] | |
| const W = 860, H = 300, ML = 56, MR = 20, MT = 14, MB = opts.groupSub ? 46 : 34; | |
| const iw = W - ML - MR, ih = H - MT - MB; | |
| const ymax = Math.max(...seriesList.flatMap(s => s.values), | |
| ...seriesList.flatMap(s => (s.cis || []).map(c => c[1])), | |
| ...(opts.baselines || []).flatMap(b => b.ci ? [b.ci[1]] : [b.value])) * 1.12; | |
| const Y = v => MT + ih - v / ymax * ih; | |
| const svgns = "http://www.w3.org/2000/svg"; | |
| const svg = document.createElementNS(svgns, "svg"); | |
| svg.setAttribute("viewBox", `0 0 ${W} ${H}`); | |
| for (let i = 0; i <= 4; i++) { | |
| const tv = ymax * i / 4; | |
| const g = document.createElementNS(svgns, "line"); | |
| g.setAttribute("x1", ML); g.setAttribute("x2", ML + iw); | |
| g.setAttribute("y1", Y(tv)); g.setAttribute("y2", Y(tv)); | |
| g.setAttribute("stroke", css("--grid")); svg.appendChild(g); | |
| const lb = document.createElementNS(svgns, "text"); | |
| lb.setAttribute("x", ML - 8); lb.setAttribute("y", Y(tv) + 4); | |
| lb.setAttribute("text-anchor", "end"); lb.setAttribute("fill", css("--muted")); | |
| lb.setAttribute("font-size", "11"); lb.textContent = tv.toFixed(2); | |
| svg.appendChild(lb); | |
| } | |
| const ax = document.createElementNS(svgns, "line"); | |
| ax.setAttribute("x1", ML); ax.setAttribute("x2", ML + iw); | |
| ax.setAttribute("y1", MT + ih); ax.setAttribute("y2", MT + ih); | |
| ax.setAttribute("stroke", css("--axis")); svg.appendChild(ax); | |
| // dashed reference lines (e.g. base-model baseline), drawn under the bars | |
| for (const b of (opts.baselines || [])) { | |
| if (b.ci) { // translucent CI band behind everything | |
| const bd = document.createElementNS(svgns, "rect"); | |
| bd.setAttribute("x", ML); bd.setAttribute("width", iw); | |
| bd.setAttribute("y", Y(b.ci[1])); bd.setAttribute("height", Y(b.ci[0]) - Y(b.ci[1])); | |
| bd.setAttribute("fill", b.color); bd.setAttribute("opacity", "0.08"); | |
| svg.appendChild(bd); | |
| } | |
| const bl = document.createElementNS(svgns, "line"); | |
| bl.setAttribute("x1", ML); bl.setAttribute("x2", ML + iw); | |
| bl.setAttribute("y1", Y(b.value)); bl.setAttribute("y2", Y(b.value)); | |
| bl.setAttribute("stroke", b.color); bl.setAttribute("stroke-width", "1.6"); | |
| bl.setAttribute("stroke-dasharray", "6 4"); bl.setAttribute("opacity", "0.85"); | |
| svg.appendChild(bl); | |
| const bt = document.createElementNS(svgns, "text"); | |
| bt.setAttribute("x", ML + iw - 4); bt.setAttribute("y", Y(b.value) - 5); | |
| bt.setAttribute("text-anchor", "end"); bt.setAttribute("fill", b.color); | |
| bt.setAttribute("font-size", "11"); | |
| bt.textContent = `${b.label} ${b.value.toFixed(3)}`; | |
| svg.appendChild(bt); | |
| } | |
| const nG = groups.length, nS = seriesList.length; | |
| const groupW = iw / nG, barW = Math.min(56, (groupW * 0.62 - 2 * (nS - 1)) / nS); | |
| const tip = document.createElement("div"); tip.className = "tip"; | |
| groups.forEach((gname, gi) => { | |
| const cx = ML + groupW * (gi + 0.5); | |
| const total = nS * barW + 2 * (nS - 1); | |
| seriesList.forEach((s, si) => { | |
| const v = s.values[gi]; | |
| const x0 = cx - total / 2 + si * (barW + 2); | |
| const yTop = Y(v), r = Math.min(4, barW / 2, (MT + ih) - yTop); | |
| const p = document.createElementNS(svgns, "path"); // rounded TOP corners only | |
| p.setAttribute("d", `M${x0} ${MT + ih} L${x0} ${yTop + r} Q${x0} ${yTop} ${x0 + r} ${yTop}` + | |
| ` L${x0 + barW - r} ${yTop} Q${x0 + barW} ${yTop} ${x0 + barW} ${yTop + r} L${x0 + barW} ${MT + ih} Z`); | |
| p.setAttribute("fill", s.color); | |
| svg.appendChild(p); | |
| const ci = s.cis && s.cis[gi]; | |
| if (ci) { // 95% CI whisker (prompt bootstrap) | |
| const wx = x0 + barW / 2, capw = Math.min(7, barW / 2 - 1); | |
| const mk = (x1, y1, x2, y2) => { | |
| const l = document.createElementNS(svgns, "line"); | |
| l.setAttribute("x1", x1); l.setAttribute("y1", y1); | |
| l.setAttribute("x2", x2); l.setAttribute("y2", y2); | |
| l.setAttribute("stroke", css("--ink")); l.setAttribute("stroke-width", "1.4"); | |
| l.setAttribute("opacity", "0.75"); svg.appendChild(l); | |
| }; | |
| mk(wx, Y(ci[0]), wx, Y(ci[1])); | |
| mk(wx - capw, Y(ci[1]), wx + capw, Y(ci[1])); | |
| mk(wx - capw, Y(ci[0]), wx + capw, Y(ci[0])); | |
| } | |
| if (!opts.noValueLabels) { | |
| const vl = document.createElementNS(svgns, "text"); // value label | |
| vl.setAttribute("x", x0 + barW / 2); | |
| vl.setAttribute("y", (ci ? Math.min(yTop, Y(ci[1])) : yTop) - 6); | |
| vl.setAttribute("text-anchor", "middle"); vl.setAttribute("fill", css("--ink")); | |
| vl.setAttribute("font-size", "11.5"); vl.setAttribute("font-variant", "tabular-nums"); | |
| vl.textContent = v.toFixed(3); | |
| svg.appendChild(vl); | |
| } | |
| const hit = document.createElementNS(svgns, "rect"); // hover target (bigger than mark) | |
| hit.setAttribute("x", x0 - 3); hit.setAttribute("y", MT); | |
| hit.setAttribute("width", barW + 6); hit.setAttribute("height", ih); | |
| hit.setAttribute("fill", "transparent"); | |
| hit.addEventListener("mousemove", ev => { | |
| const rc = el.getBoundingClientRect(); | |
| tip.innerHTML = `<div class="t-it">${gname}</div><div class="row">` + | |
| `<span style="width:9px;height:9px;border-radius:50%;background:${s.color};display:inline-block"></span>` + | |
| `<span>${s.label}</span><span class="val"> ${v.toFixed(3)}</span></div>` + | |
| (ci ? `<div class="row" style="color:var(--muted)">95% CI [${ci[0].toFixed(3)}, ${ci[1].toFixed(3)}]</div>` : ""); | |
| tip.style.display = "block"; | |
| tip.style.left = Math.min(ev.clientX - rc.left + 14, rc.width - 190) + "px"; | |
| tip.style.top = (ev.clientY - rc.top - 24) + "px"; | |
| }); | |
| hit.addEventListener("mouseleave", () => tip.style.display = "none"); | |
| svg.appendChild(hit); | |
| }); | |
| // per-group horizontal marker (e.g. this prompt's own skyline) | |
| const gm = opts.groupMarkers; | |
| if (gm && gm.values[gi] != null) { | |
| const my = Y(gm.values[gi]); | |
| const ml2 = document.createElementNS(svgns, "line"); | |
| ml2.setAttribute("x1", cx - total / 2 - 4); ml2.setAttribute("x2", cx + total / 2 + 4); | |
| ml2.setAttribute("y1", my); ml2.setAttribute("y2", my); | |
| ml2.setAttribute("stroke", gm.color); ml2.setAttribute("stroke-width", "2"); | |
| ml2.setAttribute("stroke-dasharray", "5 3"); | |
| svg.appendChild(ml2); | |
| } | |
| const gl = document.createElementNS(svgns, "text"); | |
| gl.setAttribute("x", cx); gl.setAttribute("y", MT + ih + 20); | |
| gl.setAttribute("text-anchor", "middle"); gl.setAttribute("fill", css("--ink-2")); | |
| gl.setAttribute("font-size", "12"); gl.textContent = gname; | |
| svg.appendChild(gl); | |
| if (opts.groupSub && opts.groupSub[gi]) { | |
| const gs = document.createElementNS(svgns, "text"); | |
| gs.setAttribute("x", cx); gs.setAttribute("y", MT + ih + 32); | |
| gs.setAttribute("text-anchor", "middle"); gs.setAttribute("fill", css("--muted")); | |
| gs.setAttribute("font-size", "10"); gs.textContent = opts.groupSub[gi]; | |
| svg.appendChild(gs); | |
| } | |
| }); | |
| const yl = document.createElementNS(svgns, "text"); | |
| yl.setAttribute("x", 14); yl.setAttribute("y", MT + ih / 2); | |
| yl.setAttribute("transform", `rotate(-90 14 ${MT + ih / 2})`); | |
| yl.setAttribute("text-anchor", "middle"); yl.setAttribute("fill", css("--ink-2")); | |
| yl.setAttribute("font-size", "12"); yl.textContent = opts.ylab; | |
| svg.appendChild(yl); | |
| el.innerHTML = ""; el.appendChild(svg); el.appendChild(tip); | |
| // legend row | |
| const lg = document.createElement("div"); | |
| lg.style.cssText = "display:flex;gap:18px;font-size:12.5px;color:var(--ink-2);padding:2px 0 8px 44px"; | |
| for (const s of seriesList) | |
| lg.innerHTML += `<span><span style="display:inline-block;width:10px;height:10px;border-radius:2px;background:${s.color};margin-right:5px"></span>${s.label}</span>`; | |
| for (const b of (opts.baselines || [])) | |
| lg.innerHTML += `<span><span style="display:inline-block;width:14px;height:0;border-top:2px dashed ${b.color};margin-right:5px;vertical-align:middle"></span>${b.label}</span>`; | |
| if (opts.groupMarkers) | |
| lg.innerHTML += `<span><span style="display:inline-block;width:14px;height:0;border-top:2px dashed ${opts.groupMarkers.color};margin-right:5px;vertical-align:middle"></span>${opts.groupMarkers.label}</span>`; | |
| el.appendChild(lg); | |
| } | |
| function drawEval2() { | |
| const e = EVALS.offline254; | |
| makeBarChart(document.getElementById("c-eval2"), | |
| e.M.map(m => "M = " + m), | |
| [ | |
| { label: "base model", color: css("--fail"), values: e.base }, | |
| { label: "offline SFT (iter 7500)", color: css("--fix"), values: e.sft7500 }, | |
| ], { ylab: "WACC (254 prompts)" }); | |
| } | |
| function drawEval3() { | |
| const e = EVALS.fullsplit18; | |
| makeChart(document.getElementById("c-eval3"), [ | |
| { x: e.iters, y: e.ftfix_e0, color: css("--fix"), label: "seeded Z · plain eval" }, | |
| { x: e.iters, y: e.ftzf_e0, color: css("--s2"), label: "frozen Z · plain eval" }, | |
| { x: e.ftfix_e09.iters, y: e.ftfix_e09.wacc, color: css("--s3"), label: "seeded · SMC eval" }, | |
| { x: [0, 2000], y: [EVALS.sky18.v, EVALS.sky18.v], color: css("--fail"), | |
| label: "RuntimeNoError skyline 0.244", dash: true }, | |
| ], { ylab: "held-out WACC", y0: 0, xmax: 2000 }); | |
| } | |
| function drawEval4() { | |
| const e = EVALS.probes; | |
| makeBarChart(document.getElementById("c-eval4"), e.arms, | |
| [ | |
| { label: "plain eval (ess=0)", color: css("--fix"), values: e.plain, cis: e.plain_ci }, | |
| { label: "SMC eval (ess=0.9)", color: css("--s2"), values: e.smc, cis: e.smc_ci }, | |
| ], { ylab: "held-out WACC (best ckpt)", baselines: [ | |
| { value: e.skyline, color: css("--fail"), label: "skyline E[correct | RuntimeNoError passed]", ci: e.skyline_ci }, | |
| { value: e.base_plain, color: css("--fix"), label: "base model · plain" }, | |
| { value: e.base_smc, color: css("--s2"), label: "base model · SMC" }, | |
| ] }); | |
| } | |
| function drawEval6() { | |
| const e = EVALS.full254; | |
| const armColors = [css("--muted"), css("--s2"), css("--fix"), css("--s3"), css("--fail")]; | |
| makeBarChart(document.getElementById("c-eval6a"), ["WACC (macro, 254 prompts)"], | |
| e.arms.map((a, i) => ({ label: a, color: armColors[i], values: [e.wacc[i]], cis: [e.wacc_ci[i]] })), | |
| { ylab: "WACC", baselines: [ | |
| { value: e.skyline, color: css("--fail"), label: "skyline E[correct | RuntimeNoError passed]", ci: e.skyline_ci }, | |
| ] }); | |
| makeBarChart(document.getElementById("c-eval6b"), e.ks.map(k => "pass@" + k), | |
| e.arms.map((a, i) => ({ label: a, color: armColors[i], values: e.passk[a] })), | |
| { ylab: "pass@K (proposal)", groupMarkers: { | |
| values: [e.skyline, null, null, null], color: css("--fail"), | |
| label: "skyline (pass@1 of the exact RuntimeNoError posterior)" } }); | |
| } | |
| function drawEval5() { | |
| const e = EVALS.probespp; | |
| makeBarChart(document.getElementById("c-eval5a"), e.ids, | |
| [ | |
| { label: "base model — plain eval", color: css("--muted"), values: e.plain_base }, | |
| { label: "β=0.02 iter 500 — plain eval", color: css("--fix"), values: e.plain_best }, | |
| ], { ylab: "WACC (plain, ess=0)", noValueLabels: true, groupSub: e.libs, | |
| groupMarkers: { values: e.skyline, color: css("--fail"), label: "prompt skyline E[correct | RuntimeNoError passed]" } }); | |
| makeBarChart(document.getElementById("c-eval5b"), e.ids, | |
| [ | |
| { label: "base model — SMC eval", color: css("--muted"), values: e.smc_base }, | |
| { label: "β=0.02 iter 1500 — SMC eval", color: css("--s2"), values: e.smc_best }, | |
| ], { ylab: "WACC (SMC, ess=0.9)", noValueLabels: true, groupSub: e.libs, | |
| groupMarkers: { values: e.skyline, color: css("--fail"), label: "prompt skyline E[correct | RuntimeNoError passed]" } }); | |
| } | |
| function drawEval7() { | |
| const t = EVALS.traj254, sky = EVALS.full254; | |
| makeChart(document.getElementById("c-eval7a"), [ | |
| { x: t.b01.it, y: t.b01.p1, color: css("--s2"), label: "β=0.1", dots: true }, | |
| { x: t.b002.it, y: t.b002.p1, color: css("--fix"), label: "β=0.02", dots: true }, | |
| { x: t.b002zf.it, y: t.b002zf.p1, color: css("--s3"), label: "β=0.02 frozen-Z", dots: true }, | |
| { x: t.cor.it, y: t.cor.p1, color: "#9467bd", label: "CORRECTNESS β=0.02", dots: true }, | |
| ], { ylab: "pass@1 (proposal, 254 prompts)", y0: 0.15, xmax: 19500 }); | |
| makeChart(document.getElementById("c-eval7b"), [ | |
| { x: t.b01.it, y: t.b01.wacc, color: css("--s2"), label: "β=0.1", dots: true }, | |
| { x: t.b002.it, y: t.b002.wacc, color: css("--fix"), label: "β=0.02", dots: true }, | |
| { x: t.b002zf.it, y: t.b002zf.wacc, color: css("--s3"), label: "β=0.02 frozen-Z", dots: true }, | |
| { x: t.cor.it, y: t.cor.wacc, color: "#9467bd", label: "CORRECTNESS β=0.02", dots: true }, | |
| { x: [0, 19500], y: [sky.skyline, sky.skyline], color: css("--fail"), | |
| label: "RuntimeNoError skyline 0.323", dash: true }, | |
| ], { ylab: "WACC (254 prompts)", y0: 0.25, xmax: 19500 }); | |
| } | |
| function drawEval8() { | |
| const c = EVALS.corprobe; | |
| makeChart(document.getElementById("c-eval8"), [ | |
| { x: c.kl002.it, y: c.kl002.wacc, color: css("--fix"), label: "no_error target (β=0.02)" }, | |
| { x: c.cor.it, y: c.cor.wacc, color: css("--s2"), label: "correctness target" }, | |
| { x: [0, 12500], y: [c.sky12, c.sky12], color: css("--fail"), | |
| label: "no_error skyline 0.234", dash: true }, | |
| ], { ylab: "held-out WACC (12 prompts)", y0: 0.15, xmax: 12500 }); | |
| } | |
| function drawEval8b() { | |
| const c = EVALS.cortrain; | |
| const xmax = Math.max(...c.cor002.epoch) + 0.5; | |
| const series = [ | |
| { x: c.cor002.epoch, y: c.cor002.rate, color: css("--fix"), label: "correctness arm β=0.02", dots: true }, | |
| ]; | |
| if (c.cor01 && c.cor01.epoch && c.cor01.epoch.length) | |
| series.splice(1, 0, { x: c.cor01.epoch, y: c.cor01.rate, color: css("--s2"), label: "correctness arm β=0.1", dots: true }); | |
| makeChart(document.getElementById("c-eval8b"), series, | |
| { ylab: "per-EPOCH solve rate (valid∧correct)", y0: 0, xmax: xmax, xstart: 0.5, | |
| xticks: c.cor002.epoch }); | |
| } | |
| function drawLCB() { | |
| const r = EVALS.lcbrace; | |
| makeChart(document.getElementById("c-lcb1"), [ | |
| { x: r.kl01.it, y: r.kl01.valid, color: css("--s3"), label: "β=0.1 anchor (stable, learning)" }, | |
| { x: r.kl002.it, y: r.kl002.valid, color: css("--fix"), label: "β=0.02 anchor" }, | |
| { x: r.noanchor.it, y: r.noanchor.valid, color: css("--fail"), label: "no anchor" }, | |
| { x: [0, 3300], y: [0.51, 0.51], color: css("--muted"), label: "prior rate Z̄=0.51", dash: true }, | |
| ], { ylab: "batch validity (strict target)", y0: 0, xmax: 3300 }); | |
| makeChart(document.getElementById("c-lcb2"), [ | |
| { x: r.kl01.it, y: r.kl01.gnorm, color: css("--s3"), label: "β=0.1 anchor" }, | |
| { x: r.kl002.it, y: r.kl002.gnorm, color: css("--fix"), label: "β=0.02 anchor" }, | |
| { x: r.noanchor.it, y: r.noanchor.gnorm, color: css("--fail"), label: "no anchor" }, | |
| ], { ylab: "grad norm (pre-clip, log)", log: true, xmax: 3300 }); | |
| } | |
| function drawAll() { drawModes(); drawEval1(); drawEval2(); drawEval3(); drawEval4(); drawEval5(); drawEval6(); drawEval7(); drawEval8(); drawEval8b(); drawLCB(); } | |
| document.getElementById("smooth").addEventListener("input", ev => { | |
| smoothW = +ev.target.value; | |
| document.getElementById("smoothval").textContent = smoothW; | |
| drawModes(); | |
| }); | |
| document.getElementById("metricseg").addEventListener("click", ev => { | |
| const b = ev.target.closest("button"); if (!b) return; | |
| metric = b.dataset.m; | |
| for (const btn of ev.currentTarget.querySelectorAll("button")) | |
| btn.setAttribute("aria-pressed", btn === b ? "true" : "false"); | |
| drawModes(); | |
| }); | |
| // redraw on theme flip (colors are read from CSS at draw time) | |
| new MutationObserver(drawAll).observe(document.documentElement, { attributes: true, attributeFilter: ["data-theme"] }); | |
| matchMedia("(prefers-color-scheme: dark)").addEventListener("change", drawAll); | |
| drawAll(); | |
| </script> | |
| </body> | |
| </html> | |